[Misc] fix qwen3.5 config (#34604 )

[KV Connector] Add temporary, off-by-default VLLM_DISABLE_REQUEST_ID_RANDOMIZATION workaround (#34415 )
Signed-off-by: Seiji Eicher <seiji@anyscale.com>
2026-02-16 00:43:52 -08:00 · 2026-02-14 23:26:10 -08:00 · 2026-02-14 23:25:46 -08:00 · 2026-02-14 23:25:17 -08:00 · 2026-02-14 23:24:25 -08:00 · 2026-02-14 23:08:38 -08:00
297 changed files with 10517 additions and 3716 deletions
@@ -9,8 +9,10 @@ import json
 import os
 from dataclasses import dataclass
 from importlib import util
+from pathlib import Path

 import pandas as pd
+import regex as re

 pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None
@@ -275,6 +277,131 @@ def _apply_two_decimals(
    return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")


+# -----------------------------
+# Export helpers (Excel + CSV)
+# -----------------------------
+def _sanitize_sheet_name(name: str) -> str:
+    """
+    Excel sheet constraints:
+      - max 31 chars
+      - cannot contain: : \ / ? * [ ]
+      - cannot be empty
+    """
+    name = "sheet" if name is None else str(name)
+    name = re.sub(r"[:\\/?*\[\]]", "_", name)
+    name = name.strip().strip("'")
+    name = re.sub(r"\s+", " ", name)
+    if not name:
+        name = "sheet"
+    return name[:31]
+
+
+def _group_to_sheet_base(group_cols: list[str], gkey_tuple) -> str:
+    d = dict(zip(group_cols, gkey_tuple))
+    model = d.get("Model", "model")
+    model_short = str(model).split("/")[-1]
+    ilen = d.get("Input Len", "")
+    olen = d.get("Output Len", "")
+    lens = f"_{ilen}x{olen}" if ilen != "" and olen != "" else ""
+    return _sanitize_sheet_name(f"{model_short}{lens}")
+
+
+def _write_tables_to_excel_sheet(
+    writer: pd.ExcelWriter, sheet: str, blocks: list[tuple[str, pd.DataFrame]]
+):
+    startrow = 0
+    for title, df in blocks:
+        pd.DataFrame([[title]]).to_excel(
+            writer, sheet_name=sheet, index=False, header=False, startrow=startrow
+        )
+        startrow += 1
+        df.to_excel(writer, sheet_name=sheet, index=False, startrow=startrow)
+        startrow += len(df) + 3
+
+
+def _safe_filename(s: str) -> str:
+    s = re.sub(r"[^\w\-.]+", "_", str(s).strip())
+    return s[:180] if len(s) > 180 else s
+
+
+# -----------------------------
+# vLLM environment export helper
+# -----------------------------
+def _parse_vllm_env_txt(env_path: Path) -> pd.DataFrame:
+    """Parse vllm_env.txt into a flat table (Section, Key, Value).
+
+    Supports:
+      - section headers as standalone lines (no ':' or '=')
+      - key-value lines like 'OS: Ubuntu ...'
+      - env var lines like 'HF_HOME=/data/hf'
+    """
+    lines = env_path.read_text(encoding="utf-8", errors="replace").splitlines()
+    section = "General"
+    rows: list[dict] = []
+
+    def set_section(s: str):
+        nonlocal section
+        s = (s or "").strip()
+        if s:
+            section = s
+
+    for raw in lines:
+        stripped = raw.strip()
+        if not stripped:
+            continue
+        # divider lines like =====
+        if set(stripped) <= {"="}:
+            continue
+
+        # section header heuristic: short standalone line
+        if ":" not in stripped and "=" not in stripped and len(stripped) <= 64:
+            if stripped.lower().startswith("collecting environment information"):
+                continue
+            set_section(stripped)
+            continue
+
+        # env var style: KEY=VALUE (and not a URL with :)
+        if "=" in stripped and ":" not in stripped:
+            k, v = stripped.split("=", 1)
+            k = k.strip()
+            v = v.strip()
+            if k:
+                rows.append({"Section": section, "Key": k, "Value": v})
+            continue
+
+        # key: value
+        if ":" in stripped:
+            k, v = stripped.split(":", 1)
+            k = k.strip()
+            v = v.strip()
+            if k:
+                rows.append({"Section": section, "Key": k, "Value": v})
+            continue
+
+    return pd.DataFrame(rows, columns=["Section", "Key", "Value"])
+
+
+def _load_env_df_for_inputs(args, files: list[str]) -> pd.DataFrame | None:
+    """Load vllm_env.txt next to the *original* input JSON file.
+
+    Note: when only one -f is provided, the script may split JSON into ./splits/...,
+    but vllm_env.txt typically lives next to the original benchmark_results.json.
+    """
+    base_dir: Path | None = None
+    if getattr(args, "file", None):
+        base_dir = Path(args.file[0]).resolve().parent
+    elif files:
+        base_dir = Path(files[0]).resolve().parent
+    if base_dir is None:
+        return None
+
+    env_path = base_dir / "vllm_env.txt"
+    if not env_path.exists():
+        return None
+    df = _parse_vllm_env_txt(env_path)
+    return df
+
+
 # -----------------------------
 # Valid max concurrency summary helpers
 # -----------------------------
@@ -428,7 +555,6 @@ def build_valid_max_concurrency_summary_html(

    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
@@ -436,12 +562,10 @@ def build_valid_max_concurrency_summary_html(

    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)
@@ -460,6 +584,95 @@ def build_valid_max_concurrency_summary_html(
    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
 # -----------------------------
@@ -537,6 +750,21 @@ def build_parser() -> argparse.ArgumentParser:
        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


@@ -657,7 +885,6 @@ def maybe_write_plot(
        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")

@@ -730,87 +957,151 @@ def write_report_group_first(
        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"
-            )
+    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)

-            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
+    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"
+                )

-                for metric_label in plan.data_cols:
-                    gb = metric_groupbys[metric_label]
-                    df_sorted, raw_data_cols = metric_cache[metric_label]
+                main_fh.write(group_header)

-                    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"
+                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"
                        )

-                        main_fh.write(missing)
-                        sub_fh.write(missing)
-                        continue
+                        html = render_metric_table_html(
+                            display_group, metric_label, suffix, args
+                        )
+                        main_fh.write(html)
+                        sub_fh.write(html)

-                    if conc_col not in group_df.columns:
-                        conc_col = _find_concurrency_col(group_df)
+                        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,
+                        )

-                    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
+                        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)

-                    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,
+                    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_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():
@@ -1,6 +1,4 @@
 #!/bin/bash
-
-# This script should be run inside the CI process
 # This script assumes that we are already inside the vllm/ directory
 # Benchmarking results will be available inside vllm/benchmarks/results/

@@ -9,6 +7,11 @@
 set -x
 set -o pipefail

+# Environment-driven debug controls (like ON_CPU=1)
+DRY_RUN="${DRY_RUN:-0}"
+MODEL_FILTER="${MODEL_FILTER:-}"
+DTYPE_FILTER="${DTYPE_FILTER:-}"
+
 check_gpus() {
  if command -v nvidia-smi; then
    # check the number of GPUs and GPU type.
@@ -112,13 +115,12 @@ json2envs() {
 }

 wait_for_server() {
-  # wait for vllm server to start
-  # return 1 if vllm server crashes
  local timeout_val="1200"
  timeout "$timeout_val" bash -c '
-    until curl -X POST localhost:8000/v1/completions; do
+    until curl -sf http://localhost:8000/v1/models >/dev/null; do
      sleep 1
-    done' && return 0 || return 1
+    done
+  '
 }

 kill_processes_launched_by_current_bash() {
@@ -252,37 +254,16 @@ run_benchmark_tests() {
  done
 }

-run_latency_tests() {
-  run_benchmark_tests "latency" "$1"
-}
+run_latency_tests() { run_benchmark_tests "latency" "$1"; }
+run_startup_tests() { run_benchmark_tests "startup" "$1"; }
+run_throughput_tests() { run_benchmark_tests "throughput" "$1"; }

-run_startup_tests() {
-  run_benchmark_tests "startup" "$1"
-}
-
-run_throughput_tests() {
-  run_benchmark_tests "throughput" "$1"
-}
-
-run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
-  # $1: a json file specifying serving test cases
-  #
-  # Supported JSON formats:
-  # 1) Plain format: top-level array
-  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #
-  # 2) Default parameters field + plain format tests
-  #    {
-  #      "defaults": { ... },
-  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #    }
-
-  local serving_test_file
-  serving_test_file=$1
-
-  # Iterate over serving tests
-  jq -c '
+merge_serving_tests_stream() {
+  # Emit merged serving test objects, optionally filtered by MODEL_FILTER/DTYPE_FILTER in DRY_RUN mode.
+  # This helper does NOT modify JSON; it only filters the stream in dry-run mode.
+  local serving_test_file="$1"
+  # shellcheck disable=SC2016
+  local merged='
    if type == "array" then
      # Plain format: test cases array
      .[]
@@ -304,7 +285,50 @@ run_serving_tests() {
    else
      error("Unsupported serving test file format: must be array or object with .tests")
    end
-  ' "$serving_test_file" | while read -r params; do
+  '
+
+  jq -c "$merged" "$serving_test_file" | \
+  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
+    jq -c --arg model "$MODEL_FILTER" --arg dtype "$DTYPE_FILTER" '
+      select((($model|length)==0)
+             or ((.server_parameters.model // "") == $model)
+             or ((.client_parameters.model // "") == $model))
+      | select((($dtype|length)==0) or ((.server_parameters.dtype // "") == $dtype))
+    '
+  else
+    cat
+  fi
+}
+
+run_serving_tests() {
+  # run serving tests using `vllm bench serve` command
+  # $1: a json file specifying serving test cases
+  #
+  # Supported JSON formats:
+  # 1) Plain format: top-level array
+  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #
+  # 2) Default parameters field + plain format tests
+  #    {
+  #      "defaults": { ... },
+  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #    }
+
+  local serving_test_file
+  serving_test_file=$1
+
+  # In dry-run mode, if filters are provided but no tests match, fail fast.
+  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
+    local count
+    count=$(merge_serving_tests_stream "$serving_test_file" | wc -l | tr -d ' ')
+    if [[ "$count" -eq 0 ]]; then
+      echo "No matching serving tests found in $serving_test_file for model='$MODEL_FILTER' dtype='$DTYPE_FILTER'." >&2
+      return 0
+    fi
+  fi
+
+  # Iterate over serving tests (merged + optional filtered stream)
+  merge_serving_tests_stream "$serving_test_file" | while read -r params; do
    # get the test name, and append the GPU type back to it.
    test_name=$(echo "$params" | jq -r '.test_name')
    if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -373,7 +397,7 @@ run_serving_tests() {
    echo "Server command: $server_command"
    # support remote vllm server
    client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" ]]; then
+    if [[ -z "${REMOTE_HOST}" && "${DRY_RUN:-0}" != "1" ]]; then
      bash -c "$server_command" &
      server_pid=$!
      # wait until the server is alive
@@ -384,6 +408,9 @@ run_serving_tests() {
        echo ""
        echo "vLLM failed to start within the timeout period."
      fi
+    elif [[ "${DRY_RUN:-0}" == "1" ]]; then
+        # dry-run: don't start server
+        echo "Dry Run."
    else
      server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
      if [[ ${REMOTE_PORT} ]]; then
@@ -402,9 +429,7 @@ run_serving_tests() {
    for qps in $qps_list; do
      # remove the surrounding single quote from qps
      if [[ "$qps" == *"inf"* ]]; then
-        echo "qps was $qps"
        qps="inf"
-        echo "now qps is $qps"
      fi

      # iterate over different max_concurrency
@@ -425,7 +450,9 @@ run_serving_tests() {
        echo "Running test case $test_name with qps $qps"
        echo "Client command: $client_command"

-        bash -c "$client_command"
+        if [[ "${DRY_RUN:-0}" != "1" ]]; then
+          bash -c "$client_command"
+        fi

        # record the benchmarking commands
        jq_output=$(jq -n \
@@ -443,12 +470,15 @@ run_serving_tests() {
    done

    # clean up
-    kill -9 $server_pid
-    kill_gpu_processes
+    if [[ "${DRY_RUN:-0}" != "1" ]]; then
+      kill -9 $server_pid
+      kill_gpu_processes
+    fi
  done
 }

 main() {
+
  local ARCH
  ARCH=''
  if [[ "$ON_CPU" == "1" ]]; then
@@ -458,7 +488,13 @@ main() {
     check_gpus
     ARCH="$arch_suffix"
  fi
-  check_hf_token
+
+  # DRY_RUN does not execute vLLM; do not require HF_TOKEN.
+  if [[ "${DRY_RUN:-0}" != "1" ]]; then
+    check_hf_token
+  else
+    echo "DRY_RUN=1 -> skip HF_TOKEN validation"
+  fi

  # dependencies
  (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
@@ -479,11 +515,16 @@ main() {

  # dump vllm info via vllm collect-env
  env_output=$(vllm collect-env)
-
  echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"

  # benchmarking
-  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}" || exit $?
+
+  if [[ "${DRY_RUN:-0}" == "1" ]]; then
+    echo "DRY_RUN=1 -> skip latency/startup/throughput suites"
+    exit 0
+  fi
+
  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
  run_startup_tests $QUICK_BENCHMARK_ROOT/tests/"${STARTUP_JSON:-startup-tests$ARCH.json}"
  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
@@ -0,0 +1,41 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [
+      32,
+      64,
+      128
+    ],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+      "VLLM_CPU_SGL_KERNEL": 1,
+      "VLLM_CPU_KVCACHE_SPACE": 40
+    },
+    "server_parameters": {
+      "dtype": "bfloat16",
+      "model": "jinaai/jina-embeddings-v3",
+      "trust_remote_code": ""
+    },
+    "client_parameters": {
+      "model": "jinaai/jina-embeddings-v3",
+      "backend": "openai-embeddings",
+      "endpoint": "/v1/embeddings",
+      "dataset_name": "sharegpt",
+      "dataset_path": "ShareGPT_V3_unfiltered_cleaned_split.json",
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_jina_embed_v3_tp1_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {}
+    }
+  ]
+}
@@ -0,0 +1,283 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+      "VLLM_CPU_SGL_KERNEL": 1,
+      "VLLM_CPU_KVCACHE_SPACE": 40
+    },
+    "server_parameters": {
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "tensor_parallel_size": 1,
+      "dtype": "bfloat16",
+      "distributed_executor_backend": "mp",
+      "block_size": 128,
+      "trust_remote_code": "",
+      "disable_log_stats": "",
+      "max_num_batched_tokens": 2048,
+      "max_num_seqs": 256
+    },
+    "client_parameters": {
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "backend": "vllm",
+      "ignore-eos": "",
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_llama8B_tp1_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "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": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_granite2B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen1.7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen4B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen8B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_glm9B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_gemma7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "google/gemma-7b",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "google/gemma-7b",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    }
+  ]
+}
@@ -148,136 +148,6 @@
        "random-input-len": 2048,
        "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": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_granite2B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen1.7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen4B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen8B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_glm9B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-7b",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "google/gemma-7b",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
    }
  ]
 }
@@ -2,7 +2,9 @@
 # for more info about CODEOWNERS file

 # This lists cover the "core" components of vLLM that require careful review
-/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @njhill @22quinn
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg
+/vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery
+/vllm/lora @jeejeelee
 /vllm/model_executor/layers/attention @LucasWilkinson
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
@@ -11,18 +13,34 @@
 /vllm/model_executor/layers/batch_invariant.py @yewentao256 
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
 /vllm/vllm_flash_attn @LucasWilkinson
-/vllm/lora @jeejeelee
-/vllm/reasoning @aarnphm @chaunceyjiang
-/vllm/entrypoints @aarnphm @chaunceyjiang
-/vllm/tool_parsers @aarnphm @chaunceyjiang
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg
-/vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery
 CMakeLists.txt @tlrmchlsmth @LucasWilkinson

 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
 /vllm/config @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
-/vllm/config/cache.py @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg @heheda12345
+/vllm/config/cache.py @heheda12345
+
+# Entrypoints
+/vllm/entrypoints/anthropic @mgoin @DarkLight1337
+/vllm/entrypoints/cli @hmellor @mgoin @DarkLight1337 @russellb
+/vllm/entrypoints/mcp @heheda12345
+/vllm/entrypoints/openai @aarnphm @chaunceyjiang @DarkLight1337 @russellb
+/vllm/entrypoints/openai/realtime @njhill
+/vllm/entrypoints/openai/speech_to_text @NickLucche
+/vllm/entrypoints/pooling @noooop
+/vllm/entrypoints/sagemaker @DarkLight1337
+/vllm/entrypoints/serve @njhill
+/vllm/entrypoints/*.py @njhill
+/vllm/entrypoints/chat_utils.py @DarkLight1337
+/vllm/entrypoints/llm.py @DarkLight1337
+
+# Input/Output Processing
+/vllm/sampling_params.py @njhill @NickLucche
+/vllm/pooling_params.py @noooop @DarkLight1337
+/vllm/tokenizers @DarkLight1337 @njhill
+/vllm/renderers @DarkLight1337 @njhill
+/vllm/reasoning @aarnphm @chaunceyjiang
+/vllm/tool_parsers @aarnphm @chaunceyjiang

 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
@@ -115,8 +133,8 @@ mkdocs.yaml @hmellor
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten
 /vllm/model_executor/models/voxtral*.py @patrickvonplaten
 /vllm/model_executor/models/pixtral*.py @patrickvonplaten
+/vllm/tokenizers/mistral.py @patrickvonplaten
 /vllm/transformers_utils/configs/mistral.py @patrickvonplaten
-/vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten

 # Kernels
 /vllm/v1/attention/ops/chunked_prefill_paged_decode.py @tdoublep
@@ -152,9 +170,7 @@ mkdocs.yaml @hmellor
 /examples/pooling @noooop
 /tests/models/*/pooling* @noooop
 /tests/entrypoints/pooling @noooop
-/vllm/entrypoints/pooling @noooop
 /vllm/config/pooler.py @noooop
-/vllm/pooling_params.py @noooop
 /vllm/model_executor/layers/pooler @noooop

 # Security guide and policies
@@ -43,6 +43,7 @@ from common import (
    ModelParameterSweep,
    ParameterSweep,
    ResultsFormatter,
+    batch_spec_sort_key,
    is_mla_backend,
 )

@@ -218,10 +219,13 @@ def run_model_parameter_sweep(
                        by_param_and_spec[key].append(r)
                        break

-    # Sort by param value then spec
+    # Sort by param value then spec (batch_size, q_len, kv_len)
    sorted_keys = sorted(
        by_param_and_spec.keys(),
-        key=lambda x: (int(x[0]) if x[0].isdigit() else x[0], x[1]),
+        key=lambda x: (
+            int(x[0]) if x[0].isdigit() else x[0],
+            batch_spec_sort_key(x[1]),
+        ),
    )

    current_param_value = None
@@ -330,7 +334,7 @@ def run_parameter_sweep(
                by_spec[spec] = []
            by_spec[spec].append(r)

-    for spec in sorted(by_spec.keys()):
+    for spec in sorted(by_spec.keys(), key=batch_spec_sort_key):
        results = by_spec[spec]
        best = min(results, key=lambda r: r.mean_time)
        console.print(
@@ -496,15 +500,18 @@ def main():
        if "description" in yaml_config:
            console.print(f"[dim]{yaml_config['description']}[/]")

-        # Override args with YAML values
-        # (YAML takes precedence unless CLI arg was explicitly set)
-        # Backend(s)
-        if "backend" in yaml_config:
-            args.backend = yaml_config["backend"]
-            args.backends = None
-        elif "backends" in yaml_config:
-            args.backends = yaml_config["backends"]
-            args.backend = None
+        # Override args with YAML values, but CLI args take precedence
+        # Check if CLI provided backends (they would be non-None and not default)
+        cli_backends_provided = args.backends is not None or args.backend is not None
+
+        # Backend(s) - only use YAML if CLI didn't specify
+        if not cli_backends_provided:
+            if "backend" in yaml_config:
+                args.backend = yaml_config["backend"]
+                args.backends = None
+            elif "backends" in yaml_config:
+                args.backends = yaml_config["backends"]
+                args.backend = None

        # Check for special modes
        if "mode" in yaml_config:
@@ -544,13 +551,15 @@ def main():
            args.num_kv_heads = model.get("num_kv_heads", args.num_kv_heads)
            args.block_size = model.get("block_size", args.block_size)

-        # Benchmark settings
-        if "benchmark" in yaml_config:
-            bench = yaml_config["benchmark"]
-            args.device = bench.get("device", args.device)
-            args.repeats = bench.get("repeats", args.repeats)
-            args.warmup_iters = bench.get("warmup_iters", args.warmup_iters)
-            args.profile_memory = bench.get("profile_memory", args.profile_memory)
+        # Benchmark settings (top-level keys)
+        if "device" in yaml_config:
+            args.device = yaml_config["device"]
+        if "repeats" in yaml_config:
+            args.repeats = yaml_config["repeats"]
+        if "warmup_iters" in yaml_config:
+            args.warmup_iters = yaml_config["warmup_iters"]
+        if "profile_memory" in yaml_config:
+            args.profile_memory = yaml_config["profile_memory"]

        # Parameter sweep configuration
        if "parameter_sweep" in yaml_config:
@@ -16,13 +16,32 @@ from batch_spec import get_batch_type, parse_batch_spec
 from rich.console import Console
 from rich.table import Table

+
+def batch_spec_sort_key(spec: str) -> tuple[int, int, int]:
+    """
+    Extract sorting key from batch spec: (batch_size, max_q_len, max_kv_len).
+
+    This ensures results are sorted by batch size first, then query length,
+    then sequence length, rather than alphabetically.
+    """
+    try:
+        requests = parse_batch_spec(spec)
+        batch_size = len(requests)
+        max_q_len = max(r.q_len for r in requests) if requests else 0
+        max_kv_len = max(r.kv_len for r in requests) if requests else 0
+        return (batch_size, max_q_len, max_kv_len)
+    except Exception:
+        # Fallback for unparseable specs
+        return (0, 0, 0)
+
+
 # Mock classes for vLLM attention infrastructure


 class MockHfConfig:
    """Mock HuggingFace config that satisfies vLLM's requirements."""

-    def __init__(self, mla_dims: dict):
+    def __init__(self, mla_dims: dict, index_topk: int | None = None):
        self.num_attention_heads = mla_dims["num_q_heads"]
        self.num_key_value_heads = mla_dims["num_kv_heads"]
        self.hidden_size = mla_dims["head_dim"] * mla_dims["num_q_heads"]
@@ -33,6 +52,8 @@ class MockHfConfig:
        self.qk_rope_head_dim = mla_dims["qk_rope_head_dim"]
        self.v_head_dim = mla_dims["v_head_dim"]
        self.qk_head_dim = mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"]
+        if index_topk is not None:
+            self.index_topk = index_topk

    def get_text_config(self):
        return self
@@ -83,6 +104,38 @@ class MockKVBProj:
        return (result,)  # Return as tuple to match ColumnParallelLinear API


+class MockIndexer:
+    """Mock Indexer for sparse MLA backends.
+
+    Provides topk_indices_buffer that sparse MLA backends use to determine
+    which KV cache slots to attend to for each token.
+    """
+
+    def __init__(
+        self,
+        max_num_tokens: int,
+        topk_tokens: int,
+        device: torch.device,
+    ):
+        self.topk_tokens = topk_tokens
+        self.topk_indices_buffer = torch.zeros(
+            (max_num_tokens, topk_tokens),
+            dtype=torch.int32,
+            device=device,
+        )
+
+    def fill_random_indices(self, num_tokens: int, max_kv_len: int):
+        """Fill topk_indices_buffer with random valid indices for benchmarking."""
+        indices = torch.randint(
+            0,
+            max_kv_len,
+            (num_tokens, self.topk_tokens),
+            dtype=torch.int32,
+            device=self.topk_indices_buffer.device,
+        )
+        self.topk_indices_buffer[:num_tokens] = indices
+
+
 class MockLayer(AttentionLayerBase):
    """Mock attention layer with scale parameters and impl.

@@ -327,6 +380,9 @@ class ResultsFormatter:
                specs_order.append(spec)
            by_spec[spec][r.config.backend] = r

+        # Sort specs by (batch_size, q_len, kv_len) instead of alphabetically
+        specs_order = sorted(by_spec.keys(), key=batch_spec_sort_key)
+
        # Create shortened backend names for display
        def shorten_backend_name(name: str) -> str:
            """Shorten long backend names for table display."""
@@ -493,10 +549,11 @@ def get_attention_scale(head_dim: int) -> float:

 def is_mla_backend(backend: str) -> bool:
    """
-    Check if backend is an MLA backend using the backend's is_mla() property.
+    Check if backend is an MLA backend using the AttentionBackendEnum.

    Args:
-        backend: Backend name (e.g., "CUTLASS_MLA", "FLASHINFER_MLA")
+        backend: Backend name matching AttentionBackendEnum exactly
+        (e.g., "FLASHMLA_SPARSE")

    Returns:
        True if the backend is an MLA backend, False otherwise
@@ -504,7 +561,8 @@ def is_mla_backend(backend: str) -> bool:
    from vllm.v1.attention.backends.registry import AttentionBackendEnum

    try:
-        backend_class = AttentionBackendEnum[backend.upper()].get_class()
+        backend_enum = AttentionBackendEnum[backend]
+        backend_class = backend_enum.get_class()
        return backend_class.is_mla()
-    except (KeyError, ValueError, ImportError):
+    except (KeyError, ValueError, ImportError, AttributeError):
        return False
@@ -3,7 +3,7 @@
 model:
  name: "deepseek-v3"
  num_layers: 60
-  num_q_heads: 128
+  num_q_heads: 128  # Base value, can be swept for TP simulation
  num_kv_heads: 1  # MLA uses single latent KV
  head_dim: 576
  kv_lora_rank: 512
@@ -12,6 +12,13 @@ model:
  v_head_dim: 128
  block_size: 128  # CUTLASS MLA and FlashAttn MLA use 128

+# Model parameter sweep: simulate tensor parallelism by varying num_q_heads
+# TP=1: 128 heads, TP=2: 64 heads, TP=4: 32 heads, TP=8: 16 heads
+model_parameter_sweep:
+  param_name: "num_q_heads"
+  values: [128, 64, 32, 16]
+  label_format: "{backend}_{value}h"
+
 batch_specs:
  # Small batches, varying sequence lengths
  - "16q1s512"     # 16 requests, 512 KV cache
@@ -34,28 +41,30 @@ batch_specs:
  # Very large batches
  - "128q1s1k"     # 128 requests, 1k KV cache
  - "128q1s2k"     # 128 requests, 2k KV cache
+  - "128q1s4k"     # 128 requests, 4k KV cache
+  - "128q1s8k"     # 128 requests, 8k KV cache

  # Long context
  - "32q1s16k"     # 32 requests, 16k KV cache
  - "32q1s32k"     # 32 requests, 32k KV cache

 backends:
-  - cutlass_mla
-  - flashinfer_mla
-  - flashattn_mla  # Hopper only
-  - flashmla        # Hopper only
+  - CUTLASS_MLA
+  - FLASHINFER_MLA
+  - FLASH_ATTN_MLA  # Hopper only
+  - FLASHMLA        # Hopper only

 device: "cuda:0"
-repeats: 5
-warmup_iters: 3
+repeats: 100
+warmup_iters: 10
 profile_memory: true

 # Backend-specific tuning
-cutlass_mla:
+CUTLASS_MLA:
  num_kv_splits: auto  # or specific value like 4, 8, 16

-flashattn_mla:
+FLASH_ATTN_MLA:
  reorder_batch_threshold: 512

-flashmla:
+FLASHMLA:
  reorder_batch_threshold: 1
@@ -45,10 +45,10 @@ batch_specs:
  - "4q4k_60q1s4k"          # 4 prefill + 60 decode

 backends:
-  - cutlass_mla
-  - flashinfer_mla
-  - flashattn_mla   # Hopper only
-  - flashmla        # Hopper only
+  - CUTLASS_MLA
+  - FLASHINFER_MLA
+  - FLASH_ATTN_MLA   # Hopper only
+  - FLASHMLA         # Hopper only

 device: "cuda:0"
 repeats: 5
@@ -0,0 +1,62 @@
+# MLA prefill-only benchmark configuration for sparse backends
+
+model:
+  name: "deepseek-v3"
+  num_layers: 60
+  num_q_heads: 128
+  num_kv_heads: 1
+  head_dim: 576
+  kv_lora_rank: 512
+  qk_nope_head_dim: 128
+  qk_rope_head_dim: 64
+  v_head_dim: 128
+  block_size: 128
+
+# Model parameter sweep: simulate tensor parallelism by varying num_q_heads
+# TP=1: 128 heads, TP=2: 64 heads, TP=4: 32 heads, TP=8: 16 heads
+model_parameter_sweep:
+  param_name: "num_q_heads"
+  values: [128, 64, 32, 16]
+  label_format: "{backend}_{value}h"
+
+batch_specs:
+  # Pure prefill
+  - "1q512"
+  - "1q1k"
+  - "1q2k"
+  - "1q4k"
+  - "1q8k"
+
+  # Batched pure prefill
+  - "2q512"
+  - "2q1k"
+  - "2q2k"
+  - "2q4k"
+  - "2q8k"
+  - "4q512"
+  - "4q1k"
+  - "4q2k"
+  - "4q4k"
+  - "4q8k"
+  - "8q512"
+  - "8q1k"
+  - "8q2k"
+  - "8q4k"
+  - "8q8k"
+
+  # Extend
+  - "1q512s4k"
+  - "1q512s8k"
+  - "1q1ks8k"
+  - "1q2ks8k"
+  - "1q2ks16k"
+  - "1q4ks16k"
+
+backends:
+  - FLASHMLA_SPARSE
+  - FLASHINFER_MLA_SPARSE
+
+device: "cuda:0"
+repeats: 10
+warmup_iters: 3
+profile_memory: true
@@ -6,7 +6,7 @@
 description: "Decode vs Prefill pipeline crossover analysis"

 # Test FlashAttn MLA
-backend: flashattn_mla
+backend: FLASH_ATTN_MLA

 # Mode: decode_vs_prefill comparison (special sweep mode)
 # For each batch spec, we'll test both decode and prefill pipelines
@@ -62,11 +62,10 @@ model:
  block_size: 128

 # Benchmark settings
-benchmark:
-  device: "cuda:0"
-  repeats: 15          # More repeats for spec decode variance
-  warmup_iters: 5
-  profile_memory: false
+device: "cuda:0"
+repeats: 15          # More repeats for spec decode variance
+warmup_iters: 5
+profile_memory: false

 # Output
 output:
@@ -41,18 +41,17 @@ batch_specs:

 # Backends that support query length > 1
 backends:
-  - flashattn_mla    # reorder_batch_threshold = 512
-  - flashmla          # reorder_batch_threshold = 1 (tunable)
+  - FLASH_ATTN_MLA    # reorder_batch_threshold = 512
+  - FLASHMLA          # reorder_batch_threshold = 1 (tunable)

 # FlashInfer-MLA also supports uniform spec-as-decode but with different mechanism
-# - flashinfer_mla
+# - FLASHINFER_MLA

 # Benchmark settings
-benchmark:
-  device: "cuda:0"
-  repeats: 10  # More repeats for statistical significance
-  warmup_iters: 5
-  profile_memory: false
+device: "cuda:0"
+repeats: 10  # More repeats for statistical significance
+warmup_iters: 5
+profile_memory: false

 # Test these threshold values for optimization
 parameter_sweep:
@@ -36,11 +36,11 @@ batch_specs:
  - "q1ks2k"          # 1k query, 2k sequence
  - "2q1ks4k"         # 2 requests: 1k query, 4k sequence

-# Available backends: flash, triton, flashinfer
+# Available backends: FLASH_ATTN, TRITON_ATTN, FLASHINFER
 backends:
-  - flash
-  - triton
-  - flashinfer
+  - FLASH_ATTN
+  - TRITON_ATTN
+  - FLASHINFER

 device: "cuda:0"
 repeats: 5
@@ -8,14 +8,13 @@ This module provides helpers for running MLA backends without
 needing full VllmConfig integration.
 """

-import importlib
-
 import numpy as np
 import torch
 from batch_spec import parse_batch_spec
 from common import (
    BenchmarkResult,
    MockHfConfig,
+    MockIndexer,
    MockKVBProj,
    MockLayer,
    setup_mla_dims,
@@ -62,6 +61,7 @@ def create_minimal_vllm_config(
    block_size: int = 128,
    max_num_seqs: int = 256,
    mla_dims: dict | None = None,
+    index_topk: int | None = None,
 ) -> VllmConfig:
    """
    Create minimal VllmConfig for MLA benchmarks.
@@ -73,6 +73,8 @@ def create_minimal_vllm_config(
        max_num_seqs: Maximum number of sequences
        mla_dims: Optional custom MLA dimensions dict. If not provided, uses
                  setup_mla_dims(model_name)
+        index_topk: Optional topk value for sparse MLA backends. If provided,
+                    the config will include index_topk for sparse attention.

    Returns:
        VllmConfig for benchmarking
@@ -82,7 +84,7 @@ def create_minimal_vllm_config(
        mla_dims = setup_mla_dims(model_name)

    # Create mock HF config first (avoids downloading from HuggingFace)
-    mock_hf_config = MockHfConfig(mla_dims)
+    mock_hf_config = MockHfConfig(mla_dims, index_topk=index_topk)

    # Create a temporary minimal config.json to avoid HF downloads
    # This ensures consistent ModelConfig construction without network access
@@ -120,16 +122,12 @@ def create_minimal_vllm_config(
            seed=0,
            max_model_len=32768,
            quantization=None,
-            quantization_param_path=None,
            enforce_eager=False,
-            max_context_len_to_capture=None,
-            max_seq_len_to_capture=8192,
            max_logprobs=20,
            disable_sliding_window=False,
            skip_tokenizer_init=True,
            served_model_name=None,
            limit_mm_per_prompt=None,
-            use_async_output_proc=True,
            config_format="auto",
        )
    finally:
@@ -180,56 +178,65 @@ def create_minimal_vllm_config(
 # ============================================================================


-# Backend name to class name prefix mapping
-_BACKEND_NAME_MAP = {
-    "flashattn_mla": "FlashAttnMLA",
-    "flashmla": "FlashMLA",
-    "flashinfer_mla": "FlashInferMLA",
-    "cutlass_mla": "CutlassMLA",
-}
-
-# Special properties that differ from defaults
+# Backend-specific properties that can't be inferred from the backend class
+# Keys are AttentionBackendEnum names (uppercase)
 _BACKEND_PROPERTIES = {
-    "flashmla": {
+    "FLASHMLA": {
        "query_format": "concat",  # Single concatenated tensor (vs tuple)
-        "block_size": 64,  # FlashMLA uses fixed block size
    },
-    "flashinfer_mla": {
-        "block_size": 64,  # FlashInfer MLA only supports 32 or 64
+    "FLASHMLA_SPARSE": {
+        "query_format": "concat",  # Single concatenated tensor (vs tuple)
    },
 }


 def _get_backend_config(backend: str) -> dict:
    """
-    Get backend configuration using naming conventions.
+    Get backend configuration from AttentionBackendEnum.

-    All MLA backends follow the pattern:
-    - Module: vllm.v1.attention.backends.mla.{backend}
-    - Impl: {Name}Impl
-    - Metadata: {Name}Metadata (or MLACommonMetadata)
-    - DecodeMetadata: {Name}DecodeMetadata (or MLACommonDecodeMetadata)
-    - MetadataBuilder: {Name}MetadataBuilder
+    Uses the registry to get the backend class and extract configuration
+    from its methods (get_impl_cls, get_builder_cls, is_sparse, etc.).
+
+    Args:
+        backend: Backend name matching AttentionBackendEnum exactly
+        (e.g., "FLASHMLA_SPARSE")
+
+    Returns:
+        Dict with backend configuration
    """
-    if backend not in _BACKEND_NAME_MAP:
-        raise ValueError(f"Unknown backend: {backend}")
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum

-    name = _BACKEND_NAME_MAP[backend]
+    try:
+        backend_enum = AttentionBackendEnum[backend]
+        backend_class = backend_enum.get_class()
+    except (KeyError, ValueError) as e:
+        valid_backends = [e.name for e in AttentionBackendEnum if e.name != "CUSTOM"]
+        raise ValueError(
+            f"Unknown backend: {backend}. "
+            f"Valid MLA backends: {[b for b in valid_backends if 'MLA' in b]}"
+        ) from e
+
+    # Get block size from backend class
+    block_sizes = backend_class.get_supported_kernel_block_sizes()
+    # Use first supported block size (backends typically support one for MLA)
+    block_size = block_sizes[0] if block_sizes else None
+    if hasattr(block_size, "value"):
+        # Handle MultipleOf enum
+        block_size = None
+
+    # Check if sparse via class method if available
+    is_sparse = getattr(backend_class, "is_sparse", lambda: False)()
+
+    # Get properties that can't be inferred
    props = _BACKEND_PROPERTIES.get(backend, {})

-    # Check if backend uses common metadata (FlashInfer, CUTLASS)
-    uses_common = backend in ("flashinfer_mla", "cutlass_mla")
-
    return {
-        "module": f"vllm.v1.attention.backends.mla.{backend}",
-        "impl_class": f"{name}Impl",
-        "metadata_class": "MLACommonMetadata" if uses_common else f"{name}Metadata",
-        "decode_metadata_class": "MLACommonDecodeMetadata"
-        if uses_common
-        else f"{name}DecodeMetadata",
-        "builder_class": f"{name}MetadataBuilder",
+        "backend_class": backend_class,
+        "impl_class": backend_class.get_impl_cls(),
+        "builder_class": backend_class.get_builder_cls(),
        "query_format": props.get("query_format", "tuple"),
-        "block_size": props.get("block_size", None),
+        "block_size": block_size,
+        "is_sparse": is_sparse,
    }


@@ -447,22 +454,26 @@ def _create_backend_impl(
    mla_dims: dict,
    vllm_config: VllmConfig,
    device: torch.device,
+    max_num_tokens: int = 8192,
+    index_topk: int | None = None,
 ):
    """
    Create backend implementation instance.

