[AMD][WideEP] Integrate aiter batched deepgemm

Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>

.buildkite/lm-eval-harness/configs/models-small-rocm.txt

@@ -0,0 +1,5 @@
+Qwen2.5-1.5B-Instruct.yaml
+Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
+Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
+Qwen1.5-MoE-W4A16-compressed-tensors.yaml

.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==0.4.9
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``

.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 git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
+#   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 git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
+#   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 git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
+#   pip install "lm-eval[api]>=0.4.9.2"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -60,6 +60,7 @@ def launch_lm_eval(eval_config, tp_size):
         f"add_bos_token=true,"
         f"trust_remote_code={trust_remote_code},"
         f"max_model_len={max_model_len},"
+        "allow_deprecated_quantization=True,"
     )
 
     env_vars = eval_config.get("env_vars", None)

.buildkite/performance-benchmarks/README.md

@@ -7,7 +7,7 @@ vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](http
 
 ## Performance benchmark quick overview
 
-**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators with different models.
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors, Intel® Gaudi® 3 Accelerators and Arm® Neoverse™ with different models.
 
 **Benchmarking Duration**: about 1hr.
 
@@ -23,7 +23,7 @@ bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
 
 Runtime environment variables:
 
-- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
+- `ON_CPU`: set the value to '1' on Intel® Xeon® and Arm® Neoverse™ Processors. Default value is 0.
 - `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
 - `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
 - `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
@@ -34,8 +34,9 @@ Runtime environment variables:
 
 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
 > NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
-For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
->
+> For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
+> For Arm® Neoverse™, use `tests/latency-tests-arm64-cpu.json`, `tests/throughput-tests-arm64-cpu.json`, `tests/serving-tests-arm64-cpu.json` instead.
+
 ### Latency test
 
 Here is an example of one test inside `latency-tests.json`:
@@ -175,19 +176,6 @@ If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
 
-The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
-When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
-If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
+#### Performance Results Comparison  
 
-Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
-
-|   | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
-| 0  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334                             | 294.018783                             | 1.216863 |
-
-A comparison diagram will be generated below the table.
-Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
-<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
+Follow the instructions in [performance results comparison](https://docs.vllm.ai/en/latest/benchmarking/dashboard/#performance-results-comparison) to analyze performance results and the sizing guide.

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

@@ -1,8 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
 import argparse
+import html as _html
 import json
 import os
+from dataclasses import dataclass
 from importlib import util
 
 import pandas as pd
@@ -10,27 +15,49 @@ import pandas as pd
 pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None
 
+DEFAULT_INFO_COLS = [
+    "Model",
+    "Dataset Name",
+    "Input Len",
+    "Output Len",
+    #    "TP Size",
+    #    "PP Size",
+    "# of max concurrency.",
+    "qps",
+]
 
+# Safety net: if any DataFrame leaks into to_html(), keep precision at 2.
+pd.set_option("display.precision", 2)
+pd.set_option("display.float_format", lambda x: f"{x:.2f}")
+
+
+# -----------------------------
+# Core data compare
+# -----------------------------
 def compare_data_columns(
-    files, name_column, data_column, info_cols, drop_column, debug=False
+    files: list[str],
+    name_column: str,
+    data_column: str,
+    info_cols: list[str],
+    drop_column: str,
+    debug: bool = False,
 ):
     """
     Align concatenation by keys derived from info_cols instead of row order.
     - Pick one canonical key list: subset of info_cols present in ALL files.
     - For each file: set index to those keys, aggregate duplicates
-    - (mean for metric, first for names).
+      (mean for metric, first for names).
     - Concat along axis=1 (indexes align), then reset_index so callers can
-    - group by columns.
+      group by columns.
     - If --debug, add a <file_label>_name column per file.
     """
     print("\ncompare_data_column:", data_column)
 
     frames = []
-    raw_data_cols = []
+    raw_data_cols: list[str] = []
     compare_frames = []
 
-    # 1) choose a canonical key list from info_cols that exists in ALL files
-    cols_per_file = []
+    cols_per_file: list[set] = []
     for f in files:
         try:
             df_tmp = pd.read_json(f, orient="records")
@@ -40,24 +67,20 @@ def compare_data_columns(
 
     key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
     if not key_cols:
-        # soft fallback: use any info_cols present in the first file
         key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
     if not key_cols:
         raise ValueError(
             "No common key columns found from info_cols across the input files."
         )
 
-    # 2) build a single "meta" block (keys as columns) once, aligned by the key index
     meta_added = False
 
     for file in files:
         df = pd.read_json(file, orient="records")
 
-        # Keep rows that actually have the compared metric (same as original behavior)
         if drop_column in df.columns:
             df = df.dropna(subset=[drop_column], ignore_index=True)
 
-        # Stabilize numeric key columns (harmless if missing)
         for c in (
             "Input Len",
             "Output Len",
@@ -69,32 +92,26 @@ def compare_data_columns(
             if c in df.columns:
                 df[c] = pd.to_numeric(df[c], errors="coerce")
 
-        # Ensure all key columns exist
         for c in key_cols:
             if c not in df.columns:
                 df[c] = pd.NA
 
-        # Set index = key_cols and aggregate duplicates → unique MultiIndex
         df_idx = df.set_index(key_cols, drop=False)
 
-        # meta (key columns), unique per key
         meta = df_idx[key_cols]
         if not meta.index.is_unique:
             meta = meta.groupby(level=key_cols, dropna=False).first()
 
-        # metric series for this file, aggregated to one row per key
         file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
         s = df_idx[data_column]
         if not s.index.is_unique:
             s = s.groupby(level=key_cols, dropna=False).mean()
-        s.name = file_label  # column label like original
+        s.name = file_label
 
-        # add meta once (from first file) so keys are the leftmost columns
         if not meta_added:
             frames.append(meta)
             meta_added = True
 
-        # (NEW) debug: aligned test-name column per file
         if debug and name_column in df_idx.columns:
             name_s = df_idx[name_column]
             if not name_s.index.is_unique:
@@ -106,26 +123,19 @@ def compare_data_columns(
         raw_data_cols.append(file_label)
         compare_frames.append(s)
 
-        # Generalize ratio: for any file N>=2, add ratio (fileN / file1)
         if len(compare_frames) >= 2:
             base = compare_frames[0]
             current = compare_frames[-1]
             if "P99" in data_column or "Median" in data_column:
-                ratio = base / current  # for latency
+                ratio = base / current
             else:
                 ratio = current / base
-            ratio = ratio.mask(base == 0)  # avoid inf when baseline is 0
+            ratio = ratio.mask(base == 0)
             ratio.name = f"Ratio 1 vs {len(compare_frames)}"
             frames.append(ratio)
 
-    # 4) concat on columns with aligned MultiIndex;
-    # then reset_index to return keys as columns
-    concat_df = pd.concat(frames, axis=1)
-    concat_df = concat_df.reset_index(drop=True).reset_index()
-    if "index" in concat_df.columns:
-        concat_df = concat_df.drop(columns=["index"])
+    concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
 
-    # Ensure key/info columns appear first (in your info_cols order)
     front = [c for c in info_cols if c in concat_df.columns]
     rest = [c for c in concat_df.columns if c not in front]
     concat_df = concat_df[front + rest]
@@ -134,20 +144,15 @@ def compare_data_columns(
     return concat_df, raw_data_cols
 
 
+# -----------------------------
+# Split helper
+# -----------------------------
 def split_json_by_tp_pp(
     input_file: str = "benchmark_results.json", output_root: str = "."
 ) -> list[str]:
-    """
-    Split a benchmark JSON into separate folders by (TP Size, PP Size).
-
-    Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
-    Returns: list of file paths written.
-    """
-    # Load JSON data into DataFrame
     with open(input_file, encoding="utf-8") as f:
         data = json.load(f)
 
-    # If the JSON is a dict with a list under common keys, use that list
     if isinstance(data, dict):
         for key in ("results", "serving_results", "benchmarks", "data"):
             if isinstance(data.get(key), list):
@@ -156,7 +161,6 @@ def split_json_by_tp_pp(
 
     df = pd.DataFrame(data)
 
-    # Keep only "serving" tests
     name_col = next(
         (c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
     )
@@ -165,7 +169,6 @@ def split_json_by_tp_pp(
             df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
         ].copy()
 
-    # Handle alias column names
     rename_map = {
         "tp_size": "TP Size",
         "tensor_parallel_size": "TP Size",
@@ -176,21 +179,14 @@ def split_json_by_tp_pp(
         columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
     )
 
-    # Ensure TP/PP columns exist (default to 1 if missing)
     if "TP Size" not in df.columns:
         df["TP Size"] = 1
     if "PP Size" not in df.columns:
         df["PP Size"] = 1
 
-    # make sure TP/PP are numeric ints with no NaN
-    df["TP Size"] = (
-        pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
-    )
-    df["PP Size"] = (
-        pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
-    )
+    df["TP Size"] = pd.to_numeric(df["TP Size"], errors="coerce").fillna(1).astype(int)
+    df["PP Size"] = pd.to_numeric(df["PP Size"], errors="coerce").fillna(1).astype(int)
 
-    # Split into separate folders
     saved_paths: list[str] = []
     for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
         folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
@@ -203,32 +199,9 @@ def split_json_by_tp_pp(
     return saved_paths
 
 
-def _add_limit_line(fig, y_value, label):
-    # Visible dashed line + annotation
-    fig.add_hline(
-        y=y_value,
-        line_dash="dash",
-        line_color="red" if "ttft" in label.lower() else "blue",
-        annotation_text=f"{label}: {y_value} ms",
-        annotation_position="top left",
-    )
-    # Optional: add a legend item (as a transparent helper trace)
-    if plot and plotly_found:
-        import plotly.graph_objects as go
-
-        fig.add_trace(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode="lines",
-                line=dict(
-                    dash="dash", color="red" if "ttft" in label.lower() else "blue"
-                ),
-                name=f"{label}",
-            )
-        )
-
-
+# -----------------------------
+# Styling helpers
+# -----------------------------
 def _find_concurrency_col(df: pd.DataFrame) -> str:
     for c in [
         "# of max concurrency.",
@@ -239,7 +212,6 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
     ]:
         if c in df.columns:
             return c
-    # Fallback: guess an integer-like column (harmless if unused)
     for c in df.columns:
         if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
             return c
@@ -248,8 +220,7 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
 
 def _highlight_threshold(
     df: pd.DataFrame, threshold: float
-) -> "pd.io.formats.style.Styler":
-    """Highlight numeric per-configuration columns with value <= threshold."""
+) -> pd.io.formats.style.Styler:
     conc_col = _find_concurrency_col(df)
     key_cols = [
         c
@@ -260,6 +231,7 @@ def _highlight_threshold(
         c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
     ]
     conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
+
     return df.style.map(
         lambda v: "background-color:#e6ffe6;font-weight:bold;"
         if pd.notna(v) and v <= threshold
@@ -268,7 +240,264 @@ def _highlight_threshold(
     )
 
 
-if __name__ == "__main__":
+def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
+    ratio_cols = [c for c in styler.data.columns if "ratio" in str(c).lower()]
+    if not ratio_cols:
+        return styler
+
+    styler = styler.apply(
+        lambda _: ["background-color: #fff3b0"] * len(styler.data),
+        subset=ratio_cols,
+        axis=0,
+    )
+
+    styler = styler.set_table_styles(
+        [
+            {
+                "selector": f"th.col_heading.level0.col{i}",
+                "props": [("background-color", "#fff3b0")],
+            }
+            for i, col in enumerate(styler.data.columns)
+            if col in ratio_cols
+        ],
+        overwrite=False,
+    )
+    return styler
+
+
+def _apply_two_decimals(
+    styler: pd.io.formats.style.Styler,
+) -> pd.io.formats.style.Styler:
+    df = styler.data
+    num_cols = df.select_dtypes("number").columns
+    if len(num_cols) == 0:
+        return styler
+    return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")
+
+
+# -----------------------------
+# Valid max concurrency summary helpers
+# -----------------------------
+def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
+    key_cols = [
+        c
+        for c in ["Model", "Dataset Name", "Input Len", "Output Len"]
+        if c in df.columns
+    ]
+    exclude = set(key_cols + [conc_col, "qps", "QPS"])
+
+    cols: list[str] = []
+    for c in df.columns:
+        if c in exclude:
+            continue
+        lc = str(c).lower()
+        if lc.startswith("ratio"):
+            continue
+        if lc.endswith("_name") or lc == "test name" or lc == "test_name":
+            continue
+        if pd.api.types.is_numeric_dtype(df[c]):
+            cols.append(c)
+    return cols
+
+
+def _max_concurrency_ok(
+    df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
+):
+    if df is None or conc_col not in df.columns or cfg_col not in df.columns:
+        return pd.NA
+
+    d = df[[conc_col, cfg_col]].copy()
+    d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
+    d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
+    d = d.dropna(subset=[conc_col, cfg_col])
+
+    if d.empty:
+        return pd.NA
+
+    ok = d[d[cfg_col] <= threshold]
+    if ok.empty:
+        return pd.NA
+
+    return ok[conc_col].max()
+
+
+def _value_at_concurrency(df: pd.DataFrame, conc_col: str, cfg_col: str, conc_value):
+    if (
+        df is None
+        or conc_col not in df.columns
+        or cfg_col not in df.columns
+        or pd.isna(conc_value)
+    ):
+        return pd.NA
+
+    d = df[[conc_col, cfg_col]].copy()
+    d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
+    d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
+
+    conc_value = pd.to_numeric(conc_value, errors="coerce")
+    if pd.isna(conc_value):
+        return pd.NA
+
+    hit = d[d[conc_col] == conc_value]
+    if hit.empty:
+        return pd.NA
+    return hit[cfg_col].iloc[0]
+
+
+def build_valid_max_concurrency_summary_html(
+    tput_group_df: pd.DataFrame | None,
+    ttft_group_df: pd.DataFrame | None,
+    tpot_group_df: pd.DataFrame | None,
+    conc_col: str,
+    args,
+) -> str:
+    if ttft_group_df is None and tpot_group_df is None:
+        return ""
+
+    ttft_cols = (
+        _config_value_columns(ttft_group_df, conc_col)
+        if ttft_group_df is not None
+        else []
+    )
+    tpot_cols = (
+        _config_value_columns(tpot_group_df, conc_col)
+        if tpot_group_df is not None
+        else []
+    )
+    tput_cols = (
+        _config_value_columns(tput_group_df, conc_col)
+        if tput_group_df is not None
+        else []
+    )
+
+    if ttft_group_df is not None and tpot_group_df is not None:
+        cfg_cols = [c for c in ttft_cols if c in tpot_cols]
+        if tput_group_df is not None:
+            cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
+    else:
+        cfg_cols = ttft_cols or tpot_cols
+
+    if not cfg_cols:
+        cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
+
+    rows = []
+    for cfg in cfg_cols:
+        ttft_max = (
+            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
+            if ttft_group_df is not None
+            else pd.NA
+        )
+        tpot_max = (
+            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
+            if tpot_group_df is not None
+            else pd.NA
+        )
+        both = (
+            pd.NA
+            if (pd.isna(ttft_max) or pd.isna(tpot_max))
+            else min(ttft_max, tpot_max)
+        )
+
+        tput_at_both = (
+            _value_at_concurrency(tput_group_df, conc_col, cfg, both)
+            if tput_group_df is not None
+            else pd.NA
+        )
+        ttft_at_both = (
+            _value_at_concurrency(ttft_group_df, conc_col, cfg, both)
+            if ttft_group_df is not None
+            else pd.NA
+        )
+        tpot_at_both = (
+            _value_at_concurrency(tpot_group_df, conc_col, cfg, both)
+            if tpot_group_df is not None
+            else pd.NA
+        )
+
+        rows.append(
+            {
+                "Configuration": cfg,
+                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
+                f"Max {conc_col} (Both)": both,
+                "Output Tput @ Both (tok/s)": tput_at_both,
+                "TTFT @ Both (ms)": ttft_at_both,
+                "TPOT @ Both (ms)": tpot_at_both,
+            }
+        )
+
+    summary_df = pd.DataFrame(rows)
+
+    # --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
+    for c in summary_df.columns:
+        if c == "Configuration":
+            continue
+        summary_df[c] = pd.to_numeric(summary_df[c], errors="coerce")
+
+    both_col = f"Max {conc_col} (Both)"
+
+    # --- Strict 2-decimal formatting for ALL non-Configuration columns ---
+    formatters = {}
+    for c in summary_df.columns:
+        if c == "Configuration":
+            continue
+        # default argument binds per-column formatter correctly
+        formatters[c] = lambda v: "" if pd.isna(v) else f"{float(v):.2f}"
+
+    styler = summary_df.style.format(formatters)
+
+    def _green(v):
+        return "background-color:#e6ffe6;font-weight:bold;" if pd.notna(v) else ""
+
+    if both_col in summary_df.columns:
+        styler = styler.map(_green, subset=[both_col])
+
+    title = (
+        '<div style="font-size: 1.15em; font-weight: 700; margin: 12px 0 6px 0;">'
+        "Valid Max Concurrency Summary"
+        "</div>\n"
+    )
+    return title + styler.to_html(table_attributes='border="1" class="dataframe"')
+
+
+# -----------------------------
+# Plot helper
+# -----------------------------
+def _add_limit_line(fig, y_value: float, label: str):
+    fig.add_hline(
+        y=y_value,
+        line_dash="dash",
+        line_color="red" if "ttft" in label.lower() else "blue",
+        annotation_text=f"{label}: {y_value} ms",
+        annotation_position="top left",
+    )
+    if plotly_found:
+        import plotly.graph_objects as go
+
+        fig.add_trace(
+            go.Scatter(
+                x=[None],
+                y=[None],
+                mode="lines",
+                line=dict(
+                    dash="dash",
+                    color="red" if "ttft" in label.lower() else "blue",
+                ),
+                name=label,
+            )
+        )
+
+
+# -----------------------------
+# Refactored main + group-first report
+# -----------------------------
+@dataclass(frozen=True)
+class MetricPlan:
+    data_cols: list[str]
+    drop_column: str
+
+
+def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "-f", "--file", action="append", type=str, help="input file name"
@@ -308,149 +537,289 @@ if __name__ == "__main__":
         default=100.0,
         help="Reference limit for TPOT plots (ms)",
     )
+    return parser
 
-    args = parser.parse_args()
 
+def choose_metrics(latency: str) -> MetricPlan:
+    latency = (latency or "").lower()
     drop_column = "P99"
-    name_column = "Test name"
-    info_cols = [
-        "Model",
-        "Dataset Name",
-        "Input Len",
-        "Output Len",
-        "TP Size",
-        "PP Size",
-        "# of max concurrency.",
-        "qps",
-    ]
 
-    if "median" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "Median TTFT /n",
-            "Median TPOT /n",
-        ]
-        drop_column = "P99"
-    elif "p99" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "P99 TTFT /n",
-            "P99 TPOT /n",
-        ]
+    if "median" in latency:
+        return MetricPlan(
+            data_cols=["Output Tput (tok/s)", "Median TTFT (ms)", "Median"],
+            drop_column=drop_column,
+        )
+
+    return MetricPlan(
+        data_cols=["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"],
+        drop_column=drop_column,
+    )
+
+
+def prepare_input_files(args, info_cols: list[str]) -> tuple[list[str], list[str]]:
+    if not args.file:
+        raise ValueError("No input files provided. Use -f/--file.")
 
     if len(args.file) == 1:
         files = split_json_by_tp_pp(args.file[0], output_root="splits")
         info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
     else:
         files = args.file
+
+    return files, info_cols
+
+
+def get_y_axis_col(info_cols: list[str], xaxis: str) -> str:
+    y_axis_index = info_cols.index(xaxis) if xaxis in info_cols else 6
+    return info_cols[y_axis_index]
+
+
+def get_group_cols(output_df: pd.DataFrame, info_cols: list[str]) -> list[str]:
+    filtered_info_cols = info_cols[:4]
+    group_cols = [c for c in filtered_info_cols if c in output_df.columns]
+    if not group_cols:
+        raise ValueError(
+            f"No valid group-by columns. Expected subset: {filtered_info_cols}, "
+            f"but DataFrame has: {list(output_df.columns)}"
+        )
+    return group_cols
+
+
+def normalize_group_key(name):
+    return name if isinstance(name, tuple) else (name,)
+
+
+def group_filename(name, prefix: str = "perf_comparison_") -> str:
+    name_vals = normalize_group_key(name)
+    safe = ",".join(map(str, name_vals)).replace(",", "_").replace("/", "-")
+    return f"{prefix}{safe}.html"
+
+
+def build_group_suffix(group_cols: list[str], name) -> str:
+    name_vals = normalize_group_key(name)
+    return " , ".join(f"{col} : [ {val} ] " for col, val in zip(group_cols, name_vals))
+
+
+def render_metric_table_html(
+    display_group: pd.DataFrame,
+    metric_label: str,
+    group_suffix: str,
+    args,
+) -> str:
+    title = (
+        f'<div style="font-size: 1.25em; font-weight: 600; margin: 12px 0;">'
+        f"{_html.escape(metric_label)}"
+        f" — {_html.escape(group_suffix)}"
+        f"</div>\n"
+    )
+
+    metric_name = metric_label.lower()
+    if "ttft" in metric_name:
+        styler = _highlight_threshold(display_group, args.ttft_max_ms)
+    elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
+        styler = _highlight_threshold(display_group, args.tpot_max_ms)
+    else:
+        styler = display_group.style
+
+    styler = _apply_two_decimals(styler)
+    styler = highlight_ratio_columns(styler)
+
+    return title + styler.to_html(table_attributes='border="1" class="dataframe"')
+
+
+def maybe_write_plot(
+    main_fh,
+    sub_fh,
+    group_df: pd.DataFrame,
+    raw_data_cols: list[str],
+    metric_label: str,
+    y_axis_col: str,
+    args,
+):
+    if not (args.plot and plotly_found):
+        return
+
+    import plotly.express as px
+
+    df = group_df[raw_data_cols].sort_values(by=y_axis_col)
+    df_melted = df.melt(
+        id_vars=y_axis_col,
+        var_name="Configuration",
+        value_name=metric_label,
+    )
+
+    fig = px.line(
+        df_melted,
+        x=y_axis_col,
+        y=metric_label,
+        color="Configuration",
+        title=f"{metric_label} vs {y_axis_col}",
+        markers=True,
+    )
+
+    # Ensure plot hover + y tick labels are also 2 decimals.
+    fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
+    fig.update_yaxes(tickformat=".2f")
+
+    metric_name = metric_label.lower()
+    if "ttft" in metric_name:
+        _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
+    elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
+        _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
+
+    html = fig.to_html(full_html=True, include_plotlyjs="cdn")
+    main_fh.write(html)
+    sub_fh.write(html)
+
+
+def build_group_keys(
+    df: pd.DataFrame, group_cols: list[str], sort_cols: list[str] | None = None
+):
+    if sort_cols:
+        df = df.sort_values(by=sort_cols)
+    gb = df.groupby(group_cols, dropna=False)
+    return [k for k, _ in gb]
+
+
+def write_report_group_first(
+    files: list[str], info_cols: list[str], plan: MetricPlan, args
+):
+    name_column = "Test name"
+    y_axis_col = get_y_axis_col(info_cols, args.xaxis)
+
     print("comparing : " + ", ".join(files))
-    debug = args.debug
-    plot = args.plot
-    # For Plot feature, assign y axis from one of info_cols
-    y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
-    with open("perf_comparison.html", "w") as text_file:
-        for i in range(len(data_cols_to_compare)):
-            output_df, raw_data_cols = compare_data_columns(
-                files,
-                name_column,
-                data_cols_to_compare[i],
-                info_cols,
-                drop_column,
-                debug=debug,
+
+    metric_cache: dict[str, tuple[pd.DataFrame, list[str]]] = {}
+    group_cols_canonical: list[str] | None = None
+
+    for metric_label in plan.data_cols:
+        output_df, raw_data_cols = compare_data_columns(
+            files,
+            name_column,
+            metric_label,
+            info_cols,
+            plan.drop_column,
+            debug=args.debug,
+        )
+
+        raw_data_cols = list(raw_data_cols)
+        raw_data_cols.insert(0, y_axis_col)
+
+        group_cols = get_group_cols(output_df, info_cols)
+        if group_cols_canonical is None:
+            group_cols_canonical = group_cols
+        else:
+            group_cols_canonical = [c for c in group_cols_canonical if c in group_cols]
+
+        metric_cache[metric_label] = (
+            output_df.sort_values(by=args.xaxis),
+            raw_data_cols,
+        )
+
+    if not group_cols_canonical:
+        raise ValueError("No canonical group columns found across metrics.")
+
+    first_metric = plan.data_cols[0]
+    first_df_sorted, _ = metric_cache[first_metric]
+    group_keys = build_group_keys(
+        first_df_sorted, group_cols_canonical, sort_cols=[args.xaxis]
+    )
+
+    metric_groupbys = {
+        metric_label: df.groupby(group_cols_canonical, dropna=False)
+        for metric_label, (df, _) in metric_cache.items()
+    }
+
+    with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
+        main_fh.write('<meta charset="utf-8">\n')
+        for gkey in group_keys:
+            gkey_tuple = normalize_group_key(gkey)
+            suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
+            sub_path = group_filename(gkey_tuple)
+            group_header = (
+                '<div style="font-size: 1.4em; font-weight: 700; '
+                'margin: 18px 0 10px 0;">'
+                f"{_html.escape(suffix)}"
+                "</div>\n"
             )
 
-            # For Plot feature, insert y axis from one of info_cols
-            raw_data_cols.insert(0, info_cols[y_axis_index])
+            main_fh.write(group_header)
+            with open(sub_path, "w", encoding="utf-8") as sub_fh:
+                sub_fh.write('<meta charset="utf-8">\n')
+                sub_fh.write(group_header)
+                tput_group_df = None
+                ttft_group_df = None
+                tpot_group_df = None
+                conc_col = args.xaxis
 
-            filtered_info_cols = info_cols[:-2]
-            existing_group_cols = [
-                c for c in filtered_info_cols if c in output_df.columns
-            ]
-            if not existing_group_cols:
-                raise ValueError(
-                    f"No valid group-by columns  "
-                    f"Expected subset: {filtered_info_cols}, "
-                    f"but DataFrame has: {list(output_df.columns)}"
+                for metric_label in plan.data_cols:
+                    gb = metric_groupbys[metric_label]
+                    df_sorted, raw_data_cols = metric_cache[metric_label]
+
+                    try:
+                        group_df = gb.get_group(gkey)
+                    except KeyError:
+                        missing = (
+                            '<div style="font-size: 1.1em; font-weight: 600; '
+                            'margin: 10px 0;">'
+                            f"{_html.escape(metric_label)} — missing for this group"
+                            "</div>\n"
+                        )
+
+                        main_fh.write(missing)
+                        sub_fh.write(missing)
+                        continue
+
+                    if conc_col not in group_df.columns:
+                        conc_col = _find_concurrency_col(group_df)
+
+                    mn = metric_label.lower().strip()
+                    if "tok/s" in mn:
+                        tput_group_df = group_df
+                    elif "ttft" in mn:
+                        ttft_group_df = group_df
+                    elif mn in ("p99", "median") or "tpot" in mn:
+                        tpot_group_df = group_df
+
+                    display_group = group_df.drop(
+                        columns=group_cols_canonical, errors="ignore"
+                    )
+
+                    html = render_metric_table_html(
+                        display_group, metric_label, suffix, args
+                    )
+                    main_fh.write(html)
+                    sub_fh.write(html)
+
+                    maybe_write_plot(
+                        main_fh,
+                        sub_fh,
+                        group_df=group_df,
+                        raw_data_cols=raw_data_cols,
+                        metric_label=metric_label,
+                        y_axis_col=y_axis_col,
+                        args=args,
+                    )
+
+                summary_html = build_valid_max_concurrency_summary_html(
+                    tput_group_df=tput_group_df,
+                    ttft_group_df=ttft_group_df,
+                    tpot_group_df=tpot_group_df,
+                    conc_col=conc_col,
+                    args=args,
                 )
-            # output_df_sorted = output_df.sort_values(by=existing_group_cols)
-            output_df_sorted = output_df.sort_values(by=args.xaxis)
-            output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
-            for name, group in output_groups:
-                group_name = (
-                    ",".join(map(str, name)).replace(",", "_").replace("/", "-")
-                )
-                group_html_name = "perf_comparison_" + group_name + ".html"
+                if summary_html:
+                    main_fh.write(summary_html)
+                    sub_fh.write(summary_html)
 
-                metric_name = str(data_cols_to_compare[i]).lower()
-                if "tok/s" in metric_name:
-                    html = group.to_html()
-                elif "ttft" in metric_name:
-                    styler = _highlight_threshold(group, args.ttft_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
-                elif (
-                    "tpot" in metric_name
-                    or "median" in metric_name
-                    or "p99" in metric_name
-                ):
-                    styler = _highlight_threshold(group, args.tpot_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
 
-                text_file.write(html_msgs_for_data_cols[i])
-                text_file.write(html)
-                with open(group_html_name, "a+") as sub_text_file:
-                    sub_text_file.write(html_msgs_for_data_cols[i])
-                    sub_text_file.write(html)
+def main():
+    args = build_parser().parse_args()
+    info_cols = list(DEFAULT_INFO_COLS)
+    plan = choose_metrics(args.latency)
+    files, info_cols = prepare_input_files(args, info_cols)
+    write_report_group_first(files, info_cols, plan, args)
 
-                    if plot and plotly_found:
-                        import plotly.express as px
 
-                        df = group[raw_data_cols]
-                        df_sorted = df.sort_values(by=info_cols[y_axis_index])
-                        # Melt DataFrame for plotting
-                        df_melted = df_sorted.melt(
-                            id_vars=info_cols[y_axis_index],
-                            var_name="Configuration",
-                            value_name=data_cols_to_compare[i],
-                        )
-                        title = (
-                            data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
-                        )
-                        # Create Plotly line chart
-                        fig = px.line(
-                            df_melted,
-                            x=info_cols[y_axis_index],
-                            y=data_cols_to_compare[i],
-                            color="Configuration",
-                            title=title,
-                            markers=True,
-                        )
-
-                        # ---- Add threshold lines based on metric name ----
-                        if "ttft" in metric_name:
-                            _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
-                        elif (
-                            "tpot" in metric_name
-                            or "median" in metric_name
-                            or "p99" in metric_name
-                        ):
-                            _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
-
-                        # Export to HTML
-                        text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
-                        sub_text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
+if __name__ == "__main__":
+    main()

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

@@ -49,7 +49,11 @@ check_cpus() {
     echo "Need at least 1 NUMA to run benchmarking."
     exit 1
   fi
-  declare -g gpu_type="cpu"
+  if [[ "$(uname -m)" == "aarch64" ]] || [[ "$(uname -m)" == "arm64" ]]; then
+    declare -g gpu_type="arm64-cpu"
+  else
+    declare -g gpu_type="cpu"
+  fi
   echo "GPU type is $gpu_type"
 }
 
@@ -207,8 +211,8 @@ run_latency_tests() {
 
     # check if there is enough GPU to run the test
     tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ]; then
-      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
+    if [[ "$ON_CPU" == "1" ]]; then
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size // 1')
       world_size=$(($tp*$pp))
       if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
         echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -276,8 +280,8 @@ run_throughput_tests() {
 
     # check if there is enough GPU to run the test
     tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ]; then
-      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
+    if [[ "$ON_CPU" == "1" ]]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size // 1')
       world_size=$(($tp*$pp))
       if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
         echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -393,8 +397,8 @@ run_serving_tests() {
 
     # check if there is enough resources to run the test
     tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ]; then
-      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
+    if [[ "$ON_CPU" == "1" ]]; then
+      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size // 1')
       world_size=$(($tp*$pp))
       if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
         echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -496,9 +500,9 @@ run_serving_tests() {
 main() {
   local ARCH
   ARCH=''
-  if [ "$ON_CPU" == "1" ];then
-     check_cpus
-     ARCH='-cpu'
+  if [[ "$ON_CPU" == "1" ]]; then
+    check_cpus
+    ARCH="-$gpu_type"
   else
      check_gpus
      ARCH="$arch_suffix"

.buildkite/performance-benchmarks/tests/latency-tests-arm64-cpu.json

@@ -0,0 +1,26 @@
+[
+    {
+        "test_name": "latency_llama8B_tp1",
+        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+            "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dtype": "bfloat16",
+            "distributed_executor_backend": "mp",
+            "block_size": 128,
+            "trust_remote_code": "",
+            "disable_log_stats": "",
+            "enforce_eager": "",
+            "max_num_batched_tokens": 2048,
+            "max_num_seqs": 256,
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    }
+]

.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json

@@ -0,0 +1,130 @@
+{
+  "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": "",
+      "enforce_eager": "",
+      "max_num_batched_tokens": 2048,
+      "max_num_seqs": 256,
+      "load_format": "dummy"
+    },
+    "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_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_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
+      }
+    }
+  ]
+}

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

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

.buildkite/performance-benchmarks/tests/throughput-tests-arm64-cpu.json

@@ -0,0 +1,27 @@
+[
+    {
+        "test_name": "throughput_llama8B_tp1",
+        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+            "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dtype": "bfloat16",
+            "distributed_executor_backend": "mp",
+            "block_size": 128,
+            "trust_remote_code": "",
+            "disable_log_stats": "",
+            "enforce_eager": "",
+            "max_num_batched_tokens": 2048,
+            "max_num_seqs": 256,
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    }
+]

.buildkite/release-pipeline.yaml

@@ -1,6 +1,6 @@
 steps:
   # aarch64 + CUDA builds
-  - label: "Build arm64 wheel - CUDA 12.9"
+  - label: "Build wheel - aarch64 - CUDA 12.9"
     depends_on: ~
     id: build-wheel-arm64-cuda-12-9
     agents:
@@ -11,11 +11,11 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build arm64 wheel - CUDA 13.0"
+  - label: "Build wheel - aarch64 - CUDA 13.0"
     depends_on: ~
     id: build-wheel-arm64-cuda-13-0
     agents:
@@ -26,12 +26,12 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
     env:
       DOCKER_BUILDKIT: "1"
 
   # aarch64 build
-  - label: "Build arm64 CPU wheel"
+  - label: "Build wheel - aarch64 - CPU"
     depends_on: ~
     id: build-wheel-arm64-cpu
     agents:
@@ -40,39 +40,39 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
     env:
       DOCKER_BUILDKIT: "1"
 
   # x86 + CUDA builds
-  - label: "Build wheel - CUDA 12.9"
+  - label: "Build wheel - x86_64 - CUDA 12.9"
     depends_on: ~
-    id: build-wheel-cuda-12-9
+    id: build-wheel-x86-cuda-12-9
     agents:
       queue: cpu_queue_postmerge
     commands:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh manylinux_2_31"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build wheel - CUDA 13.0"
+  - label: "Build wheel - x86_64 - CUDA 13.0"
     depends_on: ~
-    id: build-wheel-cuda-13-0
+    id: build-wheel-x86-cuda-13-0
     agents:
       queue: cpu_queue_postmerge
     commands:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
     env:
       DOCKER_BUILDKIT: "1"
 
   # x86 CPU wheel build
-  - label: "Build x86 CPU wheel"
+  - label: "Build wheel - x86_64 - CPU"
     depends_on: ~
     id: build-wheel-x86-cpu
     agents:
@@ -81,12 +81,12 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
     env:
       DOCKER_BUILDKIT: "1"
 
-  # Build release images (12.9)
-  - label: "Build release image (x86)"
+  # Build release images (CUDA 12.9)
+  - label: "Build release image - x86_64 - CUDA 12.9"
     depends_on: ~
     id: build-release-image-x86
     agents:
@@ -99,7 +99,7 @@ steps:
       - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-  - label: "Build release image (arm64)"
+  - label: "Build release image - aarch64 - CUDA 12.9"
     depends_on: ~
     id: build-release-image-arm64
     agents:
@@ -109,34 +109,93 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
 
-  # Add job to create multi-arch manifest
-  - label: "Create multi-arch manifest"
+  - label: "Create multi-arch manifest - CUDA 12.9"
     depends_on:
       - build-release-image-x86
       - build-release-image-arm64
     id: create-multi-arch-manifest
     agents:
-      queue: cpu_queue_postmerge
+      queue: small_cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
       - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
       - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-  - label: "Annotate release workflow"
+  - label: "Annotate release workflow - CUDA 12.9"
     depends_on:
       - create-multi-arch-manifest
     id: annotate-release-workflow
     agents:
-      queue: cpu_queue_postmerge
+      queue: small_cpu_queue_postmerge
     commands:
       - "bash .buildkite/scripts/annotate-release.sh"
 
+  - block: "Build CUDA 13.0 release images"
+    key: block-release-image-build-cuda-13-0
+    depends_on: ~
+
+  - label: "Build release image - x86_64 - CUDA 13.0"
+    depends_on: block-release-image-build-cuda-13-0
+    id: build-release-image-x86-cuda-13-0
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+      # re-tag to default image tag and push, just in case arm64 build fails
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+
+  - label: "Build release image - aarch64 - CUDA 13.0"
+    depends_on: block-release-image-build-cuda-13-0
+    id: build-release-image-arm64-cuda-13-0
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      # compute capability 12.0 for RTX-50 series / RTX PRO 6000 Blackwell, 12.1 for DGX Spark
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+
+  - label: "Create multi-arch manifest - CUDA 13.0"
+    depends_on:
+      - build-release-image-x86-cuda-13-0
+      - build-release-image-arm64-cuda-13-0
+    id: create-multi-arch-manifest-cuda-13-0
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
+      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+
   - input: "Provide Release version here"
     id: input-release-version
     fields:
       - text: "What is the release version?"
         key: release-version
 
