add

Signed-off-by: Roger Wang <hey@rogerw.io>

.buildkite/hardware_tests/amd.yaml

@@ -1,7 +1,6 @@
-group: Hardware - AMD Build 
+group: Hardware
 steps:
   - label: "AMD: :docker: build image"
-    key: image-build-amd
     depends_on: []
     device: amd_cpu
     no_plugin: true
@@ -10,7 +9,7 @@ steps:
       docker build
       --build-arg max_jobs=16
       --build-arg REMOTE_VLLM=1
-      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx942;gfx950'
+      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942'
       --build-arg VLLM_BRANCH=$BUILDKITE_COMMIT
       --tag "rocm/vllm-ci:${BUILDKITE_COMMIT}"
       -f docker/Dockerfile.rocm

.buildkite/image_build/image_build.sh

@@ -8,7 +8,7 @@ clean_docker_tag() {
 }
 
 print_usage_and_exit() {
-    echo "Usage: $0 <registry> <repo> <commit> <branch> <image_tag> [<image_tag_latest>]"
+    echo "Usage: $0 <registry> <repo> <commit> <branch> <vllm_use_precompiled> <vllm_merge_base_commit> <cache_from> <cache_to>"
     exit 1
 }
 
@@ -142,16 +142,11 @@ resolve_parent_commit() {
 
 print_bake_config() {
     echo "--- :page_facing_up: Resolved bake configuration"
-    # Write to a temp directory to avoid polluting the repo root (which is the
-    # Docker build context). Files left in the repo root get COPY'd into the
-    # image and can cause duplicate artifact uploads from downstream steps.
-    local bake_tmp
-    bake_tmp="$(mktemp -d)"
-    BAKE_CONFIG_FILE="${bake_tmp}/bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
+    BAKE_CONFIG_FILE="bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
     docker buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --print "${TARGET}" | tee "${BAKE_CONFIG_FILE}" || true
     echo "Saved bake config to ${BAKE_CONFIG_FILE}"
     echo "--- :arrow_down: Uploading bake config to Buildkite"
-    (cd "$(dirname "${BAKE_CONFIG_FILE}")" && buildkite-agent artifact upload "$(basename "${BAKE_CONFIG_FILE}")")
+    buildkite-agent artifact upload "${BAKE_CONFIG_FILE}"
 }
 
 #################################
@@ -159,7 +154,7 @@ print_bake_config() {
 #################################
 print_instance_info
 
-if [[ $# -lt 5 ]]; then
+if [[ $# -lt 7 ]]; then
     print_usage_and_exit
 fi
 
@@ -168,8 +163,10 @@ REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
 BRANCH=$4
-IMAGE_TAG=$5
-IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional
+VLLM_USE_PRECOMPILED=$5
+VLLM_MERGE_BASE_COMMIT=$6
+IMAGE_TAG=$7
+IMAGE_TAG_LATEST=${8:-} # only used for main branch, optional
 
 # build config
 TARGET="test-ci"
@@ -196,6 +193,8 @@ export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN
 export CACHE_TO
+export VLLM_USE_PRECOMPILED
+export VLLM_MERGE_BASE_COMMIT
 
 # print args
 echo "--- :mag: Arguments"
@@ -203,6 +202,8 @@ echo "REGISTRY: ${REGISTRY}"
 echo "REPO: ${REPO}"
 echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
 echo "BRANCH: ${BRANCH}"
+echo "VLLM_USE_PRECOMPILED: ${VLLM_USE_PRECOMPILED}"
+echo "VLLM_MERGE_BASE_COMMIT: ${VLLM_MERGE_BASE_COMMIT}"
 echo "IMAGE_TAG: ${IMAGE_TAG}"
 echo "IMAGE_TAG_LATEST: ${IMAGE_TAG_LATEST}"
 

.buildkite/image_build/image_build.yaml

@@ -5,7 +5,8 @@ steps:
     depends_on: []
     timeout_in_minutes: 600
     commands:
-    - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG $IMAGE_TAG_LATEST; else .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG; fi
+    - if [[ "$BUILDKITE_BRANCH" != "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG; fi
+    - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG $IMAGE_TAG_LATEST; fi
     retry:
       automatic:
         - exit_status: -1  # Agent was lost

.buildkite/image_build/image_build_cpu.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -24,13 +24,13 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
   --build-arg VLLM_CPU_AVX512BF16=true \
   --build-arg VLLM_CPU_AVX512VNNI=true \
   --build-arg VLLM_CPU_AMXBF16=true \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
+  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -24,10 +24,10 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
+  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
+  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu

.buildkite/image_build/image_build_hpu.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -25,10 +25,10 @@ fi
 docker build \
   --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
+  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
+  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu \
   --progress plain \
   https://github.com/vllm-project/vllm-gaudi.git
 
 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu

.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh

@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on chartqa for vllm.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``
@@ -41,4 +41,4 @@ lm_eval --model vllm-vlm \
   --tasks chartqa \
   --batch_size auto \
   --apply_chat_template \
-  --limit "$LIMIT"
+  --limit $LIMIT

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh

@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``
@@ -20,11 +20,14 @@ usage() {
     echo
 }
 
-while getopts "m:l:f:t:" OPT; do
+while getopts "m:b:l:f:t:" OPT; do
   case ${OPT} in
     m )
         MODEL="$OPTARG"
         ;;
+    b )
+        BATCH_SIZE="$OPTARG"
+        ;;
     l )
         LIMIT="$OPTARG"
         ;;

.buildkite/performance-benchmarks/scripts/compare-json-results.py

@@ -9,10 +9,8 @@ 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
@@ -277,131 +275,6 @@ 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
 # -----------------------------
@@ -555,6 +428,7 @@ 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
@@ -562,10 +436,12 @@ 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)
@@ -584,95 +460,6 @@ 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
 # -----------------------------
@@ -750,21 +537,6 @@ 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
 
 
@@ -885,6 +657,7 @@ 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")
 
@@ -957,151 +730,87 @@ def write_report_group_first(
         for metric_label, (df, _) in metric_cache.items()
     }
 
-    csv_dir = Path(args.csv_out_dir) if args.csv_out_dir else None
-    if csv_dir:
-        csv_dir.mkdir(parents=True, exist_ok=True)
+    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"
+            )
 
-    excel_path = args.excel_out or "perf_comparison.xlsx"
-    with pd.ExcelWriter(excel_path, engine="openpyxl") as xw:
-        # ---- Environment sheet (first) ----
-        env_sheet = _sanitize_sheet_name("Environment")
-        env_df = _load_env_df_for_inputs(args, files)
-        if env_df is None or env_df.empty:
-            pd.DataFrame(
-                [
-                    {
-                        "Section": "Environment",
-                        "Key": "vllm_env.txt",
-                        "Value": "NOT FOUND (or empty)",
-                    }
-                ]
-            ).to_excel(xw, sheet_name=env_sheet, index=False)
-        else:
-            env_df.to_excel(xw, sheet_name=env_sheet, index=False)
-        with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
-            main_fh.write('<meta charset="utf-8">\n')
-            for gkey in group_keys:
-                gkey_tuple = normalize_group_key(gkey)
-                suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
-                sub_path = group_filename(gkey_tuple)
-                group_header = (
-                    '<div style="font-size: 1.4em; font-weight: 700; '
-                    'margin: 18px 0 10px 0;">'
-                    f"{_html.escape(suffix)}"
-                    "</div>\n"
+            main_fh.write(group_header)
+            with open(sub_path, "w", encoding="utf-8") as sub_fh:
+                sub_fh.write('<meta charset="utf-8">\n')
+                sub_fh.write(group_header)
+                tput_group_df = None
+                ttft_group_df = None
+                tpot_group_df = None
+                conc_col = args.xaxis
+
+                for metric_label in plan.data_cols:
+                    gb = metric_groupbys[metric_label]
+                    df_sorted, raw_data_cols = metric_cache[metric_label]
+
+                    try:
+                        group_df = gb.get_group(gkey)
+                    except KeyError:
+                        missing = (
+                            '<div style="font-size: 1.1em; font-weight: 600; '
+                            'margin: 10px 0;">'
+                            f"{_html.escape(metric_label)} — missing for this group"
+                            "</div>\n"
+                        )
+
+                        main_fh.write(missing)
+                        sub_fh.write(missing)
+                        continue
+
+                    if conc_col not in group_df.columns:
+                        conc_col = _find_concurrency_col(group_df)
+
+                    mn = metric_label.lower().strip()
+                    if "tok/s" in mn:
+                        tput_group_df = group_df
+                    elif "ttft" in mn:
+                        ttft_group_df = group_df
+                    elif mn in ("p99", "median") or "tpot" in mn:
+                        tpot_group_df = group_df
+
+                    display_group = group_df.drop(
+                        columns=group_cols_canonical, errors="ignore"
+                    )
+
+                    html = render_metric_table_html(
+                        display_group, metric_label, suffix, args
+                    )
+                    main_fh.write(html)
+                    sub_fh.write(html)
+
+                    maybe_write_plot(
+                        main_fh,
+                        sub_fh,
+                        group_df=group_df,
+                        raw_data_cols=raw_data_cols,
+                        metric_label=metric_label,
+                        y_axis_col=y_axis_col,
+                        args=args,
+                    )
+
+                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,
                 )
-
-                main_fh.write(group_header)
-
-                sheet = _group_to_sheet_base(group_cols_canonical, gkey_tuple)
-                sheet_base = sheet
-                dedup_i = 1
-                while sheet in xw.sheets:
-                    dedup_i += 1
-                    sheet = _sanitize_sheet_name(f"{sheet_base}_{dedup_i}")
-
-                excel_blocks: list[tuple[str, pd.DataFrame]] = []
-
-                with open(sub_path, "w", encoding="utf-8") as sub_fh:
-                    sub_fh.write('<meta charset="utf-8">\n')
-                    sub_fh.write(group_header)
-                    tput_group_df = None
-                    ttft_group_df = None
-                    tpot_group_df = None
-                    conc_col = args.xaxis
-
-                    for metric_label in plan.data_cols:
-                        gb = metric_groupbys[metric_label]
-                        df_sorted, raw_data_cols = metric_cache[metric_label]
-
-                        try:
-                            group_df = gb.get_group(gkey)
-                        except KeyError:
-                            missing = (
-                                '<div style="font-size: 1.1em; font-weight: 600; '
-                                'margin: 10px 0;">'
-                                f"{_html.escape(metric_label)} — missing for this group"
-                                "</div>\n"
-                            )
-                            main_fh.write(missing)
-                            sub_fh.write(missing)
-                            continue
-
-                        if conc_col not in group_df.columns:
-                            conc_col = _find_concurrency_col(group_df)
-
-                        mn = metric_label.lower().strip()
-                        if "tok/s" in mn:
-                            tput_group_df = group_df
-                        elif "ttft" in mn:
-                            ttft_group_df = group_df
-                        elif mn in ("p99", "median") or "tpot" in mn:
-                            tpot_group_df = group_df
-
-                        display_group = group_df.drop(
-                            columns=group_cols_canonical, errors="ignore"
-                        )
-
-                        html = render_metric_table_html(
-                            display_group, metric_label, suffix, args
-                        )
-                        main_fh.write(html)
-                        sub_fh.write(html)
-
-                        maybe_write_plot(
-                            main_fh,
-                            sub_fh,
-                            group_df=group_df,
-                            raw_data_cols=raw_data_cols,
-                            metric_label=metric_label,
-                            y_axis_col=y_axis_col,
-                            args=args,
-                        )
-
-                        excel_blocks.append(
-                            (metric_label, display_group.reset_index(drop=True))
-                        )
-                        if csv_dir:
-                            fn = _safe_filename(
-                                f"{sheet}__{metric_label}".replace(" ", "_").replace(
-                                    "/", "_"
-                                )
-                            )
-                            display_group.to_csv(csv_dir / f"{fn}.csv", index=False)
-
-                    summary_html = build_valid_max_concurrency_summary_html(
-                        tput_group_df=tput_group_df,
-                        ttft_group_df=ttft_group_df,
-                        tpot_group_df=tpot_group_df,
-                        conc_col=conc_col,
-                        args=args,
-                    )
-                    if summary_html:
-                        main_fh.write(summary_html)
-                        sub_fh.write(summary_html)
-
-                    summary_df = build_valid_max_concurrency_summary_df(
-                        tput_group_df=tput_group_df,
-                        ttft_group_df=ttft_group_df,
-                        tpot_group_df=tpot_group_df,
-                        conc_col=conc_col,
-                        args=args,
-                    )
-                    if summary_df is not None:
-                        excel_blocks.append(
-                            ("Valid Max Concurrency Summary", summary_df)
-                        )
-                        if csv_dir:
-                            fn = _safe_filename(
-                                f"{sheet}__Valid_Max_Concurrency_Summary"
-                            )
-                            summary_df.to_csv(csv_dir / f"{fn}.csv", index=False)
-
-                _write_tables_to_excel_sheet(xw, sheet, excel_blocks)
-
-    print(f"Wrote Excel: {excel_path}")
-    if csv_dir:
-        print(f"Wrote CSVs under: {csv_dir}")
+                if summary_html:
+                    main_fh.write(summary_html)
+                    sub_fh.write(summary_html)
 
 
 def main():

.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh

@@ -1,4 +1,6 @@
 #!/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/
 
@@ -7,19 +9,14 @@
 set -x
 set -o pipefail
 
-# Environment-driven debug controls (like ON_CPU=1)
-DRY_RUN="${DRY_RUN:-0}"
-MODEL_FILTER="${MODEL_FILTER:-}"
-DTYPE_FILTER="${DTYPE_FILTER:-}"
-
 check_gpus() {
   if command -v nvidia-smi; then
     # check the number of GPUs and GPU type.
-    declare -g gpu_count=$(nvidia-smi --list-gpus | grep -c . || true)
+    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
   elif command -v amd-smi; then
-    declare -g gpu_count=$(amd-smi list | grep -c 'GPU' || true)
+    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
   elif command -v hl-smi; then
-    declare -g gpu_count=$(hl-smi --list | grep -ci "Module ID" || true)
+    declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
   fi
 
   if [[ $gpu_count -gt 0 ]]; then
@@ -47,7 +44,7 @@ check_cpus() {
   declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
   if [[ $numa_count -gt 0 ]]; then
     echo "NUMA found."
-    echo "$numa_count"
+    echo $numa_count
   else
     echo "Need at least 1 NUMA to run benchmarking."
     exit 1
@@ -115,12 +112,13 @@ 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 -sf http://localhost:8000/v1/models >/dev/null; do
+    until curl -X POST localhost:8000/v1/completions; do
       sleep 1
-    done
-  '
+    done' && return 0 || return 1
 }
 
 kill_processes_launched_by_current_bash() {
@@ -254,16 +252,37 @@ run_benchmark_tests() {
   done
 }
 
-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_latency_tests() {
+  run_benchmark_tests "latency" "$1"
+}
 
-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='
+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=$1
+
+  # Iterate over serving tests
+  jq -c '
     if type == "array" then
       # Plain format: test cases array
       .[]
@@ -285,50 +304,7 @@ merge_serving_tests_stream() {
     else
       error("Unsupported serving test file format: must be array or object with .tests")
     end
-  '
-
-  jq -c "$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
+  ' "$serving_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
     test_name=$(echo "$params" | jq -r '.test_name')
     if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -397,7 +373,7 @@ run_serving_tests() {
     echo "Server command: $server_command"
     # support remote vllm server
     client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" && "${DRY_RUN:-0}" != "1" ]]; then
+    if [[ -z "${REMOTE_HOST}" ]]; then
       bash -c "$server_command" &
       server_pid=$!
       # wait until the server is alive
@@ -408,9 +384,6 @@ 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
@@ -429,12 +402,14 @@ 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
       for max_concurrency in $max_concurrency_list; do
-        new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
+        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
         echo " new test name $new_test_name"
         # pass the tensor parallel size, the compilation mode, and the optimization
         # level to the client so that they can be used on the benchmark dashboard
@@ -450,9 +425,7 @@ run_serving_tests() {
         echo "Running test case $test_name with qps $qps"
         echo "Client command: $client_command"
 
-        if [[ "${DRY_RUN:-0}" != "1" ]]; then
-          bash -c "$client_command"
-        fi
+        bash -c "$client_command"
 
         # record the benchmarking commands
         jq_output=$(jq -n \
@@ -470,15 +443,12 @@ run_serving_tests() {
     done
 
     # clean up
-    if [[ "${DRY_RUN:-0}" != "1" ]]; then
-      kill -9 "$server_pid"
-      kill_gpu_processes
-    fi
+    kill -9 $server_pid
+    kill_gpu_processes
   done
 }
 
 main() {
-
   local ARCH
   ARCH=''
   if [[ "$ON_CPU" == "1" ]]; then
@@ -488,13 +458,7 @@ main() {
      check_gpus
      ARCH="$arch_suffix"
   fi
-
-  # 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
+  check_hf_token
 
   # dependencies
   (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
@@ -515,16 +479,11 @@ 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}" || exit $?
-
-  if [[ "${DRY_RUN:-0}" == "1" ]]; then
-    echo "DRY_RUN=1 -> skip latency/startup/throughput suites"
-    exit 0
-  fi
-
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
   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}"

.buildkite/performance-benchmarks/tests/serving-tests-cpu-embed.json

@@ -1,41 +0,0 @@
-{
-  "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": {}
-    }
-  ]
-}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-text.json

@@ -1,283 +0,0 @@
-{
-  "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
-      }
-    }
-  ]
-}

.buildkite/performance-benchmarks/tests/serving-tests-cpu.json

@@ -148,6 +148,136 @@
         "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
+      }
     }
   ]
 }

.buildkite/scripts/annotate-rocm-release.sh

@@ -25,7 +25,7 @@ S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
 S3_URL="http://${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com"
 
 # Format ROCm version for path (e.g., "7.1" -> "rocm710")
-ROCM_VERSION_PATH="rocm$(echo "${ROCM_VERSION}" | tr -d '.')"
+ROCM_VERSION_PATH="rocm$(echo ${ROCM_VERSION} | tr -d '.')"
 ROCM_PATH="rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}"
 buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
 ## ROCm Wheel and Docker Image Releases

.buildkite/scripts/cache-rocm-base-wheels.sh

@@ -83,7 +83,7 @@ case "${1:-}" in
             exit 1
         fi
 
-        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
         if [[ "$WHEEL_COUNT" -eq 0 ]]; then
             echo "ERROR: No wheels found in artifacts/rocm-base-wheels/" >&2
             exit 1
@@ -110,9 +110,9 @@ case "${1:-}" in
 
         echo ""
         echo "Downloaded wheels:"
-        find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' -exec ls -lh {} \;
+        ls -lh artifacts/rocm-base-wheels/
 
-        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
         echo ""
         echo "Total: $WHEEL_COUNT wheels"
         echo "========================================"

.buildkite/scripts/cherry-pick-from-milestone.sh

@@ -134,7 +134,7 @@ log_info "Fetching merged PRs from milestone '${MILESTONE}'..."
 
 # Store PR data in a temp file
 PR_DATA=$(mktemp)
-trap 'rm -f "$PR_DATA"' EXIT
+trap "rm -f $PR_DATA" EXIT
 
 if ! gh pr list --state merged --search "milestone:${MILESTONE}" \
     --limit 1000 \

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -27,7 +27,7 @@ function cpu_tests() {
   podman exec -it "$container_id" bash -c "
     export TORCH_COMPILE_DISABLE=1
     set -xve
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> "$HOME"/test_basic.log
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log
 
   # Run basic model test
   podman exec -it "$container_id" bash -c "
@@ -43,7 +43,7 @@ function cpu_tests() {
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-google/gemma-1.1-2b-it]
     pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
     # TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
-    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> "$HOME"/test_rest.log
+    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -16,5 +16,5 @@ echo "--- :docker: Building Docker image"
 docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g "$IMAGE_NAME" \
-        timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"
+docker run --rm --cpuset-cpus=$CORE_RANGE --cpuset-mems=$NUMA_NODE -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g $IMAGE_NAME \
+        timeout $TIMEOUT_VAL bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"

.buildkite/scripts/hardware_ci/run-hpu-test.sh

@@ -7,7 +7,7 @@ set -exuo pipefail
 # Try building the docker image
 image_name="hpu/upstream-vllm-ci:${BUILDKITE_COMMIT}"
 container_name="hpu-upstream-vllm-ci-${BUILDKITE_COMMIT}-container"
-cat <<EOF | docker build -t "${image_name}" -f - .
+cat <<EOF | docker build -t ${image_name} -f - .
 FROM gaudi-base-image:latest
 
 COPY ./ /workspace/vllm
@@ -39,12 +39,12 @@ EOF
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-remove_docker_containers() { docker rm -f "${container_name}" || true; }
+remove_docker_containers() { docker rm -f ${container_name} || true; }
 trap 'remove_docker_containers; exit $EXITCODE;' EXIT
 remove_docker_containers
 
 echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name="${container_name}" --network=host \
+docker run --rm --runtime=habana --name=${container_name} --network=host \
   -e HABANA_VISIBLE_DEVICES=all \
   -e VLLM_SKIP_WARMUP=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \

.buildkite/scripts/hardware_ci/run-npu-test.sh

@@ -41,7 +41,6 @@ get_config() {
         echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2
         exit 1
     fi
-    # shellcheck source=/dev/null
     source "${TEST_RUN_CONFIG_FILE}"
     echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}"
     return 0
@@ -49,8 +48,9 @@ get_config() {
 
 # get test running configuration.
 fetch_vllm_test_cfg
+get_config
 # Check if the function call was successful. If not, exit the script.
-if ! get_config; then
+if [ $? -ne 0 ]; then
   exit 1
 fi
 
@@ -62,14 +62,14 @@ agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
 echo "agent_idx: ${agent_idx}"
 builder_name="cachebuilder${agent_idx}"
 builder_cache_dir="/mnt/docker-cache${agent_idx}"
-mkdir -p "${builder_cache_dir}"
+mkdir -p ${builder_cache_dir}
 
 # Try building the docker image
 cat <<EOF | DOCKER_BUILDKIT=1 docker build \
-    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:"${PYPI_CACHE_HOST}" \
-    --builder "${builder_name}" --cache-from type=local,src="${builder_cache_dir}" \
-                           --cache-to type=local,dest="${builder_cache_dir}",mode=max \
-    --progress=plain --load -t "${image_name}" -f - .
+    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_HOST} \
+    --builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
+                           --cache-to type=local,dest=${builder_cache_dir},mode=max \
+    --progress=plain --load -t ${image_name} -f - .
 FROM ${BASE_IMAGE_NAME}
 
 # Define environments
@@ -116,7 +116,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
     source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -i)-linux/devlib && \
+    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
     python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/
 
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
@@ -139,7 +139,7 @@ trap remove_docker_container EXIT
 # Generate corresponding --device args based on BUILDKITE_AGENT_NAME
 # Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
 #   e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
-#   returns one argument per line: --device, /dev/davinciX, ...
+#   returns --device /dev/davinci0 --device /dev/davinci1
 parse_and_gen_devices() {
     local input="$1"
     local index cards_num
@@ -151,24 +151,29 @@ parse_and_gen_devices() {
         return 1
     fi
 
+    local devices=""
     local i=0
     while (( i < cards_num )); do
         local dev_idx=$(((index - 1)*cards_num + i ))
-        printf '%s\n' "--device"
-        printf '%s\n' "/dev/davinci${dev_idx}"
+        devices="$devices --device /dev/davinci${dev_idx}"
         ((i++))
     done
+
+    # trim leading space
+    devices="${devices#"${devices%%[![:space:]]*}"}"
+    # Output devices: assigned to the caller variable
+    printf '%s' "$devices"
 }
 
-mapfile -t device_args < <(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
+devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
 
 # Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
 # This test checks whether the OOT platform interface is functioning properly in conjunction with
 # the hardware plugin vllm-ascend.
 model_cache_dir=/mnt/modelscope${agent_idx}
-mkdir -p "${model_cache_dir}"
+mkdir -p ${model_cache_dir}
 docker run \
-    "${device_args[@]}" \
+    ${devices} \
     --device /dev/davinci_manager \
     --device /dev/devmm_svm \
     --device /dev/hisi_hdc \
@@ -177,7 +182,7 @@ docker run \
     -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
     -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v "${model_cache_dir}":/root/.cache/modelscope \
+    -v ${model_cache_dir}:/root/.cache/modelscope \
     --entrypoint="" \
     --name "${container_name}" \
     "${image_name}" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -8,7 +8,7 @@ image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 
 # Try building the docker image
-docker build -t "${image_name}" -f docker/Dockerfile.xpu .
+docker build -t ${image_name} -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() {

.buildkite/scripts/push-nightly-builds.sh

@@ -21,16 +21,16 @@ echo "Pushing original tag $ORIG_TAG_NAME$ORIG_TAG_SUFFIX to new nightly tag nam
 
 # pull original arch-dependent images from AWS ECR Public
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX"
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX"
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX
 # tag arch-dependent images
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-aarch64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-aarch64
 # push arch-dependent images to DockerHub
-docker push vllm/vllm-openai:"$TAG_NAME"-x86_64
-docker push vllm/vllm-openai:"$TAG_NAME"-aarch64
+docker push vllm/vllm-openai:$TAG_NAME-x86_64
+docker push vllm/vllm-openai:$TAG_NAME-aarch64
 # push arch-independent manifest to DockerHub
-docker manifest create vllm/vllm-openai:"$TAG_NAME" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
-docker manifest create vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
-docker manifest push vllm/vllm-openai:"$TAG_NAME"
-docker manifest push vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT"
+docker manifest create vllm/vllm-openai:$TAG_NAME vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
+docker manifest create vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
+docker manifest push vllm/vllm-openai:$TAG_NAME
+docker manifest push vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT

.buildkite/scripts/run-prime-rl-test.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Setup script for Prime-RL integration tests
+# This script prepares the environment for running Prime-RL tests with nightly vLLM
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
+PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
+
+if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
+    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
+    exit 0
+fi
+
+echo "Setting up Prime-RL integration test environment..."
+
+# Clean up any existing Prime-RL directory
+if [ -d "${PRIME_RL_DIR}" ]; then
+    echo "Removing existing Prime-RL directory..."
+    rm -rf "${PRIME_RL_DIR}"
+fi
+
+# Install UV if not available
+if ! command -v uv &> /dev/null; then
+    echo "Installing UV package manager..."
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    source $HOME/.local/bin/env
+fi
+
+# Clone Prime-RL repository at specific branch for reproducible tests
+PRIME_RL_BRANCH="integ-vllm-main"
+echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
+git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
+cd "${PRIME_RL_DIR}"
+
+echo "Setting up UV project environment..."
+export UV_PROJECT_ENVIRONMENT=/usr/local
+ln -s /usr/bin/python3 /usr/local/bin/python
+
+# Remove vllm pin from pyproject.toml
+echo "Removing vllm pin from pyproject.toml..."
+sed -i '/vllm==/d' pyproject.toml
+
+# Sync Prime-RL dependencies
+echo "Installing Prime-RL dependencies..."
+uv sync --inexact && uv sync --inexact --all-extras
+
+# Verify installation
+echo "Verifying installations..."
+uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
+
+echo "Prime-RL integration test environment setup complete!"
+
+echo "Running Prime-RL integration tests..."
+export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
+uv run pytest -vs tests/integration/test_rl.py -m gpu
+
+echo "Prime-RL integration tests completed!"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -51,14 +51,14 @@ for BACK in "${BACKENDS[@]}"; do
     --enable-eplb \
     --trust-remote-code \
     --max-model-len 2048 \
-    --all2all-backend "$BACK" \
-    --port "$PORT" &
+    --all2all-backend $BACK \
+    --port $PORT &
   SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh

@@ -47,20 +47,20 @@ for BACK in "${BACKENDS[@]}"; do
   vllm serve "$MODEL" \
     --enforce-eager \
     --enable-eplb \
-    --all2all-backend "$BACK" \
+    --all2all-backend $BACK \
     --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true}' \
-    --tensor-parallel-size "${TENSOR_PARALLEL_SIZE}" \
-    --data-parallel-size "${DATA_PARALLEL_SIZE}" \
+    --tensor-parallel-size ${TENSOR_PARALLEL_SIZE} \
+    --data-parallel-size ${DATA_PARALLEL_SIZE} \
     --enable-expert-parallel \
     --trust-remote-code \
     --max-model-len 2048 \
-    --port "$PORT" &
+    --port $PORT &
   SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -51,20 +51,20 @@ for BACK in "${BACKENDS[@]}"; do
     --tensor-parallel-size 4 \
     --enable-expert-parallel \
     --enable-eplb \
-    --all2all-backend "$BACK" \
+    --all2all-backend $BACK \
     --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
     --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
     --trust-remote-code \
     --max-model-len 2048 \
     --gpu-memory-utilization 0.9 \
     "${PLATFORM_ARGS[@]}" \
-    --port "$PORT" &
+    --port $PORT &
   SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -9,11 +9,10 @@ ENV_FILE=$1
 
 # For testing on local vm, use `set -a` to export all variables
 source /etc/environment
-# shellcheck source=/dev/null
-source "$ENV_FILE"
+source $ENV_FILE
 
 remove_docker_container() { 
-    docker rm -f "$CONTAINER_NAME" || true;
+    docker rm -f $CONTAINER_NAME || true;
 }
 
 trap remove_docker_container EXIT
@@ -42,13 +41,13 @@ echo
 echo "starting docker...$CONTAINER_NAME"
 echo    
 docker run \
- -v "$DOWNLOAD_DIR":"$DOWNLOAD_DIR" \
- --env-file "$ENV_FILE" \
+ -v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
+ --env-file $ENV_FILE \
  -e HF_TOKEN="$HF_TOKEN" \
- -e TARGET_COMMIT="$BUILDKITE_COMMIT" \
- -e MODEL="$MODEL" \
+ -e TARGET_COMMIT=$BUILDKITE_COMMIT \
+ -e MODEL=$MODEL \
  -e WORKSPACE=/workspace \
- --name "$CONTAINER_NAME" \
+ --name $CONTAINER_NAME \
  -d \
  --privileged \
  --network host \

.buildkite/scripts/tpu/run_bm.sh

@@ -42,21 +42,21 @@ echo "lanching vllm..."
 echo "logging to $VLLM_LOG"
 echo
 
-vllm serve "$MODEL" \
+vllm serve $MODEL \
  --seed 42 \
- --max-num-seqs "$MAX_NUM_SEQS" \
- --max-num-batched-tokens "$MAX_NUM_BATCHED_TOKENS" \
- --tensor-parallel-size "$TENSOR_PARALLEL_SIZE" \
+ --max-num-seqs $MAX_NUM_SEQS \
+ --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
+ --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
  --no-enable-prefix-caching \
- --download_dir "$DOWNLOAD_DIR" \
- --max-model-len "$MAX_MODEL_LEN" > "$VLLM_LOG" 2>&1 &
+ --download_dir $DOWNLOAD_DIR \
+ --max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
 
 
 echo "wait for 20 minutes.."
 echo
 # sleep 1200
 # wait for 10 minutes...
-for _ in {1..120}; do
+for i in {1..120}; do
     # TODO: detect other type of errors.
     if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
         echo "Detected RuntimeError, exiting."
@@ -78,11 +78,11 @@ echo "logging to $BM_LOG"
 echo
 vllm bench serve \
     --backend vllm \
-    --model "$MODEL"  \
+    --model $MODEL  \
     --dataset-name sonnet \
     --dataset-path benchmarks/sonnet_4x.txt \
-    --sonnet-input-len "$INPUT_LEN" \
-    --sonnet-output-len "$OUTPUT_LEN" \
+    --sonnet-input-len $INPUT_LEN \
+    --sonnet-output-len $OUTPUT_LEN \
     --ignore-eos > "$BM_LOG"
 
 echo "completed..."

.buildkite/scripts/upload-nightly-wheels.sh

@@ -76,15 +76,16 @@ mkdir -p "$INDICES_OUTPUT_DIR"
 # this indices have relative paths that could work as long as it is next to the wheel directory in s3
 # i.e., the wheels are always in s3://vllm-wheels/<commit>/
 # and indices can be placed in /<commit>/, or /nightly/, or /<version>/
-alias_args=()
-if [[ -n "$DEFAULT_VARIANT_ALIAS" ]]; then
-    alias_args=(--alias-to-default "$DEFAULT_VARIANT_ALIAS")
+if [[ ! -z "$DEFAULT_VARIANT_ALIAS" ]]; then
+    alias_arg="--alias-to-default $DEFAULT_VARIANT_ALIAS"
+else
+    alias_arg=""
 fi
 
 # HACK: we do not need regex module here, but it is required by pre-commit hook
 # To avoid any external dependency, we simply replace it back to the stdlib re module
 sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" "${alias_args[@]}"
+$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" $alias_arg
 
 # copy indices to /<commit>/ unconditionally
 echo "Uploading indices to $S3_COMMIT_PREFIX"
@@ -99,9 +100,9 @@ fi
 # re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
 if [[ "$version" != *"dev"* ]]; then
     echo "Re-generating indices for /$pure_version/"
-    rm -rf "${INDICES_OUTPUT_DIR:?}/*"
+    rm -rf "$INDICES_OUTPUT_DIR/*"
     mkdir -p "$INDICES_OUTPUT_DIR"
     # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" "${alias_args[@]}"
+    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" $alias_arg
     aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
 fi

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -7,7 +7,7 @@ SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
 RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
-GIT_VERSION=$(git describe --exact-match --tags "$BUILDKITE_COMMIT" 2>/dev/null)
+GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
 
 echo "Release version from Buildkite: $RELEASE_VERSION"
 
@@ -55,7 +55,7 @@ mkdir -p $DIST_DIR
 aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
 echo "Wheels copied to local directory"
 # generate source tarball
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" "$BUILDKITE_COMMIT"
+git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
 ls -la $DIST_DIR
 
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
@@ -65,6 +65,6 @@ if [[ -z "$PYPI_WHEEL_FILES" ]]; then
   exit 1
 fi
 
-python3 -m twine check "$PYPI_WHEEL_FILES"
-python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES"
+python3 -m twine check $PYPI_WHEEL_FILES
+python3 -m twine upload --non-interactive --verbose $PYPI_WHEEL_FILES
 echo "Wheels uploaded to PyPI"

.buildkite/scripts/upload-rocm-wheels.sh

@@ -55,7 +55,7 @@ mkdir -p all-rocm-wheels
 cp artifacts/rocm-base-wheels/*.whl all-rocm-wheels/ 2>/dev/null || true
 cp artifacts/rocm-vllm-wheel/*.whl all-rocm-wheels/ 2>/dev/null || true
 
-WHEEL_COUNT=$(find all-rocm-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+WHEEL_COUNT=$(ls all-rocm-wheels/*.whl 2>/dev/null | wc -l)
 echo "Total wheels to upload: $WHEEL_COUNT"
 
 if [ "$WHEEL_COUNT" -eq 0 ]; then
@@ -115,7 +115,7 @@ if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]] |
 fi
 
 # Extract version from vLLM wheel and update version-specific index
-VLLM_WHEEL=$(find all-rocm-wheels -maxdepth 1 -name 'vllm*.whl' 2>/dev/null | head -1)
+VLLM_WHEEL=$(ls all-rocm-wheels/vllm*.whl 2>/dev/null | head -1)
 if [ -n "$VLLM_WHEEL" ]; then
     VERSION=$(unzip -p "$VLLM_WHEEL" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
     echo "Version in wheel: $VERSION"

.buildkite/test_areas/basic_correctness.yaml

@@ -14,8 +14,3 @@ steps:
   - pytest -v -s basic_correctness/test_cumem.py
   - pytest -v -s basic_correctness/test_basic_correctness.py
   - pytest -v -s basic_correctness/test_cpu_offload.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd

.buildkite/test_areas/distributed.yaml

@@ -104,6 +104,7 @@ steps:
   # NEW rlhf examples
   - cd new_weight_syncing
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_async_new_apis.py
 
 - label: Distributed Tests (8 GPUs)(H100)
   timeout_in_minutes: 10
@@ -145,7 +146,6 @@ steps:
   num_devices: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - cd examples/offline_inference/new_weight_syncing && VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_async_new_apis.py
     - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
 
@@ -165,7 +165,6 @@ steps:
   num_devices: 2
   num_nodes: 2
   no_plugin: true
-  optional: true # TODO: revert once infra issue solved
   source_file_dependencies:
   - vllm/distributed/
   - vllm/engine/
@@ -198,18 +197,7 @@ steps:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: Pipeline + Context Parallelism (4 GPUs)
+- label: Pipeline + Context Parallelism (4 GPUs))
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/e2e_integration.yaml

@@ -28,3 +28,16 @@ steps:
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
+
+- label: Prime-RL Integration (2 GPUs)
+  timeout_in_minutes: 30
+  optional: true
+  soft_fail: true
+  num_devices: 2
+  working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/
+  - .buildkite/scripts/run-prime-rl-test.sh
+  commands:
+    - nvidia-smi
+    - bash .buildkite/scripts/run-prime-rl-test.sh

.buildkite/test_areas/entrypoints.yaml

@@ -24,11 +24,6 @@ steps:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Entrypoints Integration (API Server 1)
   timeout_in_minutes: 130

.buildkite/test_areas/misc.yaml

@@ -108,11 +108,9 @@ steps:
   timeout_in_minutes: 50
   source_file_dependencies:
   - vllm/
-  - tests/detokenizer
   - tests/multimodal
   - tests/utils_
   commands:
-  - pytest -v -s detokenizer
   - pytest -v -s -m 'not cpu_test' multimodal
   - pytest -v -s utils_
 
@@ -125,7 +123,6 @@ steps:
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/test_pooling_params.py
-  - tests/test_ray_env.py
   - tests/multimodal
   - tests/renderers
   - tests/standalone_tests/lazy_imports.py
@@ -139,7 +136,6 @@ steps:
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s test_pooling_params.py
-  - pytest -v -s test_ray_env.py
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
@@ -147,19 +143,6 @@ steps:
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
-- label: GPT-OSS Eval (H100)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  device: h100
-  optional: true
-  source_file_dependencies:
-  - tests/evals/gpt_oss
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
-
 - label: GPT-OSS Eval (B200)
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"

.buildkite/test_areas/models_basic.yaml

@@ -4,6 +4,7 @@ depends_on:
 steps:
 - label: Basic Models Tests (Initialization)
   timeout_in_minutes: 45
+  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -15,6 +16,7 @@ steps:
 
 - label: Basic Models Tests (Extra Initialization) %N
   timeout_in_minutes: 45
+  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -36,12 +38,6 @@ steps:
   - tests/models/test_registry.py
   commands:
     - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-    
 
 - label: Basic Models Test (Other CPU) # 5min
   depends_on: 

.buildkite/test_areas/models_language.yaml

@@ -4,6 +4,7 @@ depends_on:
 steps:
 - label: Language Models Tests (Standard)
   timeout_in_minutes: 25
+  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -15,6 +16,7 @@ steps:
 
 - label: Language Models Tests (Extra Standard) %N
   timeout_in_minutes: 45
+  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -30,6 +32,7 @@ steps:
 
 - label: Language Models Tests (Hybrid) %N
   timeout_in_minutes: 75
+  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -45,6 +48,7 @@ steps:
 
 - label: Language Models Test (Extended Generation) # 80min
   timeout_in_minutes: 110
+  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -58,6 +62,7 @@ steps:
 
 - label: Language Models Test (PPL)
   timeout_in_minutes: 110
+  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -67,6 +72,7 @@ steps:
 
 - label: Language Models Test (Extended Pooling)  # 36min
   timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -76,6 +82,7 @@ steps:
 
 - label: Language Models Test (MTEB)
   timeout_in_minutes: 110
+  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/samplers.yaml

@@ -12,10 +12,3 @@ steps:
   commands:
     - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-      commands:
-      - pytest -v -s samplers

.github/CODEOWNERS

@@ -2,9 +2,7 @@
 # for more info about CODEOWNERS file
 
 # This lists cover the "core" components of vLLM that require careful review
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg
-/vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery
-/vllm/lora @jeejeelee
+/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @njhill @22quinn
 /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
@@ -13,34 +11,18 @@
 /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 @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/config/cache.py @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg @heheda12345
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
@@ -55,7 +37,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/v1/kv_cache_interface.py @heheda12345
 /vllm/v1/kv_offload @ApostaC @orozery
 /vllm/v1/worker/gpu/kv_connector.py @orozery
-/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery @NickLucche
+/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery
 
 # Model runner V2
 /vllm/v1/worker/gpu @WoosukKwon
@@ -133,8 +115,8 @@ 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
@@ -170,7 +152,9 @@ 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

.gitignore

@@ -238,6 +238,3 @@ ep_kernels_workspace/
 vllm/grpc/vllm_engine_pb2.py
 vllm/grpc/vllm_engine_pb2_grpc.py
 vllm/grpc/vllm_engine_pb2.pyi
-
-# Ignore generated cpu headers 
-csrc/cpu/cpu_attn_dispatch_generated.h

CMakeLists.txt

@@ -771,25 +771,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  # DeepSeek V3 fused A GEMM kernel (requires SM 9.0+, Hopper and later)
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND DSV3_FUSED_A_GEMM_ARCHS)
-    set(DSV3_FUSED_A_GEMM_SRC "csrc/dsv3_fused_a_gemm.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${DSV3_FUSED_A_GEMM_SRC}"
-      CUDA_ARCHS "${DSV3_FUSED_A_GEMM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC ${DSV3_FUSED_A_GEMM_SRC})
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_DSV3_FUSED_A_GEMM=1")
-    message(STATUS "Building dsv3_fused_a_gemm for archs: ${DSV3_FUSED_A_GEMM_ARCHS}")
-  else()
-    message(STATUS "Not building dsv3_fused_a_gemm as no compatible archs found "
-                   "in CUDA target architectures.")
-  endif()
-
   # moe_data.cu is used by all CUTLASS MoE kernels.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
     cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
@@ -1101,27 +1082,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
                    " in CUDA target architectures")
   endif()
-
-  # DeepSeek V3 router GEMM kernel - requires SM90+
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(DSV3_ROUTER_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(DSV3_ROUTER_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND DSV3_ROUTER_GEMM_ARCHS)
-    set(DSV3_ROUTER_GEMM_SRC
-      "csrc/moe/dsv3_router_gemm_entry.cu"
-      "csrc/moe/dsv3_router_gemm_float_out.cu"
-      "csrc/moe/dsv3_router_gemm_bf16_out.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${DSV3_ROUTER_GEMM_SRC}"
-      CUDA_ARCHS "${DSV3_ROUTER_GEMM_ARCHS}")
-    list(APPEND VLLM_MOE_EXT_SRC "${DSV3_ROUTER_GEMM_SRC}")
-    message(STATUS "Building DSV3 router GEMM kernel for archs: ${DSV3_ROUTER_GEMM_ARCHS}")
-  else()
-    message(STATUS "Not building DSV3 router GEMM kernel as no compatible archs found"
-                   " (requires SM90+ and CUDA >= 12.0)")
-  endif()
 endif()
 
 message(STATUS "Enabling moe extension.")

benchmarks/attention_benchmarks/benchmark.py

@@ -43,7 +43,6 @@ from common import (
     ModelParameterSweep,
     ParameterSweep,
     ResultsFormatter,
-    batch_spec_sort_key,
     is_mla_backend,
 )
 
@@ -219,13 +218,10 @@ def run_model_parameter_sweep(
                         by_param_and_spec[key].append(r)
                         break
 
-    # Sort by param value then spec (batch_size, q_len, kv_len)
+    # Sort by param value then spec
     sorted_keys = sorted(
         by_param_and_spec.keys(),
-        key=lambda x: (
-            int(x[0]) if x[0].isdigit() else x[0],
-            batch_spec_sort_key(x[1]),
-        ),
+        key=lambda x: (int(x[0]) if x[0].isdigit() else x[0], x[1]),
     )
 
     current_param_value = None
@@ -334,7 +330,7 @@ def run_parameter_sweep(
                 by_spec[spec] = []
             by_spec[spec].append(r)
 
-    for spec in sorted(by_spec.keys(), key=batch_spec_sort_key):
+    for spec in sorted(by_spec.keys()):
         results = by_spec[spec]
         best = min(results, key=lambda r: r.mean_time)
         console.print(
@@ -500,18 +496,15 @@ def main():
         if "description" in yaml_config:
             console.print(f"[dim]{yaml_config['description']}[/]")
 
-        # 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
+        # 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
 
         # Check for special modes
         if "mode" in yaml_config:
@@ -551,15 +544,13 @@ 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 (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"]
+        # 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)
 
         # Parameter sweep configuration
         if "parameter_sweep" in yaml_config:

benchmarks/attention_benchmarks/common.py

@@ -16,32 +16,13 @@ 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, index_topk: int | None = None):
+    def __init__(self, mla_dims: dict):
         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"]
@@ -52,8 +33,6 @@ 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
@@ -104,38 +83,6 @@ 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.
 
@@ -380,9 +327,6 @@ 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."""
@@ -549,11 +493,10 @@ def get_attention_scale(head_dim: int) -> float:
 
 def is_mla_backend(backend: str) -> bool:
     """
-    Check if backend is an MLA backend using the AttentionBackendEnum.
+    Check if backend is an MLA backend using the backend's is_mla() property.
 
     Args:
-        backend: Backend name matching AttentionBackendEnum exactly
-        (e.g., "FLASHMLA_SPARSE")
+        backend: Backend name (e.g., "CUTLASS_MLA", "FLASHINFER_MLA")
 
     Returns:
         True if the backend is an MLA backend, False otherwise
@@ -561,8 +504,7 @@ def is_mla_backend(backend: str) -> bool:
     from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
     try:
-        backend_enum = AttentionBackendEnum[backend]
-        backend_class = backend_enum.get_class()
+        backend_class = AttentionBackendEnum[backend.upper()].get_class()
         return backend_class.is_mla()
-    except (KeyError, ValueError, ImportError, AttributeError):
+    except (KeyError, ValueError, ImportError):
         return False

benchmarks/attention_benchmarks/configs/mla_decode.yaml

@@ -3,7 +3,7 @@
 model:
   name: "deepseek-v3"
   num_layers: 60
-  num_q_heads: 128  # Base value, can be swept for TP simulation
+  num_q_heads: 128
   num_kv_heads: 1  # MLA uses single latent KV
   head_dim: 576
   kv_lora_rank: 512
@@ -12,13 +12,6 @@ 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
@@ -41,30 +34,28 @@ 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
-  - FLASH_ATTN_MLA  # Hopper only
-  - FLASHMLA        # Hopper only
+  - cutlass_mla
+  - flashinfer_mla
+  - flashattn_mla  # Hopper only
+  - flashmla        # Hopper only
 
 device: "cuda:0"
-repeats: 100
-warmup_iters: 10
+repeats: 5
+warmup_iters: 3
 profile_memory: true
 
 # Backend-specific tuning
-CUTLASS_MLA:
+cutlass_mla:
   num_kv_splits: auto  # or specific value like 4, 8, 16
 
-FLASH_ATTN_MLA:
+flashattn_mla:
   reorder_batch_threshold: 512
 
-FLASHMLA:
+flashmla:
   reorder_batch_threshold: 1

benchmarks/attention_benchmarks/configs/mla_mixed_batch.yaml

@@ -45,10 +45,10 @@ batch_specs:
   - "4q4k_60q1s4k"          # 4 prefill + 60 decode
 
 backends:
-  - CUTLASS_MLA
-  - FLASHINFER_MLA
-  - FLASH_ATTN_MLA   # Hopper only
-  - FLASHMLA         # Hopper only
+  - cutlass_mla
+  - flashinfer_mla
+  - flashattn_mla   # Hopper only
+  - flashmla        # Hopper only
 
 device: "cuda:0"
 repeats: 5

benchmarks/attention_benchmarks/configs/mla_prefill.yaml

@@ -1,62 +0,0 @@
-# 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

benchmarks/attention_benchmarks/configs/reorder_threshold.yaml

@@ -6,7 +6,7 @@
 description: "Decode vs Prefill pipeline crossover analysis"
 
 # Test FlashAttn MLA
-backend: FLASH_ATTN_MLA
+backend: flashattn_mla
 
 # Mode: decode_vs_prefill comparison (special sweep mode)
 # For each batch spec, we'll test both decode and prefill pipelines
@@ -62,10 +62,11 @@ model:
   block_size: 128
 
 # Benchmark settings
-device: "cuda:0"
-repeats: 15          # More repeats for spec decode variance
-warmup_iters: 5
-profile_memory: false
+benchmark:
+  device: "cuda:0"
+  repeats: 15          # More repeats for spec decode variance
+  warmup_iters: 5
+  profile_memory: false
 
 # Output
 output:

benchmarks/attention_benchmarks/configs/speculative_decode.yaml

@@ -41,17 +41,18 @@ batch_specs:
 
 # Backends that support query length > 1
 backends:
-  - FLASH_ATTN_MLA    # reorder_batch_threshold = 512
-  - FLASHMLA          # reorder_batch_threshold = 1 (tunable)
+  - flashattn_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
-device: "cuda:0"
-repeats: 10  # More repeats for statistical significance
-warmup_iters: 5
-profile_memory: false
+benchmark:
+  device: "cuda:0"
+  repeats: 10  # More repeats for statistical significance
+  warmup_iters: 5
+  profile_memory: false
 
 # Test these threshold values for optimization
 parameter_sweep:

benchmarks/attention_benchmarks/configs/standard_attention.yaml

@@ -36,11 +36,11 @@ batch_specs:
   - "q1ks2k"          # 1k query, 2k sequence
   - "2q1ks4k"         # 2 requests: 1k query, 4k sequence
 
-# Available backends: FLASH_ATTN, TRITON_ATTN, FLASHINFER
+# Available backends: flash, triton, flashinfer
 backends:
-  - FLASH_ATTN
-  - TRITON_ATTN
-  - FLASHINFER
+  - flash
+  - triton
+  - flashinfer
 
 device: "cuda:0"
 repeats: 5

benchmarks/attention_benchmarks/mla_runner.py

@@ -8,13 +8,14 @@ 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,
@@ -61,7 +62,6 @@ 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.
@@ -73,8 +73,6 @@ 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
@@ -84,7 +82,7 @@ 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, index_topk=index_topk)
+    mock_hf_config = MockHfConfig(mla_dims)
 
     # Create a temporary minimal config.json to avoid HF downloads
     # This ensures consistent ModelConfig construction without network access
@@ -122,12 +120,16 @@ 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:
@@ -178,65 +180,56 @@ def create_minimal_vllm_config(
 # ============================================================================
 
 
-# Backend-specific properties that can't be inferred from the backend class
-# Keys are AttentionBackendEnum names (uppercase)
+# 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_PROPERTIES = {
-    "FLASHMLA": {
+    "flashmla": {
         "query_format": "concat",  # Single concatenated tensor (vs tuple)
+        "block_size": 64,  # FlashMLA uses fixed block size
     },
-    "FLASHMLA_SPARSE": {
-        "query_format": "concat",  # Single concatenated tensor (vs tuple)
+    "flashinfer_mla": {
+        "block_size": 64,  # FlashInfer MLA only supports 32 or 64
     },
 }
 
 
 def _get_backend_config(backend: str) -> dict:
     """
-    Get backend configuration from AttentionBackendEnum.
+    Get backend configuration using naming conventions.
 