    Args:
-        backend_cfg: Backend configuration dict
+        backend_cfg: Backend configuration dict from _get_backend_config()
        mla_dims: MLA dimension configuration
        vllm_config: VllmConfig instance
        device: Target device
+        max_num_tokens: Maximum number of tokens for sparse indexer buffer
+        index_topk: Topk value for sparse MLA backends

    Returns:
-        Tuple of (impl, layer, builder_instance)
+        Tuple of (impl, layer, builder_instance, indexer)
    """
-    # Import backend classes
-    backend_module = importlib.import_module(backend_cfg["module"])
-    impl_class = getattr(backend_module, backend_cfg["impl_class"])
+    # Get classes from backend config (already resolved by _get_backend_config)
+    impl_class = backend_cfg["impl_class"]
+    builder_class = backend_cfg["builder_class"]

    # Calculate scale
    scale = 1.0 / np.sqrt(mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"])
@@ -474,26 +485,44 @@ def _create_backend_impl(
        v_head_dim=mla_dims["v_head_dim"],
    )

+    # Create indexer for sparse backends
+    indexer = None
+    if backend_cfg.get("is_sparse", False):
+        if index_topk is None:
+            index_topk = 2048  # Default topk for sparse MLA
+        indexer = MockIndexer(
+            max_num_tokens=max_num_tokens,
+            topk_tokens=index_topk,
+            device=device,
+        )
+
+    # Build impl kwargs
+    impl_kwargs = {
+        "num_heads": mla_dims["num_q_heads"],
+        "head_size": mla_dims["head_dim"],
+        "scale": scale,
+        "num_kv_heads": mla_dims["num_kv_heads"],
+        "alibi_slopes": None,
+        "sliding_window": None,
+        "kv_cache_dtype": "auto",
+        "logits_soft_cap": None,
+        "attn_type": "decoder",
+        "kv_sharing_target_layer_name": None,
+        "q_lora_rank": None,
+        "kv_lora_rank": mla_dims["kv_lora_rank"],
+        "qk_nope_head_dim": mla_dims["qk_nope_head_dim"],
+        "qk_rope_head_dim": mla_dims["qk_rope_head_dim"],
+        "qk_head_dim": mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"],
+        "v_head_dim": mla_dims["v_head_dim"],
+        "kv_b_proj": mock_kv_b_proj,
+    }
+
+    # Add indexer for sparse backends
+    if indexer is not None:
+        impl_kwargs["indexer"] = indexer
+
    # Create impl
-    impl = impl_class(
-        num_heads=mla_dims["num_q_heads"],
-        head_size=mla_dims["head_dim"],
-        scale=scale,
-        num_kv_heads=mla_dims["num_kv_heads"],
-        alibi_slopes=None,
-        sliding_window=None,
-        kv_cache_dtype="auto",
-        logits_soft_cap=None,
-        attn_type="decoder",
-        kv_sharing_target_layer_name=None,
-        q_lora_rank=None,
-        kv_lora_rank=mla_dims["kv_lora_rank"],
-        qk_nope_head_dim=mla_dims["qk_nope_head_dim"],
-        qk_rope_head_dim=mla_dims["qk_rope_head_dim"],
-        qk_head_dim=mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"],
-        v_head_dim=mla_dims["v_head_dim"],
-        kv_b_proj=mock_kv_b_proj,
-    )
+    impl = impl_class(**impl_kwargs)

    # Initialize DCP attributes
    if not hasattr(impl, "dcp_world_size") or impl.dcp_world_size in (None, -1):
@@ -515,9 +544,7 @@ def _create_backend_impl(

    # Create builder instance if needed
    builder_instance = None
-    if backend_cfg["builder_class"]:
-        builder_class = getattr(backend_module, backend_cfg["builder_class"])
-
+    if builder_class:
        # Populate static_forward_context so builder can find the layer
        # MockLayer inherits from AttentionLayerBase, so isinstance checks pass
        vllm_config.compilation_config.static_forward_context = {"placeholder": layer}
@@ -529,7 +556,7 @@ def _create_backend_impl(
            device=device,
        )

-    return impl, layer, builder_instance
+    return impl, layer, builder_instance, indexer


 # ============================================================================
@@ -594,6 +621,7 @@ def _run_single_benchmark(
    backend_cfg: dict,
    mla_dims: dict,
    device: torch.device,
+    indexer=None,
 ) -> BenchmarkResult:
    """
    Run a single benchmark iteration.
@@ -606,6 +634,7 @@ def _run_single_benchmark(
        backend_cfg: Backend configuration dict
        mla_dims: MLA dimension configuration
        device: Target device
+        indexer: Optional MockIndexer for sparse backends

    Returns:
        BenchmarkResult with timing statistics
@@ -613,7 +642,9 @@ def _run_single_benchmark(
    # Parse batch spec
    requests = parse_batch_spec(config.batch_spec)
    q_lens = [r.q_len for r in requests]
+    kv_lens = [r.kv_len for r in requests]
    total_q = sum(q_lens)
+    max_kv_len = max(kv_lens)

    # Determine block size
    block_size = backend_cfg["block_size"] or config.block_size
@@ -641,8 +672,16 @@ def _run_single_benchmark(
        torch.bfloat16,
    )

-    # Determine which forward method to use based on metadata
-    if metadata.decode is not None:
+    # Fill indexer with random indices for sparse backends
+    is_sparse = backend_cfg.get("is_sparse", False)
+    if is_sparse and indexer is not None:
+        indexer.fill_random_indices(total_q, max_kv_len)
+
+    # Determine which forward method to use
+    if is_sparse:
+        # Sparse backends use forward_mqa
+        forward_fn = lambda: impl.forward_mqa(decode_inputs, kv_cache, metadata, layer)
+    elif metadata.decode is not None:
        forward_fn = lambda: impl._forward_decode(
            decode_inputs, kv_cache, metadata, layer
        )
@@ -693,11 +732,13 @@ def _run_single_benchmark(
 def _run_mla_benchmark_batched(
    backend: str,
    configs_with_params: list[tuple],  # [(config, threshold, num_splits), ...]
+    index_topk: int = 2048,
 ) -> list[BenchmarkResult]:
    """
    Unified batched MLA benchmark runner for all backends.

-    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla
+    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla,
+               flashinfer_mla_sparse, flashmla_sparse

    This function reuses backend initialization across multiple benchmarks
    to avoid setup/teardown overhead.
@@ -707,6 +748,7 @@ def _run_mla_benchmark_batched(
        configs_with_params: List of (config, threshold, num_splits) tuples
            - threshold: reorder_batch_threshold (FlashAttn/FlashMLA only)
            - num_splits: num_kv_splits (CUTLASS only)
+        index_topk: Topk value for sparse MLA backends (default 2048)

    Returns:
        List of BenchmarkResult objects
@@ -730,19 +772,27 @@ def _run_mla_benchmark_batched(
    if mla_dims is None:
        mla_dims = setup_mla_dims("deepseek-v3")

+    # Determine if this is a sparse backend
+    is_sparse = backend_cfg.get("is_sparse", False)
+
    # Create and set vLLM config for MLA (reused across all benchmarks)
    vllm_config = create_minimal_vllm_config(
        model_name="deepseek-v3",  # Used only for model path
        block_size=block_size,
        mla_dims=mla_dims,  # Use custom dims from config or default
+        index_topk=index_topk if is_sparse else None,
    )

    results = []

    with set_current_vllm_config(vllm_config):
-        # Create backend impl, layer, and builder (reused across benchmarks)
-        impl, layer, builder_instance = _create_backend_impl(
-            backend_cfg, mla_dims, vllm_config, device
+        # Create backend impl, layer, builder, and indexer (reused across benchmarks)
+        impl, layer, builder_instance, indexer = _create_backend_impl(
+            backend_cfg,
+            mla_dims,
+            vllm_config,
+            device,
+            index_topk=index_topk if is_sparse else None,
        )

        # Run each benchmark with the shared impl
@@ -768,6 +818,7 @@ def _run_mla_benchmark_batched(
                    backend_cfg,
                    mla_dims,
                    device,
+                    indexer=indexer,
                )
                results.append(result)

@@ -793,20 +844,24 @@ def run_mla_benchmark(
    config,
    reorder_batch_threshold: int | None = None,
    num_kv_splits: int | None = None,
+    index_topk: int = 2048,
 ) -> BenchmarkResult | list[BenchmarkResult]:
    """
    Unified MLA benchmark runner for all backends.

-    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla
+    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla,
+               flashinfer_mla_sparse, flashmla_sparse

    Always uses batched execution internally for optimal performance.

    Args:
-        backend: Backend name (flashattn_mla, flashmla, flashinfer_mla, cutlass_mla)
+        backend: Backend name (flashattn_mla, flashmla, flashinfer_mla, cutlass_mla,
+                 flashinfer_mla_sparse, flashmla_sparse)
        config: BenchmarkConfig or list of (BenchmarkConfig, param) tuples
        reorder_batch_threshold: Threshold override for FlashAttn/FlashMLA
                                 (single config mode only)
        num_kv_splits: Number of KV splits for CUTLASS (single config mode only)
+        index_topk: Topk value for sparse MLA backends (default 2048)

    Returns:
        BenchmarkResult (single mode) or list of BenchmarkResult (batched mode)
@@ -816,9 +871,9 @@ def run_mla_benchmark(
        # Already in batched format
        if len(config) > 0 and isinstance(config[0], tuple):
            # Format: [(cfg, param), ...] where param is threshold or num_splits
-            if backend in ("flashattn_mla", "flashmla"):
+            if backend in ("flashattn_mla", "flashmla", "flashmla_sparse"):
                configs_with_params = [(cfg, param, None) for cfg, param in config]
-            else:  # cutlass_mla or flashinfer_mla
+            else:  # cutlass_mla, flashinfer_mla, or sparse backends
                configs_with_params = [(cfg, None, param) for cfg, param in config]
        else:
            # Format: [cfg, ...] - just configs
@@ -830,7 +885,7 @@ def run_mla_benchmark(
        return_single = True

    # Use unified batched execution
-    results = _run_mla_benchmark_batched(backend, configs_with_params)
+    results = _run_mla_benchmark_batched(backend, configs_with_params, index_topk)

    # Return single result or list based on input
    return results[0] if return_single else results
@@ -40,29 +40,29 @@ from vllm.v1.kv_cache_interface import FullAttentionSpec
 # ============================================================================


-_BACKEND_CONFIG = {
-    "flash": {
-        "module": "vllm.v1.attention.backends.flash_attn",
-        "backend_class": "FlashAttentionBackend",
-    },
-    "triton": {
-        "module": "vllm.v1.attention.backends.triton_attn",
-        "backend_class": "TritonAttentionBackend",
-    },
-    "flashinfer": {
-        "module": "vllm.v1.attention.backends.flashinfer",
-        "backend_class": "FlashInferBackend",
-    },
-}
-
-
 def _get_backend_config(backend: str) -> dict:
-    if backend not in _BACKEND_CONFIG:
+    """
+    Get backend configuration from AttentionBackendEnum.
+
+    Args:
+        backend: Backend name matching AttentionBackendEnum exactly
+                 (e.g., "FLASH_ATTN", "TRITON_ATTN", "FLASHINFER")
+
+    Returns:
+        Dict with backend_class
+    """
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    try:
+        backend_enum = AttentionBackendEnum[backend]
+        backend_class = backend_enum.get_class()
+    except (KeyError, ValueError) as e:
+        valid_backends = [b.name for b in AttentionBackendEnum if b.name != "CUSTOM"]
        raise ValueError(
-            f"Unknown backend: {backend}. "
-            f"Available: {', '.join(_BACKEND_CONFIG.keys())}"
-        )
-    return _BACKEND_CONFIG[backend]
+            f"Unknown backend: {backend}. Valid backends: {valid_backends}"
+        ) from e
+
+    return {"backend_class": backend_class}


@contextmanager
@@ -205,10 +205,7 @@ def _create_backend_impl(
    dtype: torch.dtype,
 ):
    """Create backend implementation instance."""
-    import importlib
-
-    backend_module = importlib.import_module(backend_cfg["module"])
-    backend_class = getattr(backend_module, backend_cfg["backend_class"])
+    backend_class = backend_cfg["backend_class"]

    scale = get_attention_scale(config.head_dim)

@@ -247,7 +244,7 @@ def _create_metadata_builder(

    # Flashinfer needs get_per_layer_parameters mocked since we don't have
    # real model layers registered
-    if backend_name == "flashinfer":
+    if backend_name == "FLASHINFER":
        import unittest.mock

        from vllm.v1.attention.backends.utils import PerLayerParameters
@@ -438,7 +435,7 @@ def run_attention_benchmark(config: BenchmarkConfig) -> BenchmarkResult:
    """
    Run standard attention benchmark with real kernels.

-    Supports: flash, triton, flashinfer
+    Supports: FLASH_ATTN, TRITON_ATTN, FLASHINFER

    Args:
        config: Benchmark configuration
@@ -453,7 +450,7 @@ def run_attention_benchmark(config: BenchmarkConfig) -> BenchmarkResult:

    requests = parse_batch_spec(config.batch_spec)

-    if config.backend == "flashinfer":
+    if config.backend == "FLASHINFER":
        requests = reorder_for_flashinfer(requests)

    q_lens = [r.q_len for r in requests]
@@ -100,13 +100,38 @@ def benchmark_config(
    dtype: torch.dtype,
    use_fp8_w8a8: bool,
    use_int8_w8a16: bool,
+    use_int4_w4a16: bool = False,
    num_iters: int = 100,
    block_quant_shape: list[int] = None,
    use_deep_gemm: bool = False,
 ) -> float:
    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
-    if use_int8_w8a16:
+    if use_int4_w4a16:
+        # Int4 packed weights: 2 int4 values per uint8 byte
+        # K dimension is packed (halved)
+        intermediate_size = shard_intermediate_size // 2  # after silu_and_mul
+        w1 = torch.randint(
+            0,
+            255,
+            (
+                num_experts,
+                shard_intermediate_size,
+                hidden_size // 2,  # int4 packing
+            ),
+            dtype=torch.uint8,
+        )
+        w2 = torch.randint(
+            0,
+            255,
+            (
+                num_experts,
+                hidden_size,
+                intermediate_size // 2,  # int4 packing
+            ),
+            dtype=torch.uint8,
+        )
+    elif use_int8_w8a16:
        w1 = torch.randint(
            -127,
            127,
@@ -140,7 +165,20 @@ def benchmark_config(
    w2_scale = None
    a1_scale = None
    a2_scale = None
-    if use_int8_w8a16:
+    if use_int4_w4a16:
+        if block_quant_shape is None:
+            raise ValueError("block_quant_shape is required for int4_w4a16")
+        group_size = block_quant_shape[1]
+        # Scales shape: (E, N, K // group_size) in fp16
+        w1_scale = torch.rand(
+            (num_experts, shard_intermediate_size, hidden_size // group_size),
+            dtype=dtype,
+        )
+        w2_scale = torch.rand(
+            (num_experts, hidden_size, intermediate_size // group_size),
+            dtype=dtype,
+        )
+    elif use_int8_w8a16:
        w1_scale = torch.randn(
            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
        )
@@ -199,6 +237,7 @@ def benchmark_config(
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            block_shape=block_quant_shape,
+            weight_dtype="int4" if use_int4_w4a16 else None,
        )

        deep_gemm_experts = None
@@ -481,6 +520,7 @@ class BenchmarkWorker:
        dtype: torch.dtype,
        use_fp8_w8a8: bool,
        use_int8_w8a16: bool,
+        use_int4_w4a16: bool = False,
        block_quant_shape: list[int] = None,
        use_deep_gemm: bool = False,
    ) -> tuple[dict[str, int], float]:
@@ -488,7 +528,10 @@ class BenchmarkWorker:

        set_random_seed(self.seed)
        dtype_str = _get_config_dtype_str(
-            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+            dtype,
+            use_int8_w8a16=use_int8_w8a16,
+            use_fp8_w8a8=use_fp8_w8a8,
+            use_int4_w4a16=use_int4_w4a16,
        )
        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
        # is the intermediate size after silu_and_mul.
@@ -519,6 +562,7 @@ class BenchmarkWorker:
            dtype,
            use_fp8_w8a8,
            use_int8_w8a16,
+            use_int4_w4a16=use_int4_w4a16,
            num_iters=100,
            block_quant_shape=block_quant_shape,
            use_deep_gemm=use_deep_gemm,
@@ -535,6 +579,7 @@ class BenchmarkWorker:
        dtype: torch.dtype,
        use_fp8_w8a8: bool,
        use_int8_w8a16: bool,
+        use_int4_w4a16: bool,
        search_space: list[dict[str, int]],
        block_quant_shape: list[int],
        use_deep_gemm: bool,
@@ -545,7 +590,7 @@ class BenchmarkWorker:
        best_config = None
        best_time = float("inf")
        if current_platform.is_rocm():
-            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
+            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16 or use_int4_w4a16)
            search_space = prune_rocm_search_space(
                num_tokens,
                shard_intermediate_size,
@@ -574,6 +619,7 @@ class BenchmarkWorker:
                        dtype,
                        use_fp8_w8a8,
                        use_int8_w8a16,
+                        use_int4_w4a16,
                        num_iters=20,
                        block_quant_shape=block_quant_shape,
                        use_deep_gemm=use_deep_gemm,
@@ -621,6 +667,7 @@ def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
            else {}
        ),
        **({"kpack": config["kpack"]} if "kpack" in config else {}),
+        **({"SPLIT_K": config["SPLIT_K"]} if "SPLIT_K" in config else {}),
    }


@@ -633,11 +680,15 @@ def save_configs(
    dtype: torch.dtype,
    use_fp8_w8a8: bool,
    use_int8_w8a16: bool,
+    use_int4_w4a16: bool,
    block_quant_shape: list[int],
    save_dir: str,
 ) -> None:
    dtype_str = _get_config_dtype_str(
-        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+        dtype,
+        use_int8_w8a16=use_int8_w8a16,
+        use_fp8_w8a8=use_fp8_w8a8,
+        use_int4_w4a16=use_int4_w4a16,
    )

    # NOTE(woosuk): The current naming convention uses w2.shape[2], which
@@ -739,6 +790,38 @@ def get_model_params(config):
    return E, topk, intermediate_size, hidden_size


+def get_quantization_group_size(config) -> int | None:
+    """Extract the quantization group size from the HF model config.
+
+    This reads directly from the HuggingFace config object (as returned by
+    ``get_config()``), not from vLLM's quantization config classes.
+
+    Supports AWQ/GPTQ-style configs (direct 'group_size' key) and
+    compressed-tensors configs (nested inside 'config_groups').
+    """
+    quantization_config = getattr(config, "quantization_config", {})
+    if not isinstance(quantization_config, dict):
+        return None
+    # AWQ / GPTQ style: group_size is a top-level key
+    gs = quantization_config.get("group_size")
+    if gs is not None:
+        return gs
+    # compressed-tensors style: group_size is nested in config_groups
+    config_groups = quantization_config.get("config_groups", {})
+    if not isinstance(config_groups, dict):
+        return None
+    for group_cfg in config_groups.values():
+        if not isinstance(group_cfg, dict):
+            continue
+        weights = group_cfg.get("weights", {})
+        if not isinstance(weights, dict):
+            continue
+        gs = weights.get("group_size")
+        if gs is not None:
+            return gs
+    return None
+
+
 def main(args: argparse.Namespace):
    print(args)

@@ -757,7 +840,20 @@ def main(args: argparse.Namespace):
    dtype = torch.float16 if current_platform.is_rocm() else config.dtype
    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    use_int4_w4a16 = args.dtype == "int4_w4a16"
    block_quant_shape = get_weight_block_size_safety(config)
+    if use_int4_w4a16:
+        group_size = get_quantization_group_size(config)
+        if group_size is None:
+            raise ValueError(
+                "Could not determine group_size from model config. "
+                "The model's quantization_config must contain a 'group_size' "
+                "field (AWQ/GPTQ) or 'config_groups.*.weights.group_size' "
+                "(compressed-tensors)."
+            )
+        # For int4_w4a16, block_shape = [0, group_size]
+        # block_shape[0]=0 means no block quantization on N dimension
+        block_quant_shape = [0, group_size]

    if args.batch_size is None:
        batch_sizes = [
@@ -811,8 +907,20 @@ def main(args: argparse.Namespace):
        return ray.get(outputs)

    if args.tune:
-        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
-        search_space = get_configs_compute_bound(is_fp16, block_quant_shape)
+        # int4_w4a16 weights are uint8-packed, not fp16; treat like fp8 for
+        # search space generation (no matrix_instr_nonkdim/kpack exploration).
+        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16 or use_int4_w4a16)
+        # For int4_w4a16, the group_size constraint on BLOCK_SIZE_K does not
+        # apply: the gptq_awq kernel handles arbitrary BLOCK_SIZE_K regardless
+        # of group_size. Skip block_quant_shape filtering to keep the full
+        # search space (e.g. BLOCK_SIZE_K=64 with group_size=128).
+        tune_block_quant_shape = None if use_int4_w4a16 else block_quant_shape
+        search_space = get_configs_compute_bound(is_fp16, tune_block_quant_shape)
+        if use_int4_w4a16:
+            # SPLIT_K is a required kernel constexpr for gptq_awq kernel;
+            # only SPLIT_K=1 is used at runtime, so fix it during tuning.
+            for cfg in search_space:
+                cfg["SPLIT_K"] = 1
        print(f"Start tuning over {len(search_space)} configurations...")
        if use_deep_gemm:
            raise ValueError(
@@ -832,6 +940,7 @@ def main(args: argparse.Namespace):
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
+                    use_int4_w4a16,
                    search_space,
                    block_quant_shape,
                    use_deep_gemm,
@@ -851,6 +960,7 @@ def main(args: argparse.Namespace):
            dtype,
            use_fp8_w8a8,
            use_int8_w8a16,
+            use_int4_w4a16,
            block_quant_shape,
            args.save_dir,
        )
@@ -869,6 +979,7 @@ def main(args: argparse.Namespace):
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
+                    use_int4_w4a16,
                    block_quant_shape,
                    use_deep_gemm,
                )
@@ -891,7 +1002,10 @@ if __name__ == "__main__":
    )
    parser.add_argument("--enable-expert-parallel", "-enable-ep", action="store_true")
    parser.add_argument(
-        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
+        "--dtype",
+        type=str,
+        choices=["auto", "fp8_w8a8", "int8_w8a16", "int4_w4a16"],
+        default="auto",
    )
    parser.add_argument("--use-deep-gemm", action="store_true")
    parser.add_argument(
@@ -2,33 +2,58 @@
 #include <torch/cuda.h>
 #include <cuda_runtime.h>

-// This function assumes that `cpu_tensor` is a CPU tensor allocated with pinned
-// memory, and that UVA (Unified Virtual Addressing) is enabled.
+// This function assumes that `cpu_tensor` is a CPU tensor,
+// and that UVA (Unified Virtual Addressing) is enabled.
 torch::Tensor get_cuda_view_from_cpu_tensor(torch::Tensor& cpu_tensor) {
  TORCH_CHECK(cpu_tensor.device().is_cpu(), "Input tensor must be on CPU");

-  // Get raw host pointer from CPU tensor
-  void* host_ptr = cpu_tensor.data_ptr();
+  // handle empty tensor
+  if (cpu_tensor.numel() == 0) {
+    return torch::empty(cpu_tensor.sizes(),
+                        cpu_tensor.options().device(torch::kCUDA));
+  }
+
+  if (cpu_tensor.is_pinned()) {
+    // If CPU tensor is pinned, directly get the device pointer.
+    void* host_ptr = const_cast<void*>(cpu_tensor.data_ptr());
+    void* device_ptr = nullptr;
+    cudaError_t err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
+    TORCH_CHECK(err == cudaSuccess,
+                "cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
+
+    return torch::from_blob(
+        device_ptr, cpu_tensor.sizes(), cpu_tensor.strides(),
+        [base = cpu_tensor](void*) {},  // keep cpu tensor alive
+        cpu_tensor.options().device(torch::kCUDA));
+  }
+
+  // If CPU tensor is not pinned, allocate a new pinned memory buffer.
+  torch::Tensor contiguous_cpu = cpu_tensor.contiguous();
+  size_t nbytes = contiguous_cpu.nbytes();
+
+  void* host_ptr = nullptr;
+  cudaError_t err = cudaHostAlloc(&host_ptr, nbytes, cudaHostAllocMapped);
+  if (err != cudaSuccess) {
+    AT_ERROR("cudaHostAlloc failed: ", cudaGetErrorString(err));
+  }
+
+  err = cudaMemcpy(host_ptr, contiguous_cpu.data_ptr(), nbytes,
+                   cudaMemcpyDefault);
+  if (err != cudaSuccess) {
+    cudaFreeHost(host_ptr);
+    AT_ERROR("cudaMemcpy failed: ", cudaGetErrorString(err));
+  }

-  // Get a device pointer corresponding to the pinned host memory
  void* device_ptr = nullptr;
-  cudaError_t err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
-  TORCH_CHECK(err == cudaSuccess,
-              "cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
+  err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
+  if (err != cudaSuccess) {
+    cudaFreeHost(host_ptr);
+    AT_ERROR("cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
+  }

-  // We'll use the same sizes, strides, and dtype as the CPU tensor.
-  // TODO: check if layout is respected.
-  auto sizes = cpu_tensor.sizes();
-  auto strides = cpu_tensor.strides();
-  auto options = cpu_tensor.options().device(torch::kCUDA);
+  auto deleter = [host_ptr](void*) { cudaFreeHost(host_ptr); };

-  // use default no-op deleter, since the memory is owned by the original CPU
-  // tensor
-  torch::Tensor cuda_tensor =
-      torch::from_blob(device_ptr, sizes, strides, options);
-
-  TORCH_CHECK(cuda_tensor.device().is_cuda(),
-              "Resulting tensor is not on CUDA device");
-
-  return cuda_tensor;
-}
+  return torch::from_blob(device_ptr, contiguous_cpu.sizes(),
+                          contiguous_cpu.strides(), deleter,
+                          contiguous_cpu.options().device(torch::kCUDA));
+}
@@ -1568,8 +1568,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
        {
  #endif
          unsigned int kOff = k + (thrd * A_CHUNK);
-          unsigned int kOffcp =
-              k_str + kOff;  // min__(K - A_CHUNK, k_str + kOff);
+          unsigned int kOffcp = min__(K - A_CHUNK, k_str + kOff);
          for (unsigned int n = 0; n < N; n += CHUNKK * sprdN) {
            __builtin_amdgcn_global_load_lds(
                (int*)(&A[min__(
@@ -6,11 +6,11 @@
 #include "cutlass_extensions/common.hpp"

 bool cutlass_sparse_scaled_mm_supported(int64_t cuda_device_capability) {
-  // sparse CUTLASS kernels need at least
+  // sparse CUTLASS kernels need exactly hopper and are not forward compatible
  //   CUDA 12.2 and SM90 (Hopper)

 #if defined CUDA_VERSION
-  return CUDA_VERSION >= 12020 && cuda_device_capability >= 90;
+  return CUDA_VERSION >= 12020 && cuda_device_capability == 90;
 #endif

  return false;
@@ -98,7 +98,7 @@ std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a) {

  TORCH_CHECK_NOT_IMPLEMENTED(
      false,
-      "No compiled cutlass_sparse_compress for a compute capability less than "
+      "No compiled cutlass_sparse_compress for a compute capability equal to "
      "CUDA device capability: ",
      version_num);
 }
@@ -349,7 +349,7 @@ void setup_kernel_smem_once() {
 void large_context_topk(
    const torch::Tensor& logits, torch::Tensor& indices,
    const torch::Tensor& seq_lens,
-    c10::optional<torch::Tensor> row_starts = c10::nullopt) {
+    std::optional<torch::Tensor> row_starts = std::nullopt) {
  TORCH_CHECK(logits.is_cuda(), "logits must be a CUDA tensor");
  TORCH_CHECK(indices.is_cuda(), "indices must be a CUDA tensor");
  TORCH_CHECK(seq_lens.is_cuda(), "seq_lens must be a CUDA tensor");
@@ -506,7 +506,6 @@ RUN apt-get update -y \
        curl \
        sudo \
        python3-pip \
-        git \
        ffmpeg \
        libsm6 \
        libxext6 \
@@ -1,5 +1,5 @@
 ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.0-complete
-ARG TRITON_BRANCH="f332c492"
+ARG TRITON_BRANCH="57c693b6"
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
 ARG PYTORCH_BRANCH="89075173"
 ARG PYTORCH_REPO="https://github.com/ROCm/pytorch.git"
@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="6af8b687"
+ARG AITER_BRANCH="v0.1.10.post2"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="2d02c6a9"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
@@ -239,7 +239,7 @@ RUN pip install pyyaml && cd aiter \
           export HIP_CLANG_PATH=/opt/sccache-wrappers \
           && sccache --show-stats; \
       fi \
-    && PREBUILD_KERNELS=1 GPU_ARCHS=${AITER_ROCM_ARCH} python3 setup.py bdist_wheel --dist-dir=dist \
+    && GPU_ARCHS=${AITER_ROCM_ARCH} python3 setup.py bdist_wheel --dist-dir=dist \
    && if [ "$USE_SCCACHE" = "1" ]; then sccache --show-stats; fi \
    && ls /app/aiter/dist/*.whl
 RUN mkdir -p /app/install && cp /app/aiter/dist/*.whl /app/install
@@ -128,6 +128,7 @@ Priority is **1 = highest** (tried first).
 | 4 | `FLASHMLA` |
 | 5 | `TRITON_MLA` |
 | 6 | `FLASHMLA_SPARSE` |
+| 7 | `FLASHINFER_MLA_SPARSE` |

 **Ampere/Hopper (SM 8.x-9.x):**

@@ -204,6 +205,7 @@ configuration.
 |---------|--------|-----------|-------------|------------|------|--------|-----------|-----|-----------------|--------------|
 | `CUTLASS_MLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
 | `FLASHINFER_MLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
+| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `bfloat16` | 32, 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |
 | `FLASHMLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
 | `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
 | `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
@@ -1,10 +1,5 @@
 # Speculative Decoding

-!!! warning
-    Please note that speculative decoding in vLLM is not yet optimized and does
-    not usually yield inter-token latency reductions for all prompt datasets or sampling parameters.
-    The work to optimize it is ongoing and can be followed here: <https://github.com/vllm-project/vllm/issues/4630>
-
 !!! warning
    Currently, speculative decoding in vLLM is not compatible with pipeline parallelism.

@@ -176,7 +176,7 @@ For the full and up-to-date list of models validated on CPU platforms, please se

 ### How to find benchmark configuration examples for supported CPU models?

-For any model listed under [Supported Models on CPU](../../models/hardware_supported_models/cpu.md), optimized runtime configurations are provided in the vLLM Benchmark Suite’s CPU test cases, defined in [cpu test cases](../../../.buildkite/performance-benchmarks/tests/serving-tests-cpu.json)
+For any model listed under [Supported Models on CPU](../../models/hardware_supported_models/cpu.md), optimized runtime configurations are provided in the vLLM Benchmark Suite’s CPU test cases, defined in cpu test cases as serving-tests-cpu.json. Full test cases for Text-only models, Multi-Modal models and Embedded models are in cpu Text-Only test cases as serving-tests-cpu-text.json, cpu Multi-Modal test cases as serving-tests-cpu-multimodal.json and cpu Embedded test cases as serving-tests-cpu-embed.json.  
 For details on how these optimized configurations are determined, see: [performance-benchmark-details](../../../.buildkite/performance-benchmarks/README.md#performance-benchmark-details).
 To benchmark the supported models using these optimized settings, follow the steps in [running vLLM Benchmark Suite manually](../../benchmarking/dashboard.md#manually-trigger-the-benchmark) and run the Benchmark Suite on a CPU environment.  

@@ -199,6 +199,28 @@ lscpu | grep "NUMA node(s):" | awk '{print $3}'
 For performance reference, users may also consult the [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm&deviceName=cpu)
 , which publishes default-model CPU results produced using the same Benchmark Suite.

+#### Dry-Run
+
+For users only need to get the optimized runtime configurations without running benchmark, a Dry-Run mode is provided.
+By passing an environment variable DRY_RUN=1 with run-performance-benchmarks.sh,
+all commands will be generated under `./benchmark/results/`.
+
+```bash
+ON_CPU=1 DRY_RUN=1 bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
+```
+
+By providing different JSON file, users can get runtime configurations for different models such as Embedded Models.
+
+```bash
+ON_CPU=1 SERVING_JSON=serving-tests-cpu-embed.json DRY_RUN=1 bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
+```
+
+By providing MODEL_FILTER and DTYPE_FILTER, only commands for related model ID and Data Type will be generated.
+
+```bash
+ON_CPU=1 SERVING_JSON=serving-tests-cpu-text.json DRY_RUN=1 MODEL_FILTER=meta-llama/Llama-3.1-8B-Instruct DTYPE_FILTER=bfloat16  bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
+```
+
 ### How to decide `VLLM_CPU_OMP_THREADS_BIND`?

 - Default `auto` thread-binding is recommended for most cases. Ideally, each OpenMP thread will be bound to a dedicated physical core respectively, threads of each rank will be bound to the same NUMA node respectively, and 1 CPU per rank will be reserved for other vLLM components when `world_size > 1`. If you have any performance problems or unexpected binding behaviours, please try to bind threads as following.
@@ -311,20 +311,31 @@ An OpenAI client example can be found here: [examples/pooling/embed/openai_embed

 [ColBERT](https://arxiv.org/abs/2004.12832) (Contextualized Late Interaction over BERT) is a retrieval model that uses per-token embeddings and MaxSim scoring for document ranking. Unlike single-vector embedding models, ColBERT retains token-level representations and computes relevance scores through late interaction, providing better accuracy while being more efficient than cross-encoders.

-vLLM supports ColBERT models for reranking tasks, automatically applying MaxSim scoring for query-document relevance:
+vLLM supports ColBERT models with multiple encoder backbones:
+
+| Architecture | Backbone | Example HF Models |
+|---|---|---|
+| `HF_ColBERT` | BERT | `answerdotai/answerai-colbert-small-v1`, `colbert-ir/colbertv2.0` |
+| `ColBERTModernBertModel` | ModernBERT | `lightonai/GTE-ModernColBERT-v1` |
+| `ColBERTJinaRobertaModel` | Jina XLM-RoBERTa | `jinaai/jina-colbert-v2` |
+
+**BERT-based ColBERT** models work out of the box:

 ```shell
 vllm serve answerdotai/answerai-colbert-small-v1
 ```

-Currently supports ColBERT models with standard BERT encoders (e.g., `answerdotai/answerai-colbert-small-v1`, `colbert-ir/colbertv2.0`).
-
-ColBERT models with modified encoder architectures are not yet supported, including BERT variants with rotary embeddings (e.g., `jinaai/jina-colbert-v2`) or other custom encoders (e.g., `LiquidAI/LFM2-ColBERT-350M`).
-
-If your standard BERT ColBERT model's config doesn't specify the architecture as `HF_ColBERT`, override it with:
+For **non-BERT backbones**, use `--hf-overrides` to set the correct architecture:

 ```shell
-vllm serve your-colbert-model --hf-overrides '{"architectures": ["HF_ColBERT"]}'
+# ModernBERT backbone
+vllm serve lightonai/GTE-ModernColBERT-v1 \
+    --hf-overrides '{"architectures": ["ColBERTModernBertModel"]}'
+
+# Jina XLM-RoBERTa backbone
+vllm serve jinaai/jina-colbert-v2 \
+    --hf-overrides '{"architectures": ["ColBERTJinaRobertaModel"]}' \
+    --trust-remote-code
 ```

 Then you can use the rerank endpoint:
@@ -363,6 +374,77 @@ curl -s http://localhost:8000/pooling -H "Content-Type: application/json" -d '{

 An example can be found here: [examples/pooling/score/colbert_rerank_online.py](../../examples/pooling/score/colbert_rerank_online.py)

+### ColQwen3 Multi-Modal Late Interaction Models
+
+ColQwen3 is based on [ColPali](https://arxiv.org/abs/2407.01449), which extends ColBERT's late interaction approach to **multi-modal** inputs. While ColBERT operates on text-only token embeddings, ColPali/ColQwen3 can embed both **text and images** (e.g. PDF pages, screenshots, diagrams) into per-token L2-normalized vectors and compute relevance via MaxSim scoring. ColQwen3 specifically uses Qwen3-VL as its vision-language backbone.
+
+| Architecture | Backbone | Example HF Models |
+|---|---|---|
+| `ColQwen3` | Qwen3-VL | `TomoroAI/tomoro-colqwen3-embed-4b`, `TomoroAI/tomoro-colqwen3-embed-8b` |
+| `OpsColQwen3Model` | Qwen3-VL | `OpenSearch-AI/Ops-Colqwen3-4B`, `OpenSearch-AI/Ops-Colqwen3-8B` |
+
+Start the server:
+
+```shell
+vllm serve TomoroAI/tomoro-colqwen3-embed-4b --max-model-len 4096
+```
+
+Then you can use the rerank endpoint:
+
+```shell
+curl -s http://localhost:8000/rerank -H "Content-Type: application/json" -d '{
+    "model": "TomoroAI/tomoro-colqwen3-embed-4b",
+    "query": "What is machine learning?",
+    "documents": [
+        "Machine learning is a subset of artificial intelligence.",
+        "Python is a programming language.",
+        "Deep learning uses neural networks."
+    ]
+}'
+```
+
+Or the score endpoint:
+
+```shell
+curl -s http://localhost:8000/score -H "Content-Type: application/json" -d '{
+    "model": "TomoroAI/tomoro-colqwen3-embed-4b",
+    "text_1": "What is the capital of France?",
+    "text_2": ["The capital of France is Paris.", "Python is a programming language."]
+}'
+```
+
+You can also get the raw token embeddings using the pooling endpoint with `token_embed` task:
+
+```shell
+curl -s http://localhost:8000/pooling -H "Content-Type: application/json" -d '{
+    "model": "TomoroAI/tomoro-colqwen3-embed-4b",
+    "input": "What is machine learning?",
+    "task": "token_embed"
+}'
+```
+
+For **image inputs**, use the chat-style `messages` field so that the vLLM multimodal processor handles them correctly:
+
+```shell
+curl -s http://localhost:8000/pooling -H "Content-Type: application/json" -d '{
+    "model": "TomoroAI/tomoro-colqwen3-embed-4b",
+    "messages": [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
+                {"type": "text", "text": "Describe the image."}
+            ]
+        }
+    ]
+}'
+```
+
+Examples can be found here:
+
+- Multi-vector retrieval: [examples/pooling/token_embed/colqwen3_token_embed_online.py](../../examples/pooling/token_embed/colqwen3_token_embed_online.py)
+- Reranking: [examples/pooling/score/colqwen3_rerank_online.py](../../examples/pooling/score/colqwen3_rerank_online.py)
+
 ### BAAI/bge-m3

 The `BAAI/bge-m3` model comes with extra weights for sparse and colbert embeddings but unfortunately in its `config.json`
@@ -728,6 +728,8 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `OpenPanguVLForConditionalGeneration` | openpangu-VL | T + I<sup>E+</sup> + V<sup>E+</sup> |`FreedomIntelligence/openPangu-VL-7B` | ✅︎ | ✅︎ |
 | `Ovis` | Ovis2, Ovis1.6 | T + I<sup>+</sup> | `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc. | | ✅︎ |
 | `Ovis2_5` | Ovis2.5 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.5-9B`, etc. | | |
+| `Ovis2_6ForCausalLM` | Ovis2.6 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.6-2B`, etc. | | |
+| `Ovis2_6_MoeForCausalLM` | Ovis2.6 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.6-30B-A3B`, etc. | | |
 | `PaddleOCRVLForConditionalGeneration` | Paddle-OCR | T + I<sup>+</sup> | `PaddlePaddle/PaddleOCR-VL`, etc. | | |
 | `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | ✅︎ | ✅︎ |
 | `Phi3VForCausalLM` | Phi-3-Vision, Phi-3.5-Vision | T + I<sup>E+</sup> | `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc. | | ✅︎ |
@@ -0,0 +1,135 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test pause/resume with Data Parallel (DP) via HTTP API.
+
+This example demonstrates coordinated pause/resume across multiple DP ranks.
+The pause synchronizes across all DP engines via all-reduce.
+
+Prerequisites:
+    Start a vLLM server with data parallelism:
+
+    $ VLLM_SERVER_DEV_MODE=1 vllm serve facebook/opt-125m \
+        --enforce-eager \
+        --data-parallel-size 4 \
+        --tensor-parallel-size 1
+
+    Then run this script:
+
+    $ python data_parallel_pause_resume.py
+
+The test verifies pause works by:
+1. Starting a streaming generation request
+2. Pausing the server mid-generation
+3. Sleeping for PAUSE_DURATION seconds
+4. Resuming the server
+5. Verifying there was a gap in token generation matching the pause duration
+"""
+
+import argparse
+import threading
+import time
+
+import requests
+from openai import OpenAI
+
+BASE_URL = "http://localhost:8000"
+MODEL_NAME = "facebook/opt-125m"
+PAUSE_DURATION = 3.0
+
+
+def pause_generation(base_url: str, mode: str = "keep") -> None:
+    """Pause generation via HTTP endpoint."""
+    url = f"{base_url}/pause"
+    response = requests.post(url, params={"mode": mode}, timeout=60)
+    response.raise_for_status()
+    print("Server paused")
+
+
+def resume_generation(base_url: str) -> None:
+    """Resume generation via HTTP endpoint."""
+    url = f"{base_url}/resume"
+    response = requests.post(url, timeout=60)
+    response.raise_for_status()
+    print("Server resumed")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-url", default=BASE_URL)
+    parser.add_argument("--model", default=MODEL_NAME)
+    args = parser.parse_args()
+
+    client = OpenAI(
+        base_url=f"{args.base_url}/v1",
+        api_key="EMPTY",
+    )
+
+    prompt = "Write a long story about a dragon. Once upon a time"
+    token_times: list[float] = []
+    pause_token_idx = 0
+    pause_triggered = threading.Event()
+
+    def generator_thread():
+        """Stream tokens and record timestamps."""
+        stream = client.completions.create(
+            model=args.model,
+            prompt=prompt,
+            max_tokens=50,
+            stream=True,
+        )
+        for chunk in stream:
+            if chunk.choices[0].text:
+                token_times.append(time.monotonic())
+                token_count = len(token_times)
+                print(f"Token {token_count}: {chunk.choices[0].text!r}")
+
+                # Signal controller after some tokens
+                if token_count >= 5 and not pause_triggered.is_set():
+                    pause_triggered.set()
+
+    def controller_thread():
+        """Pause and resume the server."""
+        nonlocal pause_token_idx
+
+        # Wait for some tokens
+        pause_triggered.wait()
+
+        print(f"\nPausing server (keep mode) at token {len(token_times)}...")
+        pause_generation(args.base_url, mode="keep")
+        pause_token_idx = len(token_times)
+        print(f"Sleeping for {PAUSE_DURATION}s...")
+
+        time.sleep(PAUSE_DURATION)
+
+        print("Resuming server...")
+        resume_generation(args.base_url)
+        print("Resumed!\n")
+
+    # Run both threads
+    gen_thread = threading.Thread(target=generator_thread)
+    ctrl_thread = threading.Thread(target=controller_thread)
+
+    gen_thread.start()
+    ctrl_thread.start()
+
+    gen_thread.join()
+    ctrl_thread.join()
+
+    # Check gap at the pause point
+    if pause_token_idx < len(token_times):
+        pause_gap = token_times[pause_token_idx] - token_times[pause_token_idx - 1]
+        print(
+            f"\nGap after pause (token {pause_token_idx} -> "
+            f"{pause_token_idx + 1}): {pause_gap:.3f}s"
+        )
+        if pause_gap >= PAUSE_DURATION * 0.9:
+            print("Test passed! Pause synchronized across DP ranks.")
+        else:
+            print(f"Test failed! Expected ~{PAUSE_DURATION}s gap, got {pause_gap:.3f}s")
+    else:
+        print("Test failed! No tokens were generated after resuming.")
+
+
+if __name__ == "__main__":
+    main()
@@ -28,7 +28,7 @@ def main():
    )

    llm = LLM(
-        model="christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM",
+        model="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
        skip_tokenizer_init=True,
        trust_remote_code=True,
        enforce_eager=True,
@@ -391,7 +391,7 @@ if __name__ == "__main__":
    parser.add_argument(
        "--model",
        type=str,
-        default="christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM",
+        default="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
        help="Path to a checkpoint file to load from.",
    )
    parser.add_argument(
@@ -14,9 +14,7 @@ import requests
 # - install TerraTorch v1.1 (or later):
 #   pip install terratorch>=v1.1
 # - start vllm in serving mode with the below args
-#   --model='christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM'
-#   --model-impl terratorch
-#   --trust-remote-code
+#   --model='ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11'
 #   --skip-tokenizer-init --enforce-eager
 #   --io-processor-plugin terratorch_segmentation
 #   --enable-mm-embeds
@@ -34,7 +32,7 @@ def main():
            "out_data_format": "b64_json",
        },
        "priority": 0,
-        "model": "christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM",
+        "model": "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
    }

    ret = requests.post(server_endpoint, json=request_payload_url)
@@ -1,15 +1,27 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-Example of using ColBERT late interaction model for reranking.
+Example of using ColBERT late interaction models for reranking and scoring.