+  - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
+    key: block-upload-release-wheels
+    depends_on:
+      - input-release-version
+      - build-wheel-x86-cuda-12-9
+      - build-wheel-x86-cuda-13-0
+      - build-wheel-x86-cpu
+      - build-wheel-arm64-cuda-12-9
+      - build-wheel-arm64-cuda-13-0
+      - build-wheel-arm64-cpu
+
+  - label: "Upload release wheels to PyPI and GitHub"
+    depends_on:
+      - block-upload-release-wheels
+    id: upload-release-wheels
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "bash .buildkite/scripts/upload-release-wheels.sh"
+
   - block: "Build CPU release image"
     key: block-cpu-release-image-build
     depends_on: ~
@@ -169,24 +228,31 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
+  - block: "Build ROCm release image"
+    key: block-rocm-release-image-build
+    depends_on: ~
+
+  - label: "Build release image (ROCm)"
+    depends_on: block-rocm-release-image-build
+    id: build-release-image-rocm
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      # Build base image first
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --tag rocm/vllm-dev:base-$BUILDKITE_COMMIT --target final --progress plain -f docker/Dockerfile.rocm_base ."
+      # Build vLLM ROCm image using the base
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg BASE_IMAGE=rocm/vllm-dev:base-$BUILDKITE_COMMIT --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm --target vllm-openai --progress plain -f docker/Dockerfile.rocm ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm"
+  
   - label: "Build and publish nightly multi-arch image to DockerHub"
     depends_on:
       - create-multi-arch-manifest
     if: build.env("NIGHTLY") == "1"
     agents:
-      queue: cpu_queue_postmerge
+      queue: small_cpu_queue_postmerge
     commands:
-      - "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:$BUILDKITE_COMMIT-x86_64"
-      - "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64"
-      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 vllm/vllm-openai:nightly-x86_64"
-      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 vllm/vllm-openai:nightly-aarch64"
-      - "docker push vllm/vllm-openai:nightly-x86_64"
-      - "docker push vllm/vllm-openai:nightly-aarch64"
-      - "docker manifest create vllm/vllm-openai:nightly vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
-      - "docker manifest create vllm/vllm-openai:nightly-$BUILDKITE_COMMIT vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
-      - "docker manifest push vllm/vllm-openai:nightly"
-      - "docker manifest push vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
+      - "bash .buildkite/scripts/push-nightly-builds.sh"
       # Clean up old nightly builds (keep only last 14)
       - "bash .buildkite/scripts/cleanup-nightly-builds.sh"
     plugins:
@@ -196,3 +262,384 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
       DOCKERHUB_USERNAME: "vllmbot"
+
+  - label: "Build and publish nightly multi-arch image to DockerHub - CUDA 13.0"
+    depends_on:
+      - create-multi-arch-manifest-cuda-13-0
+    if: build.env("NIGHTLY") == "1"
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "bash .buildkite/scripts/push-nightly-builds.sh cu130"
+      # Clean up old nightly builds (keep only last 14)
+      - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu130-nightly-"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"
+
+
+  # =============================================================================
+  # ROCm Release Pipeline (x86_64 only)
+  # =============================================================================
+  #
+  # vLLM version is determined by the Buildkite checkout (like CUDA pipeline).
+  # To build a specific version, trigger the build from that branch/tag.
+  #
+  # Environment variables for ROCm builds (set via Buildkite UI or schedule):
+  #   ROCM_PYTHON_VERSION: Python version (default: 3.12)
+  #   PYTORCH_ROCM_ARCH: GPU architectures (default: gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151)
+  #   ROCM_UPLOAD_WHEELS: Upload to S3 (default: false for nightly, true for releases)
+  #   ROCM_FORCE_REBUILD: Force rebuild base wheels, ignore S3 cache (default: false)
+  #
+  # Note: ROCm version is determined by BASE_IMAGE in docker/Dockerfile.rocm_base
+  #       (currently rocm/dev-ubuntu-22.04:7.1-complete)
+  #
+  # =============================================================================
+
+  # ROCm Input Step - Collect build configuration (manual trigger only)
+  - input: "ROCm Wheel Release Build Configuration"
+    key: input-rocm-config
+    depends_on: ~
+    if: build.source == "ui"
+    fields:
+      - text: "Python Version"
+        key: "rocm-python-version"
+        default: "3.12"
+        hint: "Python version (e.g., 3.12)"
+      - text: "GPU Architectures"
+        key: "rocm-pytorch-rocm-arch"
+        default: "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151"
+        hint: "Semicolon-separated GPU architectures"
+      - select: "Upload Wheels to S3"
+        key: "rocm-upload-wheels"
+        default: "true"
+        options:
+          - label: "No - Build only (nightly/dev)"
+            value: "false"
+          - label: "Yes - Upload to S3 (release)"
+            value: "true"
+      - select: "Force Rebuild Base Wheels"
+        key: "rocm-force-rebuild"
+        default: "false"
+        hint: "Ignore S3 cache and rebuild base wheels from scratch"
+        options:
+          - label: "No - Use cached wheels if available"
+            value: "false"
+          - label: "Yes - Rebuild even if cache exists"
+            value: "true"
+
+  # ROCm Job 1: Build ROCm Base Wheels (with S3 caching)
+  - label: ":rocm: Build ROCm Base Wheels"
+    id: build-rocm-base-wheels
+    depends_on:
+      - step: input-rocm-config
+        allow_failure: true  # Allow failure so non-UI builds can proceed (input step is skipped)
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      # Set configuration and check cache
+      - |
+        set -euo pipefail
+
+        # Get values from meta-data (set by input step) or use defaults
+        PYTHON_VERSION="$$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo '')"
+        export PYTHON_VERSION="$${PYTHON_VERSION:-3.12}"
+
+        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
+        export PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
+
+        # Check for force rebuild flag
+        ROCM_FORCE_REBUILD="$${ROCM_FORCE_REBUILD:-}"
+        if [ -z "$${ROCM_FORCE_REBUILD}" ]; then
+          ROCM_FORCE_REBUILD="$$(buildkite-agent meta-data get rocm-force-rebuild 2>/dev/null || echo '')"
+        fi
+
+        echo "========================================"
+        echo "ROCm Base Wheels Build Configuration"
+        echo "========================================"
+        echo "  PYTHON_VERSION: $${PYTHON_VERSION}"
+        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
+        echo "  ROCM_FORCE_REBUILD: $${ROCM_FORCE_REBUILD:-false}"
+        echo "========================================"
+
+        # Save resolved config for later jobs
+        buildkite-agent meta-data set "rocm-python-version" "$${PYTHON_VERSION}"
+        buildkite-agent meta-data set "rocm-pytorch-rocm-arch" "$${PYTORCH_ROCM_ARCH}"
+
+        # Check S3 cache for pre-built wheels
+        CACHE_KEY=$$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+        CACHE_PATH=$$(.buildkite/scripts/cache-rocm-base-wheels.sh path)
+        echo ""
+        echo "Cache key: $${CACHE_KEY}"
+        echo "Cache path: $${CACHE_PATH}"
+
+        # Save cache key for downstream jobs
+        buildkite-agent meta-data set "rocm-cache-key" "$${CACHE_KEY}"
+
+        CACHE_STATUS="miss"
+        if [ "$${ROCM_FORCE_REBUILD}" != "true" ]; then
+          CACHE_STATUS=$$(.buildkite/scripts/cache-rocm-base-wheels.sh check)
+        else
+          echo "Force rebuild requested, skipping cache check"
+        fi
+
+        if [ "$${CACHE_STATUS}" = "hit" ]; then
+          echo ""
+          echo "CACHE HIT! Downloading pre-built wheels..."
+          echo ""
+          .buildkite/scripts/cache-rocm-base-wheels.sh download
+
+          # Set the S3 path for the cached Docker image (for Job 2 to download)
+          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
+          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
+
+          # Mark that we used cache (for Docker image handling)
+          buildkite-agent meta-data set "rocm-used-cache" "true"
+
+          echo ""
+          echo "Cache download complete. Skipping Docker build."
+          echo "Docker image will be downloaded from: $${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
+        else
+          echo ""
+          echo "CACHE MISS. Building from scratch..."
+          echo ""
+
+          # Build full base image (for later vLLM build)
+          DOCKER_BUILDKIT=1 docker buildx build \
+            --file docker/Dockerfile.rocm_base \
+            --tag rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} \
+            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
+            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
+            --build-arg USE_SCCACHE=1 \
+            --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
+            --build-arg SCCACHE_REGION_NAME=us-west-2 \
+            --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
+            --load \
+            .
+
+          # Build debs_wheel_release stage for wheel extraction
+          DOCKER_BUILDKIT=1 docker buildx build \
+            --file docker/Dockerfile.rocm_base \
+            --tag rocm-base-debs:$${BUILDKITE_BUILD_NUMBER} \
+            --target debs_wheel_release \
+            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
+            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
+            --build-arg USE_SCCACHE=1 \
+            --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
+            --build-arg SCCACHE_REGION_NAME=us-west-2 \
+            --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
+            --load \
+            .
+
+          # Extract wheels from Docker image
+          mkdir -p artifacts/rocm-base-wheels
+          container_id=$$(docker create rocm-base-debs:$${BUILDKITE_BUILD_NUMBER})
+          docker cp $${container_id}:/app/debs/. artifacts/rocm-base-wheels/
+          docker rm $${container_id}
+          echo "Extracted base wheels:"
+          ls -lh artifacts/rocm-base-wheels/
+
+          # Upload wheels to S3 cache for future builds
+          echo ""
+          echo "Uploading wheels to S3 cache..."
+          .buildkite/scripts/cache-rocm-base-wheels.sh upload
+
+          # Export base Docker image for reuse in vLLM build
+          mkdir -p artifacts/rocm-docker-image
+          docker save rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} | gzip > artifacts/rocm-docker-image/rocm-base-image.tar.gz
+          echo "Docker image size:"
+          ls -lh artifacts/rocm-docker-image/
+
+          # Upload large Docker image to S3 (also cached by cache key)
+          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
+          echo "Uploading Docker image to $${S3_ARTIFACT_PATH}/"
+          aws s3 cp artifacts/rocm-docker-image/rocm-base-image.tar.gz "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
+
+          # Save the S3 path for downstream jobs
+          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
+
+          # Mark that we did NOT use cache
+          buildkite-agent meta-data set "rocm-used-cache" "false"
+
+          echo ""
+          echo "Build complete. Wheels cached for future builds."
+        fi
+    artifact_paths:
+      - "artifacts/rocm-base-wheels/*.whl"
+    env:
+      DOCKER_BUILDKIT: "1"
+      S3_BUCKET: "vllm-wheels"
+
+  # ROCm Job 2: Build vLLM ROCm Wheel
+  - label: ":python: Build vLLM ROCm Wheel"
+    id: build-rocm-vllm-wheel
+    depends_on:
+      - step: build-rocm-base-wheels
+        allow_failure: false
+    agents:
+      queue: cpu_queue_postmerge
+    timeout_in_minutes: 180
+    commands:
+      # Download artifacts and prepare Docker image
+      - |
+        set -euo pipefail
+
+        # Ensure git tags are up-to-date (Buildkite's default fetch doesn't update tags)
+        # This fixes version detection when tags are moved/force-pushed
+        echo "Fetching latest tags from origin..."
+        git fetch --tags --force origin
+        
+        # Log tag information for debugging version detection
+        echo "========================================"
+        echo "Git Tag Verification"
+        echo "========================================"
+        echo "Current HEAD: $(git rev-parse HEAD)"
+        echo "git describe --tags: $(git describe --tags 2>/dev/null || echo 'No tags found')"
+        echo ""
+        echo "Recent tags (pointing to commits near HEAD):"
+        git tag -l --sort=-creatordate | head -5
+        echo "setuptools_scm version detection:"
+        pip install -q setuptools_scm 2>/dev/null || true
+        python3 -c "import setuptools_scm; print('  Detected version:', setuptools_scm.get_version())" 2>/dev/null || echo "  (setuptools_scm not available in this environment)"
+        echo "========================================"
+
+        # Download wheel artifacts from current build
+        echo "Downloading wheel artifacts from current build"
+        buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
+
+        # Download Docker image from S3 (too large for Buildkite artifacts)
+        DOCKER_IMAGE_S3_PATH="$$(buildkite-agent meta-data get rocm-docker-image-s3-path 2>/dev/null || echo '')"
+        if [ -z "$${DOCKER_IMAGE_S3_PATH}" ]; then
+          echo "ERROR: rocm-docker-image-s3-path metadata not found"
+          echo "This should have been set by the build-rocm-base-wheels job"
+          exit 1
+        fi
+        echo "Downloading Docker image from $${DOCKER_IMAGE_S3_PATH}"
+        mkdir -p artifacts/rocm-docker-image
+        aws s3 cp "$${DOCKER_IMAGE_S3_PATH}" artifacts/rocm-docker-image/rocm-base-image.tar.gz
+
+        # Load base Docker image and capture the tag
+        echo "Loading base Docker image..."
+        LOAD_OUTPUT=$$(gunzip -c artifacts/rocm-docker-image/rocm-base-image.tar.gz | docker load)
+        echo "$${LOAD_OUTPUT}"
+        # Extract the actual loaded image tag from "Loaded image: <tag>" output
+        # This avoids picking up stale images (like rocm/vllm-dev:nightly) already on the agent
+        BASE_IMAGE_TAG=$$(echo "$${LOAD_OUTPUT}" | grep "Loaded image:" | sed 's/Loaded image: //')
+        if [ -z "$${BASE_IMAGE_TAG}" ]; then
+          echo "ERROR: Failed to extract image tag from docker load output"
+          echo "Load output was: $${LOAD_OUTPUT}"
+          exit 1
+        fi
+        echo "Loaded base image: $${BASE_IMAGE_TAG}"
+
+        # Prepare base wheels for Docker build context
+        mkdir -p docker/context/base-wheels
+        touch docker/context/base-wheels/.keep
+        cp artifacts/rocm-base-wheels/*.whl docker/context/base-wheels/
+        echo "Base wheels for vLLM build:"
+        ls -lh docker/context/base-wheels/
+
+        # Get GPU architectures from meta-data
+        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
+        PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
+
+        echo "========================================"
+        echo "Building vLLM wheel with:"
+        echo "  BUILDKITE_COMMIT: $${BUILDKITE_COMMIT}"
+        echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
+        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
+        echo "  BASE_IMAGE: $${BASE_IMAGE_TAG}"
+        echo "========================================"
+
+        # Build vLLM wheel using local checkout (REMOTE_VLLM=0)
+        DOCKER_BUILDKIT=1 docker build \
+          --file docker/Dockerfile.rocm \
+          --target export_vllm_wheel_release \
+          --output type=local,dest=rocm-dist \
+          --build-arg BASE_IMAGE="$${BASE_IMAGE_TAG}" \
+          --build-arg ARG_PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
+          --build-arg REMOTE_VLLM=0 \
+          --build-arg GIT_REPO_CHECK=1 \
+          --build-arg USE_SCCACHE=1 \
+          --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
+          --build-arg SCCACHE_REGION_NAME=us-west-2 \
+          --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
+          .
+
+        echo "Built vLLM wheel:"
+        ls -lh rocm-dist/*.whl
+
+        # Copy wheel to artifacts directory
+        mkdir -p artifacts/rocm-vllm-wheel
+        cp rocm-dist/*.whl artifacts/rocm-vllm-wheel/
+        echo "Final vLLM wheel:"
+        ls -lh artifacts/rocm-vllm-wheel/
+    artifact_paths:
+      - "artifacts/rocm-vllm-wheel/*.whl"
+    env:
+      DOCKER_BUILDKIT: "1"
+      S3_BUCKET: "vllm-wheels"
+
+  # ROCm Job 3: Upload Wheels to S3
+  - label: ":s3: Upload ROCm Wheels to S3"
+    id: upload-rocm-wheels
+    depends_on:
+      - step: build-rocm-vllm-wheel
+        allow_failure: false
+    agents:
+      queue: cpu_queue_postmerge
+    timeout_in_minutes: 60
+    commands:
+      # Download all wheel artifacts and run upload
+      - |
+        set -euo pipefail
+
+        # Check if upload is enabled (from env var, meta-data, or release branch)
+        ROCM_UPLOAD_WHEELS="$${ROCM_UPLOAD_WHEELS:-}"
+        if [ -z "$${ROCM_UPLOAD_WHEELS}" ]; then
+          # Try to get from meta-data (input form)
+          ROCM_UPLOAD_WHEELS="$$(buildkite-agent meta-data get rocm-upload-wheels 2>/dev/null || echo '')"
+        fi
+
+        echo "========================================"
+        echo "Upload check:"
+        echo "  ROCM_UPLOAD_WHEELS: $${ROCM_UPLOAD_WHEELS}"
+        echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
+        echo "========================================"
+
+        # Skip upload if not enabled
+        if [ "$${ROCM_UPLOAD_WHEELS}" != "true" ]; then
+          echo "Skipping S3 upload (ROCM_UPLOAD_WHEELS != true, NIGHTLY != 1, not a release branch)"
+          echo "To enable upload, set 'Upload Wheels to S3' to 'Yes' in the build configuration"
+          exit 0
+        fi
+
+        echo "Upload enabled, proceeding..."
+
+        # Download artifacts from current build
+        echo "Downloading artifacts from current build"
+        buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
+        buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .
+
+        # Run upload script
+        bash .buildkite/scripts/upload-rocm-wheels.sh
+    env:
+      DOCKER_BUILDKIT: "1"
+      S3_BUCKET: "vllm-wheels"
+
+  # ROCm Job 4: Annotate ROCm Wheel Release
+  - label: ":memo: Annotate ROCm wheel release"
+    id: annotate-rocm-release
+    depends_on:
+      - step: upload-rocm-wheels
+        allow_failure: true
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "bash .buildkite/scripts/annotate-rocm-release.sh"
+    env:
+      S3_BUCKET: "vllm-wheels"

.buildkite/scripts/annotate-release.sh

@@ -32,6 +32,7 @@ To download and upload the image:
 \`\`\`
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
 
 docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
 docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
@@ -45,6 +46,12 @@ docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 docker push vllm/vllm-openai:latest-aarch64
 docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai:rocm
+docker tag vllm/vllm-openai:rocm vllm/vllm-openai:latest-rocm
+docker tag vllm/vllm-openai:rocm vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+docker push vllm/vllm-openai:latest-rocm
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+
 docker manifest rm vllm/vllm-openai:latest
 docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
 docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64

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

@@ -0,0 +1,74 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Generate Buildkite annotation for ROCm wheel release
+
+set -ex
+
+# Get build configuration from meta-data
+# Extract ROCm version dynamically from Dockerfile.rocm_base
+# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.1-complete -> extracts "7.1"
+ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
+PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
+PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
+
+# S3 URLs
+S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
+S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
+S3_URL="https://${S3_BUCKET}.s3.${S3_REGION}.amazonaws.com"
+ROCM_PATH="rocm/${BUILDKITE_COMMIT}"
+
+buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
+## :rocm: ROCm Wheel Release
+
+### Build Configuration
+| Setting | Value |
+|---------|-------|
+| **ROCm Version** | ${ROCM_VERSION} |
+| **Python Version** | ${PYTHON_VERSION} |
+| **GPU Architectures** | ${PYTORCH_ROCM_ARCH} |
+| **Branch** | \`${BUILDKITE_BRANCH}\` |
+| **Commit** | \`${BUILDKITE_COMMIT}\` |
+
+### :package: Installation
+
+**Install from this build (by commit):**
+\`\`\`bash
+uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/{rocm_variant}/
+
+# Example:
+uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/rocm700/
+\`\`\`
+
+**Install from nightly (if published):**
+\`\`\`bash
+uv pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/
+\`\`\`
+
+### :floppy_disk: Download Wheels Directly
+
+\`\`\`bash
+# List all ROCm wheels
+aws s3 ls s3://${S3_BUCKET}/${ROCM_PATH}/
+
+# Download specific wheels
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/vllm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torch-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/triton_rocm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torchvision-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/amdsmi-*.whl .
+\`\`\`
+
+### :gear: Included Packages
+- **vllm**: vLLM with ROCm support
+- **torch**: PyTorch built for ROCm ${ROCM_VERSION}
+- **triton_rocm**: Triton built for ROCm
+- **torchvision**: TorchVision for ROCm PyTorch
+- **amdsmi**: AMD SMI Python bindings
+
+### :warning: Notes
+- These wheels are built for **ROCm ${ROCM_VERSION}** and will NOT work with CUDA GPUs
+- Supported GPU architectures: ${PYTORCH_ROCM_ARCH}
+- Platform: Linux x86_64 only
+EOF

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

@@ -0,0 +1,140 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Cache helper for ROCm base wheels
+#
+# This script manages caching of pre-built ROCm base wheels (torch, triton, etc.)
+# to avoid rebuilding them when Dockerfile.rocm_base hasn't changed.
+#
+# Usage:
+#   cache-rocm-base-wheels.sh check    - Check if cache exists, outputs "hit" or "miss"
+#   cache-rocm-base-wheels.sh upload   - Upload wheels to cache
+#   cache-rocm-base-wheels.sh download - Download wheels from cache
+#   cache-rocm-base-wheels.sh key      - Output the cache key
+#
+# Environment variables:
+#   S3_BUCKET          - S3 bucket name (default: vllm-wheels)
+#   PYTHON_VERSION     - Python version (affects cache key)
+#   PYTORCH_ROCM_ARCH  - GPU architectures (affects cache key)
+#
+# Note: ROCm version is determined by BASE_IMAGE in Dockerfile.rocm_base,
+#       so changes to ROCm version are captured by the Dockerfile hash.
+
+set -euo pipefail
+
+BUCKET="${S3_BUCKET:-vllm-wheels}"
+DOCKERFILE="docker/Dockerfile.rocm_base"
+CACHE_PREFIX="rocm/cache"
+
+# Generate hash from Dockerfile content + build args
+generate_cache_key() {
+    # Include Dockerfile content
+    if [[ ! -f "$DOCKERFILE" ]]; then
+        echo "ERROR: Dockerfile not found: $DOCKERFILE" >&2
+        exit 1
+    fi
+    local dockerfile_hash=$(sha256sum "$DOCKERFILE" | cut -c1-16)
+
+    # Include key build args that affect the output
+    # These should match the ARGs in Dockerfile.rocm_base that change the build output
+    # Note: ROCm version is determined by BASE_IMAGE in the Dockerfile, so it's captured by dockerfile_hash
+    local args_string="${PYTHON_VERSION:-}|${PYTORCH_ROCM_ARCH:-}"
+    local args_hash=$(echo "$args_string" | sha256sum | cut -c1-8)
+
+    echo "${dockerfile_hash}-${args_hash}"
+}
+
+CACHE_KEY=$(generate_cache_key)
+CACHE_PATH="s3://${BUCKET}/${CACHE_PREFIX}/${CACHE_KEY}/"
+
+case "${1:-}" in
+    check)
+        echo "Checking cache for key: ${CACHE_KEY}" >&2
+        echo "Cache path: ${CACHE_PATH}" >&2
+        echo "Variables used in cache key:" >&2
+        echo "  PYTHON_VERSION: ${PYTHON_VERSION:-<not set>}" >&2
+        echo "  PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH:-<not set>}" >&2
+
+        # Check if cache exists by listing objects
+        # We look for at least one .whl file
+        echo "Running: aws s3 ls ${CACHE_PATH}" >&2
+        S3_OUTPUT=$(aws s3 ls "${CACHE_PATH}" 2>&1) || true
+        echo "S3 ls output:" >&2
+        echo "$S3_OUTPUT" | head -5 >&2
+
+        if echo "$S3_OUTPUT" | grep -q "\.whl"; then
+            echo "hit"
+        else
+            echo "miss"
+        fi
+        ;;
+
+    upload)
+        echo "========================================"
+        echo "Uploading wheels to cache"
+        echo "========================================"
+        echo "Cache key: ${CACHE_KEY}"
+        echo "Cache path: ${CACHE_PATH}"
+        echo ""
+
+        if [[ ! -d "artifacts/rocm-base-wheels" ]]; then
+            echo "ERROR: artifacts/rocm-base-wheels directory not found" >&2
+            exit 1
+        fi
+
+        WHEEL_COUNT=$(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
+        fi
+
+        echo "Uploading $WHEEL_COUNT wheels..."
+        aws s3 cp --recursive artifacts/rocm-base-wheels/ "${CACHE_PATH}"
+
+        echo ""
+        echo "Cache upload complete!"
+        echo "========================================"
+        ;;
+
+    download)
+        echo "========================================"
+        echo "Downloading wheels from cache"
+        echo "========================================"
+        echo "Cache key: ${CACHE_KEY}"
+        echo "Cache path: ${CACHE_PATH}"
+        echo ""
+
+        mkdir -p artifacts/rocm-base-wheels
+        aws s3 cp --recursive "${CACHE_PATH}" artifacts/rocm-base-wheels/
+
+        echo ""
+        echo "Downloaded wheels:"
+        ls -lh artifacts/rocm-base-wheels/
+
+        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
+        echo ""
+        echo "Total: $WHEEL_COUNT wheels"
+        echo "========================================"
+        ;;
+
+    key)
+        echo "${CACHE_KEY}"
+        ;;
+
+    path)
+        echo "${CACHE_PATH}"
+        ;;
+
+    *)
+        echo "Usage: $0 {check|upload|download|key|path}" >&2
+        echo "" >&2
+        echo "Commands:" >&2
+        echo "  check    - Check if cache exists, outputs 'hit' or 'miss'" >&2
+        echo "  upload   - Upload wheels from artifacts/rocm-base-wheels/ to cache" >&2
+        echo "  download - Download wheels from cache to artifacts/rocm-base-wheels/" >&2
+        echo "  key      - Output the cache key" >&2
+        echo "  path     - Output the full S3 cache path" >&2
+        exit 1
+        ;;
+esac

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

@@ -0,0 +1,242 @@
+#!/bin/bash
+#
+# cherry-pick-from-milestone.sh
+# Find commits from a GitHub milestone that are missing from the current branch
+# and output them in chronological order for cherry-picking.
+#
+# Usage: ./cherry-pick-from-milestone.sh <milestone> [--dry-run] [--execute]
+#
+
+set -euo pipefail
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+usage() {
+    cat <<EOF
+Usage: $(basename "$0") <milestone> [options]
+
+Find commits from a GitHub milestone that need to be cherry-picked into the current branch.
+
+Arguments:
+    milestone       The GitHub milestone name (e.g., v0.14.0)
+
+Options:
+    --dry-run       Show the cherry-pick commands without executing (default)
+    --execute       Actually execute the cherry-picks
+    --main-branch   Specify the main branch name (default: main)
+    --help          Show this help message
+
+Examples:
+    $(basename "$0") v0.14.0
+    $(basename "$0") v0.14.0 --dry-run
+    $(basename "$0") v0.14.0 --execute
+    $(basename "$0") v0.14.0 --main-branch master
+EOF
+    exit 1
+}
+
+log_info() {
+    echo -e "${BLUE}[INFO]${NC} $1"
+}
+
+log_success() {
+    echo -e "${GREEN}[OK]${NC} $1"
+}
+
+log_warn() {
+    echo -e "${YELLOW}[WARN]${NC} $1"
+}
+
+log_error() {
+    echo -e "${RED}[ERROR]${NC} $1" >&2
+}
+
+# Default values
+MILESTONE=""
+DRY_RUN=true
+MAIN_BRANCH="main"
+
+# Parse arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --dry-run)
+            DRY_RUN=true
+            shift
+            ;;
+        --execute)
+            DRY_RUN=false
+            shift
+            ;;
+        --main-branch)
+            MAIN_BRANCH="$2"
+            shift 2
+            ;;
+        --help|-h)
+            usage
+            ;;
+        -*)
+            log_error "Unknown option: $1"
+            usage
+            ;;
+        *)
+            if [[ -z "$MILESTONE" ]]; then
+                MILESTONE="$1"
+            else
+                log_error "Unexpected argument: $1"
+                usage
+            fi
+            shift
+            ;;
+    esac
+done
+
+# Validate milestone argument
+if [[ -z "$MILESTONE" ]]; then
+    log_error "Milestone is required"
+    usage
+fi
+
+# Check if we're in a git repository
+if ! git rev-parse --is-inside-work-tree &>/dev/null; then
+    log_error "Not in a git repository"
+    exit 1
+fi
+
+# Check if gh CLI is available
+if ! command -v gh &>/dev/null; then
+    log_error "GitHub CLI (gh) is not installed"
+    exit 1
+fi
+
+# Check if authenticated with gh
+if ! gh auth status &>/dev/null; then
+    log_error "Not authenticated with GitHub CLI. Run 'gh auth login' first."
+    exit 1
+fi
+
+CURRENT_BRANCH=$(git branch --show-current)
+log_info "Current branch: ${CURRENT_BRANCH}"
+log_info "Main branch: ${MAIN_BRANCH}"
+log_info "Milestone: ${MILESTONE}"
+echo ""
+
+# Fetch latest from remote
+log_info "Fetching latest from remote..."
+git fetch origin "$MAIN_BRANCH" --quiet
+
+# Get merged PRs from the milestone, sorted by merge date
+log_info "Fetching merged PRs from milestone '${MILESTONE}'..."
+
+# Store PR data in a temp file
+PR_DATA=$(mktemp)
+trap "rm -f $PR_DATA" EXIT
+
+if ! gh pr list --state merged --search "milestone:${MILESTONE}" \
+    --limit 1000 \
+    --json number,title,mergeCommit,mergedAt \
+    --jq 'sort_by(.mergedAt) | .[] | "\(.mergeCommit.oid)\t\(.number)\t\(.title)"' > "$PR_DATA" 2>/dev/null; then
+    log_error "Failed to fetch PRs from milestone '${MILESTONE}'"
+    log_error "This could be due to:"
+    log_error "  - Milestone does not exist"
+    log_error "  - Network/authentication issues"
+    log_error "  - Invalid milestone name format"
+    exit 1
+fi
+
+if [[ ! -s "$PR_DATA" ]]; then
+    log_warn "No merged PRs found for milestone '${MILESTONE}'"
+    exit 0
+fi
+
+TOTAL_PRS=$(wc -l < "$PR_DATA")
+log_info "Found ${TOTAL_PRS} merged PR(s) in milestone"
+echo ""
+
+# Find commits that are missing from current branch
+MISSING_COMMITS=()
+MISSING_INFO=()
+
+while IFS=$'\t' read -r sha pr_number title; do
+    # Skip if SHA is empty or null
+    if [[ -z "$sha" || "$sha" == "null" ]]; then
+        log_warn "PR #${pr_number} has no merge commit SHA, skipping"
+        continue
+    fi
+    
+    # Check if this commit is already in the current branch
+    if git merge-base --is-ancestor "$sha" HEAD 2>/dev/null; then
+        log_success "PR #${pr_number} already in branch: ${title:0:60}"
+    else
+        log_warn "PR #${pr_number} MISSING: ${title:0:60}"
+        MISSING_COMMITS+=("$sha")
+        MISSING_INFO+=("$sha PR #${pr_number}: ${title}")
+    fi
+done < "$PR_DATA"
+
+echo ""
+
+if [[ ${#MISSING_COMMITS[@]} -eq 0 ]]; then
+    log_success "All PRs from milestone '${MILESTONE}' are already in the current branch!"
+    exit 0
+fi
+
+log_info "Found ${#MISSING_COMMITS[@]} missing commit(s) to cherry-pick"
+echo ""
+
+# Output the cherry-pick commands
+echo "=========================================="
+echo "Cherry-pick commands (in chronological order):"
+echo "=========================================="
+echo ""
+
+for info in "${MISSING_INFO[@]}"; do
+    echo "# $info"
+done
+echo ""
+
+echo "# Run these commands to cherry-pick all missing commits:"
+echo "git cherry-pick ${MISSING_COMMITS[*]}"
+echo ""
+
+# Or one by one
+echo "# Or cherry-pick one at a time:"
+for sha in "${MISSING_COMMITS[@]}"; do
+    echo "git cherry-pick $sha"
+done
+echo ""
+
+# Execute if requested
+if [[ "$DRY_RUN" == false ]]; then
+    echo "=========================================="
+    log_info "Executing cherry-picks..."
+    echo "=========================================="
+    
+    for i in "${!MISSING_COMMITS[@]}"; do
+        sha="${MISSING_COMMITS[$i]}"
+        info="${MISSING_INFO[$i]}"
+        
+        echo ""
+        log_info "Cherry-picking: $info"
+        
+        if git cherry-pick "$sha"; then
+            log_success "Successfully cherry-picked $sha"
+        else
+            log_error "Failed to cherry-pick $sha"
+            log_error "Resolve conflicts and run 'git cherry-pick --continue', or 'git cherry-pick --abort' to cancel"
+            exit 1
+        fi
+    done
+    
+    echo ""
+    log_success "All cherry-picks completed successfully!"
+else
+    echo "=========================================="
+    echo -e "${YELLOW}Dry run mode - no changes made${NC}"
+    echo "Run with --execute to perform the cherry-picks"
+    echo "=========================================="
+fi

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

@@ -3,7 +3,14 @@
 set -ex
 
 # Clean up old nightly builds from DockerHub, keeping only the last 14 builds
-# This script uses DockerHub API to list and delete old tags with "nightly-" prefix
+# This script uses DockerHub API to list and delete old tags with specified prefix
+# Usage: cleanup-nightly-builds.sh [TAG_PREFIX]
+# Example: cleanup-nightly-builds.sh "nightly-" or cleanup-nightly-builds.sh "cu130-nightly-"
+
+# Get tag prefix from argument, default to "nightly-" if not provided
+TAG_PREFIX="${1:-nightly-}"
+
+echo "Cleaning up tags with prefix: $TAG_PREFIX"
 
 # DockerHub API endpoint for vllm/vllm-openai repository
 REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
@@ -45,7 +52,7 @@ get_all_tags() {
         set -x
         
         # Get both last_updated timestamp and tag name, separated by |
-        local tags=$(echo "$response" | jq -r '.results[] | select(.name | startswith("nightly-")) | "\(.last_updated)|\(.name)"')
+        local tags=$(echo "$response" | jq -r --arg prefix "$TAG_PREFIX" '.results[] | select(.name | startswith($prefix)) | "\(.last_updated)|\(.name)"')
         
         if [ -z "$tags" ]; then
             break

.buildkite/scripts/generate-nightly-index.py

@@ -16,6 +16,18 @@ from urllib.parse import quote
 
 import regex as re
 
+
+def normalize_package_name(name: str) -> str:
+    """
+    Normalize package name according to PEP 503.
+    https://peps.python.org/pep-0503/#normalized-names
+
+    Replace runs of underscores, hyphens, and periods with a single hyphen,
+    and lowercase the result.
+    """
+    return re.sub(r"[-_.]+", "-", name).lower()
+
+
 if not sys.version_info >= (3, 12):
     raise RuntimeError("This script requires Python 3.12 or higher.")
 
@@ -78,7 +90,13 @@ def parse_from_filename(file: str) -> WheelFileInfo:
             version = version.removesuffix("." + variant)
     else:
         if "+" in version:
-            version, variant = version.split("+")
+            version_part, suffix = version.split("+", 1)
+            # Only treat known patterns as variants (rocmXXX, cuXXX, cpu)
+            # Git hashes and other suffixes are NOT variants
+            if suffix.startswith(("rocm", "cu", "cpu")):
+                variant = suffix
+                version = version_part
+            # Otherwise keep the full version string (variant stays None)
 
     return WheelFileInfo(
         package_name=package_name,
@@ -206,6 +224,26 @@ def generate_index_and_metadata(
         print("No wheel files found, skipping index generation.")
         return
 
+    # For ROCm builds: inherit variant from vllm wheel
+    # All ROCm wheels should share the same variant as vllm
+    rocm_variant = None
+    for file in parsed_files:
+        if (
+            file.package_name == "vllm"
+            and file.variant
+            and file.variant.startswith("rocm")
+        ):
+            rocm_variant = file.variant
+            print(f"Detected ROCm variant from vllm: {rocm_variant}")
+            break
+
+    # Apply ROCm variant to all wheels without a variant
+    if rocm_variant:
+        for file in parsed_files:
+            if file.variant is None:
+                file.variant = rocm_variant
+                print(f"Inherited variant '{rocm_variant}' for {file.filename}")
+
     # Group by variant
     variant_to_files: dict[str, list[WheelFileInfo]] = {}
     for file in parsed_files:
@@ -256,8 +294,8 @@ def generate_index_and_metadata(
 
         variant_dir.mkdir(parents=True, exist_ok=True)
 
-        # gather all package names in this variant
-        packages = set(f.package_name for f in files)
+        # gather all package names in this variant (normalized per PEP 503)
+        packages = set(normalize_package_name(f.package_name) for f in files)
         if variant == "default":
             # these packages should also appear in the "project list"
             # generate after all variants are processed
@@ -269,8 +307,10 @@ def generate_index_and_metadata(
                 f.write(project_list_str)
 
         for package in packages:
-            # filter files belonging to this package only
-            package_files = [f for f in files if f.package_name == package]
+            # filter files belonging to this package only (compare normalized names)
+            package_files = [
+                f for f in files if normalize_package_name(f.package_name) == package
+            ]
             package_dir = variant_dir / package
             package_dir.mkdir(parents=True, exist_ok=True)
             index_str, metadata_str = generate_package_index_and_metadata(
@@ -291,6 +331,7 @@ if __name__ == "__main__":
     """
     Arguments:
         --version <version> : version string for the current build (e.g., commit hash)
+        --wheel-dir <wheel_directory> : directory containing wheel files (default to be same as `version`)
         --current-objects <path_to_json> : path to JSON file containing current S3 objects listing in this version directory
         --output-dir <output_directory> : directory to store generated index files
         --alias-to-default <alias_variant_name> : (optional) alias variant name for the default variant
@@ -318,6 +359,12 @@ if __name__ == "__main__":
         required=True,
         help="Directory to store generated index files",
     )
+    parser.add_argument(
+        "--wheel-dir",
+        type=str,
+        default=None,
+        help="Directory containing wheel files (default to be same as `version`)",
+    )
     parser.add_argument(
         "--alias-to-default",
         type=str,
@@ -334,8 +381,13 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     version = args.version
-    if "/" in version or "\\" in version:
-        raise ValueError("Version string must not contain slashes.")
+    # Allow rocm/ prefix, reject other slashes and all backslashes
+    if "\\" in version:
+        raise ValueError("Version string must not contain backslashes.")
+    if "/" in version and not version.startswith("rocm/"):
+        raise ValueError(
+            "Version string must not contain slashes (except for 'rocm/' prefix)."
+        )
     current_objects_path = Path(args.current_objects)
     output_dir = Path(args.output_dir)
     if not output_dir.exists():
@@ -372,7 +424,7 @@ if __name__ == "__main__":
 
     print(f"Found {len(wheel_files)} wheel files for version {version}: {wheel_files}")
 
-    # keep only "official" files for a non-nightly version (specifed by cli args)
+    # keep only "official" files for a non-nightly version (specified by cli args)
     PY_VERSION_RE = re.compile(r"^\d+\.\d+\.\d+([a-zA-Z0-9.+-]*)?$")
     if PY_VERSION_RE.match(version):
         # upload-wheels.sh ensures no "dev" is in args.version
@@ -384,9 +436,25 @@ if __name__ == "__main__":
         print("Nightly version detected, keeping all wheel files.")
 
     # Generate index and metadata, assuming wheels and indices are stored as:
-    # s3://vllm-wheels/{version}/<wheel files>
+    # s3://vllm-wheels/{wheel_dir}/<wheel files>
     # s3://vllm-wheels/<anything>/<index files>
-    wheel_base_dir = Path(output_dir).parent / version
+    #
+    # For ROCm builds, version is "rocm/{commit}" and indices are uploaded to:
+    #   - rocm/{commit}/  (same as wheels)
+    #   - rocm/nightly/
+    #   - rocm/{version}/
+    # All these are under the "rocm/" prefix, so relative paths should be
+    # relative to "rocm/", not the bucket root.
+    if args.wheel_dir:
+        # Explicit wheel-dir provided (e.g., for version-specific indices pointing to commit dir)
+        wheel_dir = args.wheel_dir.strip().rstrip("/")
+    elif version.startswith("rocm/"):
+        # For rocm/commit, wheel_base_dir should be just the commit part
+        # so relative path from rocm/0.12.0/rocm710/vllm/ -> ../../../{commit}/
+        wheel_dir = version.split("/", 1)[1]
+    else:
+        wheel_dir = version
+    wheel_base_dir = Path(output_dir).parent / wheel_dir
     index_base_dir = Path(output_dir)
 
     generate_index_and_metadata(

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

@@ -209,12 +209,21 @@ if [[ $commands == *"--shard-id="* ]]; then
     wait "${pid}"
     STATUS+=($?)
   done
+  at_least_one_shard_with_tests=0
   for st in "${STATUS[@]}"; do
-    if [[ ${st} -ne 0 ]]; then
+    if [[ ${st} -ne 0 ]] && [[ ${st} -ne 5 ]]; then
       echo "One of the processes failed with $st"
       exit "${st}"
+    elif [[ ${st} -eq 5 ]]; then
+      echo "Shard exited with status 5 (no tests collected) - treating as success"
+    else # This means st is 0
+      at_least_one_shard_with_tests=1
     fi
   done
+  if [[ ${#STATUS[@]} -gt 0 && ${at_least_one_shard_with_tests} -eq 0 ]]; then
+    echo "All shards reported no tests collected. Failing the build."
+    exit 1
+  fi
 else
   echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
   docker run \

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

@@ -84,7 +84,7 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -x -s -v \
-    tests/lora/test_qwen2vl.py"
+    tests/lora/test_qwenvl.py"
 
   # online serving: tp+pp
   docker exec cpu-test-"$NUMA_NODE" bash -c '

.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 @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
+    && 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 @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
+    && 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/push-nightly-builds.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+set -ex
+
+# Get tag variant from argument, default to empty if not provided, should be something like "cu130".
+# Due to limits in cleanup script, we must move variants to use separate tags like "cu130-nightly",
+# otherwise they will be cleaned up together with the main "nightly" tags.
+
+TAG_VARIANT="$1"
+if [ -n "$TAG_VARIANT" ]; then
+    ORIG_TAG_SUFFIX="-$TAG_VARIANT"
+    TAG_NAME="$TAG_VARIANT-nightly"
+else
+    ORIG_TAG_SUFFIX=""
+    TAG_NAME="nightly"
+fi
+
+ORIG_TAG_NAME="$BUILDKITE_COMMIT"
+
+echo "Pushing original tag $ORIG_TAG_NAME$ORIG_TAG_SUFFIX to new nightly tag name: $TAG_NAME"
+
+# pull original arch-dependent images from AWS ECR Public
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX
+# tag arch-dependent images
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$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:$TAG_NAME-aarch64
+# push arch-dependent images to DockerHub
+docker push vllm/vllm-openai:$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

.buildkite/scripts/run-multi-node-test.sh

@@ -2,6 +2,17 @@
 
 set -euox pipefail
 
+# To detect ROCm
+# Check multiple indicators:
+if [ -e /dev/kfd ] || \
+    [ -d /opt/rocm ] || \
+    command -v rocm-smi &> /dev/null || \
+    [ -n "${ROCM_HOME:-}" ]; then
+    IS_ROCM=1
+else
+    IS_ROCM=0
+fi
+
 if [[ $# -lt 4 ]]; then
     echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
     exit 1
@@ -26,13 +37,18 @@ for command in "${COMMANDS[@]}"; do
     echo "$command"
 done
 
+
 start_network() {
     docker network create --subnet=192.168.10.0/24 docker-net
 }
 
 start_nodes() {
     for node in $(seq 0 $(($NUM_NODES-1))); do
-        GPU_DEVICES='"device='
+        if [ "$IS_ROCM" -eq 1 ]; then
+            GPU_DEVICES='--device /dev/kfd --device /dev/dri -e HIP_VISIBLE_DEVICES='
+        else
+            GPU_DEVICES='--gpus "device='
+        fi
         for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
             DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
             GPU_DEVICES+=$(($DEVICE_NUM))
@@ -40,7 +56,9 @@ start_nodes() {
                 GPU_DEVICES+=','
             fi
         done
-        GPU_DEVICES+='"'
+        if [ "$IS_ROCM" -eq 0 ]; then
+            GPU_DEVICES+='"'
+        fi
 
         # start the container in detached mode
         # things to note:
@@ -49,7 +67,7 @@ start_nodes() {
         # 3. map the huggingface cache directory to the container
         # 3. assign ip addresses to the containers (head node: 192.168.10.10, worker nodes:
         #    starting from 192.168.10.11)
-        docker run -d --gpus "$GPU_DEVICES" --shm-size=10.24gb -e HF_TOKEN \
+        docker run -d $GPU_DEVICES --shm-size=10.24gb -e HF_TOKEN \
             -v ~/.cache/huggingface:/root/.cache/huggingface --name "node$node" \
             --network docker-net --ip 192.168.10.$((10 + $node)) --rm "$DOCKER_IMAGE" \
             /bin/bash -c "tail -f /dev/null"

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -18,15 +18,18 @@ wait_for_server() {
 
 MODEL="Qwen/Qwen3-Next-80B-A3B-Instruct"
 
-# Set BACKENDS based on platform
+# Set BACKENDS and platform-specific args based on platform
 if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
   # ROCm platform
   BACKENDS=("allgather_reducescatter")
   # Disable MOE padding for ROCm since it is causing eplb to fail
   export VLLM_ROCM_MOE_PADDING=0
+  PLATFORM_ARGS=("--no-async-scheduling")
+  echo "Disabled async scheduling for ROCm platform due to issues with spec decode."
 else
   # Non-ROCm platform (CUDA/other)
   BACKENDS=("deepep_high_throughput" "deepep_low_latency")
+  PLATFORM_ARGS=()
 fi
 
 cleanup() {
@@ -54,6 +57,7 @@ for BACK in "${BACKENDS[@]}"; do
     --trust-remote-code \
     --max-model-len 2048 \
     --gpu-memory-utilization 0.9 \
+    "${PLATFORM_ARGS[@]}" \
     --port $PORT &
   SERVER_PID=$!
   wait_for_server $PORT

.buildkite/scripts/trigger-ci-build.sh

@@ -0,0 +1,227 @@
+#!/bin/bash
+#
+# trigger-ci-build.sh
+# Trigger a Buildkite CI build using the bk CLI for the current commit and branch
+# with RUN_ALL=1 and NIGHTLY=1 environment variables.
+#
+# Usage: ./trigger-ci-build.sh [options]
+#
+# Requires: bk CLI (https://buildkite.com/docs/platform/cli)
+#
+# SAFETY: Dry-run by default. Use --execute to actually trigger a build.
+#
+
+set -euo pipefail
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Default configuration
+PIPELINE="ci"
+DRY_RUN=true
+
+usage() {
+    cat <<EOF
+Usage: $(basename "$0") [options]
+
+Trigger a Buildkite CI build using the bk CLI for the current commit and branch.
+Sets RUN_ALL=1 and NIGHTLY=1 environment variables.
+
+SAFETY: Dry-run by default. Use --execute to actually trigger a build.
+
+Options:
+    --execute       Actually trigger the build (default: dry-run)
+    --pipeline      Buildkite pipeline slug (default: ${PIPELINE})
+    --commit        Override commit SHA (default: current HEAD)
+    --branch        Override branch name (default: current branch)
+    --message       Custom build message (default: auto-generated)
+    --help          Show this help message
+
+Prerequisites:
+    - bk CLI installed: brew tap buildkite/buildkite && brew install buildkite/buildkite/bk
+    - bk configured: bk configure
+
+Examples:
+    $(basename "$0")                        # Dry-run, show what would happen
+    $(basename "$0") --execute              # Actually trigger the build
+    $(basename "$0") --pipeline ci-shadow   # Dry-run with different pipeline
+EOF
+    exit 1
+}
+
+log_info() {
+    echo -e "${BLUE}[INFO]${NC} $1"
+}
+
+log_success() {
+    echo -e "${GREEN}[OK]${NC} $1"
+}
+
+log_warn() {
+    echo -e "${YELLOW}[WARN]${NC} $1"
+}
+
+log_error() {
+    echo -e "${RED}[ERROR]${NC} $1" >&2
+}
+
+# Parse arguments
+COMMIT=""
+BRANCH=""
+MESSAGE=""
+
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --execute)
+            DRY_RUN=false
+            shift
+            ;;
+        --pipeline)
+            PIPELINE="$2"
+            shift 2
+            ;;
+        --commit)
+            COMMIT="$2"
+            shift 2
+            ;;
+        --branch)
+            BRANCH="$2"
+            shift 2
+            ;;
+        --message)
+            MESSAGE="$2"
+            shift 2
+            ;;
+        --help|-h)
+            usage
+            ;;
+        -*)
+            log_error "Unknown option: $1"
+            usage
+            ;;
+        *)
+            log_error "Unexpected argument: $1"
+            usage
+            ;;
+    esac
+done
+
+# Check if bk CLI is installed
+if ! command -v bk &>/dev/null; then
+    log_error "Buildkite CLI (bk) is not installed"
+    echo ""
+    echo "Install with:"
+    echo "  brew tap buildkite/buildkite && brew install buildkite/buildkite/bk"
+    echo ""
+    echo "Then configure:"
+    echo "  bk configure"
+    exit 1
+fi
+
+# Check if we're in a git repository
+if ! git rev-parse --is-inside-work-tree &>/dev/null; then
+    log_error "Not in a git repository"
+    exit 1
+fi
+
+# Get current commit and branch if not overridden
+if [[ -z "$COMMIT" ]]; then
+    COMMIT=$(git rev-parse HEAD)
+fi
+
+if [[ -z "$BRANCH" ]]; then
+    BRANCH=$(git branch --show-current)
+    if [[ -z "$BRANCH" ]]; then
+        # Detached HEAD state - try to get branch from ref
+        BRANCH=$(git rev-parse --abbrev-ref HEAD)
+    fi
+fi
+
+# Generate default message if not provided
+if [[ -z "$MESSAGE" ]]; then
+    COMMIT_MSG=$(git log -1 --pretty=format:"%s" "$COMMIT" 2>/dev/null || echo "Manual build")
+    MESSAGE="[Manual] ${COMMIT_MSG}"
+fi
+
+# Safety check: Verify the commit exists on the remote
+log_info "Verifying commit exists on remote..."
+git fetch origin --quiet 2>/dev/null || true
+
+# Check if commit is reachable from any remote branch
+REMOTE_BRANCHES=$(git branch -r --contains "$COMMIT" 2>/dev/null || true)
+if [[ -z "$REMOTE_BRANCHES" ]]; then
+    log_error "Commit ${COMMIT} does not exist on any remote branch!"
+    echo ""
+    echo "The CI system will fail to checkout this commit."
+    echo "Please push your changes first:"
+    echo ""
+    echo "  git push origin ${BRANCH}"
+    echo ""
+    exit 1
+fi
+
+log_success "Commit found on remote branches:"
+echo "$REMOTE_BRANCHES" | head -5 | sed 's/^/  /'
+if [[ $(echo "$REMOTE_BRANCHES" | wc -l) -gt 5 ]]; then
+    echo "  ... and more"
+fi
+echo ""
+
+log_info "Pipeline: ${PIPELINE}"
+log_info "Branch: ${BRANCH}"
+log_info "Commit: ${COMMIT}"
+log_info "Message: ${MESSAGE}"
+log_info "Environment: RUN_ALL=1, NIGHTLY=1"
+echo ""
+
+# Build the command
+CMD=(bk build create
+    -y
+    -w
+    -i
+    --pipeline "${PIPELINE}"
+    --commit "${COMMIT}"
+    --branch "${BRANCH}"
+    --message "${MESSAGE}"
+    --env "RUN_ALL=1"
+    --env "NIGHTLY=1"
+)
+
+if [[ "$DRY_RUN" == true ]]; then
+    echo "=========================================="
+    log_warn "DRY-RUN MODE - No build will be triggered"
+    echo "=========================================="
+    echo ""
+    echo "Command that would be executed:"
+    echo ""
+    # Escape single quotes in values for safe shell display
+    escape_for_shell() {
+        printf '%s' "$1" | sed "s/'/'\\\\''/g"
+    }
+    echo "  bk build create \\"
+    echo "    -y \\"
+    echo "    -w \\"
+    echo "    -i \\"
+    echo "    --pipeline '$(escape_for_shell "${PIPELINE}")' \\"
+    echo "    --commit '$(escape_for_shell "${COMMIT}")' \\"
+    echo "    --branch '$(escape_for_shell "${BRANCH}")' \\"
+    echo "    --message '$(escape_for_shell "${MESSAGE}")' \\"
+    echo "    --env 'RUN_ALL=1' \\"
+    echo "    --env 'NIGHTLY=1'"
+    echo ""
+    echo "=========================================="
+    echo -e "${YELLOW}To actually trigger this build, run:${NC}"
+    echo ""
+    echo "  $0 --execute"
+    echo "=========================================="
+    exit 0
+fi
+
+log_info "Triggering build..."
+
+# Execute the command - bk will print the URL and open browser
+"${CMD[@]}"