-    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
+    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
     """
-    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+    if backend not in _BACKEND_NAME_MAP:
+        raise ValueError(f"Unknown backend: {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
+    name = _BACKEND_NAME_MAP[backend]
     props = _BACKEND_PROPERTIES.get(backend, {})
 
+    # Check if backend uses common metadata (FlashInfer, CUTLASS)
+    uses_common = backend in ("flashinfer_mla", "cutlass_mla")
+
     return {
-        "backend_class": backend_class,
-        "impl_class": backend_class.get_impl_cls(),
-        "builder_class": backend_class.get_builder_cls(),
+        "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",
         "query_format": props.get("query_format", "tuple"),
-        "block_size": block_size,
-        "is_sparse": is_sparse,
+        "block_size": props.get("block_size", None),
     }
 
 
@@ -454,26 +447,22 @@ 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 from _get_backend_config()
+        backend_cfg: Backend configuration dict
         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, indexer)
+        Tuple of (impl, layer, builder_instance)
     """
-    # Get classes from backend config (already resolved by _get_backend_config)
-    impl_class = backend_cfg["impl_class"]
-    builder_class = backend_cfg["builder_class"]
+    # Import backend classes
+    backend_module = importlib.import_module(backend_cfg["module"])
+    impl_class = getattr(backend_module, backend_cfg["impl_class"])
 
     # Calculate scale
     scale = 1.0 / np.sqrt(mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"])
@@ -485,44 +474,26 @@ 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(**impl_kwargs)
+    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,
+    )
 
     # Initialize DCP attributes
     if not hasattr(impl, "dcp_world_size") or impl.dcp_world_size in (None, -1):
@@ -544,7 +515,9 @@ def _create_backend_impl(
 
     # Create builder instance if needed
     builder_instance = None
-    if builder_class:
+    if backend_cfg["builder_class"]:
+        builder_class = getattr(backend_module, backend_cfg["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}
@@ -556,7 +529,7 @@ def _create_backend_impl(
             device=device,
         )
 
-    return impl, layer, builder_instance, indexer
+    return impl, layer, builder_instance
 
 
 # ============================================================================
@@ -621,7 +594,6 @@ def _run_single_benchmark(
     backend_cfg: dict,
     mla_dims: dict,
     device: torch.device,
-    indexer=None,
 ) -> BenchmarkResult:
     """
     Run a single benchmark iteration.
@@ -634,7 +606,6 @@ 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
@@ -642,9 +613,7 @@ 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
@@ -672,16 +641,8 @@ def _run_single_benchmark(
         torch.bfloat16,
     )
 
-    # 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:
+    # Determine which forward method to use based on metadata
+    if metadata.decode is not None:
         forward_fn = lambda: impl._forward_decode(
             decode_inputs, kv_cache, metadata, layer
         )
@@ -732,13 +693,11 @@ 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,
-               flashinfer_mla_sparse, flashmla_sparse
+    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla
 
     This function reuses backend initialization across multiple benchmarks
     to avoid setup/teardown overhead.
@@ -748,7 +707,6 @@ 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
@@ -772,27 +730,19 @@ 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, 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,
+        # Create backend impl, layer, and builder (reused across benchmarks)
+        impl, layer, builder_instance = _create_backend_impl(
+            backend_cfg, mla_dims, vllm_config, device
         )
 
         # Run each benchmark with the shared impl
@@ -818,7 +768,6 @@ def _run_mla_benchmark_batched(
                     backend_cfg,
                     mla_dims,
                     device,
-                    indexer=indexer,
                 )
                 results.append(result)
 
@@ -844,24 +793,20 @@ 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,
-               flashinfer_mla_sparse, flashmla_sparse
+    Works for: flashattn_mla, flashmla, flashinfer_mla, cutlass_mla
 
     Always uses batched execution internally for optimal performance.
 
     Args:
-        backend: Backend name (flashattn_mla, flashmla, flashinfer_mla, cutlass_mla,
-                 flashinfer_mla_sparse, flashmla_sparse)
+        backend: Backend name (flashattn_mla, flashmla, flashinfer_mla, cutlass_mla)
         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)
@@ -871,9 +816,9 @@ 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", "flashmla_sparse"):
+            if backend in ("flashattn_mla", "flashmla"):
                 configs_with_params = [(cfg, param, None) for cfg, param in config]
-            else:  # cutlass_mla, flashinfer_mla, or sparse backends
+            else:  # cutlass_mla or flashinfer_mla
                 configs_with_params = [(cfg, None, param) for cfg, param in config]
         else:
             # Format: [cfg, ...] - just configs
@@ -885,7 +830,7 @@ def run_mla_benchmark(
         return_single = True
 
     # Use unified batched execution
-    results = _run_mla_benchmark_batched(backend, configs_with_params, index_topk)
+    results = _run_mla_benchmark_batched(backend, configs_with_params)
 
     # Return single result or list based on input
     return results[0] if return_single else results

benchmarks/attention_benchmarks/runner.py

@@ -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:
-    """
-    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"]
+    if backend not in _BACKEND_CONFIG:
         raise ValueError(
-            f"Unknown backend: {backend}. Valid backends: {valid_backends}"
-        ) from e
-
-    return {"backend_class": backend_class}
+            f"Unknown backend: {backend}. "
+            f"Available: {', '.join(_BACKEND_CONFIG.keys())}"
+        )
+    return _BACKEND_CONFIG[backend]
 
 
 @contextmanager
@@ -205,7 +205,10 @@ def _create_backend_impl(
     dtype: torch.dtype,
 ):
     """Create backend implementation instance."""
-    backend_class = backend_cfg["backend_class"]
+    import importlib
+
+    backend_module = importlib.import_module(backend_cfg["module"])
+    backend_class = getattr(backend_module, backend_cfg["backend_class"])
 
     scale = get_attention_scale(config.head_dim)
 
@@ -244,7 +247,7 @@ 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
@@ -435,7 +438,7 @@ def run_attention_benchmark(config: BenchmarkConfig) -> BenchmarkResult:
     """
     Run standard attention benchmark with real kernels.
 
-    Supports: FLASH_ATTN, TRITON_ATTN, FLASHINFER
+    Supports: flash, triton, flashinfer
 
     Args:
         config: Benchmark configuration
@@ -450,7 +453,7 @@ 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]

benchmarks/auto_tune/auto_tune.sh

@@ -46,10 +46,10 @@ echo "VLLM_LOGGING_LEVEL=$VLLM_LOGGING_LEVEL"
 echo "RESULT_FILE=$RESULT"
 echo "====================== AUTO TUNEPARAMETERS ===================="
 
-rm -rf "$LOG_FOLDER"
-rm -rf "$PROFILE_PATH"
-mkdir -p "$LOG_FOLDER"
-mkdir -p "$PROFILE_PATH"
+rm -rf $LOG_FOLDER
+rm -rf $PROFILE_PATH
+mkdir -p $LOG_FOLDER
+mkdir -p $PROFILE_PATH
 
 cd "$BASE/vllm"
 
@@ -114,7 +114,7 @@ start_server() {
 
     # wait for 10 minutes...
     server_started=0
-    for _ in {1..60}; do
+    for i in {1..60}; do
         # This line checks whether the server is still alive or not,
         # since that we should always have permission to send signal to the server process.
         kill -0 $server_pid 2> /dev/null || break
@@ -145,12 +145,12 @@ run_benchmark() {
     local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
     echo "vllm_log: $vllm_log"
     echo
-    rm -f "$vllm_log"
+    rm -f $vllm_log
     pkill -if "vllm serve" || true
 
     echo "starting server..."
     # Call start_server without a profile_dir to avoid profiling overhead
-    start_server "$gpu_memory_utilization" "$max_num_seqs" "$max_num_batched_tokens" "$vllm_log" ""
+    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log ""
     result=$?
     if [[ "$result" -eq 1 ]]; then
         echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
@@ -168,15 +168,15 @@ run_benchmark() {
     # --profile flag is removed from this call
     vllm bench serve \
         --backend vllm \
-        --model "$MODEL"  \
+        --model $MODEL  \
         --dataset-name random \
         --random-input-len $adjusted_input_len \
-        --random-output-len "$OUTPUT_LEN" \
+        --random-output-len $OUTPUT_LEN \
         --ignore-eos \
         --disable-tqdm \
         --request-rate inf \
         --percentile-metrics ttft,tpot,itl,e2el \
-        --goodput e2el:"$MAX_LATENCY_ALLOWED_MS" \
+        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
         --num-prompts 1000 \
         --random-prefix-len $prefix_len \
         --host "$HOSTNAME" \
@@ -195,20 +195,20 @@ run_benchmark() {
         request_rate=$((${throughput%.*} + 1))
         while ((request_rate > 0)); do
             # clear prefix cache
-            curl -X POST http://"${HOSTNAME}":8004/reset_prefix_cache
+            curl -X POST http://${HOSTNAME}:8004/reset_prefix_cache
             sleep 5
             bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
             vllm bench serve \
                 --backend vllm \
-                --model "$MODEL"  \
+                --model $MODEL  \
                 --dataset-name random \
                 --random-input-len $adjusted_input_len \
-                --random-output-len "$OUTPUT_LEN" \
+                --random-output-len $OUTPUT_LEN \
                 --ignore-eos \
                 --disable-tqdm \
                 --request-rate $request_rate \
                 --percentile-metrics ttft,tpot,itl,e2el \
-                --goodput e2el:"$MAX_LATENCY_ALLOWED_MS" \
+                --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
                 --num-prompts 100 \
                 --random-prefix-len $prefix_len \
                 --host "$HOSTNAME" \
@@ -255,7 +255,7 @@ gpu_memory_utilization=0.98
 find_gpu_memory_utilization=0
 while (( $(echo "$gpu_memory_utilization >= 0.9" | bc -l) )); do
     # Pass empty string for profile_dir argument
-    start_server "$gpu_memory_utilization" "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log" ""
+    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log" ""
     result=$?
     if [[ "$result" -eq 0 ]]; then
         find_gpu_memory_utilization=1
@@ -274,7 +274,7 @@ fi
 
 for num_seqs in "${num_seqs_list[@]}"; do
     for num_batched_tokens in "${num_batched_tokens_list[@]}"; do
-        run_benchmark "$num_seqs" "$num_batched_tokens" "$gpu_memory_utilization"
+        run_benchmark $num_seqs $num_batched_tokens $gpu_memory_utilization
     done
 done
 echo "finish permutations"
@@ -285,7 +285,7 @@ echo "finish permutations"
 if (( $(echo "$best_throughput > 0" | bc -l) )); then
     echo
     echo "Benchmark tuning finished. Now running profiling on the best configuration found..."
-    echo "Best config: max_num_seqs: $best_max_num_seqs, max_num_batched_tokens: $best_num_batched_tokens, throughput: $best_throughput, goodput: $best_goodput"
+    echo "Best config: max_num_seqs: $best_max_num_seqs, max_num_batched_tokens: $best_num_batched_tokens, throughput: $best_throughput"
     echo
 
     vllm_log="$LOG_FOLDER/vllm_log_BEST_PROFILE.txt"
@@ -293,7 +293,7 @@ if (( $(echo "$best_throughput > 0" | bc -l) )); then
 
     # Start server with the best params and profiling ENABLED
     echo "Starting server for profiling..."
-    start_server "$gpu_memory_utilization" "$best_max_num_seqs" "$best_num_batched_tokens" "$vllm_log" "$PROFILE_PATH"
+    start_server $gpu_memory_utilization $best_max_num_seqs $best_num_batched_tokens "$vllm_log" "$PROFILE_PATH"
 
     # Run benchmark with the best params and the --profile flag
     echo "Running benchmark with profiling..."
@@ -301,15 +301,15 @@ if (( $(echo "$best_throughput > 0" | bc -l) )); then
     adjusted_input_len=$(( INPUT_LEN - prefix_len ))
     vllm bench serve \
         --backend vllm \
-        --model "$MODEL" \
+        --model $MODEL \
         --dataset-name random \
         --random-input-len $adjusted_input_len \
-        --random-output-len "$OUTPUT_LEN" \
+        --random-output-len $OUTPUT_LEN \
         --ignore-eos \
         --disable-tqdm \
-        --request-rate "$best_request_rate" \
+        --request-rate $best_request_rate \
         --percentile-metrics ttft,tpot,itl,e2el \
-        --goodput e2el:"$MAX_LATENCY_ALLOWED_MS" \
+        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
         --num-prompts 100 \
         --random-prefix-len $prefix_len \
         --host "$HOSTNAME" \

benchmarks/auto_tune/batch_auto_tune.sh

@@ -64,7 +64,7 @@ for i in $(seq 0 $(($num_runs - 1))); do
   else
     STATUS="FAILURE"
     ((FAILURE_COUNT++))
-    FAILED_RUNS+=("Run #$((i+1)): $(echo "$run_object" | jq -c .)")
+    FAILED_RUNS+=("Run #$((i+1)): $(echo $run_object | jq -c .)")
   fi
 
   RUN_OUTPUT=$(<"$RUN_OUTPUT_FILE")

benchmarks/benchmark_topk_topp.py

@@ -1,471 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Benchmark comparing Triton vs PyTorch sort-based top-k/top-p implementations.
-
-Compares:
-- apply_top_k_top_p_triton (Triton binary search)
-- apply_top_k_top_p (PyTorch sort-based)
-
-Scenarios:
-- top_k only (whole batch, partial batch)
-- top_p only (whole batch, partial batch)
-- mix of top_k and top_p
-"""
-
-import argparse
-import gc
-from dataclasses import dataclass
-
-import torch
-
-from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p_pytorch
-from vllm.v1.sample.ops.topk_topp_triton import (
-    apply_top_k_top_p_triton,
-    reset_buffer_cache,
-)
-
-
-@dataclass
-class BenchmarkConfig:
-    """Configuration for a benchmark run."""
-
-    name: str
-    batch_size: int
-    vocab_size: int
-    # k and p can be tensors or None
-    k_values: torch.Tensor | None  # [batch_size] or None
-    p_values: torch.Tensor | None  # [batch_size] or None
-    description: str
-    ops_pct: float = 0.0  # Percentage of ops relative to batch size
-
-
-def calculate_ops_pct(
-    k_values: torch.Tensor | None,
-    p_values: torch.Tensor | None,
-    vocab_size: int,
-    batch_size: int,
-) -> float:
-    """
-    Calculate the percentage of active top-k and top-p operations.
-
-    Returns percentage where 100% = batch_size ops.
-    E.g., if all rows have both top-k and top-p active, returns 200%.
-    """
-    active_ops = 0
-
-    if k_values is not None:
-        # Count rows where k < vocab_size (active top-k filtering)
-        active_ops += (k_values < vocab_size).sum().item()
-
-    if p_values is not None:
-        # Count rows where p < 1.0 (active top-p filtering)
-        active_ops += (p_values < 1.0).sum().item()
-
-    return (active_ops / batch_size) * 100 if batch_size > 0 else 0.0
-
-
-def create_logits(
-    batch_size: int, vocab_size: int, device: str = "cuda"
-) -> torch.Tensor:
-    """Create random logits mimicking a realistic LLM distribution.
-
-    Uses a Zipf-like probability distribution (rank^-1.1) converted to logits
-    via log, then randomly permuted per row. This produces a peaked distribution
-    where a small number of tokens capture most probability mass, similar to
-    real model outputs.
-    """
-    # Create Zipf-like probabilities: p(rank) ~ rank^(-alpha)
-    ranks = torch.arange(1, vocab_size + 1, dtype=torch.float32, device=device)
-    probs = ranks.pow(-1.1)
-    probs = probs / probs.sum()
-
-    # Convert to logits (log-probabilities, unnormalized is fine)
-    base_logits = probs.log()
-
-    # Broadcast to batch and randomly permute each row
-    logits = base_logits.unsqueeze(0).expand(batch_size, -1).clone()
-    for i in range(batch_size):
-        logits[i] = logits[i, torch.randperm(vocab_size, device=device)]
-
-    return logits
-
-
-def measure_memory() -> tuple[int, int]:
-    """Return (allocated, reserved) memory in bytes."""
-    torch.cuda.synchronize()
-    return torch.cuda.memory_allocated(), torch.cuda.max_memory_allocated()
-
-
-def reset_memory_stats():
-    """Reset peak memory statistics."""
-    reset_buffer_cache()
-    torch.cuda.reset_peak_memory_stats()
-    torch.cuda.empty_cache()
-    gc.collect()
-
-
-def benchmark_function(
-    func,
-    logits: torch.Tensor,
-    k: torch.Tensor | None,
-    p: torch.Tensor | None,
-    warmup_iters: int = 5,
-    benchmark_iters: int = 20,
-) -> tuple[float, int]:
-    """
-    Benchmark a function and return (avg_time_ms, peak_memory_bytes).
-
-    Returns average time in milliseconds and peak memory usage.
-    """
-    # Warmup
-    for _ in range(warmup_iters):
-        logits_copy = logits.clone()
-        func(logits_copy, k, p)
-    torch.cuda.synchronize()
-
-    # Reset memory stats before benchmark
-    reset_memory_stats()
-
-    # Benchmark
-    start_events = [
-        torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)
-    ]
-    end_events = [torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)]
-
-    for i in range(benchmark_iters):
-        logits_copy = logits.clone()
-        start_events[i].record()
-        func(logits_copy, k, p)
-        end_events[i].record()
-
-    torch.cuda.synchronize()
-
-    # Calculate timing
-    times = [
-        start_events[i].elapsed_time(end_events[i]) for i in range(benchmark_iters)
-    ]
-    avg_time = sum(times) / len(times)
-
-    # Get peak memory
-    _, peak_memory = measure_memory()
-
-    return avg_time, peak_memory
-
-
-def create_benchmark_configs(
-    batch_sizes: list[int],
-    vocab_sizes: list[int],
-    device: str = "cuda",
-) -> list[BenchmarkConfig]:
-    """Create all benchmark configurations."""
-    configs = []
-
-    for vocab_size in vocab_sizes:
-        for batch_size in batch_sizes:
-            # 1. Top-k only - whole batch (all rows have k < vocab_size)
-            k_all = torch.full((batch_size,), 50, dtype=torch.int32, device=device)
-            configs.append(
-                BenchmarkConfig(
-                    name=f"topk_whole_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=k_all,
-                    p_values=None,
-                    description=f"Top-k only (whole batch, k=50), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(k_all, None, vocab_size, batch_size),
-                )
-            )
-
-            # 2. Top-k only - partial batch (half have k=50, half have k=vocab_size)
-            k_partial = torch.full((batch_size,), 50, dtype=torch.int32, device=device)
-            k_partial[batch_size // 2 :] = vocab_size  # No filtering for second half
-            configs.append(
-                BenchmarkConfig(
-                    name=f"topk_partial_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=k_partial,
-                    p_values=None,
-                    description=f"Top-k only (partial batch, 50% k=50, 50% k=vocab), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(k_partial, None, vocab_size, batch_size),
-                )
-            )
-
-            # 3. Top-p only - whole batch (all rows have p < 1.0)
-            p_all = torch.full((batch_size,), 0.9, dtype=torch.float32, device=device)
-            configs.append(
-                BenchmarkConfig(
-                    name=f"topp_whole_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=None,
-                    p_values=p_all,
-                    description=f"Top-p only (whole batch, p=0.9), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(None, p_all, vocab_size, batch_size),
-                )
-            )
-
-            # 4. Top-p only - partial batch (half have p=0.9, half have p=1.0)
-            p_partial = torch.full(
-                (batch_size,), 0.9, dtype=torch.float32, device=device
-            )
-            p_partial[batch_size // 2 :] = 1.0  # No filtering for second half
-            configs.append(
-                BenchmarkConfig(
-                    name=f"topp_partial_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=None,
-                    p_values=p_partial,
-                    description=f"Top-p only (partial batch, 50% p=0.9, 50% p=1.0), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(None, p_partial, vocab_size, batch_size),
-                )
-            )
-
-            # 5. Mix of top-k and top-p (both applied to whole batch)
-            k_mix = torch.full((batch_size,), 100, dtype=torch.int32, device=device)
-            p_mix = torch.full((batch_size,), 0.9, dtype=torch.float32, device=device)
-            configs.append(
-                BenchmarkConfig(
-                    name=f"topk_topp_whole_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=k_mix,
-                    p_values=p_mix,
-                    description=f"Top-k + Top-p (whole batch, k=100, p=0.9), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(k_mix, p_mix, vocab_size, batch_size),
-                )
-            )
-
-            # 6. Mix with partial application (some rows k only, some p only, some both)
-            k_mixed = torch.full(
-                (batch_size,), vocab_size, dtype=torch.int32, device=device
-            )
-            p_mixed = torch.full((batch_size,), 1.0, dtype=torch.float32, device=device)
-            # First third: k only
-            third = batch_size // 3
-            k_mixed[:third] = 50
-            # Second third: p only
-            p_mixed[third : 2 * third] = 0.5
-            # Last third: both k and p
-            k_mixed[2 * third :] = 100
-            p_mixed[2 * third :] = 0.9
-            configs.append(
-                BenchmarkConfig(
-                    name=f"mixed_partial_b{batch_size}_v{vocab_size // 1000}k",
-                    batch_size=batch_size,
-                    vocab_size=vocab_size,
-                    k_values=k_mixed,
-                    p_values=p_mixed,
-                    description=f"Mixed partial (1/3 k=50, 1/3 p=0.9, 1/3 both), "
-                    f"batch={batch_size}, vocab={vocab_size}",
-                    ops_pct=calculate_ops_pct(k_mixed, p_mixed, vocab_size, batch_size),
-                )
-            )
-
-    return configs
-
-
-def format_memory(bytes_val: int) -> str:
-    """Format memory in human-readable form."""
-    if bytes_val >= 1024**3:
-        return f"{bytes_val / (1024**3):.2f} GB"
-    elif bytes_val >= 1024**2:
-        return f"{bytes_val / (1024**2):.2f} MB"
-    elif bytes_val >= 1024:
-        return f"{bytes_val / 1024:.2f} KB"
-    return f"{bytes_val} B"
-
-
-def run_benchmark(
-    configs: list[BenchmarkConfig],
-    warmup_iters: int = 5,
-    benchmark_iters: int = 20,
-    verbose: bool = True,
-):
-    """Run all benchmarks and print results."""
-    results = []
-
-    print("=" * 100)
-    print("Top-k/Top-p Benchmark: Triton vs PyTorch Sort-based")
-    print("=" * 100)
-    print()
-
-    for config in configs:
-        if verbose:
-            print(f"Running: {config.description}")
-
-        # Create fresh logits for this config
-        logits = create_logits(config.batch_size, config.vocab_size)
-
-        # Benchmark Triton
-        reset_memory_stats()
-        triton_time, triton_mem = benchmark_function(
-            apply_top_k_top_p_triton,
-            logits,
-            config.k_values,
-            config.p_values,
-            warmup_iters,
-            benchmark_iters,
-        )
-
-        # Benchmark PyTorch
-        reset_memory_stats()
-        pytorch_time, pytorch_mem = benchmark_function(
-            apply_top_k_top_p_pytorch,
-            logits,
-            config.k_values,
-            config.p_values,
-            warmup_iters,
-            benchmark_iters,
-        )
-
-        speedup = pytorch_time / triton_time if triton_time > 0 else float("inf")
-        mem_ratio = pytorch_mem / triton_mem if triton_mem > 0 else float("inf")
-
-        result = {
-            "config": config,
-            "triton_time_ms": triton_time,
-            "pytorch_time_ms": pytorch_time,
-            "triton_mem": triton_mem,
-            "pytorch_mem": pytorch_mem,
-            "speedup": speedup,
-            "mem_ratio": mem_ratio,
-        }
-        results.append(result)
-
-        if verbose:
-            print(f"  Triton:  {triton_time:.3f} ms, {format_memory(triton_mem)}")
-            print(f"  PyTorch: {pytorch_time:.3f} ms, {format_memory(pytorch_mem)}")
-            print(f"  Speedup: {speedup:.2f}x, Memory ratio: {mem_ratio:.2f}x")
-            print()
-
-        # Clean up
-        del logits
-        reset_memory_stats()
-
-    return results
-
-
-def print_summary_table(results: list[dict]):
-    """Print a summary table of results."""
-    print()
-    print("=" * 130)
-    print("SUMMARY TABLE")
-    print("=" * 130)
-    print()
-
-    # Header
-    header = (
-        f"{'Scenario':<40} {'Batch':>6} {'Vocab':>7} {'Ops%':>6} "
-        f"{'Triton (ms)':>12} {'PyTorch (ms)':>13} {'Speedup':>8} "
-        f"{'Tri Mem':>10} {'Pyt Mem':>10}"
-    )
-    print(header)
-    print("-" * 130)
-
-    # Group by scenario type
-    current_vocab = None
-    for result in results:
-        config = result["config"]
-
-        # Add separator between vocab sizes
-        if current_vocab != config.vocab_size:
-            if current_vocab is not None:
-                print("-" * 130)
-            current_vocab = config.vocab_size
-
-        scenario = config.name.split("_b")[0]  # Extract scenario name
-        print(
-            f"{scenario:<40} {config.batch_size:>6} {config.vocab_size:>7} "
-            f"{config.ops_pct:>5.0f}% "
-            f"{result['triton_time_ms']:>12.3f} {result['pytorch_time_ms']:>13.3f} "
-            f"{result['speedup']:>7.2f}x "
-            f"{format_memory(result['triton_mem']):>10} "
-            f"{format_memory(result['pytorch_mem']):>10}"
-        )
-
-    print("=" * 130)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Benchmark Triton vs PyTorch sort-based top-k/top-p implementations"
-    )
-    parser.add_argument(
-        "--batch-sizes",
-        type=int,
-        nargs="+",
-        default=[1, 4, 16, 64, 128, 512, 1024, 2048],
-        help="Batch sizes to test (default: 1 4 16 64)",
-    )
-    parser.add_argument(
-        "--vocab-sizes",
-        type=int,
-        nargs="+",
-        default=[32768, 131072],  # 32k, 128k
-        help="Vocabulary sizes to test (default: 32768 131072)",
-    )
-    parser.add_argument(
-        "--warmup-iters",
-        type=int,
-        default=5,
-        help="Number of warmup iterations (default: 5)",
-    )
-    parser.add_argument(
-        "--benchmark-iters",
-        type=int,
-        default=20,
-        help="Number of benchmark iterations (default: 20)",
-    )
-    parser.add_argument(
-        "--quiet",
-        action="store_true",
-        help="Only print summary table",
-    )
-
-    args = parser.parse_args()
-
-    # Print configuration
-    print(f"Batch sizes: {args.batch_sizes}")
-    print(f"Vocab sizes: {args.vocab_sizes}")
-    print(f"Warmup iterations: {args.warmup_iters}")
-    print(f"Benchmark iterations: {args.benchmark_iters}")
-    print()
-
-    # Check CUDA
-    if not torch.cuda.is_available():
-        print("ERROR: CUDA is not available. This benchmark requires a GPU.")
-        return
-
-    device_name = torch.cuda.get_device_name(0)
-    print(f"GPU: {device_name}")
-    print()
-
-    # Create configs
-    configs = create_benchmark_configs(
-        args.batch_sizes,
-        args.vocab_sizes,
-    )
-
-    # Run benchmarks
-    results = run_benchmark(
-        configs,
-        warmup_iters=args.warmup_iters,
-        benchmark_iters=args.benchmark_iters,
-        verbose=not args.quiet,
-    )
-
-    # Print summary
-    print_summary_table(results)
-
-
-if __name__ == "__main__":
-    main()