 ColBERT (Contextualized Late Interaction over BERT) uses per-token embeddings
 and MaxSim scoring for document reranking, providing better accuracy than
 single-vector models while being more efficient than cross-encoders.

-Start the server with:
+vLLM supports ColBERT with multiple encoder backbones. Start the server
+with one of the following:
+
+    # BERT backbone (works out of the box)
    vllm serve answerdotai/answerai-colbert-small-v1

+    # ModernBERT backbone
+    vllm serve lightonai/GTE-ModernColBERT-v1 \
+        --hf-overrides '{"architectures": ["ColBERTModernBertModel"]}'
+
+    # Jina XLM-RoBERTa backbone
+    vllm serve jinaai/jina-colbert-v2 \
+        --hf-overrides '{"architectures": ["ColBERTJinaRobertaModel"]}' \
+        --trust-remote-code
+
 Then run this script:
    python colbert_rerank_online.py
 """
@@ -18,39 +30,62 @@ import json

 import requests

-url = "http://127.0.0.1:8000/rerank"
+# Change this to match the model you started the server with
+MODEL = "answerdotai/answerai-colbert-small-v1"
+BASE_URL = "http://127.0.0.1:8000"

 headers = {"accept": "application/json", "Content-Type": "application/json"}

-data = {
-    "model": "answerdotai/answerai-colbert-small-v1",
-    "query": "What is machine learning?",
-    "documents": [
-        "Machine learning is a subset of artificial intelligence.",
-        "Python is a programming language.",
-        "Deep learning uses neural networks for complex tasks.",
-        "The weather today is sunny.",
-    ],
-}
+documents = [
+    "Machine learning is a subset of artificial intelligence.",
+    "Python is a programming language.",
+    "Deep learning uses neural networks for complex tasks.",
+    "The weather today is sunny.",
+]
+
+
+def rerank_example():
+    """Use the /rerank endpoint to rank documents by query relevance."""
+    print("=== Rerank Example ===")
+
+    data = {
+        "model": MODEL,
+        "query": "What is machine learning?",
+        "documents": documents,
+    }
+
+    response = requests.post(f"{BASE_URL}/rerank", headers=headers, json=data)
+    result = response.json()
+    print(json.dumps(result, indent=2))
+
+    print("\nRanked documents (most relevant first):")
+    for item in result["results"]:
+        doc_idx = item["index"]
+        score = item["relevance_score"]
+        print(f"  Score {score:.4f}: {documents[doc_idx]}")
+
+
+def score_example():
+    """Use the /score endpoint for pairwise query-document scoring."""
+    print("\n=== Score Example ===")
+
+    data = {
+        "model": MODEL,
+        "text_1": "What is machine learning?",
+        "text_2": [
+            "Machine learning is a subset of AI.",
+            "The weather is sunny.",
+        ],
+    }
+
+    response = requests.post(f"{BASE_URL}/score", headers=headers, json=data)
+    result = response.json()
+    print(json.dumps(result, indent=2))


 def main():
-    response = requests.post(url, headers=headers, json=data)
-
-    if response.status_code == 200:
-        print("ColBERT Rerank Request successful!")
-        result = response.json()
-        print(json.dumps(result, indent=2))
-
-        # Show ranked results
-        print("\nRanked documents (most relevant first):")
-        for item in result["results"]:
-            doc_idx = item["index"]
-            score = item["relevance_score"]
-            print(f"  Score {score:.4f}: {data['documents'][doc_idx]}")
-    else:
-        print(f"Request failed with status code: {response.status_code}")
-        print(response.text)
+    rerank_example()
+    score_example()


 if __name__ == "__main__":
@@ -0,0 +1,130 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Example of using ColQwen3 late interaction model for reranking.
+
+ColQwen3 is a multi-modal ColBERT-style model based on Qwen3-VL.
+It produces per-token embeddings and uses MaxSim scoring for retrieval
+and reranking. Supports both text and image inputs.
+
+Start the server with:
+    vllm serve TomoroAI/tomoro-colqwen3-embed-4b --max-model-len 50000
+
+Then run this script:
+    python colqwen3_rerank_online.py
+"""
+
+import requests
+
+MODEL = "TomoroAI/tomoro-colqwen3-embed-4b"
+BASE_URL = "http://127.0.0.1:8000"
+
+headers = {"accept": "application/json", "Content-Type": "application/json"}
+
+
+def rerank_text():
+    """Text-only reranking via /rerank endpoint."""
+    print("=" * 60)
+    print("1. Text reranking (/rerank)")
+    print("=" * 60)
+
+    data = {
+        "model": MODEL,
+        "query": "What is machine learning?",
+        "documents": [
+            "Machine learning is a subset of artificial intelligence.",
+            "Python is a programming language.",
+            "Deep learning uses neural networks for complex tasks.",
+            "The weather today is sunny.",
+        ],
+    }
+
+    response = requests.post(f"{BASE_URL}/rerank", headers=headers, json=data)
+
+    if response.status_code == 200:
+        result = response.json()
+        print("\n  Ranked documents (most relevant first):")
+        for item in result["results"]:
+            doc_idx = item["index"]
+            score = item["relevance_score"]
+            print(f"    [{score:.4f}] {data['documents'][doc_idx]}")
+    else:
+        print(f"  Request failed: {response.status_code}")
+        print(f"  {response.text[:300]}")
+
+
+def score_text():
+    """Text-only scoring via /score endpoint."""
+    print()
+    print("=" * 60)
+    print("2. Text scoring (/score)")
+    print("=" * 60)
+
+    query = "What is the capital of France?"
+    documents = [
+        "The capital of France is Paris.",
+        "Berlin is the capital of Germany.",
+        "Python is a programming language.",
+    ]
+
+    data = {
+        "model": MODEL,
+        "text_1": query,
+        "text_2": documents,
+    }
+
+    response = requests.post(f"{BASE_URL}/score", headers=headers, json=data)
+
+    if response.status_code == 200:
+        result = response.json()
+        print(f"\n  Query: {query}\n")
+        for item in result["data"]:
+            idx = item["index"]
+            score = item["score"]
+            print(f"    Doc {idx} (score={score:.4f}): {documents[idx]}")
+    else:
+        print(f"  Request failed: {response.status_code}")
+        print(f"  {response.text[:300]}")
+
+
+def score_text_top_n():
+    """Text reranking with top_n filtering via /rerank endpoint."""
+    print()
+    print("=" * 60)
+    print("3. Text reranking with top_n=2 (/rerank)")
+    print("=" * 60)
+
+    data = {
+        "model": MODEL,
+        "query": "What is the capital of France?",
+        "documents": [
+            "The capital of France is Paris.",
+            "Berlin is the capital of Germany.",
+            "Python is a programming language.",
+            "The Eiffel Tower is in Paris.",
+        ],
+        "top_n": 2,
+    }
+
+    response = requests.post(f"{BASE_URL}/rerank", headers=headers, json=data)
+
+    if response.status_code == 200:
+        result = response.json()
+        print(f"\n  Top {data['top_n']} results:")
+        for item in result["results"]:
+            doc_idx = item["index"]
+            score = item["relevance_score"]
+            print(f"    [{score:.4f}] {data['documents'][doc_idx]}")
+    else:
+        print(f"  Request failed: {response.status_code}")
+        print(f"  {response.text[:300]}")
+
+
+def main():
+    rerank_text()
+    score_text()
+    score_text_top_n()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,198 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+
+"""
+Example online usage of Pooling API for ColQwen3 multi-vector retrieval.
+
+ColQwen3 is a multi-modal late interaction model based on Qwen3-VL that
+produces per-token embeddings (320-dim, L2-normalized) for both text and
+image inputs. Similarity is computed via MaxSim scoring.
+
+This example mirrors the official TomoroAI inference code
+(https://huggingface.co/TomoroAI/tomoro-colqwen3-embed-4b) but uses the
+vLLM serving API instead of local HuggingFace model loading.
+
+Start the server with:
+    vllm serve TomoroAI/tomoro-colqwen3-embed-4b --max-model-len 4096
+
+Then run this script:
+    python colqwen3_token_embed_online.py
+"""
+
+import argparse
+import base64
+from io import BytesIO
+
+import numpy as np
+import requests
+from PIL import Image
+
+# ── Helpers ─────────────────────────────────────────────────
+
+
+def post_http_request(payload: dict, api_url: str) -> requests.Response:
+    headers = {"User-Agent": "Test Client"}
+    return requests.post(api_url, headers=headers, json=payload)
+
+
+def load_image(url: str) -> Image.Image:
+    """Download an image from URL (handles Wikimedia 403)."""
+    for hdrs in ({}, {"User-Agent": "Mozilla/5.0 (compatible; ColQwen3-demo/1.0)"}):
+        resp = requests.get(url, headers=hdrs, timeout=10)
+        if resp.status_code == 403:
+            continue
+        resp.raise_for_status()
+        return Image.open(BytesIO(resp.content)).convert("RGB")
+    raise RuntimeError(f"Could not fetch image from {url}")
+
+
+def encode_image_base64(image: Image.Image) -> str:
+    """Encode a PIL image to a base64 data URI."""
+    buf = BytesIO()
+    image.save(buf, format="PNG")
+    return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+
+
+def compute_maxsim(q_emb: np.ndarray, d_emb: np.ndarray) -> float:
+    """Compute ColBERT-style MaxSim score between query and document."""
+    sim = q_emb @ d_emb.T
+    return float(sim.max(axis=-1).sum())
+
+
+# ── Encode functions ────────────────────────────────────────
+
+
+def encode_queries(texts: list[str], model: str, api_url: str) -> list[np.ndarray]:
+    """Encode text queries → list of multi-vector embeddings."""
+    resp = post_http_request({"model": model, "input": texts}, api_url)
+    return [np.array(item["data"]) for item in resp.json()["data"]]
+
+
+def encode_images(image_urls: list[str], model: str, api_url: str) -> list[np.ndarray]:
+    """Encode image documents → list of multi-vector embeddings.
+
+    Images are sent via the chat-style `messages` field so that the
+    vLLM multimodal processor handles them correctly.
+    """
+    embeddings = []
+    for url in image_urls:
+        print(f"  Loading: {url.split('/')[-1]}...")
+        image = load_image(url)
+        image_uri = encode_image_base64(image)
+        resp = post_http_request(
+            {
+                "model": model,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": {"url": image_uri}},
+                            {"type": "text", "text": "Describe the image."},
+                        ],
+                    }
+                ],
+            },
+            api_url,
+        )
+        result = resp.json()
+        if resp.status_code != 200 or "data" not in result:
+            print(f"    Error ({resp.status_code}): {str(result)[:200]}")
+            continue
+        embeddings.append(np.array(result["data"][0]["data"]))
+    return embeddings
+
+
+# ── Main ────────────────────────────────────────────────────
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="TomoroAI/tomoro-colqwen3-embed-4b",
+    )
+    return parser.parse_args()
+
+
+def main(args):
+    pooling_url = f"http://{args.host}:{args.port}/pooling"
+    score_url = f"http://{args.host}:{args.port}/score"
+    model = args.model
+
+    # Same sample data as the official TomoroAI example
+    queries = [
+        "Retrieve the city of Singapore",
+        "Retrieve the city of Beijing",
+        "Retrieve the city of London",
+    ]
+    image_urls = [
+        "https://upload.wikimedia.org/wikipedia/commons/2/27/Singapore_skyline_2022.jpg",
+        "https://upload.wikimedia.org/wikipedia/commons/6/61/Beijing_skyline_at_night.JPG",
+        "https://upload.wikimedia.org/wikipedia/commons/4/49/London_skyline.jpg",
+    ]
+
+    # ── 1) Text query embeddings ────────────────────────────
+    print("=" * 60)
+    print("1. Encode text queries (multi-vector)")
+    print("=" * 60)
+    query_embeddings = encode_queries(queries, model, pooling_url)
+    for i, emb in enumerate(query_embeddings):
+        norm = float(np.linalg.norm(emb[0]))
+        print(f'  Query {i}: {emb.shape}  (L2 norm: {norm:.4f})  "{queries[i]}"')
+
+    # ── 2) Image document embeddings ────────────────────────
+    print()
+    print("=" * 60)
+    print("2. Encode image documents (multi-vector)")
+    print("=" * 60)
+    doc_embeddings = encode_images(image_urls, model, pooling_url)
+    for i, emb in enumerate(doc_embeddings):
+        print(f"  Doc {i}:   {emb.shape}  {image_urls[i].split('/')[-1]}")
+
+    # ── 3) Cross-modal MaxSim scoring ───────────────────────
+    if doc_embeddings:
+        print()
+        print("=" * 60)
+        print("3. Cross-modal MaxSim scores (text queries × image docs)")
+        print("=" * 60)
+        # Header
+        print(f"{'':>35s}", end="")
+        for j in range(len(doc_embeddings)):
+            print(f"  Doc {j:>2d}", end="")
+        print()
+        # Score matrix
+        for i, q_emb in enumerate(query_embeddings):
+            print(f"  {queries[i]:<33s}", end="")
+            for j, d_emb in enumerate(doc_embeddings):
+                score = compute_maxsim(q_emb, d_emb)
+                print(f"  {score:6.2f}", end="")
+            print()
+
+    # ── 4) Text-only /score endpoint ────────────────────────
+    print()
+    print("=" * 60)
+    print("4. Text-only late interaction scoring (/score endpoint)")
+    print("=" * 60)
+    text_query = "What is the capital of France?"
+    text_docs = [
+        "The capital of France is Paris.",
+        "Berlin is the capital of Germany.",
+        "Python is a programming language.",
+    ]
+    resp = post_http_request(
+        {"model": model, "text_1": text_query, "text_2": text_docs},
+        score_url,
+    )
+    print(f'  Query: "{text_query}"\n')
+    for item in resp.json()["data"]:
+        idx = item["index"]
+        print(f"  Doc {idx} (score={item['score']:.4f}): {text_docs[idx]}")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
@@ -7,7 +7,7 @@ requests >= 2.26.0
 tqdm
 blake3
 py-cpuinfo
-transformers @ git+https://github.com/huggingface/transformers.git@main
+transformers >= 4.56.0, < 5
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
@@ -31,7 +31,7 @@ partial-json-parser # used for parsing partial JSON outputs
 pyzmq >= 25.0.0
 msgspec
 gguf >= 0.17.0
-mistral_common[image] >= 1.9.0
+mistral_common[image] >= 1.9.1
 opencv-python-headless >= 4.13.0    # required for video IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
@@ -52,4 +52,4 @@ anthropic >= 0.71.0
 model-hosting-container-standards >= 0.1.13, < 1.0.0
 mcp
 grpcio
-grpcio-reflection
+grpcio-reflection
@@ -23,7 +23,7 @@ jiwer # required for audio tests
 timm # required for internvl test
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[image,audio] >= 1.9.0 # required for voxtral test
+mistral_common[image,audio] >= 1.9.1 # required for voxtral test
 num2words # required for smolvlm test
 opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
@@ -96,3 +96,5 @@ albumentations==1.4.6
 transformers==4.57.3
 # Pin HF Hub version
 huggingface-hub==0.36.2
+# Pin Mistral Common
+mistral-common[image,audio]==1.9.1
@@ -30,7 +30,7 @@ torchaudio==2.10.0
 torchvision==0.25.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[image,audio] >= 1.9.0 # required for voxtral test
+mistral_common[image,audio] >= 1.9.1 # required for voxtral test
 num2words # required for smolvlm test
 open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_common.py
 opencv-python-headless >= 4.13.0 # required for video test
@@ -56,7 +56,14 @@ runai-model-streamer[s3,gcs]==0.15.3
 fastsafetensors>=0.2.2 # 0.2.2 contains important fixes for multi-GPU mem usage
 pydantic>=2.12 # 2.11 leads to error on python 3.13
 decord==0.6.0
-terratorch @ git+https://github.com/IBM/terratorch.git@1.1.rc3 # required for PrithviMAE test
+terratorch >= 1.2.2 # Required for Prithvi tests
+imagehash # Required for Prithvi tests
+segmentation-models-pytorch > 0.4.0 # Required for Prithvi tests
+
 gpt-oss >= 0.0.7; python_version > '3.11'

 perceptron # required for isaac test
+
+# Newer versions of datasets require torchcoded, that makes the tests fail in CI because of a missing library.
+# Older versions are in conflict with teerratorch requirements.
+datasets>=3.3.0,<=3.6.0
@@ -1,7 +1,9 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match --torch-backend cu129 --python-platform x86_64-manylinux_2_28 --python-version 3.12
 absl-py==2.1.0
-    # via rouge-score
+    # via
+    #   rouge-score
+    #   tensorboard
 accelerate==1.0.1
    # via
    #   lm-eval
@@ -31,9 +33,7 @@ albumentations==1.4.6
    #   -r requirements/test.in
    #   terratorch
 alembic==1.16.4
-    # via
-    #   mlflow
-    #   optuna
+    # via optuna
 annotated-doc==0.0.4
    # via fastapi
 annotated-types==0.7.0
@@ -74,8 +74,6 @@ bitsandbytes==0.46.1
    #   lightning
 black==24.10.0
    # via datamodel-code-generator
-blinker==1.9.0
-    # via flask
 blobfile==3.0.0
    # via -r requirements/test.in
 bm25s==0.2.13
@@ -93,9 +91,7 @@ bounded-pool-executor==0.0.3
 buildkite-test-collector==0.1.9
    # via -r requirements/test.in
 cachetools==5.5.2
-    # via
-    #   google-auth
-    #   mlflow-skinny
+    # via google-auth
 certifi==2024.8.30
    # via
    #   fiona
@@ -106,6 +102,7 @@ certifi==2024.8.30
    #   pyproj
    #   rasterio
    #   requests
+    #   sentry-sdk
 cffi==1.17.1
    # via soundfile
 chardet==5.2.0
@@ -120,15 +117,14 @@ click==8.1.7
    #   click-plugins
    #   cligj
    #   fiona
-    #   flask
    #   jiwer
-    #   mlflow-skinny
    #   nltk
    #   rasterio
    #   ray
    #   schemathesis
    #   typer
    #   uvicorn
+    #   wandb
 click-plugins==1.1.1.2
    # via
    #   fiona
@@ -137,8 +133,6 @@ cligj==0.7.2
    # via
    #   fiona
    #   rasterio
-cloudpickle==3.1.1
-    # via mlflow-skinny
 colorama==0.4.6
    # via
    #   perceptron
@@ -163,16 +157,15 @@ cupy-cuda12x==13.6.0
    # via ray
 cycler==0.12.1
    # via matplotlib
-databricks-sdk==0.59.0
-    # via mlflow-skinny
 datamodel-code-generator==0.26.3
    # via -r requirements/test.in
 dataproperty==1.0.1
    # via
    #   pytablewriter
    #   tabledata
-datasets==3.0.2
+datasets==3.3.0
    # via
+    #   -r requirements/test.in
    #   evaluate
    #   lm-eval
    #   mteb
@@ -180,6 +173,8 @@ decorator==5.1.1
    # via librosa
 decord==0.6.0
    # via -r requirements/test.in
+diffusers==0.36.0
+    # via terratorch
 dill==0.3.8
    # via
    #   datasets
@@ -191,15 +186,11 @@ distlib==0.3.9
 dnspython==2.7.0
    # via email-validator
 docker==7.1.0
-    # via
-    #   gpt-oss
-    #   mlflow
+    # via gpt-oss
 docopt==0.6.2
    # via num2words
 docstring-parser==0.17.0
    # via jsonargparse
-efficientnet-pytorch==0.7.1
-    # via segmentation-models-pytorch
 einops==0.8.1
    # via
    #   -r requirements/test.in
@@ -217,9 +208,7 @@ encodec==0.1.1
 evaluate==0.4.3
    # via lm-eval
 fastapi==0.128.0
-    # via
-    #   gpt-oss
-    #   mlflow-skinny
+    # via gpt-oss
 fastparquet==2024.11.0
    # via genai-perf
 fastrlock==0.8.2
@@ -230,6 +219,7 @@ filelock==3.16.1
    # via
    #   blobfile
    #   datasets
+    #   diffusers
    #   huggingface-hub
    #   ray
    #   torch
@@ -237,8 +227,6 @@ filelock==3.16.1
    #   virtualenv
 fiona==1.10.1
    # via torchgeo
-flask==3.1.1
-    # via mlflow
 fonttools==4.55.0
    # via matplotlib
 fqdn==1.5.1
@@ -249,7 +237,7 @@ frozenlist==1.5.0
    # via
    #   aiohttp
    #   aiosignal
-fsspec==2024.9.0
+fsspec==2024.12.0
    # via
    #   datasets
    #   evaluate
@@ -257,6 +245,7 @@ fsspec==2024.9.0
    #   huggingface-hub
    #   lightning
    #   pytorch-lightning
+    #   tacoreader
    #   torch
 ftfy==6.3.1
    # via open-clip-torch
@@ -269,7 +258,7 @@ geopandas==1.0.1
 gitdb==4.0.12
    # via gitpython
 gitpython==3.1.44
-    # via mlflow-skinny
+    # via wandb
 google-api-core==2.24.2
    # via
    #   google-cloud-core
@@ -277,7 +266,6 @@ google-api-core==2.24.2
    #   opencensus
 google-auth==2.40.2
    # via
-    #   databricks-sdk
    #   google-api-core
    #   google-cloud-core
    #   google-cloud-storage
@@ -296,25 +284,17 @@ googleapis-common-protos==1.70.0
    # via google-api-core
 gpt-oss==0.0.8
    # via -r requirements/test.in
-graphene==3.4.3
-    # via mlflow
 graphql-core==3.2.6
-    # via
-    #   graphene
-    #   graphql-relay
-    #   hypothesis-graphql
-graphql-relay==3.2.0
-    # via graphene
+    # via hypothesis-graphql
 greenlet==3.2.3
    # via sqlalchemy
 grpcio==1.78.0
    # via
    #   grpcio-tools
    #   ray
+    #   tensorboard
 grpcio-tools==1.78.0
    # via -r requirements/test.in
-gunicorn==23.0.0
-    # via mlflow
 h11==0.14.0
    # via
    #   httpcore
@@ -338,12 +318,14 @@ httpcore==1.0.6
 httpx==0.27.2
    # via
    #   -r requirements/test.in
+    #   diffusers
    #   perceptron
    #   schemathesis
 huggingface-hub==0.36.2
    # via
    #   accelerate
    #   datasets
+    #   diffusers
    #   evaluate
    #   open-clip-torch
    #   peft
@@ -379,11 +361,13 @@ idna==3.10
    #   jsonschema
    #   requests
    #   yarl
+imagehash==4.3.2
+    # via -r requirements/test.in
 imageio==2.37.0
    # via scikit-image
 importlib-metadata==8.7.0
    # via
-    #   mlflow-skinny
+    #   diffusers
    #   opentelemetry-api
 importlib-resources==6.5.2
    # via typeshed-client
@@ -395,14 +379,10 @@ isoduration==20.11.0
    # via jsonschema
 isort==5.13.2
    # via datamodel-code-generator
-itsdangerous==2.2.0
-    # via flask
 jinja2==3.1.6
    # via
    #   datamodel-code-generator
-    #   flask
    #   genai-perf
-    #   mlflow
    #   torch
 jiwer==3.0.5
    # via -r requirements/test.in
@@ -415,12 +395,14 @@ joblib==1.4.2
    #   librosa
    #   nltk
    #   scikit-learn
-jsonargparse==4.35.0
+jsonargparse==4.46.0
    # via
    #   lightning
    #   terratorch
 jsonlines==4.0.0
    # via lm-eval
+jsonnet==0.21.0
+    # via jsonargparse
 jsonpointer==3.0.0
    # via jsonschema
 jsonschema==4.23.0
@@ -449,13 +431,13 @@ libnacl==2.1.0
    # via tensorizer
 librosa==0.10.2.post1
    # via -r requirements/test.in
-lightly==1.5.20
+lightly==1.5.22
    # via
    #   terratorch
    #   torchgeo
 lightly-utils==0.0.2
    # via lightly
-lightning==2.5.1.post0
+lightning==2.6.1
    # via
    #   terratorch
    #   torchgeo
@@ -476,12 +458,11 @@ lxml==5.3.0
 mako==1.3.10
    # via alembic
 markdown==3.8.2
-    # via mlflow
+    # via tensorboard
 markdown-it-py==3.0.0
    # via rich
 markupsafe==3.0.1
    # via
-    #   flask
    #   jinja2
    #   mako
    #   werkzeug
@@ -489,7 +470,6 @@ matplotlib==3.9.2
    # via
    #   -r requirements/test.in
    #   lightning
-    #   mlflow
    #   pycocotools
    #   torchgeo
 mbstrdecoder==1.1.3
@@ -499,12 +479,8 @@ mbstrdecoder==1.1.3
    #   typepy
 mdurl==0.1.2
    # via markdown-it-py
-mistral-common==1.9.0
+mistral-common==1.9.1
    # via -r requirements/test.in
-mlflow==2.22.0
-    # via terratorch
-mlflow-skinny==2.22.0
-    # via mlflow
 more-itertools==10.5.0
    # via lm-eval
 mpmath==1.3.0
@@ -523,8 +499,6 @@ multiprocess==0.70.16
    # via
    #   datasets
    #   evaluate
-munch==4.0.0
-    # via pretrainedmodels
 mypy-extensions==1.0.0
    # via black
 networkx==3.2.1
@@ -553,6 +527,7 @@ numpy==2.2.6
    #   cupy-cuda12x
    #   datasets
    #   decord
+    #   diffusers
    #   einx
    #   encodec
    #   evaluate
@@ -560,13 +535,13 @@ numpy==2.2.6
    #   genai-perf
    #   geopandas
    #   h5py
+    #   imagehash
    #   imageio
    #   librosa
    #   lightly
    #   lightly-utils
    #   matplotlib
    #   mistral-common
-    #   mlflow
    #   mteb
    #   numba
    #   numexpr
@@ -578,6 +553,7 @@ numpy==2.2.6
    #   perceptron
    #   pycocotools
    #   pyogrio
+    #   pywavelets
    #   rasterio
    #   rioxarray
    #   rouge-score
@@ -590,8 +566,10 @@ numpy==2.2.6
    #   shapely
    #   soxr
    #   statsmodels
+    #   tensorboard
    #   tensorboardx
    #   tensorizer
+    #   terratorch
    #   tifffile
    #   torchgeo
    #   torchmetrics
@@ -659,7 +637,6 @@ opencv-python-headless==4.13.0.90
    #   mistral-common
 opentelemetry-api==1.35.0
    # via
-    #   mlflow-skinny
    #   opentelemetry-exporter-prometheus
    #   opentelemetry-sdk
    #   opentelemetry-semantic-conventions
@@ -669,7 +646,6 @@ opentelemetry-proto==1.36.0
    # via ray
 opentelemetry-sdk==1.35.0
    # via
-    #   mlflow-skinny
    #   opentelemetry-exporter-prometheus
    #   ray
 opentelemetry-semantic-conventions==0.56b0
@@ -687,7 +663,6 @@ packaging==24.2
    #   evaluate
    #   fastparquet
    #   geopandas
-    #   gunicorn
    #   huggingface-hub
    #   hydra-core
    #   kornia
@@ -695,7 +670,6 @@ packaging==24.2
    #   lightning
    #   lightning-utilities
    #   matplotlib
-    #   mlflow-skinny
    #   optuna
    #   peft
    #   plotly
@@ -708,10 +682,12 @@ packaging==24.2
    #   rioxarray
    #   scikit-image
    #   statsmodels
+    #   tensorboard
    #   tensorboardx
    #   torchmetrics
    #   transformers
    #   typepy
+    #   wandb
    #   xarray
 pandas==2.2.3
    # via
@@ -720,8 +696,8 @@ pandas==2.2.3
    #   fastparquet
    #   genai-perf
    #   geopandas
-    #   mlflow
    #   statsmodels
+    #   tacoreader
    #   torchgeo
    #   xarray
 pathspec==0.12.1
@@ -740,7 +716,9 @@ perf-analyzer==0.1.0
    # via genai-perf
 pillow==10.4.0
    # via
+    #   diffusers
    #   genai-perf
+    #   imagehash
    #   imageio
    #   lightly-utils
    #   matplotlib
@@ -748,6 +726,7 @@ pillow==10.4.0
    #   perceptron
    #   scikit-image
    #   segmentation-models-pytorch
+    #   tensorboard
    #   torchgeo
    #   torchvision
 platformdirs==4.3.6
@@ -755,6 +734,7 @@ platformdirs==4.3.6
    #   black
    #   pooch
    #   virtualenv
+    #   wandb
 plotly==5.24.1
    # via genai-perf
 pluggy==1.5.0
@@ -769,8 +749,6 @@ portalocker==2.10.1
    # via sacrebleu
 pqdm==0.2.0
    # via -r requirements/test.in
-pretrainedmodels==0.7.4
-    # via segmentation-models-pytorch
 prometheus-client==0.22.0
    # via
    #   opentelemetry-exporter-prometheus
@@ -786,12 +764,13 @@ protobuf==6.33.2
    #   google-api-core
    #   googleapis-common-protos
    #   grpcio-tools
-    #   mlflow-skinny
    #   opentelemetry-proto
    #   proto-plus
    #   ray
+    #   tensorboard
    #   tensorboardx
    #   tensorizer
+    #   wandb
 psutil==6.1.0
    # via
    #   accelerate
@@ -801,11 +780,12 @@ py==1.11.0
    # via pytest-forked
 py-spy==0.4.0
    # via ray
-pyarrow==18.0.0
+pyarrow==23.0.0
    # via
    #   datasets
    #   genai-perf
-    #   mlflow
+    #   tacoreader
+    #   terratorch
 pyasn1==0.6.1
    # via
    #   pyasn1-modules
@@ -831,11 +811,11 @@ pydantic==2.12.0
    #   gpt-oss
    #   lightly
    #   mistral-common
-    #   mlflow-skinny
    #   mteb
    #   openai-harmony
    #   pydantic-extra-types
    #   ray
+    #   wandb
 pydantic-core==2.41.1
    # via pydantic
 pydantic-extra-types==2.10.5
@@ -873,7 +853,6 @@ pytest==8.3.5
    #   pytest-subtests
    #   pytest-timeout
    #   schemathesis
-    #   terratorch
 pytest-asyncio==0.24.0
    # via -r requirements/test.in
 pytest-cov==6.3.0
@@ -896,7 +875,6 @@ python-dateutil==2.9.0.post0
    # via
    #   arrow
    #   botocore
-    #   graphene
    #   lightly
    #   matplotlib
    #   pandas
@@ -913,6 +891,8 @@ pytz==2024.2
    # via
    #   pandas
    #   typepy
+pywavelets==1.9.0
+    # via imagehash
 pyyaml==6.0.2
    # via
    #   accelerate
@@ -923,7 +903,6 @@ pyyaml==6.0.2
    #   huggingface-hub
    #   jsonargparse
    #   lightning
-    #   mlflow-skinny
    #   omegaconf
    #   optuna
    #   peft
@@ -934,6 +913,7 @@ pyyaml==6.0.2
    #   timm
    #   transformers
    #   vocos
+    #   wandb
 rapidfuzz==3.12.1
    # via jiwer
 rasterio==1.4.3
@@ -951,6 +931,7 @@ referencing==0.35.1
    #   jsonschema-specifications
 regex==2024.9.11
    # via
+    #   diffusers
    #   nltk
    #   open-clip-torch
    #   sacrebleu
@@ -959,8 +940,8 @@ regex==2024.9.11
 requests==2.32.3
    # via
    #   buildkite-test-collector
-    #   databricks-sdk
    #   datasets
+    #   diffusers
    #   docker
    #   evaluate
    #   google-api-core
@@ -970,15 +951,16 @@ requests==2.32.3
    #   lightly
    #   lm-eval
    #   mistral-common
-    #   mlflow-skinny
    #   mteb
    #   pooch
    #   ray
    #   responses
    #   schemathesis
    #   starlette-testclient
+    #   tacoreader
    #   tiktoken
    #   transformers
+    #   wandb
 responses==0.25.3
    # via genai-perf
 rfc3339-validator==0.1.4
@@ -991,6 +973,7 @@ rich==13.9.4
    #   lightning
    #   mteb
    #   perceptron
+    #   terratorch
    #   typer
 rioxarray==0.19.0
    # via terratorch
@@ -1017,47 +1000,55 @@ sacrebleu==2.4.3
 safetensors==0.4.5
    # via
    #   accelerate
+    #   diffusers
    #   open-clip-torch
    #   peft
+    #   segmentation-models-pytorch
    #   timm
    #   transformers
 schemathesis==3.39.15
    # via -r requirements/test.in
 scikit-image==0.25.2
-    # via albumentations
+    # via
+    #   albumentations
+    #   terratorch
 scikit-learn==1.5.2
    # via
    #   albumentations
    #   librosa
    #   lm-eval
-    #   mlflow
    #   mteb
    #   sentence-transformers
+    #   terratorch
 scipy==1.13.1
    # via
    #   albumentations
    #   bm25s
+    #   imagehash
    #   librosa
-    #   mlflow
    #   mteb
    #   scikit-image
    #   scikit-learn
    #   sentence-transformers
    #   statsmodels
    #   vocos
-segmentation-models-pytorch==0.4.0
+segmentation-models-pytorch==0.5.0
    # via
+    #   -r requirements/test.in
    #   terratorch
    #   torchgeo
 sentence-transformers==5.2.0
    # via
    #   -r requirements/test.in
    #   mteb
+sentry-sdk==2.52.0
+    # via wandb
 setuptools==77.0.3
    # via
    #   grpcio-tools
    #   lightning-utilities
    #   pytablewriter
+    #   tensorboard
    #   torch
 shapely==2.1.1
    # via
@@ -1075,7 +1066,6 @@ six==1.16.0
    #   python-dateutil
    #   rfc3339-validator
    #   rouge-score
-    #   segmentation-models-pytorch
 smart-open==7.1.0
    # via ray
 smmap==5.0.2
@@ -1099,12 +1089,9 @@ soxr==0.5.0.post1
 sqlalchemy==2.0.41
    # via
    #   alembic
-    #   mlflow
    #   optuna
 sqlitedict==2.1.0
    # via lm-eval
-sqlparse==0.5.3
-    # via mlflow-skinny
 starlette==0.50.0
    # via
    #   fastapi
@@ -1124,6 +1111,8 @@ tabledata==1.3.3
    # via pytablewriter
 tabulate==0.9.0
    # via sacrebleu
+tacoreader==0.5.6
+    # via terratorch
 tblib==3.1.0
    # via -r requirements/test.in
 tcolorpy==0.1.6
@@ -1133,13 +1122,19 @@ tenacity==9.1.2
    #   gpt-oss
    #   lm-eval
    #   plotly
+tensorboard==2.20.0
+    # via terratorch
+tensorboard-data-server==0.7.2
+    # via tensorboard
 tensorboardx==2.6.4
    # via lightning
 tensorizer==2.10.1
    # via -r requirements/test.in
 termcolor==3.1.0
-    # via gpt-oss
-terratorch @ git+https://github.com/IBM/terratorch.git@07184fcf91a1324f831ff521dd238d97fe350e3e
+    # via
+    #   gpt-oss
+    #   terratorch
+terratorch==1.2.2
    # via -r requirements/test.in
 threadpoolctl==3.5.0
    # via scikit-learn
@@ -1172,7 +1167,6 @@ torch==2.10.0+cu129
    #   -r requirements/test.in
    #   accelerate
    #   bitsandbytes
-    #   efficientnet-pytorch
    #   encodec
    #   kornia
    #   lightly
@@ -1181,7 +1175,6 @@ torch==2.10.0+cu129
    #   mteb
    #   open-clip-torch
    #   peft
-    #   pretrainedmodels
    #   pytorch-lightning
    #   runai-model-streamer
    #   segmentation-models-pytorch
@@ -1213,12 +1206,11 @@ torchvision==0.25.0+cu129
    #   -r requirements/test.in
    #   lightly
    #   open-clip-torch
-    #   pretrainedmodels
    #   segmentation-models-pytorch
    #   terratorch
    #   timm
    #   torchgeo
-tqdm==4.66.6
+tqdm==4.67.3
    # via
    #   datasets
    #   evaluate
@@ -1232,10 +1224,11 @@ tqdm==4.66.6
    #   optuna
    #   peft
    #   pqdm
-    #   pretrainedmodels
    #   pytorch-lightning
    #   segmentation-models-pytorch
    #   sentence-transformers
+    #   tacoreader
+    #   terratorch
    #   tqdm-multiprocess
    #   transformers
 tqdm-multiprocess==0.0.11
@@ -1274,14 +1267,12 @@ typing-extensions==4.15.0
    #   alembic
    #   chz
    #   fastapi
-    #   graphene
    #   grpcio
    #   huggingface-hub
    #   librosa
    #   lightning
    #   lightning-utilities
    #   mistral-common
-    #   mlflow-skinny
    #   mteb
    #   opentelemetry-api
    #   opentelemetry-sdk
@@ -1299,6 +1290,7 @@ typing-extensions==4.15.0
    #   typer
    #   typeshed-client
    #   typing-inspection
+    #   wandb
 typing-inspection==0.4.2
    # via pydantic
 tzdata==2024.2
@@ -1313,25 +1305,26 @@ urllib3==2.2.3
    #   lightly
    #   requests
    #   responses
+    #   sentry-sdk
    #   tritonclient
 uvicorn==0.35.0
-    # via
-    #   gpt-oss
-    #   mlflow-skinny
+    # via gpt-oss
 vector-quantize-pytorch==1.21.2
    # via -r requirements/test.in
 virtualenv==20.31.2
    # via ray
 vocos==0.1.0
    # via -r requirements/test.in
+wandb==0.24.2
+    # via terratorch
 wcwidth==0.2.13
    # via ftfy
 webcolors==24.11.1
    # via jsonschema
 werkzeug==3.1.3
    # via
-    #   flask
    #   schemathesis
+    #   tensorboard
 word2number==1.1
    # via lm-eval
 wrapt==1.17.2
@@ -0,0 +1,430 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Autotune registered Helion kernels for optimal configurations.
+
+Usage:
+    # Autotune all registered kernels
+    python scripts/autotune_helion_kernels.py
+
+    # Autotune specific kernel
+    python scripts/autotune_helion_kernels.py --kernels silu_mul_fp8
+
+    # Autotune multiple kernels
+    python scripts/autotune_helion_kernels.py --kernels silu_mul_fp8 rms_norm_fp8
+
+    # Force re-autotuning
+    python scripts/autotune_helion_kernels.py --force
+
+    # List available kernels
+    python scripts/autotune_helion_kernels.py --list
+"""
+
+import argparse
+import sys
+import time
+from dataclasses import dataclass
+
+import torch
+
+try:
+    import helion
+
+    from vllm.kernels.helion import (
+        ConfigManager,
+        get_kernel_by_name,
+        get_registered_kernels,
+    )
+    from vllm.kernels.helion.utils import get_canonical_gpu_name
+    from vllm.logger import init_logger
+    from vllm.utils.import_utils import has_helion
+except ImportError as e:
+    print(f"Error importing vLLM: {e}")
+    print("Please ensure vLLM is installed and in your Python path")
+    sys.exit(1)
+
+logger = init_logger("vllm.scripts.autotune_helion_kernels")
+
+
+@dataclass
+class AutotuneResult:
+    status: str  # "success" | "partial" | "error" | "skipped"
+    successful: int
+    failed: int
+    configs: dict[str, "helion.Config"]
+    message: str = ""
+
+
+def list_kernels() -> None:
+    kernels = get_registered_kernels()
+
+    if not kernels:
+        print("No Helion kernels found in registry.")
+        return
+
+    print("Available Helion kernels:")
+    print("=" * 50)
+
+    for name in sorted(kernels.keys()):
+        print(f"  {name}")
+
+    print(f"\nTotal: {len(kernels)} kernels")
+
+
+def check_requirements() -> bool:
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. Helion autotuning requires GPU.")
+        return False
+
+    if not has_helion():
+        logger.error("Helion is not installed. Please install Helion package.")
+        return False
+
+    return True
+
+
+def autotune_kernel(
+    kernel_name: str,
+    platform: str,
+    config_manager: ConfigManager,
+    force: bool = False,
+    autotune_effort: str = "quick",
+) -> AutotuneResult:
+    logger.debug(
+        "Starting autotune for kernel '%s' with effort='%s'",
+        kernel_name,
+        autotune_effort,
+    )
+    kernel_wrapper = get_kernel_by_name(kernel_name)
+    if kernel_wrapper is None:
+        error_msg = f"Kernel '{kernel_name}' not found in registry"
+        logger.error(error_msg)
+        return AutotuneResult(
+            status="error",
+            message=error_msg,
+            successful=0,
+            failed=0,
+            configs={},
+        )
+
+    try:
+        inputs_dict = kernel_wrapper.get_inputs()
+    except NotImplementedError:
+        error_msg = f"Kernel '{kernel_name}' has no input generator registered"
+        logger.error(error_msg)
+        return AutotuneResult(
+            status="error",
+            message=error_msg,
+            successful=0,
+            failed=0,
+            configs={},
+        )
+
+    try:
+        logger.info(
+            "Autotuning kernel '%s' for platform '%s' with %d configs",
+            kernel_name,
+            platform,
+            len(inputs_dict),
+        )
+
+        configs_to_autotune = {}
+        if not force:
+            existing_configs = config_manager.get_platform_configs(
+                kernel_name, platform
+            )
+            for config_key, inputs in inputs_dict.items():
+                if config_key in existing_configs:
+                    logger.debug(
+                        "Config '%s' already exists for platform '%s', skipping",
+                        config_key,
+                        platform,
+                    )
+                else:
+                    configs_to_autotune[config_key] = inputs
+        else:
+            logger.debug("Force mode enabled, will re-autotune all configs")
+            configs_to_autotune = inputs_dict
+
+        if not configs_to_autotune:
+            logger.info(
+                "All configs already exist for kernel '%s' on platform '%s'. "
+                "Use --force to re-autotune.",
+                kernel_name,
+                platform,
+            )
+            return AutotuneResult(
+                status="skipped",
+                message="All configs already exist",
+                successful=0,
+                failed=0,
+                configs={},
+            )
+
+        total_start_time = time.time()
+        autotuned_configs = {}
+        failed_configs = []
+
+        for config_key, inputs in configs_to_autotune.items():
+            logger.info("Autotuning config: %s", config_key)
+            logger.debug(
+                "Input shapes: %s",
+                [getattr(inp, "shape", type(inp).__name__) for inp in inputs],
+            )
+
+            try:
+                config_start_time = time.time()
+                config = kernel_wrapper.run_autotune(inputs, autotune_effort)
+                config_duration = time.time() - config_start_time
+
+                # Save immediately for checkpointing
+                config_manager.save_configs(kernel_name, platform, {config_key: config})
+
+                autotuned_configs[config_key] = config
+                logger.debug("Config details: %s", config)
+
+                logger.info(
+                    "✓ Autotuned and saved config '%s' (%.2fs)",
+                    config_key,
+                    config_duration,
+                )
+
+            except (RuntimeError, ValueError, OSError) as e:
+                logger.exception(
+                    "Failed to autotune config '%s': %s",
+                    config_key,
+                    e,
+                )
+                failed_configs.append(config_key)
+
+        total_duration = time.time() - total_start_time
+        successful = len(autotuned_configs)
+        failed = len(failed_configs)
+
+        logger.info(
+            "Completed autotuning for kernel '%s': %d successful, %d failed (%.2fs)",
+            kernel_name,
+            successful,
+            failed,
+            total_duration,
+        )
+
+        status = "success" if failed == 0 else "partial"
+        return AutotuneResult(
+            status=status,
+            successful=successful,
+            failed=failed,
+            configs=autotuned_configs,
+        )
+
+    except (KeyError, RuntimeError, ValueError, OSError) as e:
+        error_msg = f"Unexpected error: {e}"
+        logger.exception("Failed to autotune kernel '%s': %s", kernel_name, e)
+        return AutotuneResult(
+            status="error",
+            message=error_msg,
+            successful=0,
+            failed=0,
+            configs={},
+        )
+
+
+def summarize_results(results: dict[str, AutotuneResult]) -> bool:
+    logger.info("=" * 50)
+    logger.info("Autotuning Results Summary")
+    logger.info("=" * 50)
+
+    total_successful = 0
+    total_failed = 0
+    success_kernels = []
+    partial_kernels = []
+    error_kernels = []
+    skipped_kernels = []
+
+    for kernel_name, result in results.items():
+        total_successful += result.successful
+        total_failed += result.failed
+
+        if result.status == "success":
+            success_kernels.append(f"{kernel_name} ({result.successful} configs)")
+            logger.info("✓ %s: %d configs successful", kernel_name, result.successful)
+        elif result.status == "partial":
+            partial_kernels.append(
+                f"{kernel_name} ({result.successful} ok, {result.failed} failed)"
+            )
+            logger.warning(
+                "⚠ %s: %d successful, %d failed",
+                kernel_name,
+                result.successful,
+                result.failed,
+            )
+        elif result.status == "error":
+            error_kernels.append(f"{kernel_name}: {result.message or 'Unknown error'}")
+            logger.error("✗ %s: %s", kernel_name, result.message or "Unknown error")
+        elif result.status == "skipped":
+            skipped_kernels.append(f"{kernel_name}: {result.message or 'Skipped'}")
+            logger.info("- %s: %s", kernel_name, result.message or "Skipped")
+
+    logger.info("=" * 50)
+    logger.info(
+        "Summary: %d total configs (%d successful, %d failed)",
+        total_successful + total_failed,
+        total_successful,
+        total_failed,
+    )
+    logger.info(
+        "Kernels: %d success, %d partial, %d error, %d skipped",
+        len(success_kernels),
+        len(partial_kernels),
+        len(error_kernels),
+        len(skipped_kernels),
+    )
+
+    has_failures = bool(error_kernels or partial_kernels)
+
+    if not has_failures:
+        if total_successful > 0:
+            logger.info("All configs autotuned successfully!")
+        else:
+            logger.info("No new configs were generated (all may already exist)")
+
+    return not has_failures
+
+
+def get_kernels_to_autotune(requested_kernels: list[str] | None) -> list[str]:
+    all_kernels = get_registered_kernels()
+    if not all_kernels:
+        logger.error("No Helion kernels found in registry")
+        sys.exit(1)
+
+    if not requested_kernels:
+        return list(all_kernels.keys())
+
+    if len(requested_kernels) != len(set(requested_kernels)):
+        duplicates = [
+            k for k in set(requested_kernels) if requested_kernels.count(k) > 1
+        ]
+        logger.error("Duplicate kernel names in --kernels flag: %s", duplicates)
+        sys.exit(1)
+
+    kernels_to_autotune = []
+    missing_kernels = []
+
+    for kernel_name in requested_kernels:
+        if kernel_name in all_kernels:
+            kernels_to_autotune.append(kernel_name)
+        else:
+            missing_kernels.append(kernel_name)
+
+    if missing_kernels:
+        logger.error("Kernel(s) not found: %s", missing_kernels)
+        logger.error("Available kernels: %s", list(all_kernels.keys()))
+        sys.exit(1)
+
+    return kernels_to_autotune
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Autotune Helion kernels",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__.split("Usage:")[1] if "Usage:" in __doc__ else "",
+    )
+
+    parser.add_argument(
+        "--kernels",
+        nargs="+",
+        help="Kernel(s) to autotune (default: all kernels)",
+    )
+
+    parser.add_argument(
+        "--config-dir",
+        type=str,
+        help="Config directory for config files (default: vLLM helion configs dir)",
+    )
+
+    parser.add_argument(
+        "--list",
+        action="store_true",
+        help="List available Helion kernels and exit",
+    )
+
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help=(
+            "Force re-autotuning even if configs already exist for the "
+            "platform and config keys"
+        ),
+    )
+
+    parser.add_argument(
+        "--autotune-effort",
+        type=str,
+        default="quick",
+        help=(
+            "Helion autotune effort level: 'quick' (smaller search) or "
+            "'full' (full search budget) (default: quick)"
+        ),
+    )
+
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Enable verbose logging",
+    )
+
+    args = parser.parse_args()
+
+    import logging
+
+    if args.verbose:
+        logging.getLogger("vllm").setLevel(logging.DEBUG)
+        logger.debug("Verbose mode enabled")
+        logger.debug("Arguments: %s", vars(args))
+    else:
+        logging.getLogger("vllm").setLevel(logging.INFO)
+
+    if args.list:
+        list_kernels()
+        return
+
+    if not check_requirements():
+        sys.exit(1)
+
+    platform = get_canonical_gpu_name()
+    logger.info("Detected GPU platform: %s", platform)
+
+    config_manager = (
+        ConfigManager(args.config_dir) if args.config_dir else ConfigManager()
+    )
+
+    try:
+        config_manager.ensure_base_dir_writable()
+    except OSError as e:
+        logger.error("Failed to access config directory: %s", e)
+        sys.exit(1)
+
+    kernels_to_autotune = get_kernels_to_autotune(args.kernels)
+
+    logger.info(
+        "Will autotune %d kernel(s) for platform '%s': %s",
+        len(kernels_to_autotune),
+        platform,
+        kernels_to_autotune,
+    )
+
+    results = {}
+    for kernel_name in kernels_to_autotune:
+        result = autotune_kernel(
+            kernel_name, platform, config_manager, args.force, args.autotune_effort
+        )
+        results[kernel_name] = result
+
+    success = summarize_results(results)
+    sys.exit(0 if success else 1)
+
+
+if __name__ == "__main__":
+    main()
@@ -1,10 +1,29 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+import pytest
+
 from ..utils import compare_two_settings