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

@@ -102,6 +102,7 @@ if [[ "$version" != *"dev"* ]]; then
     echo "Re-generating indices for /$pure_version/"
     rm -rf "$INDICES_OUTPUT_DIR/*"
     mkdir -p "$INDICES_OUTPUT_DIR"
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" $alias_arg
+    # 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" $alias_arg
     aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
 fi

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

@@ -0,0 +1,103 @@
+#!/usr/bin/env bash
+
+set -e
+
+BUCKET="vllm-wheels"
+SUBPATH=$BUILDKITE_COMMIT
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
+echo "Release version from Buildkite: $RELEASE_VERSION"
+GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
+if [ -z "$GIT_VERSION" ]; then
+    echo "[FATAL] Not on a git tag, cannot create release."
+    exit 1
+else
+    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
+fi
+# sanity check for version mismatch
+if [ "v$RELEASE_VERSION" != "$GIT_VERSION" ]; then
+  if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
+    echo "[WARNING] Force release and ignore version mismatch"
+  else
+    echo "[FATAL] Release version from Buildkite does not match Git version."
+    exit 1
+  fi
+fi
+
+# check pypi token
+if [ -z "$PYPI_TOKEN" ]; then
+  echo "[FATAL] PYPI_TOKEN is not set."
+  exit 1
+else
+  export TWINE_USERNAME="__token__"
+  export TWINE_PASSWORD="$PYPI_TOKEN"
+fi
+
+# check github token
+if [ -z "$GITHUB_TOKEN" ]; then
+  echo "[FATAL] GITHUB_TOKEN is not set."
+  exit 1
+else
+  export GH_TOKEN="$GITHUB_TOKEN"
+fi
+
+set -x # avoid printing secrets above
+
+# download gh CLI from github
+# Get latest gh CLI version from GitHub API
+GH_VERSION=$(curl -s https://api.github.com/repos/cli/cli/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/' | sed 's/^v//')
+if [ -z "$GH_VERSION" ]; then
+  echo "[FATAL] Failed to get latest gh CLI version from GitHub"
+  exit 1
+fi
+echo "Downloading gh CLI version: $GH_VERSION"
+GH_TARBALL="gh_${GH_VERSION}_linux_amd64.tar.gz"
+GH_URL="https://github.com/cli/cli/releases/download/v${GH_VERSION}/${GH_TARBALL}"
+GH_INSTALL_DIR="/tmp/gh-install"
+mkdir -p "$GH_INSTALL_DIR"
+pushd "$GH_INSTALL_DIR"
+curl -L -o "$GH_TARBALL" "$GH_URL"
+tar -xzf "$GH_TARBALL"
+GH_BIN=$(realpath $(find . -name "gh" -type f -executable | head -n 1))
+if [ -z "$GH_BIN" ]; then
+  echo "[FATAL] Failed to find gh CLI executable"
+  exit 1
+fi
+echo "gh CLI downloaded successfully, version: $($GH_BIN --version)"
+echo "Last 5 releases on GitHub:" # as a sanity check of gh and GH_TOKEN
+command "$GH_BIN" release list --limit 5
+popd
+
+# install twine from pypi
+python3 -m venv /tmp/vllm-release-env
+source /tmp/vllm-release-env/bin/activate
+pip install twine
+python3 -m twine --version
+
+# copy release wheels to local directory
+DIST_DIR=/tmp/vllm-release-dist
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+echo "Copying wheels to local directory"
+mkdir -p $DIST_DIR
+# include only wheels for the release version, ignore all files with "dev" or "rc" in the name
+aws s3 cp --recursive --exclude "*" --include "vllm-${RELEASE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc*" "$S3_COMMIT_PREFIX" $DIST_DIR
+echo "Wheels copied to local directory"
+# generate source tarball
+git archive --format=tar.gz --output="$DIST_DIR/vllm-${RELEASE_VERSION}.tar.gz" $BUILDKITE_COMMIT
+ls -la $DIST_DIR
+
+
+# upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
+PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${RELEASE_VERSION}*.whl" -not -name "*+*")
+if [ -z "$PYPI_WHEEL_FILES" ]; then
+  echo "No default variant wheels found, quitting..."
+  exit 1
+fi
+python3 -m twine check $PYPI_WHEEL_FILES
+python3 -m twine --non-interactive --verbose upload $PYPI_WHEEL_FILES
+echo "Wheels uploaded to PyPI"
+
+# create release on GitHub with the release version and all wheels
+command "$GH_BIN" release create $GIT_VERSION -d --latest --notes-from-tag --verify-tag $DIST_DIR/*.whl

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

@@ -0,0 +1,151 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Upload ROCm wheels to S3 with proper index generation
+#
+# Required environment variables:
+#   AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY (or IAM role)
+#   S3_BUCKET (default: vllm-wheels)
+#
+# S3 path structure:
+#   s3://vllm-wheels/rocm/{commit}/     - All wheels for this commit
+#   s3://vllm-wheels/rocm/nightly/      - Index pointing to latest nightly
+#   s3://vllm-wheels/rocm/{version}/    - Index for release versions
+
+set -ex
+
+# ======== Configuration ========
+BUCKET="${S3_BUCKET:-vllm-wheels}"
+ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
+S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
+INDICES_OUTPUT_DIR="rocm-indices"
+PYTHON="${PYTHON_PROG:-python3}"
+
+# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
+MANYLINUX_VERSION="manylinux_2_35"
+
+echo "========================================"
+echo "ROCm Wheel Upload Configuration"
+echo "========================================"
+echo "S3 Bucket: $BUCKET"
+echo "S3 Path: $ROCM_SUBPATH"
+echo "Commit: $BUILDKITE_COMMIT"
+echo "Branch: $BUILDKITE_BRANCH"
+echo "========================================"
+
+# ======== Part 0: Setup Python ========
+
+# Detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
+if [[ "$has_new_python" -eq 0 ]]; then
+    # Use new python from docker
+    # Use --user to ensure files are created with correct ownership (not root)
+    docker pull python:3-slim
+    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
+
+# ======== Part 1: Collect and prepare wheels ========
+
+# Collect all wheels
+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=$(ls all-rocm-wheels/*.whl 2>/dev/null | wc -l)
+echo "Total wheels to upload: $WHEEL_COUNT"
+
+if [ "$WHEEL_COUNT" -eq 0 ]; then
+    echo "ERROR: No wheels found to upload!"
+    exit 1
+fi
+
+# Rename linux to manylinux in wheel filenames
+for wheel in all-rocm-wheels/*.whl; do
+    if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
+        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
+        mv -- "$wheel" "$new_wheel"
+        echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
+    fi
+done
+
+echo ""
+echo "Wheels to upload:"
+ls -lh all-rocm-wheels/
+
+# ======== Part 2: Upload wheels to S3 ========
+
+echo ""
+echo "Uploading wheels to $S3_COMMIT_PREFIX"
+for wheel in all-rocm-wheels/*.whl; do
+    aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"
+done
+
+# ======== Part 3: Generate and upload indices ========
+
+# List existing wheels in commit directory
+echo ""
+echo "Generating indices..."
+obj_json="rocm-objects.json"
+aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$ROCM_SUBPATH/" --delimiter / --output json > "$obj_json"
+
+mkdir -p "$INDICES_OUTPUT_DIR"
+
+# Use the existing generate-nightly-index.py
+# HACK: Replace regex module with stdlib re (same as CUDA script)
+sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
+
+$PYTHON .buildkite/scripts/generate-nightly-index.py \
+    --version "$ROCM_SUBPATH" \
+    --current-objects "$obj_json" \
+    --output-dir "$INDICES_OUTPUT_DIR" \
+    --comment "ROCm commit $BUILDKITE_COMMIT"
+
+# Upload indices to commit directory
+echo "Uploading indices to $S3_COMMIT_PREFIX"
+aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
+
+# Update rocm/nightly/ if on main branch and not a PR
+if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]] || [[ "$NIGHTLY" == "1" ]]; then
+    echo "Updating rocm/nightly/ index..."
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/rocm/nightly/"
+fi
+
+# Extract version from vLLM wheel and update version-specific index
+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: $VERSION"
+    PURE_VERSION="${VERSION%%+*}"
+    PURE_VERSION="${PURE_VERSION%%.rocm}"
+    echo "Pure version: $PURE_VERSION"
+
+    if [[ "$VERSION" != *"dev"* ]]; then
+        echo "Updating rocm/$PURE_VERSION/ index..."
+        aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/rocm/$PURE_VERSION/"
+    fi
+fi
+
+# ======== Part 4: Summary ========
+
+echo ""
+echo "========================================"
+echo "ROCm Wheel Upload Complete!"
+echo "========================================"
+echo ""
+echo "Wheels available at:"
+echo "  s3://$BUCKET/$ROCM_SUBPATH/"
+echo ""
+echo "Install command (by commit):"
+echo "  pip install vllm --extra-index-url https://${BUCKET}.s3.amazonaws.com/$ROCM_SUBPATH/"
+echo ""
+if [[ "$BUILDKITE_BRANCH" == "main" ]] || [[ "$NIGHTLY" == "1" ]]; then
+    echo "Install command (nightly):"
+    echo "  pip install vllm --extra-index-url https://${BUCKET}.s3.amazonaws.com/rocm/nightly/"
+fi
+echo ""
+echo "Wheel count: $WHEEL_COUNT"
+echo "========================================"

.buildkite/test-amd.yaml

@@ -162,7 +162,7 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
   - pytest -v -s entrypoints/test_chat_utils.py
 
 - label: Entrypoints Integration Test (API Server 2)
@@ -199,6 +199,21 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/pooling
 
+- label: Entrypoints Integration Test (Responses API)
+  timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_1
+  # grade: Blocking
+  working_dir: "/vllm-workspace/tests"
+  fast_check: true
+  torch_nightly: true
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai/responses
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai/responses
+
 - label: Distributed Tests (4 GPUs) # 35min
   timeout_in_minutes: 50
   mirror_hardwares: [amdexperimental]
@@ -219,6 +234,9 @@ steps:
   - tests/v1/engine/test_engine_core_client.py
   - tests/distributed/test_symm_mem_allreduce.py
   commands:
+  # Work around HIP bug tracked here: https://github.com/ROCm/hip/issues/3876
+  # TODO: Remove when the bug is fixed in a future ROCm release
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
@@ -267,9 +285,10 @@ steps:
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
   commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  #- export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
+  # Work around HIP bug tracked here: https://github.com/ROCm/hip/issues/3876
+  # TODO: Remove when the bug is fixed in a future ROCm release
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: EPLB Algorithm Test # 5min
@@ -349,7 +368,9 @@ steps:
 - label: V1 Test e2e + engine # 65min
   timeout_in_minutes: 90
   mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_4
+  # The test uses 4 GPUs, but we schedule it on 8-GPU machines for stability.
+  # See discussion here: https://github.com/vllm-project/vllm/pull/31040
+  agent_pool: mi325_8
   # grade: Blocking
   source_file_dependencies:
     - vllm/
@@ -434,7 +455,7 @@ steps:
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
+    - pytest -v -s v1/attention
 
 - label: V1 Test others (CPU) # 5 mins
   mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
@@ -510,8 +531,7 @@ steps:
   - tests/samplers
   - tests/conftest.py
   commands:
-    - pytest -v -s samplers
-    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
+    - pytest -v -s -m 'not skip_v1' samplers
 
 - label: LoRA Test %N # 20min each
   timeout_in_minutes: 30
@@ -683,6 +703,17 @@ steps:
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
 
+- label: Kernels Helion Test
+  timeout_in_minutes: 30
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_1
+  source_file_dependencies:
+  - vllm/utils/import_utils.py
+  - tests/kernels/helion/
+  commands:
+    - pip install helion
+    - pytest -v -s kernels/helion/
+
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
   torch_nightly: true
@@ -725,7 +756,7 @@ steps:
 
 - label: Quantization Test # 70min
   timeout_in_minutes: 90
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -765,8 +796,9 @@ steps:
   - csrc/
   - vllm/entrypoints/openai/
   - vllm/model_executor/models/whisper.py
+  - tools/
   commands: # LMEval+Transcription WER check
-  # Transcription WER check is skipped because encoder-decoder models are not supported on ROCm, see https://github.com/vllm-project/vllm/issues/27442
+  - bash ../tools/install_torchcodec_rocm.sh || exit 1
   - pytest -s entrypoints/openai/correctness/
 
 
@@ -861,6 +893,7 @@ steps:
     # Shard slow subset of standard language models tests. Only run when model
     # source is modified, or when specified test files are modified
     - pip freeze | grep -E 'torch'
+    - export TORCH_NCCL_BLOCKING_WAIT=1
     - pytest -v -s models/language -m 'core_model and slow_test' \
              --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
              --shard-id=$$BUILDKITE_PARALLEL_JOB
@@ -878,7 +911,7 @@ steps:
   commands:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
     # Shard hybrid language model tests
     - pytest -v -s models/language/generation \
@@ -899,7 +932,7 @@ steps:
   commands:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
@@ -964,7 +997,7 @@ steps:
     - pytest -v -s models/multimodal/processing
 
 - label: Multi-Modal Models Test (Standard) # 60min
-  timeout_in_minutes: 80
+  timeout_in_minutes: 100
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -973,13 +1006,16 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pip freeze | grep -E 'torch'
-    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
+    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing --ignore models/multimodal/pooling/test_prithvi_mae.py
+    - pytest -v -s models/multimodal/pooling/test_prithvi_mae.py -m core_model
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Accuracy Eval (Small Models) # 150min - 180min
-  timeout_in_minutes: 180
+- label: Multi-Modal Accuracy Eval (Small Models) # 5min
+  timeout_in_minutes: 10
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
   # grade: Blocking
@@ -989,7 +1025,9 @@ steps:
   - vllm/inputs/
   - vllm/v1/core/
   commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
+  - export MIOPEN_DEBUG_CONV_DIRECT=0
+  - export MIOPEN_DEBUG_CONV_GEMM=0
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt
 
 - label: Multi-Modal Models Test (Extended) 1 # 60min
   timeout_in_minutes: 120
@@ -1001,10 +1039,13 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing
 
-- label: Multi-Modal Models Test (Extended) 2
+- label: Multi-Modal Models Test (Extended) 2 #60min
+  timeout_in_minutes: 120
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -1013,6 +1054,8 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
@@ -1026,6 +1069,8 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
@@ -1085,8 +1130,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -1243,13 +1288,13 @@ steps:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
     - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
     - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
 
 - label: Distributed Tests (2 GPUs) # 68min
   timeout_in_minutes: 90
@@ -1275,6 +1320,9 @@ steps:
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
+  # Work around HIP bug tracked here: https://github.com/ROCm/hip/issues/3876
+  # TODO: Remove when the bug is fixed in a future ROCm release
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -1414,7 +1462,7 @@ steps:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
 
 - label: NixlConnector PD accuracy tests (Distributed) # 30min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_4
   # grade: Blocking
   timeout_in_minutes: 30
@@ -1424,8 +1472,22 @@ steps:
     - 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
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+    - VLLM_ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: DP EP NixlConnector PD accuracy tests (Distributed) # 15min
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_4
+  # grade: Blocking
+  timeout_in_minutes: 15
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 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_rocm.txt
+    - VLLM_ATTENTION_BACKEND=ROCM_ATTN DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 ##### multi gpus test #####
 ##### A100 test #####
@@ -1497,7 +1559,7 @@ steps:
     - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - HIP_VISIBLE_DEVICES=0,1 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
+    - HIP_VISIBLE_DEVICES=0,1 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=allgather_reducescatter --disable-nccl-for-dp-synchronization
     - pytest -v -s tests/v1/distributed/test_dbo.py
 
 ##### B200 test #####
@@ -1576,6 +1638,8 @@ steps:
   - .buildkite/scripts/run-prime-rl-test.sh
   commands:
     - bash .buildkite/scripts/run-prime-rl-test.sh
+
+##### EPLB Accuracy Tests #####
 - label: DeepSeek V2-Lite Accuracy
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_4
@@ -1609,17 +1673,6 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: DeepSeek V2-Lite Async EPLB Accuracy
-  timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_4
-  # grade: Blocking
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
 
 - label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
   timeout_in_minutes: 60

.buildkite/test-pipeline.yaml

@@ -144,7 +144,7 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
   - pytest -v -s entrypoints/test_chat_utils.py
 
 - label: Entrypoints Integration Test (API Server 2)
@@ -177,6 +177,18 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/pooling
 
+- label: Entrypoints Integration Test (Responses API)
+  timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
+  working_dir: "/vllm-workspace/tests"
+  fast_check: true
+  torch_nightly: true
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai/responses
+  commands:
+  - pytest -v -s entrypoints/openai/responses
+
 - label: Distributed Tests (4 GPUs) # 35min
   timeout_in_minutes: 50
   mirror_hardwares: [amdexperimental]
@@ -387,7 +399,7 @@ steps:
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
+    - pytest -v -s v1/attention
 
 - label: V1 Test others (CPU) # 5 mins
   source_file_dependencies:
@@ -612,6 +624,56 @@ steps:
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
 
+- label: Kernels Helion Test
+  timeout_in_minutes: 30
+  gpu: h100
+  source_file_dependencies:
+  - vllm/utils/import_utils.py
+  - tests/kernels/helion/
+  commands:
+    - pip install helion
+    - pytest -v -s kernels/helion/
+
+  
+- label: Kernels FP8 MoE Test (1 H100)
+  timeout_in_minutes: 90
+  gpu: h100
+  num_gpus: 1
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_cutlass_moe.py
+    - pytest -v -s kernels/moe/test_flashinfer.py
+    - pytest -v -s kernels/moe/test_gpt_oss_triton_kernels.py
+    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py
+    - pytest -v -s kernels/moe/test_moe.py
+    # - pytest -v -s kernels/moe/test_block_fp8.py - failing on main
+    - pytest -v -s kernels/moe/test_block_int8.py
+    - pytest -v -s kernels/moe/test_triton_moe_no_act_mul.py
+    - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
+
+- label: Kernels FP8 MoE Test (2 H100s)
+  timeout_in_minutes: 90
+  gpu: h100
+  num_gpus: 2
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_deepep_deepgemm_moe.py
+    - pytest -v -s kernels/moe/test_deepep_moe.py
+    - pytest -v -s kernels/moe/test_pplx_cutlass_moe.py
+    # - pytest -v -s kernels/moe/test_pplx_moe.py - failing on main
+  
+- label: Kernels Fp4 MoE Test (B200)
+  timeout_in_minutes: 60
+  gpu: b200
+  num_gpus: 1
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_cutedsl_moe.py
+    - pytest -v -s kernels/moe/test_flashinfer_moe.py
+    - pytest -v -s kernels/moe/test_nvfp4_moe.py
+    - pytest -v -s kernels/moe/test_ocp_mx_moe.py
+
+
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
   torch_nightly: true
@@ -939,11 +1001,10 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
+- label: Blackwell Test # 23 min
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   gpu: b200
-  # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
   - csrc/attention/mla/
@@ -955,8 +1016,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -980,6 +1041,8 @@ steps:
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
+    # e2e
+    - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Blackwell Fusion and Compile Tests # 30 min
   timeout_in_minutes: 40
@@ -1034,6 +1097,48 @@ steps:
     # Run all e2e fusion tests
     - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
 
+- label: Hopper Fusion E2E Tests (H100) # 10min
+  timeout_in_minutes: 70
+  working_dir: "/vllm-workspace/"
+  gpu: h100
+  optional: true
+  source_file_dependencies:
+  - csrc/quantization/fp4/
+  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/
+  # can affect pattern matching
+  - vllm/model_executor/layers/layernorm.py
+  - vllm/model_executor/layers/activation.py
+  - vllm/model_executor/layers/quantization/input_quant_fp8.py
+  - tests/compile/test_fusion_attn.py
+  commands:
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    # skip Llama-4 since it does not fit on this device
+    - pytest -v -s tests/compile/test_fusion_attn.py -k 'not Llama-4'
+
+- label: Hopper Fusion Distributed E2E Tests (2xH100)  # 70min
+  timeout_in_minutes: 70
+  working_dir: "/vllm-workspace/"
+  gpu: h100
+  optional: true
+  num_gpus: 2
+  source_file_dependencies:
+  - csrc/quantization/fp4/
+  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/
+  # can affect pattern matching
+  - vllm/model_executor/layers/layernorm.py
+  - vllm/model_executor/layers/activation.py
+  - vllm/model_executor/layers/quantization/input_quant_fp8.py
+  - tests/compile/distributed/test_fusions_e2e.py
+  commands:
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    # Run all e2e fusion tests
+    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
+    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
+
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
@@ -1105,17 +1210,18 @@ steps:
   - vllm/model_executor/models/
   - tests/distributed/
   - tests/examples/offline_inference/data_parallel.py
+  - .buildkite/scripts/run-multi-node-test.sh
   commands:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
     - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
     - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
 
 - label: Distributed Tests (2 GPUs) # 68min
   timeout_in_minutes: 90
@@ -1267,8 +1373,8 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
 
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  timeout_in_minutes: 30
+- label: NixlConnector PD accuracy tests (Distributed) # 40min
+  timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -1276,7 +1382,18 @@ steps:
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
+    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: DP EP NixlConnector PD accuracy tests (Distributed) # 15min
+  timeout_in_minutes: 15
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 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
+    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 
 ##### multi gpus test #####
@@ -1321,22 +1438,39 @@ steps:
     - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
-##### H200 test #####
-- label: Distributed Tests (H200) # optional
-  gpu: h200
+- label: Sequence Parallel Tests (H100) # 60 min
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/"
+  gpu: h100
+  optional: true
+  num_gpus: 2
+  commands:
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    # Run sequence parallel tests
+    - pytest -v -s tests/distributed/test_sequence_parallel.py
+    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
+
+- label: Distributed Tests (H100) # optional
+  gpu: h100
   optional: true
   working_dir: "/vllm-workspace/"
   num_gpus: 2
   commands:
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
-    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
+    - 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
 
+##### H200 test #####
+
+- label: LM Eval Large Models (H200) # optional
+  timeout_in_minutes: 60
+  gpu: h200
+  optional: true
+  num_gpus: 8
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
+
 ##### B200 test #####
 - label: Distributed Tests (B200) # optional
   gpu: b200
@@ -1359,6 +1493,7 @@ steps:
   - vllm/
   - .buildkite/scripts/run-prime-rl-test.sh
   commands:
+    - nvidia-smi
     - bash .buildkite/scripts/run-prime-rl-test.sh
 
 - label: DeepSeek V2-Lite Accuracy
@@ -1387,3 +1522,26 @@ 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
+
+##### MoE Refactor (Temporary) Tests #####
+
+- label: MoE Refactor Integration Test (H100 - TEMPORARY) # optional
+  gpu: h100
+  optional: true
+  num_gpus: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
+  
+- label: MoE Refactor Integration Test (B200 - TEMPORARY) # optional
+  gpu: b200
+  optional: true
+  num_gpus: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
+
+- label: MoE Refactor Integration Test (B200 DP - TEMPORARY) # optional
+  gpu: b200
+  optional: true
+  num_gpus: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt

.buildkite/test_areas/attention.yaml

@@ -18,4 +18,4 @@ steps:
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
+    - pytest -v -s v1/attention

.buildkite/test_areas/distributed.yaml

@@ -145,7 +145,7 @@ steps:
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
+    - CUDA_VISIBLE_DEVICES=1,2 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
 
 - label: Distributed Tests (2 GPUs)(B200)
@@ -171,7 +171,7 @@ steps:
   - tests/distributed/
   - tests/examples/offline_inference/data_parallel.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
   timeout_in_minutes: 30
@@ -182,7 +182,7 @@ steps:
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
+    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: Pipeline + Context Parallelism (4 GPUs))
   timeout_in_minutes: 60

.buildkite/test_areas/entrypoints.yaml

@@ -34,10 +34,9 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
   - pytest -v -s entrypoints/test_chat_utils.py
 
-
 - label: Entrypoints Integration (API Server 2)
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
@@ -64,6 +63,14 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/pooling
 
+- label: Entrypoints Integration (Responses API)
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai/responses
+  commands:
+  - pytest -v -s entrypoints/openai/responses
 
 - label: Entrypoints V1
   timeout_in_minutes: 50

.buildkite/test_areas/kernels.yaml

@@ -90,8 +90,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py

.github/CODEOWNERS

@@ -3,7 +3,6 @@
 
 # This lists cover the "core" components of vLLM that require careful review
 /vllm/attention @LucasWilkinson
-/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @njhill @22quinn
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
@@ -15,6 +14,7 @@
 /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
 CMakeLists.txt @tlrmchlsmth @LucasWilkinson
@@ -26,6 +26,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
+/vllm/v1/attention/backend.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/v1/attention/backends/mla @pavanimajety
 /vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
@@ -116,15 +117,15 @@ mkdocs.yaml @hmellor
 /vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
 
 # Kernels
-/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
-/vllm/attention/ops/triton_unified_attention.py @tdoublep
+/vllm/v1/attention/ops/chunked_prefill_paged_decode.py @tdoublep
+/vllm/v1/attention/ops/triton_unified_attention.py @tdoublep
 
 # ROCm related: specify owner with write access to notify AMD folks for careful code review
 /vllm/**/*rocm* @tjtanaa
 /docker/Dockerfile.rocm* @gshtras @tjtanaa
 /vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
 /vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
-/vllm/attention/ops/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/ops/rocm*.py @gshtras @tjtanaa
 /vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
 /csrc/rocm @gshtras @tjtanaa
 /requirements/*rocm* @tjtanaa
@@ -152,7 +153,7 @@ mkdocs.yaml @hmellor
 /vllm/entrypoints/pooling @noooop
 /vllm/config/pooler.py @noooop
 /vllm/pooling_params.py @noooop
-/vllm/model_executor/layers/pooler.py @noooop
+/vllm/model_executor/layers/pooler @noooop
 
 # Security guide and policies
 /docs/usage/security.md @russellb

.github/mergify.yml

@@ -222,10 +222,10 @@ pull_request_rules:
       - files~=^csrc/rocm/
       - files~=^docker/Dockerfile.rocm
       - files~=^requirements/rocm.*\.txt
-      - files~=^vllm/attention/backends/rocm.*\.py
-      - files~=^vllm/attention/ops/rocm.*\.py
       - files~=^vllm/model_executor/layers/fused_moe/rocm.*\.py
+      - files~=^vllm/v1/attention/backends/rocm.*\.py
       - files~=^vllm/v1/attention/backends/mla/rocm.*\.py
+      - files~=^vllm/v1/attention/ops/rocm.*\.py
       - files~=^tests/kernels/.*_rocm.*\.py
       - files=vllm/platforms/rocm.py
       - title~=(?i)AMD
@@ -414,6 +414,18 @@ pull_request_rules:
       remove:
         - needs-rebase
 
+- name: label-bug
+  description: Automatically apply bug label
+  conditions:
+    - label != stale
+    - or:
+      - title~=(?i)\bbug\b
+      - title~=(?i)\bbugfix\b
+  actions:
+    label:
+      add:
+        - bug
+
 - name: label-kv-connector
   description: Automatically apply kv-connector label
   conditions:

.gitignore

@@ -227,3 +227,8 @@ ep_kernels_workspace/
 
 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
+
+# Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
+vllm/grpc/vllm_engine_pb2.py
+vllm/grpc/vllm_engine_pb2_grpc.py
+vllm/grpc/vllm_engine_pb2.pyi

.pre-commit-config.yaml

@@ -147,6 +147,13 @@ repos:
     entry: python tools/pre_commit/validate_config.py
     language: python
     additional_dependencies: [regex]
+  - id: validate-docker-versions
+    name: Validate docker/versions.json matches Dockerfile
+    entry: python tools/generate_versions_json.py --check
+    language: python
+    files: ^docker/(Dockerfile|versions\.json)$
+    pass_filenames: false
+    additional_dependencies: [dockerfile-parse]
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

CMakeLists.txt

@@ -282,6 +282,7 @@ endif()
 set(VLLM_EXT_SRC
   "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
   "csrc/cache_kernels.cu"
+  "csrc/cache_kernels_fused.cu"
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
   "csrc/attention/merge_attn_states.cu"
@@ -799,24 +800,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   else()
     cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
   endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/moe/blockwise_scaled_group_mm_sm100.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
-    message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
-                     "if you intend on running FP8 quantized MoE models on Blackwell.")
-    else()
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
-                     "in CUDA target architectures")
-    endif()
-  endif()
 
   #
   # Machete kernels

README.md

@@ -14,51 +14,8 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
----
-Join us at the [PyTorch Conference, October 22-23](https://events.linuxfoundation.org/pytorch-conference/) and [Ray Summit, November 3-5](https://www.anyscale.com/ray-summit/2025) in San Francisco for our latest updates on vLLM and to meet the vLLM team! Register now for the largest vLLM community events of the year!
-
----
-
-*Latest News* 🔥
-
-- [2025/11] We hosted [vLLM Bangkok Meetup](https://luma.com/v0f647nv). We explored vLLM and LMCache inference and low-resource language adaptation with speakers from Embedded LLM, AMD, and Red Hat. Please find the meetup slides [here](https://drive.google.com/drive/folders/1H0DS57F8HQ5q3kSOSoRmucPJWL3E0A_X?usp=sharing).
-- [2025/11] We hosted [the first vLLM Europe Meetup in Zurich](https://luma.com/0gls27kb) focused on quantization, distributed inference, and reinforcement learning at scale with speakers from Mistral, IBM, and Red Hat. Please find the meetup slides [here](https://docs.google.com/presentation/d/1UC9PTLCHYXQpOmJDSFg6Sljra3iVXzc09DeEI7dnxMc/edit?usp=sharing) and recording [here](https://www.youtube.com/watch?v=6m6ZE6yVEDI)
-- [2025/11] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/xSrYXjNgr1HbCP4ExYNG1w) focusing on distributed inference and diverse accelerator support with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1nQJ8ZkLSjKxvu36sSHaceVXtttbLvvu-?usp=drive_link).
-- [2025/10] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/__xb4OyOsImz-9eAVrdlcg) focused on hands-on vLLM inference optimization! Please find the meetup slides [here](https://drive.google.com/drive/folders/1KqwjsFJLfEsC8wlDugnrR61zsWHt94Q6).
-- [2025/09] We hosted [vLLM Toronto Meetup](https://luma.com/e80e0ymm) focused on tackling inference at scale and speculative decoding with speakers from NVIDIA and Red Hat! Please find the meetup slides [here](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing).
-- [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA).
-- [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing).
-- [2025/08] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg) focusing on building, developing, and integrating with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH).
-- [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
-- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
-
-<details>
-<summary>Previous News</summary>
-
-- [2025/08] We hosted [vLLM Korea Meetup](https://luma.com/cgcgprmh) with Red Hat and Rebellions! We shared the latest advancements in vLLM along with project spotlights from the vLLM Korea community. Please find the meetup slides [here](https://drive.google.com/file/d/1bcrrAE1rxUgx0mjIeOWT6hNe2RefC5Hm/view).
-- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
-- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
-- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
-- [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
-- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
-- [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
-- [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
-- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
-- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
-- [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
-- [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
-- [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
-- [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
-- [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
-- [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
-- [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
-- [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
-- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
-- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
-- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
-- [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
-
-</details>
+🔥 We have built a vllm website to help you get started with vllm. Please visit [vllm.ai](https://vllm.ai) to learn more.
+For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
 
 ---
 
@@ -118,50 +75,6 @@ Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
 We welcome and value any contributions and collaborations.
 Please check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.
 
-## Sponsors
-
-vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
-
-<!-- Note: Please sort them in alphabetical order. -->
-<!-- Note: Please keep these consistent with docs/community/sponsors.md -->
-Cash Donations:
-
-- a16z
-- Dropbox
-- Sequoia Capital
-- Skywork AI
-- ZhenFund
-
-Compute Resources:
-
-- Alibaba Cloud
-- AMD
-- Anyscale
-- Arm
-- AWS
-- Crusoe Cloud
-- Databricks
-- DeepInfra
-- Google Cloud
-- IBM
-- Intel
-- Lambda Lab
-- Nebius
-- Novita AI
-- NVIDIA
-- Red Hat
-- Replicate
-- Roblox
-- RunPod
-- Trainy
-- UC Berkeley
-- UC San Diego
-- Volcengine
-
-Slack Sponsor: Anyscale
-
-We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
-
 ## Citation
 
 If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
@@ -182,7 +95,7 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
-- For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
+- For collaborations and partnerships, please contact us at [collaboration@vllm.ai](mailto:collaboration@vllm.ai)
 <!-- --8<-- [end:contact-us] -->
 
 ## Media Kit

RELEASE.md

@@ -1,47 +1,30 @@
 # Releasing vLLM
 
-vLLM releases offer a reliable version of the code base, packaged into a binary format that can be conveniently accessed via PyPI. These releases also serve as key milestones for the development team to communicate with the community about newly available features, improvements, and upcoming changes that could affect users, including potential breaking changes.
+vLLM releases offer a reliable version of the code base, packaged into a binary format that can be conveniently accessed via [PyPI](https://pypi.org/project/vllm). These releases also serve as key milestones for the development team to communicate with the community about newly available features, improvements, and upcoming changes that could affect users, including potential breaking changes.
 
-## Release Versioning
+## Release Cadence and Versioning
 
-vLLM uses a “right-shifted” versioning scheme where a new patch release is out every 2 weeks. And patch releases contain features and bug fixes (as opposed to semver where patch release contains only backwards-compatible bug fixes). When critical fixes need to be made, special release post1 is released.
+We aim to have a regular release every 2 weeks. Since v0.12.0, regular releases increment the minor version rather than patch version. The list of past releases can be found [here](https://vllm.ai/releases).
 
-* _major_ major architectural milestone and when incompatible API changes are made, similar to PyTorch 2.0.
-* _minor_ major features
-* _patch_ features and backwards-compatible bug fixes
-* _post1_ or _patch-1_ backwards-compatible bug fixes, either explicit or implicit post release
+Our version numbers are expressed in the form `vX.Y.Z`, where `X` is the major version, `Y` is the minor version, and `Z` is the patch version. They are incremented according to the following rules:
 
-## Release Cadence
+* _Major_ releases are reserved for architectural milestones involving sweeping API changes, similar to PyTorch 2.0.
+* _Minor_ releases correspond to regular releases, which include new features, bug fixes and other backwards-compatible changes.
+* _Patch_ releases correspond to special releases for new models, as well as emergency patches for critical performance, functionality and security issues.
 
-Patch release is released on bi-weekly basis. Post release 1-3 days after patch release and uses same branch as patch release.
-Following is the release cadence for year 2025. All future release dates below are tentative. Please note: Post releases are optional.
+This versioning scheme is similar to [SemVer](https://semver.org/) for compatibility purposes, except that backwards compatibility is only guaranteed for a limited number of minor releases (see our [deprecation policy](https://docs.vllm.ai/en/latest/contributing/deprecation_policy) for details).
 
-| Release Date | Patch release versions | Post Release versions |
-| --- | --- | --- |
-| Jan 2025 | 0.7.0 | --- |
-| Feb 2025 | 0.7.1, 0.7.2, 0.7.3  | --- |
-| Mar 2025 | 0.7.4, 0.7.5 | --- |
-| Apr 2025 | 0.7.6, 0.7.7 | --- |
-| May 2025 | 0.7.8, 0.7.9 | --- |
-| Jun 2025 | 0.7.10, 0.7.11 | --- |
-| Jul 2025 | 0.7.12, 0.7.13 | --- |
-| Aug 2025 | 0.7.14, 0.7.15 | --- |
-| Sep 2025 | 0.7.16, 0.7.17 | --- |
-| Oct 2025 | 0.7.18, 0.7.19 | --- |
-| Nov 2025 | 0.7.20, 0.7.21 | --- |
-| Dec 2025 | 0.7.22, 0.7.23 | --- |
-
-## Release branch
+## Release Branch
 
 Each release is built from a dedicated release branch.
 
-* For _major_, _minor_, _patch_ releases, the release branch cut is performed 1-2 days before release is live.
-* For post releases, previously cut release branch is reused
-* Release builds are triggered via push to RC tag like vX.Y.Z-rc1 . This enables us to build and test multiple RCs for each release.
-* Final tag : vX.Y.Z does not trigger the build but used for Release notes and assets.
-* After branch cut is created we monitor the main branch for any reverts and apply these reverts to a release branch.
+* For _major_ and _minor_ releases, the release branch cut is performed 1-2 days before release is live.
+* For _patch_ releases, previously cut release branch is reused.
+* Release builds are triggered via push to RC tag like `vX.Y.Z-rc1`. This enables us to build and test multiple RCs for each release.
+* Final tag: `vX.Y.Z` does not trigger the build but used for Release notes and assets.
+* After branch cut is created, we monitor the main branch for any reverts and apply these reverts to a release branch.
 