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -13,7 +13,6 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from tqdm import tqdm
 
 import vllm._custom_ops as ops
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8,
@@ -292,7 +291,6 @@ def print_timers(timers: Iterable[TMeasurement]):
     compare.print()
 
 
-@default_vllm_config()
 def main():
     torch.set_default_device("cuda")
     bench_params = get_bench_params()

benchmarks/kernels/bench_block_fp8_gemm.py

@@ -8,7 +8,6 @@ os.environ["VLLM_USE_DEEP_GEMM"] = "0"
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     W8A8BlockFp8LinearOp,
 )
@@ -41,7 +40,6 @@ DEEPSEEK_V3_SHAPES = [
 ]
 
 
-@default_vllm_config()
 def build_w8a8_block_fp8_runner(M, N, K, block_size, device, use_cutlass):
     """Build runner function for w8a8 block fp8 matmul."""
     factor_for_scale = 1e-2

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -7,7 +7,6 @@ from unittest.mock import patch
 import pandas as pd
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
@@ -85,7 +84,6 @@ def calculate_diff(
 configs = []
 
 
-@default_vllm_config()
 def benchmark_quantization(
     batch_size,
     hidden_size,

benchmarks/kernels/benchmark_activation.py

@@ -7,7 +7,6 @@ import itertools
 import torch
 
 import vllm.model_executor.layers.activation  # noqa F401
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.custom_op import op_registry
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -19,7 +18,6 @@ intermediate_size = [3072, 9728, 12288]
 configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
 
 
-@default_vllm_config()
 def benchmark_activation(
     batch_size: int,
     seq_len: int,

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -11,7 +11,6 @@ import torch
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
@@ -162,7 +161,7 @@ def bench_run(
                 w2_fp8q_cutlass,
                 topk_weights,
                 topk_ids,
-                activation=MoEActivation.SILU,
+                activation="silu",
                 global_num_experts=num_experts,
             )
     torch.cuda.synchronize()

benchmarks/kernels/benchmark_fused_collective.py

@@ -408,18 +408,18 @@ def run_benchmarks(
 
     rms_eps = 1e-6
     results = {}
+    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
     use_oneshot_options = [False] if no_oneshot else [True, False]
 
+    # Create RMSNorm and QuantFP8 layers once for native benchmarks
+
     if "none" in quant_modes:
         # Standard AllReduce + RMSNorm
-        # Re-create VllmFusedAllreduce per config so CustomOp binds the
-        # correct forward method (native vs custom kernel).
         for custom_op in ["-rms_norm", "+rms_norm"]:
             with set_current_vllm_config(
                 VllmConfig(compilation_config=CompilationConfig(custom_ops=[custom_op]))
             ):
                 try:
-                    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                     suffix = (
                         "_custom_rms_norm" if "+" in custom_op else "_native_rms_norm"
                     )
@@ -438,7 +438,6 @@ def run_benchmarks(
             VllmConfig(compilation_config=CompilationConfig(custom_ops=["-rms_norm"]))
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm,
                     fullgraph=True,
@@ -483,7 +482,7 @@ def run_benchmarks(
                 "_custom_rms_norm" if "+" in rms_norm_custom_op else "_native_rms_norm"
             )
             for quant_fp8_custom_op in ["-quant_fp8", "+quant_fp8"]:
-                op_suffix = suffix + (
+                suffix += (
                     "_custom_quant_fp8"
                     if "+" in quant_fp8_custom_op
                     else "_native_quant_fp8"
@@ -496,17 +495,16 @@ def run_benchmarks(
                     )
                 ):
                     try:
-                        vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                         time_ms = benchmark_operation(
                             vllm_fused_allreduce.allreduce_rmsnorm_fp8_quant,
                             input_tensor,
                             residual=residual,
                             scale_factor=scale_fp8,
                         )
-                        results[f"standard_allreduce{op_suffix}"] = time_ms
+                        results[f"standard_allreduce{suffix}"] = time_ms
                     except Exception as e:
                         logger.error("Standard AllReduce+RMSNorm+FP8 failed: %s", e)
-                        results[f"standard_allreduce{op_suffix}"] = float("inf")
+                        results[f"standard_allreduce{suffix}"] = float("inf")
 
         # Standard AllReduce + RMSNorm + FP8 Quant Native Compiled
         with set_current_vllm_config(
@@ -517,7 +515,6 @@ def run_benchmarks(
             )
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_fp8_quant_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm_fp8_quant,
                     fullgraph=True,
@@ -583,7 +580,6 @@ def run_benchmarks(
                 )
             ):
                 try:
-                    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                     time_ms = benchmark_operation(
                         vllm_fused_allreduce.allreduce_rmsnorm_fp4_quant,
                         input_tensor,
@@ -602,7 +598,6 @@ def run_benchmarks(
             VllmConfig(compilation_config=CompilationConfig(custom_ops=["-rms_norm"]))
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_fp4_quant_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm_fp4_quant,
                     fullgraph=True,

benchmarks/kernels/benchmark_layernorm.py

@@ -5,14 +5,12 @@ import time
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
-@default_vllm_config()
 def main(
     num_tokens: int,
     hidden_size: int,

benchmarks/kernels/benchmark_moe.py

@@ -16,7 +16,6 @@ import torch
 from ray.experimental.tqdm_ray import tqdm
 
 from vllm.model_executor.layers.fused_moe import fused_topk
-from vllm.model_executor.layers.fused_moe.activation import MoEActivation
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -100,38 +99,13 @@ 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_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:
+    if use_int8_w8a16:
         w1 = torch.randint(
             -127,
             127,
@@ -165,20 +139,7 @@ def benchmark_config(
     w2_scale = None
     a1_scale = None
     a2_scale = None
-    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:
+    if use_int8_w8a16:
         w1_scale = torch.randn(
             (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
         )
@@ -237,7 +198,6 @@ 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
@@ -251,8 +211,7 @@ def benchmark_config(
                         hidden_dim=hidden_size,
                         intermediate_size_per_partition=shard_intermediate_size,
                         num_local_experts=num_experts,
-                        num_logical_experts=num_experts,
-                        activation=MoEActivation.SILU,
+                        activation="silu",
                         moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
                         in_dtype=init_dtype,
                         routing_method=RoutingMethodType.TopK,
@@ -520,7 +479,6 @@ 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]:
@@ -528,10 +486,7 @@ 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,
-            use_int4_w4a16=use_int4_w4a16,
+            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
         )
         # NOTE(woosuk): The current naming convention uses w2.shape[2], which
         # is the intermediate size after silu_and_mul.
@@ -562,7 +517,6 @@ 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,
@@ -579,7 +533,6 @@ 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,
@@ -590,7 +543,7 @@ class BenchmarkWorker:
         best_config = None
         best_time = float("inf")
         if current_platform.is_rocm():
-            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16 or use_int4_w4a16)
+            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
             search_space = prune_rocm_search_space(
                 num_tokens,
                 shard_intermediate_size,
@@ -619,7 +572,6 @@ 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,
@@ -667,7 +619,6 @@ 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 {}),
     }
 
 
@@ -680,15 +631,11 @@ 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,
-        use_int4_w4a16=use_int4_w4a16,
+        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
     )
 
     # NOTE(woosuk): The current naming convention uses w2.shape[2], which
@@ -790,38 +737,6 @@ 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)
 
@@ -840,20 +755,7 @@ 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 = [
@@ -907,20 +809,8 @@ def main(args: argparse.Namespace):
         return ray.get(outputs)
 
     if args.tune:
-        # 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
+        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
+        search_space = get_configs_compute_bound(is_fp16, block_quant_shape)
         print(f"Start tuning over {len(search_space)} configurations...")
         if use_deep_gemm:
             raise ValueError(
@@ -940,7 +830,6 @@ def main(args: argparse.Namespace):
                     dtype,
                     use_fp8_w8a8,
                     use_int8_w8a16,
-                    use_int4_w4a16,
                     search_space,
                     block_quant_shape,
                     use_deep_gemm,
@@ -960,7 +849,6 @@ def main(args: argparse.Namespace):
             dtype,
             use_fp8_w8a8,
             use_int8_w8a16,
-            use_int4_w4a16,
             block_quant_shape,
             args.save_dir,
         )
@@ -979,7 +867,6 @@ def main(args: argparse.Namespace):
                     dtype,
                     use_fp8_w8a8,
                     use_int8_w8a16,
-                    use_int4_w4a16,
                     block_quant_shape,
                     use_deep_gemm,
                 )
@@ -1002,10 +889,7 @@ 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", "int4_w4a16"],
-        default="auto",
+        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
     )
     parser.add_argument("--use-deep-gemm", action="store_true")
     parser.add_argument(

benchmarks/kernels/benchmark_moe_defaults.py

@@ -1,278 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Benchmark comparing old vs new default fused MoE configs.
-
-Runs the triton fused_moe kernel with three configurations for each scenario:
-  1. Tuned config (from JSON file, if available) — the target to match
-  2. Old default (the hardcoded defaults before this change)
-  3. New default (the improved defaults)
-
-Usage:
-    python benchmarks/kernels/benchmark_moe_defaults.py
-
-Produces a table showing kernel time (us) and speedup of new vs old defaults.
-"""
-
-import torch
-
-from vllm.model_executor.layers.fused_moe import fused_topk, override_config
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
-from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_experts,
-    get_default_config,
-    get_moe_configs,
-)
-from vllm.platforms import current_platform
-from vllm.triton_utils import triton
-from vllm.utils.torch_utils import set_random_seed
-
-FP8_DTYPE = current_platform.fp8_dtype()
-
-
-def old_default_config(M, E, N, K, topk, dtype=None, block_shape=None):
-    """The original defaults before https://github.com/vllm-project/vllm/pull/34846,
-    for comparison."""
-    if dtype == "fp8_w8a8" and block_shape is not None:
-        return {
-            "BLOCK_SIZE_M": 64,
-            "BLOCK_SIZE_N": block_shape[0],
-            "BLOCK_SIZE_K": block_shape[1],
-            "GROUP_SIZE_M": 32,
-            "SPLIT_K": 1,
-            "num_warps": 4,
-            "num_stages": 3 if not current_platform.is_rocm() else 2,
-        }
-    elif M <= E:
-        return {
-            "BLOCK_SIZE_M": 16,
-            "BLOCK_SIZE_N": 32,
-            "BLOCK_SIZE_K": 64,
-            "GROUP_SIZE_M": 1,
-            "SPLIT_K": 1,
-        }
-    else:
-        return {
-            "BLOCK_SIZE_M": 64,
-            "BLOCK_SIZE_N": 64,
-            "BLOCK_SIZE_K": 32,
-            "GROUP_SIZE_M": 8,
-            "SPLIT_K": 1,
-        }
-
-
-def benchmark_config(
-    config,
-    M,
-    E,
-    N,
-    K,
-    topk,
-    dtype,
-    use_fp8=False,
-    block_shape=None,
-    num_iters=100,
-):
-    """Time a single kernel config. Returns kernel time in microseconds."""
-    init_dtype = torch.float16 if use_fp8 else dtype
-
-    a = torch.randn(M, K, device="cuda", dtype=init_dtype) / 10
-    w1 = torch.randn(E, 2 * N, K, device="cuda", dtype=init_dtype) / 10
-    w2 = torch.randn(E, K, N, device="cuda", dtype=init_dtype) / 10
-
-    w1_scale = None
-    w2_scale = None
-    a1_scale = None
-    a2_scale = None
-    if use_fp8:
-        if block_shape is not None:
-            bsn, bsk = block_shape
-            n_tiles_w1 = triton.cdiv(2 * N, bsn)
-            k_tiles_w1 = triton.cdiv(K, bsk)
-            n_tiles_w2 = triton.cdiv(K, bsn)
-            k_tiles_w2 = triton.cdiv(N, bsk)
-            w1_scale = torch.rand(
-                E, n_tiles_w1, k_tiles_w1, device="cuda", dtype=torch.float32
-            )
-            w2_scale = torch.rand(
-                E, n_tiles_w2, k_tiles_w2, device="cuda", dtype=torch.float32
-            )
-        else:
-            w1_scale = torch.rand(E, device="cuda", dtype=torch.float32)
-            w2_scale = torch.rand(E, device="cuda", dtype=torch.float32)
-        a1_scale = torch.rand(1, device="cuda", dtype=torch.float32)
-        a2_scale = torch.rand(1, device="cuda", dtype=torch.float32)
-        # Only weights are stored in fp8; activations stay in bf16/fp16
-        # and get dynamically quantized inside the kernel.
-        w1 = w1.to(FP8_DTYPE)
-        w2 = w2.to(FP8_DTYPE)
-
-    quant_config = FusedMoEQuantConfig.make(
-        quant_dtype=torch.float8_e4m3fn if use_fp8 else None,
-        w1_scale=w1_scale,
-        w2_scale=w2_scale,
-        a1_scale=a1_scale,
-        a2_scale=a2_scale,
-        block_shape=block_shape,
-    )
-
-    gating = torch.randn(M, E, device="cuda", dtype=torch.float32)
-
-    # Warmup
-    for _ in range(20):
-        with override_config(config):
-            topk_weights, topk_ids, _ = fused_topk(a, gating, topk, renormalize=True)
-            fused_experts(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                quant_config=quant_config,
-            )
-    torch.cuda.synchronize()
-
-    # Benchmark
-    start = torch.cuda.Event(enable_timing=True)
-    end = torch.cuda.Event(enable_timing=True)
-    start.record()
-    for _ in range(num_iters):
-        with override_config(config):
-            topk_weights, topk_ids, _ = fused_topk(a, gating, topk, renormalize=True)
-            fused_experts(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                quant_config=quant_config,
-            )
-    end.record()
-    torch.cuda.synchronize()
-    return start.elapsed_time(end) / num_iters * 1000  # ms -> us
-
-
-# Model configurations: (name, E, N, K, topk, dtype_str, use_fp8, block_shape)
-# N = moe_intermediate_size // tp_size (the value used in config file lookup)
-MODELS = [
-    # --- Few experts ---
-    ("Mixtral bf16", 8, 7168, 4096, 2, None, False, None),
-    ("Mixtral fp8", 8, 7168, 4096, 2, "fp8_w8a8", True, None),
-    # --- Many experts: real model shapes at tp=1 ---
-    # Qwen2-MoE-57B: E=60, topk=4, N=1408, K=2048
-    ("Qwen2-MoE bf16", 60, 1408, 2048, 4, None, False, None),
-    # DeepSeek-V2: E=64, topk=6, N=1407, K=4096
-    # (use 1408 to avoid odd alignment; real model is 1407)
-    ("DeepSeek-V2 bf16", 64, 1408, 4096, 6, None, False, None),
-    # OLMoE-7B: E=64, topk=8, N=2048, K=2048
-    ("OLMoE bf16", 64, 2048, 2048, 8, None, False, None),
-    # GLM-4-100B-A10B: E=128, topk=8, N=1408, K=4096
-    ("GLM-4-MoE bf16", 128, 1408, 4096, 8, None, False, None),
-    # Qwen3-30B-A3B: E=128, topk=8, N=768, K=2048
-    ("Qwen3-MoE bf16", 128, 768, 2048, 8, None, False, None),
-    # DeepSeek-V3 / MiMo-V2-Flash: E=256, topk=8, N=2048, K=7168
-    ("DeepSeek-V3 bf16", 256, 2048, 7168, 8, None, False, None),
-    # Qwen3.5-70B-A22B (Qwen3-Next): E=512, topk=10, N=512, K=2048
-    ("Qwen3-Next bf16", 512, 512, 2048, 10, None, False, None),
-    # E=128 N=1856 bf16
-    ("E128 N1856 bf16", 128, 1856, 4096, 8, None, False, None),
-    # E=256 N=512 bf16 (DS-V3 tp=4)
-    ("DS-V3 tp4 bf16", 256, 512, 7168, 8, None, False, None),
-    # E=512 N=512 bf16 (Qwen3-Next tp=1)
-    ("Qwen3-Next bf16", 512, 512, 2048, 10, None, False, None),
-    # E=512 N=256 bf16 (Qwen3-Next tp=2)
-    ("Qwen3-Next tp2", 512, 256, 2048, 10, None, False, None),
-    # --- FP8 block quant (many experts) ---
-    # DS-V3 tp=4: E=256, N=512, fp8 block
-    ("DS-V3 tp4 fp8blk", 256, 512, 7168, 8, "fp8_w8a8", True, [128, 128]),
-    # DS-V3 tp=8: E=256, N=256, fp8 block
-    ("DS-V3 tp8 fp8blk", 256, 256, 7168, 8, "fp8_w8a8", True, [128, 128]),
-    # Qwen3-Next tp=2 fp8 block
-    ("Qwen3-Next tp2 fp8blk", 512, 256, 2048, 10, "fp8_w8a8", True, [128, 128]),
-]
-
-BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
-
-
-def main():
-    set_random_seed(0)
-    torch.set_default_device("cuda")
-    dtype = torch.bfloat16
-
-    for name, E, N, K, topk, dtype_str, use_fp8, block_shape in MODELS:
-        print(f"\n{'=' * 90}")
-        print(f"  {name}  (E={E}, N={N}, K={K}, topk={topk})")
-        print(f"{'=' * 90}")
-
-        # Try to load tuned config
-        block_n = block_shape[0] if block_shape else None
-        block_k = block_shape[1] if block_shape else None
-        tuned = get_moe_configs(E, N, dtype_str, block_n, block_k)
-        has_tuned = tuned is not None
-        print(f"  Tuned config available: {has_tuned}")
-
-        hdr = (
-            f"{'Batch':>6} | {'Tuned (us)':>11} | {'Old (us)':>11} | "
-            f"{'New (us)':>11} | {'New/Old':>8} | {'New/Tuned':>10}"
-        )
-        print(f"  {hdr}")
-        print(f"  {'-' * len(hdr)}")
-
-        for M in BATCH_SIZES:
-            old_cfg = old_default_config(M, E, N, K, topk, dtype_str, block_shape)
-            new_cfg = get_default_config(M, E, N, K, topk, dtype_str, block_shape)
-
-            if has_tuned:
-                tuned_cfg = tuned[min(tuned.keys(), key=lambda x: abs(x - M))]
-                t_tuned = benchmark_config(
-                    tuned_cfg,
-                    M,
-                    E,
-                    N,
-                    K,
-                    topk,
-                    dtype,
-                    use_fp8=use_fp8,
-                    block_shape=block_shape,
-                )
-            else:
-                t_tuned = None
-
-            t_old = benchmark_config(
-                old_cfg,
-                M,
-                E,
-                N,
-                K,
-                topk,
-                dtype,
-                use_fp8=use_fp8,
-                block_shape=block_shape,
-            )
-            t_new = benchmark_config(
-                new_cfg,
-                M,
-                E,
-                N,
-                K,
-                topk,
-                dtype,
-                use_fp8=use_fp8,
-                block_shape=block_shape,
-            )
-
-            ratio_new_old = t_new / t_old
-            tuned_str = f"{t_tuned:11.2f}" if t_tuned else f"{'N/A':>11}"
-            ratio_tuned = f"{t_new / t_tuned:10.2f}x" if t_tuned else f"{'N/A':>10}"
-            # flag regressions where new default is >5% slower than old
-            marker = " <--" if ratio_new_old > 1.05 else ""
-
-            print(
-                f"  {M:>6} | {tuned_str} | {t_old:11.2f} | {t_new:11.2f} "
-                f"| {ratio_new_old:7.2f}x | {ratio_tuned}{marker}"
-            )
-
-
-if __name__ == "__main__":
-    main()

benchmarks/kernels/benchmark_mrope.py

@@ -36,7 +36,6 @@ from typing import Any
 import numpy as np
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.transformers_utils.config import get_config
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -79,7 +78,6 @@ def calculate_stats(times: list[float]) -> dict[str, float]:
     }
 
 
-@default_vllm_config()
 def benchmark_mrope(
     model_name: str,
     num_tokens: int,

benchmarks/kernels/benchmark_rope.py

@@ -5,7 +5,6 @@ import itertools
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -30,7 +29,6 @@ def get_benchmark(head_size, rotary_dim, is_neox_style, device):
             args={},
         )
     )
-    @default_vllm_config()
     def benchmark(batch_size, seq_len, num_heads, provider):
         dtype = torch.bfloat16
         max_position = 8192

benchmarks/run_structured_output_benchmark.sh

@@ -71,7 +71,7 @@ while [[ $# -gt 0 ]]; do
       usage
       ;;
     *)
-      printf "Unknown argument: %s\n" "$1"
+      echo "Unknown argument: $1\n"
       usage
       ;;
   esac
@@ -84,17 +84,15 @@ mkdir -p "$OUTPUT_DIR"
 QPS_VALUES=(25 20 15 10 5 1)
 
 # Common parameters
-COMMON_PARAMS=(
-  --backend "$BACKEND"
-  --model "$MODEL"
-  --dataset "$DATASET"
-  --structured-output-ratio "$STRUCTURED_OUTPUT_RATIO"
-  --save-results
-  --result-dir "$OUTPUT_DIR"
-  --output-len "$MAX_NEW_TOKENS"
-  --port "$PORT"
-  --tokenizer-mode "$TOKENIZER_MODE"
-)
+COMMON_PARAMS="--backend $BACKEND \
+               --model $MODEL \
+               --dataset $DATASET \
+               --structured-output-ratio $STRUCTURED_OUTPUT_RATIO \
+               --save-results \
+               --result-dir $OUTPUT_DIR \
+               --output-len $MAX_NEW_TOKENS \
+               --port $PORT \
+               --tokenizer-mode $TOKENIZER_MODE"
 
 echo "Starting structured output benchmark with model: $MODEL"
 echo "Backend: $BACKEND"
@@ -111,17 +109,17 @@ for qps in "${QPS_VALUES[@]}"; do
   GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "unknown")
 
   # Construct filename for this run
-  FILENAME="${BACKEND}_${qps}qps_$(basename "$MODEL")_${DATASET}_${GIT_HASH}_${GIT_BRANCH}.json"
+  FILENAME="${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"
 
   NUM_PROMPTS=$(echo "$TOTAL_SECONDS * $qps" | bc)
   NUM_PROMPTS=${NUM_PROMPTS%.*}  # Remove fractional part
   echo "Running benchmark with $NUM_PROMPTS prompts"
 