-def test_cpu_offload():
+@pytest.mark.parametrize("disable_pin_memory", [False, True])
+@pytest.mark.parametrize("disable_uva", [False, True])
+def test_cpu_offload(disable_pin_memory, disable_uva):
+    env_vars = {
+        "VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY": str(int(disable_pin_memory)),
+        "VLLM_WEIGHT_OFFLOADING_DISABLE_UVA": str(int(disable_uva)),
+    }
+
+    args = ["--cpu-offload-gb", "1"]
+
+    # cuda graph only works with UVA offloading
+    if disable_uva:
+        args.append("--enforce-eager")
+
    compare_two_settings(
-        "hmellor/tiny-random-LlamaForCausalLM", [], ["--cpu-offload-gb", "1"]
+        model="hmellor/tiny-random-LlamaForCausalLM",
+        arg1=[],
+        arg2=args,
+        env1=None,
+        env2=env_vars,
    )
@@ -3,6 +3,7 @@

 import pytest

+from vllm.config.model import ModelConfig
 from vllm.config.multimodal import MultiModalConfig
 from vllm.v1.attention.backends.registry import AttentionBackendEnum

@@ -23,3 +24,20 @@ def test_mm_encoder_attn_backend_hash_updates():
        mm_encoder_attn_backend=AttentionBackendEnum.FLASH_ATTN
    ).compute_hash()
    assert base_hash != overridden_hash
+
+
+def test_language_model_only_does_not_affect_mm_hash():
+    """language_model_only does not affect the ViT computation graph,
+    so it should not change the multimodal config hash."""
+    base_hash = MultiModalConfig().compute_hash()
+    lm_only_hash = MultiModalConfig(language_model_only=True).compute_hash()
+    assert base_hash == lm_only_hash
+
+
+def test_language_model_only_affects_model_hash():
+    """language_model_only affects the LM computation graph,
+    so it should change the model config hash."""
+    model = "llava-hf/llava-1.5-7b-hf"
+    base_hash = ModelConfig(model).compute_hash()
+    lm_only_hash = ModelConfig(model, language_model_only=True).compute_hash()
+    assert base_hash != lm_only_hash
@@ -419,7 +419,6 @@ class HfRunner:
            self.tokenizer: "PreTrainedTokenizer | PreTrainedTokenizerFast" = (
                AutoTokenizer.from_pretrained(
                    model_name,
-                    dtype=dtype,
                    trust_remote_code=trust_remote_code,
                )
            )
@@ -430,7 +429,6 @@ class HfRunner:

        self.processor = AutoProcessor.from_pretrained(
            model_name,
-            dtype=dtype,
            trust_remote_code=trust_remote_code,
        )
        if skip_tokenizer_init:
@@ -39,7 +39,6 @@ def test_min_tokens_with_stop(min_tokens: int, stop: str, truth: str):
        mm_features=None,
        sampling_params=params,
        pooling_params=None,
-        eos_token_id=None,
        arrival_time=0.0,
        lora_request=None,
        cache_salt=None,
@@ -35,7 +35,6 @@ def _make_request(stop, include_stop_str_in_output: bool, min_tokens: int = 0):
        mm_features=None,
        sampling_params=params,
        pooling_params=None,
-        eos_token_id=None,
        arrival_time=0.0,
        lora_request=None,
        cache_salt=None,
@@ -19,6 +19,8 @@ from vllm.distributed import (
    tensor_model_parallel_all_reduce,
    tensor_model_parallel_reduce_scatter,
 )
+from vllm.distributed.parallel_state import GroupCoordinator, TensorMetadata
+from vllm.v1.worker.gpu_worker import AsyncIntermediateTensors

 from ..utils import (
    init_test_distributed_environment,
@@ -200,6 +202,111 @@ def send_recv_tensor_dict_test_worker(
        torch.testing.assert_close(recv_dict["f"], test_dict["f"])


+class _DummyWork:
+    def __init__(self) -> None:
+        self.wait_calls = 0
+
+    def wait(self) -> None:
+        self.wait_calls += 1
+
+
+class _DummyAllGatherGroup:
+    def __init__(self, world_size: int, rank_in_group: int) -> None:
+        self.world_size = world_size
+        self.rank_in_group = rank_in_group
+
+    def all_gather(self, t: torch.Tensor, dim: int = 0) -> torch.Tensor:
+        # duplicate local slice across ranks.
+        assert dim == 0
+        return torch.cat([t for _ in range(self.world_size)], dim=0)
+
+
+def _make_group_for_unit_test(
+    rank_in_group: int = 0, world_size: int = 2
+) -> GroupCoordinator:
+    # avoid running GroupCoordinator.__init__ (it wires up real process groups).
+    g = GroupCoordinator.__new__(GroupCoordinator)
+    g.world_size = world_size
+    g.rank_in_group = rank_in_group
+    g.ranks = list(range(world_size))
+    g.use_cpu_custom_send_recv = False
+    g.device_group = None
+    g.cpu_group = None
+    return g
+
+
+def test_irecv_tensor_dict_send_allgather_postprocess_binds_keys(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    def fake_irecv(t: torch.Tensor, *args: Any, **kwargs: Any) -> _DummyWork:
+        t.fill_(1)
+        return _DummyWork()
+
+    monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
+    monkeypatch.setattr(torch.distributed, "irecv", fake_irecv)
+
+    g = _make_group_for_unit_test(rank_in_group=0, world_size=2)
+    # 2 tensors so we can catch late-binding bugs in postprocess closures.
+    metadata_list = [
+        ("a", TensorMetadata("cpu", torch.int32, torch.Size([4]))),
+        ("b", TensorMetadata("cpu", torch.int32, torch.Size([4]))),
+    ]
+    g.recv_object = lambda src=None: metadata_list  # type: ignore[method-assign]
+
+    ag = _DummyAllGatherGroup(world_size=2, rank_in_group=0)
+    td, handles, postprocess = g.irecv_tensor_dict(all_gather_group=ag)
+
+    assert td is not None
+    assert len(handles) == 2
+    assert len(postprocess) == 2
+
+    # before postprocess, dict holds the TP slice (shape 2).
+    assert td["a"].shape == torch.Size([2])
+    assert td["b"].shape == torch.Size([2])
+
+    # simulate worker-side "defer wait": wait + postprocess later.
+    for handle in handles:
+        handle.wait()
+    for fn in postprocess:
+        fn()
+
+    # after postprocess, dict values are reconstructed to full shape (shape 4),
+    # and each key should be updated independently
+    assert td["a"].shape == torch.Size([4])
+    assert td["b"].shape == torch.Size([4])
+    torch.testing.assert_close(td["a"], torch.ones(4, dtype=torch.int32))
+    torch.testing.assert_close(td["b"], torch.ones(4, dtype=torch.int32))
+
+
+def test_async_intermediate_tensors_lazy_wait() -> None:
+    work = _DummyWork()
+    post_calls = {"n": 0}
+
+    def post() -> None:
+        post_calls["n"] += 1
+
+    it = AsyncIntermediateTensors(
+        {"x": torch.tensor([1])},
+        comm_handles=[work],
+        comm_postprocess=[post],
+    )
+
+    # accessing non-tensor attributes should not trigger wait.
+    assert it.kv_connector_output is None
+    assert work.wait_calls == 0
+    assert post_calls["n"] == 0
+
+    # first access of `.tensors` triggers wait + postprocess.
+    _ = it.tensors
+    assert work.wait_calls == 1
+    assert post_calls["n"] == 1
+
+    # subsequent access should not re-wait.
+    _ = it.tensors
+    assert work.wait_calls == 1
+    assert post_calls["n"] == 1
+
+
@ray.remote(num_gpus=1, max_calls=1)
 def send_recv_test_worker(
    monkeypatch: pytest.MonkeyPatch,
@@ -4,8 +4,17 @@
 """Unit tests for ResponsesRequest.to_sampling_params() parameter mapping."""

 import pytest
+import torch
+from openai.types.responses.response_format_text_json_schema_config import (
+    ResponseFormatTextJSONSchemaConfig,
+)
+from pydantic import ValidationError

-from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+    ResponseTextConfig,
+)
+from vllm.sampling_params import StructuredOutputsParams


 class TestResponsesRequestSamplingParams:
@@ -76,9 +85,6 @@ class TestResponsesRequestSamplingParams:

    def test_seed_bounds_validation(self):
        """Test that seed values outside torch.long bounds are rejected."""
-        import torch
-        from pydantic import ValidationError
-
        # Test seed below minimum
        with pytest.raises(ValidationError) as exc_info:
            ResponsesRequest(
@@ -111,3 +117,40 @@ class TestResponsesRequestSamplingParams:
            seed=torch.iinfo(torch.long).max,
        )
        assert request_max.seed == torch.iinfo(torch.long).max
+
+    def test_structured_outputs_passed_through(self):
+        """Test that structured_outputs field is passed to SamplingParams."""
+        structured_outputs = StructuredOutputsParams(grammar="root ::= 'hello'")
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            structured_outputs=structured_outputs,
+        )
+
+        sampling_params = request.to_sampling_params(default_max_tokens=1000)
+
+        assert sampling_params.structured_outputs is not None
+        assert sampling_params.structured_outputs.grammar == "root ::= 'hello'"
+
+    def test_structured_outputs_and_json_schema_conflict(self):
+        """Test that specifying both structured_outputs and json_schema raises."""
+        structured_outputs = StructuredOutputsParams(grammar="root ::= 'hello'")
+        text_config = ResponseTextConfig()
+        text_config.format = ResponseFormatTextJSONSchemaConfig(
+            type="json_schema",
+            name="test",
+            schema={"type": "object"},
+        )
+        request = ResponsesRequest(
+            model="test-model",
+            input="test input",
+            structured_outputs=structured_outputs,
+            text=text_config,
+        )
+
+        with pytest.raises(ValueError) as exc_info:
+            request.to_sampling_params(default_max_tokens=1000)
+
+        assert "Cannot specify both structured_outputs and text.format" in str(
+            exc_info.value
+        )
@@ -45,7 +45,6 @@ class MockModelConfig:
    multimodal_config = MultiModalConfig()
    hf_config = MockHFConfig()
    hf_text_config = MockHFConfig()
-    logits_processor_pattern = None
    logits_processors: list[str] | None = None
    diff_sampling_param: dict | None = None
    allowed_local_media_path: str = ""
@@ -55,16 +54,22 @@ class MockModelConfig:
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
    skip_tokenizer_init = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False

    def get_diff_sampling_param(self):
        return self.diff_sampling_param or {}


+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig
+
+
 def _build_renderer(model_config: MockModelConfig):
    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)

-    return HfRenderer(
-        model_config,
+    return HfRenderer.from_config(
+        MockVllmConfig(model_config),
        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
    )

@@ -44,7 +44,6 @@ class MockModelConfig:
    tokenizer_revision = None
    multimodal_config = MultiModalConfig()
    hf_config = MockHFConfig()
-    logits_processor_pattern = None
    logits_processors: list[str] | None = None
    diff_sampling_param: dict | None = None
    allowed_local_media_path: str = ""
@@ -54,11 +53,17 @@ class MockModelConfig:
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
    skip_tokenizer_init = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False

    def get_diff_sampling_param(self):
        return self.diff_sampling_param or {}


+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig
+
+
 def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
    models = OpenAIServingModels(
        engine_client=engine,
@@ -74,8 +79,8 @@ def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
 def _build_renderer(model_config: MockModelConfig):
    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)

-    return HfRenderer(
-        model_config,
+    return HfRenderer.from_config(
+        MockVllmConfig(model_config),
        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
    )

@@ -23,6 +23,7 @@ class TestGptOssStructuralTagsIntegration:
        """Create a mock tokenizer."""
        tokenizer = Mock()
        tokenizer.encode = Mock(return_value=[1, 2, 3, 4, 5])
+        tokenizer.vocab = {"<|end|>": 6}
        return tokenizer

    @pytest.fixture
@@ -45,7 +45,6 @@ class MockModelConfig:
    multimodal_config: MultiModalConfig = field(default_factory=MultiModalConfig)
    hf_config: MockHFConfig = field(default_factory=MockHFConfig)
    logits_processors: list[str] | None = None
-    logits_processor_pattern: str | None = None
    diff_sampling_param: dict | None = None
    allowed_local_media_path: str = ""
    allowed_media_domains: list[str] | None = None
@@ -53,11 +52,17 @@ class MockModelConfig:
    generation_config: str = "auto"
    skip_tokenizer_init: bool = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False

    def get_diff_sampling_param(self):
        return self.diff_sampling_param or {}


+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig
+
+
 class MockLoRAResolver(LoRAResolver):
    async def resolve_lora(
        self, base_model_name: str, lora_name: str
@@ -91,8 +96,8 @@ def register_mock_resolver():
 def _build_renderer(model_config: MockModelConfig):
    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)

-    return HfRenderer(
-        model_config,
+    return HfRenderer.from_config(
+        MockVllmConfig(model_config),
        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
    )

@@ -27,15 +27,6 @@ MISTRAL_FORMAT_ARGS = [
 MODEL_NAME = "mistralai/Voxtral-Mini-4B-Realtime-2602"


-def _audio_to_base64_pcm16(path: str, target_sr: int = 16000) -> str:
-    """Load audio file, convert to PCM16 @ target sample rate, base64 encode."""
-    audio, _ = librosa.load(path, sr=target_sr, mono=True)
-    # Convert float32 [-1, 1] to int16 [-32768, 32767]
-    audio_int16 = (audio * 32767).astype(np.int16)
-    audio_bytes = audio_int16.tobytes()
-    return base64.b64encode(audio_bytes).decode("utf-8")
-
-
 def _get_websocket_url(server: RemoteOpenAIServer) -> str:
    """Convert HTTP URL to WebSocket URL for realtime endpoint."""
    http_url = server.url_root
@@ -74,12 +65,11 @@ def mary_had_lamb_audio_chunks() -> list[str]:

@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.skip(reason="Voxtral streaming is not yet public")
 async def test_multi_chunk_streaming(
    model_name, mary_had_lamb_audio_chunks, rocm_aiter_fa_attention
 ):
    """Test streaming multiple audio chunks before committing."""
-    server_args = ["--enforce-eager"]
+    server_args = ["--enforce-eager", "--max-model-len", "2048"]

    if model_name.startswith("mistralai"):
        server_args += MISTRAL_FORMAT_ARGS
@@ -521,7 +521,6 @@ class MockModelConfig:
    hf_config = MockHFConfig()
    hf_text_config = MockHFConfig()
    logits_processors: list[str] | None = None
-    logits_processor_pattern = None
    diff_sampling_param: dict | None = None
    allowed_local_media_path: str = ""
    allowed_media_domains: list[str] | None = None
@@ -530,16 +529,22 @@ class MockModelConfig:
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
    skip_tokenizer_init: bool = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False

    def get_diff_sampling_param(self):
        return self.diff_sampling_param or {}


+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig
+
+
 def _build_renderer(model_config: MockModelConfig):
    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)

-    return HfRenderer(
-        model_config,
+    return HfRenderer.from_config(
+        MockVllmConfig(model_config),
        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
    )

@@ -750,8 +755,10 @@ async def test_serving_chat_mistral_token_ids_prompt_is_validated():
    mock_engine.io_processor = MagicMock()

    mock_tokenizer = MagicMock(spec=MistralTokenizer)
-    mock_renderer = MistralRenderer(mock_engine.model_config, tokenizer_kwargs={})
-    mock_renderer._tokenizer = mock_tokenizer
+    mock_renderer = MistralRenderer(
+        MockVllmConfig(mock_engine.model_config),
+        tokenizer=mock_tokenizer,
+    )
    # Force the Mistral chat template renderer to return token IDs.
    # Choose a prompt length that is < max_model_len, but large enough that
    # adding max_tokens should exceed the model context window.
@@ -789,8 +796,10 @@ async def test_serving_chat_mistral_token_ids_prompt_too_long_is_rejected():
    mock_engine.io_processor = MagicMock()

    mock_tokenizer = MagicMock(spec=MistralTokenizer)
-    mock_renderer = MistralRenderer(mock_engine.model_config, tokenizer_kwargs={})
-    mock_renderer._tokenizer = mock_tokenizer
+    mock_renderer = MistralRenderer(
+        MockVllmConfig(mock_engine.model_config),
+        tokenizer=mock_tokenizer,
+    )
    # prompt_token_ids length == max_model_len should be rejected for
    # completion-like requests (ChatCompletionRequest).
    mock_renderer.render_messages_async = AsyncMock(
@@ -125,6 +125,7 @@ class TestInitializeToolSessions:
        engine_client = MagicMock()

        model_config = MagicMock()
+        model_config.max_model_len = 100
        model_config.hf_config.model_type = "test"
        model_config.get_diff_sampling_param.return_value = {}
        engine_client.model_config = model_config
@@ -212,6 +213,7 @@ class TestValidateGeneratorInput:
        engine_client = MagicMock()

        model_config = MagicMock()
+        model_config.max_model_len = 100
        model_config.hf_config.model_type = "test"
        model_config.get_diff_sampling_param.return_value = {}
        engine_client.model_config = model_config
@@ -231,9 +233,6 @@ class TestValidateGeneratorInput:
            chat_template_content_format="auto",
        )

-        # Set max_model_len for testing
-        instance.max_model_len = 100
-
        return instance

    def test_validate_generator_input(self, serving_responses_instance):
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+import os
+
 import httpx
 import pytest
 import pytest_asyncio
@@ -46,6 +48,27 @@ def server(request):
        "--max-model-len",
        "1024",
        "--enforce-eager",
+        # On ROCm (e.g. MI355X/gfx950), bf16 GEMM results can differ by
+        # 1 ULP when the batch dimension (M) changes, because different M
+        # values cause the Tensile backend to select different tile
+        # configurations with different fp32 accumulation orders. With
+        # prefix caching, cache-miss prefills compute all tokens in one
+        # pass (large M) while cache-hit requests compute only the
+        # uncached suffix (small M), seeding a divergence that amplifies
+        # through the residual stream and flips argmax tokens.
+        # See: https://github.com/vllm-project/vllm/issues/33123
+        #
+        # Either disable prefix caching entirely, or enable it with
+        # --deterministic-prefix-caching which forces cache-miss prefills
+        # to split at block boundaries so the suffix GEMM shape is always
+        # identical regardless of cache state.
+        #
+        # Option A: disable prefix caching
+        "--no-enable-prefix-caching",
+        #
+        # Option B: deterministic prefix caching
+        # "--enable-prefix-caching",
+        # "--deterministic-prefix-caching",
    ]

    extra_args = getattr(request, "param", None)
@@ -56,7 +79,11 @@ def server(request):
            else [str(extra_args)]
        )

-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+    envs = os.environ.copy()
+    # See: https://github.com/vllm-project/vllm/pull/33493#issuecomment-3888060787
+    envs["VLLM_ROCM_USE_SKINNY_GEMM"] = "0"
+
+    with RemoteOpenAIServer(MODEL_NAME, args, env_dict=envs) as remote_server:
        yield remote_server


@@ -13,13 +13,12 @@ from vllm.platforms import current_platform
 from ...utils import RemoteOpenAIServer

 MODEL_NAME = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-MAXIMUM_VIDEOS = 4
+MAXIMUM_VIDEOS = 3

 TEST_VIDEO_URLS = [
-    "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4",
-    "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ElephantsDream.mp4",
-    "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerBlazes.mp4",
-    "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4",
+    "https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4",
+    "https://github.com/opencv/opencv/raw/refs/tags/4.12.0/samples/data/vtest.avi",
+    "https://github.com/opencv/opencv/raw/refs/tags/4.12.0/samples/data/Megamind.avi",
 ]


@@ -8,10 +8,8 @@ import requests
 from tests.utils import RemoteOpenAIServer
 from vllm.entrypoints.pooling.score.protocol import RerankResponse, ScoreResponse

-# ColBERT model - using answerai-colbert-small-v1 as it's a smaller model
 MODEL_NAME = "answerdotai/answerai-colbert-small-v1"
-COLBERT_DIM = 96  # This model uses 96-dimensional output
-DTYPE = "half"
+COLBERT_DIM = 96
 MAX_MODEL_LEN = 512


@@ -26,129 +24,119 @@ def server():
        yield remote_server


-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_colbert_rerank(server: RemoteOpenAIServer, model_name: str):
-    """Test ColBERT rerank endpoint."""
-    query = "What is the capital of France?"
-    documents = [
-        "The capital of Brazil is Brasilia.",
-        "The capital of France is Paris.",
-    ]
+class TestColBERTOnline:
+    def test_rerank(self, server: RemoteOpenAIServer):
+        """Test ColBERT rerank endpoint."""
+        query = "What is the capital of France?"
+        documents = [
+            "The capital of Brazil is Brasilia.",
+            "The capital of France is Paris.",
+        ]

-    rerank_response = requests.post(
-        server.url_for("rerank"),
-        json={
-            "model": model_name,
-            "query": query,
-            "documents": documents,
-        },
-    )
-    rerank_response.raise_for_status()
-    rerank = RerankResponse.model_validate(rerank_response.json())
+        rerank_response = requests.post(
+            server.url_for("rerank"),
+            json={
+                "model": MODEL_NAME,
+                "query": query,
+                "documents": documents,
+            },
+        )
+        rerank_response.raise_for_status()
+        rerank = RerankResponse.model_validate(rerank_response.json())

-    assert rerank.id is not None
-    assert rerank.results is not None
-    assert len(rerank.results) == 2
+        assert rerank.id is not None
+        assert rerank.results is not None
+        assert len(rerank.results) == 2

-    # The relevant document (Paris) should have higher score
-    paris_result = next(r for r in rerank.results if r.index == 1)
-    brazil_result = next(r for r in rerank.results if r.index == 0)
+        paris_result = next(r for r in rerank.results if r.index == 1)
+        brazil_result = next(r for r in rerank.results if r.index == 0)

-    assert paris_result.relevance_score > brazil_result.relevance_score
+        assert paris_result.relevance_score > brazil_result.relevance_score

+    def test_rerank_top_n(self, server: RemoteOpenAIServer):
+        """Test ColBERT rerank with top_n parameter."""
+        query = "What is the capital of France?"
+        documents = [
+            "The capital of Brazil is Brasilia.",
+            "The capital of France is Paris.",
+            "Machine learning is a field of AI.",
+        ]

-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_colbert_rerank_top_n(server: RemoteOpenAIServer, model_name: str):
-    """Test ColBERT rerank with top_n parameter."""
-    query = "What is the capital of France?"
-    documents = [
-        "The capital of Brazil is Brasilia.",
-        "The capital of France is Paris.",
-        "Machine learning is a field of AI.",
-    ]
+        rerank_response = requests.post(
+            server.url_for("rerank"),
+            json={
+                "model": MODEL_NAME,
+                "query": query,
+                "documents": documents,
+                "top_n": 2,
+            },
+        )
+        rerank_response.raise_for_status()
+        rerank = RerankResponse.model_validate(rerank_response.json())

-    rerank_response = requests.post(
-        server.url_for("rerank"),
-        json={
-            "model": model_name,
-            "query": query,
-            "documents": documents,
-            "top_n": 2,
-        },
-    )
-    rerank_response.raise_for_status()
-    rerank = RerankResponse.model_validate(rerank_response.json())
+        assert len(rerank.results) == 2
+        assert rerank.results[0].index == 1

-    assert len(rerank.results) == 2
-    # Top result should be about Paris (index 1)
-    assert rerank.results[0].index == 1
+    def test_score(self, server: RemoteOpenAIServer):
+        """Test ColBERT score endpoint."""
+        text_1 = "What is the capital of France?"
+        text_2 = ["The capital of France is Paris.", "Python is a language."]

+        score_response = requests.post(
+            server.url_for("score"),
+            json={
+                "model": MODEL_NAME,
+                "text_1": text_1,
+                "text_2": text_2,
+            },
+        )
+        score_response.raise_for_status()
+        score = ScoreResponse.model_validate(score_response.json())

-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_colbert_score(server: RemoteOpenAIServer, model_name: str):
-    """Test ColBERT score endpoint."""
-    text_1 = "What is the capital of France?"
-    text_2 = ["The capital of France is Paris.", "Python is a language."]
+        assert score.id is not None
+        assert score.data is not None
+        assert len(score.data) == 2

-    score_response = requests.post(
-        server.url_for("score"),
-        json={
-            "model": model_name,
-            "text_1": text_1,
-            "text_2": text_2,
-        },
-    )
-    score_response.raise_for_status()
-    score = ScoreResponse.model_validate(score_response.json())
+        assert score.data[0].score > score.data[1].score

-    assert score.id is not None
-    assert score.data is not None
-    assert len(score.data) == 2
+    def test_token_embed(self, server: RemoteOpenAIServer):
+        """Test ColBERT token_embed task via pooling endpoint."""
+        text = "What is the capital of France?"

-    # The relevant document should have higher score
-    assert score.data[0].score > score.data[1].score
+        pooling_response = requests.post(
+            server.url_for("pooling"),
+            json={
+                "model": MODEL_NAME,
+                "input": text,
+                "task": "token_embed",
+            },
+        )
+        pooling_response.raise_for_status()
+        pooling = pooling_response.json()

+        assert "data" in pooling
+        assert len(pooling["data"]) == 1

-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_colbert_token_embed(server: RemoteOpenAIServer, model_name: str):
-    """Test ColBERT token_embed task via pooling endpoint."""
-    text = "What is the capital of France?"
+        embeddings = pooling["data"][0]["data"]
+        assert isinstance(embeddings, list)
+        assert len(embeddings) > 0
+        assert len(embeddings[0]) == COLBERT_DIM

-    pooling_response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": text,
-            "task": "token_embed",
-        },
-    )
-    pooling_response.raise_for_status()
-    pooling = pooling_response.json()
+    def test_embed_not_supported(self, server: RemoteOpenAIServer):
+        """Test that ColBERT model does not support 'embed' task."""
+        task = "embed"
+        text = "What is the capital of France?"