-## Release Cherry-Pick Criteria
+### Cherry-Pick Criteria
 
 After branch cut, we approach finalizing the release branch with clear criteria on what cherry picks are allowed in. Note: a cherry pick is a process to land a PR in the release branch after branch cut. These are typically limited to ensure that the team has sufficient time to complete a thorough round of testing on a stable code base.
 

benchmarks/benchmark_batch_invariance.py

@@ -104,7 +104,6 @@ def run_benchmark_with_batch_invariant(
     random.seed(seed)
 
     # Set environment variables
-    os.environ["VLLM_ATTENTION_BACKEND"] = backend
     if batch_invariant:
         os.environ["VLLM_BATCH_INVARIANT"] = "1"
     else:
@@ -140,6 +139,7 @@ def run_benchmark_with_batch_invariant(
             max_model_len=max_model_len,
             dtype="bfloat16",
             tensor_parallel_size=tp_size,
+            attention_config={"backend": backend},
             enable_prefix_caching=False,
         )
         init_time = time.perf_counter() - start_init

benchmarks/benchmark_ngram_proposer.py

@@ -135,7 +135,6 @@ def benchmark_batched_propose(args):
             block_sizes=[16],
         )
         dummy_input_batch._req_ids = list(str(id) for id in range(args.num_req))
-        dummy_input_batch.spec_decode_unsupported_reqs = ()
         dummy_input_batch.num_tokens_no_spec = [args.num_token] * args.num_req
         dummy_input_batch.token_ids_cpu = np.random.randint(
             0, 20, (args.num_req, args.num_token)
@@ -151,10 +150,8 @@ def benchmark_batched_propose(args):
             start = time.time()
             runner.drafter.propose(
                 sampled_token_ids,
-                dummy_input_batch.req_ids,
                 dummy_input_batch.num_tokens_no_spec,
                 dummy_input_batch.token_ids_cpu,
-                dummy_input_batch.spec_decode_unsupported_reqs,
             )
             end = time.time()
             print(f"Iteration time (s): {end - start}")

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -343,7 +343,9 @@ def bench(
         return bench_int8(dtype, m, k, n, label, sub_label)
     if dtype == torch.float8_e4m3fn:
         return bench_fp8(dtype, m, k, n, label, sub_label)
-    raise ValueError("unsupported type")
+    raise ValueError(
+        f"Unsupported dtype {dtype}: should be one of torch.int8, torch.float8_e4m3fn."
+    )
 
 
 # runner

benchmarks/kernels/bench_nvfp4_quant.py

@@ -0,0 +1,177 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import copy
+import itertools
+
+import torch
+from weight_shapes import WEIGHT_SHAPES
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
+from vllm.triton_utils import triton
+from vllm.utils.flashinfer import flashinfer_fp4_quantize
+
+if not current_platform.has_device_capability(100):
+    raise RuntimeError("NVFP4 requires compute capability of 10.0 (Blackwell)")
+
+FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
+FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
+
+PROVIDER_CFGS = {
+    "vllm": dict(backend="vllm", enabled=True),
+    "flashinfer": dict(backend="flashinfer", enabled=True),
+}
+
+_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
+
+
+def compute_global_scale(tensor: torch.Tensor) -> torch.Tensor:
+    """Compute global scale for FP4 quantization."""
+    amax = torch.abs(tensor).max().to(torch.float32)
+    return FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / amax
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
+        x_log=False,
+        line_arg="provider",
+        line_vals=_enabled,
+        line_names=_enabled,
+        ylabel="us (lower is better)",
+        plot_name="NVFP4 Input Quantization Latency (us)",
+        args={},
+    )
+)
+def benchmark(batch_size, provider, N, K):
+    M = batch_size
+    device = "cuda"
+    dtype = torch.bfloat16
+
+    # Create input tensor
+    a = torch.randn((M, K), device=device, dtype=dtype)
+
+    # Compute global scale for activation
+    a_global_scale = compute_global_scale(a)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    cfg = PROVIDER_CFGS[provider]
+
+    if cfg["backend"] == "vllm":
+        # vLLM's FP4 quantization
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: ops.scaled_fp4_quant(a, a_global_scale),
+            quantiles=quantiles,
+        )
+    elif cfg["backend"] == "flashinfer":
+        # FlashInfer's FP4 quantization
+        # Use is_sf_swizzled_layout=True to match vLLM's output format
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: flashinfer_fp4_quantize(
+                a, a_global_scale, is_sf_swizzled_layout=True
+            ),
+            quantiles=quantiles,
+        )
+
+    # Convert ms to us for better readability at small batch sizes
+    to_us = lambda t_ms: t_ms * 1000
+    return to_us(ms), to_us(max_ms), to_us(min_ms)
+
+
+def prepare_shapes(args):
+    out = []
+    for model, tp_size in itertools.product(args.models, args.tp_sizes):
+        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
+            KN[tp_dim] //= tp_size
+            KN.append(model)
+            out.append(KN)
+    return out
+
+
+def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
+    """Test accuracy between vLLM and FlashInfer FP4 quantization."""
+    # Create input tensor
+    a = torch.randn((M, K), device=device, dtype=dtype)
+
+    # Compute global scale
+    a_global_scale = compute_global_scale(a)
+
+    # vLLM quantization
+    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(a, a_global_scale)
+
+    # FlashInfer quantization (with swizzled layout to match vLLM's output)
+    flashinfer_fp4, flashinfer_scale = flashinfer_fp4_quantize(
+        a, a_global_scale, is_sf_swizzled_layout=True
+    )
+    flashinfer_scale = flashinfer_scale.view(torch.float8_e4m3fn)
+
+    # Compare outputs
+    torch.testing.assert_close(
+        vllm_fp4,
+        flashinfer_fp4,
+    )
+    print(f"M={M}, K={K}, dtype={dtype}: PASSED")
+
+
+def test_accuracy():
+    """Run accuracy tests across various shapes."""
+    print("\n" + "=" * 60)
+    print("Running accuracy tests: vLLM vs FlashInfer")
+    print("=" * 60)
+
+    device = "cuda"
+    dtype = torch.bfloat16
+
+    # Test various batch sizes and hidden dimensions
+    Ms = [1, 1024]
+    Ks = [4096]
+
+    for M in Ms:
+        for K in Ks:
+            _test_accuracy_once(M, K, dtype, device)
+
+    print("\nAll accuracy tests passed!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Benchmark NVFP4 quantization: vLLM vs FlashInfer"
+    )
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=["meta-llama/Llama-3.1-8B-Instruct"],
+        choices=list(WEIGHT_SHAPES.keys()),
+    )
+    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default=None,
+        help="Path to save benchmark results",
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="Run accuracy tests",
+    )
+    args = parser.parse_args()
+
+    if args.accuracy:
+        test_accuracy()
+
+    for K, N, model in prepare_shapes(args):
+        print(f"\n{model}, N={N} K={K}")
+        benchmark.run(
+            print_data=True,
+            save_path=args.save_path,
+            N=N,
+            K=K,
+        )
+
+    print("\nBenchmark finished!")

benchmarks/kernels/benchmark_activation.py

@@ -7,11 +7,10 @@ import itertools
 import torch
 
 import vllm.model_executor.layers.activation  # noqa F401
-from vllm.model_executor.custom_op import CustomOp
-from vllm.platforms import current_platform
+from vllm.model_executor.custom_op import op_registry
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 batch_size_range = [1, 16, 128]
 seq_len_range = [1, 16, 64, 1024, 4096]
@@ -30,18 +29,18 @@ def benchmark_activation(
     device = "cuda"
     num_tokens = batch_size * seq_len
     dim = intermediate_size
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     if func_name == "gelu_and_mul":
-        layer = CustomOp.op_registry[func_name](approximate="none")
+        layer = op_registry[func_name](approximate="none")
     elif func_name == "gelu_and_mul_tanh":
-        layer = CustomOp.op_registry["gelu_and_mul"](approximate="tanh")
+        layer = op_registry["gelu_and_mul"](approximate="tanh")
     elif func_name == "fatrelu_and_mul":
         threshold = 0.5
-        layer = CustomOp.op_registry[func_name](threshold)
+        layer = op_registry[func_name](threshold)
     else:
-        layer = CustomOp.op_registry[func_name]()
+        layer = op_registry[func_name]()
 
     x = torch.randn(num_tokens, dim, dtype=dtype, device=device)
     compiled_layer = torch.compile(layer.forward_native)

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -6,15 +6,20 @@ kernel. Both kernels take in fp8 quantized weights and 16-bit activations,
 but use different quantization strategies and backends.
 """
 
-import nvtx
 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.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
+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
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.v1.worker.workspace import init_workspace_manager
 
 # Weight shapes for different models: [num_experts, topk, hidden_size,
 # intermediate_size]
@@ -58,6 +63,7 @@ def bench_run(
     per_out_ch: bool,
     mkn: tuple[int, int, int],
 ):
+    init_workspace_manager(torch.cuda.current_device())
     (m, k, n) = mkn
 
     dtype = torch.half
@@ -120,85 +126,6 @@ def bench_run(
     # Force per-tensor quantization for all cases
     per_act_token = False
 
-    # Create stride tensors for CUTLASS
-    ab_strides1 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
-    ab_strides2 = torch.full((num_experts,), n, dtype=torch.int64, device=device)
-    c_strides1 = torch.full((num_experts,), 2 * n, dtype=torch.int64, device=device)
-    c_strides2 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
-
-    def run_triton_moe(
-        a: torch.Tensor,
-        w1: torch.Tensor,
-        w2: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        w1_scale: torch.Tensor,
-        w2_scale: torch.Tensor,
-        a1_scale: torch.Tensor,
-        a2_scale: torch.Tensor,
-        num_repeats: int,
-    ):
-        quant_config = fp8_w8a8_moe_quant_config(
-            w1_scale=w1_scale,
-            w2_scale=w2_scale,
-            a1_scale=a1_scale,
-            a2_scale=a2_scale,
-            per_act_token_quant=per_act_token,
-            per_out_ch_quant=per_out_ch,
-        )
-
-        for _ in range(num_repeats):
-            fused_experts(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                quant_config=quant_config,
-            )
-
-    def run_cutlass_moe_fp8(
-        a: torch.Tensor,
-        w1: torch.Tensor,
-        w2: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        ab_strides1: torch.Tensor,
-        ab_strides2: torch.Tensor,
-        c_strides1: torch.Tensor,
-        c_strides2: torch.Tensor,
-        w1_scale: torch.Tensor,
-        w2_scale: torch.Tensor,
-        a1_scale: torch.Tensor,
-        a2_scale: torch.Tensor,
-        num_repeats: int,
-    ):
-        quant_config = fp8_w8a8_moe_quant_config(
-            w1_scale=w1_scale,
-            w2_scale=w2_scale,
-            a1_scale=a1_scale,
-            a2_scale=a2_scale,
-            per_act_token_quant=per_act_token,
-            per_out_ch_quant=per_out_ch,
-        )
-
-        for _ in range(num_repeats):
-            with nvtx.annotate("cutlass_moe_fp8", color="blue"):
-                cutlass_moe_fp8(
-                    a=a,
-                    w1_q=w1,
-                    w2_q=w2,
-                    topk_weights=topk_weights,
-                    topk_ids=topk_ids,
-                    ab_strides1=ab_strides1,
-                    ab_strides2=ab_strides2,
-                    c_strides1=c_strides1,
-                    c_strides2=c_strides2,
-                    quant_config=quant_config,
-                    activation="silu",
-                    global_num_experts=num_experts,
-                )
-
     # Pre-create quantization config to avoid creating it inside CUDA graph
     quant_config = fp8_w8a8_moe_quant_config(
         w1_scale=w1_scale,
@@ -209,23 +136,31 @@ def bench_run(
         per_out_ch_quant=per_out_ch,
     )
 
+    fn = mk.FusedMoEModularKernel(
+        MoEPrepareAndFinalizeNoEP(),
+        CutlassExpertsFp8(
+            moe_config=make_dummy_moe_config(
+                num_experts=num_experts,
+                hidden_dim=k,
+                intermediate_size_per_partition=n,
+                in_dtype=a.dtype,
+            ),
+            quant_config=quant_config,
+        ),
+    )
+
     # Create CUDA graphs for CUTLASS (match benchmark_moe.py pattern exactly)
     cutlass_stream = torch.cuda.Stream()
     cutlass_graph = torch.cuda.CUDAGraph()
     with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
         # Capture 10 invocations like benchmark_moe.py
         for _ in range(10):
-            cutlass_moe_fp8(
-                a=a,
-                w1_q=w1_fp8q_cutlass,
-                w2_q=w2_fp8q_cutlass,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                ab_strides1=ab_strides1,
-                ab_strides2=ab_strides2,
-                c_strides1=c_strides1,
-                c_strides2=c_strides2,
-                quant_config=quant_config,
+            fn(
+                a,
+                w1_fp8q_cutlass,
+                w2_fp8q_cutlass,
+                topk_weights,
+                topk_ids,
                 activation="silu",
                 global_num_experts=num_experts,
             )
@@ -297,6 +232,10 @@ def bench_run(
 
 
 def main(args):
+    # Initialize workspace manager (required for CUTLASS MoE kernels)
+    device = torch.device("cuda:0")
+    init_workspace_manager(device)
+
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")

benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py

@@ -11,16 +11,24 @@ import nvtx
 import torch
 import torch.utils.benchmark as benchmark
 
+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.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import (
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
+from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+    CutlassExpertsFp4,
+)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.scalar_type import scalar_types
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.v1.worker.workspace import init_workspace_manager
 
 WEIGHT_SHAPES_MOE = {
     "nvidia/DeepSeek-R1-FP4": [
@@ -187,19 +195,23 @@ def bench_run(
             g1_alphas=w1_gs,
             g2_alphas=w2_gs,
         )
+
+        kernel = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
+            CutlassExpertsFp4(
+                make_dummy_moe_config(),
+                quant_config=quant_config,
+            ),
+        )
+
         for _ in range(num_repeats):
             with nvtx.annotate("cutlass_moe_fp4", color="green"):
-                cutlass_moe_fp4(
-                    a=a,
-                    w1_fp4=w1_fp4,
-                    w2_fp4=w2_fp4,
+                kernel(
+                    hidden_states=a,
+                    w1=w1_fp4,
+                    w2=w2_fp4,
                     topk_weights=topk_weights,
                     topk_ids=topk_ids,
-                    m=m,
-                    n=n,
-                    k=k,
-                    e=num_experts,
-                    quant_config=quant_config,
                 )
 
     def run_cutlass_from_graph(
@@ -229,20 +241,23 @@ def bench_run(
             g2_alphas=w2_gs,
         )
 
+        kernel = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
+            CutlassExpertsFp4(
+                make_dummy_moe_config(),
+                quant_config=quant_config,
+            ),
+        )
+
         with set_current_vllm_config(
             VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
         ):
-            return cutlass_moe_fp4(
-                a=a,
-                w1_fp4=w1_fp4,
-                w2_fp4=w2_fp4,
+            return kernel(
+                hidden_states=a,
+                w1=w1_fp4,
+                w2=w2_fp4,
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
-                m=m,
-                n=n,
-                k=k,
-                e=num_experts,
-                quant_config=quant_config,
             )
 
     def run_triton_from_graph(
@@ -441,6 +456,10 @@ def bench_run(
 
 
 def main(args):
+    # Initialize workspace manager (required for CUTLASS MoE kernels)
+    device = torch.device("cuda:0")
+    init_workspace_manager(device)
+
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")

benchmarks/kernels/benchmark_device_communicators.py

@@ -293,7 +293,7 @@ class CommunicatorBenchmark:
                     graph = torch.cuda.CUDAGraph()
                     graph_pool = torch.cuda.graph_pool_handle()
                     set_graph_pool_id(graph_pool)
-                    with torch.cuda.graph(graph, pool=graph_pool):
+                    with torch.cuda.graph(graph, pool=graph_pool, stream=stream):
                         for _ in range(CUDA_GRAPH_CAPTURE_CYCLES):
                             allreduce_fn(graph_input)
 

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -5,15 +5,21 @@ import torch
 import torch.utils.benchmark as benchmark
 from benchmark_shapes import WEIGHT_SHAPES_MOE
 
+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.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
+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,
 )
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.v1.worker.workspace import init_workspace_manager
 
 DEFAULT_MODELS = [
     "mistralai/Mixtral-8x7B-Instruct-v0.1",
@@ -44,6 +50,7 @@ def bench_run(
     per_out_ch: bool,
     mkn: tuple[int, int, int],
 ):
+    init_workspace_manager(torch.cuda.current_device())
     label = "Quant Matmul"
 
     sub_label = (
@@ -81,11 +88,6 @@ def bench_run(
         a, score, topk, renormalize=False
     )
 
-    ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
-    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
-    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
-    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
-
     def run_triton_moe(
         a: torch.Tensor,
         w1: torch.Tensor,
@@ -119,10 +121,6 @@ def bench_run(
         w2: torch.Tensor,
         w1_scale: torch.Tensor,
         w2_scale: torch.Tensor,
-        ab_strides1: torch.Tensor,
-        ab_strides2: torch.Tensor,
-        c_strides1: torch.Tensor,
-        c_strides2: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         per_act_token: bool,
@@ -134,31 +132,29 @@ def bench_run(
             per_act_token_quant=per_act_token,
         )
 
-        for _ in range(num_repeats):
-            cutlass_moe_fp8(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                ab_strides1,
-                ab_strides2,
-                c_strides1,
-                c_strides2,
+        fn = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
+            CutlassExpertsFp8(
+                moe_config=make_dummy_moe_config(
+                    num_experts=w2.shape[0],
+                    hidden_dim=w2.shape[1],
+                    intermediate_size_per_partition=w2.shape[2],
+                    in_dtype=a.dtype,
+                ),
                 quant_config=quant_config,
-            )
+            ),
+        )
+
+        for _ in range(num_repeats):
+            fn(a, w1, w2, topk_weights, topk_ids)
 
     def run_cutlass_from_graph(
         a: torch.Tensor,
         a_scale: torch.Tensor,
-        w1_q: torch.Tensor,
-        w2_q: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
         w1_scale: torch.Tensor,
         w2_scale: torch.Tensor,
-        ab_strides1: torch.Tensor,
-        ab_strides2: torch.Tensor,
-        c_strides1: torch.Tensor,
-        c_strides2: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
     ):
@@ -168,21 +164,23 @@ def bench_run(
             per_act_token_quant=per_act_token,
         )
 
+        fn = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
+            CutlassExpertsFp8(
+                moe_config=make_dummy_moe_config(
+                    num_experts=w2.shape[0],
+                    hidden_dim=w2.shape[1],
+                    intermediate_size_per_partition=w2.shape[2],
+                    in_dtype=a.dtype,
+                ),
+                quant_config=quant_config,
+            ),
+        )
+
         with set_current_vllm_config(
             VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
         ):
-            return cutlass_moe_fp8(
-                a,
-                w1_q,
-                w2_q,
-                topk_weights,
-                topk_ids,
-                ab_strides1,
-                ab_strides2,
-                c_strides1,
-                c_strides2,
-                quant_config=quant_config,
-            )
+            return fn(a, w1, w2, topk_weights, topk_ids)
 
     def run_triton_from_graph(
         a: torch.Tensor,
@@ -226,10 +224,6 @@ def bench_run(
             w2_q,
             w1_scale,
             w2_scale,
-            ab_strides1,
-            ab_strides2,
-            c_strides1,
-            c_strides2,
             topk_weights,
             topk_ids,
         )
@@ -267,10 +261,6 @@ def bench_run(
         "w1_scale": w1_scale,
         "w2_scale": w2_scale,
         "per_act_token": per_act_token,
-        "ab_strides1": ab_strides1,
-        "ab_strides2": ab_strides2,
-        "c_strides1": c_strides1,
-        "c_strides2": c_strides2,
         # cuda graph params
         "cutlass_graph": cutlass_graph,
         "triton_graph": triton_graph,
@@ -329,10 +319,6 @@ def bench_run(
         w2_q,
         w1_scale,
         w2_scale,
-        ab_strides1,
-        ab_strides2,
-        c_strides1,
-        c_strides2,
         topk_weights,
         topk_ids,
         per_act_token,
@@ -341,7 +327,7 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, ab_strides1, ab_strides2, c_strides1, c_strides2, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
@@ -364,6 +350,10 @@ def bench_run(
 
 
 def main(args):
+    # Initialize workspace manager (required for CUTLASS MoE kernels)
+    device = torch.device("cuda:0")
+    init_workspace_manager(device)
+
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")

benchmarks/kernels/benchmark_layernorm.py

@@ -6,9 +6,8 @@ import time
 import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
@@ -22,7 +21,7 @@ def main(
     num_warmup_iters: int = 5,
     num_iters: int = 100,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device("cuda")
 
     layer = RMSNorm(hidden_size).to(dtype=dtype)

benchmarks/kernels/benchmark_moe.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
+import gc
 import json
 import os
 import time
@@ -14,18 +15,64 @@ import ray
 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.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
     _get_config_dtype_str,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+    TritonOrDeepGemmExperts,
+)
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
+# Default interval for clearing Triton JIT cache during tuning
+# Set to 0 to disable automatic cache clearing
+_CACHE_CLEAR_INTERVAL_ENV = "VLLM_MOE_TUNE_CACHE_CLEAR_INTERVAL"
+TRITON_CACHE_CLEAR_INTERVAL = int(os.environ.get(_CACHE_CLEAR_INTERVAL_ENV, "50"))
+
+
+def clear_triton_cache():
+    """Clear Triton JIT compilation cache and Python/CUDA memory.
+
+    This helps prevent OOM during tuning with large models (many experts).
+    """
+    # Force Python garbage collection
+    gc.collect()
+
+    # Clear CUDA memory cache
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    # Try to clear Triton's runtime cache
+    try:
+        if (
+            hasattr(triton, "runtime")
+            and hasattr(triton.runtime, "cache")
+            and hasattr(triton.runtime.cache, "clear")
+        ):
+            triton.runtime.cache.clear()
+    except ImportError:
+        # Triton not installed, skip cache clearing
+        pass
+    except AttributeError:
+        # Triton version doesn't have expected cache API
+        pass
+    except Exception as e:
+        print(f"Warning: Failed to clear Triton cache: {e}")
+
+    # Additional garbage collection after clearing caches
+    gc.collect()
+
 
 def ensure_divisibility(numerator, denominator, text):
     """Ensure that numerator is divisible by the denominator."""
@@ -154,10 +201,36 @@ def benchmark_config(
             block_shape=block_quant_shape,
         )
 
+        deep_gemm_experts = None
+        if use_deep_gemm:
+            deep_gemm_experts = mk.FusedMoEModularKernel(
+                prepare_finalize=MoEPrepareAndFinalizeNoEP(),
+                fused_experts=TritonOrDeepGemmExperts(
+                    moe_config=FusedMoEConfig(
+                        num_experts=num_experts,
+                        experts_per_token=topk,
+                        hidden_dim=hidden_size,
+                        intermediate_size_per_partition=shard_intermediate_size,
+                        num_local_experts=num_experts,
+                        activation="silu",
+                        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+                        in_dtype=init_dtype,
+                        routing_method=RoutingMethodType.TopK,
+                        device="cuda",
+                    ),
+                    quant_config=quant_config,
+                ),
+            )
+
         with override_config(config):
             topk_weights, topk_ids, token_expert_indices = fused_topk(
                 x, input_gating, topk, renormalize=not use_deep_gemm
             )
+
+            if use_deep_gemm:
+                return deep_gemm_experts(
+                    x, w1, w2, topk_weights, topk_ids, inplace=True
+                )
             return fused_experts(
                 x,
                 w1,
@@ -166,7 +239,6 @@ def benchmark_config(
                 topk_ids,
                 inplace=True,
                 quant_config=quant_config,
-                allow_deep_gemm=use_deep_gemm,
             )
 
     # JIT compilation & warmup
@@ -390,7 +462,7 @@ def merge_unique_dicts(list1, list2):
 class BenchmarkWorker:
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
-        current_platform.seed_everything(seed)
+        set_random_seed(seed)
         self.seed = seed
         # Get the device ID to allocate tensors and kernels
         # on the respective GPU. This is required for Ray to work
@@ -410,7 +482,7 @@ class BenchmarkWorker:
         block_quant_shape: list[int] = None,
         use_deep_gemm: bool = False,
     ) -> tuple[dict[str, int], float]:
-        current_platform.seed_everything(self.seed)
+        set_random_seed(self.seed)
         dtype_str = _get_config_dtype_str(
             dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
         )
@@ -483,7 +555,7 @@ class BenchmarkWorker:
                 need_device_guard = True
 
         with torch.cuda.device(self.device_id) if need_device_guard else nullcontext():
-            for config in tqdm(search_space):
+            for idx, config in enumerate(tqdm(search_space)):
                 try:
                     kernel_time = benchmark_config(
                         config,
@@ -506,6 +578,19 @@ class BenchmarkWorker:
                 if kernel_time < best_time:
                     best_time = kernel_time
                     best_config = config
+
+                # Periodically clear Triton JIT cache to prevent OOM
+                # This is especially important for large models with many experts
+                if (
+                    TRITON_CACHE_CLEAR_INTERVAL > 0
+                    and idx > 0
+                    and idx % TRITON_CACHE_CLEAR_INTERVAL == 0
+                ):
+                    clear_triton_cache()
+
+        # Final cleanup after tuning completes
+        clear_triton_cache()
+
         now = datetime.now()
         print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
         assert best_config is not None
@@ -590,6 +675,7 @@ def main(args: argparse.Namespace):
         "DeepseekV3ForCausalLM",
         "DeepseekV32ForCausalLM",
         "Glm4MoeForCausalLM",
+        "Glm4MoeLiteForCausalLM",
         "NemotronHForCausalLM",
     ):
         E = config.n_routed_experts

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -18,6 +18,7 @@ from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
@@ -261,7 +262,7 @@ def benchmark_unpermute(
 class BenchmarkWorker:
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
-        current_platform.seed_everything(seed)
+        set_random_seed(seed)
         self.seed = seed
         # Get the device ID to allocate tensors and kernels
         # on the respective GPU. This is required for Ray to work
@@ -279,7 +280,7 @@ class BenchmarkWorker:
         use_int8_w8a16: bool,
         use_customized_permute: bool = False,
     ) -> tuple[dict[str, int], float]:
-        current_platform.seed_everything(self.seed)
+        set_random_seed(self.seed)
 
         permute_time = benchmark_permute(
             num_tokens,
@@ -329,6 +330,7 @@ def main(args: argparse.Namespace):
         config.architectures[0] == "DeepseekV3ForCausalLM"
         or config.architectures[0] == "DeepseekV2ForCausalLM"
         or config.architectures[0] == "Glm4MoeForCausalLM"
+        or config.architectures[0] == "Glm4MoeLiteForCausalLM"
     ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok

benchmarks/kernels/benchmark_mrope.py

@@ -37,9 +37,9 @@ import numpy as np
 import torch
 
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import set_random_seed
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -94,7 +94,7 @@ def benchmark_mrope(
     benchmark_iter: int = 100,
     csv_writer=None,
 ):
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device(device)
     # the parameters to compute the q k v size based on tp_size
     mrope_helper_class = get_rope(

benchmarks/kernels/benchmark_paged_attention.py

@@ -13,6 +13,7 @@ from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random,
+    set_random_seed,
 )
 
 logger = init_logger(__name__)
@@ -38,7 +39,7 @@ def main(
     device: str = "cuda",
     kv_cache_dtype: str | None = None,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     scale = float(1.0 / (head_size**0.5))
     query = torch.empty(

benchmarks/kernels/benchmark_quant.py

@@ -6,9 +6,8 @@ import time
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
@@ -23,7 +22,7 @@ def main(
     num_warmup_iters: int = 5,
     num_iters: int = 100,
 ) -> None:
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
     torch.set_default_device("cuda")
 
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -8,11 +8,11 @@ from tabulate import tabulate
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random,
+    set_random_seed,
 )
 
 logger = init_logger(__name__)
@@ -36,7 +36,7 @@ def run_benchmark(
     if kv_cache_dtype == "fp8" and head_size % 16:
         raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
 
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     # create random key / value tensors [T, H, D].

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -7,15 +7,15 @@ import torch
 from tabulate import tabulate
 
 from vllm import _custom_ops as ops
-from vllm.attention.ops.triton_reshape_and_cache_flash import (
-    triton_reshape_and_cache_flash,
-)
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
     create_kv_caches_with_random_flash,
+    set_random_seed,
+)
+from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
+    triton_reshape_and_cache_flash,
 )
 
 logger = init_logger(__name__)
@@ -49,7 +49,7 @@ def run_benchmark(
     if implementation == "triton" and kv_cache_layout == "HND":
         return float("nan")  # Triton does not support HND layout yet.
 
-    current_platform.seed_everything(42)
+    set_random_seed(42)
     torch.set_default_device(device)
 
     # create random key / value tensors [T, H, D].

benchmarks/kernels/benchmark_silu_mul_fp8_quant.py

@@ -23,9 +23,9 @@ import torch
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
     persistent_masked_m_silu_mul_quant,
 )
-from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
+from vllm.utils.torch_utils import set_random_seed
 
 
 @triton.jit
@@ -207,7 +207,7 @@ def benchmark(
 ):
     def generate_data(seed_offset=0):
         """Generate input data with given seed offset"""
-        current_platform.seed_everything(42 + seed_offset)
+        set_random_seed(42 + seed_offset)
         y = torch.rand((E, T, 2 * H), dtype=torch.bfloat16, device="cuda").contiguous()
 
         if gen_strategy == "random_imbalanced":

benchmarks/kernels/cpu/benchmark_cpu_attn.py

@@ -0,0 +1,272 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import functools
+import time
+
+import numpy as np
+import torch
+
+from vllm._custom_ops import (
+    cpu_attention_with_kv_cache,
+    cpu_attn_get_scheduler_metadata,
+    cpu_attn_reshape_and_cache,
+)
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.v1.attention.backends.cpu_attn import CPUAttentionBackend, _get_attn_isa
+
+
+def get_attn_isa(
+    block_size: int | None = None,
+    dtype: torch.dtype | None = None,
+):
+    if block_size and dtype:
+        return _get_attn_isa(dtype, block_size)
+    else:
+        if current_platform.get_cpu_architecture() == CpuArchEnum.ARM:
+            return "neon"
+        elif torch._C._cpu._is_amx_tile_supported():
+            return "amx"
+        else:
+            return "vec"
+
+
+# rand number generation takes too much time, cache rand tensors
+@functools.lru_cache(maxsize=128, typed=False)
+def tensor_cache(
+    elem_num: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    tensor = torch.randn(elem_num, dtype=dtype)
+    return tensor
+
+
+@torch.inference_mode()
+def main(
+    seq_lens: list[tuple[int, int]],
+    num_heads: tuple[int, int],
+    head_size: int,
+    sliding_window: int = None,
+    dtype: torch.dtype = torch.bfloat16,
+    block_size: int = 128,
+    num_blocks: int = 4096,
+    use_sink: bool = False,
+    enable_kv_split: bool = False,
+    isa: str | None = None,
+    seed: int = 0,
+    iters: int = 20,
+) -> None:
+    current_platform.seed_everything(seed)
+    num_seqs = len(seq_lens)
+    query_lens = [x[0] for x in seq_lens]
+    kv_lens = [x[1] for x in seq_lens]
+    num_query_heads = num_heads[0]
+    num_kv_heads = num_heads[1]
+    assert num_query_heads % num_kv_heads == 0
+    max_kv_len = max(kv_lens)
+    window_size = (sliding_window - 1, 0) if sliding_window is not None else (-1, -1)
+    scale = head_size**-0.5
+    token_num = sum(query_lens)
+
+    if isa is None:
+        isa = get_attn_isa(block_size, dtype)
+
+    s_aux = (
+        15 * torch.rand((num_query_heads,), dtype=torch.bfloat16) if use_sink else None
+    )
+
+    query = tensor_cache(
+        elem_num=token_num * num_query_heads * head_size,
+        dtype=dtype,
+    )
+    query = query.view(
+        token_num,
+        num_query_heads,
+        head_size,
+    )
+
+    key_value = tensor_cache(
+        elem_num=2 * num_blocks * num_kv_heads * block_size * head_size,
+        dtype=dtype,
+    )
+    key_value = key_value.view(
+        2,
+        num_blocks,
+        block_size,
+        num_kv_heads,
+        head_size,
+    )
+    key_cache, value_cache = key_value.unbind(0)
+
+    # KV cache for CPU attention
+    packed_key_cache = torch.empty(
+        num_blocks, num_kv_heads, block_size, head_size, dtype=dtype
+    )
+    packed_value_cache = torch.empty_like(packed_key_cache)
+
+    cu_query_lens = torch.tensor([0] + query_lens, dtype=torch.int32).cumsum(
+        dim=0, dtype=torch.int32
+    )
+    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
+    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
+    block_tables = torch.randint(
+        0, num_blocks, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
+    )
+
+    # use reshape_and_cache to pack key_cache and value_cache
+    slot_mapping = torch.arange(0, num_blocks * block_size, dtype=torch.int64)
+    cpu_attn_reshape_and_cache(
+        key=key_cache.view(-1, num_kv_heads, head_size),
+        value=value_cache.view(-1, num_kv_heads, head_size),
+        key_cache=packed_key_cache,
+        value_cache=packed_value_cache,
+        slot_mapping=slot_mapping,
+        isa=isa,
+    )
+
+    metadata = cpu_attn_get_scheduler_metadata(
+        num_reqs=num_seqs,
+        num_heads=num_query_heads,
+        num_kv_heads=num_kv_heads,
+        head_dim=head_size,
+        seq_lens=kv_lens_tensor,
+        dtype=dtype,
+        query_start_loc=cu_query_lens,
+        causal=True,
+        sliding_window_size=sliding_window if sliding_window is not None else -1,
+        isa=isa,
+        enable_kv_split=enable_kv_split,
+    )
+
+    out_with_split = torch.empty_like(query)
+
+    def run_benchmark(iters: int) -> list[float]:
+        times = []
+        for _ in range(iters):
+            start_time = time.perf_counter_ns()
+            cpu_attention_with_kv_cache(
+                query=query,
+                key_cache=packed_key_cache,
+                value_cache=packed_value_cache,
+                output=out_with_split,
+                query_start_loc=cu_query_lens,
+                seq_lens=kv_lens_tensor,
+                scale=scale,
+                causal=True,
+                alibi_slopes=None,
+                sliding_window=window_size,
+                block_table=block_tables,
+                softcap=0,
+                scheduler_metadata=metadata,
+                s_aux=s_aux,
+            )
+            end_time = time.perf_counter_ns()
+            times.append((end_time - start_time) / 1e6)
+        return times
+
+    # warmup
+    run_benchmark(5)
+    # benchmark
+    times = run_benchmark(iters)
+
+    time_min = min(times)
+    time_max = max(times)
+    time_mean = np.mean(times)
+    time_std = np.std(times)
+
+    print("\tmin (ms) = ", time_min)
+    print("\tmax (ms) = ", time_max)
+    print("\tmean (ms) = ", time_mean)
+    print("\tstd = ", time_std)
+    print("\tmedian (ms) = ", np.median(times))
+
+
+def generate_seq_lens(
+    batch_size: int,
+    q_len_min: int,
+    q_len_max: int,
+    kv_len_min: int,
+    kv_len_max: int,
+    seed: int = 0,
+) -> list[tuple[int, int]]:
+    assert 1 <= q_len_min <= q_len_max
+    assert 1 <= kv_len_min <= kv_len_max
+    assert kv_len_max >= q_len_min
+
+    g = torch.Generator(device="cpu").manual_seed(seed)
+
+    def rint(lo: int, hi: int) -> int:
+        return torch.randint(lo, hi + 1, (1,), generator=g).item()
+
+    seq_lens: list[tuple[int, int]] = []
+    for _ in range(batch_size):
+        # ensure q <= kv
+        kv = rint(max(kv_len_min, q_len_min), kv_len_max)
+        q = rint(q_len_min, min(q_len_max, kv))
+        seq_lens.append((q, kv))
+
+    return seq_lens
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
+    parser.add_argument("--batch-size", type=int, default=64)
+    parser.add_argument("--q-len-min", type=int, default=512)
+    parser.add_argument("--q-len-max", type=int, default=512)
+    parser.add_argument("--kv-len-min", type=int, default=512)
+    parser.add_argument("--kv-len-max", type=int, default=512)
+    parser.add_argument("--num-blocks", type=int, default=4096)
+
+    parser.add_argument("--sliding-window", type=int, default=None)
+    parser.add_argument("--num-query-heads", type=int, default=32)
+    parser.add_argument("--num-kv-heads", type=int, default=8)
+    parser.add_argument(
+        "--head-size",
+        type=int,
+        choices=CPUAttentionBackend.get_supported_head_sizes(),
+        default=128,
+    )
+    parser.add_argument("--enable-kv-split", action="store_true")
+    parser.add_argument("--block-size", type=int, choices=[32, 64, 128], default=128)
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="bfloat16"
+    )
+    parser.add_argument("--use-sink", action="store_true")
+    parser.add_argument(
+        "--isa", type=str, choices=["vec", "neon", "amx", "vec16"], default=None
+    )
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--iters", type=int, default=20)
+
+    args = parser.parse_args()
+    print(args)
+
+    seq_lens = generate_seq_lens(
+        args.batch_size,
+        args.q_len_min,
+        args.q_len_max,
+        args.kv_len_min,
+        args.kv_len_max,
+        args.seed,
+    )
+
+    print("batch (query len, kv len) = ", seq_lens)
+
+    main(
+        seq_lens=seq_lens,
+        num_heads=(args.num_query_heads, args.num_kv_heads),
+        head_size=args.head_size,
+        sliding_window=args.sliding_window,
+        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+        block_size=args.block_size,
+        num_blocks=args.num_blocks,
+        use_sink=args.use_sink,
+        enable_kv_split=args.enable_kv_split,
+        isa=args.isa
+        if args.isa is not None
+        else get_attn_isa(args.block_size, STR_DTYPE_TO_TORCH_DTYPE[args.dtype]),
+        seed=args.seed,
+        iters=args.iters,
+    )

benchmarks/kernels/cpu/benchmark_cpu_fused_moe.py

@@ -0,0 +1,175 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import sys
+import time
+
+import numpy as np
+import torch
+
+from vllm.platforms import current_platform
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+# Check if CPU MoE operations are available
+try:
+    from vllm._custom_ops import cpu_fused_moe, cpu_prepack_moe_weight
+except (ImportError, AttributeError) as e:
+    print("ERROR: CPU fused MoE operations are not available on this platform.")
+    print("This benchmark requires x86 CPU with proper vLLM CPU extensions compiled.")
+    print(
+        "The cpu_fused_moe kernel is typically available on Linux x86_64 "
+        "with AVX2/AVX512."
+    )
+    print(f"Import error: {e}")
+    sys.exit(1)
+
+# ISA selection following test_cpu_fused_moe.py pattern
+ISA_CHOICES = ["amx", "vec"] if torch._C._cpu._is_amx_tile_supported() else ["vec"]
+
+
+@torch.inference_mode()
+def main(
+    batch_size: int,
+    expert_num: int,
+    hidden_size: int,
+    intermediate_size: int,
+    topk_num: int,
+    use_bias: bool = False,
+    dtype: torch.dtype = torch.bfloat16,
+    activation: str = "silu",
+    isa: str = "vec",
+    seed: int = 0,
+    iters: int = 20,
+) -> None:
+    current_platform.seed_everything(seed)
+    # up_dim = 2 * intermediate_size for gate + up projection
+    up_dim = 2 * intermediate_size
+
+    input_tensor = torch.randn((batch_size, hidden_size), dtype=dtype) / (
+        0.5 * hidden_size**0.5
+    )
+
+    w13 = torch.randn((expert_num, up_dim, hidden_size), dtype=dtype) / (
+        0.5 * hidden_size**0.5
+    )
+    w2 = torch.randn((expert_num, hidden_size, intermediate_size), dtype=dtype) / (
+        0.5 * intermediate_size**0.5
+    )
+
+    w13_bias = None
+    w2_bias = None
+    if use_bias:
+        w13_bias = torch.randn((expert_num, up_dim), dtype=dtype) / (0.5 * up_dim**0.5)
+        w2_bias = torch.randn((expert_num, hidden_size), dtype=dtype) / (
+            0.5 * hidden_size**0.5
+        )
+
+    router_logits = torch.randn((batch_size, expert_num), dtype=dtype)
+    score = torch.softmax(router_logits, dim=-1, dtype=torch.float32)
+    topk_weights, topk_ids = torch.topk(score, topk_num)
+    topk_ids = topk_ids.to(torch.int32)
+
+    packed_w13 = cpu_prepack_moe_weight(w13, isa)
+    packed_w2 = cpu_prepack_moe_weight(w2, isa)
+
+    def run_benchmark(iters: int) -> list[float]:
+        times = []
+        for _ in range(iters):
+            start_time = time.perf_counter_ns()
+            _ = cpu_fused_moe(
+                input_tensor,
+                packed_w13,
+                packed_w2,
+                w13_bias,
+                w2_bias,
+                topk_weights,
+                topk_ids,
+                activation,
+                isa,
+            )
+            end_time = time.perf_counter_ns()
+            times.append((end_time - start_time) / 1e6)
+        return times
+
+    # warmup
+    run_benchmark(5)
+    # benchmark
+    times = run_benchmark(iters)
+
+    if not times:
+        print("No iterations to measure. Set --iters > 0.")
+        return
+
+    time_min = min(times)
+    time_max = max(times)
+    time_mean = np.mean(times)
+    time_std = np.std(times)
+
+    print("\tmin (ms) = ", time_min)
+    print("\tmax (ms) = ", time_max)
+    print("\tmean (ms) = ", time_mean)
+    print("\tstd = ", time_std)
+    print("\tmedian (ms) = ", np.median(times))
+
+    # Calculate throughput metrics
+    # FLOPs estimation: 2 * batch * topk * (hidden * up_dim + intermediate * hidden)
+    flops_per_token = (
+        2 * topk_num * (hidden_size * up_dim + intermediate_size * hidden_size)
+    )
+    total_flops = batch_size * flops_per_token
+    tflops = total_flops / (time_mean * 1e-3) / 1e12
+    print(f"\tthroughput (TFLOP/s) = {tflops:.4f}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the CPU fused MoE kernel.")
+    parser.add_argument("--batch-size", type=int, default=64)
+    parser.add_argument("--expert-num", type=int, default=8)
+    parser.add_argument("--hidden-size", type=int, default=2880)
+    parser.add_argument("--intermediate-size", type=int, default=2880)
+    parser.add_argument(
+        "--topk-num",
+        type=int,
+        default=None,
+        help="Number of experts to route each token to (default: expert_num // 2)",
+    )
+    parser.add_argument("--use-bias", action="store_true")
+    parser.add_argument(
+        "--activation",
+        type=str,
+        choices=["silu", "swigluoai"],
+        default="silu",
+        help="Activation function",
+    )
+    parser.add_argument(
+        "--isa",
+        type=str,
+        choices=ISA_CHOICES,
+        default=ISA_CHOICES[0],
+        help=f"ISA to use (available: {ISA_CHOICES})",
+    )
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--iters", type=int, default=20)
+
+    args = parser.parse_args()
+
+    # Default topk_num to expert_num // 2, minimum 1
+    topk_num = (
+        args.topk_num if args.topk_num is not None else max(args.expert_num // 2, 1)
+    )
+
+    print(args)
+
+    main(
+        batch_size=args.batch_size,
+        expert_num=args.expert_num,
+        hidden_size=args.hidden_size,
+        intermediate_size=args.intermediate_size,
+        topk_num=topk_num,
+        use_bias=args.use_bias,
+        dtype=torch.bfloat16,  # Following test_cpu_fused_moe.py
+        activation=args.activation,
+        isa=args.isa,
+        seed=args.seed,
+        iters=args.iters,
+    )

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG 46d64a8ebef03fa50b4ae74937276a5c940e3f95
+        GIT_TAG 526781394b33d9888e4c41952e692266267dd8bf
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -55,16 +55,43 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_SOURCES
         ${flashmla_SOURCE_DIR}/csrc/torch_api.cpp
-        ${flashmla_SOURCE_DIR}/csrc/pybind.cpp
-        ${flashmla_SOURCE_DIR}/csrc/smxx/get_mla_metadata.cu
-        ${flashmla_SOURCE_DIR}/csrc/smxx/mla_combine.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/splitkv_mla.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/splitkv_mla.cu
+
+        # Misc kernels for decoding
+        ${flashmla_SOURCE_DIR}/csrc/smxx/decode/get_decoding_sched_meta/get_decoding_sched_meta.cu
+        ${flashmla_SOURCE_DIR}/csrc/smxx/decode/combine/combine.cu
+
+        # sm90 dense decode
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/instantiations/fp16.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/instantiations/bf16.cu
+
+        # sm90 sparse decode
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/model1_persistent_h64.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/model1_persistent_h128.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/v32_persistent_h64.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/v32_persistent_h128.cu
+
+        # sm90 sparse prefill
         ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/fwd.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/sparse_fp8/splitkv_mla.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k512.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k512_topklen.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k576.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k576_topklen.cu
+
+        # sm100 dense prefill & backward
         ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_fwd_sm100.cu
         ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_bwd_sm100.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd.cu
+
+        # sm100 sparse prefill
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head64/instantiations/phase1_k512.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head64/instantiations/phase1_k576.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head128/instantiations/phase1_k512.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head128/instantiations/phase1_k576.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd_for_small_topk/head128/instantiations/phase1_prefill_k512.cu
+
+        # sm100 sparse decode
+        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/head64/instantiations/v32.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/head64/instantiations/model1.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd_for_small_topk/head128/instantiations/phase1_decode_k512.cu
     )
 
     set(FlashMLA_Extension_SOURCES
@@ -76,6 +103,7 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc
+        ${flashmla_SOURCE_DIR}/csrc/kerutils/include
         ${flashmla_SOURCE_DIR}/csrc/sm90
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include
         ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include
@@ -83,7 +111,6 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_Extension_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc
-        ${flashmla_SOURCE_DIR}/csrc/sm90
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include
         ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include
@@ -110,9 +137,12 @@ if(FLASH_MLA_ARCHS)
 
     # Keep Stable ABI for the module, but *not* for CUDA/C++ files.
     # This prevents Py_LIMITED_API from affecting nvcc and C++ compiles.
+    # Also enable C++20 for the FlashMLA sources (required for std::span, requires, etc.)
     target_compile_options(_flashmla_C PRIVATE
         $<$<COMPILE_LANGUAGE:CUDA>:-UPy_LIMITED_API>
-        $<$<COMPILE_LANGUAGE:CXX>:-UPy_LIMITED_API>)
+        $<$<COMPILE_LANGUAGE:CXX>:-UPy_LIMITED_API>
+        $<$<COMPILE_LANGUAGE:CXX>:-std=c++20>
+        $<$<COMPILE_LANGUAGE:CUDA>:-std=c++20>)
 
     define_extension_target(
         _flashmla_extension_C

cmake/external_projects/qutlass.cmake

@@ -31,10 +31,15 @@ if(NOT qutlass_SOURCE_DIR)
 endif()
 message(STATUS "[QUTLASS] QuTLASS is available at ${qutlass_SOURCE_DIR}")
 