   # Run the benchmark
-  python "$SCRIPT_DIR/benchmark_serving_structured_output.py" "${COMMON_PARAMS[@]}" \
-    --request-rate "$qps" \
+  python "$SCRIPT_DIR/benchmark_serving_structured_output.py" $COMMON_PARAMS \
+    --request-rate $qps \
     --result-filename "$FILENAME" \
-    --num-prompts "$NUM_PROMPTS"
+    --num-prompts $NUM_PROMPTS
 
   echo "Completed benchmark with QPS: $qps"
   echo "----------------------------------------"

cmake/cpu_extension.cmake

@@ -18,7 +18,6 @@ set(ENABLE_AVX512 $ENV{VLLM_CPU_AVX512})
 set(ENABLE_AVX512BF16 $ENV{VLLM_CPU_AVX512BF16})
 set(ENABLE_AVX512VNNI $ENV{VLLM_CPU_AVX512VNNI})
 set(ENABLE_AMXBF16 $ENV{VLLM_CPU_AMXBF16})
-set(ENABLE_ARM_BF16 $ENV{VLLM_CPU_ARM_BF16})
 
 include_directories("${CMAKE_SOURCE_DIR}/csrc")
 
@@ -116,10 +115,6 @@ else()
         set(AVX512_FOUND ON)
         message(STATUS "AVX512 support enabled via VLLM_CPU_AVX512 environment variable")
     endif()
-    if (ENABLE_ARM_BF16)
-        set(ARM_BF16_FOUND ON)
-        message(STATUS "ARM BF16 support enabled via VLLM_CPU_ARM_BF16 environment variable")
-    endif()
 endif()
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG 692917b1cda61b93ac9ee2d846ec54e75afe87b1
+        GIT_TAG c2afa9cb93e674d5a9120a170a6da57b89267208
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 5824e6e2008271063c3229ab3e7032bd74abbbc6
+          GIT_TAG 188be16520ceefdc625fdf71365585d2ee348fe2
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/activation_kernels.cu

@@ -14,8 +14,7 @@ struct alignas(32) u32x8_t {
 };
 
 __device__ __forceinline__ void ld256(u32x8_t& val, const u32x8_t* ptr) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000 && \
-    defined(CUDA_VERSION) && CUDA_VERSION >= 12090
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000
   asm volatile("ld.global.nc.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];\n"
                : "=r"(val.u0), "=r"(val.u1), "=r"(val.u2), "=r"(val.u3),
                  "=r"(val.u4), "=r"(val.u5), "=r"(val.u6), "=r"(val.u7)
@@ -36,8 +35,7 @@ __device__ __forceinline__ void ld256(u32x8_t& val, const u32x8_t* ptr) {
 }
 
 __device__ __forceinline__ void st256(u32x8_t& val, u32x8_t* ptr) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000 && \
-    defined(CUDA_VERSION) && CUDA_VERSION >= 12090
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000
   asm volatile("st.global.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};\n"
                :
                : "l"(ptr), "r"(val.u0), "r"(val.u1), "r"(val.u2), "r"(val.u3),

csrc/cache_kernels.cu

@@ -1305,8 +1305,7 @@ void indexer_k_quant_and_cache(
   const at::cuda::OptionalCUDAGuard device_guard(device_of(k));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  static const std::string kv_cache_dtype = "fp8_e4m3";
-  DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), kv_cache_dtype,
+  DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), "fp8_e4m3",
                              CALL_INDEXER_K_QUANT_AND_CACHE);
 }
 

csrc/cpu/cpu_fused_moe.cpp

@@ -147,7 +147,7 @@ void fused_moe_impl(scalar_t* __restrict__ output, scalar_t* __restrict__ input,
                     const int32_t token_num, const int32_t expert_num,
                     const int32_t topk_num, const int32_t input_size_13,
                     const int32_t output_size_13, const int32_t input_size_2,
-                    const int32_t output_size_2, const bool skip_weighted) {
+                    const int32_t output_size_2) {
   using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
   constexpr int32_t gemm_n_tile_size = gemm_t::NSize;
   constexpr int32_t gemm_m_tile_size = gemm_t::MaxMSize;
@@ -582,11 +582,6 @@ void fused_moe_impl(scalar_t* __restrict__ output, scalar_t* __restrict__ input,
         scalar_t* __restrict__ curr_output_buffer =
             output + token_id * output_size_2;
 
-        if (skip_weighted) {
-          // Only for topk_num == 1
-          *curr_weight = 1.0f;
-        }
-
         if (topk_num > 1) {
           {
             int32_t w2_output_idx = curr_expand_token_id_index_buffer[0];
@@ -704,7 +699,7 @@ void cpu_fused_moe(
     const std::optional<torch::Tensor>& w2_bias,  // [expert_num, output_size_2]
     const torch::Tensor& topk_weights,            // [token_num, k], float32
     const torch::Tensor& topk_id,                 // [token_num, k], int32
-    const bool skip_weighted, const std::string& act, const std::string& isa) {
+    const std::string& act, const std::string& isa) {
   const int32_t token_num = input.size(0);
   const int32_t input_size_13 = input.size(1);
   const int64_t input_stride = input.stride(0);
@@ -716,8 +711,6 @@ void cpu_fused_moe(
   const int32_t topk_num = topk_id.size(1);
   const FusedMOEAct act_type = get_act_type(act);
   cpu_utils::ISA isa_type = cpu_utils::get_isa(isa);
-  TORCH_CHECK(!skip_weighted || topk_num == 1,
-              "skip_weighted is only supported for topk=1 on CPU");
 
   VLLM_DISPATCH_FLOATING_TYPES(w13.scalar_type(), "cpu_fused_moe", [&]() {
     CPU_ISA_DISPATCH_IMPL(isa_type, [&]() {
@@ -728,7 +721,7 @@ void cpu_fused_moe(
           w2_bias.has_value() ? w2_bias->data_ptr<scalar_t>() : nullptr,
           topk_weights.data_ptr<float>(), topk_id.data_ptr<int32_t>(), act_type,
           token_num, expert_num, topk_num, input_size_13, output_size_13,
-          input_size_2, output_size_2, skip_weighted);
+          input_size_2, output_size_2);
     });
   });
 }

csrc/cpu/torch_bindings.cpp

@@ -119,8 +119,8 @@ void cpu_fused_moe(torch::Tensor& output, const torch::Tensor& input,
                    const std::optional<torch::Tensor>& w13_bias,
                    const std::optional<torch::Tensor>& w2_bias,
                    const torch::Tensor& topk_weights,
-                   const torch::Tensor& topk_id, const bool skip_weighted,
-                   const std::string& act, const std::string& isa);
+                   const torch::Tensor& topk_id, const std::string& act,
+                   const std::string& isa);
 
 TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
@@ -320,7 +320,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "cpu_fused_moe(Tensor(a0!) output, Tensor input, Tensor w13, Tensor w2, "
       "Tensor? w13_bias, Tensor? w2_bias, Tensor topk_weights, Tensor topk_id, "
-      "bool skip_weighted, "
       "str act, str isa) -> ()");
   ops.impl("cpu_fused_moe", torch::kCPU, &cpu_fused_moe);
 #endif

csrc/cuda_view.cu

@@ -2,58 +2,33 @@
 #include <torch/cuda.h>
 #include <cuda_runtime.h>
 
-// This function assumes that `cpu_tensor` is a CPU tensor,
-// and that UVA (Unified Virtual Addressing) is enabled.
+// This function assumes that `cpu_tensor` is a CPU tensor allocated with pinned
+// memory, 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");
 
-  // 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 raw host pointer from CPU tensor
+  void* host_ptr = cpu_tensor.data_ptr();
 
+  // Get a device pointer corresponding to the pinned host memory
   void* device_ptr = nullptr;
-  err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
-  if (err != cudaSuccess) {
-    cudaFreeHost(host_ptr);
-    AT_ERROR("cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
-  }
+  cudaError_t err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
+  TORCH_CHECK(err == cudaSuccess,
+              "cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
 
-  auto deleter = [host_ptr](void*) { cudaFreeHost(host_ptr); };
+  // 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);
 
-  return torch::from_blob(device_ptr, contiguous_cpu.sizes(),
-                          contiguous_cpu.strides(), deleter,
-                          contiguous_cpu.options().device(torch::kCUDA));
-}
+  // 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;
+}

csrc/dsv3_fused_a_gemm.cu

@@ -1,747 +0,0 @@
-/*
- * Adapted from
- * https://github.com/sgl-project/sglang/blob/main/sgl-kernel/csrc/gemm/dsv3_fused_a_gemm.cu
- * which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/619709fc33bd5dc268f19d6a741fe7ed51c0f8f5/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3FusedAGemm.cu
- *
- * Copyright (c) 2019-2024, NVIDIA CORPORATION.  All rights reserved.
- * Copyright (c) 2021, NAVER Corp.  Authored by CLOVA.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-#include <torch/all.h>
-
-#include "core/registration.h"
-
-#include <cstdlib>
-#include <mutex>
-
-namespace {
-
-inline int getSMVersion() {
-  auto* props = at::cuda::getCurrentDeviceProperties();
-  return props->major * 10 + props->minor;
-}
-
-inline bool getEnvEnablePDL() {
-  static std::once_flag flag;
-  static bool enablePDL = false;
-  std::call_once(flag, [&]() {
-    if (getSMVersion() >= 90) {
-      char const* env = std::getenv("TRTLLM_ENABLE_PDL");
-      enablePDL = env && env[0] == '1' && env[1] == '\0';
-    }
-  });
-  return enablePDL;
-}
-
-}  // namespace
-
-using bf16_t = __nv_bfloat16;
-
-__device__ void hmma_16_8_16_f32acc_bf16ab(float (&d_reg)[4],
-                                           const bf16_t (&a_reg)[8],
-                                           const bf16_t (&b_reg)[4],
-                                           float const (&c_reg)[4]) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t a0 = *reinterpret_cast<uint32_t const*>(a_reg + 0);
-  uint32_t a1 = *reinterpret_cast<uint32_t const*>(a_reg + 2);
-  uint32_t a2 = *reinterpret_cast<uint32_t const*>(a_reg + 4);
-  uint32_t a3 = *reinterpret_cast<uint32_t const*>(a_reg + 6);
-  uint32_t b0 = *reinterpret_cast<uint32_t const*>(b_reg + 0);
-  uint32_t b1 = *reinterpret_cast<uint32_t const*>(b_reg + 2);
-  asm volatile(
-      "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 "
-      "{%0,  %1,  %2,  %3},"
-      "{%4,  %5,  %6,  %7},"
-      "{%8,  %9},"
-      "{%10, %11, %12, %13};\n"
-      : "=f"(d_reg[0]), "=f"(d_reg[1]), "=f"(d_reg[2]), "=f"(d_reg[3])
-      : "r"(a0), "r"(a1), "r"(a2), "r"(a3), "r"(b0), "r"(b1), "f"(d_reg[0]),
-        "f"(d_reg[1]), "f"(d_reg[2]), "f"(d_reg[3]));
-#endif
-}
-
-extern "C" {
-__device__ uint32_t __nvvm_get_smem_pointer(void*);
-}
-
-__device__ void ldgsts_128(void const* gPtr, void* sPtr, uint32_t pred) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  if (pred) {
-    uint32_t smemPtrAsUint32 = __nvvm_get_smem_pointer(sPtr);
-    asm volatile("cp.async.cg.shared.global.L2::128B [%0], [%1], %2;\n" ::"r"(
-                     smemPtrAsUint32),
-                 "l"(gPtr), "n"(16));
-  }
-#endif
-}
-
-__device__ void ldsm_x4(void* smem_ptr, uint32_t* reg_ptr) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  asm volatile(
-      "ldmatrix.sync.aligned.x4.m8n8.shared.b16 {%0, %1, %2, %3}, [%4];\n"
-      : "=r"(reg_ptr[0]), "=r"(reg_ptr[1]), "=r"(reg_ptr[2]), "=r"(reg_ptr[3])
-      : "r"(__nvvm_get_smem_pointer(smem_ptr)));
-#endif
-}
-
-template <class Type>
-__device__ int apply_swizzle_343_on_elem_row_col(int row_idx_, int col_idx_) {
-  uint32_t row_idx = *reinterpret_cast<uint32_t*>(&row_idx_);
-  uint32_t col_idx = *reinterpret_cast<uint32_t*>(&col_idx_);
-  row_idx = row_idx % 8;
-  row_idx = row_idx * (16 / sizeof(Type));
-  col_idx = col_idx ^ row_idx;
-  return *reinterpret_cast<int*>(&col_idx);
-}
-
-__device__ void initialize_barrier(
-    uint64_t* smem_barrier,  // 64 bits user-manged barrier in smem
-    int thread_count =
-        1)  // Thread count expected to arrive/wait on this barrier
-{
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t smem_int_ptr = __nvvm_get_smem_pointer(smem_barrier);
-  asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;\n" ::"r"(smem_int_ptr),
-               "r"(thread_count));
-#endif
-}
-
-// Barrier wait
-__device__ void wait_barrier(
-    uint64_t* smem_barrier,  // 64 bits user-manged barrier in smem
-    int phase_bit)           // Current phase bit the barrier waiting to flip
-{
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t smem_int_ptr = __nvvm_get_smem_pointer(smem_barrier);
-  asm volatile(
-      "{\n"
-      ".reg .pred                P1;\n"
-      "LAB_WAIT:\n"
-      "mbarrier.try_wait.parity.shared::cta.b64 P1, [%0], %1;\n"
-      "@P1                       bra DONE;\n"
-      "bra                   LAB_WAIT;\n"
-      "DONE:\n"
-      "}\n" ::"r"(smem_int_ptr),
-      "r"(phase_bit));
-#endif
-}
-
-__device__ bool try_wait_barrier(uint64_t* smem_ptr, int phase_bit) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t wait_complete;
-  uint32_t smem_int_ptr = __nvvm_get_smem_pointer(smem_ptr);
-  asm volatile(
-      "{\n\t"
-      ".reg .pred P1; \n\t"
-      "mbarrier.try_wait.parity.shared::cta.b64 P1, [%1], %2; \n\t"
-      "selp.b32 %0, 1, 0, P1; \n\t"
-      "}"
-      : "=r"(wait_complete)
-      : "r"(smem_int_ptr), "r"(phase_bit));
-  return static_cast<bool>(wait_complete);
-#endif
-  return false;
-}
-
-// Barrier arrive
-__device__ void arrive_barrier(
-    uint64_t* smem_barrier)  // 64 bits user-manged barrier in smem
-{
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t smem_int_ptr = __nvvm_get_smem_pointer(smem_barrier);
-  asm volatile(
-      "{\n"
-      ".reg .b64 state; \n"
-      "mbarrier.arrive.shared::cta.b64   state, [%0];\n"
-      "}\n" ::"r"(smem_int_ptr));
-#endif
-}
-
-__device__ void ldgsts_arrive(uint64_t* smem_barrier) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  uint32_t smem_int_ptr = __nvvm_get_smem_pointer(smem_barrier);
-  asm volatile("cp.async.mbarrier.arrive.noinc.shared.b64 [%0];"
-               :
-               : "r"(smem_int_ptr));
-#endif
-}
-
-template <int gemm_k, int tile_m, int tile_k, int stage_cnt>
-struct GmemLoaderA {
-  static constexpr int elem_bytes = 2;
-  static constexpr int vec_bytes = 16;
-  static constexpr int vec_elems = vec_bytes / elem_bytes;
-  static constexpr int thread_cnt = 64;
-  static_assert((tile_m * tile_k) % (vec_elems * thread_cnt) == 0);
-  static constexpr int a_inst_cnt_per_iter =
-      (tile_m * tile_k) / (vec_elems * thread_cnt);
-  static_assert(gemm_k % tile_k == 0);
-  static constexpr int k_iter_cnt = gemm_k / tile_k;
-
-  // Extra params to keep the order of k reduction...
-  static constexpr int mma_warp_cnt = 4;
-  static constexpr int per_mma_warp_k = tile_k / mma_warp_cnt;
-  static constexpr int k_each_chunk = gemm_k / mma_warp_cnt;
-
- private:
-  __device__ int k_project(int tile_k_idx) {
-    return (tile_k_idx / per_mma_warp_k * k_each_chunk) +
-           (tile_k_idx % per_mma_warp_k);
-  }
-
- public:
-  __device__ GmemLoaderA(bf16_t const* gmem_a_local_, bf16_t* smem_a_,
-                         uint64_t* smem_barrier_)
-      : gmem_a(gmem_a_local_),
-        smem_a(smem_a_),
-        smem_barrier(smem_barrier_),
-        local_tid(threadIdx.x % thread_cnt) {}
-
-  __device__ void prepare() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  // swizzle, that's what we want.
-  #pragma unroll
-    for (int i = 0; i < a_inst_cnt_per_iter; i++) {
-      int linear_idx = local_tid * vec_elems + i * thread_cnt * vec_elems;
-      int m_idx = linear_idx / tile_k;
-      int k_idx = linear_idx % tile_k;
-      k_idx = apply_swizzle_343_on_elem_row_col<bf16_t>(m_idx, k_idx);
-      a_smem_offsets[i] = m_idx * tile_k + k_idx;
-    }
-#endif
-  }
-
-  __device__ void issue_mainloop() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  #pragma unroll 1
-    for (int loop_idx = 0; loop_idx < k_iter_cnt; loop_idx++) {
-      if (need_wait) {
-        wait_barrier(smem_barrier + 1 + stage_idx * 2, phase_bit);
-      }
-      int next_stage_idx = stage_idx + 1;
-      int next_phase_bit =
-          next_stage_idx == stage_cnt ? phase_bit ^ 1 : phase_bit;
-      next_stage_idx = next_stage_idx == stage_cnt ? 0 : next_stage_idx;
-      if (loop_idx != k_iter_cnt - 1) {
-        need_wait = !try_wait_barrier(smem_barrier + 1 + next_stage_idx * 2,
-                                      next_phase_bit);
-      }
-
-  #pragma unroll
-      for (int i = 0; i < a_inst_cnt_per_iter; i++) {
-        int smem_offset = a_smem_offsets[i];
-        bf16_t* smem_ptr_this_iter =
-            smem_a + stage_idx * tile_m * tile_k + smem_offset;
-        int linear_idx = local_tid * vec_elems + i * thread_cnt * vec_elems;
-        int m_idx = linear_idx / tile_k;
-        int k_idx = linear_idx % tile_k;
-        int gmem_offset = m_idx * gemm_k + k_project(k_idx);
-        bf16_t const* gmem_ptr_this_iter = gmem_a + gmem_offset;
-        ldgsts_128(gmem_ptr_this_iter, smem_ptr_this_iter, true);
-      }
-      ldgsts_arrive(smem_barrier + stage_idx * 2);
-
-      stage_idx = next_stage_idx;
-      phase_bit = next_phase_bit;
-      gmem_a += per_mma_warp_k;
-    }
-#endif
-  }
-
-  bf16_t const* gmem_a;
-  bf16_t* smem_a;
-  uint64_t* smem_barrier;
-  int local_tid;
-  int stage_idx = 0;
-  int phase_bit = 1;
-  bool need_wait = true;
-
-  // per smem_stage, store with swizzle information
-  int a_smem_offsets[a_inst_cnt_per_iter];
-};
-
-template <int gemm_k, int tile_n, int tile_k, int stage_cnt>
-struct GmemLoaderB {
-  static constexpr int elem_bytes = 2;
-  static constexpr int vec_bytes = 16;
-  static constexpr int vec_elems = vec_bytes / elem_bytes;
-  static constexpr int thread_cnt = 64;
-  static_assert((tile_n * tile_k) % (vec_elems * thread_cnt) == 0);
-  static constexpr int b_inst_cnt_per_iter =
-      (tile_n * tile_k) / (vec_elems * thread_cnt);
-  static_assert(gemm_k % tile_k == 0);
-  static constexpr int k_iter_cnt = gemm_k / tile_k;
-
-  // Extra params to keep the order of k reduction...
-  static constexpr int mma_warp_cnt = 4;
-  static constexpr int per_mma_warp_k = tile_k / mma_warp_cnt;
-  static constexpr int k_each_chunk = gemm_k / mma_warp_cnt;
-
- private:
-  __device__ int k_project(int tile_k_idx) {
-    return (tile_k_idx / per_mma_warp_k * k_each_chunk) +
-           (tile_k_idx % per_mma_warp_k);
-  }
-
- public:
-  __device__ GmemLoaderB(bf16_t const* gmem_b_local_, bf16_t* smem_b_,
-                         uint64_t* smem_barrier_, int gemm_n_)
-      : gmem_b(gmem_b_local_),
-        smem_b(smem_b_),
-        smem_barrier(smem_barrier_),
-        gemm_n(gemm_n_),
-        local_tid(threadIdx.x % thread_cnt) {}
-
-  __device__ void prepare() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  // swizzle, that's what we want.
-  #pragma unroll
-    for (int i = 0; i < b_inst_cnt_per_iter; i++) {
-      int linear_idx = local_tid * vec_elems + i * thread_cnt * vec_elems;
-      int n_idx = linear_idx / tile_k;
-      int k_idx = linear_idx % tile_k;
-      k_idx = apply_swizzle_343_on_elem_row_col<bf16_t>(n_idx, k_idx);
-      b_smem_offsets[i] = n_idx * tile_k + k_idx;
-      preds[i] = n_idx < gemm_n;
-    }
-#endif
-  }
-
-  __device__ void issue_mainloop() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-    asm volatile("griddepcontrol.wait;");
-  #pragma unroll 1
-    for (int loop_idx = 0; loop_idx < k_iter_cnt; loop_idx++) {
-      if (need_wait) {
-        wait_barrier(smem_barrier + 1 + stage_idx * 2, phase_bit);
-      }
-      int next_stage_idx = stage_idx + 1;
-      int next_phase_bit =
-          next_stage_idx == stage_cnt ? phase_bit ^ 1 : phase_bit;
-      next_stage_idx = next_stage_idx == stage_cnt ? 0 : next_stage_idx;
-      if (loop_idx != k_iter_cnt - 1) {
-        need_wait = !try_wait_barrier(smem_barrier + 1 + next_stage_idx * 2,
-                                      next_phase_bit);
-      }
-  #pragma unroll
-      for (int i = 0; i < b_inst_cnt_per_iter; i++) {
-        int smem_offset = b_smem_offsets[i];
-        bf16_t* smem_ptr_this_iter =
-            smem_b + stage_idx * tile_n * tile_k + smem_offset;
-        int linear_idx = local_tid * vec_elems + i * thread_cnt * vec_elems;
-        int n_idx = linear_idx / tile_k;
-        int k_idx = linear_idx % tile_k;
-        int gmem_offset = n_idx * gemm_k + k_project(k_idx);
-        bf16_t const* gmem_ptr_this_iter = gmem_b + gmem_offset;
-        ldgsts_128(gmem_ptr_this_iter, smem_ptr_this_iter, preds[i]);
-      }
-      ldgsts_arrive(smem_barrier + stage_idx * 2);
-
-      stage_idx = next_stage_idx;
-      phase_bit = next_phase_bit;
-      gmem_b += per_mma_warp_k;
-    }
-#endif
-  }
-
-  bf16_t const* gmem_b;
-  bf16_t* smem_b;
-  uint64_t* smem_barrier;
-  int gemm_n;
-  int local_tid;
-  int stage_idx = 0;
-  int phase_bit = 1;
-  bool need_wait = true;
-
-  // per smem_stage, store with swizzle information
-  int b_smem_offsets[b_inst_cnt_per_iter];
-  uint32_t preds[b_inst_cnt_per_iter];
-};
-
-template <int gemm_m, int gemm_k, int tile_m, int tile_n, int tile_k,
-          int stage_cnt>
-struct MmaComputer {
-  static constexpr int elem_bytes = 2;
-  static constexpr int thread_cnt = 128;
-  static_assert(gemm_k % tile_k == 0);
-  static_assert(tile_k % (thread_cnt / 32) == 0);
-  static constexpr int per_warp_tile_k = tile_k / (thread_cnt / 32);
-  static constexpr int k_iter_cnt = gemm_k / tile_k;
-  static constexpr int k_phase_cnt = per_warp_tile_k / 16;
-  static constexpr int m_iter_cnt = (tile_m + 15) / 16;
-  static constexpr int n_iter_cnt =
-      (tile_n + 7) /
-      8;  // Possible to have non-1 n_iter_cnt for ab_swap m16 case.
-  static_assert(m_iter_cnt == 1);
-  static_assert(n_iter_cnt == 1 || n_iter_cnt == 2);
-
-  __device__ MmaComputer(bf16_t* gmem_c_local_, bf16_t* smem_a_,
-                         bf16_t* smem_b_, uint64_t* smem_barrier_,
-                         int warp_idx_, int gemm_n_)
-      : gmem_c(gmem_c_local_),
-        smem_a(smem_a_),
-        smem_b(smem_b_),
-        smem_barrier(smem_barrier_),
-        warp_idx(warp_idx_ - (thread_cnt / 32)),
-        gemm_n(gemm_n_) {}
-
- private:
-  __device__ constexpr int internal_b_atom_func(int tid) {
-    if constexpr (tile_n < 8) {
-      return (tid % tile_n) + ((tid % 8) / tile_n * 0) + tid / 8 * 8 * tile_n;
-    } else {
-      return (tid % 8) + ((tid % 32) / 8 * (tile_n * 8));
-    }
-  }
-
- public:
-  __device__ void prepare() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  #pragma unroll
-    for (int i = 0; i < k_phase_cnt; i++) {
-      int linear_idx = (lane_idx % 16) + (lane_idx / 16) * 128 + i * 256;
-      int m_idx = linear_idx % tile_m;
-      int k_idx = linear_idx / tile_m + warp_k_offset_in_tile_k;
-      k_idx = apply_swizzle_343_on_elem_row_col<bf16_t>(m_idx, k_idx);
-      a_smem_offsets[0][i] = m_idx * tile_k + k_idx;
-    }
-  #pragma unroll
-    for (int n_iter_idx = 0; n_iter_idx < n_iter_cnt; n_iter_idx++) {
-  #pragma unroll
-      for (int i = 0; i < k_phase_cnt; i += 2) {  // Special i+=2 for B.
-        int linear_idx =
-            internal_b_atom_func(lane_idx) + i * tile_n * 16 + n_iter_idx * 8;
-        int n_idx = linear_idx % tile_n;
-        int k_idx = linear_idx / tile_n + warp_k_offset_in_tile_k;
-        k_idx = apply_swizzle_343_on_elem_row_col<bf16_t>(n_idx, k_idx);
-        b_smem_offsets[n_iter_idx][i] = n_idx * tile_k + k_idx;
-      }
-    }
-#endif
-  }
-
-  __device__ void issue_mainloop() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  #pragma unroll 1
-    for (int loop_idx = 0; loop_idx < k_iter_cnt; loop_idx++) {
-      wait_barrier(smem_barrier + 0 + stage_idx * 2, phase_bit);
-
-  #pragma unroll
-      for (int i = 0; i < k_phase_cnt; i++) {
-        int smem_offset = a_smem_offsets[0][i];
-        bf16_t* smem_ptr_this_iter =
-            smem_a + stage_idx * tile_m * tile_k + smem_offset;
-        ldsm_x4(smem_ptr_this_iter, reinterpret_cast<uint32_t*>(a_reg[0][i]));
-      }
-
-  #pragma unroll
-      for (int n_iter_idx = 0; n_iter_idx < n_iter_cnt; n_iter_idx++) {
-  #pragma unroll
-        for (int i = 0; i < k_phase_cnt; i += 2) {
-          int smem_offset = b_smem_offsets[n_iter_idx][i];
-          bf16_t* smem_ptr_this_iter =
-              smem_b + stage_idx * tile_n * tile_k + smem_offset;
-          ldsm_x4(smem_ptr_this_iter,
-                  reinterpret_cast<uint32_t*>(b_reg[n_iter_idx][i]));
-        }
-      }
-
-  #pragma unroll
-      for (int k_iter_idx = 0; k_iter_idx < k_phase_cnt; k_iter_idx++) {
-  #pragma unroll
-        for (int n_iter_idx = 0; n_iter_idx < n_iter_cnt; n_iter_idx++) {
-          hmma_16_8_16_f32acc_bf16ab(
-              acc_reg[0][n_iter_idx], a_reg[0][k_iter_idx],
-              b_reg[n_iter_idx][k_iter_idx], acc_reg[0][n_iter_idx]);
-        }
-      }
-      ::arrive_barrier(smem_barrier + 1 + stage_idx * 2);
-      stage_idx += 1;
-      phase_bit = stage_idx == stage_cnt ? phase_bit ^ 1 : phase_bit;
-      stage_idx = stage_idx == stage_cnt ? 0 : stage_idx;
-    }
-#endif
-  }
-
-  __device__ void epi() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-    asm volatile("bar.sync %0, %1;" : : "r"(1), "r"(thread_cnt));
-    // reorganize the acc_reg
-    constexpr int thread_m = 2;
-    constexpr int thread_n = 2 * n_iter_cnt;
-    constexpr int cta_mma_n = n_iter_cnt * 8;
-    float acc_reg_reorg[thread_m][thread_n];
-
-    for (int i = 0; i < thread_m; i++) {
-      for (int j = 0; j < thread_n; j++) {
-        acc_reg_reorg[i][j] = acc_reg[0][j / 2][(j % 2) + (i * 2)];
-      }
-    }
-
-    // 4 x cosize(smem_c_layout)
-    float* smem_c = reinterpret_cast<float*>(smem_a);
-    // coord -> index
-    auto smem_c_index_func = [&](int m_idx, int n_idx) {
-      int group_rows = 32 / cta_mma_n;
-      int group_cnt = 2;
-      return (m_idx % group_rows * cta_mma_n) +
-             (m_idx / group_rows * (32 + group_cnt)) + n_idx;
-    };
-    constexpr int cosize_smem_c = ((tile_m * cta_mma_n) / 32) * (32 + 2);
-
-  // This should be optimized to STS.64 but can not be STS.128 due to the bank
-  // index.
-  #pragma unroll
-    for (int m_idx_thread = 0; m_idx_thread < thread_m; m_idx_thread++) {
-  #pragma unroll
-      for (int n_idx_thread = 0; n_idx_thread < thread_n; n_idx_thread++) {
-        int m_idx = (lane_idx / 4) + m_idx_thread * 8;
-        int n_idx =
-            ((lane_idx % 4) * 2) + (n_idx_thread % 2) + (n_idx_thread / 2) * 8;
-        smem_c[cosize_smem_c * warp_idx + smem_c_index_func(m_idx, n_idx)] =
-            acc_reg_reorg[m_idx_thread][n_idx_thread];
-      }
-    }
-    asm volatile("bar.sync %0, %1;" : : "r"(1), "r"(thread_cnt));
-
-    if (warp_idx == 0) {
-      constexpr int final_acc_reg_cnt = (tile_m * tile_n + 31) / 32;
-      float acc_final[final_acc_reg_cnt]{};
-
-  #pragma unroll
-      for (int reg_idx = 0; reg_idx < final_acc_reg_cnt; reg_idx++) {
-        int linear_idx = reg_idx * 32 + lane_idx;
-        int m_idx = linear_idx % tile_m;
-        int n_idx = linear_idx / tile_m;
-        acc_final[reg_idx] +=
-            smem_c[smem_c_index_func(m_idx, n_idx) + 0 * cosize_smem_c] +
-            smem_c[smem_c_index_func(m_idx, n_idx) + 1 * cosize_smem_c] +
-            smem_c[smem_c_index_func(m_idx, n_idx) + 2 * cosize_smem_c] +
-            smem_c[smem_c_index_func(m_idx, n_idx) + 3 * cosize_smem_c];
-      }
-
-  #pragma unroll
-      for (int reg_idx = 0; reg_idx < final_acc_reg_cnt; reg_idx++) {
-        int linear_idx = reg_idx * 32 + lane_idx;
-        int m_idx = linear_idx % tile_m;
-        int n_idx = linear_idx / tile_m;
-        if (m_idx < tile_m && n_idx < gemm_n) {
-          gmem_c[n_idx * gemm_m + m_idx] = acc_final[reg_idx];
-        }
-      }
-    }
-#endif
-  }
-
-  bf16_t* gmem_c;
-  bf16_t* smem_a;
-  bf16_t* smem_b;
-  uint64_t* smem_barrier;
-  int warp_idx;
-  int gemm_n;
-  int stage_idx = 0;
-  int phase_bit = 0;
-  int lane_idx = threadIdx.x % 32;
-  int warp_k_offset_in_tile_k = warp_idx * per_warp_tile_k;
-
-  int a_smem_offsets[m_iter_cnt][k_phase_cnt];
-  int b_smem_offsets[n_iter_cnt][k_phase_cnt];
-
-  bf16_t a_reg[m_iter_cnt][k_phase_cnt][8];
-  bf16_t b_reg[n_iter_cnt][k_phase_cnt][4];
-  float acc_reg[m_iter_cnt][n_iter_cnt][4]{};
-};
-
-// AB swapped, kernel is k-major, k-major, m-major
-template <int batch_size, int gemm_m, int gemm_k, int tile_m, int tile_n,
-          int tile_k, int stage_cnt>
-__global__ __launch_bounds__(256, 1) void fused_a_gemm_kernel(
-    bf16_t* output, bf16_t const* mat_a, bf16_t const* mat_b, int gemm_n) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
-  constexpr int load_thread_cnt = 128;
-  constexpr int compute_thread_cnt = 128;
-  constexpr int thread_cnt = load_thread_cnt + compute_thread_cnt;
-  (void)thread_cnt;
-  static_assert(gemm_m % 16 == 0);
-  static_assert(gemm_k % tile_k == 0);
-  static_assert(gemm_m % tile_m == 0);
-  static_assert(
-      tile_k == 128 || tile_k == 256 || tile_k == 512 ||
-      tile_k == 1024);  // tile_k must be larger than 64 since 4 warp splitK.
-  static_assert(tile_m == 16);
-  constexpr int g2s_vec_bytes = 16;
-  constexpr int a_elem_bytes = 2;
-  constexpr int b_elem_bytes = 2;
-  static_assert((tile_m * a_elem_bytes + tile_n * b_elem_bytes) * tile_k *
-                    stage_cnt <=
-                225 * 1024);
-  static_assert((tile_m * tile_k * a_elem_bytes) %
-                    (load_thread_cnt * g2s_vec_bytes) ==
-                0);
-  static_assert((tile_n * tile_k * b_elem_bytes) %
-                    (load_thread_cnt * g2s_vec_bytes) ==
-                0);
-
-  extern __shared__ char smem[];
-  uint64_t* smem_barrier = reinterpret_cast<uint64_t*>(
-      smem);  // producer,consumer; producer,consumer; ...
-  bf16_t* smem_a = reinterpret_cast<bf16_t*>(smem + (stage_cnt * 8 * 2 + 1024) /
-                                                        1024 * 1024);
-  bf16_t* smem_b = smem_a + tile_m * tile_k * stage_cnt;
-
-  int cta_m_idx = tile_m * blockIdx.x;
-  int cta_n_idx = tile_n * blockIdx.y;
-  bf16_t const* gmem_a_local = mat_a + cta_m_idx * gemm_k;
-  bf16_t const* gmem_b_local = mat_b + cta_n_idx * gemm_k;
-  bf16_t* gmem_c_local = output + cta_n_idx * gemm_m + cta_m_idx;
-
-  int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
-
-  if (warp_idx == 4) {
-    for (int i = 0; i < stage_cnt; i++) {
-      initialize_barrier(smem_barrier + i * 2 + 0,
-                         load_thread_cnt);  // producer
-      initialize_barrier(smem_barrier + i * 2 + 1,
-                         compute_thread_cnt);  // consumer
-    }
-  }
-  __syncthreads();
-
-  if (warp_idx < 2) {
-    GmemLoaderA<gemm_k, tile_m, tile_k, stage_cnt> a_loader(
-        gmem_a_local, smem_a, smem_barrier);
-    a_loader.prepare();
-    a_loader.issue_mainloop();
-  } else if (warp_idx < 4) {
-    GmemLoaderB<gemm_k, tile_n, tile_k, stage_cnt> b_loader(
-        gmem_b_local, smem_b, smem_barrier, gemm_n);
-    b_loader.prepare();
-    b_loader.issue_mainloop();
-  } else {
-    MmaComputer<gemm_m, gemm_k, tile_m, tile_n, tile_k, stage_cnt> mma_computer(
-        gmem_c_local, smem_a, smem_b, smem_barrier, warp_idx, gemm_n);
-    mma_computer.prepare();
-    mma_computer.issue_mainloop();
-    mma_computer.epi();
-  }
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, int kHdIn, int kHdOut, int kTileN>
-void invokeFusedAGemm(T* output, T const* mat_a, T const* mat_b, int num_tokens,
-                      cudaStream_t const stream) {
-  constexpr int gemm_m = kHdOut;  // 2112
-  int const gemm_n = num_tokens;  // 1-16
-  constexpr int gemm_k = kHdIn;   // 7168
-  constexpr int batch_size = 1;
-  std::swap(mat_a, mat_b);
-  constexpr int tile_m = 16;
-  constexpr int tile_n = kTileN;                        // 8 or 16
-  constexpr int tile_k = std::max(256, 1024 / tile_n);  // 256
-  constexpr int max_stage_cnt =
-      1024 * 192 / ((tile_m + tile_n) * tile_k * sizeof(bf16_t));
-  constexpr int k_iter_cnt = gemm_k / tile_k;
-  constexpr int stage_cnt =
-      k_iter_cnt > max_stage_cnt ? max_stage_cnt : k_iter_cnt;
-  int cta_m_cnt = gemm_m / tile_m;
-  int cta_n_cnt = (gemm_n + tile_n - 1) / tile_n;
-  constexpr int barrier_bytes = (stage_cnt * 16 + 1023) / 1024 * 1024;
-  constexpr int smem_bytes =
-      ((tile_m * 2 + tile_n * 2) * tile_k * stage_cnt + barrier_bytes + 1023) /
-      1024 * 1024;
-
-  dim3 grid(cta_m_cnt, cta_n_cnt, 1);
-  dim3 block_size(256);
-  cudaLaunchConfig_t config;
-  config.gridDim = grid;
-  config.blockDim = block_size;
-  config.dynamicSmemBytes = smem_bytes;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = getEnvEnablePDL();
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  if (smem_bytes >= (48 * 1024)) {
-    cudaFuncSetAttribute(fused_a_gemm_kernel<batch_size, gemm_m, gemm_k, tile_m,
-                                             tile_n, tile_k, stage_cnt>,
-                         cudaFuncAttributeMaxDynamicSharedMemorySize,
-                         smem_bytes);
-  }
-  cudaLaunchKernelEx(&config,
-                     fused_a_gemm_kernel<batch_size, gemm_m, gemm_k, tile_m,
-                                         tile_n, tile_k, stage_cnt>,
-                     output, mat_a, mat_b, gemm_n);
-}
-
-template void invokeFusedAGemm<__nv_bfloat16, 7168, 2112, 8>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, int num_tokens,
-    cudaStream_t);
-
-template void invokeFusedAGemm<__nv_bfloat16, 7168, 2112, 16>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, int num_tokens,
-    cudaStream_t);
-
-void dsv3_fused_a_gemm(torch::Tensor& output, torch::Tensor const& mat_a,
-                       torch::Tensor const& mat_b) {
-  TORCH_CHECK(mat_a.dim() == 2 && mat_b.dim() == 2 && output.dim() == 2);
-  int const num_tokens = mat_a.size(0);
-  int const hd_in = mat_a.size(1);
-  int const hd_out = mat_b.size(1);
-
-  constexpr int kHdIn = 7168;
-  constexpr int kHdOut = 2112;
-  TORCH_CHECK(num_tokens >= 1 && num_tokens <= 16,
-              "required 1 <= mat_a.shape[0] <= 16")
-  TORCH_CHECK(hd_in == kHdIn, "required mat_a.shape[1] == 7168")
-  TORCH_CHECK(hd_out == kHdOut, "required mat_b.shape[1] == 2112")
-  TORCH_CHECK(output.size(0) == num_tokens,
-              "required output.shape[0] == mat_a.shape[0]")
-  TORCH_CHECK(output.size(1) == hd_out,
-              "required output.shape[1] == mat_b.shape[1]")
-
-  TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");
-  TORCH_CHECK(output.stride(1) == 1, "output must be a row major tensor");
-  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");
-
-  TORCH_CHECK(mat_a.scalar_type() == torch::kBFloat16 &&
-                  mat_b.scalar_type() == torch::kBFloat16,
-              "Only BFloat16 input dtype is supported")
-  TORCH_CHECK(output.scalar_type() == torch::kBFloat16,
-              "Only BFloat16 output dtype is supported")
-
-  TORCH_CHECK(getSMVersion() >= 90, "required CUDA ARCH >= SM_90");
-
-  auto stream = at::cuda::getCurrentCUDAStream(mat_a.get_device());
-  if (num_tokens <= 8) {
-    invokeFusedAGemm<__nv_bfloat16, kHdIn, kHdOut, 8>(
-        reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-        reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-        reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), num_tokens,
-        stream);
-  } else {
-    invokeFusedAGemm<__nv_bfloat16, kHdIn, kHdOut, 16>(
-        reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-        reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-        reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), num_tokens,
-        stream);
-  }
-}