-    assert "data" in pooling
-    assert len(pooling["data"]) == 1
+        response = requests.post(
+            server.url_for("pooling"),
+            json={
+                "model": MODEL_NAME,
+                "input": text,
+                "task": task,
+            },
+        )

-    # Token embeddings should be 2D
-    embeddings = pooling["data"][0]["data"]
-    assert isinstance(embeddings, list)
-    assert len(embeddings) > 0  # Should have tokens
-    assert len(embeddings[0]) == COLBERT_DIM
-
-
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_colbert_embed_not_supported(server: RemoteOpenAIServer, model_name: str):
-    """Test that ColBERT model does not support 'embed' task."""
-    task = "embed"
-    text = "What is the capital of France?"
-
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": text,
-            "task": task,
-        },
-    )
-
-    assert response.json()["error"]["type"] == "BadRequestError"
-    assert response.json()["error"]["message"].startswith(f"Unsupported task: {task!r}")
+        assert response.json()["error"]["type"] == "BadRequestError"
+        assert response.json()["error"]["message"].startswith(
+            f"Unsupported task: {task!r}"
+        )
@@ -4,8 +4,6 @@ from typing import NamedTuple

 import pytest
 import torch
-from packaging.version import Version
-from transformers import __version__ as TRANSFORMERS_VERSION

 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
@@ -46,31 +44,13 @@ class MRoPETestInfo(NamedTuple):
    marks: list[pytest.MarkDecorator] = []


-TRANSFORMERS_BASE_VERSION = Version(TRANSFORMERS_VERSION).base_version
-
 MODELS_TO_TEST = [
    MRoPETestInfo(model_name="zai-org/GLM-4.1V-9B-Thinking"),
    MRoPETestInfo(model_name="Qwen/Qwen2-VL-7B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2-VL-72B-Instruct"),
    MRoPETestInfo(model_name="Qwen/Qwen2.5-VL-72B-Instruct"),
-    MRoPETestInfo(
-        model_name="Qwen/Qwen3-VL-4B-Instruct",
-        marks=[
-            pytest.mark.skipif(
-                Version(TRANSFORMERS_BASE_VERSION) < Version("4.57.0"),
-                reason="Qwen3-VL only available after Transformers v4.57",
-            )
-        ],
-    ),
-    MRoPETestInfo(
-        model_name="Qwen/Qwen3-VL-30B-A3B-Instruct",
-        marks=[
-            pytest.mark.skipif(
-                Version(TRANSFORMERS_BASE_VERSION) < Version("4.57.0"),
-                reason="Qwen3-VL only available after Transformers v4.57",
-            )
-        ],
-    ),
+    MRoPETestInfo(model_name="Qwen/Qwen3-VL-4B-Instruct"),
+    MRoPETestInfo(model_name="Qwen/Qwen3-VL-30B-A3B-Instruct"),
 ]

 num_tokens_list = [11, 8192]
@@ -554,10 +554,18 @@ class TestKernelRegistry:
    """Test suite for kernel registry functionality."""

    def setup_method(self):
-        """Clear the registry before each test."""
+        """Save and clear the registry before each test."""
+        from vllm.kernels.helion.register import _REGISTERED_KERNELS
+
+        self._saved_registry = dict(_REGISTERED_KERNELS)
+        _REGISTERED_KERNELS.clear()
+
+    def teardown_method(self):
+        """Restore the registry after each test."""
        from vllm.kernels.helion.register import _REGISTERED_KERNELS

        _REGISTERED_KERNELS.clear()
+        _REGISTERED_KERNELS.update(self._saved_registry)

    def test_get_registered_kernels_returns_copy(self):
        """Test get_registered_kernels returns copy of registry."""
@@ -0,0 +1,331 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.utils.import_utils import has_helion
+
+if not has_helion():
+    pytest.skip(
+        "Helion is not installed. Install with: pip install vllm[helion]",
+        allow_module_level=True,
+    )
+
+from vllm.kernels.helion.config_manager import ConfigManager
+from vllm.kernels.helion.ops.silu_mul_fp8 import (
+    pick_silu_mul_fp8_config,
+    silu_mul_fp8,
+    silu_mul_fp8_baseline,
+)
+
+
+def skip_if_platform_unsupported():
+    try:
+        from vllm.kernels.helion.utils import get_canonical_gpu_name
+
+        if not torch.cuda.is_available():
+            pytest.skip("CUDA not available")
+
+        platform = get_canonical_gpu_name()
+
+        try:
+            config_manager = ConfigManager.get_instance()
+        except RuntimeError:
+            config_manager = ConfigManager()
+
+        configs = config_manager.get_platform_configs("silu_mul_fp8", platform)
+        if len(configs) == 0:
+            pytest.skip("Current GPU platform not supported for silu_mul_fp8 kernel")
+
+    except (ImportError, RuntimeError, KeyError):
+        pytest.skip("Error detecting platform support for silu_mul_fp8 kernel")
+
+
+@pytest.fixture(autouse=True)
+def reset_config_manager_singleton():
+    ConfigManager.reset_instance()
+    ConfigManager()
+    yield
+    ConfigManager.reset_instance()
+
+
+class TestSiluMulFp8ConfigPicker:
+    def test_config_picker_exact_match(self):
+        config_keys = [
+            "intermediate_2048_batchsize_256",
+            "intermediate_4096_batchsize_256",
+        ]
+
+        input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        args = (input_tensor, scale)
+
+        selected_key = pick_silu_mul_fp8_config(args, config_keys)
+        assert selected_key == "intermediate_2048_batchsize_256"
+
+    def test_config_picker_closest_match(self):
+        config_keys = [
+            "intermediate_2048_batchsize_256",
+            "intermediate_4096_batchsize_256",
+        ]
+        # Use 7000 (intermediate_size=3500) which is closer to 4096 than 2048
+        input_tensor = torch.randn(32, 7000, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        args = (input_tensor, scale)
+
+        selected_key = pick_silu_mul_fp8_config(args, config_keys)
+        assert selected_key == "intermediate_4096_batchsize_256"
+
+    def test_config_picker_fallback_to_default(self):
+        config_keys = ["default", "some_other_key"]
+
+        input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        args = (input_tensor, scale)
+
+        selected_key = pick_silu_mul_fp8_config(args, config_keys)
+        assert selected_key == "default"
+
+    def test_config_picker_no_configs(self):
+        config_keys: list[str] = []
+
+        input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        args = (input_tensor, scale)
+
+        selected_key = pick_silu_mul_fp8_config(args, config_keys)
+        assert selected_key is None
+
+    @pytest.mark.parametrize("intermediate_size", [2048, 4096, 5120])
+    def test_config_picker_different_sizes(self, intermediate_size):
+        config_keys = [
+            "intermediate_2048_batchsize_256",
+            "intermediate_4096_batchsize_256",
+            "intermediate_5120_batchsize_256",
+        ]
+
+        input_tensor = torch.randn(
+            32, 2 * intermediate_size, dtype=torch.bfloat16, device="cuda"
+        )
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        args = (input_tensor, scale)
+
+        selected_key = pick_silu_mul_fp8_config(args, config_keys)
+        expected_key = f"intermediate_{intermediate_size}_batchsize_256"
+        assert selected_key == expected_key
+
+
+class TestSiluMulFp8Correctness:
+    @pytest.mark.parametrize("batch_size", [1, 8, 32, 128])
+    @pytest.mark.parametrize("intermediate_size", [2048, 3000, 3500, 4096, 5000])
+    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+    def test_silu_mul_fp8_correctness(self, batch_size, intermediate_size, dtype):
+        skip_if_platform_unsupported()
+
+        input_size = 2 * intermediate_size
+        input_tensor = torch.randn(batch_size, input_size, dtype=dtype, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        reference_output = silu_mul_fp8_baseline(input_tensor, scale)
+        helion_output = silu_mul_fp8(input_tensor, scale)
+
+        assert helion_output.shape == reference_output.shape
+        assert helion_output.dtype == torch.float8_e4m3fn
+        assert reference_output.dtype == torch.float8_e4m3fn
+
+        ref_f32 = reference_output.to(torch.float32)
+        helion_f32 = helion_output.to(torch.float32)
+        # FP8 E4M3 has limited precision. Values near quantization boundaries
+        # can round differently due to intermediate precision differences.
+        torch.testing.assert_close(
+            helion_f32,
+            ref_f32,
+            atol=0.05,
+            rtol=0.05,
+            msg=f"Mismatch at batch={batch_size}, size={intermediate_size}",
+        )
+
+    def test_silu_mul_fp8_shape_inference(self):
+        skip_if_platform_unsupported()
+        batch_size, input_size = 32, 8192
+        intermediate_size = input_size // 2
+
+        input_tensor = torch.randn(
+            batch_size, input_size, dtype=torch.bfloat16, device="cuda"
+        )
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        output = silu_mul_fp8(input_tensor, scale)
+
+        expected_shape = (batch_size, intermediate_size)
+        assert output.shape == expected_shape
+        assert output.dtype == torch.float8_e4m3fn
+
+    def test_silu_mul_fp8_scale_variations(self):
+        skip_if_platform_unsupported()
+        batch_size, input_size = 16, 4096
+
+        input_tensor = torch.randn(
+            batch_size, input_size, dtype=torch.bfloat16, device="cuda"
+        )
+
+        scales = [0.1, 0.5, 1.0, 2.0, 10.0]
+
+        for scale_val in scales:
+            scale = torch.tensor([scale_val], dtype=torch.float32, device="cuda")
+
+            reference_output = silu_mul_fp8_baseline(input_tensor, scale)
+            helion_output = silu_mul_fp8(input_tensor, scale)
+            ref_f32 = reference_output.to(torch.float32)
+            helion_f32 = helion_output.to(torch.float32)
+
+            torch.testing.assert_close(
+                helion_f32,
+                ref_f32,
+                atol=0.05,
+                rtol=0.05,
+                msg=f"Mismatch for scale={scale_val}",
+            )
+
+    @pytest.mark.parametrize(
+        "shape",
+        [
+            (1, 4096),
+            (16, 4096),
+            (128, 4096),
+            (1024, 4096),
+            (1, 8192),
+            (16, 8192),
+            (128, 8192),
+        ],
+    )
+    def test_silu_mul_fp8_various_shapes(self, shape):
+        skip_if_platform_unsupported()
+
+        input_tensor = torch.randn(*shape, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        reference_output = silu_mul_fp8_baseline(input_tensor, scale)
+        helion_output = silu_mul_fp8(input_tensor, scale)
+
+        assert helion_output.shape == reference_output.shape
+
+        ref_f32 = reference_output.to(torch.float32)
+        helion_f32 = helion_output.to(torch.float32)
+
+        torch.testing.assert_close(
+            helion_f32, ref_f32, atol=0.05, rtol=0.05, msg=f"Mismatch for shape={shape}"
+        )
+
+
+def silu_mul_fp8_pytorch(input: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
+    """Pure PyTorch reference using F.silu.
+
+    This matches vLLM's SiluAndMul.forward_native exactly:
+    F.silu(x[..., :d]) * x[..., d:]
+    """
+    d = input.shape[-1] // 2
+    result = F.silu(input[..., :d]) * input[..., d:]
+    return (result.to(torch.float32) / scale).to(torch.float8_e4m3fn)
+
+
+class TestSiluMulFp8PytorchReference:
+    """Tests comparing Helion kernel against pure PyTorch implementation.
+
+    Uses tighter tolerance since both use PyTorch's FP8 conversion
+    (same rounding mode), unlike the vLLM C++ baseline which uses
+    NVIDIA's hardware FP8 conversion with different rounding.
+    """
+
+    @pytest.mark.parametrize("batch_size", [1, 8, 32, 128, 256])
+    @pytest.mark.parametrize("intermediate_size", [1024, 2048, 4096])
+    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+    def test_silu_mul_fp8_vs_pytorch(self, batch_size, intermediate_size, dtype):
+        skip_if_platform_unsupported()
+
+        input_tensor = torch.randn(
+            batch_size, 2 * intermediate_size, dtype=dtype, device="cuda"
+        )
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        pytorch_output = silu_mul_fp8_pytorch(input_tensor, scale)
+        helion_output = silu_mul_fp8(input_tensor, scale)
+
+        assert helion_output.shape == pytorch_output.shape
+        assert helion_output.dtype == torch.float8_e4m3fn
+
+        pytorch_f32 = pytorch_output.to(torch.float32)
+        helion_f32 = helion_output.to(torch.float32)
+
+        # Tolerance accounts for FP8 quantization boundary effects
+        torch.testing.assert_close(
+            helion_f32,
+            pytorch_f32,
+            atol=0.05,
+            rtol=0.05,
+            msg=(
+                f"Mismatch at batch={batch_size}, size={intermediate_size}, "
+                f"dtype={dtype}"
+            ),
+        )
+
+    @pytest.mark.parametrize(
+        "shape",
+        [
+            (1, 2, 4096),  # 3D input
+            (2, 4, 2048),  # 3D input
+            (1, 1, 1, 8192),  # 4D input
+        ],
+    )
+    def test_silu_mul_fp8_multidim_vs_pytorch(self, shape):
+        skip_if_platform_unsupported()
+
+        input_tensor = torch.randn(*shape, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        pytorch_output = silu_mul_fp8_pytorch(input_tensor, scale)
+        helion_output = silu_mul_fp8(input_tensor, scale)
+
+        assert helion_output.shape == pytorch_output.shape
+
+        pytorch_f32 = pytorch_output.to(torch.float32)
+        helion_f32 = helion_output.to(torch.float32)
+
+        torch.testing.assert_close(
+            helion_f32,
+            pytorch_f32,
+            atol=0.05,
+            rtol=0.05,
+            msg=f"Mismatch for shape={shape}",
+        )
+
+
+class TestSiluMulFp8Integration:
+    def test_kernel_registration_integration(self):
+        from vllm.kernels.helion.register import get_registered_kernels
+
+        registered_kernels = get_registered_kernels()
+        assert "silu_mul_fp8" in registered_kernels
+
+        kernel_wrapper = registered_kernels["silu_mul_fp8"]
+        assert kernel_wrapper.op_name == "silu_mul_fp8"
+        assert kernel_wrapper._config_picker is not None
+
+    def test_fake_impl_functionality(self):
+        skip_if_platform_unsupported()
+        from vllm.kernels.helion.register import get_registered_kernels
+
+        input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+        registered_kernels = get_registered_kernels()
+        kernel_wrapper = registered_kernels["silu_mul_fp8"]
+        fake_impl = kernel_wrapper._fake_impl
+
+        fake_output = fake_impl(input_tensor, scale)
+
+        expected_shape = (32, 2048)
+        assert fake_output.shape == expected_shape
+        assert fake_output.dtype == torch.float8_e4m3fn
+        assert fake_output.device == input_tensor.device
@@ -71,7 +71,8 @@ def quant_fp8_per_tensor_batches(a):

    for i in range(num_batches):
        a_fp8, a_global_sf = input_to_float8(a[i])
-        a_global_sf = 1.0 / a_global_sf
+        if a_global_sf.numel() == 1:
+            a_global_sf = a_global_sf.view(1, 1)
        a_quant.append(a_fp8)
        a_scales.append(a_global_sf)

@@ -81,6 +82,20 @@ def quant_fp8_per_tensor_batches(a):
    return result_a_quant, result_a_scales


+def check_accuracy(ref_output, actual_output, atol=0.1, rtol=0.85, percent=0.925):
+    close = torch.isclose(ref_output, actual_output, atol=atol, rtol=rtol)
+    match_ratio = close.float().mean()
+    assert match_ratio >= percent, (
+        f"Match ratio {match_ratio:.4f} is below the threshold {percent:.4f}"
+    )
+
+    mismatch_percent = 1.0 - match_ratio.item()
+    assert mismatch_percent <= 1 - percent, (
+        f"Mismatch percentage {mismatch_percent:.4f} is above the threshold "
+        f"{1 - percent:.4f}"
+    )
+
+
@dataclass
 class TestData:
    hidden_states: torch.Tensor
@@ -104,14 +119,16 @@ class TestData:
        is_gated = activation.is_gated

        hidden_states = torch.randn((m, k), device="cuda", dtype=torch.bfloat16) / 10
-        w13 = torch.randn(
-            (e, (2 * n) if is_gated else n, k), device="cuda", dtype=torch.bfloat16
+        w13 = (
+            torch.randn(
+                (e, (2 * n) if is_gated else n, k), device="cuda", dtype=torch.bfloat16
+            )
+            / 10
        )
-        w2 = torch.randn((e, k, n), device="cuda", dtype=torch.bfloat16)
+        w2 = torch.randn((e, k, n), device="cuda", dtype=torch.bfloat16) / 10

        # Scale to fp8
        _, a1_scale = input_to_float8(hidden_states)
-        a1_scale = 1.0 / a1_scale
        a2_scale = torch.scalar_tensor(1.0).to(device="cuda").to(dtype=torch.float32)
        w13_quantized, w13_weight_scale = quant_fp8_per_tensor_batches(w13)
        w2_quantized, w2_weight_scale = quant_fp8_per_tensor_batches(w2)
@@ -124,14 +141,16 @@ class TestData:
        layer.w2_input_scale = a2_scale
        layer.w13_weight_scale = w13_weight_scale
        layer.w2_weight_scale = w2_weight_scale
+        layer.activation = activation
        # Setup dummy config.
        layer.moe_parallel_config = mk.FusedMoEParallelConfig.make_no_parallel()

        # flashinfer expects swapped rows for w13
-        layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
+        if is_gated:
+            layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
        if is_trtllm:
            rotate_weights_for_fi_trtllm_fp8_per_tensor_moe(
-                layer.w13_weight, layer.w2_weight
+                layer.w13_weight, layer.w2_weight, is_gated
            )
            register_scales_for_trtllm_fp8_per_tensor_moe(
                layer,
@@ -162,12 +181,14 @@ class TestData:
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
+@pytest.mark.parametrize("activation", [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL])
 def test_flashinfer_per_tensor_moe_fp8_no_graph(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
+    activation: MoEActivation,
    monkeypatch,
 ):
    if not current_platform.has_device_capability(100):
@@ -175,7 +196,9 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
    set_random_seed(7)
    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
    with set_current_vllm_config(vllm_config):
-        td = TestData.make_moe_tensors_8bit(m, k, n, e, is_trtllm=True)
+        td = TestData.make_moe_tensors_8bit(
+            m, k, n, e, is_trtllm=True, activation=activation
+        )

        score = torch.randn((m, e), device="cuda", dtype=torch.bfloat16)
        topk_weights, topk_ids = Llama4MoE.custom_routing_function(
@@ -200,7 +223,7 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=False,
-            activation=MoEActivation.SILU,
+            activation=activation,
            global_num_experts=e,
            expert_map=None,
            apply_router_weight_on_input=True,
@@ -219,7 +242,13 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
            apply_router_weight_on_input=True,
        )

-        torch.testing.assert_close(output, flashinfer_output, atol=5.5e-2, rtol=1e-2)
+        check_accuracy(
+            ref_output=output,
+            actual_output=flashinfer_output,
+            atol=0.1,
+            rtol=0.85,
+            percent=0.925,
+        )


@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@@ -320,8 +349,13 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
            expert_map=None,
            apply_router_weight_on_input=True,
        )
-        torch.testing.assert_close(
-            output, flashinfer_cutlass_output, atol=5.5e-2, rtol=1e-2
+
+        check_accuracy(
+            ref_output=output,
+            actual_output=flashinfer_cutlass_output,
+            atol=0.1,
+            rtol=0.85,
+            percent=0.925,
        )


@@ -364,3 +398,80 @@ def test_convert_moe_weights_to_flashinfer_trtllm_block_layout(

    assert w13_converted.shape[0] == num_experts
    assert w2_converted.shape[0] == num_experts
+
+
+def test_flashinfer_blockscale_fp8_none_expert_group(monkeypatch):
+    """Test that flashinfer_fused_moe_blockscale_fp8 handles num_expert_group=None.
+
+    Regression test for https://github.com/vllm-project/vllm/issues/34477
+    MiniMax-M2.1 uses sigmoid scoring with e_score_correction_bias but no
+    grouped top-k, resulting in num_expert_group=None. This triggered a crash
+    in the flashinfer kernel when DeepSeekV3 routing was selected.
+    """
+    if not current_platform.has_device_capability(100):
+        pytest.skip("Test requires SM >= 100 (Blackwell)")
+
+    import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401
+    from tests.kernels.quant_utils import native_per_token_group_quant_fp8
+
+    set_random_seed(7)
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
+
+    e = 16  # num_experts (must be divisible by 4)
+    topk = 6  # top_k > 1 triggers DeepSeekV3 routing with sigmoid
+    m, n, k = 10, 4096, 5120
+    block_shape = [128, 128]
+    block_k = block_shape[1]
+
+    with set_current_vllm_config(vllm_config):
+        # Create BF16 hidden states
+        x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16) / 10
+
+        # Create FP8 block-scale quantized weights
+        w13_bf16 = torch.randn((e, 2 * n, k), device="cuda", dtype=torch.bfloat16) / 10
+        w2_bf16 = torch.randn((e, k, n), device="cuda", dtype=torch.bfloat16) / 10
+
+        # Quantize weights per-block to FP8
+        w13_fp8_list, w13_scale_list = [], []
+        w2_fp8_list, w2_scale_list = [], []
+        for i in range(e):
+            wq, ws = native_per_token_group_quant_fp8(w13_bf16[i], block_k)
+            w13_fp8_list.append(wq)
+            w13_scale_list.append(ws)
+
+            wq, ws = native_per_token_group_quant_fp8(w2_bf16[i], block_k)
+            w2_fp8_list.append(wq)
+            w2_scale_list.append(ws)
+
+        w13_fp8 = torch.stack(w13_fp8_list)
+        w13_scale = torch.stack(w13_scale_list)
+        w2_fp8 = torch.stack(w2_fp8_list)
+        w2_scale = torch.stack(w2_scale_list)
+
+        # DeepSeekV3 routing uses float32 logits + optional bias
+        routing_logits = torch.randn((m, e), device="cuda", dtype=torch.float32)
+        routing_bias = torch.randn(e, device="cuda", dtype=torch.float32)
+
+        # This should NOT crash with num_expert_group=None
+        output = torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
+            routing_logits=routing_logits,
+            routing_bias=routing_bias,
+            x=x,
+            w13_weight=w13_fp8,
+            w13_weight_scale_inv=w13_scale,
+            w2_weight=w2_fp8,
+            w2_weight_scale_inv=w2_scale,
+            global_num_experts=e,
+            top_k=topk,
+            num_expert_group=None,
+            topk_group=None,
+            intermediate_size=n,
+            expert_offset=0,
+            local_num_experts=e,
+            block_shape=block_shape,
+            routing_method_type=RoutingMethodType.DeepSeekV3,
+            routed_scaling=1.0,
+        )
+
+        assert output is not None
+        assert output.shape == (m, k)
@@ -7,11 +7,6 @@ import vllm._custom_ops as ops
 from tests.kernels.quant_utils import per_block_cast_to_int8
 from tests.kernels.quantization.nvfp4_utils import FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import (
-    TritonExperts,
-    fused_experts,
-    fused_topk,
-)
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
@@ -24,10 +19,15 @@ from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
    BatchedTritonExperts,
    NaiveBatchedExperts,
 )
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    TritonExperts,
+    fused_experts,
+)
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
    MoEPrepareAndFinalizeNoEP,
 )
+from vllm.model_executor.layers.fused_moe.router.fused_topk_router import fused_topk
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.utils.deep_gemm import per_block_cast_to_fp8
 from vllm.utils.math_utils import round_up
@@ -507,7 +507,8 @@ def test_apc_single_prompt_block_align_alignment(
    vllm_runner_kwargs["enable_prefix_caching"] = True
    with vllm_runner(**vllm_runner_kwargs) as vllm_model:
        # Retrieve the default mamba state block size
-        mamba_block_size = vllm_model.llm.llm_engine.cache_config.mamba_block_size
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+        mamba_block_size = vllm_config.cache_config.mamba_block_size

    # In case the hybrid model does not have the
    # "mamba_block_size" assume a fixed constant
@@ -660,7 +661,8 @@ def test_apc_multiple_prompts_block_align_alignment(
    vllm_runner_kwargs["enable_prefix_caching"] = True
    with vllm_runner(**vllm_runner_kwargs) as vllm_model:
        # Retrieve the default mamba state block size
-        mamba_block_size = vllm_model.llm.llm_engine.cache_config.mamba_block_size
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+        mamba_block_size = vllm_config.cache_config.mamba_block_size

    # In case the hybrid model does not have the
    # "mamba_block_size" assume a fixed constant
@@ -25,7 +25,8 @@ def test_classify_models(
    with vllm_runner(
        model, max_model_len=512, dtype=dtype, enable_prefix_caching=True
    ) as vllm_model:
-        cache_config = vllm_model.llm.llm_engine.cache_config
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+        cache_config = vllm_config.cache_config
        assert cache_config.enable_prefix_caching

        # First Run
@@ -74,7 +75,8 @@ def test_embed_models(
        max_model_len=None,
        enable_prefix_caching=True,
    ) as vllm_model:
-        cache_config = vllm_model.llm.llm_engine.cache_config
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+        cache_config = vllm_config.cache_config
        assert cache_config.enable_prefix_caching

        # First Run
@@ -106,5 +108,6 @@ def test_non_causal_models(
    hf_runner, vllm_runner, example_prompts, model: str, dtype: str
 ) -> None:
    with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
-        cache_config = vllm_model.llm.llm_engine.cache_config
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+        cache_config = vllm_config.cache_config
        assert not cache_config.enable_prefix_caching
@@ -1,16 +1,47 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests for ColBERT late interaction scoring."""
+"""Tests for ColBERT late interaction scoring.
+
+Tests are parametrized across multiple ColBERT backbones to ensure the
+generic ColBERT support works with different encoder architectures.
+"""

 import pytest
 import torch

 from vllm.entrypoints.pooling.score.utils import compute_maxsim_score

-# ColBERT model - using answerai-colbert-small-v1 as it's a smaller model
-# suitable for testing (based on BERT-base)
-COLBERT_MODEL = "answerdotai/answerai-colbert-small-v1"
-COLBERT_DIM = 96  # This model uses 96-dimensional output
+# -----------------------------------------------------------------------
+# Model definitions: (model_name, colbert_dim, extra vllm_runner kwargs)
+# -----------------------------------------------------------------------
+COLBERT_MODELS = {
+    "bert": {
+        "model": "answerdotai/answerai-colbert-small-v1",
+        "colbert_dim": 96,
+        "max_model_len": 512,
+        "extra_kwargs": {},
+    },
+    "modernbert": {
+        "model": "lightonai/GTE-ModernColBERT-v1",
+        "colbert_dim": 128,
+        "max_model_len": 299,
+        "extra_kwargs": {
+            "hf_overrides": {
+                "architectures": ["ColBERTModernBertModel"],
+            },
+        },
+    },
+    "jina": {
+        "model": "jinaai/jina-colbert-v2",
+        "colbert_dim": 128,
+        "max_model_len": 8192,
+        "extra_kwargs": {
+            "hf_overrides": {
+                "architectures": ["ColBERTJinaRobertaModel"],
+            },
+        },
+    },
+}

 TEXTS_1 = [
    "What is the capital of France?",
@@ -25,80 +56,121 @@ TEXTS_2 = [
 DTYPE = "half"


+# -----------------------------------------------------------------------
+# Fixtures
+# -----------------------------------------------------------------------
+
+
+@pytest.fixture(params=list(COLBERT_MODELS.keys()), scope="module")
+def colbert_spec(request):
+    """Return the model spec dict for the current parametrization."""
+    return COLBERT_MODELS[request.param]
+
+
@pytest.fixture(scope="module")
-def colbert_model_name():
-    return COLBERT_MODEL
+def colbert_model_name(colbert_spec):
+    return colbert_spec["model"]


-def test_colbert_token_embed(vllm_runner, colbert_model_name):
+@pytest.fixture(scope="module")
+def colbert_dim(colbert_spec):
+    return colbert_spec["colbert_dim"]
+
+
+@pytest.fixture(scope="module")
+def colbert_max_model_len(colbert_spec):
+    return colbert_spec["max_model_len"]
+
+
+@pytest.fixture(scope="module")
+def colbert_extra_kwargs(colbert_spec):
+    return colbert_spec["extra_kwargs"]
+
+
+# -----------------------------------------------------------------------
+# Tests
+# -----------------------------------------------------------------------
+
+
+def test_colbert_token_embed(
+    vllm_runner,
+    colbert_model_name,
+    colbert_dim,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
    """Test that ColBERT model produces token embeddings."""
    with vllm_runner(
        colbert_model_name,
        runner="pooling",
        dtype=DTYPE,
-        max_model_len=512,
+        max_model_len=colbert_max_model_len,
        enforce_eager=True,
+        **colbert_extra_kwargs,
    ) as vllm_model:
-        # Get token embeddings for a single text
        outputs = vllm_model.token_embed([TEXTS_1[0]])

        assert len(outputs) == 1
-        # Token embeddings should be 2D: [num_tokens, colbert_dim]
        emb = torch.tensor(outputs[0])
        assert emb.dim() == 2
-        assert emb.shape[1] == COLBERT_DIM
-        # Should have at least a few tokens
+        assert emb.shape[1] == colbert_dim
        assert emb.shape[0] > 1


-def test_colbert_late_interaction_1_to_1(vllm_runner, colbert_model_name):
+def test_colbert_late_interaction_1_to_1(
+    vllm_runner,
+    colbert_model_name,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
    """Test ColBERT late interaction scoring with 1:1 query-document pair."""
    with vllm_runner(
        colbert_model_name,
        runner="pooling",
        dtype=DTYPE,
-        max_model_len=512,
+        max_model_len=colbert_max_model_len,
        enforce_eager=True,
+        **colbert_extra_kwargs,
    ) as vllm_model:
-        # Get token embeddings
        q_outputs = vllm_model.token_embed([TEXTS_1[0]])
        d_outputs = vllm_model.token_embed([TEXTS_2[0]])

        q_emb = torch.tensor(q_outputs[0])
        d_emb = torch.tensor(d_outputs[0])

-        # Compute MaxSim manually
        manual_score = compute_maxsim_score(q_emb, d_emb).item()

-        # Use the score API (which should internally use _late_interaction_score)
        vllm_scores = vllm_model.score(TEXTS_1[0], TEXTS_2[0])

        assert len(vllm_scores) == 1
        assert vllm_scores[0] == pytest.approx(manual_score, rel=0.01)


-def test_colbert_late_interaction_1_to_N(vllm_runner, colbert_model_name):
+def test_colbert_late_interaction_1_to_N(
+    vllm_runner,
+    colbert_model_name,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
    """Test ColBERT late interaction scoring with 1:N query-documents."""
    with vllm_runner(
        colbert_model_name,
        runner="pooling",
        dtype=DTYPE,
-        max_model_len=512,
+        max_model_len=colbert_max_model_len,
        enforce_eager=True,
+        **colbert_extra_kwargs,
    ) as vllm_model:
-        # Get token embeddings
        q_outputs = vllm_model.token_embed([TEXTS_1[0]])
        d_outputs = vllm_model.token_embed(TEXTS_2)

        q_emb = torch.tensor(q_outputs[0])

-        # Compute MaxSim manually for each document
        manual_scores = []
        for d_out in d_outputs:
            d_emb = torch.tensor(d_out)
            manual_scores.append(compute_maxsim_score(q_emb, d_emb).item())

-        # Use the score API
        vllm_scores = vllm_model.score(TEXTS_1[0], TEXTS_2)

        assert len(vllm_scores) == 2
@@ -106,27 +178,30 @@ def test_colbert_late_interaction_1_to_N(vllm_runner, colbert_model_name):
            assert vllm_scores[i] == pytest.approx(manual_scores[i], rel=0.01)


-def test_colbert_late_interaction_N_to_N(vllm_runner, colbert_model_name):
+def test_colbert_late_interaction_N_to_N(
+    vllm_runner,
+    colbert_model_name,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
    """Test ColBERT late interaction scoring with N:N query-documents."""
    with vllm_runner(
        colbert_model_name,
        runner="pooling",
        dtype=DTYPE,
-        max_model_len=512,
+        max_model_len=colbert_max_model_len,
        enforce_eager=True,
+        **colbert_extra_kwargs,
    ) as vllm_model:
-        # Get token embeddings
        q_outputs = vllm_model.token_embed(TEXTS_1)
        d_outputs = vllm_model.token_embed(TEXTS_2)

-        # Compute MaxSim manually for each pair
        manual_scores = []
        for q_out, d_out in zip(q_outputs, d_outputs):
            q_emb = torch.tensor(q_out)
            d_emb = torch.tensor(d_out)
            manual_scores.append(compute_maxsim_score(q_emb, d_emb).item())

-        # Use the score API
        vllm_scores = vllm_model.score(TEXTS_1, TEXTS_2)

        assert len(vllm_scores) == 2
@@ -134,8 +209,13 @@ def test_colbert_late_interaction_N_to_N(vllm_runner, colbert_model_name):
            assert vllm_scores[i] == pytest.approx(manual_scores[i], rel=0.01)


-def test_colbert_relevance_ordering(vllm_runner, colbert_model_name):
-    """Test that ColBERT scores relevant documents higher than irrelevant ones."""
+def test_colbert_relevance_ordering(
+    vllm_runner,
+    colbert_model_name,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
+    """Test that ColBERT scores relevant documents higher than irrelevant."""
    query = "What is machine learning?"
    documents = [
        "Machine learning is a subset of artificial intelligence.",
@@ -147,48 +227,73 @@ def test_colbert_relevance_ordering(vllm_runner, colbert_model_name):
        colbert_model_name,
        runner="pooling",
        dtype=DTYPE,
-        max_model_len=512,
+        max_model_len=colbert_max_model_len,
        enforce_eager=True,
+        **colbert_extra_kwargs,
    ) as vllm_model:
        scores = vllm_model.score(query, documents)

        assert len(scores) == 3
-        # ML-related documents should score higher than unrelated Python doc
-        # Document 0 (ML definition) should be most relevant
-        # Document 2 (Deep learning) should also be relevant
-        # Document 1 (Python) should be least relevant
        assert scores[0] > scores[1], "ML doc should score higher than Python doc"
        assert scores[2] > scores[1], "DL doc should score higher than Python doc"


-def test_colbert_embed_not_supported(vllm_runner, colbert_model_name):
+def test_colbert_embed_not_supported(
+    vllm_runner,
+    colbert_model_name,
+    colbert_max_model_len,
+    colbert_extra_kwargs,
+):
    """Test that ColBERT model does not support 'embed' task."""
    with (
        vllm_runner(
            colbert_model_name,
            runner="pooling",
            dtype=DTYPE,
-            max_model_len=512,
+            max_model_len=colbert_max_model_len,
            enforce_eager=True,
+            **colbert_extra_kwargs,
        ) as vllm_model,
        pytest.raises(ValueError, match="Embedding API is not supported"),
    ):
        vllm_model.embed([TEXTS_1[0]])


-def test_colbert_hf_comparison(vllm_runner, colbert_model_name):
-    """Test that vLLM ColBERT produces same embeddings as HuggingFace."""
+# -----------------------------------------------------------------------
+# Per-model HuggingFace comparison tests
+# -----------------------------------------------------------------------
+
+
+def _assert_embeddings_close(vllm_outputs, hf_embeddings):
+    """Assert that vLLM and HuggingFace embeddings match."""
+    for i, (hf_emb, vllm_out) in enumerate(zip(hf_embeddings, vllm_outputs)):
+        vllm_emb = torch.tensor(vllm_out).float()
+
+        assert hf_emb.shape == vllm_emb.shape, (
+            f"Shape mismatch for text {i}: HF {hf_emb.shape} vs vLLM {vllm_emb.shape}"
+        )
+
+        torch.testing.assert_close(
+            vllm_emb,
+            hf_emb,
+            rtol=1e-2,
+            atol=1e-2,
+            msg=f"Embedding mismatch for text {i}",
+        )
+
+
+def test_colbert_hf_comparison_bert(vllm_runner):
+    """Test that vLLM ColBERT produces same embeddings as HuggingFace (BERT)."""
    import torch.nn.functional as F
    from huggingface_hub import hf_hub_download
    from safetensors.torch import load_file
    from transformers import AutoTokenizer, BertModel

+    model_name = COLBERT_MODELS["bert"]["model"]
    test_texts = [TEXTS_1[0], TEXTS_2[0]]

-    # Get vLLM embeddings first (to avoid GPU memory contention)
-    # Use fp32 to match HuggingFace default precision for fair comparison
    with vllm_runner(
-        colbert_model_name,
+        model_name,
        runner="pooling",
        dtype="float32",
        max_model_len=512,
@@ -196,14 +301,11 @@ def test_colbert_hf_comparison(vllm_runner, colbert_model_name):
    ) as vllm_model:
        vllm_outputs = vllm_model.token_embed(test_texts)

-    # Get HuggingFace reference embeddings on CPU
-    # Load the base BERT model and manually apply the ColBERT linear projection
-    hf_tokenizer = AutoTokenizer.from_pretrained(colbert_model_name)
-    hf_bert = BertModel.from_pretrained(colbert_model_name)
+    hf_tokenizer = AutoTokenizer.from_pretrained(model_name)
+    hf_bert = BertModel.from_pretrained(model_name)
    hf_bert.eval()

-    # Load the ColBERT linear weights from safetensors
-    weights_path = hf_hub_download(colbert_model_name, filename="model.safetensors")
+    weights_path = hf_hub_download(model_name, filename="model.safetensors")
    weights = load_file(weights_path)
    linear_weight = weights["linear.weight"]  # [96, 384]

@@ -212,36 +314,103 @@ def test_colbert_hf_comparison(vllm_runner, colbert_model_name):
        inputs = hf_tokenizer(text, return_tensors="pt")
        with torch.no_grad():
            outputs = hf_bert(**inputs)
-            # Get last hidden state: [1, seq_len, 384]
            hidden_states = outputs.last_hidden_state
-            # Apply ColBERT linear projection: [1, seq_len, 96]
            token_emb = F.linear(hidden_states, linear_weight)
-            # L2 normalize
            token_emb = F.normalize(token_emb, p=2, dim=-1)
            hf_embeddings.append(token_emb.squeeze(0).float())

-    # Compare embeddings
-    for i, (hf_emb, vllm_out) in enumerate(zip(hf_embeddings, vllm_outputs)):
-        vllm_emb = torch.tensor(vllm_out).float()
+    _assert_embeddings_close(vllm_outputs, hf_embeddings)

-        # Print first few components for debugging
-        print(f"\n=== Text {i}: '{test_texts[i][:30]}...' ===")
-        print(f"HF shape: {hf_emb.shape}, vLLM shape: {vllm_emb.shape}")
-        print(f"HF first token, first 10 dims:   {hf_emb[0, :10].tolist()}")
-        print(f"vLLM first token, first 10 dims: {vllm_emb[0, :10].tolist()}")
-        print(f"HF last token, first 10 dims:    {hf_emb[-1, :10].tolist()}")
-        print(f"vLLM last token, first 10 dims:  {vllm_emb[-1, :10].tolist()}")

-        # Should have same shape
-        assert hf_emb.shape == vllm_emb.shape, (
-            f"Shape mismatch for text {i}: HF {hf_emb.shape} vs vLLM {vllm_emb.shape}"
-        )
+def test_colbert_hf_comparison_modernbert(vllm_runner):
+    """Test that vLLM ColBERT produces same embeddings as HuggingFace
+    (ModernBERT)."""
+    import torch.nn.functional as F
+    from huggingface_hub import hf_hub_download
+    from safetensors.torch import load_file
+    from transformers import AutoModel, AutoTokenizer