-cuda_archs_loose_intersection(QUTLASS_ARCHS "12.0a;10.0a" "${CUDA_ARCHS}")
-if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND QUTLASS_ARCHS)
+if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
+  cuda_archs_loose_intersection(QUTLASS_ARCHS "12.0a;10.0f" "${CUDA_ARCHS}")
+else()
+  cuda_archs_loose_intersection(QUTLASS_ARCHS "12.0a;10.0a;10.3a" "${CUDA_ARCHS}")
+endif()
 
-  if(QUTLASS_ARCHS MATCHES "10\\.0a")
+if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
+
+  if(QUTLASS_ARCHS MATCHES "10\\.(0a|3a|0f)")
     set(QUTLASS_TARGET_CC 100)
   elseif(QUTLASS_ARCHS MATCHES "12\\.0a")
     set(QUTLASS_TARGET_CC 120)

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 86f8f157cf82aa2342743752b97788922dd7de43
+          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/cache.h

@@ -9,16 +9,6 @@
 void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
                  const torch::Tensor& block_mapping);
 
-// Note: the key_caches and value_caches vectors are constant but
-// not the Tensors they contain. The vectors need to be const refs
-// in order to satisfy pytorch's C++ operator registration code.
-void copy_blocks(std::vector<torch::Tensor> const& key_caches,
-                 std::vector<torch::Tensor> const& value_caches,
-                 const torch::Tensor& block_mapping);
-
-void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
-                     const torch::Tensor& block_mapping);
-
 void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
                        torch::Tensor& key_cache, torch::Tensor& value_cache,
                        torch::Tensor& slot_mapping,
@@ -37,6 +27,13 @@ void concat_and_cache_mla(torch::Tensor& kv_c, torch::Tensor& k_pe,
                           const std::string& kv_cache_dtype,
                           torch::Tensor& scale);
 
+// NOTE: k_pe and kv_c order is flipped compared to concat_and_cache_mla
+void concat_and_cache_mla_rope_fused(
+    torch::Tensor& positions, torch::Tensor& q_pe, torch::Tensor& k_pe,
+    torch::Tensor& kv_c, torch::Tensor& rope_cos_sin_cache, bool rope_is_neox,
+    torch::Tensor& kv_cache_slot_mapping, torch::Tensor& kv_cache,
+    const std::string& kv_cache_dtype, torch::Tensor& kv_cache_quant_scale);
+
 // Just for unittest
 void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
                  const double scale, const std::string& kv_cache_dtype);

csrc/cache_kernels.cu

@@ -119,94 +119,6 @@ __global__ void copy_blocks_mla_kernel(
 
 }  // namespace vllm
 
-// Note: the key_caches and value_caches vectors are constant but
-// not the Tensors they contain. The vectors need to be const refs
-// in order to satisfy pytorch's C++ operator registration code.
-void copy_blocks(std::vector<torch::Tensor> const& key_caches,
-                 std::vector<torch::Tensor> const& value_caches,
-                 const torch::Tensor& block_mapping) {
-  int num_layers = key_caches.size();
-  TORCH_CHECK(num_layers == value_caches.size());
-  if (num_layers == 0) {
-    return;
-  }
-  torch::Device cache_device = key_caches[0].device();
-  TORCH_CHECK(cache_device.is_cuda());
-
-  // Create data structures for the kernel.
-  // Create an array of pointers to the key and value caches.
-  int64_t key_cache_ptrs[num_layers];
-  int64_t value_cache_ptrs[num_layers];
-  for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
-    key_cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(key_caches[layer_idx].data_ptr());
-    value_cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
-  }
-
-  // block_mapping is a 2D tensor with shape (num_pairs, 2).
-  int num_pairs = block_mapping.size(0);
-
-  // Move the data structures to the GPU.
-  // NOTE: This synchronizes the CPU and GPU.
-  torch::Tensor key_cache_ptrs_tensor =
-      torch::from_blob(key_cache_ptrs, {num_layers}, torch::kInt64)
-          .to(cache_device);
-  torch::Tensor value_cache_ptrs_tensor =
-      torch::from_blob(value_cache_ptrs, {num_layers}, torch::kInt64)
-          .to(cache_device);
-
-  // Launch the kernel.
-  const int numel_per_block = key_caches[0][0].numel();
-  dim3 grid(num_layers, num_pairs);
-  dim3 block(std::min(1024, numel_per_block));
-  const at::cuda::OptionalCUDAGuard device_guard(cache_device);
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
-      key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
-        vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            key_cache_ptrs_tensor.data_ptr<int64_t>(),
-            value_cache_ptrs_tensor.data_ptr<int64_t>(),
-            block_mapping.data_ptr<int64_t>(), numel_per_block);
-      }));
-}
-
-// copy blocks kernel for MLA (assumes a joint KV-cache)
-void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
-                     const torch::Tensor& block_mapping) {
-  int num_layers = kv_caches.size();
-  if (num_layers == 0) {
-    return;
-  }
-  torch::Device cache_device = kv_caches[0].device();
-  TORCH_CHECK(cache_device.is_cuda(), "kv_cache must be on CUDA");
-
-  std::vector<int64_t> cache_ptrs(num_layers);
-  for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
-    cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(kv_caches[layer_idx].data_ptr());
-  }
-  torch::Tensor cache_ptrs_tensor =
-      torch::from_blob(cache_ptrs.data(), {num_layers}, torch::kInt64)
-          .to(cache_device);
-
-  int num_pairs = block_mapping.size(0);
-  // We use the stride instead of numel in case the cache is padded for memory
-  // alignment reasons, we assume the blocks data (inclusive of any padding)
-  // is contiguous in memory
-  int mem_footprint_per_block = kv_caches[0].stride(0);
-  dim3 grid(num_layers, num_pairs);
-  dim3 block(std::min(1024, mem_footprint_per_block));
-  const at::cuda::OptionalCUDAGuard device_guard(cache_device);
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
-      kv_caches[0].scalar_type(), "copy_blocks_mla_kernel", ([&] {
-        vllm::copy_blocks_mla_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            cache_ptrs_tensor.data_ptr<int64_t>(),
-            block_mapping.data_ptr<int64_t>(), mem_footprint_per_block);
-      }));
-}
-
 namespace vllm {
 
 // Used to copy/convert one element
@@ -539,9 +451,6 @@ __global__ void indexer_k_quant_and_cache_kernel(
   for (int i = 0; i < VEC_SIZE; i++) {
     amax = fmaxf(amax, fabsf(float(k_val_ptr[i])));
   }
-#ifndef USE_ROCM
-  __syncwarp();
-#endif
 
   // Reduced amax
   for (int mask = 16; mask > 0; mask /= 2) {
@@ -551,9 +460,7 @@ __global__ void indexer_k_quant_and_cache_kernel(
     amax = fmaxf(amax, __shfl_xor_sync(unsigned(-1), amax, mask));
 #endif
   }
-#ifndef USE_ROCM
-  __syncwarp();
-#endif
+
 #if defined(__gfx942__)
   float scale = fmaxf(amax, 1e-4) / 224.0f;
 #else

csrc/cache_kernels_fused.cu

@@ -0,0 +1,279 @@
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include "cuda_compat.h"
+#include "dispatch_utils.h"
+
+#include "quantization/w8a8/fp8/common.cuh"
+#ifdef USE_ROCM
+  #include "quantization/w8a8/fp8/amd/quant_utils.cuh"
+#else
+  #include "quantization/w8a8/fp8/nvidia/quant_utils.cuh"
+#endif
+
+#ifdef USE_ROCM
+  #include <hip/hip_bf16.h>
+typedef __hip_bfloat16 __nv_bfloat16;
+#endif
+
+namespace vllm {
+
+// NOTE Be EXTRA careful with raw_kv_scalar_t, for __half and __nv_bfloat16 it's
+// using u16 as the backing type.
+template <typename qk_t, bool IS_NEOX, typename raw_kv_scalar_t,
+          typename cache_t, Fp8KVCacheDataType kv_dt>
+__global__ void concat_and_cache_mla_rope_fused_kernel(
+    const int64_t* __restrict__ positions,  // [num_tokens]
+    qk_t* __restrict__ q_pe,        // [num_tokens, num_q_heads, rot_dim]
+    qk_t* __restrict__ k_pe,        // [num_tokens, rot_dim]
+    const qk_t* __restrict__ kv_c,  // [num_tokens, kv_lora_rank]
+    const qk_t* __restrict__ rope_cos_sin_cache,  // [max_position, 2,
+                                                  // rot_dim // 2]
+    const int rot_dim, const int64_t q_pe_stride_token,
+    const int64_t q_pe_stride_head, const int64_t k_pe_stride,
+    const int64_t kv_c_stride, const int num_q_heads,
+    cache_t* __restrict__ kv_cache,  // [num_blocks, block_size, (kv_lora_rank +
+                                     // rot_dim)]
+    const int64_t* __restrict__ kv_cache_slot_mapping,  // [num_tokens]
+    const int block_stride, const int entry_stride, const int kv_lora_rank,
+    const int block_size, const float* kv_cache_quant_scale) {
+  // Each thread block is responsible for one token.
+  const int64_t token_idx = blockIdx.x;
+  const int64_t pos = positions[token_idx];
+
+  const qk_t* cos_sin_ptr = rope_cos_sin_cache + pos * rot_dim;
+
+  const int embed_dim = rot_dim / 2;
+
+  // Q ROPE
+  const int nq = num_q_heads * embed_dim;
+  for (int i = threadIdx.x; i < nq; i += blockDim.x) {
+    int head_idx = i / embed_dim;
+    int pair_idx = i % embed_dim;
+
+    // NOTE: Would be nice to have interleaved sin/cos so we could just load
+    // both at the same time.
+    qk_t cos = VLLM_LDG(cos_sin_ptr + pair_idx);
+    qk_t sin = VLLM_LDG(cos_sin_ptr + pair_idx + embed_dim);
+
+    qk_t* q_pe_head_ptr =
+        q_pe + token_idx * q_pe_stride_token + head_idx * q_pe_stride_head;
+
+    int pair_idx_x, pair_idx_y;
+    if constexpr (IS_NEOX) {
+      // GPT-NeoX style rotary embedding.
+      pair_idx_x = pair_idx;
+      pair_idx_y = embed_dim + pair_idx;
+    } else {
+      // GPT-J style rotary embedding.
+      pair_idx_x = pair_idx * 2;
+      pair_idx_y = pair_idx * 2 + 1;
+    }
+
+    qk_t x_src = q_pe_head_ptr[pair_idx_x];
+    qk_t y_src = q_pe_head_ptr[pair_idx_y];
+
+    qk_t x_dst = x_src * cos - y_src * sin;
+    qk_t y_dst = y_src * cos + x_src * sin;
+
+    q_pe_head_ptr[pair_idx_x] = x_dst;
+    q_pe_head_ptr[pair_idx_y] = y_dst;
+  }
+
+  const int64_t slot_idx = kv_cache_slot_mapping[token_idx];
+  const int64_t block_idx = slot_idx / block_size;
+  const int64_t entry_idx = slot_idx % block_size;
+
+  // NOTE: slot_idx can be -1 if the token is padded
+  if (slot_idx < 0) {
+    return;
+  }
+
+  // K with 1 HEAD
+  for (int i = threadIdx.x; i < embed_dim; i += blockDim.x) {
+    int pair_idx = i;
+
+    qk_t cos = VLLM_LDG(cos_sin_ptr + pair_idx);
+    qk_t sin = VLLM_LDG(cos_sin_ptr + pair_idx + embed_dim);
+
+    qk_t* k_pe_head_ptr = k_pe + token_idx * k_pe_stride;
+
+    int pair_idx_x, pair_idx_y;
+    if constexpr (IS_NEOX) {
+      // GPT-NeoX style rotary embedding.
+      pair_idx_x = pair_idx;
+      pair_idx_y = embed_dim + pair_idx;
+    } else {
+      // GPT-J style rotary embedding.
+      pair_idx_x = pair_idx * 2;
+      pair_idx_y = pair_idx * 2 + 1;
+    }
+
+    qk_t x_src = k_pe_head_ptr[pair_idx_x];
+    qk_t y_src = k_pe_head_ptr[pair_idx_y];
+
+    qk_t x_dst = x_src * cos - y_src * sin;
+    qk_t y_dst = y_src * cos + x_src * sin;
+
+    k_pe_head_ptr[pair_idx_x] = x_dst;
+    k_pe_head_ptr[pair_idx_y] = y_dst;
+
+    // NOTE Why is this monster necessary?
+    // When K is of type float16, the actual template replacement for
+    // raw_kv_scalar_t with be u16. That's why it's used at the last moment
+    // otherwise CUDA ALU would break.
+    const raw_kv_scalar_t raw_x_value =
+        *reinterpret_cast<const raw_kv_scalar_t*>(&x_dst);
+    const raw_kv_scalar_t raw_y_value =
+        *reinterpret_cast<const raw_kv_scalar_t*>(&y_dst);
+
+    cache_t* kv_cache_ptr = kv_cache + block_idx * block_stride +
+                            entry_idx * entry_stride + kv_lora_rank;
+
+    // MLA Cache Store
+    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+      kv_cache_ptr[pair_idx_x] = raw_x_value;
+      kv_cache_ptr[pair_idx_y] = raw_y_value;
+    } else {
+      kv_cache_ptr[pair_idx_x] =
+          fp8::scaled_convert<cache_t, raw_kv_scalar_t, kv_dt>(
+              raw_x_value, *kv_cache_quant_scale);
+      kv_cache_ptr[pair_idx_y] =
+          fp8::scaled_convert<cache_t, raw_kv_scalar_t, kv_dt>(
+              raw_y_value, *kv_cache_quant_scale);
+    }
+  }
+
+  // NOPE
+  for (int i = threadIdx.x; i < kv_lora_rank; i += blockDim.x) {
+    const qk_t* src_ptr = kv_c + token_idx * kv_c_stride + i;
+    const raw_kv_scalar_t src_value =
+        *reinterpret_cast<const raw_kv_scalar_t*>(src_ptr);
+
+    cache_t* kv_cache_ptr =
+        kv_cache + block_idx * block_stride + entry_idx * entry_stride;
+
+    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+      kv_cache_ptr[i] = src_value;
+    } else {
+      kv_cache_ptr[i] = fp8::scaled_convert<cache_t, raw_kv_scalar_t, kv_dt>(
+          src_value, *kv_cache_quant_scale);
+    }
+  }
+}
+
+}  // namespace vllm
+
+#define CALL_CONCAT_AND_CACHE_MLA_ROPE_FUSED(RAW_KV_T, CACHE_T, KV_DTYPE)      \
+  do {                                                                         \
+    VLLM_DISPATCH_FLOATING_TYPES(q_pe.scalar_type(), "qk_scalar_type", [&] {   \
+      using qk_t = scalar_t;                                                   \
+      if (rope_is_neox) {                                                      \
+        vllm::concat_and_cache_mla_rope_fused_kernel<qk_t, true, RAW_KV_T,     \
+                                                     CACHE_T, KV_DTYPE>        \
+            <<<grid, block, 0, stream>>>(                                      \
+                positions.data_ptr<int64_t>(), q_pe.data_ptr<qk_t>(),          \
+                k_pe.data_ptr<qk_t>(), kv_c.data_ptr<qk_t>(),                  \
+                rope_cos_sin_cache.data_ptr<qk_t>(), rot_dim,                  \
+                q_pe_stride_token, q_pe_stride_head, k_pe_stride, kv_c_stride, \
+                num_q_heads, reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),  \
+                kv_cache_slot_mapping.data_ptr<int64_t>(), block_stride,       \
+                entry_stride, kv_lora_rank, block_size,                        \
+                kv_cache_quant_scale.data_ptr<float>());                       \
+      } else {                                                                 \
+        vllm::concat_and_cache_mla_rope_fused_kernel<qk_t, false, RAW_KV_T,    \
+                                                     CACHE_T, KV_DTYPE>        \
+            <<<grid, block, 0, stream>>>(                                      \
+                positions.data_ptr<int64_t>(), q_pe.data_ptr<qk_t>(),          \
+                k_pe.data_ptr<qk_t>(), kv_c.data_ptr<qk_t>(),                  \
+                rope_cos_sin_cache.data_ptr<qk_t>(), rot_dim,                  \
+                q_pe_stride_token, q_pe_stride_head, k_pe_stride, kv_c_stride, \
+                num_q_heads, reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),  \
+                kv_cache_slot_mapping.data_ptr<int64_t>(), block_stride,       \
+                entry_stride, kv_lora_rank, block_size,                        \
+                kv_cache_quant_scale.data_ptr<float>());                       \
+      }                                                                        \
+    });                                                                        \
+  } while (false)
+
+// Executes RoPE on q_pe and k_pe, then writes k_pe and kv_c in the kv cache.
+// q_pe and k_pe are modified in place.
+// Replaces DeepseekScalingRotaryEmbedding.self.rotary_emb and
+// concat_and_cache_mla.
+void concat_and_cache_mla_rope_fused(
+    torch::Tensor& positions,           // [num_tokens]
+    torch::Tensor& q_pe,                // [num_tokens, num_q_heads, rot_dim]
+    torch::Tensor& k_pe,                // [num_tokens, rot_dim]
+    torch::Tensor& kv_c,                // [num_tokens, kv_lora_rank]
+    torch::Tensor& rope_cos_sin_cache,  // [max_position, rot_dim]
+    bool rope_is_neox,
+    torch::Tensor&
+        kv_cache_slot_mapping,  // [num_tokens] or [num_actual_tokens]
+    torch::Tensor&
+        kv_cache,  // [num_blocks, block_size, (kv_lora_rank + rot_dim)]
+    const std::string& kv_cache_dtype, torch::Tensor& kv_cache_quant_scale) {
+  const int64_t num_tokens = q_pe.size(0);
+
+  const int num_q_heads = q_pe.size(1);
+  const int rot_dim = q_pe.size(2);
+  const int kv_lora_rank = kv_c.size(1);
+
+  TORCH_CHECK(positions.size(0) >=
+              num_tokens);  // CUDA Graphs might pad this for us
+  TORCH_CHECK_EQ(positions.dim(), 1);
+  TORCH_CHECK_EQ(positions.scalar_type(), c10::ScalarType::Long);
+
+  TORCH_CHECK_EQ(q_pe.size(0), num_tokens);
+  TORCH_CHECK_EQ(q_pe.size(1), num_q_heads);
+  TORCH_CHECK_EQ(q_pe.size(2), rot_dim);
+  TORCH_CHECK_EQ(q_pe.dim(), 3);
+
+  TORCH_CHECK_EQ(k_pe.size(0), num_tokens);
+  TORCH_CHECK_EQ(k_pe.size(1), rot_dim);
+  TORCH_CHECK_EQ(k_pe.dim(), 2);
+  TORCH_CHECK_EQ(k_pe.scalar_type(), q_pe.scalar_type());
+
+  TORCH_CHECK_EQ(kv_c.size(0), num_tokens);
+  TORCH_CHECK_EQ(kv_c.size(1), kv_lora_rank);
+  TORCH_CHECK_EQ(kv_c.dim(), 2);
+  TORCH_CHECK_EQ(kv_c.scalar_type(), q_pe.scalar_type());
+  TORCH_CHECK_EQ(kv_c.dtype(), q_pe.dtype());
+
+  TORCH_CHECK_EQ(rope_cos_sin_cache.size(1), rot_dim);
+  TORCH_CHECK_EQ(rope_cos_sin_cache.scalar_type(), q_pe.scalar_type());
+
+  TORCH_CHECK_EQ(kv_cache_slot_mapping.size(0), num_tokens);
+  TORCH_CHECK_EQ(kv_cache_slot_mapping.scalar_type(), c10::ScalarType::Long);
+
+  TORCH_CHECK_EQ(kv_cache.size(2), kv_lora_rank + rot_dim);
+  TORCH_CHECK_EQ(kv_cache.dim(), 3);
+
+  TORCH_CHECK_EQ(kv_cache_quant_scale.numel(), 1);
+  TORCH_CHECK_EQ(kv_cache_quant_scale.scalar_type(), c10::ScalarType::Float);
+
+  int64_t q_pe_stride_token = q_pe.stride(0);
+  int64_t q_pe_stride_head = q_pe.stride(1);
+
+  int64_t k_pe_stride = k_pe.stride(0);
+  int64_t kv_c_stride = kv_c.stride(0);
+
+  int block_size = kv_cache.size(1);
+
+  int block_stride = kv_cache.stride(0);
+  int entry_stride = kv_cache.stride(1);
+
+  int rope_block_size = std::min(num_q_heads * rot_dim / 2, 512);
+  int mla_block_size = kv_lora_rank;
+  int thread_block_size =
+      std::min(std::max(rope_block_size, mla_block_size), 512);
+
+  dim3 grid(num_tokens, 1, 1);
+  dim3 block(thread_block_size, 1, 1);
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(positions));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype,
+                             CALL_CONCAT_AND_CACHE_MLA_ROPE_FUSED);
+}

csrc/cpu/cpu_attn.cpp

@@ -15,6 +15,7 @@
 
 #ifdef __aarch64__
   #include "cpu_attn_neon.hpp"
+  // NEON requires head_dim to be a multiple of 32
   #define NEON_DISPATCH(...)                                                   \
     case cpu_attention::ISA::NEON: {                                           \
       using attn_impl = cpu_attention::AttentionImpl<cpu_attention::ISA::NEON, \
@@ -36,7 +37,9 @@
     switch (HEAD_DIM) {                                         \
       CPU_ATTN_DISPATCH_CASE(32, __VA_ARGS__)                   \
       CPU_ATTN_DISPATCH_CASE(64, __VA_ARGS__)                   \
+      CPU_ATTN_DISPATCH_CASE(80, __VA_ARGS__)                   \
       CPU_ATTN_DISPATCH_CASE(96, __VA_ARGS__)                   \
+      CPU_ATTN_DISPATCH_CASE(112, __VA_ARGS__)                  \
       CPU_ATTN_DISPATCH_CASE(128, __VA_ARGS__)                  \
       CPU_ATTN_DISPATCH_CASE(160, __VA_ARGS__)                  \
       CPU_ATTN_DISPATCH_CASE(192, __VA_ARGS__)                  \

csrc/cpu/cpu_attn_amx.hpp

@@ -377,7 +377,7 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
       const int32_t q_heads_per_kv, const int64_t q_num_stride,
       const int64_t q_head_stride, const float scale) {
     constexpr int64_t bytes_per_head = head_dim * sizeof(scalar_t);
-    static_assert(bytes_per_head % AMX_TILE_ROW_BYTES == 0);
+    // static_assert(bytes_per_head % AMX_TILE_ROW_BYTES == 0);
     constexpr int64_t head_size_block_num = bytes_per_head / AMX_TILE_ROW_BYTES;
     constexpr int64_t head_elem_num_pre_block =
         AMX_TILE_ROW_BYTES / sizeof(scalar_t);

csrc/cpu/cpu_attn_neon.hpp

@@ -264,7 +264,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
   constexpr static ISA ISAType = ISA::NEON;
   constexpr static bool scale_on_logits = false;  // apply scale on q_buffer
 
-  static_assert(HeadDim % HeadDimAlignment == 0);
+  //  static_assert(HeadDim % HeadDimAlignment == 0);
   // the gemm micro kernel is Mx8
   static_assert(HeadDimAlignment % 8 == 0);
   static_assert(BlockSizeAlignment % 8 == 0);

csrc/cpu/utils.cpp

@@ -24,6 +24,8 @@ std::string init_cpu_threads_env(const std::string& cpu_ids) {
 #ifndef VLLM_NUMA_DISABLED
 std::string init_cpu_threads_env(const std::string& cpu_ids) {
   bitmask* omp_cpu_mask = numa_parse_cpustring_all(cpu_ids.c_str());
+  TORCH_CHECK(omp_cpu_mask != nullptr,
+              "Failed to parse CPU string: " + cpu_ids);
   TORCH_CHECK(omp_cpu_mask->size > 0);
   std::vector<int> omp_cpu_ids;
   omp_cpu_ids.reserve(omp_cpu_mask->size);
@@ -44,20 +46,12 @@ std::string init_cpu_threads_env(const std::string& cpu_ids) {
 
   // Memory node binding
   if (numa_available() != -1) {
-    int mem_node_id = numa_node_of_cpu(omp_cpu_ids.front());
     std::set<int> node_ids;
     for (const auto& cpu_id : omp_cpu_ids) {
       int node_id = numa_node_of_cpu(cpu_id);
       if (node_id != -1) {
         node_ids.insert(node_id);
       }
-      if (node_id != mem_node_id) {
-        TORCH_WARN("CPU ", cpu_id, " is on NUMA node ", node_id, ", but CPU ",
-                   omp_cpu_ids.front(), " is on NUMA node ", mem_node_id,
-                   ". All CPUs should be on the same NUMA node for optimal "
-                   "performance. Memory will be bound to NUMA node ",
-                   mem_node_id, ".");
-      }
     }
     // Concatenate all node_ids into a single comma-separated string
     if (!node_ids.empty()) {
@@ -70,7 +64,7 @@ std::string init_cpu_threads_env(const std::string& cpu_ids) {
       }
 
       bitmask* mask = numa_parse_nodestring(node_ids_str.c_str());
-      bitmask* src_mask = numa_get_membind();
+      bitmask* src_mask = numa_get_mems_allowed();
 
       int pid = getpid();
 
@@ -83,15 +77,46 @@ std::string init_cpu_threads_env(const std::string& cpu_ids) {
                      std::to_string(errno));
         }
 
-        // restrict memory allocation node.
-        numa_set_membind(mask);
+        // Restrict memory allocation to the selected NUMA node(s).
+        // Enhances memory locality for the threads bound to those NUMA CPUs.
+        if (node_ids.size() > 1) {
+          errno = 0;
+          numa_set_interleave_mask(mask);
+          if (errno != 0) {
+            TORCH_WARN("numa_set_interleave_mask failed. errno: " +
+                       std::to_string(errno));
+          } else {
+            TORCH_WARN(
+                "NUMA binding: Using INTERLEAVE policy for memory "
+                "allocation across multiple NUMA nodes (nodes: " +
+                node_ids_str +
+                "). Memory allocations will be "
+                "interleaved across the specified NUMA nodes.");
+          }
+        } else {
+          errno = 0;
+          numa_set_membind(mask);
+          if (errno != 0) {
+            TORCH_WARN("numa_set_membind failed. errno: " +
+                       std::to_string(errno));
+          } else {
+            TORCH_WARN(
+                "NUMA binding: Using MEMBIND policy for memory "
+                "allocation on the NUMA nodes (" +
+                node_ids_str +
+                "). Memory allocations will be "
+                "strictly bound to these NUMA nodes.");
+          }
+        }
+
         numa_set_strict(1);
 
         numa_free_nodemask(mask);
         numa_free_nodemask(src_mask);
       } else {
-        TORCH_WARN("numa_parse_nodestring or numa_get_membind failed. errno: " +
-                   std::to_string(errno));
+        TORCH_WARN(
+            "numa_parse_nodestring or numa_get_run_node_mask failed. errno: " +
+            std::to_string(errno));
       }
     }
   }

csrc/cpu/utils.hpp

@@ -37,10 +37,12 @@ struct VecTypeTrait<c10::BFloat16> {
 };
 #endif
 
+#if !defined(__powerpc__)
 template <>
 struct VecTypeTrait<c10::Half> {
   using vec_t = vec_op::FP16Vec16;
 };
+#endif
 
 struct Counter {
   std::atomic<int64_t> counter;

csrc/fused_qknorm_rope_kernel.cu

@@ -107,7 +107,8 @@ __global__ void fusedQKNormRopeKernel(
     void const* k_weight_void,       // RMSNorm weights for key
     void const* cos_sin_cache_void,  // Pre-computed cos/sin cache
     int64_t const* position_ids,     // Position IDs for RoPE
-    int const num_tokens             // Number of tokens
+    int const num_tokens,            // Number of tokens
+    int const rotary_dim             // Dimension for RoPE
 ) {
 #if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
   if constexpr ((std::is_same_v<scalar_t_in, c10::BFloat16>) ||
@@ -227,56 +228,59 @@ __global__ void fusedQKNormRopeKernel(
 
     // Calculate cache pointer for this position - similar to
     // pos_encoding_kernels.cu
-    T_cache const* cache_ptr = cos_sin_cache + pos_id * head_dim;
-    int const embed_dim = head_dim / 2;
+    T_cache const* cache_ptr = cos_sin_cache + pos_id * rotary_dim;
+    int const embed_dim = rotary_dim / 2;
     T_cache const* cos_ptr = cache_ptr;
     T_cache const* sin_ptr = cache_ptr + embed_dim;
-
-    if constexpr (interleave) {
-      // Perform interleaving. Use pre-computed cos/sin values.
+    int const rotary_lanes = rotary_dim / numElemsPerThread;  // rotary range
+    if (laneId < rotary_lanes) {
+      if constexpr (interleave) {
+        // Perform interleaving. Use pre-computed cos/sin values.
 #pragma unroll
-      for (int i = 0; i < numElemsPerThread / 2; ++i) {
-        int const idx0 = 2 * i;
-        int const idx1 = 2 * i + 1;
+        for (int i = 0; i < numElemsPerThread / 2; ++i) {
+          int const idx0 = 2 * i;
+          int const idx1 = 2 * i + 1;
+          // Global dimension index in the head
+          int const dim_idx = laneId * numElemsPerThread + idx0;
 
-        float const val0 = elements[idx0];
-        float const val1 = elements[idx1];
+          float const val0 = elements[idx0];
+          float const val1 = elements[idx1];
 
-        int const dim_idx = laneId * numElemsPerThread + idx0;
-        int const half_dim = dim_idx / 2;
-        float const cos_val =
-            CacheConverter::convert(VLLM_LDG(cos_ptr + half_dim));
-        float const sin_val =
-            CacheConverter::convert(VLLM_LDG(sin_ptr + half_dim));
+          int const half_dim = dim_idx / 2;
+          float const cos_val =
+              CacheConverter::convert(VLLM_LDG(cos_ptr + half_dim));
+          float const sin_val =
+              CacheConverter::convert(VLLM_LDG(sin_ptr + half_dim));
 
-        elements[idx0] = val0 * cos_val - val1 * sin_val;
-        elements[idx1] = val0 * sin_val + val1 * cos_val;
-      }
-    } else {
-      // Before data exchange with in warp, we need to sync.
-      __syncwarp();
-      // Get the data from the other half of the warp. Use pre-computed cos/sin
-      // values.
-#pragma unroll
-      for (int i = 0; i < numElemsPerThread; i++) {
-        elements2[i] = __shfl_xor_sync(FINAL_MASK, elements[i], 16);
-        if (laneId < 16) {
-          elements2[i] = -elements2[i];
+          elements[idx0] = val0 * cos_val - val1 * sin_val;
+          elements[idx1] = val0 * sin_val + val1 * cos_val;
         }
+      } else {
+        // Before data exchange with in warp, we need to sync.
+        __syncwarp();
+        int pairOffset = (rotary_dim / 2) / numElemsPerThread;
+        // Get the data from the other half of the warp. Use pre-computed
+        // cos/sin values.
+#pragma unroll
+        for (int i = 0; i < numElemsPerThread; i++) {
+          elements2[i] = __shfl_xor_sync(FINAL_MASK, elements[i], pairOffset);
 
-        int dim_idx = laneId * numElemsPerThread + i;
-        dim_idx = (dim_idx * 2) % head_dim;
-        int half_dim = dim_idx / 2;
-        // Use pre-computed cos/sin from cache
-        float cos_val = CacheConverter::convert(VLLM_LDG(cos_ptr + half_dim));
-        float sin_val = CacheConverter::convert(VLLM_LDG(sin_ptr + half_dim));
+          if (laneId < pairOffset) {
+            elements2[i] = -elements2[i];
+          }
+          int dim_idx = laneId * numElemsPerThread + i;
 
-        elements[i] = elements[i] * cos_val + elements2[i] * sin_val;
+          dim_idx = (dim_idx * 2) % rotary_dim;
+          int half_dim = dim_idx / 2;
+          float cos_val = CacheConverter::convert(VLLM_LDG(cos_ptr + half_dim));
+          float sin_val = CacheConverter::convert(VLLM_LDG(sin_ptr + half_dim));
+
+          elements[i] = elements[i] * cos_val + elements2[i] * sin_val;
+        }
+        // __shfl_xor_sync does not provide memfence. Need to sync again.
+        __syncwarp();
       }
-      // __shfl_xor_sync does not provide memfence. Need to sync again.
-      __syncwarp();
     }
-
     // Store.
     {
       vec_T vec;
@@ -312,10 +316,10 @@ template <typename scalar_t_in, typename scalar_t_cache>
 void launchFusedQKNormRope(void* qkv, int const num_tokens,
                            int const num_heads_q, int const num_heads_k,
                            int const num_heads_v, int const head_dim,
-                           float const eps, void const* q_weight,
-                           void const* k_weight, void const* cos_sin_cache,
-                           bool const interleave, int64_t const* position_ids,
-                           cudaStream_t stream) {
+                           int const rotary_dim, float const eps,
+                           void const* q_weight, void const* k_weight,
+                           void const* cos_sin_cache, bool const interleave,
+                           int64_t const* position_ids, cudaStream_t stream) {
   constexpr int blockSize = 256;
 
   int const warpsPerBlock = blockSize / 32;
@@ -332,7 +336,7 @@ void launchFusedQKNormRope(void* qkv, int const num_tokens,
         fusedQKNormRopeKernel<scalar_t_in, scalar_t_cache, 64, INTERLEAVE>
             <<<gridDim, blockDim, 0, stream>>>(
                 qkv, num_heads_q, num_heads_k, num_heads_v, eps, q_weight,
-                k_weight, cos_sin_cache, position_ids, num_tokens);
+                k_weight, cos_sin_cache, position_ids, num_tokens, rotary_dim);
       });
       break;
     case 128:
@@ -340,7 +344,7 @@ void launchFusedQKNormRope(void* qkv, int const num_tokens,
         fusedQKNormRopeKernel<scalar_t_in, scalar_t_cache, 128, INTERLEAVE>
             <<<gridDim, blockDim, 0, stream>>>(
                 qkv, num_heads_q, num_heads_k, num_heads_v, eps, q_weight,
-                k_weight, cos_sin_cache, position_ids, num_tokens);
+                k_weight, cos_sin_cache, position_ids, num_tokens, rotary_dim);
       });
       break;
     case 256:
@@ -348,7 +352,7 @@ void launchFusedQKNormRope(void* qkv, int const num_tokens,
         fusedQKNormRopeKernel<scalar_t_in, scalar_t_cache, 256, INTERLEAVE>
             <<<gridDim, blockDim, 0, stream>>>(
                 qkv, num_heads_q, num_heads_k, num_heads_v, eps, q_weight,
-                k_weight, cos_sin_cache, position_ids, num_tokens);
+                k_weight, cos_sin_cache, position_ids, num_tokens, rotary_dim);
       });
       break;
     default:
@@ -392,8 +396,11 @@ void fused_qk_norm_rope(
               "Query weights size must match head dimension");
   TORCH_CHECK(k_weight.size(0) == head_dim,
               "Key weights size must match head dimension");
-  TORCH_CHECK(cos_sin_cache.size(1) == head_dim,
-              "Cos/sin cache dimension must match head_dim");
+
+  TORCH_CHECK(cos_sin_cache.size(1) % 2 == 0, "rotary_dim must be even");
+  TORCH_CHECK(cos_sin_cache.size(1) <= head_dim,
+              "rotary_dim must be less than or equal to head_dim");
+
   TORCH_CHECK(qkv.scalar_type() == q_weight.scalar_type() &&
                   qkv.scalar_type() == k_weight.scalar_type(),
               "qkv, q_weight and k_weight must have the same dtype");
@@ -419,7 +426,8 @@ void fused_qk_norm_rope(
               qkv.data_ptr(), static_cast<int>(num_tokens),
               static_cast<int>(num_heads_q), static_cast<int>(num_heads_k),
               static_cast<int>(num_heads_v), static_cast<int>(head_dim),
-              static_cast<float>(eps), q_weight.data_ptr(), k_weight.data_ptr(),
+              static_cast<int>(cos_sin_cache.size(1)), static_cast<float>(eps),
+              q_weight.data_ptr(), k_weight.data_ptr(),
               cos_sin_cache.data_ptr(), !is_neox,
               reinterpret_cast<int64_t const*>(position_ids.data_ptr()),
               stream);

csrc/moe/grouped_topk_kernels.cu

@@ -31,8 +31,6 @@ namespace moe {
 
 constexpr unsigned FULL_WARP_MASK = 0xffffffff;
 constexpr int32_t WARP_SIZE = 32;
-constexpr int32_t BLOCK_SIZE = 512;
-constexpr int32_t NUM_WARPS_PER_BLOCK = BLOCK_SIZE / WARP_SIZE;
 
 namespace warp_topk {
 
@@ -65,14 +63,6 @@ __forceinline__ __device__ bool is_better_than(T val, T baseline, idxT index,
   return res;
 }
 
-template <typename T, typename idxT>
-int calc_smem_size_for_block_wide(int num_of_warp, int64_t k) {
-  int64_t cache_topk = (sizeof(T) + sizeof(idxT)) * num_of_warp * k;
-  int64_t n = std::max<int>(num_of_warp / 2 * k, num_of_warp * WARP_SIZE);
-  return max(cache_topk,
-             round_up_to_multiple_of<256>(n * sizeof(T)) + n * sizeof(idxT));
-}
-
 template <int size, bool ascending, bool reverse, typename T, typename idxT,
           bool is_stable>
 struct BitonicMerge {
@@ -267,6 +257,15 @@ class WarpSort {
     }
   }
 
+  // Accessors for per-lane selected value/index.
+  // NOTE: For the common case `capacity == WARP_SIZE`, `max_arr_len_ == 1`
+  // and callers should use `i == 0`.
+  __device__ __forceinline__ idxT get_idx(int i = 0) const {
+    return idx_arr_[i];
+  }
+
+  __device__ __forceinline__ T get_val(int i = 0) const { return val_arr_[i]; }
+
  protected:
   static constexpr int max_arr_len_ = capacity / WARP_SIZE;
 
@@ -285,6 +284,7 @@ class WarpSelect : public WarpSort<capacity, greater, T, idxT, is_stable> {
   __device__ WarpSelect(idxT k, T dummy)
       : WarpSort<capacity, greater, T, idxT, is_stable>(k, dummy),
         k_th_(dummy),
+        k_th_idx_(0),
         k_th_lane_((k - 1) % WARP_SIZE) {
     extern __shared__ char smem_buf[];  // extern __shared__ T smem_buf[];
 
@@ -346,9 +346,6 @@ class WarpSelect : public WarpSort<capacity, greater, T, idxT, is_stable> {
       idxT idx = (lane_ < smem_buf_len_) ? idx_smem_[lane_] : 0;
       merge_buf_(val, idx);
     }
-
-    // after done(), smem is used for merging results among warps
-    __syncthreads();
   }
 
  private:
@@ -457,8 +454,8 @@ __device__ inline T apply_scoring(T val) {
   }
 }
 
-template <typename T, ScoringFunc SF>
-__device__ void topk_with_k2(T* output, T const* input, T const* bias,
+template <typename T, typename BiasT, ScoringFunc SF>
+__device__ void topk_with_k2(T* output, T const* input, BiasT const* bias,
                              cg::thread_block_tile<32> const& tile,
                              int32_t const lane_id,
                              int const num_experts_per_group) {
@@ -469,7 +466,7 @@ __device__ void topk_with_k2(T* output, T const* input, T const* bias,
   if (num_experts_per_group > WARP_SIZE) {
     for (int i = lane_id; i < num_experts_per_group; i += WARP_SIZE) {
       T value = apply_scoring<SF>(input[i]);
-      value = value + bias[i];
+      value = value + static_cast<T>(bias[i]);
 
       if (value > largest) {
         second_largest = largest;
@@ -481,7 +478,7 @@ __device__ void topk_with_k2(T* output, T const* input, T const* bias,
   } else {
     for (int i = lane_id; i < num_experts_per_group; i += WARP_SIZE) {
       T value = apply_scoring<SF>(input[i]);
-      value = value + bias[i];
+      value = value + static_cast<T>(bias[i]);
       largest = value;
     }
   }
@@ -503,254 +500,186 @@ __device__ void topk_with_k2(T* output, T const* input, T const* bias,
   }
 }
 
-template <typename T, ScoringFunc SF>
-__global__ void topk_with_k2_kernel(T* output, T* input, T const* bias,
-                                    int64_t const num_tokens,
-                                    int64_t const num_cases,
-                                    int64_t const n_group,
-                                    int64_t const num_experts_per_group) {
-  int32_t warp_id = threadIdx.x / WARP_SIZE;
-  int32_t lane_id = threadIdx.x % WARP_SIZE;
-
-  int32_t case_id = blockIdx.x * NUM_WARPS_PER_BLOCK + warp_id;
-  if (case_id < num_cases) {
-    input += case_id * num_experts_per_group;
-    // bias is per expert group, offset to current group
-    int32_t group_id = case_id % n_group;
-    T const* group_bias = bias + group_id * num_experts_per_group;
-    output += case_id;
-
-    cg::thread_block block = cg::this_thread_block();
-    cg::thread_block_tile<32> tile = cg::tiled_partition<32>(block);
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-    asm volatile("griddepcontrol.wait;");
-#endif
-    topk_with_k2<T, SF>(output, input, group_bias, tile, lane_id,
-                        num_experts_per_group);
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, typename IdxT, ScoringFunc SF, int NGroup = -1>
-__global__ void group_idx_and_topk_idx_kernel(
-    T* scores, T const* group_scores, float* topk_values, IdxT* topk_indices,
-    T const* bias, int64_t const num_tokens, int64_t const n_group,
-    int64_t const topk_group, int64_t const topk, int64_t const num_experts,
-    int64_t const num_experts_per_group, bool renormalize,
+template <typename T, typename BiasT, typename IdxT, ScoringFunc SF>
+__global__ void grouped_topk_fused_kernel(
+    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 renormalize,
     double routed_scaling_factor) {
-  int32_t warp_id = threadIdx.x / WARP_SIZE;
-  int32_t lane_id = threadIdx.x % WARP_SIZE;
-  int32_t case_id =
-      blockIdx.x * NUM_WARPS_PER_BLOCK + warp_id;  // one per token
-  scores += case_id * num_experts;
-  group_scores += case_id * n_group;
-  topk_values += case_id * topk;
-  topk_indices += case_id * topk;
+  int32_t const token_id = static_cast<int32_t>(blockIdx.x);
+  if (token_id >= num_tokens) {
+    return;
+  }
 
-  constexpr bool kUseStaticNGroup = (NGroup > 0);
-  // use int32 to avoid implicit conversion
-  int32_t const n_group_i32 =
-      kUseStaticNGroup ? NGroup : static_cast<int32_t>(n_group);
+  int32_t const warp_id = threadIdx.x / WARP_SIZE;
+  int32_t const lane_id = threadIdx.x % WARP_SIZE;
 
-  int32_t align_num_experts_per_group =
-      warp_topk::round_up_to_multiple_of<WARP_SIZE>(num_experts_per_group);
+  int32_t const n_group_i32 = static_cast<int32_t>(n_group);
+  int32_t const topk_group_i32 = static_cast<int32_t>(topk_group);
+  int32_t const topk_i32 = static_cast<int32_t>(topk);
+  int32_t const num_experts_i32 = static_cast<int32_t>(num_experts);
+
+  int32_t const num_warps = blockDim.x / WARP_SIZE;
+  if (warp_id >= n_group_i32 || num_warps < n_group_i32) {
+    return;
+  }
+
+  int32_t const num_experts_per_group = num_experts_i32 / n_group_i32;
+
+  T* scores_token = scores + static_cast<int64_t>(token_id) * num_experts;
 
   cg::thread_block block = cg::this_thread_block();
   cg::thread_block_tile<32> tile = cg::tiled_partition<32>(block);
 