csrc/moe/dsv3_router_gemm_bf16_out.cu

@@ -1,291 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "dsv3_router_gemm_utils.h"
-
-// Custom FMA implementation using PTX assembly instructions
-__device__ __forceinline__ void fma(float2& d, float2 const& a, float2 const& b,
-                                    float2 const& c) {
-  asm volatile("fma.rn.f32x2 %0, %1, %2, %3;\n"
-               : "=l"(reinterpret_cast<uint64_t&>(d))
-               : "l"(reinterpret_cast<uint64_t const&>(a)),
-                 "l"(reinterpret_cast<uint64_t const&>(b)),
-                 "l"(reinterpret_cast<uint64_t const&>(c)));
-}
-
-// Convert 8 bfloat16 values from a uint4 to float array - optimized conversion
-template <int VPT>
-__device__ __forceinline__ void bf16_uint4_to_float8(uint4 const& vec,
-                                                     float* dst) {
-  __nv_bfloat16* bf16_ptr =
-      reinterpret_cast<__nv_bfloat16*>(const_cast<uint4*>(&vec));
-
-#pragma unroll
-  for (int i = 0; i < VPT; i++) {
-    dst[i] = __bfloat162float(bf16_ptr[i]);
-  }
-}
-
-template <typename T, int kBlockSize, int VPT, int kNumTokens, int kNumExperts,
-          int kHiddenDim>
-__global__ __launch_bounds__(128, 1) void router_gemm_kernel_bf16_output(
-    __nv_bfloat16* out, T const* mat_a, T const* mat_b) {
-  // Each block handles one expert column
-  int const n_idx = blockIdx.x;
-  int const tid = threadIdx.x;
-  constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;
-  // Constants for this kernel
-  constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
-  constexpr int k_iterations =
-      kHiddenDim / k_elems_per_k_iteration;  // Total K iterations
-
-  // Initialize accumulators for all M rows
-  float acc[kNumTokens] = {};
-
-  // Shared memory for warp-level reduction
-  __shared__ float sm_reduction[kNumTokens][kNumWarps];  // kNumWarps
-
-  // B matrix is in column-major order, so we can directly load a column for the
-  // n_idx expert
-  T const* b_col = mat_b + n_idx * kHiddenDim;
-
-  // Pre-compute k_base values for each iteration to help compiler optimize
-  int k_bases[k_iterations];
-#pragma unroll
-  for (int ki = 0; ki < k_iterations; ki++) {
-    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.wait;");
-#endif
-
-  // Process the GEMM in chunks
-  for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = k_bases[ki];
-
-    // Load B matrix values using vector load (8 bf16 values)
-    uint4 b_vec = *reinterpret_cast<uint4 const*>(b_col + k_base);
-
-    // Convert B values to float
-    float b_float[VPT];
-    bf16_uint4_to_float8<VPT>(b_vec, b_float);
-
-// Process each token
-#pragma unroll
-    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-      // Load both rows of A matrix using vector loads
-      uint4 a_vec = *reinterpret_cast<uint4 const*>(
-          mat_a + (m_idx * kHiddenDim) + k_base);
-
-      // Convert A values to float
-      float a_float[VPT];
-      bf16_uint4_to_float8<VPT>(a_vec, a_float);
-
-// Process elements in this chunk
-#pragma unroll
-      for (int k = 0; k < VPT; k++) {
-        float a = a_float[k];
-        float b = b_float[k];
-        acc[m_idx] += a * b;
-      }
-    }
-  }
-
-  // Perform warp-level reduction
-  int const warpSize = 32;
-  int const warpId = tid / warpSize;
-  int const laneId = tid % warpSize;
-
-  // Register for warp-level reduction results
-  float warp_result[kNumTokens];
-
-#pragma unroll
-  for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-    warp_result[m_idx] = acc[m_idx];
-  }
-
-// Perform warp-level reduction using optimized butterfly pattern
-#pragma unroll
-  for (int m = 0; m < kNumTokens; m++) {
-    float sum = warp_result[m];
-
-    // Butterfly reduction pattern
-    sum += __shfl_xor_sync(0xffffffff, sum, 16);
-    sum += __shfl_xor_sync(0xffffffff, sum, 8);
-    sum += __shfl_xor_sync(0xffffffff, sum, 4);
-    sum += __shfl_xor_sync(0xffffffff, sum, 2);
-    sum += __shfl_xor_sync(0xffffffff, sum, 1);
-
-    // Only the first thread in each warp stores to shared memory
-    if (laneId == 0) {
-      sm_reduction[m][warpId] = sum;
-    }
-  }
-
-  __syncthreads();
-
-  // Final reduction across warps (only first thread)
-  if (tid == 0) {
-#pragma unroll
-    for (int m = 0; m < kNumTokens; m++) {
-      float final_sum = 0.0f;
-
-// Sum across the kNumWarps
-#pragma unroll
-      for (int w = 0; w < kNumWarps; w++) {
-        final_sum += sm_reduction[m][w];
-      }
-
-      // Write final result
-      out[m * kNumExperts + n_idx] = __float2bfloat16(final_sum);
-    }
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmBf16Output(__nv_bfloat16* output, T const* mat_a,
-                                T const* mat_b, cudaStream_t stream) {
-  constexpr int VPT = 16 / sizeof(T);
-  constexpr int kBlockSize = 128;
-  cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts;
-  config.blockDim = kBlockSize;
-  config.dynamicSmemBytes = 0;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = getEnvEnablePDL();
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  cudaLaunchKernelEx(
-      &config,
-      router_gemm_kernel_bf16_output<T, kBlockSize, VPT, kNumTokens,
-                                     kNumExperts, kHiddenDim>,
-      output, mat_a, mat_b);
-}
-
-// Template instantiations for DEFAULT_NUM_EXPERTS experts
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 1, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 2, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 3, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 4, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 5, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 6, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 7, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 8, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 9, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 10, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 11, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 12, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 13, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 14, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for KIMI_K2_NUM_EXPERTS experts
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 1, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 2, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 3, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 4, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 5, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 6, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 7, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 8, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 9, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 10, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 11, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 12, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 13, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 14, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);

csrc/moe/dsv3_router_gemm_entry.cu

@@ -1,163 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "dsv3_router_gemm_utils.h"
-
-static constexpr int DEFAULT_NUM_EXPERTS = 256;
-static constexpr int KIMI_K2_NUM_EXPERTS = 384;
-static constexpr int DEFAULT_HIDDEN_DIM = 7168;
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmFloatOutput(float* output, T const* mat_a, T const* mat_b,
-                                 cudaStream_t stream);
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmBf16Output(__nv_bfloat16* output, T const* mat_a,
-                                T const* mat_b, cudaStream_t stream);
-
-template <int kBegin, int kEnd, int kNumExperts, int kHiddenDim>
-struct LoopUnroller {
-  static void unroll_float_output(int num_tokens, float* output,
-                                  __nv_bfloat16 const* input,
-                                  __nv_bfloat16 const* weights,
-                                  cudaStream_t stream) {
-    if (num_tokens == kBegin) {
-      invokeRouterGemmFloatOutput<__nv_bfloat16, kBegin, kNumExperts,
-                                  kHiddenDim>(output, input, weights, stream);
-    } else {
-      LoopUnroller<kBegin + 1, kEnd, kNumExperts,
-                   kHiddenDim>::unroll_float_output(num_tokens, output, input,
-                                                    weights, stream);
-    }
-  }
-
-  static void unroll_bf16_output(int num_tokens, __nv_bfloat16* output,
-                                 __nv_bfloat16 const* input,
-                                 __nv_bfloat16 const* weights,
-                                 cudaStream_t stream) {
-    if (num_tokens == kBegin) {
-      invokeRouterGemmBf16Output<__nv_bfloat16, kBegin, kNumExperts,
-                                 kHiddenDim>(output, input, weights, stream);
-    } else {
-      LoopUnroller<kBegin + 1, kEnd, kNumExperts,
-                   kHiddenDim>::unroll_bf16_output(num_tokens, output, input,
-                                                   weights, stream);
-    }
-  }
-};
-
-template <int kEnd, int kNumExperts, int kHiddenDim>
-struct LoopUnroller<kEnd, kEnd, kNumExperts, kHiddenDim> {
-  static void unroll_float_output(int num_tokens, float* output,
-                                  __nv_bfloat16 const* input,
-                                  __nv_bfloat16 const* weights,
-                                  cudaStream_t stream) {
-    if (num_tokens == kEnd) {
-      invokeRouterGemmFloatOutput<__nv_bfloat16, kEnd, kNumExperts, kHiddenDim>(
-          output, input, weights, stream);
-    } else {
-      throw std::invalid_argument("Invalid num_tokens, only supports 1 to 16");
-    }
-  }
-
-  static void unroll_bf16_output(int num_tokens, __nv_bfloat16* output,
-                                 __nv_bfloat16 const* input,
-                                 __nv_bfloat16 const* weights,
-                                 cudaStream_t stream) {
-    if (num_tokens == kEnd) {
-      invokeRouterGemmBf16Output<__nv_bfloat16, kEnd, kNumExperts, kHiddenDim>(
-          output, input, weights, stream);
-    } else {
-      throw std::invalid_argument("Invalid num_tokens, only supports 1 to 16");
-    }
-  }
-};
-
-void dsv3_router_gemm(at::Tensor& output,       // [num_tokens, num_experts]
-                      const at::Tensor& mat_a,  // [num_tokens, hidden_dim]
-                      const at::Tensor& mat_b   // [num_experts, hidden_dim]
-) {
-  TORCH_CHECK(output.dim() == 2 && mat_a.dim() == 2 && mat_b.dim() == 2);
-
-  const int num_tokens = mat_a.size(0);
-  const int num_experts = mat_b.size(0);
-  const int hidden_dim = mat_a.size(1);
-
-  TORCH_CHECK(mat_a.size(1) == mat_b.size(1),
-              "mat_a and mat_b must have the same hidden_dim");
-  TORCH_CHECK(hidden_dim == DEFAULT_HIDDEN_DIM,
-              "Expected hidden_dim=", DEFAULT_HIDDEN_DIM,
-              ", but got hidden_dim=", hidden_dim);
-  TORCH_CHECK(
-      num_experts == DEFAULT_NUM_EXPERTS || num_experts == KIMI_K2_NUM_EXPERTS,
-      "Expected num_experts=", DEFAULT_NUM_EXPERTS,
-      " or num_experts=", KIMI_K2_NUM_EXPERTS,
-      ", but got num_experts=", num_experts);
-  TORCH_CHECK(num_tokens >= 1 && num_tokens <= 16,
-              "currently num_tokens must be less than or equal to 16 for "
-              "router_gemm");
-  TORCH_CHECK(mat_a.dtype() == at::kBFloat16, "mat_a must be bf16");
-  TORCH_CHECK(mat_b.dtype() == at::kBFloat16, "mat_b must be bf16");
-  TORCH_CHECK(output.dtype() == at::kFloat || output.dtype() == at::kBFloat16,
-              "output must be float32 or bf16");
-
-  auto const sm = getSMVersion();
-  TORCH_CHECK(sm >= 90 && sm <= 103, "required SM_103 >= CUDA ARCH >= SM_90");
-
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  if (output.dtype() == at::kFloat) {
-    if (num_experts == DEFAULT_NUM_EXPERTS) {
-      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_float_output(
-              num_tokens, reinterpret_cast<float*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    } else if (num_experts == KIMI_K2_NUM_EXPERTS) {
-      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_float_output(
-              num_tokens, reinterpret_cast<float*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    }
-  } else if (output.dtype() == at::kBFloat16) {
-    if (num_experts == DEFAULT_NUM_EXPERTS) {
-      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_bf16_output(
-              num_tokens,
-              reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    } else if (num_experts == KIMI_K2_NUM_EXPERTS) {
-      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_bf16_output(
-              num_tokens,
-              reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    }
-  }
-}