-        # Should have same values (with tolerance for fp16)
-        torch.testing.assert_close(
-            vllm_emb,
-            hf_emb,
-            rtol=1e-2,
-            atol=1e-2,
-            msg=f"Embedding mismatch for text {i}",
-        )
+    spec = COLBERT_MODELS["modernbert"]
+    model_name = spec["model"]
+    test_texts = [TEXTS_1[0], TEXTS_2[0]]
+
+    with vllm_runner(
+        model_name,
+        runner="pooling",
+        dtype="float32",
+        max_model_len=spec["max_model_len"],
+        enforce_eager=True,
+        **spec["extra_kwargs"],
+    ) as vllm_model:
+        vllm_outputs = vllm_model.token_embed(test_texts)
+
+    hf_tokenizer = AutoTokenizer.from_pretrained(model_name)
+    hf_model = AutoModel.from_pretrained(model_name)
+    hf_model.eval()
+
+    # Load projection from sentence-transformers 1_Dense layer
+    dense_path = hf_hub_download(model_name, filename="1_Dense/model.safetensors")
+    dense_weights = load_file(dense_path)
+    linear_weight = dense_weights["linear.weight"]  # [128, 768]
+
+    hf_embeddings = []
+    for text in test_texts:
+        inputs = hf_tokenizer(text, return_tensors="pt")
+        with torch.no_grad():
+            outputs = hf_model(**inputs)
+            hidden_states = outputs.last_hidden_state
+            token_emb = F.linear(hidden_states, linear_weight)
+            token_emb = F.normalize(token_emb, p=2, dim=-1)
+            hf_embeddings.append(token_emb.squeeze(0).float())
+
+    _assert_embeddings_close(vllm_outputs, hf_embeddings)
+
+
+def test_colbert_hf_comparison_jina(vllm_runner):
+    """Test that vLLM ColBERT produces same embeddings as HuggingFace
+    (Jina XLM-RoBERTa)."""
+    import torch.nn.functional as F
+    from huggingface_hub import hf_hub_download
+    from safetensors.torch import load_file
+    from transformers import AutoModel, AutoTokenizer
+
+    spec = COLBERT_MODELS["jina"]
+    model_name = spec["model"]
+    test_texts = [TEXTS_1[0], TEXTS_2[0]]
+
+    with vllm_runner(
+        model_name,
+        runner="pooling",
+        dtype="float32",
+        max_model_len=spec["max_model_len"],
+        enforce_eager=True,
+        **spec["extra_kwargs"],
+    ) as vllm_model:
+        vllm_outputs = vllm_model.token_embed(test_texts)
+
+    hf_tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+    )
+    hf_model = AutoModel.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+    )
+    hf_model.eval()
+
+    # Load projection from main checkpoint
+    weights_path = hf_hub_download(model_name, filename="model.safetensors")
+    weights = load_file(weights_path)
+    linear_weight = weights["linear.weight"]  # [128, 1024]
+
+    hf_embeddings = []
+    for text in test_texts:
+        inputs = hf_tokenizer(text, return_tensors="pt")
+        with torch.no_grad():
+            outputs = hf_model(**inputs)
+            hidden_states = outputs.last_hidden_state
+            token_emb = F.linear(hidden_states.float(), linear_weight.float())
+            token_emb = F.normalize(token_emb, p=2, dim=-1)
+            hf_embeddings.append(token_emb.squeeze(0).float())
+
+    _assert_embeddings_close(vllm_outputs, hf_embeddings)
@@ -961,12 +961,6 @@ VLM_TEST_SETTINGS = {
                limit_mm_per_prompt={"image": 4},
            )
        ],
-        marks=[
-            pytest.mark.skipif(
-                Version(TRANSFORMERS_VERSION) == Version("4.57.1"),
-                reason="This model is broken in Transformers v4.57.1",
-            )
-        ],
    ),
    # regression test for https://github.com/vllm-project/vllm/issues/15122
    "qwen2_5_vl-windows-attention": VLMTestInfo(
@@ -4,16 +4,18 @@
 import json

 import pytest
-import pytest_asyncio
 from mistral_common.audio import Audio
 from mistral_common.protocol.instruct.chunk import AudioChunk, RawAudio, TextChunk
 from mistral_common.protocol.instruct.messages import UserMessage
+from transformers import VoxtralForConditionalGeneration

 from vllm.tokenizers.mistral import MistralTokenizer

 from ....conftest import AudioTestAssets
 from ....utils import RemoteOpenAIServer
+from ...utils import check_logprobs_close
 from .test_ultravox import MULTI_AUDIO_PROMPT, run_multi_audio_test
+from .vlm_utils import model_utils

 MODEL_NAME = "mistralai/Voxtral-Mini-3B-2507"
 MISTRAL_FORMAT_ARGS = [
@@ -26,40 +28,21 @@ MISTRAL_FORMAT_ARGS = [
 ]


-@pytest.fixture()
-def server(request, audio_assets: AudioTestAssets):
-    args = [
-        "--enforce-eager",
-        "--limit-mm-per-prompt",
-        json.dumps({"audio": len(audio_assets)}),
-    ] + MISTRAL_FORMAT_ARGS
-
-    with RemoteOpenAIServer(
-        MODEL_NAME, args, env_dict={"VLLM_AUDIO_FETCH_TIMEOUT": "30"}
-    ) as remote_server:
-        yield remote_server
-
-
-@pytest_asyncio.fixture
-async def client(server):
-    async with server.get_async_client() as async_client:
-        yield async_client
-
-
-def _get_prompt(audio_assets, question):
+def _get_prompt(audio_assets: AudioTestAssets, question: str) -> list[int]:
+    """Build a token-ID prompt via mistral_common for vLLM offline inference."""
    tokenizer = MistralTokenizer.from_pretrained(MODEL_NAME)

    audios = [
-        Audio.from_file(str(audio_assets[i].get_local_path()), strict=False)
-        for i in range(len(audio_assets))
+        Audio.from_file(str(asset.get_local_path()), strict=False)
+        for asset in audio_assets
    ]
    audio_chunks = [
        AudioChunk(input_audio=RawAudio.from_audio(audio)) for audio in audios
    ]

-    text_chunk = TextChunk(text=question)
-    messages = [UserMessage(content=[*audio_chunks, text_chunk]).to_openai()]
-
+    messages = [
+        UserMessage(content=[*audio_chunks, TextChunk(text=question)]).to_openai()
+    ]
    return tokenizer.apply_chat_template(messages=messages)


@@ -77,7 +60,7 @@ def test_models_with_multiple_audios(
    vllm_prompt = _get_prompt(audio_assets, MULTI_AUDIO_PROMPT)
    run_multi_audio_test(
        vllm_runner,
-        [(vllm_prompt, [audio.audio_and_sample_rate for audio in audio_assets])],
+        [(vllm_prompt, [a.audio_and_sample_rate for a in audio_assets])],  # type: ignore[list-item]
        MODEL_NAME,
        dtype=dtype,
        max_tokens=max_tokens,
@@ -86,30 +69,142 @@ def test_models_with_multiple_audios(
    )


-@pytest.mark.asyncio
-async def test_online_serving(client, audio_assets: AudioTestAssets):
-    """Exercises online serving with/without chunked prefill enabled."""
+def test_online_serving(vllm_runner, audio_assets: AudioTestAssets):
+    """Two-layer accuracy and serving validation using Mistral format.

-    def asset_to_chunk(asset):
+    1. Offline vLLM greedy output (runs first to avoid CUDA fork issues
+       with multiprocessing - see vlm_utils/core.py).
+    2. Online OpenAI-compatible API output must match offline — validates
+       that the serving path (chat template, audio encoding, tokenization)
+       does not corrupt anything.
+
+    Steps run sequentially so each releases the GPU before the next starts.
+    """
+
+    question = f"What's happening in these {len(audio_assets)} audio clips?"
+    max_tokens = 10
+    audio_data = [asset.audio_and_sample_rate for asset in audio_assets]
+
+    vllm_prompt = _get_prompt(audio_assets, question)
+    with vllm_runner(
+        MODEL_NAME,
+        dtype="half",
+        enforce_eager=True,
+        tokenizer_mode="mistral",
+        config_format="mistral",
+        load_format="mistral",
+        limit_mm_per_prompt={"audio": len(audio_assets)},
+    ) as vllm_model:
+        offline_outputs = vllm_model.generate_greedy(
+            [vllm_prompt],
+            max_tokens,
+            audios=[audio_data],
+        )
+
+    offline_text = offline_outputs[0][1]
+    assert offline_text, "Offline vLLM inference produced empty output"
+
+    def _asset_to_openai_chunk(asset):
        audio = Audio.from_file(str(asset.get_local_path()), strict=False)
        audio.format = "wav"
-        audio_dict = AudioChunk.from_audio(audio).to_openai()
-        return audio_dict
+        return AudioChunk.from_audio(audio).to_openai()

-    audio_chunks = [asset_to_chunk(asset) for asset in audio_assets]
-    text = f"What's happening in these {len(audio_assets)} audio clips?"
    messages = [
        {
            "role": "user",
-            "content": [*audio_chunks, {"type": "text", "text": text}],
+            "content": [
+                *[_asset_to_openai_chunk(a) for a in audio_assets],
+                {"type": "text", "text": question},
+            ],
        }
    ]

-    chat_completion = await client.chat.completions.create(
-        model=MODEL_NAME, messages=messages, max_tokens=10
+    server_args = [
+        "--enforce-eager",
+        "--limit-mm-per-prompt",
+        json.dumps({"audio": len(audio_assets)}),
+        *MISTRAL_FORMAT_ARGS,
+    ]
+
+    with RemoteOpenAIServer(
+        MODEL_NAME,
+        server_args,
+        env_dict={"VLLM_AUDIO_FETCH_TIMEOUT": "30"},
+    ) as remote_server:
+        client = remote_server.get_client()
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=messages,
+            max_tokens=max_tokens,
+            temperature=0,
+        )
+
+    assert len(completion.choices) == 1
+    choice = completion.choices[0]
+    assert choice.finish_reason == "length"
+    assert choice.message.content == offline_text, (
+        f"Online serving output does not match offline inference.\n"
+        f"  Online:  {choice.message.content!r}\n"
+        f"  Offline: {offline_text!r}"
    )

-    assert len(chat_completion.choices) == 1
-    choice = chat_completion.choices[0]
-    assert choice.message.content == "In the first audio clip, you hear a brief"
-    assert choice.finish_reason == "length"
+
+def test_hf_reference(hf_runner, vllm_runner, audio_assets: AudioTestAssets):
+    """Compare vLLM Mistral-format output against HF Transformers reference.
+
+    Instead of requiring an exact text match (which is brittle across
+    attention backends), we compare per-token logprobs using the standard
+    check_logprobs_close helper: when tokens diverge at a position, each
+    runner's chosen token must appear in the other's top-k logprobs.
+
+    Marked xfail(strict=False) so remaining edge-case mismatches
+    don't block CI.
+    """
+    question = f"What's happening in these {len(audio_assets)} audio clips?"
+    max_tokens = 10
+    num_logprobs = 5
+    audio_data = [asset.audio_and_sample_rate for asset in audio_assets]
+
+    vllm_prompt = _get_prompt(audio_assets, question)
+    with vllm_runner(
+        MODEL_NAME,
+        dtype="half",
+        enforce_eager=True,
+        tokenizer_mode="mistral",
+        config_format="mistral",
+        load_format="mistral",
+        limit_mm_per_prompt={"audio": len(audio_assets)},
+    ) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy_logprobs(
+            [vllm_prompt],
+            max_tokens,
+            num_logprobs,
+            audios=[audio_data],
+        )
+    assert vllm_outputs[0][1], "vLLM inference produced empty output"
+
+    with hf_runner(
+        MODEL_NAME,
+        dtype="half",
+        auto_cls=VoxtralForConditionalGeneration,
+    ) as hf_model:
+        hf_model = model_utils.voxtral_patch_hf_runner(hf_model)
+        hf_outputs = hf_model.generate_greedy_logprobs_limit(
+            [question],
+            max_tokens,
+            num_logprobs,
+            audios=[audio_data],
+        )
+    assert hf_outputs[0][1], "HF Transformers produced empty output"
+
+    print(
+        f"HF Reference Comparison\n"
+        f"  vLLM: {vllm_outputs[0][1]!r}\n"
+        f"  HF:   {hf_outputs[0][1]!r}"
+    )
+    check_logprobs_close(
+        outputs_0_lst=vllm_outputs,
+        outputs_1_lst=hf_outputs,
+        name_0="vllm",
+        name_1="hf",
+    )
@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import asyncio
 from dataclasses import asdict

 import pytest
@@ -10,14 +9,13 @@ from mistral_common.protocol.transcription.request import (
    StreamingMode,
    TranscriptionRequest,
 )
-from mistral_common.tokens.tokenizers.audio import AudioConfig
 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy

 from vllm import LLM, EngineArgs, SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.engine.arg_utils import AsyncEngineArgs
-from vllm.inputs.data import TokensPrompt
-from vllm.v1.engine.async_llm import AsyncLLM, StreamingInput
+from vllm.v1.engine.async_llm import AsyncLLM

 MODEL_NAME = "mistralai/Voxtral-Mini-4B-Realtime-2602"
 ENGINE_CONFIG = dict(
@@ -29,7 +27,7 @@ ENGINE_CONFIG = dict(
    load_format="mistral",
    tokenizer_mode="mistral",
    enforce_eager=True,
-    gpu_memory_utilization=0.4,
+    gpu_memory_utilization=0.9,
 )


@@ -73,7 +71,6 @@ def async_engine() -> AsyncLLM:
    return AsyncLLM.from_engine_args(engine_args)


-@pytest.mark.skip(reason="Voxtral streaming is not yet public")
 def test_voxtral_realtime_forward(audio_assets, tokenizer, engine):
    audio_config = tokenizer.instruct_tokenizer.tokenizer.audio

@@ -115,137 +112,46 @@ def test_voxtral_realtime_forward(audio_assets, tokenizer, engine):
    assert texts == EXPECTED_TEXT


-class RealTimeAudioInput:
-    """
-    This class is used to stream an audio file just as
-    if it would be streamed in real-time.
-    """
-
-    def __init__(self, tokenizer: MistralTokenizer) -> None:
-        self._tokenizer = tokenizer
-        self._config: AudioConfig = (
-            self._tokenizer.instruct_tokenizer.audio_encoder.audio_config
-        )
-
-        self._look_ahead_in_ms = self._config.streaming_look_ahead_ms
-        self._look_back_in_ms = self._config.streaming_look_back_ms
-
-        self._sampling_rate = self._config.sampling_rate
-
-        self._audio: Audio | None = None
-
-        # mutable objects
-        self._start = 0
-
-        n_left_pad_samples = (
-            self._config.raw_audio_length_per_tok * self._config.n_left_pad_tokens
-        )
-        self._end = self.streaming_delay + n_left_pad_samples + self.streaming_size
-        self._queue: asyncio.Queue[StreamingInput | None] = asyncio.Queue()
-
-    @classmethod
-    async def create(cls, audio: Audio, tokenizer: MistralTokenizer):
-        self = cls(tokenizer)
-
-        # we're doing "OFFLINE" encoding here to right & left pad the audio since
-        # we have access to the whole audio
-        # if we'd do an actual online realtime streaming application we
-        # should instead pass `StreamingMode.ONLINE`
-        req = TranscriptionRequest(
-            streaming=StreamingMode.OFFLINE,
-            audio=RawAudio.from_audio(audio),
-            language=None,
-        )
-        audio_enc = self._tokenizer.encode_transcription(req)
-        self._audio = audio_enc.audios[0]
-
-        # add first request
-        await self.add_tokens(audio_enc.tokens)
-
-        return self
-
-    @property
-    def look_ahead(self) -> int:
-        return self._get_len_in_samples(self._look_ahead_in_ms)
-
-    @property
-    def look_back(self) -> int:
-        return self._get_len_in_samples(self._look_back_in_ms)
-
-    @property
-    def streaming_delay(self) -> int:
-        return self._get_len_in_samples(self._config.transcription_delay_ms)
-
-    @property
-    def streaming_size(self) -> int:
-        stream_size_in_ms = 1000 / self._config.frame_rate
-        return self._get_len_in_samples(stream_size_in_ms)
-
-    def _get_len_in_samples(self, len_in_ms: float) -> int:
-        _len_in_s = self._sampling_rate * len_in_ms / 1000
-        assert _len_in_s.is_integer(), _len_in_s
-        len_in_s = int(_len_in_s)
-
-        return len_in_s
-
-    async def add_tokens(self, tokens: list[int]) -> None:
-        assert self._audio is not None
-        if self._start >= len(self._audio.audio_array):
-            self.stop()
-            return
-
-        _end = self._end + self.look_ahead
-        _start = max(0, self._start - self.look_back)
-
-        multi_modal_data = {"audio": (self._audio.audio_array[_start:_end], None)}
-
-        prompt = TokensPrompt(
-            prompt_token_ids=tokens, multi_modal_data=multi_modal_data
-        )
-
-        await self._queue.put(StreamingInput(prompt))
-
-        # increase
-        self._start = self._end
-        self._end = self._end + self.streaming_size
-
-    def stop(self):
-        self._queue.put_nowait(None)
-
-    async def generator(self):
-        while (item := await self._queue.get()) is not None:
-            yield item
-
-
@pytest.mark.asyncio
-@pytest.mark.skip(reason="Voxtral streaming is not yet public")
 async def test_voxtral_realtime_generator(audio_assets, tokenizer, async_engine):
+    # Lazy import to avoid CUDA-reinitialization error
+    from vllm.model_executor.models.voxtral_realtime import VoxtralRealtimeBuffer
+
    sampling_params = SamplingParams(temperature=0.0, max_tokens=1)
+    audio_config = tokenizer.instruct_tokenizer.audio_encoder.audio_config

    output_tokens_list = []
    for i, audio_asset in enumerate(audio_assets):
        output_tokens = []
        audio = Audio.from_file(audio_asset.get_local_path(), strict=False)
-        streaming_input = await RealTimeAudioInput.create(
-            audio=audio, tokenizer=tokenizer
+
+        req = TranscriptionRequest(
+            streaming=StreamingMode.OFFLINE,
+            audio=RawAudio.from_audio(audio),
+            language=None,
        )
+        audio_enc = tokenizer.encode_transcription(req)
+
+        buffer = VoxtralRealtimeBuffer(audio_config, audio_enc.tokens)
+        await buffer.append_audio(audio_enc.audios[0].audio_array)
+        await buffer.append_audio(None)

        request_id = f"session-{i}"

        async for resp in async_engine.generate(
-            prompt=streaming_input.generator(),
+            prompt=buffer.get_input_stream(),
            sampling_params=sampling_params,
            request_id=request_id,
        ):
            tokens = resp.outputs[0].token_ids[-1:]
-
            output_tokens.extend(tokens)
-            await streaming_input.add_tokens(tokens)
+            await buffer.append_tokens(tokens)

        output_tokens_list.append(output_tokens)

-    texts = [tokenizer.decode(output_tokens) for output_tokens in output_tokens_list]
-
+    texts = [
+        tokenizer.decode(output_tokens, special_token_policy=SpecialTokenPolicy.IGNORE)
+        for output_tokens in output_tokens_list
+    ]
    texts[1] = texts[1].replace("a base hit", "OBS").replace("oh my", "oh, my")
-
    assert texts == EXPECTED_TEXT
@@ -1215,3 +1215,91 @@ def tarsier_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
        hf_processor.patch_size = vision_encoder_info.get_patch_size()

    return hf_model
+
+
+def voxtral_patch_hf_runner(hf_model: "HfRunner") -> "HfRunner":
+    """Patch HfRunner for Voxtral's conversation-based processor.
+
+    Two issues in HfRunner require patching:
+
+    1. VoxtralProcessor requires ``apply_chat_template()`` with conversation
+       dicts (accepting ``url``, ``path``, or ``base64`` audio) rather than
+       the standard ``processor(text=, audio=, sampling_rate=)`` interface.
+    2. HfRunner.get_inputs cannot handle multi-audio per prompt because it
+       mis-unpacks ``[(arr1, sr1), (arr2, sr2)]`` via a ``len == 2`` check.
+
+    We override ``get_inputs`` to build conversation dicts and call
+    ``apply_chat_template`` directly, bypassing both issues. We also wrap
+    ``model.generate`` to strip prompt tokens before decoding, since
+    HfRunner.generate calls batch_decode on the full sequence (prompt +
+    generated).
+    """
+
+    import base64
+    import io
+
+    import soundfile as sf
+
+    processor = hf_model.processor
+
+    def _audio_to_base64(audio_array, sample_rate: int) -> str:
+        """Encode a numpy audio array as a base64 WAV string."""
+        buf = io.BytesIO()
+        sf.write(buf, audio_array, int(sample_rate), format="WAV")
+        return base64.b64encode(buf.getvalue()).decode("ascii")
+
+    def patched_get_inputs(prompts, images=None, videos=None, audios=None, **kwargs):
+        all_inputs = []
+        for i, prompt in enumerate(prompts):
+            content: list[dict] = []
+
+            if audios is not None and audios[i] is not None:
+                items = audios[i]
+                if not isinstance(items, list):
+                    items = [items]
+                for item in items:
+                    if isinstance(item, (list, tuple)) and len(item) == 2:
+                        arr, sr = item
+                    else:
+                        arr, sr = item, 16_000
+                    content.append(
+                        {
+                            "type": "audio",
+                            "base64": _audio_to_base64(arr, sr),
+                        }
+                    )
+
+            content.append({"type": "text", "text": prompt})
+
+            inputs = processor.apply_chat_template(
+                [{"role": "user", "content": content}]
+            )
+            if hasattr(inputs, "to"):
+                inputs = inputs.to(dtype=hf_model.dtype)
+            all_inputs.append(inputs)
+
+        return all_inputs
+
+    _orig_generate = hf_model.model.generate
+
+    def patched_generate(*args, **kwargs):
+        """Strip prompt tokens so only generated tokens are decoded."""
+        input_ids = kwargs.get("input_ids")
+        if input_ids is None and args:
+            input_ids = args[0]
+        prompt_len = input_ids.shape[1] if input_ids is not None else 0
+
+        output = _orig_generate(*args, **kwargs)
+        if prompt_len:
+            if isinstance(output, torch.Tensor):
+                output = output[:, prompt_len:]
+            else:
+                # GenerateDecoderOnlyOutput - trim sequences but preserve
+                # scores/logits so generate_greedy_logprobs_limit can
+                # extract per-token logprobs.
+                output.sequences = output.sequences[:, prompt_len:]
+        return output
+
+    hf_model.get_inputs = patched_get_inputs  # type: ignore[method-assign, assignment]
+    hf_model.model.generate = patched_generate  # type: ignore[method-assign]
+    return hf_model
@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for ColQwen3 late interaction model for multi-modal retrieval.
+
+ColQwen3 is a multi-vector retrieval model based on Qwen3-VL backbone with
+ColBERT-style late interaction scoring (MaxSim). It produces per-token
+embeddings for both text and image inputs.
+"""
+
+import pytest
+import torch
+
+from ....conftest import VllmRunner
+
+MODELS = [
+    "TomoroAI/tomoro-colqwen3-embed-4b",
+    "OpenSearch-AI/Ops-Colqwen3-4B",
+]
+
+EMBED_DIMS = {
+    "TomoroAI/tomoro-colqwen3-embed-4b": 320,
+    "OpenSearch-AI/Ops-Colqwen3-4B": 2560,
+}
+
+TEXT_QUERIES = [
+    "What is the capital of France?",
+    "Describe the contents of the document.",
+]
+
+TEXT_DOCUMENTS = [
+    "The capital of France is Paris.",
+    "This document contains important financial data.",
+]
+
+DTYPE = "half"
+
+
+def _run_token_embed_test(
+    vllm_runner: type[VllmRunner],
+    model: str,
+    *,
+    dtype: str,
+) -> None:
+    """Verify per-token embedding shape and L2 normalization."""
+    with vllm_runner(
+        model,
+        runner="pooling",
+        dtype=dtype,
+        max_model_len=4096,
+        enforce_eager=True,
+    ) as vllm_model:
+        outputs = vllm_model.token_embed([TEXT_QUERIES[0]])
+
+        assert len(outputs) == 1
+        emb = torch.tensor(outputs[0])
+        # Token embeddings should be 2D: [num_tokens, embed_dim]
+        assert emb.dim() == 2
+        assert emb.shape[1] == EMBED_DIMS[model]
+        assert emb.shape[0] > 1
+
+        # Verify L2 normalization
+        norms = torch.norm(emb, p=2, dim=-1)
+        torch.testing.assert_close(
+            norms,
+            torch.ones_like(norms),
+            rtol=1e-2,
+            atol=1e-2,
+        )
+
+
+def _run_late_interaction_test(
+    vllm_runner: type[VllmRunner],
+    model: str,
+    *,
+    dtype: str,
+) -> None:
+    """Verify MaxSim scoring matches manual computation."""
+    from vllm.entrypoints.pooling.score.utils import compute_maxsim_score
+
+    with vllm_runner(
+        model,
+        runner="pooling",
+        dtype=dtype,
+        max_model_len=4096,
+        enforce_eager=True,
+    ) as vllm_model:
+        q_outputs = vllm_model.token_embed([TEXT_QUERIES[0]])
+        d_outputs = vllm_model.token_embed([TEXT_DOCUMENTS[0]])
+
+        q_emb = torch.tensor(q_outputs[0])
+        d_emb = torch.tensor(d_outputs[0])
+
+        manual_score = compute_maxsim_score(q_emb, d_emb).item()
+
+        vllm_scores = vllm_model.score(TEXT_QUERIES[0], TEXT_DOCUMENTS[0])
+
+        assert len(vllm_scores) == 1
+        assert vllm_scores[0] == pytest.approx(manual_score, rel=0.01)
+
+
+def _run_relevance_test(
+    vllm_runner: type[VllmRunner],
+    model: str,
+    *,
+    dtype: str,
+) -> None:
+    """Verify that relevant documents score higher than irrelevant ones."""
+    query = "What is machine learning?"
+    documents = [
+        "Machine learning is a subset of artificial intelligence.",
+        "The weather forecast shows rain tomorrow.",
+        "Deep learning uses neural networks for complex tasks.",
+    ]
+
+    with vllm_runner(
+        model,
+        runner="pooling",
+        dtype=dtype,
+        max_model_len=4096,
+        enforce_eager=True,
+    ) as vllm_model:
+        scores = vllm_model.score(query, documents)
+
+        assert len(scores) == 3
+        assert scores[0] > scores[1], "ML doc should score higher than weather doc"
+        assert scores[2] > scores[1], "DL doc should score higher than weather doc"
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", [DTYPE])
+def test_colqwen3_token_embed(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    _run_token_embed_test(vllm_runner, model, dtype=dtype)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", [DTYPE])
+def test_colqwen3_late_interaction_scoring(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    _run_late_interaction_test(vllm_runner, model, dtype=dtype)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", [DTYPE])
+def test_colqwen3_relevance_ordering(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    _run_relevance_test(vllm_runner, model, dtype=dtype)
@@ -40,7 +40,7 @@ def _run_test(
        vllm_model.llm.encode(prompt, pooling_task="plugin")


-MODELS = ["mgazz/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
+MODELS = ["ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]


@pytest.mark.core_model
@@ -184,22 +184,42 @@ def get_text_token_prompts(
    text_prompt: str | None
    token_prompt: list[int]
    if isinstance(tokenizer, MistralTokenizer):
-        images = parsed_data.get("image", [])
-        request = ChatCompletionRequest(
-            messages=[
-                UserMessage(
-                    content=[
-                        TextChunk(text=""),
-                        *(ImageChunk(image=image) for image in images),
-                    ]
-                ),
-            ]
+        # ChatCompletionRequest only supports ImageChunk natively;
+        # for other modalities (e.g. audio), fall back to the model's
+        # own dummy inputs builder which knows the right placeholders.
+        has_non_image = any(
+            k != "image" and count > 0 for k, count in mm_counts.items()
        )
-        res = tokenizer.mistral.encode_chat_completion(request)

-        # Mistral does not support decode_tokens with skip_special_tokens=False
-        text_prompt = None
-        token_prompt = res.tokens
+        if has_non_image:
+            inputs = dummy_inputs.get_dummy_processor_inputs(
+                model_config.max_model_len,
+                mm_counts,
+            )
+            text_prompt = None
+            token_prompt = (
+                inputs.prompt
+                if isinstance(inputs.prompt, list)
+                else tokenizer.encode(inputs.prompt, add_special_tokens=False)
+            )
+        else:
+            images = parsed_data.get("image", [])
+            request = ChatCompletionRequest(
+                messages=[
+                    UserMessage(
+                        content=[
+                            TextChunk(text=""),
+                            *(ImageChunk(image=image) for image in images),
+                        ]
+                    ),
+                ]
+            )
+            res = tokenizer.mistral.encode_chat_completion(request)
+
+            # Mistral does not support decode_tokens with
+            # skip_special_tokens=False
+            text_prompt = None
+            token_prompt = res.tokens
    else:
        inputs = dummy_inputs.get_dummy_processor_inputs(
            model_config.max_model_len,
@@ -441,6 +461,13 @@ def test_processing_correctness(
            "Qwen-VL tokenizer requires downloading a font file from "
            "servers that often refuse connections in CI"
        )
+    if model_id == "mistralai/Voxtral-Mini-4B-Realtime-2602":
+        pytest.skip(
+            "Voxtral Realtime doesn't make use of any place-holder"
+            "tokens and hence cannot pass the processing "
+            "correctness test as is. Let's revisit adapting this "
+            "test once more realtime models exist."
+        )
    if model_id == "internlm/Intern-S1-Pro":
        # FIXME(Isotr0py): Fix later.
        pytest.skip("Tokenization issue. Fix later")
@@ -168,6 +168,7 @@ def test_get_image_size_with_most_features(
        image_width=max_image_size.width,
        image_height=max_image_size.height,
        processor=hf_processor,
+        mm_kwargs=hf_processor_mm_kwargs,
    )

    prompt = "<start_of_image>"
@@ -3,7 +3,9 @@
 """Tests for Idefics3's multimodal preprocessing kwargs."""

 import pytest
+from packaging.version import Version
 from transformers import Idefics3Config
+from transformers import __version__ as TRANSFORMERS_VERSION

 from vllm.multimodal import MULTIMODAL_REGISTRY

@@ -11,6 +13,10 @@ from ....conftest import ImageTestAssets
 from ...utils import build_model_context


+@pytest.mark.skipif(
+    Version(TRANSFORMERS_VERSION) < Version("5.2.0"),
+    reason="See https://github.com/huggingface/transformers/pull/43948",
+)
@pytest.mark.parametrize("model_id", ["HuggingFaceM4/Idefics3-8B-Llama3"])
@pytest.mark.parametrize(
    ("mm_processor_kwargs", "expected_toks_per_img"),
@@ -63,7 +69,11 @@ def test_processor_override(

    # Ensure the placeholders format are correct
    hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
-    hf_processed_inputs = hf_processor(text=prompt, images=mm_data["image"])
+    hf_processed_inputs = hf_processor(
+        text=prompt,
+        images=mm_data["image"],
+        **processor.info.ctx.get_merged_mm_kwargs(hf_processor_mm_kwargs),
+    )
    assert processed_inputs["prompt_token_ids"] == hf_processed_inputs["input_ids"][0]

    # Ensure we have the right number of placeholders per num_crops size
@@ -82,6 +82,7 @@ def test_get_image_size_with_most_features(
        image_width=max_image_size.width,
        image_height=max_image_size.height,
        image_processor=hf_processor.image_processor,
+        mm_kwargs=hf_processor_mm_kwargs,
    )

    prompt = "<|vision_start|><|image_pad|><|vision_end|>"
@@ -3,7 +3,9 @@
 """Tests for smolvlm's multimodal preprocessing kwargs."""

 import pytest
+from packaging.version import Version
 from transformers import SmolVLMConfig
+from transformers import __version__ as TRANSFORMERS_VERSION

 from vllm.multimodal import MULTIMODAL_REGISTRY

@@ -11,6 +13,10 @@ from ....conftest import ImageTestAssets
 from ...utils import build_model_context


+@pytest.mark.skipif(
+    Version(TRANSFORMERS_VERSION) < Version("5.2.0"),
+    reason="See https://github.com/huggingface/transformers/pull/43948",
+)
@pytest.mark.parametrize("model_id", ["HuggingFaceTB/SmolVLM2-2.2B-Instruct"])
@pytest.mark.parametrize(
    ("mm_processor_kwargs", "expected_toks_per_img"),
@@ -63,7 +69,11 @@ def test_processor_override(

    # Ensure the placeholders format are correct
    hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
-    hf_processed_inputs = hf_processor(text=prompt, images=mm_data["image"])
+    hf_processed_inputs = hf_processor(
+        text=prompt,
+        images=mm_data["image"],
+        **processor.info.ctx.get_merged_mm_kwargs(hf_processor_mm_kwargs),
+    )
    assert processed_inputs["prompt_token_ids"] == hf_processed_inputs["input_ids"][0]

    # Ensure we have the right number of placeholders per num_crops size
@@ -529,6 +529,15 @@ _EMBEDDING_EXAMPLE_MODELS = {
    # [Text-only]
    "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
    "HF_ColBERT": _HfExamplesInfo("answerdotai/answerai-colbert-small-v1"),
+    "ColBERTModernBertModel": _HfExamplesInfo(
+        "lightonai/GTE-ModernColBERT-v1",
+        hf_overrides={"architectures": ["ColBERTModernBertModel"]},
+    ),
+    "ColBERTJinaRobertaModel": _HfExamplesInfo(
+        "jinaai/jina-colbert-v2",
+        trust_remote_code=True,
+        hf_overrides={"architectures": ["ColBERTJinaRobertaModel"]},
+    ),
    "BgeM3EmbeddingModel": _HfExamplesInfo("BAAI/bge-m3"),
    "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),
    "Gemma3TextModel": _HfExamplesInfo("google/embeddinggemma-300m"),
@@ -588,6 +597,12 @@ _EMBEDDING_EXAMPLE_MODELS = {
        "TIGER-Lab/VLM2Vec-Full", trust_remote_code=True
    ),
    "Qwen2VLForConditionalGeneration": _HfExamplesInfo("MrLight/dse-qwen2-2b-mrl-v1"),
+    "ColQwen3": _HfExamplesInfo(
+        "TomoroAI/tomoro-colqwen3-embed-4b", trust_remote_code=True
+    ),
+    "OpsColQwen3Model": _HfExamplesInfo(
+        "OpenSearch-AI/Ops-Colqwen3-4B", trust_remote_code=True
+    ),
    "SiglipModel": _HfExamplesInfo("google/siglip-base-patch16-224"),
    "PrithviGeoSpatialMAE": _HfExamplesInfo(
        "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
@@ -915,6 +930,12 @@ _MULTIMODAL_EXAMPLE_MODELS = {
        },
    ),
    "Ovis2_5": _HfExamplesInfo("AIDC-AI/Ovis2.5-2B", trust_remote_code=True),
+    "Ovis2_6ForCausalLM": _HfExamplesInfo(
+        "AIDC-AI/Ovis2.6-2B", is_available_online=False, trust_remote_code=True
+    ),
+    "Ovis2_6_MoeForCausalLM": _HfExamplesInfo(
+        "AIDC-AI/Ovis2.6-30B-A3B", trust_remote_code=True
+    ),
    "PaddleOCRVLForConditionalGeneration": _HfExamplesInfo(
        "PaddlePaddle/PaddleOCR-VL",
        trust_remote_code=True,
@@ -1031,13 +1052,12 @@ _MULTIMODAL_EXAMPLE_MODELS = {
    ),
    "VoxtralForConditionalGeneration": _HfExamplesInfo(
        "mistralai/Voxtral-Mini-3B-2507",
-        # disable this temporarily until we support HF format
-        is_available_online=False,
+        tokenizer_mode="mistral",
    ),
    "VoxtralRealtimeGeneration": _HfExamplesInfo(
-        "<place-holder>",
-        # disable this temporarily until we support HF format
-        is_available_online=False,
+        "mistralai/Voxtral-Mini-4B-Realtime-2602",
+        enforce_eager=True,
+        tokenizer_mode="mistral",
    ),
    # [Encoder-decoder]
    "NemotronParseForConditionalGeneration": _HfExamplesInfo(
@@ -113,25 +113,6 @@ class TestGGUFModelLoader:
        assert result == "/path/to/model.gguf"
        mock_isfile.assert_called_once_with("/path/to/model.gguf")

-    @patch("vllm.model_executor.model_loader.gguf_loader.hf_hub_download")
-    @patch("os.path.isfile", return_value=False)
-    def test_prepare_weights_https_url(self, mock_isfile, mock_hf_download):
-        """Test _prepare_weights with HTTPS URL."""
-        load_config = LoadConfig(load_format="gguf")
-        loader = GGUFModelLoader(load_config)
-
-        mock_hf_download.return_value = "/downloaded/model.gguf"
-
-        # Create a simple mock ModelConfig with only the model attribute
-        model_config = MagicMock()
-        model_config.model = "https://huggingface.co/model.gguf"
-
-        result = loader._prepare_weights(model_config)
-        assert result == "/downloaded/model.gguf"
-        mock_hf_download.assert_called_once_with(
-            url="https://huggingface.co/model.gguf"
-        )
-
    @patch("vllm.model_executor.model_loader.gguf_loader.hf_hub_download")
    @patch("os.path.isfile", return_value=False)
    def test_prepare_weights_repo_filename(self, mock_isfile, mock_hf_download):
@@ -13,7 +13,7 @@ from tests.utils import create_new_process_for_each_test
    "model",
    [
        "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
-        "mgazz/Prithvi_v2_eo_300_tl_unet_agb",
+        "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars",
    ],
 )
 def test_inference(
@@ -44,12 +44,8 @@ datamodule_config: DataModuleConfig = {
    "no_label_replace": -1,
    "num_workers": 8,
    "test_transform": [
-        albumentations.Resize(
-            always_apply=False, height=448, interpolation=1, p=1, width=448
-        ),
-        albumentations.pytorch.ToTensorV2(
-            transpose_mask=False, always_apply=True, p=1.0
-        ),
+        albumentations.Resize(height=448, interpolation=1, p=1, width=448),
+        albumentations.pytorch.ToTensorV2(transpose_mask=False, p=1.0),
    ],
 }

@@ -1,18 +1,39 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import base64
+import io

+import imagehash
 import pytest
 import requests
+from PIL import Image

 from tests.utils import RemoteOpenAIServer
 from vllm.config import VllmConfig
 from vllm.entrypoints.pooling.pooling.protocol import IOProcessorResponse
 from vllm.plugins.io_processors import get_io_processor

-MODEL_NAME = "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"
+models_config = {
+    "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11": {
+        "image_url": "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff",  # noqa: E501
+        "out_hash": "aa6d92ad25926a5e",
+        "plugin": "prithvi_to_tiff",
+    },
+    "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars": {
+        "image_url": "https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars/resolve/main/examples/subsetted_512x512_HLS.S30.T10SEH.2018190.v1.4_merged.tif",  # noqa: E501
+        "out_hash": "c07f4f602da73552",
+        "plugin": "prithvi_to_tiff",
+    },
+}

-image_url = "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff"  # noqa: E501
+
+def _compute_image_hash(base64_data: str) -> str:
+    # Decode the base64 output and create image from byte stream
+    decoded_image = base64.b64decode(base64_data)
+    image = Image.open(io.BytesIO(decoded_image))
+
+    # Compute perceptual hash of the output image
+    return str(imagehash.phash(image))


 def test_loading_missing_plugin():
@@ -22,33 +43,39 @@ def test_loading_missing_plugin():