-  extern __shared__ char smem_buf[];  // NOTE: reuse the shared memory here to
-                                      // store the target topk idx
-  int32_t* s_topk_idx = reinterpret_cast<int32_t*>(smem_buf);
-  T* s_topk_value =
-      reinterpret_cast<T*>(s_topk_idx + NUM_WARPS_PER_BLOCK * topk) +
-      warp_id * topk;
-  s_topk_idx += warp_id * topk;
+  extern __shared__ char smem_buf[];
+  // warpSelect internal staging buffer layout
+  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;
 
-  T value = neg_inf<T>();
-  T topk_group_value = neg_inf<T>();
-  int32_t num_equalto_topkth_group;
+  // user-managed shared memory starts after warpSelect internal staging.
+  uintptr_t ptr_u = reinterpret_cast<uintptr_t>(smem_buf + internal_bytes);
+  ptr_u = (ptr_u + 15) & ~static_cast<uintptr_t>(15);  // align to 16B
+  T* s_group_scores = reinterpret_cast<T*>(ptr_u);
 
 #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
   asm volatile("griddepcontrol.wait;");  // I think all prolog can be put before
                                          // acqbulk because it's ptr arithmetic
 #endif
 
-  if (case_id < num_tokens) {
-    // calculate group_idx
-    int32_t target_num_min =
-        WARP_SIZE - n_group_i32 + static_cast<int32_t>(topk_group);
-    // The check is necessary to avoid abnormal input
-    if (lane_id < n_group_i32 && is_finite(group_scores[lane_id])) {
-      value = group_scores[lane_id];
-    }
+  // phase 1: per-group scan
+  int32_t const group_offset = warp_id * num_experts_per_group;
+  topk_with_k2<T, BiasT, SF>(s_group_scores + warp_id,
+                             scores_token + group_offset, bias + group_offset,
+                             tile, lane_id, num_experts_per_group);
 
-    int count_equal_to_top_value = WARP_SIZE - n_group_i32;
-    int pre_count_equal_to_top_value = 0;
-    // Use loop to find the largset top_group
-    while (count_equal_to_top_value < target_num_min) {
-      topk_group_value = cg::reduce(tile, value, cg::greater<T>());
-      if (value == topk_group_value) {
-        value = neg_inf<T>();
-      }
-      pre_count_equal_to_top_value = count_equal_to_top_value;
-      count_equal_to_top_value =
-          __popc(__ballot_sync(FULL_WARP_MASK, (value == neg_inf<T>())));
-    }
-    num_equalto_topkth_group = target_num_min - pre_count_equal_to_top_value;
-  }
   __syncthreads();
 
+  // phase 2: warp0 selects groups + merges candidates to final topk
+  if (warp_id != 0) {
+    return;
+  }
+
+  topk_values += static_cast<int64_t>(token_id) * topk;
+  topk_indices += static_cast<int64_t>(token_id) * topk;
+
+  // select topk_group groups by group score
   warp_topk::WarpSelect</*capability*/ WARP_SIZE, /*greater*/ true, T, int32_t,
                         /* is_stable */ true>
-      queue((int32_t)topk, neg_inf<T>());
+      group_sel(static_cast<int32_t>(topk_group_i32), neg_inf<T>());
 
-  int count_equalto_topkth_group = 0;
-  bool if_proceed_next_topk = topk_group_value != neg_inf<T>();
-  if (case_id < num_tokens && if_proceed_next_topk) {
-    auto process_group = [&](int i_group) {
-      if ((group_scores[i_group] > topk_group_value) ||
-          ((group_scores[i_group] == topk_group_value) &&
-           (count_equalto_topkth_group < num_equalto_topkth_group))) {
-        int32_t offset = i_group * num_experts_per_group;
-        for (int32_t i = lane_id; i < align_num_experts_per_group;
-             i += WARP_SIZE) {
-          T candidates = neg_inf<T>();
-          if (i < num_experts_per_group) {
-            // apply scoring function (if any) and add bias
-            T input = scores[offset + i];
-            if (is_finite(input)) {
-              T score = apply_scoring<SF>(input);
-              candidates = score + bias[offset + i];
-            }
-          }
-          queue.add(candidates, offset + i);
-        }
-        if (group_scores[i_group] == topk_group_value) {
-          count_equalto_topkth_group++;
+  // all lanes must participate in WarpSelect::add().
+  T gscore = (lane_id < n_group_i32) ? s_group_scores[lane_id] : neg_inf<T>();
+  group_sel.add(gscore, lane_id);
+  group_sel.done();
+
+  // proceed only if the k-th selected group score is not -inf
+  bool proceed = false;
+  if (topk_group_i32 > 0) {
+    int const kth_lane = topk_group_i32 - 1;
+    // broadcast the k-th selected group score to all lanes
+    T kth_val = __shfl_sync(FULL_WARP_MASK, group_sel.get_val(0), kth_lane);
+    proceed = (kth_val != neg_inf<T>());
+  }
+
+  if (!proceed) {
+    for (int i = lane_id; i < topk_i32; i += WARP_SIZE) {
+      topk_indices[i] = static_cast<IdxT>(i);
+      topk_values[i] = 1.0f / static_cast<float>(topk_i32);
+    }
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+    asm volatile("griddepcontrol.launch_dependents;");
+#endif
+    return;
+  }
+
+  // merge per-group topk candidates for selected groups, then select topk
+  warp_topk::WarpSelect</*capability*/ WARP_SIZE, /*greater*/ true, T, int32_t,
+                        /* is_stable */ true>
+      expert_sel(static_cast<int32_t>(topk_i32), neg_inf<T>());
+
+  // selected group ids reside in lanes [0, topk_group)
+  int32_t sel_gid_lane = (lane_id < topk_group_i32) ? group_sel.get_idx(0) : 0;
+
+  // add candidates from selected groups to expert_sel
+  for (int32_t g = 0; g < topk_group_i32; ++g) {
+    int32_t gid = __shfl_sync(FULL_WARP_MASK, sel_gid_lane, g);
+    int32_t const offset = gid * num_experts_per_group;
+    int32_t const align_num_experts_per_group =
+        warp_topk::round_up_to_multiple_of<WARP_SIZE>(num_experts_per_group);
+    for (int32_t i = lane_id; i < align_num_experts_per_group; i += WARP_SIZE) {
+      // all lanes must call `add()` the same number of times.
+      T cand = neg_inf<T>();
+      int32_t idx = 0;
+      if (i < num_experts_per_group) {
+        idx = offset + i;
+        T input = scores_token[idx];
+        if (is_finite(input)) {
+          T score = apply_scoring<SF>(input);
+          cand = score + static_cast<T>(bias[idx]);
         }
       }
-    };
-
-    if constexpr (kUseStaticNGroup) {
-#pragma unroll
-      for (int i_group = 0; i_group < NGroup; ++i_group) {
-        process_group(i_group);
-      }
-    } else {
-      for (int i_group = 0; i_group < n_group_i32; ++i_group) {
-        process_group(i_group);
-      }
-    }
-    queue.done();
-    // Get the topk_idx
-    queue.dumpIdx(s_topk_idx);
-  }
-
-  // Load the valid score value
-  // Calculate the summation
-  float topk_sum = 1e-20;
-  if (case_id < num_tokens && if_proceed_next_topk) {
-    for (int i = lane_id;
-         i < warp_topk::round_up_to_multiple_of<WARP_SIZE>(topk);
-         i += WARP_SIZE) {
-      T value = cuda_cast<T, float>(0.0f);
-      if (i < topk) {
-        // Load the score value (without bias) for normalization
-        T input = scores[s_topk_idx[i]];
-        value = apply_scoring<SF>(input);
-        s_topk_value[i] = value;
-      }
-      if (renormalize) {
-        topk_sum +=
-            cg::reduce(tile, cuda_cast<float, T>(value), cg::plus<float>());
-      }
+      expert_sel.add(cand, idx);
     }
   }
+  expert_sel.done();
 
-  __syncthreads();
-
-  if (case_id < num_tokens) {
-    if (if_proceed_next_topk) {
-      float scale = routed_scaling_factor;
-      if (renormalize) {
-        scale /= topk_sum;
-      }
-      for (int i = lane_id; i < topk; i += WARP_SIZE) {
-        float base = cuda_cast<float, T>(s_topk_value[i]);
-        float value = base * scale;
-        topk_indices[i] = s_topk_idx[i];
-        topk_values[i] = value;
-      }
-    } else {
-      for (int i = lane_id; i < topk; i += WARP_SIZE) {
-        topk_indices[i] = i;
-        topk_values[i] = 1.0f / topk;
-      }
-    }
-    // Note: when if_proceed_next_topk==false, choose the first 8 experts as the
-    // default result.
+  // compute unbiased routing weights + optional renorm.
+  float lane_unbiased = 0.0f;
+  IdxT lane_idx = 0;
+  if (lane_id < topk_i32) {
+    lane_idx = static_cast<IdxT>(expert_sel.get_idx(0));
+    T in = scores_token[static_cast<int32_t>(lane_idx)];
+    lane_unbiased = cuda_cast<float, T>(apply_scoring<SF>(in));
   }
+
+  float topk_sum = 1e-20f;
+  if (renormalize) {
+    topk_sum += cg::reduce(tile, lane_unbiased, cg::plus<float>());
+  }
+
+  float scale = static_cast<float>(routed_scaling_factor);
+  if (renormalize) {
+    scale /= topk_sum;
+  }
+
+  if (lane_id < topk_i32) {
+    topk_indices[lane_id] = lane_idx;
+    topk_values[lane_id] = lane_unbiased * scale;
+  }
+
 #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
   asm volatile("griddepcontrol.launch_dependents;");
 #endif
 }
 
-template <typename T, typename IdxT, ScoringFunc SF>
-inline void launch_group_idx_and_topk_kernel(
-    cudaLaunchConfig_t const& config, T* scores, T* group_scores,
-    float* topk_values, IdxT* topk_indices, T const* bias,
-    int64_t const num_tokens, int64_t const n_group, int64_t const topk_group,
-    int64_t const topk, int64_t const num_experts,
-    int64_t const num_experts_per_group, bool const renormalize,
-    double const routed_scaling_factor) {
-  auto launch = [&](auto* kernel_instance2) {
-    cudaLaunchKernelEx(&config, kernel_instance2, scores, group_scores,
-                       topk_values, topk_indices, bias, num_tokens, n_group,
-                       topk_group, topk, num_experts, num_experts_per_group,
-                       renormalize, routed_scaling_factor);
-  };
-
-  switch (n_group) {
-    case 4: {
-      launch(&group_idx_and_topk_idx_kernel<T, IdxT, SF, 4>);
-      break;
-    }
-    case 8: {
-      launch(&group_idx_and_topk_idx_kernel<T, IdxT, SF, 8>);
-      break;
-    }
-    case 16: {
-      launch(&group_idx_and_topk_idx_kernel<T, IdxT, SF, 16>);
-      break;
-    }
-    case 32: {
-      launch(&group_idx_and_topk_idx_kernel<T, IdxT, SF, 32>);
-      break;
-    }
-    default: {
-      launch(&group_idx_and_topk_idx_kernel<T, IdxT, SF>);
-      break;
-    }
-  }
-}
-
-template <typename T, typename IdxT>
-void invokeNoAuxTc(T* scores, T* group_scores, float* topk_values,
-                   IdxT* topk_indices, T const* bias, int64_t const num_tokens,
+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,
                    int const scoring_func, bool enable_pdl = false,
                    cudaStream_t const stream = 0) {
-  int64_t num_cases = num_tokens * n_group;
-  int64_t topk_with_k2_num_blocks = (num_cases - 1) / NUM_WARPS_PER_BLOCK + 1;
   cudaLaunchConfig_t config;
-  config.gridDim = topk_with_k2_num_blocks;
-  config.blockDim = BLOCK_SIZE;
-  config.dynamicSmemBytes = 0;
+  // 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;
@@ -758,71 +687,46 @@ void invokeNoAuxTc(T* scores, T* group_scores, float* topk_values,
   config.numAttrs = 1;
   config.attrs = attrs;
   auto const sf = static_cast<ScoringFunc>(scoring_func);
-  int64_t const num_experts_per_group = num_experts / n_group;
-  auto launch_topk_with_k2 = [&](auto* kernel_instance1) {
-    cudaLaunchKernelEx(&config, kernel_instance1, group_scores, scores, bias,
-                       num_tokens, num_cases, n_group, num_experts_per_group);
-  };
   switch (sf) {
     case SCORING_NONE: {
-      auto* kernel_instance1 = &topk_with_k2_kernel<T, SCORING_NONE>;
-      launch_topk_with_k2(kernel_instance1);
-      break;
+      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;
     }
     case SCORING_SIGMOID: {
-      auto* kernel_instance1 = &topk_with_k2_kernel<T, SCORING_SIGMOID>;
-      launch_topk_with_k2(kernel_instance1);
-      break;
+      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");
   }
-
-  int64_t topk_with_k_group_num_blocks =
-      (num_tokens - 1) / NUM_WARPS_PER_BLOCK + 1;
-  size_t dynamic_smem_in_bytes =
-      warp_topk::calc_smem_size_for_block_wide<T, int32_t>(NUM_WARPS_PER_BLOCK,
-                                                           topk);
-  config.gridDim = topk_with_k_group_num_blocks;
-  config.blockDim = BLOCK_SIZE;
-  config.dynamicSmemBytes = dynamic_smem_in_bytes;
-  config.stream = stream;
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = enable_pdl;
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  switch (sf) {
-    case SCORING_NONE: {
-      launch_group_idx_and_topk_kernel<T, IdxT, SCORING_NONE>(
-          config, scores, group_scores, topk_values, topk_indices, bias,
-          num_tokens, n_group, topk_group, topk, num_experts,
-          num_experts_per_group, renormalize, routed_scaling_factor);
-      break;
-    }
-    case SCORING_SIGMOID: {
-      launch_group_idx_and_topk_kernel<T, IdxT, SCORING_SIGMOID>(
-          config, scores, group_scores, topk_values, topk_indices, bias,
-          num_tokens, n_group, topk_group, topk, num_experts,
-          num_experts_per_group, renormalize, routed_scaling_factor);
-      break;
-    }
-    default:
-      TORCH_CHECK(false, "Unsupported scoring_func in invokeNoAuxTc");
-  }
 }
 
-#define INSTANTIATE_NOAUX_TC(T, IdxT)                                       \
-  template void invokeNoAuxTc<T, IdxT>(                                     \
-      T * scores, T * group_scores, float* topk_values, IdxT* topk_indices, \
-      T 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,           \
+#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,            \
       int const scoring_func, bool enable_pdl, cudaStream_t const stream);
 
-INSTANTIATE_NOAUX_TC(float, int32_t);
-INSTANTIATE_NOAUX_TC(half, int32_t);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, int32_t);
+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
 
@@ -831,21 +735,26 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
     int64_t topk, bool renormalize, double routed_scaling_factor,
     torch::Tensor const& bias, int64_t scoring_func = 0) {
   auto data_type = scores.scalar_type();
+  auto bias_type = bias.scalar_type();
   auto input_size = scores.sizes();
   int64_t num_tokens = input_size[0];
   int64_t num_experts = input_size[1];
   TORCH_CHECK(input_size.size() == 2, "scores must be a 2D Tensor");
+  TORCH_CHECK(n_group > 0, "n_group must be positive");
+  TORCH_CHECK(topk > 0, "topk must be positive");
+  TORCH_CHECK(topk_group > 0, "topk_group must be positive");
+  TORCH_CHECK(topk_group <= n_group, "topk_group must be <= n_group");
   TORCH_CHECK(num_experts % n_group == 0,
               "num_experts should be divisible by n_group");
   TORCH_CHECK(n_group <= 32,
               "n_group should be smaller than or equal to 32 for now");
   TORCH_CHECK(topk <= 32, "topk should be smaller than or equal to 32 for now");
+  TORCH_CHECK(topk <= topk_group * (num_experts / n_group),
+              "topk must be <= topk_group * (num_experts / n_group)");
   TORCH_CHECK(scoring_func == vllm::moe::SCORING_NONE ||
                   scoring_func == vllm::moe::SCORING_SIGMOID,
               "scoring_func must be SCORING_NONE (0) or SCORING_SIGMOID (1)");
 
-  torch::Tensor group_scores = torch::empty(
-      {num_tokens, n_group}, torch::dtype(data_type).device(torch::kCUDA));
   // Always output float32 for topk_values (eliminates Python-side conversion)
   torch::Tensor topk_values = torch::empty(
       {num_tokens, topk}, torch::dtype(torch::kFloat32).device(torch::kCUDA));
@@ -854,39 +763,59 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
 
   auto stream = c10::cuda::getCurrentCUDAStream(scores.get_device());
 
+#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) {
     case torch::kFloat16:
       // Handle Float16
-      vllm::moe::invokeNoAuxTc<half, int32_t>(
-          reinterpret_cast<half*>(scores.mutable_data_ptr()),
-          reinterpret_cast<half*>(group_scores.mutable_data_ptr()),
-          reinterpret_cast<float*>(topk_values.mutable_data_ptr()),
-          reinterpret_cast<int32_t*>(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);
+      LAUNCH_KERNEL(half, int32_t);
       break;
     case torch::kFloat32:
       // Handle Float32
-      vllm::moe::invokeNoAuxTc<float, int32_t>(
-          reinterpret_cast<float*>(scores.mutable_data_ptr()),
-          reinterpret_cast<float*>(group_scores.mutable_data_ptr()),
-          reinterpret_cast<float*>(topk_values.mutable_data_ptr()),
-          reinterpret_cast<int32_t*>(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);
+      LAUNCH_KERNEL(float, int32_t);
       break;
     case torch::kBFloat16:
       // Handle BFloat16
-      vllm::moe::invokeNoAuxTc<__nv_bfloat16, int32_t>(
-          reinterpret_cast<__nv_bfloat16*>(scores.mutable_data_ptr()),
-          reinterpret_cast<__nv_bfloat16*>(group_scores.mutable_data_ptr()),
-          reinterpret_cast<float*>(topk_values.mutable_data_ptr()),
-          reinterpret_cast<int32_t*>(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);
+      LAUNCH_KERNEL(__nv_bfloat16, int32_t);
       break;
     default:
       // Handle other data types
@@ -894,5 +823,6 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
           "Invalid dtype, only supports float16, float32, and bfloat16");
       break;
   }
+#undef LAUNCH_KERNEL
   return {topk_values, topk_indices};
 }

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -58,7 +58,7 @@ TEMPLATE = (
     "( MARLIN_KERNEL_PARAMS );"
 )
 
-THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128)]
+THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128), (128, 64, 128)]
 
 THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
 

csrc/moe/marlin_moe_wna16/kernel.h

@@ -7,20 +7,20 @@
 #include "quantization/gptq_marlin/marlin_dtypes.cuh"
 #include "core/scalar_type.hpp"
 
-#define MARLIN_KERNEL_PARAMS                                                  \
-  const int4 *__restrict__ A, const int4 *__restrict__ B,                     \
-      int4 *__restrict__ C, int4 *__restrict__ C_tmp,                         \
-      const int4 *__restrict__ b_bias_ptr,                                    \
-      const float *__restrict__ a_scales_ptr,                                 \
-      const int4 *__restrict__ scales_ptr,                                    \
-      const uint16_t *__restrict__ global_scale_ptr,                          \
-      const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx,         \
-      const int32_t *__restrict__ sorted_token_ids_ptr,                       \
-      const int32_t *__restrict__ expert_ids_ptr,                             \
-      const int32_t *__restrict__ num_tokens_past_padded_ptr,                 \
-      const float *__restrict__ topk_weights_ptr, int top_k,                  \
-      bool mul_topk_weights, bool is_ep, int num_groups, int prob_m,          \
-      int prob_n, int prob_k, int *locks, bool has_bias, bool use_atomic_add, \
+#define MARLIN_KERNEL_PARAMS                                          \
+  const int4 *__restrict__ A, const int4 *__restrict__ B,             \
+      int4 *__restrict__ C, int4 *__restrict__ C_tmp,                 \
+      const int4 *__restrict__ b_bias_ptr,                            \
+      const float *__restrict__ a_scales_ptr,                         \
+      const int4 *__restrict__ scales_ptr,                            \
+      const uint16_t *__restrict__ global_scale_ptr,                  \
+      const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx, \
+      const int32_t *__restrict__ sorted_token_ids_ptr,               \
+      const int32_t *__restrict__ expert_ids_ptr,                     \
+      const int32_t *__restrict__ num_tokens_past_padded_ptr,         \
+      const float *__restrict__ topk_weights_ptr, int top_k,          \
+      bool mul_topk_weights, int num_groups, int prob_m, int prob_n,  \
+      int prob_k, int *locks, bool has_bias, bool use_atomic_add,     \
       bool use_fp32_reduce
 
 namespace MARLIN_NAMESPACE_NAME {

csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -71,7 +71,6 @@ __global__ void Marlin(
     const float* __restrict__ topk_weights_ptr,              // moe top weights
     int top_k,              // num of experts per token
     bool mul_topk_weights,  // mul topk weights or not
-    bool is_ep,             // expert parallelism
     int num_groups,         // number of scale groups per output channel
     int prob_m,             // batch dimension m
     int prob_n,             // output dimension n
@@ -273,7 +272,6 @@ __global__ void Marlin(
     const float* __restrict__ topk_weights_ptr,              // moe top weights
     int top_k,              // num of experts per token
     bool mul_topk_weights,  // mul topk weights or not
-    bool is_ep,             // expert parallelism
     int num_groups,         // number of scale groups per output channel
     int prob_m,             // batch dimension m
     int prob_n,             // output dimension n
@@ -376,14 +374,6 @@ __global__ void Marlin(
 
   // parallel: num valid moe blocks
   int parallel = num_tokens_past_padded / moe_block_size;
-  int num_valid_blocks = parallel;
-  if (is_ep) {
-    for (int i = 0; i < parallel; i++) {
-      if (expert_ids_ptr[i] == -1) num_valid_blocks--;
-    }
-  }
-  int num_invalid_blocks = parallel - num_valid_blocks;
-  parallel = num_valid_blocks;
 
   int k_tiles = prob_k / 16 / thread_k_blocks;
   int n_tiles = prob_n / 16 / thread_n_blocks;
@@ -538,22 +528,8 @@ __global__ void Marlin(
     if (par_id >= parallel) return;
 
     old_expert_id = expert_id;
-    if (num_invalid_blocks > 0) {
-      int skip_count = par_id;
-      for (int i = 0; i < num_tokens_past_padded / moe_block_size; i++) {
-        expert_id = expert_ids_ptr[i];
-        if (expert_id != -1) {
-          if (skip_count == 0) {
-            block_id = i;
-            break;
-          };
-          skip_count--;
-        };
-      }
-    } else {
-      block_id = par_id;
-      expert_id = expert_ids_ptr[block_id];
-    }
+    block_id = par_id;
+    expert_id = expert_ids_ptr[block_id];
 
     if constexpr (b_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
       uint16_t val = global_scale_ptr[expert_id];

csrc/moe/marlin_moe_wna16/ops.cu

@@ -126,14 +126,16 @@ thread_config_t small_batch_thread_configs[] = {
 
     // thread_k, thread_n, num_threads
     {128, 128, 256},
-    {64, 128, 128}};
+    {64, 128, 128},
+    {128, 64, 128}};
 
 thread_config_t large_batch_thread_configs[] = {
     // Ordered by priority
 
     // thread_k, thread_n, num_threads
     {64, 256, 256},
-    {64, 128, 128}};
+    {64, 128, 128},
+    {128, 64, 128}};
 
 typedef struct {
   int blocks_per_sm;
@@ -336,14 +338,14 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
                void* perm, void* a_tmp, void* sorted_token_ids,
                void* expert_ids, void* num_tokens_past_padded,
                void* topk_weights, int moe_block_size, int num_experts,
-               int top_k, bool mul_topk_weights, bool is_ep, int prob_m,
-               int prob_n, int prob_k, void* workspace,
-               vllm::ScalarType const& a_type, vllm::ScalarType const& b_type,
-               vllm::ScalarType const& c_type, vllm::ScalarType const& s_type,
-               bool has_bias, bool has_act_order, bool is_k_full, bool has_zp,
-               int num_groups, int group_size, int dev, cudaStream_t stream,
-               int thread_k, int thread_n, int sms, int blocks_per_sm,
-               bool use_atomic_add, bool use_fp32_reduce, bool is_zp_float) {
+               int top_k, bool mul_topk_weights, int prob_m, int prob_n,
+               int prob_k, void* workspace, vllm::ScalarType const& a_type,
+               vllm::ScalarType const& b_type, vllm::ScalarType const& c_type,
+               vllm::ScalarType const& s_type, bool has_bias,
+               bool has_act_order, bool is_k_full, bool has_zp, int num_groups,
+               int group_size, int dev, cudaStream_t stream, int thread_k,
+               int thread_n, int sms, int blocks_per_sm, bool use_atomic_add,
+               bool use_fp32_reduce, bool is_zp_float) {
   int thread_m_blocks = div_ceil(moe_block_size, 16);
   bool m_block_size_8 = moe_block_size == 8;
   bool is_a_8bit = a_type.size_bits() == 8;
@@ -523,7 +525,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
   kernel<<<blocks, num_threads, max_shared_mem, stream>>>(
       A_ptr, B_ptr, C_ptr, C_tmp_ptr, bias_ptr, a_s_ptr, b_s_ptr, g_s_ptr, zp_ptr, g_idx_ptr,
       sorted_token_ids_ptr, expert_ids_ptr, num_tokens_past_padded_ptr,
-      topk_weights_ptr, top_k, mul_topk_weights, is_ep, num_groups, prob_m,
+      topk_weights_ptr, top_k, mul_topk_weights, num_groups, prob_m,
       prob_n, prob_k, locks, has_bias, use_atomic_add, use_fp32_reduce);
   // clang-format on
 }
@@ -541,7 +543,7 @@ torch::Tensor moe_wna16_marlin_gemm(
     std::optional<torch::Tensor> const& perm_or_none, torch::Tensor& workspace,
     torch::Tensor& sorted_token_ids, torch::Tensor& expert_ids,
     torch::Tensor& num_tokens_past_padded, torch::Tensor& topk_weights,
-    int64_t moe_block_size, int64_t top_k, bool mul_topk_weights, bool is_ep,
+    int64_t moe_block_size, int64_t top_k, bool mul_topk_weights,
     vllm::ScalarTypeId const& b_type_id, int64_t size_m, int64_t size_n,
     int64_t size_k, bool is_k_full, bool use_atomic_add, bool use_fp32_reduce,
     bool is_zp_float, int64_t thread_k, int64_t thread_n,
@@ -855,9 +857,9 @@ torch::Tensor moe_wna16_marlin_gemm(
       perm.data_ptr(), a_tmp.data_ptr(), sorted_token_ids.data_ptr(),
       expert_ids.data_ptr(), num_tokens_past_padded.data_ptr(),
       topk_weights.data_ptr(), moe_block_size, num_experts, top_k,
-      mul_topk_weights, is_ep, size_m, size_n, size_k, workspace.data_ptr(),
-      a_type, b_type, c_type, s_type, has_bias, has_act_order, is_k_full,
-      has_zp, num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
+      mul_topk_weights, size_m, size_n, size_k, workspace.data_ptr(), a_type,
+      b_type, c_type, s_type, has_bias, has_act_order, is_k_full, has_zp,
+      num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
       thread_k, thread_n, sms, blocks_per_sm, use_atomic_add, use_fp32_reduce,
       is_zp_float);
 
@@ -866,4 +868,4 @@ torch::Tensor moe_wna16_marlin_gemm(
 
 TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
   m.impl("moe_wna16_marlin_gemm", &moe_wna16_marlin_gemm);
-}
+}

csrc/moe/moe_permute_unpermute_op.cu

@@ -42,7 +42,7 @@ void moe_permute(
   auto sort_workspace = torch::empty(
       {sorter_size},
       torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
-  auto copy_topk_ids = topk_ids.clone();  // copy topk_ids for preprocess
+  torch::Tensor topk_ids_for_sort = topk_ids;
   auto permuted_experts_id = torch::empty_like(topk_ids);
   auto sorted_row_idx = torch::empty_like(inv_permuted_idx);
 
@@ -62,12 +62,13 @@ void moe_permute(
     const int* expert_map_ptr = get_ptr<int>(expert_map.value());
     valid_num_ptr =
         get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
-    preprocessTopkIdLauncher(get_ptr<int>(copy_topk_ids), n_token * topk,
+    topk_ids_for_sort = topk_ids.clone();
+    preprocessTopkIdLauncher(get_ptr<int>(topk_ids_for_sort), n_token * topk,
                              expert_map_ptr, n_expert, stream);
   }
   // expert sort topk expert id and scan expert id get expert_first_token_offset
   sortAndScanExpert(
-      get_ptr<int>(copy_topk_ids), get_ptr<int>(token_expert_indices),
+      get_ptr<const int>(topk_ids_for_sort), get_ptr<int>(token_expert_indices),
       get_ptr<int>(permuted_experts_id), get_ptr<int>(sorted_row_idx),
       get_ptr<int64_t>(expert_first_token_offset), n_token, n_expert,
       n_local_expert, topk, sorter, get_ptr<int>(sort_workspace), stream);

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -109,7 +109,7 @@ void computeExpertFirstTokenOffset(int const* sorted_indices,
       sorted_indices, total_indices, num_experts, expert_first_token_offset);
 }
 
-void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
+void sortAndScanExpert(const int* expert_for_source_row, const int* source_rows,
                        int* permuted_experts, int* permuted_rows,
                        int64_t* expert_first_token_offset, int num_rows,
                        int num_experts, int num_experts_per_node, int k,

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.h

@@ -48,7 +48,7 @@ void computeExpertFirstTokenOffset(int const* sorted_indices,
                                    int64_t* expert_first_token_offset,
                                    cudaStream_t stream);
 
-void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
+void sortAndScanExpert(const int* expert_for_source_row, const int* source_rows,
                        int* permuted_experts, int* permuted_rows,
                        int64_t* expert_first_token_offset, int num_rows,
                        int num_experts, int num_experts_per_node, int k,

csrc/moe/torch_bindings.cpp

@@ -71,7 +71,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "Tensor sorted_token_ids,"
       "Tensor! expert_ids, Tensor! num_tokens_past_padded,"
       "Tensor! topk_weights, int moe_block_size, int top_k, "
-      "bool mul_topk_weights, bool is_ep, int b_type_id,"
+      "bool mul_topk_weights, int b_type_id,"
       "int size_m, int size_n, int size_k,"
       "bool is_full_k, bool use_atomic_add,"
       "bool use_fp32_reduce, bool is_zp_float,"

csrc/ops.h

@@ -2,6 +2,7 @@
 
 #include <optional>
 #include <torch/library.h>
+#include <tuple>
 
 #include "core/scalar_type.hpp"
 
@@ -259,11 +260,10 @@ void get_cutlass_moe_mm_data(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
-void get_cutlass_moe_mm_problem_sizes(
-    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
-    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
-    std::optional<bool> force_swap_ab = std::nullopt);
+void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
+    const torch::Tensor& expert_first_token_offset,
+    torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
+    const int64_t n, const int64_t k, const bool swap_ab);
 
 void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
                                   torch::Tensor& problem_sizes1,
@@ -301,6 +301,12 @@ void scaled_fp4_experts_quant(
     torch::Tensor const& input_offset_by_experts,
     torch::Tensor const& output_scale_offset_by_experts);
 
+void silu_and_mul_scaled_fp4_experts_quant(
+    torch::Tensor& output, torch::Tensor& output_scale,
+    torch::Tensor const& input, torch::Tensor const& input_global_scale,
+    torch::Tensor const& input_offset_by_experts,
+    torch::Tensor const& output_scale_offset_by_experts);
+
 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,
@@ -335,8 +341,9 @@ torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
 
 void gptq_shuffle(torch::Tensor q_weight, torch::Tensor q_perm, int64_t bit);
 
-void static_scaled_fp8_quant(torch::Tensor& out, torch::Tensor const& input,
-                             torch::Tensor const& scale);
+void static_scaled_fp8_quant(
+    torch::Tensor& out, torch::Tensor const& input, torch::Tensor const& scale,
+    std::optional<std::tuple<int64_t, int64_t>> group_shape = std::nullopt);
 
 void dynamic_scaled_fp8_quant(torch::Tensor& out, torch::Tensor const& input,
                               torch::Tensor& scale);

csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu

@@ -31,37 +31,6 @@
 
 namespace vllm {
 
-// silu in float32
-__device__ __forceinline__ float silu(float x) {
-  return __fdividef(x, (1.f + __expf(-x)));
-}
-
-__device__ __forceinline__ float2 silu2(float2 x) {
-  return make_float2(silu(x.x), silu(x.y));
-}
-
-template <class Type>
-__inline__ __device__ PackedVec<Type> compute_silu_mul(PackedVec<Type>& vec,
-                                                       PackedVec<Type>& vec2) {
-  PackedVec<Type> result;
-  using packed_type = typename TypeConverter<Type>::Type;
-
-#pragma unroll
-  for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; ++i) {
-    // silu_mul in float32
-    if constexpr (std::is_same_v<Type, half>) {
-      float2 silu_vec = silu2(__half22float2(vec.elts[i]));
-      result.elts[i] =
-          __float22half2_rn(__fmul2_rn(silu_vec, __half22float2(vec2.elts[i])));
-    } else {
-      float2 silu_vec = silu2(__bfloat1622float2(vec.elts[i]));
-      result.elts[i] = __float22bfloat162_rn(
-          __fmul2_rn(silu_vec, __bfloat1622float2(vec2.elts[i])));
-    }
-  }
-  return result;
-}
-
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
 __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
@@ -74,6 +43,9 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
                 "Vec size is not matched.");
 
+  // Precompute SF layout parameter (constant for entire kernel).
+  int32_t const numKTiles = (numCols + 63) / 64;
+
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
@@ -101,7 +73,7 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
       auto sf_out =
           cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                              CVT_FP4_NUM_THREADS_PER_SF>(
-              rowIdx, colIdx, numCols, SFout);
+              rowIdx, colIdx, numKTiles, SFout);
 
       out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(out_silu_mul, SFScaleVal,
                                                      sf_out);

csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu

@@ -62,7 +62,9 @@ __global__ void __get_group_gemm_starts(
     ElementSF* a_scales_base_as_int, ElementSF* b_scales_base_as_int,
     ElementAccumulator* alphas_base_as_int, const int32_t* expert_offsets,
     const int32_t* sf_offsets, const int32_t* problem_sizes_as_shapes,
-    const int K, const int N) {
+    int64_t* a_strides, int64_t* b_strides, int64_t* c_strides,
+    const int64_t a_stride_val, const int64_t b_stride_val,
+    const int64_t c_stride_val, const int K, const int N) {
   int64_t expert_id = threadIdx.x;
   if (expert_id >= gridDim.x * blockDim.x) {
     return;
@@ -103,6 +105,11 @@ __global__ void __get_group_gemm_starts(
   // Shape of alpha = [E]
   alpha_offsets[expert_id] = alphas_base_as_int + expert_id;
 
+  // Initialize strides (constant across all experts, avoids separate kernels)
+  a_strides[expert_id] = a_stride_val;
+  b_strides[expert_id] = b_stride_val;
+  c_strides[expert_id] = c_stride_val;
+
   LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
   LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
 
@@ -135,7 +142,11 @@ __global__ void __get_group_gemm_starts(
             static_cast<float*>(alphas.data_ptr()),                           \
             static_cast<int32_t*>(expert_offsets.data_ptr()),                 \
             static_cast<int32_t*>(sf_offsets.data_ptr()),                     \
-            static_cast<int32_t*>(problem_sizes.data_ptr()), K, N);           \
+            static_cast<int32_t*>(problem_sizes.data_ptr()),                  \
+            static_cast<int64_t*>(a_strides.data_ptr()),                      \
+            static_cast<int64_t*>(b_strides.data_ptr()),                      \
+            static_cast<int64_t*>(c_strides.data_ptr()), a_stride_val,        \
+            b_stride_val, c_stride_val, K, N);                                \
   }
 
 template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
@@ -144,6 +155,9 @@ void run_get_group_gemm_starts(
     const torch::Tensor& out_starts, const torch::Tensor& a_scales_starts,
     const torch::Tensor& b_scales_starts, const torch::Tensor& alpha_starts,
     const torch::Tensor& layout_sfa, const torch::Tensor& layout_sfb,
+    const torch::Tensor& a_strides, const torch::Tensor& b_strides,
+    const torch::Tensor& c_strides, int64_t a_stride_val, int64_t b_stride_val,
+    int64_t c_stride_val,
     /*these are used for their base addresses*/
     torch::Tensor const& a_tensors, torch::Tensor const& b_tensors,
     torch::Tensor const& out_tensors, torch::Tensor const& a_scales,
@@ -269,17 +283,16 @@ void run_fp4_blockwise_scaled_group_mm_sm100(
   torch::Tensor alpha_ptrs = torch::empty(num_experts, options_int);
   torch::Tensor layout_sfa = torch::empty({num_experts, 5}, options_int);
   torch::Tensor layout_sfb = torch::empty({num_experts, 5}, options_int);
-  torch::Tensor c_strides1 =
-      torch::full({num_experts}, output.stride(0), options_int);
-  torch::Tensor a_strides1 =
-      torch::full({num_experts}, a.stride(0) * 2, options_int);
-  torch::Tensor b_strides1 =
-      torch::full({num_experts}, b.stride(1) * 2, options_int);
+  torch::Tensor a_strides1 = torch::empty(num_experts, options_int);
+  torch::Tensor b_strides1 = torch::empty(num_experts, options_int);
+  torch::Tensor c_strides1 = torch::empty(num_experts, options_int);
 
   run_get_group_gemm_starts<LayoutSFA, LayoutSFB, ScaleConfig>(
       a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, alpha_ptrs,
-      layout_sfa, layout_sfb, a, b, output, a_blockscale, b_blockscales, alphas,
-      expert_offsets, sf_offsets, problem_sizes, M, N, K);
+      layout_sfa, layout_sfb, a_strides1, b_strides1, c_strides1,
+      a.stride(0) * 2, b.stride(1) * 2, output.stride(0), a, b, output,
+      a_blockscale, b_blockscales, alphas, expert_offsets, sf_offsets,
+      problem_sizes, M, N, K);
 
   // Create an instance of the GEMM
   Gemm gemm_op;
@@ -444,17 +457,16 @@ void run_fp4_blockwise_scaled_group_mm_sm120(
   torch::Tensor alpha_ptrs = torch::empty(num_experts, options_int);
   torch::Tensor layout_sfa = torch::empty({num_experts, 5}, options_int);
   torch::Tensor layout_sfb = torch::empty({num_experts, 5}, options_int);
-  torch::Tensor c_strides1 =
-      torch::full({num_experts}, output.stride(0), options_int);
-  torch::Tensor a_strides1 =
-      torch::full({num_experts}, a.stride(0) * 2, options_int);
-  torch::Tensor b_strides1 =
-      torch::full({num_experts}, b.stride(1) * 2, options_int);
+  torch::Tensor a_strides1 = torch::empty(num_experts, options_int);
+  torch::Tensor b_strides1 = torch::empty(num_experts, options_int);
+  torch::Tensor c_strides1 = torch::empty(num_experts, options_int);
 
   run_get_group_gemm_starts<LayoutSFA, LayoutSFB, ScaleConfig>(
       a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, alpha_ptrs,
-      layout_sfa, layout_sfb, a, b, output, a_blockscale, b_blockscales, alphas,
-      expert_offsets, sf_offsets, problem_sizes, M, N, K);
+      layout_sfa, layout_sfb, a_strides1, b_strides1, c_strides1,
+      a.stride(0) * 2, b.stride(1) * 2, output.stride(0), a, b, output,
+      a_blockscale, b_blockscales, alphas, expert_offsets, sf_offsets,
+      problem_sizes, M, N, K);
 
   // Create an instance of the GEMM
   Gemm gemm_op;

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -25,13 +25,18 @@
 #include <cuda_fp8.h>
 #include "dispatch_utils.h"
 
+#include "cuda_utils.h"
 #include "nvfp4_utils.cuh"
 #include "launch_bounds_utils.h"
 
 namespace vllm {
 
+// NVFP4 quantization kernel for experts (low-latency path).
+// When FUSE_SILU_MUL=true, expects input with gate||up layout and fuses
+// SiLU(gate)*up before quantization.
 // Use UE4M3 by default.
-template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
+template <class Type, bool FUSE_SILU_MUL = false, bool UE8M0_SF = false,
+          bool SMALL_NUM_EXPERTS = false>
 __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                     float const* SFScale, uint32_t* out, uint32_t* SFout,
@@ -44,8 +49,13 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
                 "Vec size is not matched.");
 
+  // Precompute SF layout parameter (constant for entire kernel).
+  int32_t const numKTiles = (numCols + 63) / 64;
+
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
+  // When fusing SiLU+Mul, input has gate || up layout (doubled width)
+  int inColsPerRow = FUSE_SILU_MUL ? colsPerRow * 2 : colsPerRow;
 
   // Each global thread processes one element
   for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
@@ -54,13 +64,6 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     int rowIdx = globalIdx / colsPerRow;
     int colIdx = globalIdx % colsPerRow;
 
-    int64_t inOffset = rowIdx * colsPerRow + colIdx;
-    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
-    // Get the output tensor offset.
-    // Same as inOffset because 8 elements are packed into one uint32_t.
-    int64_t outOffset = inOffset;
-    auto& out_pos = out[outOffset];
-
     // Find index within the experts using different strategies based on expert
     // count
     int rowIdx_in_expert = 0;
@@ -107,29 +110,46 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
       }
     }
 
+    // Load input and optionally apply fused SiLU+Mul
+    int64_t inOffset = rowIdx * inColsPerRow + colIdx;
+    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+    PackedVec quant_input;
+    if constexpr (FUSE_SILU_MUL) {
+      PackedVec in_vec_up =
+          reinterpret_cast<PackedVec const*>(in)[inOffset + colsPerRow];
+      quant_input = compute_silu_mul(in_vec, in_vec_up);
+    } else {
+      quant_input = in_vec;
+    }
+
+    // Get the output tensor offset.
+    // Same as inOffset because 8 elements are packed into one uint32_t.
+    int64_t outOffset = rowIdx * colsPerRow + colIdx;
+    auto& out_pos = out[outOffset];
+
     // Get the global scaling factor, which will be applied to the SF.
     // Note SFScale is the same as next GEMM's alpha, which is
     // (448.f / (Alpha_A / 6.f)).
     float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[expert_idx];
 
-    int factor = CVT_FP4_SF_VEC_SIZE * 4;
-    // The actual output_scales dim is computed from the padded numCols.
-    int32_t numCols_padded = (numCols + factor - 1) / factor * factor;
-    int numCols_SFout = numCols_padded / CVT_FP4_SF_VEC_SIZE / 4;
     uint32_t* SFout_in_expert =
-        SFout + output_scale_offset_by_experts[expert_idx] * numCols_SFout;
+        SFout + output_scale_offset_by_experts[expert_idx] * numKTiles;
 
     auto sf_out =
         cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                            CVT_FP4_NUM_THREADS_PER_SF>(
-            rowIdx_in_expert, colIdx, numCols, SFout_in_expert);
+            rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
   }
 }
 