csrc/moe/dsv3_router_gemm_float_out.cu

@@ -1,291 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "dsv3_router_gemm_utils.h"
-
-// Custom FMA implementation using PTX assembly instructions
-__device__ __forceinline__ void fma(float2& d, float2 const& a, float2 const& b,
-                                    float2 const& c) {
-  asm volatile("fma.rn.f32x2 %0, %1, %2, %3;\n"
-               : "=l"(reinterpret_cast<uint64_t&>(d))
-               : "l"(reinterpret_cast<uint64_t const&>(a)),
-                 "l"(reinterpret_cast<uint64_t const&>(b)),
-                 "l"(reinterpret_cast<uint64_t const&>(c)));
-}
-
-// Convert 8 bfloat16 values from a uint4 to float array - optimized conversion
-template <int VPT>
-__device__ __forceinline__ void bf16_uint4_to_float8(uint4 const& vec,
-                                                     float* dst) {
-  __nv_bfloat16* bf16_ptr =
-      reinterpret_cast<__nv_bfloat16*>(const_cast<uint4*>(&vec));
-
-#pragma unroll
-  for (int i = 0; i < VPT; i++) {
-    dst[i] = __bfloat162float(bf16_ptr[i]);
-  }
-}
-
-template <typename T, int kBlockSize, int VPT, int kNumTokens, int kNumExperts,
-          int kHiddenDim>
-__global__ __launch_bounds__(128, 1) void router_gemm_kernel_float_output(
-    float* out, T const* mat_a, T const* mat_b) {
-  // Each block handles one expert column
-  int const n_idx = blockIdx.x;
-  int const tid = threadIdx.x;
-  constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;
-  // Constants for this kernel
-  constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
-  constexpr int k_iterations =
-      kHiddenDim / k_elems_per_k_iteration;  // Total K iterations
-
-  // Initialize accumulators for all M rows
-  float acc[kNumTokens] = {};
-
-  // Shared memory for warp-level reduction
-  __shared__ float sm_reduction[kNumTokens][kNumWarps];  // kNumWarps
-
-  // B matrix is in column-major order, so we can directly load a column for the
-  // n_idx expert
-  T const* b_col = mat_b + n_idx * kHiddenDim;
-
-  // Pre-compute k_base values for each iteration to help compiler optimize
-  int k_bases[k_iterations];
-#pragma unroll
-  for (int ki = 0; ki < k_iterations; ki++) {
-    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.wait;");
-#endif
-
-  // Process the GEMM in chunks
-  for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = k_bases[ki];
-
-    // Load B matrix values using vector load (8 bf16 values)
-    uint4 b_vec = *reinterpret_cast<uint4 const*>(b_col + k_base);
-
-    // Convert B values to float
-    float b_float[VPT];
-    bf16_uint4_to_float8<VPT>(b_vec, b_float);
-
-// Process each token
-#pragma unroll
-    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-      // Load both rows of A matrix using vector loads
-      uint4 a_vec = *reinterpret_cast<uint4 const*>(
-          mat_a + (m_idx * kHiddenDim) + k_base);
-
-      // Convert A values to float
-      float a_float[VPT];
-      bf16_uint4_to_float8<VPT>(a_vec, a_float);
-
-// Process elements in this chunk
-#pragma unroll
-      for (int k = 0; k < VPT; k++) {
-        float a = a_float[k];
-        float b = b_float[k];
-        acc[m_idx] += a * b;
-      }
-    }
-  }
-
-  // Perform warp-level reduction
-  int const warpSize = 32;
-  int const warpId = tid / warpSize;
-  int const laneId = tid % warpSize;
-
-  // Register for warp-level reduction results
-  float warp_result[kNumTokens];
-
-#pragma unroll
-  for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-    warp_result[m_idx] = acc[m_idx];
-  }
-
-// Perform warp-level reduction using optimized butterfly pattern
-#pragma unroll
-  for (int m = 0; m < kNumTokens; m++) {
-    float sum = warp_result[m];
-
-    // Butterfly reduction pattern
-    sum += __shfl_xor_sync(0xffffffff, sum, 16);
-    sum += __shfl_xor_sync(0xffffffff, sum, 8);
-    sum += __shfl_xor_sync(0xffffffff, sum, 4);
-    sum += __shfl_xor_sync(0xffffffff, sum, 2);
-    sum += __shfl_xor_sync(0xffffffff, sum, 1);
-
-    // Only the first thread in each warp stores to shared memory
-    if (laneId == 0) {
-      sm_reduction[m][warpId] = sum;
-    }
-  }
-
-  __syncthreads();
-
-  // Final reduction across warps (only first thread)
-  if (tid == 0) {
-#pragma unroll
-    for (int m = 0; m < kNumTokens; m++) {
-      float final_sum = 0.0f;
-
-// Sum across the kNumWarps
-#pragma unroll
-      for (int w = 0; w < kNumWarps; w++) {
-        final_sum += sm_reduction[m][w];
-      }
-
-      // Write final result
-      out[m * kNumExperts + n_idx] = final_sum;
-    }
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmFloatOutput(float* output, T const* mat_a, T const* mat_b,
-                                 cudaStream_t stream) {
-  constexpr int VPT = 16 / sizeof(T);
-  constexpr int kBlockSize = 128;
-  cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts;
-  config.blockDim = kBlockSize;
-  config.dynamicSmemBytes = 0;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = getEnvEnablePDL();
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  cudaLaunchKernelEx(
-      &config,
-      router_gemm_kernel_float_output<T, kBlockSize, VPT, kNumTokens,
-                                      kNumExperts, kHiddenDim>,
-      output, mat_a, mat_b);
-}
-
-// Template instantiations for DEFAULT_NUM_EXPERTS experts
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 1, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 2, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 3, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 4, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 5, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 6, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 7, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 8, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 9, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 10, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 11, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 12, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 13, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 14, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for KIMI_K2_NUM_EXPERTS experts
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 1, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 2, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 3, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 4, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 5, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 6, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 7, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 8, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 9, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 10, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 11, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 12, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 13, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 14, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);

csrc/moe/dsv3_router_gemm_utils.h

@@ -1,43 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cstdlib>
-#include <mutex>
-
-inline int getSMVersion() {
-  auto* props = at::cuda::getCurrentDeviceProperties();
-  return props->major * 10 + props->minor;
-}
-
-inline bool getEnvEnablePDL() {
-  static std::once_flag flag;
-  static bool enablePDL = false;
-  std::call_once(flag, [&]() {
-    if (getSMVersion() >= 90) {
-      const char* env = std::getenv("TRTLLM_ENABLE_PDL");
-      enablePDL = env && env[0] == '1' && env[1] == '\0';
-    }
-  });
-  return enablePDL;
-}

csrc/moe/grouped_topk_kernels.cu

@@ -1,6 +1,6 @@
 /*
  * Adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/v1.3.0rc2/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu
+ * https://github.com/NVIDIA/TensorRT-LLM/blob/v0.21.0/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu
  * Copyright (c) 2025, The vLLM team.
  * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION &
  * AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
@@ -17,10 +17,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "moeTopKFuncs.cuh"
 #include <c10/cuda/CUDAStream.h>
 #include <torch/all.h>
-#include <cmath>
 #include <cuda_fp16.h>
 #include <cuda_bf16.h>
 #include <cuda/std/limits>
@@ -32,17 +30,7 @@ namespace vllm {
 namespace moe {
 
 constexpr unsigned FULL_WARP_MASK = 0xffffffff;
-static constexpr int WARP_SIZE = 32;
-static constexpr int NumNemotronExperts = 512;
-static constexpr int NumKimiK2Experts = 384;
-static constexpr int NumDeepseekExperts = 256;
-static constexpr int MaxSupportedExpertCount =
-    std::max({NumNemotronExperts, NumKimiK2Experts, NumDeepseekExperts});
-static constexpr int MaxNumExpertsUnit = 128;
-static constexpr int NumTopGroupScores = 2;
-static constexpr int DefaultMaxNumTopExperts = 8;
-static constexpr int MaxSupportedTopExperts = 22;
-static constexpr int MaxNumTopGroups = 4;
+constexpr int32_t WARP_SIZE = 32;
 
 namespace warp_topk {
 
@@ -669,335 +657,76 @@ __global__ void grouped_topk_fused_kernel(
 #endif
 }
 
-template <typename T, typename BiasT, typename IdxT, ScoringFunc SF,
-          int MaxNumExperts, bool UseGroups,
-          int MaxNumTopExperts = DefaultMaxNumTopExperts>
-__global__ void grouped_topk_fused_small_expert_count_kernel(
-    T* scores, float* topkValues, IdxT* topkIndices, BiasT const* routingBias,
-    int64_t const numTokens, int64_t const numGroup, int64_t const topkGroup,
-    int64_t const topk, int64_t const numExperts,
-    int64_t const numExpertsPerGroup, bool const renormalize,
-    double const routedScalingFactor) {
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  cudaGridDependencySynchronize();
-#endif
-  // declare shared memory structure
-  // number of experts is bounded by number of threads
-  __shared__ float __attribute((aligned(128))) smemScoreSigmoid[MaxNumExperts];
-  __shared__ float __attribute((aligned(128))) smemScoreBias[MaxNumExperts];
-  // number of expert groups is bounded by number of warps
-  int constexpr NumWarps = MaxNumExperts / WARP_SIZE;
-  __shared__ float __attribute((aligned(128))) smemGroupScores[NumWarps];
-
-  // needed for warp reduce
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  // for the final reduction of weight norm, only some lanes need to participate
-  int32_t laneIdx = threadIdx.x % WARP_SIZE;
-  int32_t warpIdx = __shfl_sync(0xffffffff, threadIdx.x / WARP_SIZE, 0);
-
-  if constexpr (UseGroups) {
-    if (warpIdx >= numGroup) {
-      return;
-    }
-  }
-  // note that for invalid scores, we simply use a negative value:
-  // they work well even with the compacted format used in topK, and
-  // sigmoid / bias activated scores cannot be negative
-  const float invalidScoreFloat = float{-INFINITY};
-
-  // load bias already; each warp represents one expert group
-  auto threadExpert = threadIdx.x;
-  bool expertSelected = threadExpert < numExperts;
-  if constexpr (UseGroups) {
-    threadExpert = warpIdx * numExpertsPerGroup + laneIdx;
-    expertSelected = laneIdx < numExpertsPerGroup;
-  }
-
-  auto scoreIdx = int64_t{blockIdx.x} * int64_t{numExperts} + threadExpert;
-  auto biasVal = expertSelected ? static_cast<float>(routingBias[threadExpert])
-                                : invalidScoreFloat;
-  topkValues += blockIdx.x * topk;
-  topkIndices += blockIdx.x * topk;
-
-  // get our assigned thread score; each warp represents one expert group
-  float score =
-      expertSelected ? static_cast<float>(scores[scoreIdx]) : invalidScoreFloat;
-  auto scoreSigmoid = apply_scoring<SF>(score);
-  // write the sigmoid score to shared for later use
-  if (expertSelected) {
-    smemScoreSigmoid[threadExpert] = scoreSigmoid;
-  }
-
-  // get the score with bias
-  // note that with invalid values, because sigmoid is < 1 and bias is -1,
-  // we must get a negative value, which is smaller than any valid value
-  auto scoreBias = float{scoreSigmoid + float{biasVal}};
-
-  if (expertSelected) {
-    smemScoreBias[threadExpert] = scoreBias;
-  }
-
-  // registers for top group score reduction
-  float topExpGroupScores[NumTopGroupScores];
-  [[maybe_unused]] int32_t topExpGroupIdx[NumTopGroupScores];
-  float topGroups[MaxNumTopGroups];  // bound of numGroup
-  int32_t topGroupIdx[MaxNumTopGroups];
-  float expertScoreGroup[MaxNumTopGroups];
-  int32_t expertIdxGroup[MaxNumTopGroups];
-  float topScores[MaxNumTopExperts];  // bound of topk
-  int32_t topExperts[MaxNumTopExperts];
-
-  if constexpr (UseGroups) {
-    reduce_topk::reduceTopK(warp, topExpGroupScores, topExpGroupIdx, scoreBias,
-                            threadExpert,
-                            /* minValue */ invalidScoreFloat);
-
-    // get the final group score and write it to shared
-    if (warp.thread_rank() == 0) {
-      auto groupScore = topExpGroupScores[0] + topExpGroupScores[1];
-      smemGroupScores[warpIdx] = groupScore;
-    }
-  }
-
-  // make group scores available to all warps
-  __syncthreads();
-
-  if constexpr (UseGroups) {
-    if (warpIdx == 0) {
-      // a single warp performs the selection of top groups, and goes on to
-      // select the final experts
-      float groupScore =
-          laneIdx < numGroup ? smemGroupScores[laneIdx] : invalidScoreFloat;
-
-      reduce_topk::reduceTopK(warp, topGroups, topGroupIdx, groupScore, laneIdx,
-                              /* minValue */ invalidScoreFloat);
-      // final expert selection: get relevant indexes and scores from shared
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {  // bound of numGroup
-        auto groupIdx = topGroupIdx[ii];
-        expertIdxGroup[ii] = groupIdx * numExpertsPerGroup + laneIdx;
-
-        expertScoreGroup[ii] = (ii < topkGroup) && expertSelected
-                                   ? smemScoreBias[expertIdxGroup[ii]]
-                                   : invalidScoreFloat;
-      }
-
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup, /* minValue */ invalidScoreFloat,
-                              topk);
-    }
-  } else if constexpr (MaxNumExperts > MaxNumExpertsUnit) {
-    // without groups, and the expert number is larger than MaxNumExpertsUnit,
-    // we need to use multiple warps to calculate the intermediate topk results
-
-    int constexpr NumExpertWarps = (MaxNumExperts - 1) / MaxNumExpertsUnit + 1;
-    int constexpr NumInterTopK = NumExpertWarps * MaxNumTopExperts;
-    __shared__ float
-        __attribute((aligned(128))) smemInterTopScores[NumInterTopK];
-    __shared__ int32_t
-        __attribute((aligned(128))) smemInterTopExperts[NumInterTopK];
-    if (warpIdx < NumExpertWarps) {
-      int offset = warpIdx * WARP_SIZE * MaxNumTopGroups;
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {
-        auto expertIdx = ii * WARP_SIZE + laneIdx;
-        expertIdxGroup[ii] = offset + expertIdx;
-        expertScoreGroup[ii] = offset + expertIdx < numExperts
-                                   ? smemScoreBias[offset + expertIdx]
-                                   : invalidScoreFloat;
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup,
-                              /* minValue */ invalidScoreFloat, topk);
-
-      if (laneIdx < topk) {
-        smemInterTopScores[warpIdx * MaxNumTopExperts + laneIdx] =
-            topScores[laneIdx];
-        smemInterTopExperts[warpIdx * MaxNumTopExperts + laneIdx] =
-            topExperts[laneIdx];
-      } else if (laneIdx >= topk && laneIdx < MaxNumTopExperts) {
-        smemInterTopScores[warpIdx * MaxNumTopExperts + laneIdx] =
-            invalidScoreFloat;
-        smemInterTopExperts[warpIdx * MaxNumTopExperts + laneIdx] =
-            MaxNumExperts - 1;
-      }
-    }
-    __syncthreads();
-    if (warpIdx == 0) {
-      int constexpr NumInterTopKPerThread = (NumInterTopK - 1) / WARP_SIZE + 1;
-      float intermediateScore[NumInterTopKPerThread];
-      int32_t intermediateExpert[NumInterTopKPerThread];
-      for (int i = laneIdx; i < NumInterTopKPerThread * WARP_SIZE;
-           i += WARP_SIZE) {
-        int ii = i / WARP_SIZE;
-        if (i < NumInterTopK) {
-          intermediateScore[ii] = smemInterTopScores[i];
-          intermediateExpert[ii] = smemInterTopExperts[i];
-        } else {
-          intermediateScore[ii] = invalidScoreFloat;
-          intermediateExpert[ii] = MaxNumExperts - 1;
-        }
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, intermediateScore,
-                              intermediateExpert,
-                              /* minValue */ invalidScoreFloat, topk);
-    }
-  } else {
-    // without groups, and the expert number is smaller than MaxNumExpertsUnit
-    // each thread just takes `MaxNumTopGroups` experts
-    if (warpIdx == 0) {
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {
-        auto expertIdx = ii * WARP_SIZE + laneIdx;
-        expertIdxGroup[ii] = expertIdx;
-        expertScoreGroup[ii] = expertIdx < numExperts ? smemScoreBias[expertIdx]
-                                                      : invalidScoreFloat;
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup,
-                              /* minValue */ invalidScoreFloat, topk);
-    }
-  }
-
-  if (warpIdx == 0) {
-    // determine our lane's expert index and write to output
-    int32_t expertIdx =
-        laneIdx < topk ? topExperts[laneIdx] : MaxNumExperts - 1;
-    float scoreNorm = laneIdx < topk ? smemScoreSigmoid[expertIdx] : 0.F;
-    float finalScore = static_cast<float>(scoreNorm * routedScalingFactor);
-    // norm the value
-    if (renormalize) {
-      auto redNorm = cg::reduce(warp, scoreNorm, cg::plus<float>{});
-      finalScore /= (redNorm + 1e-20);
-    }
-    // store the topk scores and experts to output
-    if (laneIdx < topk) {
-      topkValues[laneIdx] = finalScore;
-      topkIndices[laneIdx] = expertIdx;
-    }
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  cudaTriggerProgrammaticLaunchCompletion();
-#endif
-}
-
-template <typename T, typename BiasT, typename IdxT, ScoringFunc SF>
+template <typename T, typename BiasT, typename IdxT>
 void invokeNoAuxTc(T* scores, float* topk_values, IdxT* topk_indices,
                    BiasT const* bias, int64_t const num_tokens,
                    int64_t const num_experts, int64_t const n_group,
                    int64_t const topk_group, int64_t const topk,
                    bool const renormalize, double const routed_scaling_factor,
-                   bool enable_pdl = false, cudaStream_t const stream = 0) {
+                   int const scoring_func, bool enable_pdl = false,
+                   cudaStream_t const stream = 0) {
   cudaLaunchConfig_t config;
+  // One block per token; one warp per group.
+  config.gridDim = static_cast<uint32_t>(num_tokens);
+  config.blockDim = static_cast<uint32_t>(n_group) * WARP_SIZE;
+  // Dynamic shared memory: WarpSelect staging + per-group topk buffers.
+  int32_t const num_warps = static_cast<int32_t>(n_group);
+  size_t const val_bytes =
+      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
+  size_t const val_bytes_aligned =
+      warp_topk::round_up_to_multiple_of<256>(val_bytes);
+  size_t const idx_bytes =
+      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
+  size_t const internal_bytes = val_bytes_aligned + idx_bytes;
+  size_t const extra_bytes = 16 + static_cast<size_t>(n_group) * sizeof(T);
+  config.dynamicSmemBytes = internal_bytes + extra_bytes;
   config.stream = stream;
   cudaLaunchAttribute attrs[1];
   attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
   attrs[0].val.programmaticStreamSerializationAllowed = enable_pdl;
   config.numAttrs = 1;
   config.attrs = attrs;
-
-  // Check if we can use the optimized
-  // grouped_topk_fused_small_expert_count_kernel
-  bool const is_single_group =
-      (n_group == 1) && (topk_group == 1) &&
-      (num_experts <= MaxSupportedExpertCount) &&
-      (topk <= DefaultMaxNumTopExperts || topk == MaxSupportedTopExperts);
-
-  int64_t const experts_per_group = num_experts / n_group;
-  bool const is_multi_group =
-      (n_group > 1) && (num_experts <= NumDeepseekExperts) &&
-      (experts_per_group <= WARP_SIZE) &&
-      (experts_per_group * topk_group <= MaxNumExpertsUnit) &&
-      (topk <= DefaultMaxNumTopExperts) && (topk_group <= MaxNumTopGroups);
-
-  if (is_single_group || is_multi_group) {
-    auto* kernel_instance =
-        &grouped_topk_fused_small_expert_count_kernel<T, BiasT, IdxT, SF,
-                                                      NumDeepseekExperts, true>;
-    int num_threads = NumDeepseekExperts;
-    if (is_single_group) {
-      // Special case for Nemotron, which selects top 22 from 512 experts, and 1
-      // group only.
-      if (num_experts == NumNemotronExperts && n_group == 1 &&
-          topk == MaxSupportedTopExperts) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, NumNemotronExperts, false,
-            MaxSupportedTopExperts>;
-        num_threads = NumNemotronExperts;
-      } else if (num_experts > NumKimiK2Experts &&
-                 num_experts <= MaxSupportedExpertCount) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, MaxSupportedExpertCount, false>;
-        num_threads = MaxSupportedExpertCount;
-      } else if (num_experts > MaxNumExpertsUnit &&
-                 num_experts <= NumKimiK2Experts) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, NumKimiK2Experts, false>;
-        num_threads = NumKimiK2Experts;
-      } else {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, MaxNumExpertsUnit, false>;
-        num_threads = MaxNumExpertsUnit;
-      }
+  auto const sf = static_cast<ScoringFunc>(scoring_func);
+  switch (sf) {
+    case SCORING_NONE: {
+      auto* kernel_instance =
+          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_NONE>;
+      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
+                         topk_indices, bias, num_tokens, num_experts, n_group,
+                         topk_group, topk, renormalize, routed_scaling_factor);
+      return;
     }
-    config.gridDim = num_tokens;
-    config.blockDim = num_threads;
-    config.dynamicSmemBytes = 0;
-    cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                       topk_indices, bias, num_tokens, n_group, topk_group,
-                       topk, num_experts, num_experts / n_group, renormalize,
-                       routed_scaling_factor);
-  } else {
-    auto* kernel_instance = &grouped_topk_fused_kernel<T, BiasT, IdxT, SF>;
-    // One block per token; one warp per group.
-    config.gridDim = static_cast<uint32_t>(num_tokens);
-    config.blockDim = static_cast<uint32_t>(n_group) * WARP_SIZE;
-    // Dynamic shared memory: WarpSelect staging + per-group topk buffers.
-    int32_t const num_warps = static_cast<int32_t>(n_group);
-    size_t const val_bytes =
-        static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
-    size_t const val_bytes_aligned =
-        warp_topk::round_up_to_multiple_of<256>(val_bytes);
-    size_t const idx_bytes =
-        static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
-    size_t const internal_bytes = val_bytes_aligned + idx_bytes;
-    size_t const extra_bytes = 16 + static_cast<size_t>(n_group) * sizeof(T);
-    config.dynamicSmemBytes = internal_bytes + extra_bytes;
-    cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                       topk_indices, bias, num_tokens, num_experts, n_group,
-                       topk_group, topk, renormalize, routed_scaling_factor);
+    case SCORING_SIGMOID: {
+      auto* kernel_instance =
+          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_SIGMOID>;
+      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
+                         topk_indices, bias, num_tokens, num_experts, n_group,
+                         topk_group, topk, renormalize, routed_scaling_factor);
+      return;
+    }
+    default:
+      // should be guarded by higher level checks.
+      TORCH_CHECK(false, "Unsupported scoring_func in invokeNoAuxTc");
   }
 }
 