@pytest.fixture(scope="function")
-def server():
+def server(model_name, plugin):
    args = [
        "--runner",
        "pooling",
        "--enforce-eager",
-        "--trust-remote-code",
        "--skip-tokenizer-init",
        # Limit the maximum number of parallel requests
        # to avoid the model going OOM in CI.
        "--max-num-seqs",
        "32",
        "--io-processor-plugin",
-        "prithvi_to_tiff",
-        "--model-impl",
-        "terratorch",
+        plugin,
        "--enable-mm-embeds",
    ]

-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+    with RemoteOpenAIServer(model_name, args) as remote_server:
        yield remote_server


@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize(
+    "model_name, image_url, plugin, expected_hash",
+    [
+        (model_name, config["image_url"], config["plugin"], config["out_hash"])
+        for model_name, config in models_config.items()
+    ],
+)
 async def test_prithvi_mae_plugin_online(
    server: RemoteOpenAIServer,
    model_name: str,
+    image_url: str | dict,
+    plugin: str,
+    expected_hash: str,
 ):
    request_payload_url = {
        "data": {
@@ -74,16 +101,25 @@ async def test_prithvi_mae_plugin_online(

    # verify the output is formatted as expected for this plugin
    plugin_data = parsed_response.data
-
    assert all(plugin_data.get(attr) for attr in ["type", "format", "data"])

-    # We just check that the output is a valid base64 string.
-    # Raises an exception and fails the test if the string is corrupted.
-    base64.b64decode(plugin_data["data"])
+    # Compute the output image hash and compare it against the expected hash
+    image_hash = _compute_image_hash(plugin_data["data"])
+    assert image_hash == expected_hash, (
+        f"Image hash mismatch: expected {expected_hash}, got {image_hash}"
+    )


-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
+@pytest.mark.parametrize(
+    "model_name, image_url, plugin, expected_hash",
+    [
+        (model_name, config["image_url"], config["plugin"], config["out_hash"])
+        for model_name, config in models_config.items()
+    ],
+)
+def test_prithvi_mae_plugin_offline(
+    vllm_runner, model_name: str, image_url: str | dict, plugin: str, expected_hash: str
+):
    img_prompt = dict(
        data=image_url,
        data_format="url",
@@ -96,13 +132,12 @@ def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
        runner="pooling",
        skip_tokenizer_init=True,
        enable_mm_embeds=True,
-        trust_remote_code=True,
        enforce_eager=True,
        # Limit the maximum number of parallel requests
        # to avoid the model going OOM in CI.
-        max_num_seqs=1,
-        model_impl="terratorch",
-        io_processor_plugin="prithvi_to_tiff",
+        max_num_seqs=32,
+        io_processor_plugin=plugin,
+        default_torch_num_threads=1,
    ) as llm_runner:
        pooler_output = llm_runner.get_llm().encode(img_prompt, pooling_task="plugin")
    output = pooler_output[0].outputs
@@ -110,6 +145,8 @@ def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
    # verify the output is formatted as expected for this plugin
    assert all(hasattr(output, attr) for attr in ["type", "format", "data"])

-    # We just check that the output is a valid base64 string.
-    # Raises an exception and fails the test if the string is corrupted.
-    base64.b64decode(output.data)
+    # Compute the output image hash and compare it against the expected hash
+    image_hash = _compute_image_hash(output.data)
+    assert image_hash == expected_hash, (
+        f"Image hash mismatch: expected {expected_hash}, got {image_hash}"
+    )
@@ -17,7 +17,9 @@ def gpt_oss_tokenizer():

 USER_MESSAGE_START = "<|start|>user<|message|>"
 REASONING_SECTION_START = "<|end|><|start|>assistant<|channel|>analysis<|message|>"
-ASSISTANT_CONTENT_START_PREFIX = "<|end|><|start|>assistant<|channel|>final"
+END = "<|end|>"
+ASSISTANT_START = "<|start|>assistant"
+ASSISTANT_CONTENT_START_PREFIX = END + ASSISTANT_START + "<|channel|>final"
 ASSISTANT_CONTENT_START_SUFFIX = "<|message|>"
 ASSISTANT_CONTENT_START = (
    ASSISTANT_CONTENT_START_PREFIX + ASSISTANT_CONTENT_START_SUFFIX
@@ -97,6 +99,20 @@ COMPLEX_CONTENT_2 = {
    "is_reasoning_end": True,
 }

+MULTI_TURN_CONTENT = {
+    "output": USER_MESSAGE_START
+    + "1st turn user message"
+    + REASONING_SECTION_START
+    + "1st turn reasoning"
+    + ASSISTANT_CONTENT_START
+    + "1st turn response"
+    + END
+    + USER_MESSAGE_START
+    + "2nd turn user message"
+    + END
+    + ASSISTANT_START,
+    "is_reasoning_end": False,
+}
 TEST_CASES = [
    BASIC_CONTENT,
    BASIC_REASONING_ONLY,
@@ -106,6 +122,7 @@ TEST_CASES = [
    COMPLEX_CONTENT_1,
    COMPLEX_CONTENT_1_WITH_CONTENT,
    COMPLEX_CONTENT_2,
+    MULTI_TURN_CONTENT,
 ]


@@ -38,6 +38,12 @@ class MockModelConfig:
    enable_prompt_embeds: bool = True
    skip_tokenizer_init: bool = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False
+
+
+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig


@dataclass
@@ -72,16 +78,17 @@ def _build_renderer(
    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)

    renderer = HfRenderer(
-        model_config,
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        MockVllmConfig(model_config),
+        tokenizer=(
+            None
+            if model_config.skip_tokenizer_init
+            else DummyTokenizer(
+                truncation_side=truncation_side,
+                max_chars_per_token=max_chars_per_token,
+            )
+        ),
    )

-    if not model_config.skip_tokenizer_init:
-        renderer._tokenizer = DummyTokenizer(
-            truncation_side=truncation_side,
-            max_chars_per_token=max_chars_per_token,
-        )
-
    return renderer


@@ -104,14 +111,14 @@ class TestValidatePrompt:
        renderer = _build_renderer(MockModelConfig())

        with pytest.raises(ValueError, match="at least one prompt"):
-            renderer.render_prompts(_preprocess_prompt(renderer.config, []))
+            renderer.render_prompts(_preprocess_prompt(renderer.model_config, []))

    def test_invalid_type(self):
        renderer = _build_renderer(MockModelConfig())

        with pytest.raises(TypeError, match="should be a list of integers"):
            renderer.render_prompts(
-                _preprocess_prompt(renderer.config, [[1, 2], ["foo", "bar"]])  # type: ignore[arg-type]
+                _preprocess_prompt(renderer.model_config, [[1, 2], ["foo", "bar"]])  # type: ignore[arg-type]
            )


@@ -120,7 +127,9 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig())

        tokens = [101, 7592, 2088]
-        prompts = renderer.render_prompts(_preprocess_prompt(renderer.config, tokens))
+        prompts = renderer.render_prompts(
+            _preprocess_prompt(renderer.model_config, tokens)
+        )
        results = renderer.tokenize_prompts(
            prompts,
            TokenizeParams(max_total_tokens=100),
@@ -134,7 +143,7 @@ class TestRenderPrompt:

        token_lists = [[101, 7592, 2088], [102, 1234, 5678, 9012], [103, 4567]]
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, token_lists)
+            _preprocess_prompt(renderer.model_config, token_lists)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -151,7 +160,7 @@ class TestRenderPrompt:

        text_input = "x" * 10
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, text_input)
+            _preprocess_prompt(renderer.model_config, text_input)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -166,7 +175,7 @@ class TestRenderPrompt:

        text_list_input = ["x" * 10, "x" * 12, "x" * 14]
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, text_list_input)
+            _preprocess_prompt(renderer.model_config, text_list_input)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -181,7 +190,7 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig())

        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, "x" * 200)
+            _preprocess_prompt(renderer.model_config, "x" * 200)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -195,7 +204,7 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig())

        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, "x" * 200)
+            _preprocess_prompt(renderer.model_config, "x" * 200)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -209,7 +218,7 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig())

        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, "x" * 200)
+            _preprocess_prompt(renderer.model_config, "x" * 200)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -224,7 +233,7 @@ class TestRenderPrompt:

        long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]  # 10 tokens
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, long_tokens)
+            _preprocess_prompt(renderer.model_config, long_tokens)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -240,7 +249,7 @@ class TestRenderPrompt:

        long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]  # 10 tokens
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, long_tokens)
+            _preprocess_prompt(renderer.model_config, long_tokens)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -257,7 +266,7 @@ class TestRenderPrompt:
        # Exceeds max_total_tokens and max_total_tokens * VLLM_MAX_CHARS_PER_TOKEN
        long_tokens = "x" * 150
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, long_tokens)
+            _preprocess_prompt(renderer.model_config, long_tokens)
        )

        with pytest.raises(
@@ -270,7 +279,7 @@ class TestRenderPrompt:
            )

        # Should not even attempt tokenization
-        assert renderer._tokenizer._captured_encode_kwargs == {}
+        assert renderer.tokenizer._captured_encode_kwargs == {}

    def test_text_max_length_exceeded_nonobvious(self):
        renderer = _build_renderer(MockModelConfig(), max_chars_per_token=2)
@@ -278,7 +287,7 @@ class TestRenderPrompt:
        # Exceeds max_total_tokens but not max_total_tokens * VLLM_MAX_CHARS_PER_TOKEN
        long_tokens = "x" * 150
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, long_tokens)
+            _preprocess_prompt(renderer.model_config, long_tokens)
        )

        with pytest.raises(
@@ -291,15 +300,15 @@ class TestRenderPrompt:
            )

        # Should only tokenize the first max_total_tokens + 1 tokens
-        assert renderer._tokenizer._captured_encode_kwargs["truncation"] is True
-        assert renderer._tokenizer._captured_encode_kwargs["max_length"] == 101
+        assert renderer.tokenizer._captured_encode_kwargs["truncation"] is True
+        assert renderer.tokenizer._captured_encode_kwargs["max_length"] == 101

    def test_token_max_length_exceeded(self):
        renderer = _build_renderer(MockModelConfig())

        long_tokens = list(range(150))  # Exceeds max_total_tokens=100
        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, long_tokens)
+            _preprocess_prompt(renderer.model_config, long_tokens)
        )

        with pytest.raises(
@@ -315,7 +324,7 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig(skip_tokenizer_init=True))

        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, "Hello world")
+            _preprocess_prompt(renderer.model_config, "Hello world")
        )

        with pytest.raises(ValueError, match="`skip_tokenizer_init=True`"):
@@ -328,7 +337,9 @@ class TestRenderPrompt:
        renderer = _build_renderer(MockModelConfig())

        tokens = [1, 2, 3, 4]
-        prompts = renderer.render_prompts(_preprocess_prompt(renderer.config, tokens))
+        prompts = renderer.render_prompts(
+            _preprocess_prompt(renderer.model_config, tokens)
+        )
        results = renderer.tokenize_prompts(
            prompts,
            TokenizeParams(
@@ -358,7 +369,7 @@ class TestRenderEmbedPrompt:
        embed_bytes = self._create_test_embed_bytes(tensor_input)

        prompts = renderer.render_prompts(
-            _preprocess_prompt(renderer.config, embed_bytes)
+            _preprocess_prompt(renderer.model_config, embed_bytes)
        )
        results = renderer.tokenize_prompts(
            prompts,
@@ -379,7 +390,7 @@ class TestRenderEmbedPrompt:

        prompts = renderer.render_prompts(
            _preprocess_prompt(
-                renderer.config,
+                renderer.model_config,
                [self._create_test_embed_bytes(t) for t in tensor_inputs],
            )
        )
@@ -400,7 +411,7 @@ class TestRenderEmbedPrompt:

        prompts = renderer.render_prompts(
            _preprocess_prompt(
-                renderer.config, self._create_test_embed_bytes(tensor_input)
+                renderer.model_config, self._create_test_embed_bytes(tensor_input)
            )
        )
        results = renderer.tokenize_prompts(
@@ -427,7 +438,7 @@ class TestRenderEmbedPrompt:

            prompts = renderer.render_prompts(
                _preprocess_prompt(
-                    renderer.config, self._create_test_embed_bytes(tensor_input)
+                    renderer.model_config, self._create_test_embed_bytes(tensor_input)
                )
            )
            results = renderer.tokenize_prompts(
@@ -446,7 +457,7 @@ class TestRenderEmbedPrompt:

        prompts = renderer.render_prompts(
            _preprocess_prompt(
-                renderer.config, self._create_test_embed_bytes(tensor_input)
+                renderer.model_config, self._create_test_embed_bytes(tensor_input)
            )
        )
        results = renderer.tokenize_prompts(
@@ -466,7 +477,7 @@ class TestRenderEmbedPrompt:

        prompts = renderer.render_prompts(
            _preprocess_prompt(
-                renderer.config,
+                renderer.model_config,
                [text_input, self._create_test_embed_bytes(tensor_input)],
            )
        )
@@ -36,6 +36,12 @@ class MockModelConfig:
    enable_prompt_embeds: bool = True
    skip_tokenizer_init: bool = False
    is_encoder_decoder: bool = False
+    is_multimodal_model: bool = False
+
+
+@dataclass
+class MockVllmConfig:
+    model_config: MockModelConfig


@pytest.mark.asyncio
@@ -50,8 +56,10 @@ async def test_async_mistral_tokenizer_does_not_block_event_loop():
    mock_model_config = MockModelConfig(skip_tokenizer_init=True)
    mock_tokenizer = Mock(spec=MistralTokenizer)
    mock_tokenizer.apply_chat_template = mocked_apply_chat_template
-    mock_renderer = MistralRenderer(mock_model_config, tokenizer_kwargs={})
-    mock_renderer._tokenizer = mock_tokenizer
+    mock_renderer = MistralRenderer(
+        MockVllmConfig(mock_model_config),
+        tokenizer=mock_tokenizer,
+    )

    task = mock_renderer.render_messages_async([], ChatParams())

@@ -3,7 +3,7 @@

 import pytest

-from vllm.config import ModelConfig
+from vllm.config import ModelConfig, VllmConfig
 from vllm.inputs.preprocess import InputPreprocessor

 pytestmark = pytest.mark.cpu_test
@@ -20,7 +20,8 @@ pytestmark = pytest.mark.cpu_test
 )
 def test_preprocessor_always_mm_code_path(model_id, prompt):
    model_config = ModelConfig(model=model_id)
-    input_preprocessor = InputPreprocessor(model_config)
+    vllm_config = VllmConfig(model_config=model_config)
+    input_preprocessor = InputPreprocessor(vllm_config)

    # HF processor adds sep token
    tokenizer = input_preprocessor.get_tokenizer()
@@ -67,7 +67,6 @@ def _run_incremental_decode(
        mm_features=None,
        sampling_params=params,
        pooling_params=None,
-        eos_token_id=None,
        arrival_time=0.0,
        lora_request=None,
        cache_salt=None,
@@ -1123,7 +1123,7 @@ rectangle

    # Encode all content tokens at once
    all_token_ids = step3p5_tokenizer.encode(model_output, add_special_tokens=False)
-    eos_token_id = getattr(step3p5_tokenizer, "eos_token_id", None)
+    eos_token_id = step3p5_tokenizer.eos_token_id

    # Include EOS token in delta_token_ids if available
    if eos_token_id is not None:
@@ -1,11 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for the FlashMLA sparse backend utilities."""
+"""Unit tests for the sparse MLA backends and utilities."""

 import math
 from types import MethodType, SimpleNamespace

-import numpy as np
 import pytest
 import torch

@@ -24,7 +23,21 @@ from vllm import _custom_ops as ops
 from vllm.config import set_current_vllm_config
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.platforms import current_platform
+
+# TODO: Integrate ROCMAiterMLASparseBackend for ROCm.
+# The ROCm sparse MLA backend (rocm_aiter_mla_sparse.py) has a compatible
+# forward_mqa interface but needs validation on ROCm hardware.
+if not current_platform.is_cuda():
+    pytest.skip(
+        "Sparse MLA backend tests currently only support CUDA. "
+        "ROCm support requires integrating ROCMAiterMLASparseBackend.",
+        allow_module_level=True,
+    )
+
 from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.backends.mla.flashinfer_mla_sparse import (
+    FlashInferMLASparseBackend,
+)
 from vllm.v1.attention.backends.mla.flashmla_sparse import (
    FlashMLASparseBackend,
    triton_convert_req_index_to_global_index,
@@ -156,32 +169,48 @@ def _quantize_dequantize_fp8_ds_mla(
    return dequant_kv_c, dequant_k_pe


-@pytest.mark.parametrize("batch_name", list(SPARSE_BACKEND_BATCH_SPECS.keys()))
-@pytest.mark.parametrize("kv_cache_dtype", ["fp8_ds_mla", "auto"])
-@pytest.mark.parametrize("tensor_parallel_size", [1, 2, 4])
-@pytest.mark.skipif(
-    torch.cuda.get_device_capability() < (9, 0),
-    reason="FlashMLASparseBackend requires CUDA 9.0 or higher",
+@pytest.mark.parametrize(
+    "backend_cls",
+    [FlashMLASparseBackend, FlashInferMLASparseBackend],
+    ids=["FlashMLA", "FlashInfer"],
 )
+@pytest.mark.parametrize("batch_name", list(SPARSE_BACKEND_BATCH_SPECS.keys()))
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8", "fp8_ds_mla"])
+@pytest.mark.parametrize("tensor_parallel_size", [1, 2, 4])
+@pytest.mark.parametrize("block_size", [32, 64])
 def test_sparse_backend_decode_correctness(
    default_vllm_config,
    dist_init,
+    backend_cls,
    batch_name,
    kv_cache_dtype,
    tensor_parallel_size,
+    block_size,
    workspace_init,
 ):
-    if current_platform.is_rocm():
-        pytest.skip("ROCm does not support fp8_ds_mla data type for kv cache.")
+    if kv_cache_dtype not in backend_cls.supported_kv_cache_dtypes:
+        pytest.skip(f"{backend_cls.get_name()} does not support {kv_cache_dtype}")

-    if not torch.cuda.is_available():
-        pytest.skip("CUDA is required for sparse MLA decode test")
+    supported_block_sizes = backend_cls.get_supported_kernel_block_sizes()
+    if block_size not in supported_block_sizes:
+        pytest.skip(
+            f"{backend_cls.get_name()} does not support block_size={block_size}"
+        )
+
+    if backend_cls == FlashMLASparseBackend:
+        ok, reason = flashmla.is_flashmla_sparse_supported()
+        if not ok:
+            pytest.skip(reason)
+    elif backend_cls == FlashInferMLASparseBackend:
+        if not current_platform.has_device_capability(100):
+            pytest.skip("FlashInferMLASparseBackend requires SM 10.0 or higher")
+
+    batch_spec = SPARSE_BACKEND_BATCH_SPECS[batch_name]
+    use_fp8_ds_mla_quantization = kv_cache_dtype == "fp8_ds_mla"

    device = torch.device("cuda")
    dtype = torch.bfloat16

-    batch_spec = SPARSE_BACKEND_BATCH_SPECS[batch_name]
-
    # Model hyper-parameters (kept intentionally small for the unit test)
    total_num_heads = 128
    # Compute per-rank heads for simulated TP
@@ -192,11 +221,10 @@ def test_sparse_backend_decode_correctness(
    qk_rope_head_dim = 64
    v_head_dim = 128
    head_size = kv_lora_rank + qk_rope_head_dim
-    topk_tokens = 2048
+    topk_tokens = 128

    max_seqlen = max(batch_spec.seq_lens)
    total_cache_tokens = sum(batch_spec.seq_lens)
-    block_size = 64

    # Note: We use TP=1 to avoid multi-GPU requirements in CI.
    # The test simulates head partitioning via mocked methods below.
@@ -247,11 +275,55 @@ def test_sparse_backend_decode_correctness(
    seq_lens = batch_spec.seq_lens
    query_lens = batch_spec.query_lens

+    # Pre-compute positions and sparse indices for all tokens.
+    # We need these BEFORE computing the reference to use sparse attention masks.
+    total_query_tokens = sum(query_lens)
+    positions = []
+    for i in range(batch_spec.batch_size):
+        s_len = seq_lens[i]
+        q_len = query_lens[i]
+        ctx_len = s_len - q_len
+        for q_idx in range(q_len):
+            positions.append(ctx_len + q_idx)
+
+    # Create sparse indices with UNIQUE per-token offsets to catch bugs where
+    # the kernel uses wrong indices for some tokens (e.g., due to incorrect
+    # tensor shapes like [1, num_tokens, ...] instead of [num_tokens, 1, ...]).
+    # Also include -1 masked indices to verify the kernel handles them correctly.
+    sparse_indices = torch.empty(
+        total_query_tokens, topk_tokens, dtype=torch.int32, device=device
+    )
+    for tok_idx in range(total_query_tokens):
+        max_valid_idx = positions[tok_idx]
+        offset = tok_idx * 7  # Prime number for varied offsets
+        # Use only half the topk indices as valid, mask the rest with -1
+        # This tests that the kernel correctly ignores -1 indices
+        num_valid = min(topk_tokens // 2, max_valid_idx + 1)
+        if num_valid > 0:
+            valid_range = torch.arange(num_valid, device=device, dtype=torch.int32)
+            tok_indices = (valid_range + offset) % (max_valid_idx + 1)
+            # Pad with -1 for the remaining positions
+            tok_indices = torch.cat(
+                [
+                    tok_indices,
+                    torch.full(
+                        (topk_tokens - num_valid,), -1, device=device, dtype=torch.int32
+                    ),
+                ]
+            )
+        else:
+            tok_indices = torch.full(
+                (topk_tokens,), -1, device=device, dtype=torch.int32
+            )
+            tok_indices[0] = 0  # At least one valid index
+        sparse_indices[tok_idx] = tok_indices
+
    all_q_vllm, all_kv_c_vllm, all_k_pe_vllm = [], [], []
    kv_c_contexts, k_pe_contexts = [], []
    reference_outputs = []

    kv_cache_scale = torch.tensor(1.0, dtype=torch.float32, device=device)
+    global_token_idx = 0

    for i in range(batch_spec.batch_size):
        s_len = seq_lens[i]
@@ -268,40 +340,53 @@ def test_sparse_backend_decode_correctness(
        kv_c_full = torch.rand(s_len, kv_lora_rank, dtype=dtype, device=device)
        k_pe_full = torch.rand(s_len, 1, qk_rope_head_dim, dtype=dtype, device=device)

-        # SM100 (Blackwell) uses float -> e8m0 -> bf16 scale conversion
-        # which truncates scales to powers of 2. Simulate this in reference.
-        is_sm100 = torch.cuda.get_device_capability()[0] >= 10
-        kv_c_full, k_pe_full = _quantize_dequantize_fp8_ds_mla(
-            kv_c_full,
-            k_pe_full.squeeze(1),
-            block_size=vllm_config.cache_config.block_size,
-            scale=kv_cache_scale,
-            simulate_sm100_e8m0_scales=is_sm100,
-        )
+        if use_fp8_ds_mla_quantization:
+            is_sm100 = torch.cuda.get_device_capability()[0] >= 10
+            kv_c_full, k_pe_squeezed = _quantize_dequantize_fp8_ds_mla(
+                kv_c_full,
+                k_pe_full.squeeze(1),
+                block_size=block_size,
+                scale=kv_cache_scale,
+                simulate_sm100_e8m0_scales=is_sm100,
+            )
+            k_pe_full = k_pe_squeezed.unsqueeze(1)

        q_nope, q_pe = q_c.split([qk_nope_head_dim, qk_rope_head_dim], dim=-1)
        ql_nope = torch.einsum("qnh,lnh->qnl", q_nope, W_UK)
        q_mqa = torch.cat([ql_nope, q_pe], dim=-1)

-        k_mqa = torch.cat([kv_c_full, k_pe_full], dim=-1)
-        k_mqa = k_mqa.unsqueeze(1).expand(-1, num_heads, -1)
-        v_mqa = kv_c_full.unsqueeze(1).expand(-1, num_heads, -1)
+        k_mqa = torch.cat([kv_c_full, k_pe_full.squeeze(1)], dim=-1)
+        v_mqa = kv_c_full

-        attn_mask = torch.ones(q_len, s_len, dtype=torch.bool, device=device)
-        causal_mask = torch.tril(torch.ones(q_len, q_len, device=device))
-        attn_mask[:, ctx_len:] = causal_mask
+        # Compute sparse SDPA reference per query token using its sparse indices
+        for q_idx in range(q_len):
+            tok_sparse_idx = sparse_indices[global_token_idx]
+            valid_mask = tok_sparse_idx >= 0
+            valid_indices = tok_sparse_idx[valid_mask].long()

-        q_sdpa_in = q_mqa.unsqueeze(0).transpose(1, 2)
-        k_sdpa_in = k_mqa.unsqueeze(0).transpose(1, 2)
-        v_sdpa_in = v_mqa.unsqueeze(0).transpose(1, 2)
+            q_tok = q_mqa[q_idx : q_idx + 1]  # [1, num_heads, head_dim]
+            k_sparse = k_mqa[valid_indices]  # [num_valid, head_dim]
+            v_sparse = v_mqa[valid_indices]  # [num_valid, kv_lora_rank]

-        sdpa_out = torch.nn.functional.scaled_dot_product_attention(
-            q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=scale
-        )
-        sdpa_out = sdpa_out.transpose(1, 2).squeeze(0)
+            k_sparse = k_sparse.unsqueeze(1).expand(-1, num_heads, -1)
+            v_sparse = v_sparse.unsqueeze(1).expand(-1, num_heads, -1)

-        sdpa_out = torch.einsum("qnl,lnv->qnv", sdpa_out, W_UV)
-        reference_outputs.append(sdpa_out.flatten(start_dim=-2))
+            # SDPA: [1, num_heads, 1, head_dim] x [1, num_heads, num_valid, head_dim]
+            q_sdpa_in = q_tok.unsqueeze(0).transpose(1, 2)
+            k_sdpa_in = k_sparse.unsqueeze(0).transpose(1, 2)
+            v_sdpa_in = v_sparse.unsqueeze(0).transpose(1, 2)
+
+            sdpa_out = torch.nn.functional.scaled_dot_product_attention(
+                q_sdpa_in, k_sdpa_in, v_sdpa_in, scale=scale
+            )
+            sdpa_out = sdpa_out.transpose(1, 2).squeeze(
+                0
+            )  # [1, num_heads, kv_lora_rank]
+
+            sdpa_out = torch.einsum("qnl,lnv->qnv", sdpa_out, W_UV)
+            reference_outputs.append(sdpa_out.flatten(start_dim=-2))
+
+            global_token_idx += 1

        all_q_vllm.append(q_c)
        all_kv_c_vllm.append(kv_c_full[ctx_len:])
@@ -334,42 +419,18 @@ def test_sparse_backend_decode_correctness(
        num_blocks=vllm_config.cache_config.num_gpu_blocks,
        common_attn_metadata=common_attn_metadata,
        randomize_blocks=False,
-        kv_cache_dtype=vllm_config.cache_config.cache_dtype,
+        kv_cache_dtype=kv_cache_dtype if use_fp8_ds_mla_quantization else "auto",
        scale=kv_cache_scale,
    )

-    builder_cls = FlashMLASparseBackend.get_builder_cls()
+    builder_cls = backend_cls.get_builder_cls()
    builder = builder_cls(kv_cache_spec, ["placeholder"], vllm_config, device)
    metadata = builder.build(
        common_prefix_len=0, common_attn_metadata=common_attn_metadata
    )

-    starts = np.asarray(common_attn_metadata.query_start_loc_cpu, dtype=np.int32)
-    seg_lengths = np.diff(starts)
-    positions = np.arange(starts[-1], dtype=np.int32) - np.repeat(
-        starts[:-1], seg_lengths
-    )
-    seq_lengths = np.asarray(common_attn_metadata.seq_lens.cpu(), dtype=np.int32)
-    prefix_lengths = seq_lengths - seg_lengths
-    positions += np.repeat(prefix_lengths, seg_lengths)
-
-    pos_gpu = torch.as_tensor(positions, device=device, dtype=torch.int32)
-    topk = metadata.topk_tokens
-    debug_indices = torch.arange(topk, device=device, dtype=torch.int32).unsqueeze(0)
-    token_positions = pos_gpu.unsqueeze(1)
-    causal_mask = debug_indices <= token_positions
-    debug_indices = torch.where(
-        causal_mask, debug_indices, torch.full_like(debug_indices, -1)
-    )
-
-    # FlashMLASparseImpl now reads top-k indices from the indexer-provided
-    # buffer, so emulate that contract with a simple namespace mock.
-    debug_indices = debug_indices.expand(metadata.num_actual_tokens, -1).clone()
-    mock_indexer = SimpleNamespace(topk_indices_buffer=debug_indices)
-
-    ok, reason = flashmla.is_flashmla_sparse_supported()
-    if not ok:
-        pytest.skip(reason)
+    # Use the pre-computed sparse_indices for the mock indexer
+    mock_indexer = SimpleNamespace(topk_indices_buffer=sparse_indices)

    kv_b_proj_weight = torch.cat([W_UK, W_UV], dim=-1)
    kv_b_proj_weight = kv_b_proj_weight.view(
@@ -383,7 +444,7 @@ def test_sparse_backend_decode_correctness(
    ).to(device=device, dtype=dtype)
    mock_kv_b_proj.weight = torch.nn.Parameter(kv_b_proj_weight.T.contiguous())

-    impl_cls = FlashMLASparseBackend.get_impl_cls()
+    impl_cls = backend_cls.get_impl_cls()
    with set_current_vllm_config(vllm_config):
        impl = impl_cls(
            num_heads=num_heads,
@@ -441,7 +502,7 @@ def test_sparse_backend_decode_correctness(

    # FP8 quantization introduces some error, but should be within reasonable bounds
    # BF16 (auto) should be very accurate, FP8 allows slightly more tolerance
-    if kv_cache_dtype == "fp8_ds_mla":
+    if kv_cache_dtype.startswith("fp8"):
        torch.testing.assert_close(backend_output, sdpa_reference, rtol=0.05, atol=0.05)
    else:
        torch.testing.assert_close(backend_output, sdpa_reference, rtol=0.01, atol=0.01)
@@ -636,3 +697,63 @@ def test_triton_convert_req_index_to_global_index_with_prefill_workspace(block_s
 def test_split_prefill_chunks(seq_lens, max_buf, expected):
    out = split_prefill_chunks(seq_lens, max_buf)
    assert out == expected
+
+
+def test_triton_convert_returns_valid_counts():
+    """Test that return_valid_counts correctly counts non-negative indices."""
+    device = torch.device("cuda")
+    num_tokens = 8
+    num_requests = 2
+    max_blocks_per_req = 10
+    block_size = 64
+    num_topk_tokens = 128
+
+    req_id = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1], dtype=torch.int32, device=device)
+    block_table = torch.arange(
+        num_requests * max_blocks_per_req, dtype=torch.int32, device=device
+    ).view(num_requests, max_blocks_per_req)
+
+    # Create token indices with varying numbers of valid entries
+    # Token 0: 64 valid, 64 invalid (-1)
+    # Token 1: 32 valid, 96 invalid
+    # Token 2: 128 valid (all)
+    # Token 3: 1 valid, 127 invalid
+    # etc.
+    token_indices = torch.full(
+        (num_tokens, num_topk_tokens), -1, dtype=torch.int32, device=device
+    )
+    expected_valid = []
+    for i in range(num_tokens):
+        num_valid = [64, 32, 128, 1, 64, 32, 128, 1][i]
+        token_indices[i, :num_valid] = torch.arange(
+            num_valid, dtype=torch.int32, device=device
+        ) % (block_size * max_blocks_per_req)
+        expected_valid.append(num_valid)
+
+    expected_valid_tensor = torch.tensor(
+        expected_valid, dtype=torch.int32, device=device
+    )
+
+    # Test with return_valid_counts=True
+    result, valid_counts = triton_convert_req_index_to_global_index(
+        req_id,
+        block_table,
+        token_indices,
+        BLOCK_SIZE=block_size,
+        NUM_TOPK_TOKENS=num_topk_tokens,
+        return_valid_counts=True,
+    )
+
+    torch.testing.assert_close(valid_counts, expected_valid_tensor, rtol=0, atol=0)
+
+    # Test that return_valid_counts=False returns only the indices
+    result_only = triton_convert_req_index_to_global_index(
+        req_id,
+        block_table,
+        token_indices,
+        BLOCK_SIZE=block_size,
+        NUM_TOPK_TOKENS=num_topk_tokens,
+        return_valid_counts=False,
+    )
+    assert isinstance(result_only, torch.Tensor)
+    torch.testing.assert_close(result_only, result, rtol=0, atol=0)
@@ -84,13 +84,15 @@ def make_request(
            )
            mm_features.append(mm_feature)

+    sampling_params = SamplingParams(max_tokens=17)
+    sampling_params.update_from_generation_config({}, eos_token_id=100)
+
    return Request(
        request_id=request_id,
        prompt_token_ids=prompt_token_ids,
        mm_features=mm_features if mm_features else None,
-        sampling_params=SamplingParams(max_tokens=17),
+        sampling_params=sampling_params,
        pooling_params=None,
-        eos_token_id=100,
        lora_request=None,
        cache_salt=cache_salt,
        block_hasher=get_request_block_hasher(block_size, hash_fn),
@@ -75,13 +75,15 @@ def make_request(
            )
            mm_features.append(mm_feature)

+    sampling_params = SamplingParams(max_tokens=17, prompt_logprobs=prompt_logprobs)
+    sampling_params.update_from_generation_config({}, eos_token_id=100)
+
    return Request(
        request_id=request_id,
        prompt_token_ids=prompt_token_ids,
        mm_features=mm_features if mm_features else None,
-        sampling_params=SamplingParams(max_tokens=17, prompt_logprobs=prompt_logprobs),
+        sampling_params=sampling_params,
        pooling_params=None,
-        eos_token_id=100,
        lora_request=lora_request,
        cache_salt=cache_salt,
        block_hasher=get_request_block_hasher(block_size, hash_fn),
@@ -742,6 +744,12 @@ def _make_hybrid_kv_cache_config(
            shapes=(1, 1),
            dtypes=(torch.float32,),
        ),
+        "mamba_align": lambda: MambaSpec(
+            block_size=block_size,
+            shapes=(1, 1),
+            dtypes=(torch.float32,),
+            mamba_cache_mode="align",
+        ),
    }

    kv_cache_groups = [
@@ -960,6 +968,46 @@ def test_prefill_hybrid_model_combinations_eagle(
    manager.free(req1)


+def test_prefill_hybrid_model_mamba_align():
+    """Test that MambaManager.cache_blocks() handles null blocks in align mode.
+
+    Regression test for https://github.com/vllm-project/vllm/issues/34361.
+    In mamba_cache_mode="align", allocate_new_blocks() pads req_to_blocks with
+    null blocks. cache_full_blocks() correctly skips them, but
+    MambaManager.cache_blocks() must also skip null blocks when tracking
+    cached_blocks_this_step.
+    """
+    block_size = 16
+    num_blocks = 30
+
+    kv_cache_config = _make_hybrid_kv_cache_config(
+        block_size, num_blocks, ["full", "mamba_align"]
+    )
+    manager = KVCacheManager(
+        kv_cache_config,
+        max_model_len=8192,
+        enable_caching=True,
+        hash_block_size=block_size,
+    )
+
+    hash_fn = sha256
+
+    # 3 full blocks (48 tokens) + 7 partial tokens = 55 tokens total
+    all_token_ids = [i for i in range(3) for _ in range(block_size)] + [3] * 7
+
+    # First request: allocate_slots should not crash with the assertion error
+    # in MambaManager.cache_blocks() when null blocks are present.
+    req0 = make_request("0", all_token_ids, block_size, hash_fn)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req0)
+    assert num_computed_tokens == 0
+
+    blocks = manager.allocate_slots(req0, 55, num_computed_tokens, computed_blocks)
+    assert blocks is not None
+    assert len(blocks.get_block_ids()) == 2  # full_attn + mamba groups
+
+    manager.free(req0)
+
+
 def test_prefill_plp():
    """Test prefill with APC and some prompt logprobs (plp) requests.