-// Kernel for LARGE_M_TOPK = true (large m_topk optimized version)
-template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
+// NVFP4 quantization kernel for LARGE_M_TOPK = true (large m_topk optimized
+// version). When FUSE_SILU_MUL=true, expects input with gate||up layout and
+// fuses SiLU(gate)*up before quantization.
+template <class Type, bool FUSE_SILU_MUL = false, bool UE8M0_SF = false,
+          bool SMALL_NUM_EXPERTS = false>
 __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
     cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                     float const* SFScale, uint32_t* out, uint32_t* SFout,
@@ -140,6 +160,10 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
                 "Vec size is not matched.");
+
+  // Precompute SF layout parameter (constant for entire kernel).
+  int32_t const numKTiles = (numCols + 63) / 64;
+
   extern __shared__ uint32_t shared_input_offsets[];
 
   // Load input offsets into shared memory.
@@ -163,6 +187,8 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
 
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
+  // When fusing SiLU+Mul, input has gate || up layout (doubled width)
+  int inColsPerRow = FUSE_SILU_MUL ? colsPerRow * 2 : colsPerRow;
 
   // Each global thread processes one element
   for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
@@ -171,11 +197,6 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
     int rowIdx = globalIdx / colsPerRow;
     int colIdx = globalIdx % colsPerRow;
 
-    int64_t inOffset = rowIdx * colsPerRow + colIdx;
-    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
-    int64_t outOffset = inOffset;
-    auto& out_pos = out[outOffset];
-
     // Find expert using binary search for better performance with large m_topk
     int rowIdx_in_expert = 0;
     int expert_idx = 0;
@@ -200,34 +221,43 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
       }
     }
 
+    // Load input and optionally apply fused SiLU+Mul
+    int64_t inOffset = rowIdx * inColsPerRow + colIdx;
+    PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+    PackedVec quant_input;
+    if constexpr (FUSE_SILU_MUL) {
+      PackedVec in_vec_up =
+          reinterpret_cast<PackedVec const*>(in)[inOffset + colsPerRow];
+      quant_input = compute_silu_mul(in_vec, in_vec_up);
+    } else {
+      quant_input = in_vec;
+    }
+
+    int64_t outOffset = rowIdx * colsPerRow + colIdx;
+    auto& out_pos = out[outOffset];
+
     float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[expert_idx];
 
-    int factor = CVT_FP4_SF_VEC_SIZE * 4;
-    int32_t numCols_padded = (numCols + factor - 1) / factor * factor;
-    int numCols_SFout = numCols_padded / CVT_FP4_SF_VEC_SIZE / 4;
     uint32_t* SFout_in_expert =
-        SFout + output_scale_offset_by_experts[expert_idx] * numCols_SFout;
+        SFout + output_scale_offset_by_experts[expert_idx] * numKTiles;
 
     auto sf_out =
         cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                            CVT_FP4_NUM_THREADS_PER_SF>(
-            rowIdx_in_expert, colIdx, numCols, SFout_in_expert);
+            rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
   }
 }
 
-template <typename T>
+template <typename T, bool FUSE_SILU_MUL = false>
 void quant_impl(void* output, void* output_scale, void* input,
                 void* input_global_scale, void* input_offset_by_experts,
                 void* output_scale_offset_by_experts, int m_topk, int k,
                 int n_experts, cudaStream_t stream) {
-  // TODO: this multiProcessorCount should be cached.
-  int device;
-  cudaGetDevice(&device);
-  int multiProcessorCount;
-  cudaDeviceGetAttribute(&multiProcessorCount, cudaDevAttrMultiProcessorCount,
-                         device);
+  int multiProcessorCount =
+      get_device_attribute(cudaDevAttrMultiProcessorCount, -1);
 
   // Grid, Block size.
   // Each thread converts 8 values.
@@ -249,7 +279,7 @@ void quant_impl(void* output, void* output_scale, void* input,
   if (blockRepeat > 1) {
     size_t shared_mem_size = (n_experts + 1) * sizeof(uint32_t);
     if (n_experts >= 4) {
-      cvt_fp16_to_fp4<T, false, false>
+      cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, false>
           <<<grid, block, shared_mem_size, stream>>>(
               m_topk, k, reinterpret_cast<T*>(input),
               reinterpret_cast<float*>(input_global_scale),
@@ -259,34 +289,37 @@ void quant_impl(void* output, void* output_scale, void* input,
               reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
               n_experts);
     } else {
-      cvt_fp16_to_fp4<T, false, true><<<grid, block, shared_mem_size, stream>>>(
-          m_topk, k, reinterpret_cast<T*>(input),
-          reinterpret_cast<float*>(input_global_scale),
-          reinterpret_cast<uint32_t*>(output),
-          reinterpret_cast<uint32_t*>(output_scale),
-          reinterpret_cast<uint32_t*>(input_offset_by_experts),
-          reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
-          n_experts);
+      cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, true>
+          <<<grid, block, shared_mem_size, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<float*>(input_global_scale),
+              reinterpret_cast<uint32_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts);
     }
   } else {
     if (n_experts >= 16) {
-      cvt_fp16_to_fp4<T, false, false><<<grid, block, 0, stream>>>(
-          m_topk, k, reinterpret_cast<T*>(input),
-          reinterpret_cast<float*>(input_global_scale),
-          reinterpret_cast<uint32_t*>(output),
-          reinterpret_cast<uint32_t*>(output_scale),
-          reinterpret_cast<uint32_t*>(input_offset_by_experts),
-          reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
-          n_experts, /* bool low_latency */ true);
+      cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, false>
+          <<<grid, block, 0, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<float*>(input_global_scale),
+              reinterpret_cast<uint32_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts, /* bool low_latency */ true);
     } else {
-      cvt_fp16_to_fp4<T, false, true><<<grid, block, 0, stream>>>(
-          m_topk, k, reinterpret_cast<T*>(input),
-          reinterpret_cast<float*>(input_global_scale),
-          reinterpret_cast<uint32_t*>(output),
-          reinterpret_cast<uint32_t*>(output_scale),
-          reinterpret_cast<uint32_t*>(input_offset_by_experts),
-          reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
-          n_experts, /* bool low_latency */ true);
+      cvt_fp16_to_fp4<T, FUSE_SILU_MUL, false, true>
+          <<<grid, block, 0, stream>>>(
+              m_topk, k, reinterpret_cast<T*>(input),
+              reinterpret_cast<float*>(input_global_scale),
+              reinterpret_cast<uint32_t*>(output),
+              reinterpret_cast<uint32_t*>(output_scale),
+              reinterpret_cast<uint32_t*>(input_offset_by_experts),
+              reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
+              n_experts, /* bool low_latency */ true);
     }
   }
 }
@@ -307,19 +340,19 @@ constexpr auto FLOAT = at::ScalarType::Float;
 constexpr auto INT = at::ScalarType::Int;
 constexpr auto UINT8 = at::ScalarType::Byte;
 
-void scaled_fp4_experts_quant_sm1xxa(
-    torch::Tensor& output, torch::Tensor& output_scale,
+// Common validation for fp4 experts quantization entry points.
+static void validate_fp4_experts_quant_inputs(
+    torch::Tensor const& output, torch::Tensor const& output_scale,
     torch::Tensor const& input, torch::Tensor const& input_global_scale,
     torch::Tensor const& input_offset_by_experts,
-    torch::Tensor const& output_scale_offset_by_experts) {
-  CHECK_INPUT(output, "output must be a CUDA tensor");
-  CHECK_INPUT(output_scale, "output_scale must be a CUDA tensor");
-  CHECK_INPUT(input, "input must be a CUDA tensor");
-  CHECK_INPUT(input_global_scale, "input_global_scale must be a CUDA tensor");
-  CHECK_INPUT(input_offset_by_experts,
-              "input_offset_by_experts must be a CUDA tensor");
-  CHECK_INPUT(output_scale_offset_by_experts,
-              "output_scale_offset_by_experts must be a CUDA tensor");
+    torch::Tensor const& output_scale_offset_by_experts, int64_t m_topk,
+    int64_t k) {
+  CHECK_INPUT(output, "output");
+  CHECK_INPUT(output_scale, "output_scale");
+  CHECK_INPUT(input, "input");
+  CHECK_INPUT(input_global_scale, "input_global_scale");
+  CHECK_INPUT(input_offset_by_experts, "input_offset_by_experts");
+  CHECK_INPUT(output_scale_offset_by_experts, "output_scale_offset_by_experts");
 
   TORCH_CHECK(output.dim() == 2);
   TORCH_CHECK(output_scale.dim() == 2);
@@ -338,8 +371,6 @@ void scaled_fp4_experts_quant_sm1xxa(
   TORCH_CHECK(output_scale.scalar_type() == INT);
 
   const int BLOCK_SIZE = 16;
-  auto m_topk = input.size(0);
-  auto k = input.size(1);
   TORCH_CHECK(k % BLOCK_SIZE == 0, "k must be a multiple of 16");
   auto n_experts = input_global_scale.size(0);
   TORCH_CHECK(input_offset_by_experts.size(0) == n_experts + 1);
@@ -351,7 +382,21 @@ void scaled_fp4_experts_quant_sm1xxa(
   int padded_k = (scales_k + (4 - 1)) / 4 * 4;
   // 4 means 4 fp8 values are packed into one int32
   TORCH_CHECK(output_scale.size(1) * 4 == padded_k);
+}
 
+void scaled_fp4_experts_quant_sm1xxa(
+    torch::Tensor& output, torch::Tensor& output_scale,
+    torch::Tensor const& input, torch::Tensor const& input_global_scale,
+    torch::Tensor const& input_offset_by_experts,
+    torch::Tensor const& output_scale_offset_by_experts) {
+  auto m_topk = input.size(0);
+  auto k = input.size(1);
+
+  validate_fp4_experts_quant_inputs(output, output_scale, input,
+                                    input_global_scale, input_offset_by_experts,
+                                    output_scale_offset_by_experts, m_topk, k);
+
+  auto n_experts = input_global_scale.size(0);
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream =
       at::cuda::getCurrentCUDAStream(input.get_device());
@@ -359,7 +404,38 @@ void scaled_fp4_experts_quant_sm1xxa(
   VLLM_DISPATCH_HALF_TYPES(
       input.scalar_type(), "nvfp4_experts_quant_kernel", [&] {
         using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
-        vllm::quant_impl<cuda_type>(
+        vllm::quant_impl<cuda_type, /*FUSE_SILU_MUL=*/false>(
+            output.data_ptr(), output_scale.data_ptr(), input.data_ptr(),
+            input_global_scale.data_ptr(), input_offset_by_experts.data_ptr(),
+            output_scale_offset_by_experts.data_ptr(), m_topk, k, n_experts,
+            stream);
+      });
+}
+
+void silu_and_mul_scaled_fp4_experts_quant_sm1xxa(
+    torch::Tensor& output, torch::Tensor& output_scale,
+    torch::Tensor const& input, torch::Tensor const& input_global_scale,
+    torch::Tensor const& input_offset_by_experts,
+    torch::Tensor const& output_scale_offset_by_experts) {
+  auto m_topk = input.size(0);
+  // Input has gate || up layout, so k = input.size(1) / 2
+  auto k_times_2 = input.size(1);
+  TORCH_CHECK(k_times_2 % 2 == 0, "input width must be even (gate || up)");
+  auto k = k_times_2 / 2;
+
+  validate_fp4_experts_quant_inputs(output, output_scale, input,
+                                    input_global_scale, input_offset_by_experts,
+                                    output_scale_offset_by_experts, m_topk, k);
+
+  auto n_experts = input_global_scale.size(0);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  const cudaStream_t stream =
+      at::cuda::getCurrentCUDAStream(input.get_device());
+
+  VLLM_DISPATCH_HALF_TYPES(
+      input.scalar_type(), "silu_mul_nvfp4_experts_quant_kernel", [&] {
+        using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
+        vllm::quant_impl<cuda_type, /*FUSE_SILU_MUL=*/true>(
             output.data_ptr(), output_scale.data_ptr(), input.data_ptr(),
             input_global_scale.data_ptr(), input_offset_by_experts.data_ptr(),
             output_scale_offset_by_experts.data_ptr(), m_topk, k, n_experts,

csrc/quantization/fp4/nvfp4_quant_entry.cu

@@ -41,6 +41,15 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,
                                      torch::Tensor& input_sf);
 #endif
 
+#if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
+    (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
+void silu_and_mul_scaled_fp4_experts_quant_sm1xxa(
+    torch::Tensor& output, torch::Tensor& output_scale,
+    torch::Tensor const& input, torch::Tensor const& input_global_scale,
+    torch::Tensor const& input_offset_by_experts,
+    torch::Tensor const& output_scale_offset_by_experts);
+#endif
+
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
                       torch::Tensor& output_sf, torch::Tensor const& input_sf) {
 #if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
@@ -74,3 +83,18 @@ void silu_and_mul_nvfp4_quant(torch::Tensor& output, torch::Tensor& output_sf,
   TORCH_CHECK_NOT_IMPLEMENTED(
       false, "No compiled silu_and_mul nvfp4 quantization kernel");
 }
+
+void silu_and_mul_scaled_fp4_experts_quant(
+    torch::Tensor& output, torch::Tensor& output_scale,
+    torch::Tensor const& input, torch::Tensor const& input_global_scale,
+    torch::Tensor const& input_offset_by_experts,
+    torch::Tensor const& output_scale_offset_by_experts) {
+#if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
+    (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
+  return silu_and_mul_scaled_fp4_experts_quant_sm1xxa(
+      output, output_scale, input, input_global_scale, input_offset_by_experts,
+      output_scale_offset_by_experts);
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false, "No compiled silu_and_mul nvfp4 experts quantization kernel");
+}

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -35,7 +35,13 @@ template <typename Int>
 __host__ __device__ inline Int round_up(Int x, Int y) {
   static_assert(std::is_integral_v<Int>,
                 "round_up argument must be integral type");
-  return (x + y - 1) / y * y;
+  return ((x + y - 1) / y) * y;
+}
+
+// Compute effective rows for grid configuration with swizzled SF layouts.
+inline int computeEffectiveRows(int m) {
+  constexpr int ROW_TILE = 128;
+  return round_up(m, ROW_TILE);
 }
 
 // Use UE4M3 by default.
@@ -49,81 +55,57 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
                 "Vec size is not matched.");
 
+  // Precompute SF layout parameter (constant for entire kernel).
+  int32_t const numKTiles = (numCols + 63) / 64;
+
   int sf_m = round_up<int>(numRows, 128);
   int sf_n_unpadded = numCols / CVT_FP4_SF_VEC_SIZE;
   int sf_n_int = round_up<int>(sf_n_unpadded, 4) / 4;
-  for (int row = numRows + blockIdx.x; row < sf_m; row += gridDim.x) {
-    // Each thread writes 4 uint32_t elements.
-    for (int col = sf_n_unpadded + threadIdx.x * 4; col < sf_n_int;
-         col += blockDim.x * 4) {
-      SFout[row * sf_n_int + col] = 0x00;
-    }
-  }
+  int num_padded_cols = sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE;
 
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
   float const global_scale = SFScale == nullptr ? 1.0f : SFScale[0];
 
-  // Input tensor row/col loops.
-  for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    for (int colIdx = threadIdx.x; colIdx < numCols / CVT_FP4_ELTS_PER_THREAD;
+  // Iterate over all rows and cols including padded ones -
+  //  ensures we visit every single scale factor address to initialize it.
+  for (int rowIdx = blockIdx.x; rowIdx < sf_m; rowIdx += gridDim.x) {
+    for (int colIdx = threadIdx.x;
+         colIdx < num_padded_cols / CVT_FP4_ELTS_PER_THREAD;
          colIdx += blockDim.x) {
+      int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
+
+      PackedVec in_vec;
       int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
-      PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
-      // Get the output tensor offset.
-      // Same as inOffset because 8 elements are packed into one uint32_t.
-      int64_t outOffset = inOffset;
-      auto& out_pos = out[outOffset];
+
+      // If we are outside valid rows OR outside valid columns -> Use Zeros
+      if (rowIdx >= numRows || elem_idx >= numCols) {
+        memset(&in_vec, 0, sizeof(PackedVec));
+
+      } else {
+        // Valid Region: Load actual data
+        in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+      }
 
       auto sf_out =
           cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                              CVT_FP4_NUM_THREADS_PER_SF>(
-              rowIdx, colIdx, numCols, SFout);
+              rowIdx, colIdx, numKTiles, SFout);
 
-      out_pos =
+      auto out_val =
           cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, global_scale, sf_out);
+
+      // We do NOT write output for padding because the 'out' tensor is not
+      // padded.
+      if (rowIdx < numRows && elem_idx < numCols) {
+        // Same as inOffset because 8 elements are packed into one uint32_t.
+        out[inOffset] = out_val;
+      }
     }
   }
 }
 
-template <typename T>
-void invokeFP4Quantization(int m, int n, T const* input, float const* SFScale,
-                           int64_t* output, int32_t* SFOuput, bool useUE8M0,
-                           int multiProcessorCount, cudaStream_t stream) {
-  // Grid, Block size.
-  // Each thread converts 8 values.
-  dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
-  // Get number of blocks per SM
-  int const numBlocksPerSM =
-      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
-  dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
-
-  // Launch the cvt kernel.
-  if (useUE8M0) {
-    cvt_fp16_to_fp4<T, true><<<grid, block, 0, stream>>>(
-        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output),
-        reinterpret_cast<uint32_t*>(SFOuput));
-  } else {
-    cvt_fp16_to_fp4<T, false><<<grid, block, 0, stream>>>(
-        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output),
-        reinterpret_cast<uint32_t*>(SFOuput));
-  }
-}
-
-// Instantiate the function.
-template void invokeFP4Quantization(int m, int n, half const* input,
-                                    float const* SFScale, int64_t* output,
-                                    int32_t* SFOuput, bool useUE8M0,
-                                    int multiProcessorCount,
-                                    cudaStream_t stream);
-
-template void invokeFP4Quantization(int m, int n, __nv_bfloat16 const* input,
-                                    float const* SFScale, int64_t* output,
-                                    int32_t* SFOuput, bool useUE8M0,
-                                    int multiProcessorCount,
-                                    cudaStream_t stream);
-
 }  // namespace vllm
 
 void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
@@ -147,13 +129,19 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
 
-  // We don't support e8m0 scales at this moment.
-  bool useUE8M0 = false;
+  // Grid, Block size. Each thread converts 8 values.
+  dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
+  int const numBlocksPerSM =
+      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
+  int effectiveRows = vllm::computeEffectiveRows(m);
+  dim3 grid(std::min(effectiveRows, multiProcessorCount * numBlocksPerSM));
 
   VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
     using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
     auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
-    vllm::invokeFP4Quantization(m, n, input_ptr, input_sf_ptr, output_ptr,
-                                sf_out, useUE8M0, multiProcessorCount, stream);
+    // NOTE: We don't support e8m0 scales at this moment.
+    vllm::cvt_fp16_to_fp4<cuda_type, false><<<grid, block, 0, stream>>>(
+        m, n, input_ptr, input_sf_ptr, reinterpret_cast<uint32_t*>(output_ptr),
+        reinterpret_cast<uint32_t*>(sf_out));
   });
-}
+}

csrc/quantization/fp4/nvfp4_utils.cuh

@@ -128,51 +128,42 @@ inline __device__ float reciprocal_approximate_ftz(float a) {
   return b;
 }
 
+// Compute SF output offset for swizzled tensor core layout.
+// SF layout: [numMTiles, numKTiles, 32, 4, 4]
+// Caller must precompute: numKTiles = (numCols + 63) / 64
 template <class SFType, int CVT_FP4_NUM_THREADS_PER_SF>
-__device__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(int rowIdx, int colIdx,
-                                                       int numCols,
-                                                       SFType* SFout) {
+__device__ __forceinline__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(
+    int rowIdx, int colIdx, int32_t numKTiles, SFType* SFout) {
   static_assert(CVT_FP4_NUM_THREADS_PER_SF == 1 ||
                 CVT_FP4_NUM_THREADS_PER_SF == 2);
 
   // One pair of threads write one SF to global memory.
   // TODO: stage through smem for packed STG.32
   // is it better than STG.8 from 4 threads ?
-  if (threadIdx.x % CVT_FP4_NUM_THREADS_PER_SF == 0) {
-    // SF vector index (16 elements share one SF in the K dimension).
-    int32_t kIdx = colIdx / CVT_FP4_NUM_THREADS_PER_SF;
-    int32_t mIdx = rowIdx;
-
-    // SF layout [numMTiles, numKTiles, 32 (mTile), 4 (mTile), 4(kTile)]
-    // --> index [mTileIdx, kTileIdx, outerMIdx, innerMIdx, innerKIdx]
-
-    int32_t mTileIdx = mIdx / (32 * 4);
-    // SF vector size 16.
-    int factor = CVT_FP4_SF_VEC_SIZE * 4;
-    int32_t numKTiles = (numCols + factor - 1) / factor;
-    int64_t mTileStride = numKTiles * 32 * 4 * 4;
-
-    int32_t kTileIdx = (kIdx / 4);
-    int64_t kTileStride = 32 * 4 * 4;
-
-    // M tile layout [32, 4] is column-major.
-    int32_t outerMIdx = (mIdx % 32);
-    int64_t outerMStride = 4 * 4;
-
-    int32_t innerMIdx = (mIdx % (32 * 4)) / 32;
-    int64_t innerMStride = 4;
-
-    int32_t innerKIdx = (kIdx % 4);
-    int64_t innerKStride = 1;
-
-    // Compute the global offset.
-    int64_t SFOffset = mTileIdx * mTileStride + kTileIdx * kTileStride +
-                       outerMIdx * outerMStride + innerMIdx * innerMStride +
-                       innerKIdx * innerKStride;
-
-    return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
+  if (threadIdx.x % CVT_FP4_NUM_THREADS_PER_SF != 0) {
+    return nullptr;
   }
-  return nullptr;
+
+  // SF vector index (16 elements share one SF in the K dimension).
+  int32_t kIdx = colIdx / CVT_FP4_NUM_THREADS_PER_SF;
+  int32_t mIdx = rowIdx;
+
+  // Decompose indices using bitwise ops (all divisors are powers of 2).
+  // SF layout [numMTiles, numKTiles, 32 (mTile), 4 (mTile), 4(kTile)]
+  int32_t mTileIdx = mIdx >> 7;         // mIdx / 128
+  int32_t outerMIdx = mIdx & 31;        // mIdx % 32
+  int32_t innerMIdx = (mIdx >> 5) & 3;  // (mIdx / 32) % 4
+  int32_t kTileIdx = kIdx >> 2;         // kIdx / 4
+  int32_t innerKIdx = kIdx & 3;         // kIdx % 4
+
+  // Compute global SF offset: mTileIdx * (numKTiles * 512) + kTileIdx * 512 +
+  //                           outerMIdx * 16 + innerMIdx * 4 + innerKIdx
+  // Use bitwise OR for non-overlapping lower bits.
+  int64_t SFOffset = (static_cast<int64_t>(mTileIdx) * numKTiles + kTileIdx)
+                         << 9 |
+                     (outerMIdx << 4) | (innerMIdx << 2) | innerKIdx;
+
+  return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
 }
 
 // Quantizes the provided PackedVec into the uint32_t output
@@ -248,4 +239,34 @@ __device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
   return e2m1Vec;
 }
 
+// silu in float32
+__device__ __forceinline__ float silu(float x) {
+  return __fdividef(x, (1.f + __expf(-x)));
+}
+
+__device__ __forceinline__ float2 silu2(float2 x) {
+  return make_float2(silu(x.x), silu(x.y));
+}
+
+template <class Type>
+__inline__ __device__ PackedVec<Type> compute_silu_mul(
+    const PackedVec<Type>& x_vec, const PackedVec<Type>& y_vec) {
+  PackedVec<Type> result;
+
+#pragma unroll
+  for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; ++i) {
+    // silu_mul in float32
+    if constexpr (std::is_same_v<Type, half>) {
+      float2 silu_vec = silu2(__half22float2(x_vec.elts[i]));
+      result.elts[i] = __float22half2_rn(
+          __fmul2_rn(silu_vec, __half22float2(y_vec.elts[i])));
+    } else {
+      float2 silu_vec = silu2(__bfloat1622float2(x_vec.elts[i]));
+      result.elts[i] = __float22bfloat162_rn(
+          __fmul2_rn(silu_vec, __bfloat1622float2(y_vec.elts[i])));
+    }
+  }
+  return result;
+}
+
 }  // namespace vllm

csrc/quantization/gptq/q_gemm.cu

@@ -233,11 +233,6 @@ __global__ void gemm_half_q_half_gptq_4bit_kernel(
   // Zero output
   if (n >= size_n) return;
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < m_count; m++)
-      *((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
-  }
-
   __syncthreads();
 
   // Find initial group
@@ -372,11 +367,6 @@ __global__ void gemm_half_q_half_gptq_2bit_kernel(
   // Zero output
   if (n >= size_n) return;
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < m_count; m++)
-      *((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
-  }
-
   __syncthreads();
 
   // Find initial group
@@ -494,11 +484,6 @@ __global__ void gemm_half_q_half_gptq_3bit_kernel(
   // Zero output
   if (n >= size_n) return;
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < m_count; m++)
-      *((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
-  }
-
   __syncthreads();
 
   // Find initial group
@@ -623,11 +608,6 @@ __global__ void gemm_half_q_half_gptq_8bit_kernel(
   // Zero output
   if (n >= size_n) return;
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < m_count; m++)
-      *((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
-  }
-
   __syncthreads();
 
   // Find initial group
@@ -1224,9 +1204,6 @@ __global__ void gemm_half_q_half_alt_4bit_kernel(
         __halves2half2(__int2half_rn(val & 0xF), __int2half_rn(val >> 4));
   }
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < b_end; m++) mul[(b + m) * width + w] = __int2half_rn(0);
-  }
   __syncthreads();
 
   int i = width * h + w;
@@ -1319,9 +1296,6 @@ __global__ void gemm_half_q_half_alt_8bit_kernel(
     }
   }
 
-  if (blockIdx.z == 0) {
-    for (int m = 0; m < b_end; m++) mul[(b + m) * width + w] = __int2half_rn(0);
-  }
   __syncthreads();
 
   int i = width * h + w;
@@ -1857,7 +1831,7 @@ torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
                         bool use_exllama, bool use_v2_format, int64_t bit) {
   const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
   auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
-  at::Tensor c = torch::empty({a.size(0), b_q_weight.size(1)}, options);
+  at::Tensor c = torch::zeros({a.size(0), b_q_weight.size(1)}, options);
   at::Tensor temp_dq = torch::empty(
       {b_q_weight.size(0) * 32 / bit, b_q_weight.size(1)}, options);
 

csrc/quantization/gptq_marlin/generate_kernels.py

@@ -70,15 +70,6 @@ QUANT_CONFIGS = [
         "thread_m_blocks": THREAD_M_BLOCKS,
         "group_blocks": [-1, 2, 4, 8],
     },
-    # HQQ
-    {
-        "a_type": ["kFloat16"],
-        "b_type": "kU4",
-        "thread_configs": THREAD_CONFIGS,
-        "thread_m_blocks": THREAD_M_BLOCKS,
-        "group_blocks": [4],
-        "is_zp_float": True,
-    },
     # GPTQ-INT4
     {
         "b_type": "kU4B8",

csrc/quantization/w8a8/cutlass/moe/blockwise_scaled_group_mm_sm100.cu

@@ -1,373 +0,0 @@
-#include "core/registration.h"
-
-#include <torch/all.h>
-#include <cutlass/arch/arch.h>
-
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <c10/cuda/CUDAStream.h>
-
-#include "cute/tensor.hpp"
-#include "cutlass/tensor_ref.h"
-#include "cutlass/epilogue/collective/default_epilogue.hpp"
-#include "cutlass/epilogue/thread/linear_combination.h"
-#include "cutlass/gemm/dispatch_policy.hpp"
-#include "cutlass/gemm/group_array_problem_shape.hpp"
-#include "cutlass/gemm/collective/collective_builder.hpp"
-#include "cutlass/epilogue/collective/collective_builder.hpp"
-#include "cutlass/gemm/device/gemm_universal_adapter.h"
-#include "cutlass/gemm/kernel/gemm_universal.hpp"
-
-#include "cutlass/util/command_line.h"
-#include "cutlass/util/distribution.h"
-#include "cutlass/util/host_tensor.h"
-#include "cutlass/util/packed_stride.hpp"
-#include "cutlass/util/tensor_view_io.h"
-#include "cutlass/util/reference/device/gemm.h"
-#include "cutlass/util/reference/device/tensor_compare.h"
-#include "cutlass/util/reference/host/tensor_fill.h"
-#include "cutlass/util/reference/host/gett.hpp"
-#include "cutlass/util/reference/host/tensor_norm.h"
-#include "cutlass/util/reference/host/tensor_compare.h"
-#include <cassert>
-
-using namespace cute;
-
-template <typename ElementAB, typename ElementC, typename ElementAccumulator,
-          typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
-__global__ void get_ggemm_starts(
-    int32_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
-    ElementC** out_offsets, ElementAccumulator** a_scale_offsets,
-    ElementAccumulator** b_scale_offsets, ElementAB* a_base_as_int,
-    ElementAB* b_base_as_int, ElementC* out_base_as_int,
-    ElementAccumulator* a_scale_base_as_int,
-    ElementAccumulator* b_scale_base_as_int, LayoutSFA* layout_sfa_base_as_int,
-    LayoutSFB* layout_sfb_base_as_int, int* problem_sizes) {
-  int expert_id = threadIdx.x;
-
-  if (expert_id >= gridDim.x * blockDim.x) {
-    return;
-  }
-
-  int m = problem_sizes[expert_id * 3];
-  int n = problem_sizes[expert_id * 3 + 1];
-  int k = problem_sizes[expert_id * 3 + 2];
-
-  int32_t expert_offset = expert_offsets[expert_id];
-  int a_stride = expert_offset * k;
-  int b_stride = expert_id * k * n;
-  int a_scale_stride = expert_offset * k / 128;
-  int b_scale_stride = expert_id * k * n / 128 / 128;
-
-  a_offsets[expert_id] = a_base_as_int + a_stride;
-  b_offsets[expert_id] = b_base_as_int + b_stride;
-  out_offsets[expert_id] = out_base_as_int + expert_offset * n;
-  a_scale_offsets[expert_id] = a_scale_base_as_int + a_scale_stride;
-  b_scale_offsets[expert_id] = b_scale_base_as_int + b_scale_stride;
-
-  LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
-  LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
-
-  *layout_sfa_ptr =
-      ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(m, n, k, 1));
-  *layout_sfb_ptr =
-      ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(m, n, k, 1));
-}
-
-#define __CALL_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE, LayoutSFA, LayoutSFB, \
-                                 ScaleConfig)                                 \
-  else if (out_tensors.dtype() == TENSOR_C_TYPE) {                            \
-    get_ggemm_starts<cutlass::float_e4m3_t, C_TYPE, float, LayoutSFA,         \
-                     LayoutSFB, ScaleConfig><<<1, num_experts, 0, stream>>>(  \
-        static_cast<int32_t*>(expert_offsets.data_ptr()),                     \
-        static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),              \
-        static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()),              \
-        static_cast<C_TYPE**>(out_ptrs.data_ptr()),                           \
-        static_cast<float**>(a_scales_ptrs.data_ptr()),                       \
-        static_cast<float**>(b_scales_ptrs.data_ptr()),                       \
-        static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()),            \
-        static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()),            \
-        static_cast<C_TYPE*>(out_tensors.data_ptr()),                         \
-        static_cast<float*>(a_scales.data_ptr()),                             \
-        static_cast<float*>(b_scales.data_ptr()),                             \
-        reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),                  \
-        reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()),                  \
-        static_cast<int*>(problem_sizes.data_ptr()));                         \
-  }
-
-template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
-void run_get_ggemm_starts(
-    torch::Tensor const& expert_offsets, torch::Tensor& a_ptrs,
-    torch::Tensor& b_ptrs, torch::Tensor& out_ptrs,
-    torch::Tensor& a_scales_ptrs, torch::Tensor& b_scales_ptrs,
-    torch::Tensor const& a_tensors, torch::Tensor const& b_tensors,
-    torch::Tensor out_tensors, torch::Tensor const& a_scales,
-    torch::Tensor const& b_scales, torch::Tensor const& layout_sfa,
-    torch::Tensor const& layout_sfb, torch::Tensor const& problem_sizes) {
-  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(out_tensors.size(1) % 128 == 0 or out_tensors.size(0) % 128 == 0);
-  TORCH_CHECK(a_tensors.size(1) % 128 == 0 or a_tensors.size(0) % 128 == 0);
-
-  int num_experts = (int)expert_offsets.size(0);
-  auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
-
-  if (false) {
-  }
-  __CALL_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t, LayoutSFA,
-                           LayoutSFB, ScaleConfig)
-  __CALL_GET_STARTS_KERNEL(torch::kFloat16, cutlass::half_t, LayoutSFA,
-                           LayoutSFB, ScaleConfig)
-  else {
-    TORCH_CHECK(false, "Unsupported output tensor type");
-  }
-}
-
-template <typename OutType, typename ScheduleConfig, typename LayoutD>
-void run_blockwise_scaled_group_mm(
-    torch::Tensor& out_ptrs, const torch::Tensor& a_ptrs,
-    const torch::Tensor& b_ptrs, const torch::Tensor& a_scales_ptrs,
-    const torch::Tensor& b_scales_ptrs, const torch::Tensor& stride_a,
-    const torch::Tensor& stride_b, const torch::Tensor& stride_c,
-    const torch::Tensor& layout_sfa, const torch::Tensor& layout_sfb,
-    const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
-  using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
-
-  // Types
-  using ElementA = cutlass::float_e4m3_t;
-  using ElementB = cutlass::float_e4m3_t;
-  using ElementC = OutType;
-  using ElementD = ElementC;
-  using ElementAccumulator = float;
-  using LayoutA = cutlass::layout::RowMajor;
-  using LayoutB = cutlass::layout::ColumnMajor;
-  using LayoutC = LayoutD;
-
-  // Alignments
-  static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
-  static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
-  static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
-
-  using ArchTag = cutlass::arch::Sm100;
-  using OperatorClass = cutlass::arch::OpClassTensorOp;
-
-  using CollectiveEpilogue =
-      typename cutlass::epilogue::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, typename ScheduleConfig::MmaTileShape,
-          typename ScheduleConfig::ClusterShape,
-          cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
-          ElementAccumulator, void, LayoutC*, AlignmentC, ElementD, LayoutC*,
-          AlignmentC, typename ScheduleConfig::EpilogueSchedule>::CollectiveOp;
-
-  using CollectiveMainloop =
-      typename cutlass::gemm::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, ElementA,
-          cute::tuple<LayoutA*, typename ScheduleConfig::LayoutSFA*>,
-          AlignmentA, ElementB,
-          cute::tuple<LayoutB*, typename ScheduleConfig::LayoutSFB*>,
-          AlignmentB, ElementAccumulator, typename ScheduleConfig::MmaTileShape,
-          typename ScheduleConfig::ClusterShape,
-          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
-              sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          typename ScheduleConfig::KernelSchedule>::CollectiveOp;
-
-  using GemmKernel =
-      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
-                                           CollectiveEpilogue, void>;
-
-  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
-  using StrideA = typename Gemm::GemmKernel::InternalStrideA;
-  using StrideB = typename Gemm::GemmKernel::InternalStrideB;
-  using StrideC = typename Gemm::GemmKernel::InternalStrideC;
-  using StrideD = typename Gemm::GemmKernel::InternalStrideD;
-
-  using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
-  int num_experts = (int)expert_offsets.size(0);
-
-  Gemm gemm_op;
-
-  // Mainloop Arguments
-  typename GemmKernel::MainloopArguments mainloop_args{
-      static_cast<const ElementA**>(a_ptrs.data_ptr()),
-      static_cast<StrideA*>(stride_a.data_ptr()),
-      static_cast<const ElementB**>(b_ptrs.data_ptr()),
-      static_cast<StrideB*>(stride_b.data_ptr()),
-      static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
-      reinterpret_cast<typename ScheduleConfig::LayoutSFA*>(
-          layout_sfa.data_ptr()),
-      static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
-      reinterpret_cast<typename ScheduleConfig::LayoutSFB*>(
-          layout_sfb.data_ptr())};
-
-  int device_id = a_ptrs.device().index();
-  static const cutlass::KernelHardwareInfo hw_info{
-      device_id, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
-                     device_id)};
-
-  // Epilogue Arguments
-  typename GemmKernel::EpilogueArguments epilogue_args{
-      {},  // epilogue.thread
-      nullptr,
-      static_cast<StrideC*>(stride_c.data_ptr()),
-      static_cast<ElementD**>(out_ptrs.data_ptr()),
-      static_cast<StrideC*>(stride_c.data_ptr())};
-
-  UnderlyingProblemShape* problem_sizes_as_shapes =
-      static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
-
-  // Gemm Arguments
-  typename GemmKernel::Arguments args{
-      cutlass::gemm::GemmUniversalMode::kGrouped,
-      {num_experts, problem_sizes_as_shapes, nullptr},
-      mainloop_args,
-      epilogue_args,
-      hw_info};
-
-  at::cuda::CUDAGuard device_guard{(char)a_ptrs.device().index()};
-  const cudaStream_t stream =
-      at::cuda::getCurrentCUDAStream(a_ptrs.get_device());
-
-  auto can_implement_status = gemm_op.can_implement(args);
-  TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
-              "Failed to implement GEMM");
-
-  size_t workspace_size = gemm_op.get_workspace_size(args);
-  auto const workspace_options =
-      torch::TensorOptions().dtype(torch::kUInt8).device(a_ptrs.device());
-  auto workspace = torch::empty(workspace_size, workspace_options);
-
-  auto status = gemm_op.initialize(args, workspace.data_ptr(), stream);
-  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
-
-  status = gemm_op.run(stream);
-  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
-}
-
-template <typename OutType>
-void blockwise_scaled_group_mm_dispatch_shape(
-    torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
-    const torch::Tensor& scales_a, const torch::Tensor& scales_b,
-    const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
-  struct MmaConfig {
-    using ElementA = cutlass::float_e4m3_t;
-    using KernelSchedule =
-        cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
-    using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
-    using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<
-        1, 128, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
-    using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
-    using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
-    using LayoutC = cutlass::layout::RowMajor;
-    using MmaTileShape = Shape<_128, _128, _128>;
-    using ClusterShape = Shape<_1, _1, _1>;
-  };
-
-  int num_experts = (int)expert_offsets.size(0);
-
-  auto a_ptrs = torch::empty(
-      {num_experts},
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto b_ptrs = torch::empty(
-      {num_experts},
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto out_ptrs = torch::empty(
-      {num_experts},
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto a_scales_ptrs = torch::empty(
-      {num_experts},
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto b_scales_ptrs = torch::empty(
-      {num_experts},
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-
-  auto layout_sfa = torch::empty(
-      {num_experts, 5},
-      torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
-  auto layout_sfb = torch::empty(
-      {num_experts, 5},
-      torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
-
-  auto stride_a = torch::full(
-      {num_experts}, a.size(1),
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto stride_b = torch::full(
-      {num_experts}, a.size(1),
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-  auto stride_c = torch::full(
-      {num_experts}, output.size(1),
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
-
-  torch::TensorOptions options_int =
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device());
-
-  run_get_ggemm_starts<typename MmaConfig::LayoutSFA,
-                       typename MmaConfig::LayoutSFB,
-                       typename MmaConfig::ScaleConfig>(
-      expert_offsets, a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, a,
-      b, output, scales_a, scales_b, layout_sfa, layout_sfb, problem_sizes);
-
-  run_blockwise_scaled_group_mm<OutType, MmaConfig,
-                                typename MmaConfig::LayoutC>(
-      out_ptrs, a_ptrs, b_ptrs, a_scales_ptrs, b_scales_ptrs, stride_a,
-      stride_b, stride_c, layout_sfa, layout_sfb, problem_sizes,
-      expert_offsets);
-}
-
-void cutlass_blockwise_scaled_grouped_mm(
-    torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
-    const torch::Tensor& scales_a, const torch::Tensor& scales_b,
-    const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
-  TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
-  TORCH_CHECK(problem_sizes.size(1) == 3,
-              "problem_sizes must have shape (num_experts, 3)");
-  TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
-              "Number of experts in problem_sizes must match expert_offsets");
-  TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
-              "problem_sizes must be int32");
-  TORCH_CHECK(a.scalar_type() == torch::kFloat8_e4m3fn,
-              "a must be kFloat8_e4m3fn");
-  TORCH_CHECK(b.scalar_type() == torch::kFloat8_e4m3fn,
-              "b must be kFloat8_e4m3fn");
-  TORCH_CHECK(output.scalar_type() == torch::kBFloat16 ||
-                  output.scalar_type() == torch::kHalf,
-              "output must be bfloat16 or half");
-  TORCH_CHECK(scales_a.scalar_type() == torch::kFloat32,
-              "scales_a must be float32");
-  TORCH_CHECK(scales_b.scalar_type() == torch::kFloat32,
-              "scales_b must be float32");
-  TORCH_CHECK(expert_offsets.scalar_type() == torch::kInt32,
-              "expert_offsets must be int32");
-
-  TORCH_CHECK(output.dim() == 2, "output must be 2D tensor");
-  TORCH_CHECK(a.dim() == 2, "a must be 2D tensor");
-  TORCH_CHECK(b.dim() == 3, "b must be 3D tensor");
-  TORCH_CHECK(scales_a.dim() == 2, "scales_a must be 2D tensor");
-  TORCH_CHECK(scales_b.dim() == 3, "scales_b must be 3D tensor");
-  TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
-  TORCH_CHECK(problem_sizes.size(1) == 3,
-              "problem_sizes must have shape (num_experts, 3)");
-  TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
-              "Number of experts in problem_sizes must match expert_offsets");
-  TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
-              "problem_sizes must be int32");
-  TORCH_CHECK(expert_offsets.dim() == 1, "expert_offsets must be 1D tensor");
-
-#if defined(ENABLE_CUTLASS_MOE_SM100) && ENABLE_CUTLASS_MOE_SM100
-  if (output.scalar_type() == torch::kBFloat16) {
-    blockwise_scaled_group_mm_dispatch_shape<cutlass::bfloat16_t>(
-        output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
-  } else if (output.scalar_type() == torch::kFloat16) {
-    blockwise_scaled_group_mm_dispatch_shape<cutlass::half_t>(
-        output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
-  } else {
-    TORCH_CHECK(false, "Unsupported output tensor type");
-  }
-#endif
-}
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("cutlass_blockwise_scaled_grouped_mm",
-         &cutlass_blockwise_scaled_grouped_mm);
-}

csrc/quantization/w8a8/cutlass/moe/moe_data.cu

@@ -3,6 +3,8 @@
 #include <c10/cuda/CUDAGuard.h>
 #include <torch/all.h>
 
+#include "dispatch_utils.h"
+
 #include <iostream>
 
 constexpr uint64_t THREADS_PER_EXPERT = 512;
@@ -114,43 +116,105 @@ inline void launch_compute_problem_sizes(const torch::Tensor& topk_ids,
                                          const bool swap_ab) {
   int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
 
-  const int32_t* topk_ptr = static_cast<const int32_t*>(topk_ids.data_ptr());
-  int32_t* ps1_ptr = static_cast<int32_t*>(problem_sizes1.data_ptr());
-  int32_t* ps2_ptr = static_cast<int32_t*>(problem_sizes2.data_ptr());
-  int32_t* atomic_ptr = static_cast<int32_t*>(atomic_buffer.data_ptr());
+  auto const* topk_ptr = topk_ids.data_ptr<int32_t>();
+  auto* ps1_ptr = problem_sizes1.data_ptr<int32_t>();
+  auto* ps2_ptr = problem_sizes2.data_ptr<int32_t>();
+  auto* atomic_ptr = atomic_buffer.data_ptr<int32_t>();
 