-#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT, SF)                             \
-  template void invokeNoAuxTc<T, BiasT, IdxT, SF>(                           \
+#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT)                                 \
+  template void invokeNoAuxTc<T, BiasT, IdxT>(                               \
       T * scores, float* topk_values, IdxT* topk_indices, BiasT const* bias, \
       int64_t const num_tokens, int64_t const num_experts,                   \
       int64_t const n_group, int64_t const topk_group, int64_t const topk,   \
       bool const renormalize, double const routed_scaling_factor,            \
-      bool enable_pdl, cudaStream_t const stream);
+      int const scoring_func, bool enable_pdl, cudaStream_t const stream);
 
-INSTANTIATE_NOAUX_TC(float, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(float, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t, SCORING_NONE);
+INSTANTIATE_NOAUX_TC(float, float, int32_t);
+INSTANTIATE_NOAUX_TC(float, half, int32_t);
+INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t);
+INSTANTIATE_NOAUX_TC(half, float, int32_t);
+INSTANTIATE_NOAUX_TC(half, half, int32_t);
+INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t);
 }  // end namespace moe
 }  // namespace vllm
 
@@ -1033,53 +762,46 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       {num_tokens, topk}, torch::dtype(torch::kInt32).device(torch::kCUDA));
 
   auto stream = c10::cuda::getCurrentCUDAStream(scores.get_device());
-  auto const sf = static_cast<vllm::moe::ScoringFunc>(scoring_func);
 
-#define LAUNCH_KERNEL_SF(T, BiasT, IdxT)                                      \
-  do {                                                                        \
-    switch (sf) {                                                             \
-      case vllm::moe::SCORING_NONE:                                           \
-        vllm::moe::invokeNoAuxTc<T, BiasT, IdxT, vllm::moe::SCORING_NONE>(    \
-            reinterpret_cast<T*>(scores.mutable_data_ptr()),                  \
-            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),         \
-            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),         \
-            reinterpret_cast<BiasT const*>(bias.data_ptr()), num_tokens,      \
-            num_experts, n_group, topk_group, topk, renormalize,              \
-            routed_scaling_factor, false, stream);                            \
-        break;                                                                \
-      case vllm::moe::SCORING_SIGMOID:                                        \
-        vllm::moe::invokeNoAuxTc<T, BiasT, IdxT, vllm::moe::SCORING_SIGMOID>( \
-            reinterpret_cast<T*>(scores.mutable_data_ptr()),                  \
-            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),         \
-            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),         \
-            reinterpret_cast<BiasT const*>(bias.data_ptr()), num_tokens,      \
-            num_experts, n_group, topk_group, topk, renormalize,              \
-            routed_scaling_factor, false, stream);                            \
-        break;                                                                \
-      default:                                                                \
-        throw std::invalid_argument("Unsupported scoring_func");              \
-        break;                                                                \
-    }                                                                         \
-  } while (0)
-
-#define LAUNCH_KERNEL(T, IdxT)                                         \
-  do {                                                                 \
-    switch (bias_type) {                                               \
-      case torch::kFloat16:                                            \
-        LAUNCH_KERNEL_SF(T, half, IdxT);                               \
-        break;                                                         \
-      case torch::kFloat32:                                            \
-        LAUNCH_KERNEL_SF(T, float, IdxT);                              \
-        break;                                                         \
-      case torch::kBFloat16:                                           \
-        LAUNCH_KERNEL_SF(T, __nv_bfloat16, IdxT);                      \
-        break;                                                         \
-      default:                                                         \
-        throw std::invalid_argument(                                   \
-            "Invalid bias dtype, only supports float16, float32, and " \
-            "bfloat16");                                               \
-        break;                                                         \
-    }                                                                  \
+#define LAUNCH_KERNEL(T, IdxT)                                               \
+  do {                                                                       \
+    switch (bias_type) {                                                     \
+      case torch::kFloat16:                                                  \
+        vllm::moe::invokeNoAuxTc<T, half, IdxT>(                             \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<half const*>(bias.data_ptr()), num_tokens,      \
+            num_experts, n_group, topk_group, topk, renormalize,             \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      case torch::kFloat32:                                                  \
+        vllm::moe::invokeNoAuxTc<T, float, IdxT>(                            \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<float const*>(bias.data_ptr()), num_tokens,     \
+            num_experts, n_group, topk_group, topk, renormalize,             \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      case torch::kBFloat16:                                                 \
+        vllm::moe::invokeNoAuxTc<T, __nv_bfloat16, IdxT>(                    \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<__nv_bfloat16 const*>(bias.data_ptr()),         \
+            num_tokens, num_experts, n_group, topk_group, topk, renormalize, \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      default:                                                               \
+        throw std::invalid_argument(                                         \
+            "Invalid bias dtype, only supports float16, float32, and "       \
+            "bfloat16");                                                     \
+        break;                                                               \
+    }                                                                        \
   } while (0)
 
   switch (data_type) {
@@ -1102,6 +824,5 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       break;
   }
 #undef LAUNCH_KERNEL
-#undef LAUNCH_KERNEL_SF
   return {topk_values, topk_indices};
 }

csrc/moe/moeTopKFuncs.cuh

@@ -1,257 +0,0 @@
-/*
- * Adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/v1.3.0rc2/cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh
- * Copyright (c) 2026, The vLLM team.
- * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION. All rights
- * reserved. SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <cub/cub.cuh>
-
-namespace vllm {
-namespace moe {
-namespace reduce_topk {
-namespace cg = cooperative_groups;
-static constexpr int kWARP_SIZE = 32;
-
-template <typename T_>
-struct TopKRedType {
-  using T = T_;
-  static_assert(
-      std::is_same_v<T, float> || std::is_same_v<T, half> ||
-          std::is_same_v<T, __nv_bfloat16> || std::is_same_v<T, int>,
-      "Top K reduction only implemented for int, float, float16 and bfloat16");
-
-  using TypeCmp = std::conditional_t<sizeof(T) == 4, uint64_t, uint32_t>;
-  using IdxT = std::conditional_t<sizeof(T) == 4, int32_t, int16_t>;
-
-  static constexpr int kMoveBits = (sizeof(T) == 4) ? 32 : 16;
-  static constexpr int kMaxIdx = 65535;
-  TypeCmp compValIdx;
-
-  static __host__ __device__ inline TypeCmp makeCmpVal(T val, int32_t idx = 0) {
-    auto valueBits = cub::Traits<T>::TwiddleIn(
-        reinterpret_cast<typename cub::Traits<T>::UnsignedBits&>(val));
-    TypeCmp compactTmp = valueBits;
-    compactTmp = (compactTmp << kMoveBits) | (0xFFFF & (kMaxIdx - idx));
-    // Use 65535 minus idx to give higher priority to elements with smaller
-    // indices.
-    return compactTmp;
-  }
-
-  static __host__ __device__ void unpack(T& value, int32_t& index,
-                                         TypeCmp cmp) {
-    // Since “65535-idx” is always smaller than 65536 and positive, we can
-    // directly use it as the lower 16 bits
-    index = kMaxIdx - static_cast<int32_t>((cmp & 0xFFFF));
-
-    auto compactTmp = cmp >> kMoveBits;
-    auto valueBits = cub::Traits<T>::TwiddleOut(
-        reinterpret_cast<typename cub::Traits<T>::UnsignedBits&>(compactTmp));
-    value = reinterpret_cast<T&>(valueBits);
-  }
-
-  __host__ __device__ TopKRedType() = default;
-
-  __host__ __device__ TopKRedType(T val, int32_t idx)
-      : compValIdx(makeCmpVal(val, idx)) {}
-
-  __host__ __device__ operator TypeCmp() const noexcept { return compValIdx; }
-
-  __device__ inline TypeCmp reduce(
-      cg::thread_block_tile<kWARP_SIZE> const& warp) {
-    return cg::reduce(warp, compValIdx, cg::greater<TypeCmp>{});
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <int K_, bool Enable_>
-struct TopKIdx {
-  // by default, empty
-};
-
-template <int K_>
-struct TopKIdx<K_, true> {
-  static constexpr int K = K_;
-  int32_t val[K];
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#define TOPK_SWAP(I, J)                                         \
-  {                                                             \
-    auto pairMin = min(topK[I].compValIdx, topK[J].compValIdx); \
-    auto pairMax = max(topK[I].compValIdx, topK[J].compValIdx); \
-    topK[I].compValIdx = pairMax;                               \
-    topK[J].compValIdx = pairMin;                               \
-  }
-
-template <int N, typename RedType>
-struct Sort;
-
-template <typename RedType>
-struct Sort<1, RedType> {
-  static __device__ void run(RedType* topK) {}
-};
-
-template <typename RedType>
-struct Sort<2, RedType> {
-  static __device__ void run(RedType* topK) { TOPK_SWAP(0, 1); }
-};
-
-template <typename RedType>
-struct Sort<3, RedType> {
-  static __device__ void run(RedType* topK) {
-    TOPK_SWAP(0, 1);
-    TOPK_SWAP(1, 2);
-    TOPK_SWAP(0, 1);
-  }
-};
-
-template <typename RedType>
-struct Sort<4, RedType> {
-  static __device__ void run(RedType* topK) {
-    TOPK_SWAP(0, 2);
-    TOPK_SWAP(1, 3);
-    TOPK_SWAP(0, 1);
-    TOPK_SWAP(2, 3);
-    TOPK_SWAP(1, 2);
-  }
-};
-
-template <int K, typename Type>
-__forceinline__ __device__ void reduceTopK(
-    cg::thread_block_tile<kWARP_SIZE> const& warp, Type (&out)[K],
-    int32_t (&outIdx)[K], Type value, int32_t idx, Type const minValue,
-    int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  using RedType = TopKRedType<Type>;
-  RedType topK{value, idx};
-  typename RedType::TypeCmp packedMax{};
-#pragma unroll
-  for (int kk = 0; kk < actualK; ++kk) {
-    topK =
-        kk > 0 && packedMax == topK.compValIdx ? RedType{minValue, idx} : topK;
-    // get the next largest value
-    packedMax = topK.reduce(warp);
-    RedType::unpack(out[kk], outIdx[kk], packedMax);
-  }
-};
-
-template <int K, typename Type, int N, bool IsSorted = false>
-__device__ void reduceTopKFunc(cg::thread_block_tile<kWARP_SIZE> const& warp,
-                               Type (&out)[K], int32_t (&outIdx)[K],
-                               Type (&value)[N], int32_t (&idx)[N],
-                               Type minValue, int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  static_assert(N > 0, "Top K must have N > 0");
-  static_assert(N < 5,
-                "Only support candidates number less than or equal to 128");
-  using RedType = TopKRedType<Type>;
-  RedType topK[N];
-#pragma unroll
-  for (int nn = 0; nn < N; ++nn) {
-    topK[nn] = RedType{value[nn], idx[nn]};
-  }
-
-  if constexpr (!IsSorted) {
-    Sort<N, RedType>::run(topK);
-  }
-  typename RedType::TypeCmp packedMax{};
-#pragma unroll
-  for (int kk = 0; kk < actualK; ++kk) {
-    bool update = kk > 0 && packedMax == topK[0].compValIdx;
-#pragma unroll
-    for (int nn = 0; nn < N; ++nn) {
-      topK[nn] = update && nn == N - 1 ? RedType{minValue, idx[nn]}
-                 : update              ? topK[nn + 1]
-                                       : topK[nn];
-    }
-    // get the next largest value
-    packedMax = topK[0].reduce(warp);
-    RedType::unpack(out[kk], outIdx[kk], packedMax);
-  }
-};
-
-template <int K, typename Type, int N>
-__forceinline__ __device__ void reduceTopK(
-    cg::thread_block_tile<kWARP_SIZE> const& warp, Type (&out)[K],
-    int32_t (&outIdx)[K], Type (&value)[N], int32_t (&idx)[N],
-    Type const minValue, int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  static_assert(N > 0, "Top K must have N > 0");
-  static_assert(
-      N <= 16,
-      "Only support candidates number less than or equal to 16*32=512");
-  static_assert(N <= 4 || N % 4 == 0,
-                "Only support candidates number is a multiple of 4*32=128 or "
-                "less than or equal to 4");
-  using RedType = TopKRedType<Type>;
-
-  if constexpr (N <= 4) {
-    reduceTopKFunc<K, Type, N>(warp, out, outIdx, value, idx, minValue,
-                               actualK);
-  } else {
-    constexpr int numLoops = N / 4;
-    constexpr int numResults = (numLoops * K - 1) / kWARP_SIZE + 1;
-
-    Type topKBufferValue[numResults];
-    int32_t topKBufferIdx[numResults];
-    int32_t laneIdx = threadIdx.x % kWARP_SIZE;
-
-    for (int ii = 0; ii < numResults; ++ii) {
-      topKBufferValue[ii] = minValue;
-      topKBufferIdx[ii] = ii * kWARP_SIZE - 1;
-    }
-    for (int loop = 0; loop < numLoops; ++loop) {
-      int start = loop * 4;
-      Type topKValue[K];
-      int32_t topKIdx[K];
-      Type inValue[4];
-      int32_t inIdx[4];
-      for (int i = 0; i < 4; ++i) {
-        inValue[i] = value[start + i];
-        inIdx[i] = idx[start + i];
-      }
-      reduceTopKFunc<K, Type, 4>(warp, topKValue, topKIdx, inValue, inIdx,
-                                 minValue, actualK);
-      int inOffset = laneIdx % K;
-      if (laneIdx >= loop * K && laneIdx < (loop + 1) * K) {
-        topKBufferValue[0] = topKValue[inOffset];
-        topKBufferIdx[0] = topKIdx[inOffset];
-      }
-      if (loop == numLoops - 1 && (laneIdx < (numLoops * K - kWARP_SIZE))) {
-        topKBufferValue[1] = topKValue[inOffset];
-        topKBufferIdx[1] = topKIdx[inOffset];
-      }
-    }
-
-    reduceTopKFunc<K, Type, numResults>(warp, out, outIdx, topKBufferValue,
-                                        topKBufferIdx, minValue, actualK);
-  }
-};
-
-#undef TOPK_SWAP
-
-}  // namespace reduce_topk
-}  // namespace moe
-}  // namespace vllm

csrc/moe/moe_ops.h

@@ -55,15 +55,4 @@ bool moe_permute_unpermute_supported();
 
 void shuffle_rows(const torch::Tensor& input_tensor,
                   const torch::Tensor& dst2src_map,
-                  torch::Tensor& output_tensor);
-
-#ifndef USE_ROCM
-// DeepSeek V3 optimized router GEMM kernel for SM90+
-// Computes output = mat_a @ mat_b.T where:
-//   mat_a: [num_tokens, hidden_dim] in bf16
-//   mat_b: [num_experts, hidden_dim] in bf16
-//   output: [num_tokens, num_experts] in bf16 or fp32
-// Supports num_tokens in [1, 16], num_experts in {256, 384}, hidden_dim = 7168
-void dsv3_router_gemm(torch::Tensor& output, const torch::Tensor& mat_a,
-                      const torch::Tensor& mat_b);
-#endif
+                  torch::Tensor& output_tensor);

csrc/moe/torch_bindings.cpp

@@ -124,10 +124,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "routed_scaling_factor, Tensor bias, int scoring_func) -> (Tensor, "
       "Tensor)");
   m.impl("grouped_topk", torch::kCUDA, &grouped_topk);
-
-  // DeepSeek V3 optimized router GEMM for SM90+
-  m.def("dsv3_router_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
-  m.impl("dsv3_router_gemm", torch::kCUDA, &dsv3_router_gemm);
 #endif
 }
 

csrc/ops.h

@@ -315,9 +315,7 @@ void silu_and_mul_scaled_fp4_experts_quant(
 void per_token_group_quant_fp8(const torch::Tensor& input,
                                torch::Tensor& output_q, torch::Tensor& output_s,
                                int64_t group_size, double eps, double fp8_min,
-                               double fp8_max, bool scale_ue8m0,
-                               bool dummy_is_scale_transposed,
-                               bool dummy_is_tma_aligned);
+                               double fp8_max, bool scale_ue8m0);
 
 void per_token_group_quant_int8(const torch::Tensor& input,
                                 torch::Tensor& output_q,
@@ -410,8 +408,3 @@ void qr_all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
                    int64_t quant_level, bool cast_bf2half = false);
 int64_t qr_max_size();
 #endif
-
-#ifndef USE_ROCM
-void dsv3_fused_a_gemm(torch::Tensor& output, torch::Tensor const& mat_a,
-                       torch::Tensor const& mat_b);
-#endif

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

@@ -97,7 +97,7 @@ __global__ void rms_norm_per_block_quant_kernel(
     scalar_t const* __restrict__ input,   // [..., hidden_size]
     scalar_t const* __restrict__ weight,  // [hidden_size]
     float const* scale_ub, float const var_epsilon, int32_t const hidden_size,
-    scalar_t* __restrict__ residual = nullptr, int64_t outer_scale_stride = 1) {
+    scalar_t* __restrict__ residual = nullptr) {
   float rms;
   // Compute RMS
   // Always able to vectorize due to constraints on hidden_size
@@ -108,8 +108,7 @@ __global__ void rms_norm_per_block_quant_kernel(
   // Always able to vectorize due to constraints on hidden_size and group_size
   vllm::vectorized::compute_dynamic_per_token_scales<
       scalar_t, scalar_out_t, has_residual, is_scale_transposed, group_size>(
-      nullptr, scales, input, weight, rms, scale_ub, hidden_size, residual,
-      outer_scale_stride);
+      nullptr, scales, input, weight, rms, scale_ub, hidden_size, residual);
 
   // RMS Norm + Quant
   // Always able to vectorize due to constraints on hidden_size
@@ -120,8 +119,7 @@ __global__ void rms_norm_per_block_quant_kernel(
   vllm::vectorized::norm_and_quant<
       scalar_t, scalar_out_t, std::is_same_v<scalar_out_t, int8_t>,
       has_residual, is_scale_transposed, group_size>(
-      out, input, weight, rms, scales, hidden_size, residual,
-      outer_scale_stride);
+      out, input, weight, rms, scales, hidden_size, residual);
 }
 
 }  // namespace vllm
@@ -227,8 +225,7 @@ void rms_norm_per_block_quant_dispatch(
                                                  : nullptr,
                             var_epsilon, hidden_size,
                             has_residual ? residual->data_ptr<scalar_in_t>()
-                                         : nullptr,
-                            scales.stride(1));
+                                         : nullptr);
                   });
             });
           });
@@ -260,11 +257,6 @@ void rms_norm_per_block_quant(torch::Tensor& out, torch::Tensor const& input,
   TORCH_CHECK(group_size == 128 || group_size == 64,
               "Unsupported group size: ", group_size);
 
-  if (scales.stride(1) > 1) {
-    TORCH_CHECK(is_scale_transposed,
-                "Outer scale stride must be 1 when scales are not transposed");
-  }
-
   rms_norm_per_block_quant_dispatch(out, input, weight, scales, group_size,
                                     var_epsilon, scale_ub, residual,
                                     is_scale_transposed);

csrc/quantization/fused_kernels/layernorm_utils.cuh

@@ -74,7 +74,7 @@ __device__ void compute_dynamic_per_token_scales(
     scalar_t const* __restrict__ input, scalar_t const* __restrict__ weight,
     float const rms, float const* __restrict__ scale_ub,
     int32_t const hidden_size, scalar_t const* __restrict__ residual = nullptr,
-    int32_t const group_size = 0, int64_t outer_scale_stride = 1) {
+    int32_t const group_size = 0) {
   float block_absmax_val_maybe = 0.0f;
   constexpr scalar_out_t qmax{quant_type_max_v<scalar_out_t>};
   __syncthreads();
@@ -133,9 +133,7 @@ __device__ void compute_dynamic_per_token_scales(
       scale = max(scale / qmax, min_scaling_factor<scalar_out_t>::val());
       // Global output store
       if constexpr (is_scale_transposed) {
-        int64_t const scale_rows = (gridDim.x + outer_scale_stride - 1) /
-                                   outer_scale_stride * outer_scale_stride;
-        all_token_scales[(threadIdx.x / threads_per_group) * scale_rows +
+        all_token_scales[(threadIdx.x / threads_per_group) * gridDim.x +
                          blockIdx.x] = scale;
       } else {
         all_token_scales[blockIdx.x * num_groups +
@@ -182,11 +180,13 @@ __device__ void compute_dynamic_per_token_scales(
 
 template <typename scalar_t, typename scalar_out_t, bool is_scale_inverted,
           bool has_residual = false, bool is_scale_transposed = false>
-__device__ void norm_and_quant(
-    scalar_out_t* __restrict__ output, scalar_t const* __restrict__ input,
-    scalar_t const* __restrict__ weight, float const rms, float* const scale,
-    int32_t const hidden_size, scalar_t* __restrict__ residual = nullptr,
-    int32_t const group_size = 0, int64_t outer_scale_stride = 1) {
+__device__ void norm_and_quant(scalar_out_t* __restrict__ output,
+                               scalar_t const* __restrict__ input,
+                               scalar_t const* __restrict__ weight,
+                               float const rms, float* const scale,
+                               int32_t const hidden_size,
+                               scalar_t* __restrict__ residual = nullptr,
+                               int32_t const group_size = 0) {
   int64_t const token_offset = blockIdx.x * static_cast<int64_t>(hidden_size);
 
   for (auto i = threadIdx.x; i < hidden_size; i += blockDim.x) {
@@ -202,9 +202,7 @@ __device__ void norm_and_quant(
     int64_t scale_idx = 0;
     if (group_size > 0) {
       if constexpr (is_scale_transposed) {
-        int64_t const scale_rows = (gridDim.x + outer_scale_stride - 1) /
-                                   outer_scale_stride * outer_scale_stride;
-        scale_idx = (i / group_size) * scale_rows + blockIdx.x;
+        scale_idx = (i / group_size) * gridDim.x + blockIdx.x;
       } else {
         scale_idx = blockIdx.x * (hidden_size / group_size) + i / group_size;
       }
@@ -288,8 +286,8 @@ __device__ void compute_dynamic_per_token_scales(
     float* __restrict__ token_scale, float* __restrict__ all_token_scales,
     scalar_t const* __restrict__ input, scalar_t const* __restrict__ weight,
     float const rms, float const* __restrict__ scale_ub,
-    int32_t const hidden_size, scalar_t const* __restrict__ residual = nullptr,
-    int64_t outer_scale_stride = 1) {
+    int32_t const hidden_size,
+    scalar_t const* __restrict__ residual = nullptr) {
   constexpr scalar_out_t qmax{quant_type_max_v<scalar_out_t>};
 
   const int VEC_SIZE = 4;
@@ -384,9 +382,7 @@ __device__ void compute_dynamic_per_token_scales(
       scale = max(scale / qmax, min_scaling_factor<scalar_out_t>::val());
       // Global output store
       if constexpr (is_scale_transposed) {
-        int64_t const scale_rows = (gridDim.x + outer_scale_stride - 1) /
-                                   outer_scale_stride * outer_scale_stride;
-        all_token_scales[(threadIdx.x / threads_per_group) * scale_rows +
+        all_token_scales[(threadIdx.x / threads_per_group) * gridDim.x +
                          blockIdx.x] = scale;
       } else {
         all_token_scales[blockIdx.x * num_groups +
@@ -467,8 +463,7 @@ __device__ void norm_and_quant(scalar_out_t* __restrict__ output,
                                scalar_t const* __restrict__ weight,
                                float const rms, float* const scale,
                                int32_t const hidden_size,
-                               scalar_t* __restrict__ residual = nullptr,
-                               int64_t outer_scale_stride = 1) {
+                               scalar_t* __restrict__ residual = nullptr) {
   int64_t const token_offset = blockIdx.x * static_cast<int64_t>(hidden_size);
 
   // Vectorized input/output/weight/residual to better utilize memory bandwidth.
@@ -521,9 +516,7 @@ __device__ void norm_and_quant(scalar_out_t* __restrict__ output,
       int64_t const num_groups = hidden_size / group_size;
       int64_t scale_idx = 0;
       if constexpr (is_scale_transposed) {
-        int64_t const scale_rows = (gridDim.x + outer_scale_stride - 1) /
-                                   outer_scale_stride * outer_scale_stride;
-        scale_idx = (i * VEC_SIZE / group_size) * scale_rows + blockIdx.x;
+        scale_idx = (i * VEC_SIZE / group_size) * gridDim.x + blockIdx.x;
       } else {
         scale_idx = blockIdx.x * num_groups + i * VEC_SIZE / group_size;
       }

csrc/quantization/w8a8/fp8/per_token_group_quant.cu

@@ -379,9 +379,7 @@ void per_token_group_quant_8bit_packed(const torch::Tensor& input,
 void per_token_group_quant_fp8(const torch::Tensor& input,
                                torch::Tensor& output_q, torch::Tensor& output_s,
                                int64_t group_size, double eps, double fp8_min,
-                               double fp8_max, bool scale_ue8m0,
-                               bool dummy_is_scale_transposed = false,
-                               bool dummy_is_tma_aligned = false) {
+                               double fp8_max, bool scale_ue8m0) {
   per_token_group_quant_8bit(input, output_q, output_s, group_size, eps,
                              fp8_min, fp8_max, scale_ue8m0);
 }