@@ -48,10 +48,9 @@ def _create_random_request(

    request_id = uuid.uuid4().hex

-    sampling_params = SamplingParams(
-        ignore_eos=False,
-        max_tokens=max_tokens,
-    )
+    sampling_params = SamplingParams(ignore_eos=False, max_tokens=max_tokens)
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
+
    mm_features = []
    for j, position in enumerate(mm_positions):
        identifier = f"{request_id}_hash_{j}"
@@ -79,7 +78,6 @@ def _create_random_request(
        sampling_params=sampling_params,
        pooling_params=None,
        mm_features=mm_features if mm_features else None,
-        eos_token_id=EOS_TOKEN_ID,
        arrival_time=arrival_time,
        priority=priority,
        block_hasher=block_hasher,
@@ -469,8 +469,7 @@ def test_stop_via_update_from_output():

    # Test case 4: Ignore EOS flag
    scheduler = create_scheduler(num_speculative_tokens=2)
-    requests = create_requests(num_requests=1, max_tokens=10)
-    requests[0].sampling_params.ignore_eos = True
+    requests = create_requests(num_requests=1, max_tokens=10, ignore_eos=True)
    requests[0].num_computed_tokens = requests[0].num_tokens
    scheduler.requests[requests[0].request_id] = requests[0]
    scheduler.running.append(requests[0])
@@ -515,12 +514,12 @@ def test_check_stop_min_tokens():
        max_tokens=20,
        min_tokens=5,
    )
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
    request = Request(
        request_id="0",
        prompt_token_ids=[0, 1, 2],
        sampling_params=sampling_params,
        pooling_params=None,
-        eos_token_id=EOS_TOKEN_ID,
    )
    # Simulate having generated 3 output tokens (less than min_tokens=5)
    request.append_output_token_ids([10, 11, EOS_TOKEN_ID])  # EOS token present
@@ -551,12 +550,12 @@ def test_check_stop_min_tokens():
        max_tokens=20,
        min_tokens=0,
    )
+    sampling_params_no_min.update_from_generation_config({}, EOS_TOKEN_ID)
    request_no_min = Request(
        request_id="1",
        prompt_token_ids=[0, 1, 2],
        sampling_params=sampling_params_no_min,
        pooling_params=None,
-        eos_token_id=EOS_TOKEN_ID,
    )
    request_no_min.append_output_token_ids([10, EOS_TOKEN_ID])

@@ -571,12 +570,12 @@ def test_check_stop_min_tokens():
        min_tokens=5,
        stop_token_ids=[42],
    )
+    sampling_params_stop.update_from_generation_config({}, EOS_TOKEN_ID)
    request_stop = Request(
        request_id="2",
        prompt_token_ids=[0, 1, 2],
        sampling_params=sampling_params_stop,
        pooling_params=None,
-        eos_token_id=EOS_TOKEN_ID,
    )
    # Only 3 output tokens, less than min_tokens=5, but has stop token
    request_stop.append_output_token_ids([10, 11, 42])
@@ -1877,6 +1876,7 @@ def create_requests_with_priority(
        stop_token_ids=stop_token_ids,
        prompt_logprobs=prompt_logprobs,
    )
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
    requests = []

    if mm_hashes_list is not None:
@@ -1938,7 +1938,6 @@ def create_requests_with_priority(
            sampling_params=sampling_params,
            pooling_params=None,
            mm_features=mm_features if mm_features else None,
-            eos_token_id=EOS_TOKEN_ID,
            arrival_time=arrival_times[i],
            priority=priorities[i],
            block_hasher=block_hasher,
@@ -2429,13 +2428,13 @@ def test_schedule_skip_tokenizer_init_structured_output_request():
        max_tokens=16,
        structured_outputs=structured_outputs_params,
    )
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
    request = Request(
        request_id="0",
        prompt_token_ids=[0, 1],
        mm_features=None,
        sampling_params=sampling_params,
        pooling_params=None,
-        eos_token_id=EOS_TOKEN_ID,
    )
    scheduler.add_request(request)
    output = scheduler.schedule()
@@ -174,6 +174,7 @@ def create_requests(
    num_tokens: int = 10,
    mm_hashes_list: list[list[str]] | None = None,
    mm_positions: list[list[PlaceholderRange]] | None = None,
+    ignore_eos: bool = False,
    max_tokens: int = 16,
    stop_token_ids: list[int] | None = None,
    prompt_logprobs: int | None = None,
@@ -188,11 +189,12 @@ def create_requests(

    block_hasher = get_request_block_hasher(block_size, sha256)
    sampling_params = SamplingParams(
-        ignore_eos=False,
+        ignore_eos=ignore_eos,
        max_tokens=max_tokens,
        stop_token_ids=stop_token_ids,
        prompt_logprobs=prompt_logprobs,
    )
+    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)
    requests = []

    if mm_hashes_list is not None:
@@ -250,7 +252,6 @@ def create_requests(
            sampling_params=sampling_params,
            pooling_params=None,
            mm_features=mm_features if mm_features else None,
-            eos_token_id=EOS_TOKEN_ID,
            block_hasher=block_hasher,
        )
        requests.append(request)
@@ -12,6 +12,7 @@ from vllm import SamplingParams
 from vllm.config import VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.inputs import PromptType
+from vllm.outputs import RequestOutput
 from vllm.platforms import current_platform
 from vllm.sampling_params import RequestOutputKind
 from vllm.v1.engine.async_llm import AsyncLLM
@@ -181,3 +182,145 @@ async def test_load(
            assert slogger.finished_req_count > NUM_REQUESTS // (DP_SIZE + 1), (
                f"requests are imbalanced: {stats_loggers}"
            )
+
+
+# =============================================================================
+# DP Pause/Resume Tests
+# =============================================================================
+
+DP_PAUSE_MODEL = "hmellor/tiny-random-LlamaForCausalLM"
+DP_PAUSE_PROMPT = "This is a test of data parallel pause"
+
+
+@pytest.mark.asyncio
+async def test_dp_pause_resume_basic():
+    """Pausing from the client (one call) pauses all DP ranks; resume clears it."""
+    if current_platform.is_rocm():
+        pytest.skip("DP pause tests use mp backend only")
+    with ExitStack() as after:
+        engine_args = AsyncEngineArgs(
+            model=DP_PAUSE_MODEL,
+            enforce_eager=True,
+            tensor_parallel_size=int(os.getenv("TP_SIZE", 1)),
+            data_parallel_size=DP_SIZE,
+            data_parallel_backend="mp",
+        )
+        engine = AsyncLLM.from_engine_args(engine_args)
+        after.callback(engine.shutdown)
+
+        assert not await engine.is_paused()
+        await engine.pause_generation(mode="abort")
+        assert await engine.is_paused()
+        await engine.resume_generation()
+        assert not await engine.is_paused()
+
+        # Engine still works after resume
+        sampling_params = SamplingParams(max_tokens=5)
+        async for out in engine.generate(
+            request_id="after-resume",
+            prompt=DP_PAUSE_PROMPT,
+            sampling_params=sampling_params,
+        ):
+            pass
+        assert out.finished
+
+
+@pytest.mark.asyncio
+async def test_dp_pause_abort():
+    """Pause with abort from one client aborts in-flight requests on all DP ranks."""
+    if current_platform.is_rocm():
+        pytest.skip("DP pause tests use mp backend only")
+    with ExitStack() as after:
+        engine_args = AsyncEngineArgs(
+            model=DP_PAUSE_MODEL,
+            enforce_eager=True,
+            tensor_parallel_size=int(os.getenv("TP_SIZE", 1)),
+            data_parallel_size=DP_SIZE,
+            data_parallel_backend="mp",
+        )
+        engine = AsyncLLM.from_engine_args(engine_args)
+        after.callback(engine.shutdown)
+
+        # Start several requests so they are distributed across ranks
+        sampling_params = SamplingParams(max_tokens=500, ignore_eos=True)
+        num_requests = 4
+        outputs_by_id: dict[str, list[RequestOutput]] = {}
+
+        async def gen(rid: str):
+            out_list: list[RequestOutput] = []
+            outputs_by_id[rid] = out_list
+            async for out in engine.generate(
+                request_id=rid,
+                prompt=DP_PAUSE_PROMPT,
+                sampling_params=sampling_params,
+            ):
+                out_list.append(out)
+            return out_list[-1] if out_list else None
+
+        tasks = [asyncio.create_task(gen(f"req-{i}")) for i in range(num_requests)]
+        # Wait for some tokens on at least one request
+        while not any(len(o) >= 2 for o in outputs_by_id.values()):
+            await asyncio.sleep(0.02)
+
+        await engine.pause_generation(mode="abort")
+
+        finals = await asyncio.gather(*tasks)
+        for i, final in enumerate(finals):
+            assert final is not None, f"req-{i} had no output"
+            assert final.finished
+            assert final.outputs[0].finish_reason == "abort"
+
+        assert await engine.is_paused()
+        await engine.resume_generation()
+        assert not await engine.is_paused()
+
+        # New request completes after resume
+        async for out in engine.generate(
+            request_id="after-abort",
+            prompt=DP_PAUSE_PROMPT,
+            sampling_params=SamplingParams(max_tokens=5),
+        ):
+            pass
+        assert out.finished
+        assert not engine.output_processor.has_unfinished_requests()
+
+
+@pytest.mark.asyncio
+async def test_dp_pause_keep_then_resume():
+    """Pause with keep queues new requests; resume allows them to run."""
+    if current_platform.is_rocm():
+        pytest.skip("DP pause tests use mp backend only")
+    with ExitStack() as after:
+        engine_args = AsyncEngineArgs(
+            model=DP_PAUSE_MODEL,
+            enforce_eager=True,
+            tensor_parallel_size=int(os.getenv("TP_SIZE", 1)),
+            data_parallel_size=DP_SIZE,
+            data_parallel_backend="mp",
+        )
+        engine = AsyncLLM.from_engine_args(engine_args)
+        after.callback(engine.shutdown)
+
+        await engine.pause_generation(mode="keep")
+        assert await engine.is_paused()
+
+        request_done = asyncio.Event()
+
+        async def gen():
+            async for out in engine.generate(
+                request_id="queued-keep",
+                prompt=DP_PAUSE_PROMPT,
+                sampling_params=SamplingParams(max_tokens=5),
+            ):
+                pass
+            request_done.set()
+            return out
+
+        task = asyncio.create_task(gen())
+        await asyncio.sleep(0.2)
+        assert not request_done.is_set()
+
+        await engine.resume_generation()
+        final = await asyncio.wait_for(task, timeout=10.0)
+        assert final.finished
+        assert not await engine.is_paused()
@@ -11,8 +11,10 @@ import datasets
 import pytest
 import torch

+from tests.utils import create_new_process_for_each_test
 from vllm import LLM, SamplingParams, TokensPrompt
 from vllm.config import CacheConfig
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.model_executor.layers.mamba.mamba_utils import MambaStateCopyFunc
 from vllm.sequence import IntermediateTensors
 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
@@ -74,14 +76,11 @@ def get_fake_sample_fn() -> SamplerOutput:
                ),
                logprobs_tensors=None,
            )
-        num_sampled_tokens = spec_decode_metadata.cu_num_sampled_tokens[0].item() + 1
        accpeted_tokens = prompt_token_ids[
            first_token_id_index : first_token_id_index
            + min(num_accepted_tokens, logits.shape[0])
        ]
-        sampled_token_ids = accpeted_tokens + [-1] * (
-            num_sampled_tokens - len(accpeted_tokens)
-        )
+        sampled_token_ids = accpeted_tokens
        return SamplerOutput(
            sampled_token_ids=torch.tensor(
                [sampled_token_ids], device="cuda", dtype=torch.int32
@@ -103,6 +102,7 @@ def get_fake_propose_draft_token_ids_fn():
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
        common_attn_metadata: CommonAttentionMetadata,
+        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
    ) -> list[list[int]]:
        num_computed_tokens_cpu_tensor = self.input_batch.num_computed_tokens_cpu_tensor
        num_computed_tokens = num_computed_tokens_cpu_tensor[0].item()
@@ -121,7 +121,24 @@ def get_fake_propose_draft_token_ids_fn():
                first_token_id_index : first_token_id_index + num_speculative_tokens
            ]
        ]
-        return proposed_draft_token_ids
+
+        next_token_ids = torch.tensor(
+            prompt_token_ids[
+                first_token_id_index - 1 : first_token_id_index
+                - 1
+                + num_accepted_tokens
+            ],
+            device="cuda",
+            dtype=torch.int32,
+        )
+
+        valid_sampled_tokens_count = torch.tensor(
+            [num_accepted_tokens], device="cuda", dtype=torch.int32
+        )
+
+        self._copy_valid_sampled_token_count(next_token_ids, valid_sampled_tokens_count)
+
+        return torch.tensor(proposed_draft_token_ids, device="cuda", dtype=torch.int32)

    return fake_propose_draft_token_ids_fn

@@ -181,6 +198,7 @@ mamba_kv_cache_dict = {}

 def get_fake_execute_model_fn(original_execute_model_fn: Callable):
    last_num_computed_tokens = 0
+    num_prompt_tokens = None

    def fake_execute_model_fn(
        self: GPUModelRunner,
@@ -198,10 +216,30 @@ def get_fake_execute_model_fn(original_execute_model_fn: Callable):
            mamba_group_id
        ].layer_names[0]
        nonlocal last_num_computed_tokens
+        nonlocal num_prompt_tokens
+
+        if (
+            len(scheduler_output.scheduled_new_reqs) > 0
+            and scheduler_output.scheduled_new_reqs[0].prompt_token_ids is not None
+        ):
+            # record number of prompt tokens
+            num_prompt_tokens = len(
+                scheduler_output.scheduled_new_reqs[0].prompt_token_ids
+            )
+
        if len(scheduler_output.scheduled_cached_reqs.req_ids) > 0:
            num_computed_tokens = (
                scheduler_output.scheduled_cached_reqs.num_computed_tokens[0]
            )
+            if (
+                self.num_spec_tokens
+                and num_prompt_tokens is not None
+                and num_computed_tokens > num_prompt_tokens
+            ):
+                # NOTE (tdoublep) with async scheduling, the scheduler does not have an
+                # accurate measure of the number of computed tokens; we need to subtract
+                # the number of reject tokens from the previous timestep.
+                num_computed_tokens -= num_speculative_tokens + 1 - num_accepted_tokens
            if (
                num_computed_tokens // BLOCK_SIZE
                > last_num_computed_tokens // BLOCK_SIZE
@@ -401,6 +439,9 @@ def _run_ref_mamba_state_worker():
        }
        torch.save(cpu_state_ref, "mamba_kv_cache_dict_ref.pth")
        mamba_kv_cache_dict.clear()
+        del engine
+        torch.cuda.empty_cache()
+        cleanup_dist_env_and_memory()
    except Exception:
        traceback.print_exc()
        raise
@@ -473,10 +514,7 @@ def apply_patch(monkeypatch: pytest.MonkeyPatch):
    monkeypatch.setattr(mamba_utils, "do_mamba_copy_block", fake_copy_fn)


-@pytest.mark.skip(
-    reason="Skipping test_mamba_prefix_cache because it is based on spec "
-    "decode which is not allowed now."
-)
+@create_new_process_for_each_test()
 def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
    run_ref_mamba_state_in_subprocess()
    apply_patch(monkeypatch)
@@ -490,9 +528,9 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
            step_actions=[
                StepAction(0, 554, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(554, 4, [], (-1, -1), (-1, -1)),
-                StepAction(555, 4, [], (-1, -1), (-1, -1)),
+                StepAction(555, 4, [1, 1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(556, 4, [], (-1, -1), (-1, -1)),
-                StepAction(557, 4, [1, 1, 1, 1, 1], (0, 1), (-1, -1)),
+                StepAction(557, 4, [], (0, 1), (-1, -1)),
                StepAction(558, 4, [], (-1, -1), (-1, -1)),
                StepAction(559, 4, [], (-1, -1), (1, 0)),
                StepAction(560, 4, [], (-1, -1), (-1, -1)),
@@ -507,8 +545,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
            step_actions=[
                StepAction(0, 554, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(554, 4, [], (-1, -1), (-1, -1)),
-                StepAction(556, 4, [], (-1, -1), (-1, -1)),
-                StepAction(558, 4, [1, 1, 1, 1, 1], (1, 1), (2, 0)),
+                StepAction(556, 4, [1, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(558, 4, [], (1, 1), (2, 0)),
                StepAction(560, 4, [], (-1, -1), (-1, -1)),
                StepAction(562, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
@@ -523,7 +561,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
                StepAction(555, 4, [], (-1, -1), (-1, -1)),
                StepAction(557, 4, [1, 1, 1, 1, 1], (1, 1), (-1, -1)),
                StepAction(559, 4, [], (-1, -1), (1, 0)),
-                StepAction(561, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(561, 4, [], (-1, -1), (-1, -1)),
+                StepAction(563, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_3_1": TestConfig(
@@ -533,9 +572,10 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
            step_actions=[
                StepAction(0, 553, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(553, 4, [], (-1, -1), (-1, -1)),
-                StepAction(556, 4, [], (-1, -1), (-1, -1)),
-                StepAction(559, 4, [1, 1, 1, 1, 1], (2, 1), (1, 0)),
-                StepAction(562, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(556, 4, [1, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(559, 4, [], (2, 1), (1, 0)),
+                StepAction(562, 4, [], (-1, -1), (-1, -1)),
+                StepAction(565, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_3_2": TestConfig(
@@ -558,7 +598,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
                StepAction(0, 555, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(555, 4, [], (-1, -1), (-1, -1)),
                StepAction(558, 4, [1, 1, 1, 1, 1], (2, 1), (2, 0)),
-                StepAction(561, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(561, 4, [], (-1, -1), (-1, -1)),
+                StepAction(564, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_4_1": TestConfig(
@@ -569,8 +610,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
                StepAction(0, 553, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(553, 4, [], (-1, -1), (-1, -1)),
                StepAction(557, 4, [1, 1, 1, 1, 1], (3, 1), (3, 0)),
-                StepAction(561, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
-                StepAction(565, 4, [], (-1, -1), (-1, -1)),
+                StepAction(561, 4, [], (-1, -1), (-1, -1)),
+                StepAction(565, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_4_2": TestConfig(
@@ -581,8 +622,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
                StepAction(0, 554, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(554, 4, [], (-1, -1), (-1, -1)),
                StepAction(558, 4, [1, 1, 1, 1, 1], (3, 1), (2, 0)),
-                StepAction(562, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
-                StepAction(566, 4, [], (-1, -1), (-1, -1)),
+                StepAction(562, 4, [], (-1, -1), (-1, -1)),
+                StepAction(566, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_4_3": TestConfig(
@@ -593,7 +634,8 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
                StepAction(0, 555, [1, 1, 1, 1], (-1, -1), (-1, -1)),
                StepAction(555, 4, [], (-1, -1), (-1, -1)),
                StepAction(559, 4, [1, 1, 1, 1, 1], (3, 1), (1, 0)),
-                StepAction(563, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
+                StepAction(563, 4, [], (-1, -1), (-1, -1)),
+                StepAction(567, 4, [0, 1, 1, 1, 1], (-1, -1), (-1, -1)),
            ],
        ),
        "accept_4_4": TestConfig(
@@ -762,3 +804,6 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
        mamba_state_ref = torch.load("mamba_kv_cache_dict_ref.pth")
        check_mamba_state_equal(mamba_state_ref, mamba_kv_cache_dict, keys_to_check)
        mamba_kv_cache_dict.clear()
+    del engine
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
@@ -161,7 +161,8 @@ def test_pooling_prefix_cache(vllm_runner, monkeypatch):
            assert chunks[0] <= prompt1_len
            assert chunks[0] < prompt2_len

-            cache_config = llm.get_llm().llm_engine.cache_config
+            vllm_config = llm.get_llm().llm_engine.vllm_config
+            cache_config = vllm_config.cache_config
            print(f"{cache_config=}")
            # Prefixes are cached in blocks
            assert (prompt2_len - chunks[0]) % cache_config.block_size == 0
@@ -19,7 +19,7 @@ import pytest
 import pytest_asyncio

 from vllm import SamplingParams
-from vllm.inputs import StreamingInput
+from vllm.engine.protocol import StreamingInput
 from vllm.outputs import RequestOutput
 from vllm.platforms import current_platform
 from vllm.sampling_params import RequestOutputKind
@@ -708,9 +708,7 @@ async def test_pause_resume_basic():
        # Test all modes with no requests in flight
        for mode in ("abort", "wait", "keep"):
            await engine.pause_generation(mode=mode)
-            # "keep" only freezes the scheduler; it does not set _paused
-            if mode != "keep":
-                assert await engine.is_paused()
+            assert await engine.is_paused()
            await engine.resume_generation()
            assert not await engine.is_paused()

@@ -808,6 +806,53 @@ async def test_pause_abort():
        assert final_output2.finished


+@pytest.mark.asyncio
+async def test_pause_then_abort_queued_request():
+    """Test that aborting a request that was submitted while paused (in
+    _paused_adds_queue) aborts it and notifies the client; the request does
+    not run after resume.
+    """
+    with ExitStack() as after:
+        with set_default_torch_num_threads(1):
+            engine = AsyncLLM.from_engine_args(TEXT_ENGINE_ARGS)
+        after.callback(engine.shutdown)
+
+        request_id = "abort-queued-request"
+        sampling_params = SamplingParams(max_tokens=20, ignore_eos=True)
+        outputs: list[RequestOutput] = []
+
+        # Pause first so the next add goes to _paused_adds_queue
+        await engine.pause_generation(mode="keep")
+        assert await engine.is_paused()
+
+        async def gen():
+            async for out in engine.generate(
+                request_id=request_id,
+                prompt=TEXT_PROMPT,
+                sampling_params=sampling_params,
+            ):
+                outputs.append(out)
+            return outputs[-1] if outputs else None
+
+        gen_task = asyncio.create_task(gen())
+
+        # Give the request time to reach the engine and sit in _paused_adds_queue
+        await asyncio.sleep(0.2)
+
+        # Abort the queued request
+        await engine.abort(request_id, internal=False)
+
+        # Resume so the engine can process and deliver the abort output
+        await engine.resume_generation()
+
+        final_output = await asyncio.wait_for(gen_task, timeout=10.0)
+        assert final_output is not None
+        assert final_output.finished
+        assert final_output.outputs[0].finish_reason == "abort"
+        # Request was never run, so no tokens
+        assert len(final_output.outputs[0].token_ids) == 0
+
+
@pytest.mark.asyncio
 async def test_pause_wait():
    """Test that mode='wait' waits for in-flight requests to complete."""
@@ -54,7 +54,6 @@ def make_request() -> EngineCoreRequest:
        mm_features=None,
        sampling_params=SamplingParams(),
        pooling_params=None,
-        eos_token_id=None,
        arrival_time=time.time(),
        lora_request=None,
        cache_salt=None,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
JJJYmmmandRoger Wang	6d98f91c13	[Misc] fix qwen3.5 config (#34604 )	2026-02-16 00:43:52 -08:00
Seiji EicherandGitHub	79c7e09235	[KV Connector] Add temporary, off-by-default `VLLM_DISABLE_REQUEST_ID_RANDOMIZATION` workaround (#34415 ) Signed-off-by: Seiji Eicher <seiji@anyscale.com>	2026-02-14 23:26:10 -08:00
haosdentandGitHub	79f3fab05a	[Bugfix] Handle num_expert_group=None in flashinfer block-scale FP8 MoE (#34494 ) Signed-off-by: haosdent <haosdent@gmail.com>	2026-02-14 23:25:46 -08:00
Vadim GimpelsonandGitHub	604b9eaec5	[BUGFIX] Fix accuracy regression for NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 with TP>1 (#34476 ) Signed-off-by: Vadim Gimpelson <vadim.gimpelson@gmail.com>	2026-02-14 23:25:17 -08:00
Stanislav Kirillov GitHub Stanislav Kirillov Cyrus Leung	50dbd6c9e6	[bugfix] Fix critical bug when reporting for all paths where handler.create_error_response is used (#34516 ) Signed-off-by: Stanislav Kirillov <stas@nebius.com> Co-authored-by: Stanislav Kirillov <stas@nebius.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>	2026-02-14 23:24:25 -08:00
Andreas KaratzasandGitHub	98bcc6ca59	[CI][Entrypoints] Validate detokenize token IDs to prevent int64 overflow causing 500 (#34468 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-14 23:08:38 -08:00
Andreas KaratzasandGitHub	f13e86d8dd	[Kernels] Fix Helion GPU utils to use platform-agnostic device name API (#34537 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-14 20:29:23 -08:00
Woosuk KwonandGitHub	9ca768c740	[Model Runner V2] Minor cleanup for Sampler (#34563 ) Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>	2026-02-14 18:29:03 -08:00
Thomas ParnellandGitHub	d5fe3f702c	[Hybrid] Enable mamba prefix cache "align" mode with async scheduling (#33997 ) Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>	2026-02-14 13:15:56 -08:00
Cyrus LeungandGitHub	73391a1baa	[Renderer] Move InputPreprocessor into Renderer (1/2) (#34510 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com>	2026-02-14 10:14:21 -08:00
Andreas KaratzasandGitHub	b3c14229b0	[ROCm][CI] Guard sparse MLA backend imports for ROCm compatibility in tests (#34538 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-14 07:32:09 -08:00
Roger WangandGitHub	2f186635cb	[Bugfix] Fix Qwen3.5 config loading (#34554 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2026-02-14 03:56:11 -08:00
Christian Pinto GitHub Cyrus Leung	342a7cda2d	[Misc] Update tests and examples for Prithvi/Terratorch models (#34416 ) Signed-off-by: Christian Pinto <christian.pinto@ibm.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>	2026-02-13 23:03:51 -08:00
Kata CoderandGitHub	d1ea65d0a1	[new model] add COLQwen3 code & Inference (#34398 ) Signed-off-by: craftsangjae <craftsangjae@gmail.com> Signed-off-by: katacoder <craftsangjae@gmail.com>	2026-02-14 12:15:19 +08:00
Andreas KaratzasandGitHub	de42abb366	[CI] Heavy refactoring of Voxtral multimodal audio model tests (#34294 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-13 20:04:29 -08:00
Julien DenizeandGitHub	60ca7981bc	Add explicit validation error for tool calls. (#34438 ) Signed-off-by: juliendenize <julien.denize@mistral.ai>	2026-02-13 20:04:01 -08:00
Christian S. Perone GitHub Cyrus Leung	0ef5b9147b	fix: use `__annotations__` instead of `get_type_hints()` for dynamic `kwargs` detection (#34527 ) Signed-off-by: Christian S. Perone <christian.perone@gmail.com> Signed-off-by: Christian S. Perone <perone@users.noreply.github.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>	2026-02-13 20:03:37 -08:00
Shiyan DengandGitHub	ed242652d7	[bug] Make sure get_modality_with_max_tokens is deterministic (#34533 ) Signed-off-by: Shiyan Deng <dsy842974287@meta.com>	2026-02-13 20:02:59 -08:00
Wei ZhaoandGitHub	b37b679770	[Feature][Perf] Support Selective CPU Weight Offloading (#34535 ) Signed-off-by: wzhao18 <wzhao18.sz@gmail.com>	2026-02-13 20:02:24 -08:00
Andreas KaratzasandGitHub	a0638d052d	[Bugfix] Fix ROCm UVA CPU weight offloading broken by #32993 (#34543 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-13 20:01:42 -08:00
Harry HuangandGitHub	c027541eaf	[Hybrid] Enable spec decoding in mamba cache align mode (#33705 ) Signed-off-by: huanghaoyan.hhy <huanghaoyan.hhy@alibaba-inc.com>	2026-02-13 13:02:28 -08:00
Ben Browning GitHub Cyrus Leung Cyrus Leung	fd267bc7b7	[Bugfix]: Fix structured output in multi-turn gpt-oss (#34454 ) Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>	2026-02-13 11:12:48 -08:00
Michael GoinandGitHub	bfaa559305	Revert "[Bugfix] Fix fused MoE IMA (sans chunking) by using int64 for strides" (#34530 )	2026-02-13 10:35:29 -08:00
Richard ZouandGitHub	87789c8364	[Misc] vLLM's --enforce-eager should turn off compile and cudagraphs only (#34523 ) Signed-off-by: Richard Zou <zou3519@gmail.com>	2026-02-13 09:52:20 -08:00
Pushpinder SinghandGitHub	bcd65c1f6a	[Bugfix] Replace c10::optional with std::optional in topk kernel (#34467 ) Signed-off-by: Pushpinder Singh <pushpindersingh135@gmail.com>	2026-02-13 08:30:23 -08:00
Wei Zhao GitHub Michael Goin	59d53066d8	[Feature] Support CPU Offloading without Pytorch Pinned Memory that leads to doubled allocation (#32993 ) Signed-off-by: wzhao18 <wzhao18.sz@gmail.com> Co-authored-by: Michael Goin <mgoin64@gmail.com>	2026-02-13 08:11:26 -08:00
LoganJane GitHub Isotr0py	4a9952ec1b	[Bugfix] Add quant_config in ViT of Kimi-K2.5 (#34501 ) Signed-off-by: LoganJane <LoganJane73@hotmail.com> Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>	2026-02-13 16:05:34 +00:00
Roger WangandGitHub	1dae7b7843	[Bugfix] Exclude `language_model_only` key from MM AOT compile hash but include in model one (#34508 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2026-02-13 13:59:00 +00:00
Roger WangandGitHub	5885e330ef	[Misc] Port Qwen3.5 Configs (#34512 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2026-02-13 05:24:25 -08:00
Ilya BoytsovandGitHub	071d863e20	Extend ColBERT support to non-standard BERT backbones (#34170 ) Signed-off-by: Ilya Boytsov <ilya.boytsov@aleph-alpha.com>	2026-02-13 09:53:09 +00:00
Woosuk KwonandGitHub	0916e7960b	[GDN] Use CPU tensors to build GDN metadata (#34498 ) Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>	2026-02-13 01:24:45 -08:00
Wentao Ye GitHub Nick Hill	3d2a026fd0	[Feature] Pipeline Parallel Async send/recv, 2.9% E2E throughput improvement (#33368 ) Signed-off-by: yewentao256 <zhyanwentao@126.com> Signed-off-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Co-authored-by: Nick Hill <nhill@redhat.com>	2026-02-13 16:38:16 +08:00
Aaron Hao GitHubgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>Nick Hill	dddbff4624	[Core] Move pause and resume functions into engine (#34125 ) Signed-off-by: ahao-anyscale <ahao@anyscale.com> Signed-off-by: Aaron Hao <ahao@anyscale.com> Signed-off-by: hao-aaron <ahao@anyscale.com> Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Nick Hill <nickhill123@gmail.com>	2026-02-13 00:15:10 -08:00
Martin HickeyandGitHub	47e9b63e1a	[KVConnector] Clean up redundant code in KV connectors (#34147 ) Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>	2026-02-13 00:14:30 -08:00
Matthias Gehre GitHub TJian	934acddef9	[Perf] fused_moe: add int4_w4a16 benchmark support and tuning config (#34130 ) Signed-off-by: Matthias Gehre <matthias.gehre@amd.com> Co-authored-by: TJian <tunjian.tan@embeddedllm.com>	2026-02-13 00:14:27 -08:00
Marek MichalowskiandGitHub	742d214d6e	[Bugfix] fix the import path in moe test utils.py (#34245 ) Signed-off-by: Marek Michalowski <marek.michalowski@arm.com>	2026-02-13 00:13:45 -08:00
haosdentandGitHub	4137c5dfa7	[Bug Fix] Fix MambaManager.cache_blocks() crash on null blocks in align mode (#34418 ) Signed-off-by: haosdent <haosdent@gmail.com>	2026-02-13 00:13:22 -08:00
Harry HuangandGitHub	7a8a46ddcb	[BugFix] Fix and optimize max_num_blocks_per_req calculation for MambaSpec (#34440 ) Signed-off-by: huanghaoyan.hhy <huanghaoyan.hhy@alibaba-inc.com>	2026-02-13 00:13:14 -08:00
myselvessandGitHub	bcf0731aa0	[New Model] support new model ovis2.6 (#34426 ) Signed-off-by: myselvess <23743269+myselvess@users.noreply.github.com>	2026-02-13 00:12:45 -08:00
Cyrus Leung GitHubgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	ec090c2429	[Refactor] Call renderer for online IO processor request (#34490 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-12 22:48:45 -08:00
Roger WangandGitHub	eea3024f43	[Bugfix] Fix mamba state dtype setting for Qwen3-Next and Qwen3.5 (#34489 ) Signed-off-by: Roger Wang <hey@rogerw.io>	2026-02-12 22:48:42 -08:00
Cyrus LeungandGitHub	2f308214c0	[Refactor] Pass full VllmConfig to Renderer (#34485 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 22:48:38 -08:00
Cyrus LeungandGitHub	1b4e8e53f8	[CI/Build] Fix CUDA re-initialization error in distributed model tests (#34491 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-13 06:43:53 +00:00
haosdentandGitHub	dcf6ee8592	[Bugfix] Fix encoder cache underestimation for GLM-4V/GLM-OCR single image (#34483 ) Signed-off-by: haosdent <haosdent@gmail.com>	2026-02-12 21:04:06 -08:00
Cyrus LeungandGitHub	372b2e762a	[Bugfix] Standardize getting number of image patches/tokens (#34358 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 20:47:01 -08:00
Andreas KaratzasandGitHub	6afa587d31	[ROCm][CI] Fix serving tokens test failures (#34047 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-13 11:27:53 +08:00
Cyrus LeungandGitHub	94ed6cf6ea	Add new sections to CODEOWNERS (#34309 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 18:39:28 -08:00
Harry HuangandGitHub	bf37812ca7	[Hybrid] Fix and optimize block-aligned splitting in mamba cache align mode (#33706 ) Signed-off-by: huanghaoyan.hhy <huanghaoyan.hhy@alibaba-inc.com>	2026-02-12 18:21:52 -08:00
Frank Wang GitHub Roger Wang	b86bf4417e	[Bugfix] Fix Random Dataset Prefix Length Inaccuracy (#33907 ) Signed-off-by: frankwang28 <frank.wbb@hotmail.com> Co-authored-by: Roger Wang <hey@rogerw.io>	2026-02-12 18:21:19 -08:00
Yanan CaoandGitHub	de13dd781f	[Kernel] [Helion] [5/N] Add Helion Autotuning infrastructure (#34025 ) Signed-off-by: Yanan Cao <gmagogsfm@gmail.com>	2026-02-12 18:21:05 -08:00
LoganJane GitHub Isotr0py	62788f99a4	[Bugfix] Delete unused redundant code in Kimi-K2.5 (#34427 ) Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>	2026-02-12 18:18:42 -08:00
Cyrus LeungandGitHub	ea5ff3a1f6	[Refactor] Simplify BOS/EOS token handling (#34435 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 18:18:24 -08:00
bnellnmandGitHub	04ea31baab	[Bugfix] Remove assert that's no longer valid (#34443 ) Signed-off-by: Bill Nell <bnell@redhat.com>	2026-02-12 18:18:15 -08:00
Harry HuangandGitHub	6f019e6e0a	[BugFix] Add block_size validation for mamba cache align mode (#34445 ) Signed-off-by: huanghaoyan.hhy <huanghaoyan.hhy@alibaba-inc.com>	2026-02-12 18:18:07 -08:00
Zhuohan LiandGitHub	d707678dfb	Fix num_logprobs parameter description in sampler.py (#34451 ) Signed-off-by: Zhuohan Li <zhuohan123@gmail.com>	2026-02-12 18:18:03 -08:00
Cyrus LeungandGitHub	fc22cae4ac	[CI/Build] Update video URLs for testing (#34446 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 18:15:36 -08:00
Yanan CaoandGitHub	96161fe978	[Kernel] [Helion] [4/N] Add silu_mul_fp8 Helion kernel (#33373 ) Signed-off-by: Yanan Cao <gmagogsfm@gmail.com>	2026-02-12 18:13:12 -08:00
Jaewon GitHub Lu Fang	4453ba8d9e	[Core] Profiler improvements and lazy initialization (#33198 ) Signed-off-by: Jaewon Lee <jaewon@meta.com> Co-authored-by: Lu Fang <30275821+houseroad@users.noreply.github.com>	2026-02-12 16:16:38 -08:00
Jaewon GitHub Lu Fang	aa181c923b	[Core] Add sleep level 0 mode with enqueue/wait pattern (#33195 ) Signed-off-by: Jaewon Lee <jaewon@meta.com> Co-authored-by: Lu Fang <30275821+houseroad@users.noreply.github.com>	2026-02-12 16:16:25 -08:00
Alec S GitHub Alec Solder	be7370daf3	[Frontend] Enable generic structured_outputs for responses API (#33709 ) Signed-off-by: Alec Solder <alecs@fb.com> Co-authored-by: Alec Solder <alecs@fb.com>	2026-02-12 16:15:48 -08:00
Mengtao (Martin) Yuan GitHub Martin Yuan	9ea1f598ce	Use paged_attention_v1 for sliding window decode in rocm_aiter_fa (#34378 ) Signed-off-by: Martin Yuan <myuan@meta.com> Co-authored-by: Martin Yuan <myuan@meta.com>	2026-02-12 16:14:43 -08:00
amitz-nvandGitHub	f120bd42d3	[Kernel] Support Flashinfer trtllm fused MoE non gated FP8 & NVFP4 (#33506 ) Signed-off-by: amitz-nv <203509407+amitz-nv@users.noreply.github.com>	2026-02-12 13:06:58 -08:00
Hashem HashemiandGitHub	fac4e96940	small adjustment to wvSplitKrc (#34410 ) Signed-off-by: Hashem Hashemi <hashem.hashemi@amd.com>	2026-02-12 20:26:36 +00:00
Michael GoinandGitHub	6d4e27ce29	[Bugfix] Enforce DeepGEMM when using sparse_attn_indexer on CUDA (#34374 ) Signed-off-by: mgoin <mgoin64@gmail.com>	2026-02-12 12:08:06 -08:00
Andreas KaratzasandGitHub	4c078fa546	[ROCm][CI] Pin TorchCodec to v0.10.0 for ROCm compatibility (#34447 ) Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-02-12 18:47:34 +00:00
Patrick von Platen GitHubgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	6c0baee610	[Voxtral Realtime] Refactor & Improve buffering logic (#34428 ) Signed-off-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-12 09:46:43 -08:00
Patrick von PlatenandGitHub	1100a97621	[Voxstral Realtime] Enable tests (#33803 ) Signed-off-by: Patrick von Platen <patrick.v.platen@gmail.com>	2026-02-12 09:43:24 -08:00
xuebwang-amd GitHub TJian	766e167821	[ROCm][quantization] improve OCP weight quant parser robust (#34431 ) Signed-off-by: xuebwang-amd <xuebwang@amd.com> Co-authored-by: TJian <tunjian.tan@embeddedllm.com>	2026-02-12 09:40:19 -08:00
Isotr0pyandGitHub	becbe24808	[Bugfix] Remove broken raw url GGUF model loading support (#34433 ) Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>	2026-02-12 09:40:01 -08:00
Harry MellorandGitHub	679ca5d8d3	Fix MoE for the Transformers modelling backend (#34436 ) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>	2026-02-12 09:29:42 -08:00
Matthew Bonanni GitHub Lucas Wilkinson Lucas Wilkinson	f2c47886fd	[Attention] Add FlashInfer Sparse MLA backend (#33451 ) Signed-off-by: Matthew Bonanni <mbonanni@redhat.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: Lucas Wilkinson <LucasWilkinson@users.noreply.github.com>	2026-02-12 17:21:54 +00:00
Nicolò LucchesiandGitHub	334c715e0f	[Docs] Spec decoding docs warning removal (#34439 ) Signed-off-by: NickLucche <nlucches@redhat.com>	2026-02-12 09:01:51 -08:00
Aaron HaoandGitHub	7b5a8b4a9d	[BUG] Reset running requests when clearing cache for pause/resume (#34382 ) Signed-off-by: hao-aaron <ahao@anyscale.com>	2026-02-12 16:19:13 +00:00
daniserebandGitHub	dea63512bb	Add config file for fused MoE for Nemotron (TP4, B200) (#34411 ) Signed-off-by: Daniel Serebrenik <daserebrenik@nvidia.com>	2026-02-12 06:09:55 -08:00
Douglas Lehr GitHub Doug Lehr Gregory Shtrasberg tjtanaavllm	8a798be929	[ROCm] Enable MXFP4 MoE weight pre-shuffling on gfx950 and update aiter (#34192 ) Signed-off-by: Doug Lehr <douglehr@amd.com> Co-authored-by: Doug Lehr <douglehr@amd.com> Co-authored-by: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com> Co-authored-by: tjtanaavllm <tunjian.tan@amd.com>	2026-02-12 05:06:33 -08:00
Cyrus LeungandGitHub	fb455ed547	[V0 Deprecation] Remove code related to per-request logits processors (#34400 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-12 20:44:28 +08:00
baonudesifeizhaiandGitHub	f5897613fb	Fix Mistral config remap to accept compressed-tensors quantization #34028 (#34104 ) Signed-off-by: baonudesifeizhai <baonudesifeizhai@gmail.com>	2026-02-12 08:22:06 +00:00
Louie TsaiandGitHub	55a1a9563a	Vllm CPU benchmark suite improvement (#34128 ) Signed-off-by: louie-tsai <louie.tsai@intel.com>	2026-02-12 16:04:44 +08:00
AllenDou GitHub zixiao	386bfe5d08	[bugfix] refactor FunASR's _get_data_parser (#34397 ) Signed-off-by: zixiao <shunli.dsl@alibaba-inc.com> Co-authored-by: zixiao <shunli.dsl@alibaba-inc.com>	2026-02-12 07:26:49 +00:00
Kyle Sayers GitHub Michael Goin	e9cd691132	[Bugfix] Fix Sparse24 Compressed Tensors models (#33446 ) Signed-off-by: Kyle Sayers <kylesayrs@gmail.com> Co-authored-by: Michael Goin <mgoin64@gmail.com>	2026-02-11 23:15:16 -08:00
Yichuan Wang GitHub Cursor	80f2ba6ea6	Fix DeepSeek-OCR tensor validation for all size variants (#34085 ) Co-authored-by: Cursor <cursoragent@cursor.com>	2026-02-11 22:50:23 -08:00
Lucas Wilkinson GitHub Bill Nell	136b0bfa59	[BugFix] Fix DP chunking (#34379 ) Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Signed-off-by: Bill Nell <bnell@redhat.com> Co-authored-by: Bill Nell <bnell@redhat.com>	2026-02-12 06:44:03 +00:00
Cyrus LeungandGitHub	b96f7314b4	[Refactor] Pass Renderer to Input Processor (#34329 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-11 19:38:11 -08:00
Cyrus LeungandGitHub	ced2a92f40	[Refactor] Move validation to params definitions (#34362 ) Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>	2026-02-11 19:33:15 -08:00
Runkai TaoandGitHub	e1d97c38f8	[Bug Fix] Fix `naive_block_assignment` always defaulting to False due to arg misalignment (#33848 ) Signed-off-by: Runkai Tao <rt572@physics.rutgers.edu>	2026-02-12 11:30:57 +08:00
Michael GoinandGitHub	ec12d39d44	[Bugfix] Fix MTP accuracy for GLM-5 (#34385 ) Signed-off-by: mgoin <mgoin64@gmail.com>	2026-02-12 11:08:19 +08:00