-  if (swap_ab) {
-    compute_problem_sizes<true><<<num_experts, num_threads, 0, stream>>>(
+  VLLM_DISPATCH_BOOL(swap_ab, SwapAB, [&] {
+    compute_problem_sizes<SwapAB><<<num_experts, num_threads, 0, stream>>>(
         topk_ptr, ps1_ptr, ps2_ptr, atomic_ptr,
         static_cast<int>(topk_ids.numel()), static_cast<int>(n),
         static_cast<int>(k));
-  } else {
-    compute_problem_sizes<false><<<num_experts, num_threads, 0, stream>>>(
-        topk_ptr, ps1_ptr, ps2_ptr, atomic_ptr,
-        static_cast<int>(topk_ids.numel()), static_cast<int>(n),
-        static_cast<int>(k));
-  }
+  });
 }
 }  // namespace
 
-void get_cutlass_moe_mm_problem_sizes_caller(
-    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
-    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
-    std::optional<bool> force_swap_ab = std::nullopt) {
-  auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
-  auto options_int32 =
-      torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
-  torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
+template <bool SWAP_AB>
+__global__ void compute_problem_sizes_from_expert_offsets(
+    const int64_t* __restrict__ expert_first_token_offset,
+    int32_t* __restrict__ problem_sizes1, int32_t* __restrict__ problem_sizes2,
+    const int num_experts, const int n, const int k) {
+  int const expert_id = blockIdx.x * blockDim.x + threadIdx.x;
+  if (expert_id >= num_experts) {
+    return;
+  }
 
-  // Swap-AB should be disabled for FP4 path
-  bool may_swap_ab =
-      force_swap_ab.value_or((!blockscale_offsets.has_value()) &&
-                             (topk_ids.numel() <= SWAP_AB_THRESHOLD));
+  int64_t const m64 = expert_first_token_offset[expert_id + 1] -
+                      expert_first_token_offset[expert_id];
+  int32_t const m = static_cast<int32_t>(m64);
 
-  launch_compute_problem_sizes(topk_ids, problem_sizes1, problem_sizes2,
-                               atomic_buffer, num_experts, n, k, stream,
-                               may_swap_ab);
+  int32_t* ps1 = problem_sizes1 + expert_id * 3;
+  int32_t* ps2 = problem_sizes2 + expert_id * 3;
+
+  if constexpr (!SWAP_AB) {
+    // [M, 2*N, K]
+    ps1[0] = m;
+    ps1[1] = 2 * n;
+    ps1[2] = k;
+    // [M, K, N]
+    ps2[0] = m;
+    ps2[1] = k;
+    ps2[2] = n;
+  } else {
+    // swap logical M/N in the problem shape
+    // [2*N, M, K]
+    ps1[0] = 2 * n;
+    ps1[1] = m;
+    ps1[2] = k;
+    // [K, M, N]
+    ps2[0] = k;
+    ps2[1] = m;
+    ps2[2] = n;
+  }
+}
+
+void get_cutlass_moe_mm_problem_sizes_from_expert_offsets_caller(
+    const torch::Tensor& expert_first_token_offset,
+    torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
+    const int64_t n, const int64_t k, const bool swap_ab) {
+  TORCH_CHECK(expert_first_token_offset.is_cuda(),
+              "expert_first_token_offset must be a CUDA tensor");
+  TORCH_CHECK(expert_first_token_offset.dtype() == torch::kInt64,
+              "expert_first_token_offset must be int64");
+
+  TORCH_CHECK(problem_sizes1.is_cuda() && problem_sizes2.is_cuda(),
+              "problem_sizes must be CUDA tensors");
+  TORCH_CHECK(problem_sizes1.dtype() == torch::kInt32 &&
+                  problem_sizes2.dtype() == torch::kInt32,
+              "problem_sizes must be int32");
+  TORCH_CHECK(problem_sizes1.is_contiguous() && problem_sizes2.is_contiguous(),
+              "problem_sizes must be contiguous");
+  TORCH_CHECK(problem_sizes1.dim() == 2 && problem_sizes2.dim() == 2,
+              "problem_sizes must be 2D tensors");
+  TORCH_CHECK(problem_sizes1.size(1) == 3 && problem_sizes2.size(1) == 3,
+              "problem_sizes second dim must be 3");
+  TORCH_CHECK(problem_sizes1.sizes() == problem_sizes2.sizes(),
+              "problem_sizes1 and problem_sizes2 must have same shape");
+
+  int64_t const num_experts64 = problem_sizes1.size(0);
+  TORCH_CHECK(expert_first_token_offset.numel() == num_experts64 + 1,
+              "expert_first_token_offset must have num_experts + 1 elements");
+  TORCH_CHECK(num_experts64 <= INT32_MAX, "num_experts must fit in int32");
+  TORCH_CHECK(n <= INT32_MAX && k <= INT32_MAX, "n and k must fit in int32");
+
+  int const num_experts = static_cast<int>(num_experts64);
+  auto stream = at::cuda::getCurrentCUDAStream(
+      expert_first_token_offset.device().index());
+
+  int const threads = (num_experts < 256) ? num_experts : 256;
+  int const blocks = (num_experts + threads - 1) / threads;
+
+  auto const* offsets_ptr = expert_first_token_offset.data_ptr<int64_t>();
+  auto* ps1_ptr = problem_sizes1.data_ptr<int32_t>();
+  auto* ps2_ptr = problem_sizes2.data_ptr<int32_t>();
+
+  VLLM_DISPATCH_BOOL(swap_ab, SwapAB, [&] {
+    compute_problem_sizes_from_expert_offsets<SwapAB>
+        <<<blocks, threads, 0, stream>>>(offsets_ptr, ps1_ptr, ps2_ptr,
+                                         num_experts, static_cast<int>(n),
+                                         static_cast<int>(k));
+  });
 }
 
 void get_cutlass_moe_mm_data_caller(

csrc/quantization/w8a8/cutlass/scaled_mm_entry.cu

@@ -77,11 +77,10 @@ void get_cutlass_moe_mm_data_caller(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
-void get_cutlass_moe_mm_problem_sizes_caller(
-    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
-    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
-    std::optional<bool> force_swap_ab = std::nullopt);
+void get_cutlass_moe_mm_problem_sizes_from_expert_offsets_caller(
+    const torch::Tensor& expert_first_token_offset,
+    torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
+    const int64_t n, const int64_t k, const bool swap_ab);
 
 void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
                                          torch::Tensor& problem_sizes1,
@@ -301,24 +300,22 @@ void get_cutlass_moe_mm_data(
       version_num, ". Required capability: 90, 100, or 120");
 }
 
-void get_cutlass_moe_mm_problem_sizes(
-    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
-    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
-    std::optional<bool> force_swap_ab = std::nullopt) {
+void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
+    const torch::Tensor& expert_first_token_offset,
+    torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
+    const int64_t n, const int64_t k, const bool swap_ab) {
   int32_t version_num = get_sm_version_num();
 #if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
     (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
     (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
-  get_cutlass_moe_mm_problem_sizes_caller(topk_ids, problem_sizes1,
-                                          problem_sizes2, num_experts, n, k,
-                                          blockscale_offsets, force_swap_ab);
+  get_cutlass_moe_mm_problem_sizes_from_expert_offsets_caller(
+      expert_first_token_offset, problem_sizes1, problem_sizes2, n, k, swap_ab);
   return;
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(
       false,
-      "No compiled get_cutlass_moe_mm_problem_sizes: no cutlass_scaled_mm "
-      "kernel for CUDA device capability: ",
+      "No compiled get_cutlass_moe_mm_problem_sizes_from_expert_offsets: "
+      "no cutlass_scaled_mm kernel for CUDA device capability: ",
       version_num, ". Required capability: 90, 100, or 120");
 }
 

csrc/quantization/w8a8/fp8/common.cu

@@ -4,28 +4,77 @@
 #include "quantization/vectorization_utils.cuh"
 #include <c10/cuda/CUDAGuard.h>
 #include <ATen/cuda/Exceptions.h>
+#include <tuple>
 
 namespace vllm {
 
-template <typename scalar_t, typename fp8_type>
-__global__ void scaled_fp8_quant_kernel_strided(
+// STRIDE_I_ZERO: true if scale_stride_i == 0 (per-tensor or per-channel)
+// STRIDE_J_ZERO: true if scale_stride_j == 0 (per-tensor or per-token)
+template <typename scalar_t, typename fp8_type, bool STRIDE_I_ZERO,
+          bool STRIDE_J_ZERO>
+__global__ void scaled_fp8_quant_kernel_strided_group_shape(
     fp8_type* __restrict__ out, const scalar_t* __restrict__ input,
     const float* __restrict__ scale, int hidden_size, int64_t in_row_stride,
-    int64_t out_row_stride) {
-  const int64_t token_idx = blockIdx.x;  // one token per block
+    int64_t out_row_stride, int group_m, int group_n, int64_t scale_stride_i,
+    int64_t scale_stride_j) {
+  const int64_t token_idx = blockIdx.x;
   const int tid = threadIdx.x;
 
   const scalar_t* token_in = input + token_idx * in_row_stride;
   fp8_type* token_out = out + token_idx * out_row_stride;
 
-  const float inv_scale = 1.0f / (*scale);
+  // Precompute row-level base offset for scale access (compile-time eliminated
+  // when STRIDE_I_ZERO)
+  const int64_t scale_row_base =
+      STRIDE_I_ZERO ? 0
+                    : static_cast<int>(token_idx) / group_m * scale_stride_i;
 
-  vectorize_with_alignment<16>(
-      token_in, token_out, hidden_size, tid, blockDim.x,
-      [=] __device__(fp8_type & dst, const scalar_t& src) {
-        dst = scaled_fp8_conversion<true, fp8_type>(static_cast<float>(src),
-                                                    inv_scale);
-      });
+  auto get_inv_scale = [&](int gj) {
+    return 1.0f / scale[scale_row_base + gj * scale_stride_j];
+  };
+
+  int cached_gj = -1;
+  float cached_inv_scale = 0.0f;
+  auto get_inv_scale_cached = [&](int gj) {
+    if (gj != cached_gj) {
+      cached_inv_scale = 1.0f / scale[scale_row_base + gj * scale_stride_j];
+      cached_gj = gj;
+    }
+    return cached_inv_scale;
+  };
+
+  constexpr int VEC_SIZE = 16;  // FP8 so vectorize to 128 bits
+  auto scaled_fp8_conversion_vectorized = [&](const scalar_t* in, fp8_type* out,
+                                              int size, float inv_scale) {
+    vectorize_with_alignment<VEC_SIZE>(
+        in, out, size, tid, blockDim.x,
+        [=] __device__(fp8_type & dst, const scalar_t& src) {
+          dst = scaled_fp8_conversion<true, fp8_type>(static_cast<float>(src),
+                                                      inv_scale);
+        });
+  };
+
+  if (STRIDE_J_ZERO && hidden_size % VEC_SIZE == 0) {
+    // Per-tensor or per-token: single scale per row, vectorize full row
+    scaled_fp8_conversion_vectorized(token_in, token_out, hidden_size,
+                                     get_inv_scale(0));
+  } else if (group_n % VEC_SIZE == 0) {
+    // Multiple column groups with vectorization
+    const int num_groups_n = hidden_size / group_n;
+
+    for (int gj = 0; gj < num_groups_n; gj++) {
+      scaled_fp8_conversion_vectorized(token_in + gj * group_n,
+                                       token_out + gj * group_n, group_n,
+                                       get_inv_scale(gj));
+    }
+  } else {
+    // Scalar path for small column groups (group_n < VEC_SIZE)
+    for (int n = tid; n < hidden_size; n += blockDim.x) {
+      const int gj = n / group_n;
+      token_out[n] = scaled_fp8_conversion<true, fp8_type>(
+          static_cast<float>(token_in[n]), get_inv_scale_cached(gj));
+    }
+  }
 }
 
 template <typename scalar_t, typename fp8_type>
@@ -133,17 +182,116 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel_strided(
 
 }  // namespace vllm
 
-void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
-                             torch::Tensor const& input,  // [..., d]
-                             torch::Tensor const& scale)  // [1]
+void static_scaled_fp8_quant(
+    torch::Tensor& out,          // [..., d]
+    torch::Tensor const& input,  // [..., d]
+    torch::Tensor const& scale,  // various shapes
+    std::optional<std::tuple<int64_t, int64_t>>
+        opt_group_shape)  // optional explicit (group_m, group_n)
 {
   TORCH_CHECK(input.stride(-1) == 1,
               "last dimension of input must be contiguous");
   TORCH_CHECK(out.stride(-1) == 1,
               "last dimension of output must be contiguous");
 
-  const int hidden_size = input.size(-1);
-  const int num_tokens = input.numel() / hidden_size;
+  const int hidden_size = input.size(-1);              // N (columns)
+  const int num_tokens = input.numel() / hidden_size;  // M (rows)
+
+  // Determine group_m, group_n, and scale strides from scale shape
+  // Scale indexing: scale[gi * scale_stride_j + gj * scale_stride_i]
+  // where gi = m / group_m, gj = n / group_n
+  int group_m, group_n;
+  int64_t scale_stride_i, scale_stride_j;
+
+  if (scale.dim() == 0 || scale.numel() == 1) {
+    // Per-tensor: one scale for the entire tensor
+    group_m = num_tokens;
+    group_n = hidden_size;
+    scale_stride_i = 0;
+    scale_stride_j = 0;
+  } else if (scale.dim() == 1) {
+    // 1D scale: require explicit group_shape to disambiguate per-channel vs
+    // per-token (avoids edge case where num_tokens == hidden_size)
+    TORCH_CHECK(opt_group_shape.has_value(),
+                "1D scale requires explicit group_shape to disambiguate "
+                "per-channel vs per-token quantization. "
+                "Use group_shape=(-1, 1) for per-channel or group_shape=(1, "
+                "-1) for per-token.");
+
+    const auto& [opt_group_m, opt_group_n] = opt_group_shape.value();
+    group_m = opt_group_m == -1 ? num_tokens : static_cast<int>(opt_group_m);
+    group_n = opt_group_n == -1 ? hidden_size : static_cast<int>(opt_group_n);
+
+    // Validate the explicit group shape matches the 1D scale
+    const int64_t scale_len = scale.numel();
+    const int64_t expected_scale_m = num_tokens / group_m;
+    const int64_t expected_scale_n = hidden_size / group_n;
+    const int64_t expected_scale_numel = expected_scale_m * expected_scale_n;
+
+    TORCH_CHECK(scale_len == expected_scale_numel, "1D scale length (",
+                scale_len, ") does not match expected size (",
+                expected_scale_numel, ") for group_shape (", opt_group_m, ", ",
+                opt_group_n, ") with input shape (", num_tokens, ", ",
+                hidden_size, ")");
+
+    // For 1D scale, determine strides based on which dim is trivial
+    // Scale indexing: scale[gi * scale_stride_i + gj * scale_stride_j]
+    // where gi = m / group_m (row group), gj = n / group_n (col group)
+    if (expected_scale_m == 1) {
+      // Per-channel style: one scale in M dim, scale varies along N
+      // gi = 0 always, gj varies, so stride_1 traverses the scale
+      scale_stride_i = 0;
+      scale_stride_j = scale.stride(0);
+    } else if (expected_scale_n == 1) {
+      // Per-token style: one scale in N dim, scale varies along M
+      // gj = 0 always, gi varies, so stride_0 traverses the scale
+      scale_stride_i = scale.stride(0);
+      scale_stride_j = 0;
+    } else {
+      TORCH_CHECK(
+          false,
+          "1D scale can only be used when one of the scale dimensions is 1. "
+          "For 2D group scaling, use a 2D scale tensor.");
+    }
+  } else if (scale.dim() == 2) {
+    // 2D scale: infer group sizes from scale dimensions (or use explicit if
+    // provided)
+    const int64_t scale_size_0 = scale.size(0);
+    const int64_t scale_size_1 = scale.size(1);
+
+    TORCH_CHECK(num_tokens % scale_size_0 == 0, "num_tokens (", num_tokens,
+                ") must be divisible by scale.size(0) (", scale_size_0, ")");
+    TORCH_CHECK(hidden_size % scale_size_1 == 0, "hidden_size (", hidden_size,
+                ") must be divisible by scale.size(1) (", scale_size_1, ")");
+
+    // Infer from 2D scale shape
+    int inferred_group_m = num_tokens / scale_size_0;
+    int inferred_group_n = hidden_size / scale_size_1;
+
+    // Use explicit if provided, otherwise use inferred
+    if (opt_group_shape.has_value()) {
+      const auto& [opt_group_m, opt_group_n] = opt_group_shape.value();
+      group_m = opt_group_m == -1 ? num_tokens : static_cast<int>(opt_group_m);
+      group_n = opt_group_n == -1 ? hidden_size : static_cast<int>(opt_group_n);
+
+      // Validate explicit matches inferred
+      TORCH_CHECK(group_m == inferred_group_m && group_n == inferred_group_n,
+                  "Explicit group_shape (", opt_group_m, ", ", opt_group_n,
+                  ") does not match inferred group shape (", inferred_group_m,
+                  ", ", inferred_group_n, ") from 2D scale tensor shape (",
+                  scale_size_0, ", ", scale_size_1, ")");
+    } else {
+      group_m = inferred_group_m;
+      group_n = inferred_group_n;
+    }
+
+    scale_stride_i = scale.stride(0);
+    scale_stride_j = scale.stride(1);
+  } else {
+    TORCH_CHECK(false, "scale must be 0D, 1D, or 2D tensor, but got ",
+                scale.dim(), "D");
+  }
+
   const int block_size = 256;
   dim3 grid(num_tokens);
   dim3 block(block_size);
@@ -153,15 +301,23 @@ void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
 
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  // Dispatch to template-specialized kernel based on stride pattern
   VLLM_DISPATCH_FLOATING_TYPES(
       input.scalar_type(), "scaled_fp8_quant_kernel_scalar_type", [&] {
         VLLM_DISPATCH_FP8_TYPES(
             out.scalar_type(), "scaled_fp8_quant_kernel_fp8_type", [&] {
-              vllm::scaled_fp8_quant_kernel_strided<scalar_t, fp8_t>
-                  <<<grid, block, 0, stream>>>(
-                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
-                      scale.data_ptr<float>(), hidden_size, in_row_stride,
-                      out_row_stride);
+              VLLM_DISPATCH_BOOL(scale_stride_i == 0, S0_ZERO, [&] {
+                VLLM_DISPATCH_BOOL(scale_stride_j == 0, S1_ZERO, [&] {
+                  vllm::scaled_fp8_quant_kernel_strided_group_shape<
+                      scalar_t, fp8_t, S0_ZERO, S1_ZERO>
+                      <<<grid, block, 0, stream>>>(
+                          out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
+                          scale.data_ptr<float>(), hidden_size, in_row_stride,
+                          out_row_stride, group_m, group_n, scale_stride_i,
+                          scale_stride_j);
+                });
+              });
             });
       });
 }

csrc/rocm/ops.h

@@ -9,6 +9,10 @@ torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
                        const std::optional<at::Tensor>& in_bias,
                        const int64_t CuCount);
 
+torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
+                         const std::optional<at::Tensor>& in_bias,
+                         const int64_t CuCount);
+
 void wvSplitKQ(const at::Tensor& in_a, const at::Tensor& in_b,
                const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
                const at::Tensor& scale_a, const at::Tensor& scale_b,

csrc/rocm/skinny_gemms.cu

@@ -287,6 +287,11 @@ torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
     V0 += (s.x + s.y);                                                        \
   }
 
+// To avoid LLVM silently upcasting to double
+__device__ inline unsigned int min__(uint32_t a, uint32_t b) {
+  return min(a, b);
+}
+
 #if defined(__HIP__GFX9__)  // TODO: Add NAVI support
 // This version targets cases where A[] fits LDS capacity
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
@@ -334,11 +339,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min__(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -633,11 +638,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min__(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -954,11 +959,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   //----------------------------------------------------
   #define PCML
   #ifndef PCML
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min__(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -975,7 +980,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
              ? kFit
              : (kFit - kFit % TUC);  // round up to multiple of TUC
   // if (kFit == 0) kFit = TUC;
-  kFit = min(kFit, K);
+  kFit = min__(kFit, K);
 
   float sum[N][YTILE];
   scalar8 sum4[N][YTILE];
@@ -1251,6 +1256,7 @@ int mindiv(int N, int div1, int div2) {
   }
   for (int i = 12; i >= 0; i--)
     if (rnds[0] == rnds[i]) return (div2 - i);
+  return 0;
 }
 
 torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
@@ -1352,6 +1358,536 @@ torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
   return out_c;
 }
 
+#if defined(__gfx950__)  // TODO: Add NAVI support
+  // This version targets big A[] cases, where it is much larger than LDS
+  // capacity
+  #define WVSPLITKRC_1KPASS
+template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
+          int UNRL, int N, int GrpsShrB>
+
+__global__ void __launch_bounds__(WvPrGrp* THRDS)
+    __attribute__((amdgpu_waves_per_eu(1, 1)))
+    wvSplitKrc_(const int actlN, const int K, const int M, const int Bx,
+                const int By, const scalar_t* __restrict__ B,
+                const scalar_t* __restrict__ A,
+                const scalar_t* __restrict__ BIAS, float* glbl, scalar_t* C,
+                const int CuCount) {
+  // Use upper half of glbl buffer for atomic reduce counting
+  int* cntr = (int*)(&glbl[M * N]);
+
+  constexpr int NTILE = 16;
+  constexpr int WVLDS_ = (NTILE * THRDS * A_CHUNK);
+  constexpr int APAD = 1;
+  constexpr int ASTRD = 64;
+  constexpr int BPAD = 1;
+  constexpr int BSTRD = 64;
+  constexpr int WVLDS = ((WVLDS_ + (WVLDS_ / BSTRD) * 4 * BPAD));
+
+  constexpr int max_lds_len = LDS_SIZE / 2;
+
+  using scalar16 =
+      __attribute__((__vector_size__((A_CHUNK * 2) * sizeof(float)))) float;
+  using scalar8 =
+      __attribute__((__vector_size__((A_CHUNK / 2) * sizeof(float)))) float;
+  using half4 =
+      __attribute__((__vector_size__((A_CHUNK / 2) * sizeof(__bf16)))) __bf16;
+  union bigType {
+    scalar_t h[A_CHUNK];
+    float f[A_CHUNK / 2];
+    unsigned int i[A_CHUNK / 2];
+    float2 f2[A_CHUNK / 4];
+    unsigned long l[A_CHUNK / 4];
+    double d[A_CHUNK / 4];
+    half4 h4[A_CHUNK / 4];
+    scalar8 h8;
+  };
+  using big4 = __attribute__((__vector_size__(4 * sizeof(bigType)))) __bf16;
+
+  __shared__ scalar_t stg[WvPrGrp * WVLDS / GrpsShrB];
+  unsigned int* myStg = (unsigned int*)(&stg[WVLDS * (threadIdx.y / GrpsShrB)]);
+  __shared__ scalar_t s[max_lds_len - WvPrGrp * WVLDS / GrpsShrB];
+
+  #ifndef WVSPLITKRC_1KPASS
+  constexpr int TUC_ = (THRDS * UNRL * A_CHUNK);
+  // find biggest k size that fits padded into LDS
+  constexpr uint32_t kFit__ = (max_lds_len - WvPrGrp * WVLDS / GrpsShrB) / N;
+  constexpr uint32_t kFit_ = (kFit__ * ASTRD) / (APAD + ASTRD);
+  uint32_t kFit = kFit_ - (kFit_ % TUC_);
+  uint32_t kfitsPerRdc = (K + kFit - 1) / kFit;
+
+  // find best k split to fill the CUs
+  if (((K + kfitsPerRdc * kFit - 1) / (kfitsPerRdc * kFit)) * numCuWithFullK <=
+      CuCount)
+    while (true) {
+      while (kFit > TUC_) {
+        uint32_t kFit_ = kFit - TUC_;
+        if (((K + (kfitsPerRdc * kFit_ - 1)) / (kfitsPerRdc * kFit_)) *
+                numCuWithFullK >
+            CuCount)
+          break;
+        kFit = kFit_;
+      }
+      if (((K + ((kfitsPerRdc - 1) * kFit - 1)) / ((kfitsPerRdc - 1) * kFit)) *
+              numCuWithFullK <=
+          CuCount)
+        kfitsPerRdc--;
+      else
+        break;
+    }
+  #else
+  int constexpr kFit = 512;
+  int constexpr kfitsPerRdc = 1;
+  #endif
+
+  bool doRdc = (kfitsPerRdc * kFit < K);
+  uint32_t numCuWithFullK =
+      ((M + (WvPrGrp * YTILE / GrpsShrB) - 1) / (WvPrGrp * YTILE / GrpsShrB));
+  uint32_t Mmod = numCuWithFullK * (WvPrGrp * YTILE / GrpsShrB);
+
+  // given above k-split, find this wave's position
+  uint32_t kFitPdd = kFit + (kFit / ASTRD) * APAD;
+  uint32_t m0 = (blockIdx.x * WvPrGrp / GrpsShrB) * YTILE;
+  uint32_t m1 = ((threadIdx.y % WvPrGrp) / GrpsShrB) * YTILE;
+  uint32_t m = (m0 + m1) % Mmod;
+  const uint32_t k_str = (m0 / Mmod) * kFit * kfitsPerRdc;
+  uint32_t k_end = (m0 / Mmod + 1) * kFit * kfitsPerRdc;
+  const uint32_t k_rnd = (K + kFit * kfitsPerRdc - 1) / (kFit * kfitsPerRdc);
+
+  scalar8 sum4[N / NTILE / GrpsShrB][1];
+  bigType bigB_[YTILE / GrpsShrB][UNRL];
+  const uint32_t bLoader = (threadIdx.y % GrpsShrB);
+  uint32_t kBase = 0;
+  if (k_str >= K) return;
+  if (m >= Mmod) return;
+
+  bool noreloada = false;
+  constexpr bool FAST_UNSAFE_RDC_INIT = false;
+
+  #ifdef WVSPLITKRC_1KPASS
+  // Early glbl init, B[] loading, if 1KPASS
+  if constexpr (FAST_UNSAFE_RDC_INIT) {
+    if (m + (threadIdx.x % 16) < M)
+      if (doRdc)
+        if (k_str == 0) {
+          int mindx = m + (threadIdx.x % 16);
+          int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
+                       (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+          int adr_ = mindx + M * nindx_ / 4;
+          __hip_atomic_store(&cntr[adr_], 0, __ATOMIC_RELAXED,
+                             __HIP_MEMORY_SCOPE_AGENT);
+          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+            for (uint32_t j = 0; j < 4; j++) {
+              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+              int adr = mindx + M * nindx;
+              __hip_atomic_store(&glbl[adr], 0, __ATOMIC_RELAXED,
+                                 __HIP_MEMORY_SCOPE_AGENT);
+            }
+          }
+        }
+  }
+
+    // Load first B[] chunk
+    #pragma unroll
+  for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+    uint32_t k = k_str + k2 * THRDS * A_CHUNK;
+    uint32_t k_ = k + threadIdx.x * A_CHUNK;
+    const scalar_t* B_ = &B[min__(k_, K - A_CHUNK)];
+    #pragma unroll
+    for (uint32_t y = 0; y < YTILE / GrpsShrB; y++)
+      bigB_[y][k2].h8 = (loadnt(
+          (scalar8*)(&B_[min__(y * GrpsShrB + bLoader + m, M - 1) * K])));
+  }
+  {
+  #else
+  while (m < Mmod) {
+  #endif
+
+  #ifndef WVSPLITKRC_1KPASS
+    if constexpr (FAST_UNSAFE_RDC_INIT) {
+      if (m + (threadIdx.x % 16) < M)
+        if (doRdc)
+          if (k_str == 0) {
+            int mindx = m + (threadIdx.x % 16);
+            int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
+                         (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+            int adr_ = mindx + M * nindx_ / 4;
+            __hip_atomic_store(&cntr[adr_], 0, __ATOMIC_RELAXED,
+                               __HIP_MEMORY_SCOPE_AGENT);
+            for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+              for (uint32_t j = 0; j < 4; j++) {
+                int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                            (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+                int adr = mindx + M * nindx;
+                __hip_atomic_store(&glbl[adr], 0, __ATOMIC_RELAXED,
+                                   __HIP_MEMORY_SCOPE_AGENT);
+              }
+            }
+          }
+    }
+
+  #endif
+
+  #ifndef WVSPLITKRC_1KPASS
+    for (uint32_t k1 = k_str; k1 < k_end; k1 += THRDS * A_CHUNK * UNRL) {
+  #else
+    const uint32_t k1 = k_str;
+    {
+  #endif
+  #ifndef WVSPLITKRC_1KPASS
+      const bool reloada = (!noreloada) &&
+                           ((k1 == k_str) || (k1 == k_str + kBase + kFit)) &&
+                           (k1 < k_end);
+      // load next chunk of A[] to LDS
+      if (reloada) {
+        if (k1 != k_str) kBase += kFit;
+        __syncthreads();
+  #else
+      const bool reloada = (!noreloada) &&
+                           ((k1 == k_str) || (k1 == k_str + kBase + kFit)) &&
+                           (k1 < k_end);
+      if (reloada) {
+  #endif
+        constexpr int sprdN = 4;
+        const uint32_t thrd = ((threadIdx.y / sprdN) * THRDS + threadIdx.x);
+
+  #ifndef WVSPLITKRC_1KPASS
+    #pragma unroll
+        for (int k = 0; k < kFit; k += THRDS * (WvPrGrp / sprdN) * A_CHUNK) {
+  #else
+        const unsigned int k = 0;
+        {
+  #endif
+          unsigned int kOff = k + (thrd * A_CHUNK);
+          unsigned int kOffcp = min__(K - A_CHUNK, k_str + kOff);
+          const unsigned int k_in = kOffcp + ((threadIdx.y % sprdN)) * K;
+          const unsigned int k_ot = kOff + ((threadIdx.y % sprdN)) * kFitPdd;
+          for (unsigned int n = 0; n < N / 2; n += sprdN) {
+            __builtin_amdgcn_global_load_lds((int*)(&A[k_in + n * K]),
+                                             (int*)(&s[(k_ot + n * kFitPdd)]),
+                                             16, 0, 0);
+            if (((threadIdx.y % sprdN)) + n + N / 2 >= actlN) continue;
+            __builtin_amdgcn_global_load_lds(
+                (int*)(&A[k_in + (n + N / 2) * K]),
+                (int*)(&s[(k_ot + (n + N / 2) * kFitPdd)]), 16, 0, 0);
+          }
+
+          // Stage loaded B[] to LDS for MFMA swizzling...
+          for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+            uint32_t k = k1 + k2 * THRDS * A_CHUNK;
+            uint32_t k_ = k + threadIdx.x * A_CHUNK;
+            const bool oob_k = (k_ >= K);
+            for (uint32_t y = 0; y < YTILE / GrpsShrB; y++) {
+              uint32_t idx = threadIdx.x * 4 +
+                             (y * GrpsShrB + bLoader) * ((THRDS + BPAD) * 4);
+              // zero out if oob
+              *((scalar8*)&myStg[idx]) =
+                  (oob_k || (y * GrpsShrB + bLoader + m >= M))
+                      ? 0
+                      : bigB_[y][k2].h8;
+            }
+          }
+        }
+      }
+    }
+
+  #ifndef WVSPLITKRC_1KPASS
+    // Fire load of next B[] chunk...
+    if ((k1 + THRDS * A_CHUNK * UNRL < k_end) &&
+        (k1 + THRDS * A_CHUNK * UNRL < K))
+    #pragma unroll
+      for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+        uint32_t k = k1 + THRDS * A_CHUNK * UNRL + k2 * THRDS * A_CHUNK;
+        uint32_t k_ = k + threadIdx.x * A_CHUNK;
+        const scalar_t* B_ = &B[min__(k_, K - A_CHUNK)];
+    #pragma unroll
+        for (uint32_t y = 0; y < YTILE / GrpsShrB; y++)
+          bigB_[y][k2].h8 = (loadnt(
+              (scalar8*)(&B_[min__(y * GrpsShrB + bLoader + m, M - 1) * K])));
+      }
+  #endif
+
+    // B[] staging is cooperative across GrpsShrB, so sync here before reading
+    // back
+    __syncthreads();
+
+    // read back B[] swizzled for MFMA...
+    bigType bigB[YTILE][UNRL];
+    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+      for (uint32_t y = 0; y < YTILE; y++) {
+        unsigned int idx = (threadIdx.x % YTILE) * ((THRDS + BPAD) * 4) +
+                           (threadIdx.x / YTILE) * 4 + y * 16;
+        bigB[y][k2].h8 = *((scalar8*)&myStg[idx]);
+      }
+    }
+
+    // rReadback A[] swizzled for MFMA...
+    bigType bigA[N / GrpsShrB][UNRL];
+  #pragma unroll
+    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+      uint32_t k = k1 + k2 * THRDS * A_CHUNK - kBase - k_str;
+  #pragma unroll
+      for (uint32_t nt = 0; nt < N / GrpsShrB; nt += NTILE)
+  #pragma unroll
+        for (uint32_t n = 0; n < NTILE; n++) {
+          uint32_t idxa = (nt + (threadIdx.x % NTILE) +
+                           (N / GrpsShrB) * (threadIdx.y % GrpsShrB)) *
+                              kFitPdd +
+                          A_CHUNK * ((threadIdx.x / NTILE) + n * 4) + k;
+          bigA[nt + n][k2] = *((const bigType*)(&(s[idxa])));
+        }
+    }
+
+    // Do the MFMAs
+  #pragma unroll
+    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
+  #pragma unroll
+      for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+        if constexpr (std::is_same_v<scalar_t, half>) {
+          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
+              bigA[nt * NTILE + 0][k2].h4[0], bigB[0][k2].h4[0],
+              (k1 == k_str) ? ((scalar8){0}) : sum4[nt][0], 0, 0, 0);
+          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
+              bigA[nt * NTILE + 0][k2].h4[1], bigB[0][k2].h4[1], sum4[nt][0], 0,
+              0, 0);
+        } else {  // bf16
+          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
+              bigA[nt * NTILE + 0][k2].h4[0], bigB[0][k2].h4[0],
+              (k1 == k_str) ? ((scalar8){0}) : sum4[nt][0], 0, 0, 0);
+          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
+              bigA[nt * NTILE + 0][k2].h4[1], bigB[0][k2].h4[1], sum4[nt][0], 0,
+              0, 0);
+        }
+  #pragma unroll
+        for (uint32_t j = 1; j < YTILE; j++) {
+          if constexpr (std::is_same_v<scalar_t, half>) {
+            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
+                bigA[nt * NTILE + j][k2].h4[0], bigB[j][k2].h4[0], sum4[nt][0],
+                0, 0, 0);
+            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
+                bigA[nt * NTILE + j][k2].h4[1], bigB[j][k2].h4[1], sum4[nt][0],
+                0, 0, 0);
+          } else {  // bf16
+            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
+                bigA[nt * NTILE + j][k2].h4[0], bigB[j][k2].h4[0], sum4[nt][0],
+                0, 0, 0);
+            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
+                bigA[nt * NTILE + j][k2].h4[1], bigB[j][k2].h4[1], sum4[nt][0],
+                0, 0, 0);
+          }
+        }
+      }
+    }
+  }
+
+  if (!doRdc) {
+    if (m + (threadIdx.x % 16) < M) {
+      scalar_t biases[N / NTILE / GrpsShrB][4] = {0};
+      if (BIAS)
+        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+          for (uint32_t j = 0; j < 4; j++) {
+            int mindx = m + (threadIdx.x % 16);
+            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+            biases[nt][j] = BIAS[(mindx % Bx) + (nindx % By) * M];
+          }
+        }
+      for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+        for (uint32_t j = 0; j < 4; j++) {
+          int mindx = m + (threadIdx.x % 16);
+          int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                      (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+          int adr = mindx + M * nindx;
+          if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+            if (BIAS) sum4[nt][0][j] += __bfloat162float(biases[nt][j]);
+            C[adr] = __float2bfloat16(sum4[nt][0][j]);
+          } else {
+            if (BIAS) sum4[nt][0][j] += __half2float(biases[nt][j]);
+            C[adr] = __float2half(sum4[nt][0][j]);
+          }
+        }
+      }
+    }
+  } else {
+    if (m + (threadIdx.x % 16) < M) {
+      int my_cntr;
+      if (!BIAS) {
+        int mindx = m + (threadIdx.x % 16);
+        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++)
+          for (uint32_t j = 0; j < 4; j++) {
+            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+            int adr = mindx + M * nindx;
+            atomicAdd(&glbl[adr], sum4[nt][0][j]);
+          }
+        int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
+                     (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+        int adr_ = mindx + M * nindx_ / 4;
+        my_cntr = atomicAdd(&cntr[adr_], 1);
+        float vals[N / NTILE / GrpsShrB][4] = {};
+        if (my_cntr + 1 == k_rnd) {
+          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+            for (uint32_t j = 0; j < 4; j++) {
+              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+              int adr = mindx + M * nindx;
+              vals[nt][j] = glbl[adr];
+            }
+          }
+          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+            for (uint32_t j = 0; j < 4; j++) {
+              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+              if (nindx >= actlN) break;
+              int adr = mindx + M * nindx;
+              if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+                C[adr] = __float2bfloat16(vals[nt][j]);
+              } else {
+                C[adr] = __float2half(vals[nt][j]);
+              }
+            }
+          }
+        }
+      } else {
+        int mindx = m + (threadIdx.x % 16);
+        scalar_t biases[N / NTILE / GrpsShrB][4] = {};
+        // Atomic add the output, read biases
+        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++)
+          for (uint32_t j = 0; j < 4; j++) {
+            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+            int adr = mindx + M * nindx;
+            atomicAdd(&glbl[adr], sum4[nt][0][j]);
+            biases[nt][j] = BIAS[(mindx % Bx) + (nindx % By) * M];
+          }
+        int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
+                     (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+        int adr_ = mindx + M * nindx_ / 4;
+        // Update the complete counter
+        my_cntr = atomicAdd(&cntr[adr_], 1);
+        float vals[N / NTILE / GrpsShrB][4] = {};
+        // If we're the last k-shard, read back the value and convert...
+        if (my_cntr + 1 == k_rnd) {
+          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+            for (uint32_t j = 0; j < 4; j++) {
+              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+              int adr = mindx + M * nindx;
+              vals[nt][j] = glbl[adr];
+            }
+          }
+          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
+            for (uint32_t j = 0; j < 4; j++) {
+              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
+                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
+              if (nindx >= actlN) break;
+              int adr = mindx + M * nindx;
+              if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+                vals[nt][j] += __bfloat162float(biases[nt][j]);
+                C[adr] = __float2bfloat16(vals[nt][j]);
+              } else {
+                vals[nt][j] += __half2float(biases[nt][j]);
+                C[adr] = __float2half(vals[nt][j]);
+              }
+            }
+          }
+        }
+      }
+    }
+
+  #ifndef WVSPLITKRC_1KPASS
+    m0 += CuCount * WvPrGrp * YTILE / GrpsShrB;
+    m = (m0 + m1) % Mmod;
+    k_str = (m0 / Mmod) * kFit * kfitsPerRdc;
+    k_end = (m0 / Mmod + 1) * kFit * kfitsPerRdc;
+    if (k_str >= K) break;
+    kBase = 0;
+  #endif
+  }
+}
+#else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
+template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
+          int UNRL, int N, int GrpsShrB>
+__global__ void wvSplitKrc_(const int actlN, const int K, const int M,
+                            const int Bx, const int By, const scalar_t* B,
+                            const scalar_t* __restrict__ A,
+                            const scalar_t* __restrict__ BIAS, float* glbl,
+                            // int* cntr,
+                            scalar_t* C, const int CuCount){UNREACHABLE_CODE}
+#endif  // defined(__HIP__GFX9__) TODO: Add NAVI support
+
+torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
+                         const std::optional<at::Tensor>& in_bias,
+                         const int64_t CuCount) {
+  auto M_in = in_a.size(0);
+  auto N_in = in_b.size(0);
+  auto K_in = in_a.size(1);
+  auto Bx_in =
+      (in_bias.has_value() && in_bias->numel() > 0)
+          ? (in_bias->sizes().size() == 2) ? in_bias->size(1) : in_bias->size(0)
+          : 1;
+  auto By_in = (in_bias.has_value() && in_bias->numel() > 0 &&
+                in_bias->sizes().size() == 2)
+                   ? in_bias->size(0)
+                   : 1;
+
+  TORCH_CHECK(in_a.dtype() == in_b.dtype());
+  TORCH_CHECK(K_in % 8 == 0, "k % 8 == 0");
+  TORCH_CHECK(in_a.dtype() == torch::kFloat16 ||
+              in_a.dtype() == torch::kBFloat16);
+
+  auto out_c = torch::empty(
+      {N_in, M_in},
+      torch::TensorOptions().dtype(in_b.dtype()).device(in_b.device()));
+
+  auto N_p2 = 1U << (32 - __builtin_clz(N_in - 1));
+  auto axl_glbl = torch::empty(
+      {N_p2 + N_p2 / 4, M_in + M_in / 4},
+      torch::TensorOptions().dtype(torch::kFloat32).device(in_b.device()));
+  axl_glbl.zero_();  // disable for FAST_UNSAFE_RDC_INIT
+
+  dim3 grid(CuCount);
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(in_a));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  // const int max_lds_len = get_lds_size() / 2;
+
+#define WVSPLITKrc(_WvPrGrp, _YTILE, _UNRL, _N, _GrpsShrB)                     \
+  {                                                                            \
+    dim3 block(64, _WvPrGrp);                                                  \
+    wvSplitKrc_<fptype, 64, _YTILE, _WvPrGrp, 8, _UNRL, _N, _GrpsShrB>         \
+        <<<grid, block, 0, stream>>>(N_in, K_in, M_in, Bx_in, By_in, af4, bf4, \
+                                     biasf4, glbl, c, CuCount);                \
+  }
+
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(in_b.scalar_type(), "wvSplitKrc", [&] {
+    using fptype = typename scalar<scalar_t>::type;
+    fptype* af4 = reinterpret_cast<fptype*>(in_a.data_ptr());
+    const fptype* bf4 = reinterpret_cast<const fptype*>(in_b.data_ptr());
+    const fptype* biasf4 =
+        (in_bias.has_value() && in_bias->numel() > 0)
+            ? reinterpret_cast<const fptype*>(in_bias->data_ptr())
+            : nullptr;
+    fptype* c = reinterpret_cast<fptype*>(out_c.data_ptr());
+    auto glbl = axl_glbl.data_ptr<float>();
+    switch (N_p2) {
+      case 16:
+        WVSPLITKrc(4, 16, 1, 16, 1) break;
+      case 32:
+        WVSPLITKrc(4, 16, 1, 32, 2) break;
+      case 64:
+        WVSPLITKrc(4, 16, 1, 64, 2) break;
+      case 128:
+        WVSPLITKrc(4, 16, 1, 128, 4) break;
+      default:
+        throw std::runtime_error(
+            "Unsupported N value: " + std::to_string(M_in) + "," +
+            std::to_string(K_in) + "," + std::to_string(N_in));
+    }
+  });
+  return out_c;
+}
+
 #if defined(__HIP__MI3XX__)  // TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
@@ -1381,7 +1917,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   __shared__ fp8_t s[max_lds_len];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min__(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();
@@ -1570,7 +2106,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   __shared__ fp8_t s[max_lds_len];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min__(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();