Use Blackwell FlashInfer MXFP4 MoE by default if available (#23008)

Signed-off-by: mgoin <mgoin64@gmail.com>

.buildkite/nightly-benchmarks/README.md

@@ -7,7 +7,7 @@ This directory contains two sets of benchmark for vllm.
 - Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
 - Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
 
-See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
 
 ## Performance benchmark quick overview
 
@@ -138,28 +138,20 @@ The raw benchmarking results (in the format of json files) are in the `Artifacts
 
 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.
+`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.
 
-Here is an example using the script to compare result_a and result_b without detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json --ignore_test_name`
-
-|    | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|----------------------------------------|----------------------------------------|----------|
-| 0  | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | 241.620334                             | 294.018783                             | 1.216863 |
-| 2  | 218.298905                             | 262.664916                             | 1.203235 |
-| 3  | 242.743860                             | 299.816190                             | 1.235113 |
-
-Here is an example using the script to compare result_a and result_b with detail test name.
+Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output lenght, max concurrency and qps.
 `python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
 
-|   | results_a/benchmark_results.json_name | results_a/benchmark_results.json | results_b/benchmark_results.json_name | results_b/benchmark_results.json | perf_ratio        |
-|---|---------------------------------------------|----------------------------------------|---------------------------------------------|----------------------------------------|----------|
-| 0 | serving_llama8B_tp1_sharegpt_qps_1          | 142.633982                             | serving_llama8B_tp1_sharegpt_qps_1          | 156.526018                             | 1.097396 |
-| 1 | serving_llama8B_tp1_sharegpt_qps_16         | 241.620334                             | serving_llama8B_tp1_sharegpt_qps_16         | 294.018783                             | 1.216863 |
-| 2 | serving_llama8B_tp1_sharegpt_qps_4          | 218.298905                             | serving_llama8B_tp1_sharegpt_qps_4          | 262.664916                             | 1.203235 |
-| 3 | serving_llama8B_tp1_sharegpt_qps_inf        | 242.743860                             | serving_llama8B_tp1_sharegpt_qps_inf        | 299.816190                             | 1.235113 |
-| 4 | serving_llama8B_tp2_random_1024_128_qps_1   | 96.613390                              | serving_llama8B_tp4_random_1024_128_qps_1   | 108.404853                             | 1.122048 |
+|   | 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" />
 
 ## Nightly test details
 

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

@@ -1,24 +1,38 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import argparse
+import json
+import os
 
 import pandas as pd
 
 
 def compare_data_columns(
-    files, name_column, data_column, drop_column, ignore_test_name=False
+    files, name_column, data_column, info_cols, drop_column, debug=False
 ):
     print("\ncompare_data_column: " + data_column)
     frames = []
+    raw_data_cols = []
     compare_frames = []
     for file in files:
         data_df = pd.read_json(file)
         serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
-        if ignore_test_name is False:
+        # Show all info columns in the first couple columns
+        if not frames:
+            for col in info_cols:
+                if col not in serving_df.columns:
+                    print(f"Skipping missing column: {col}")
+                    continue
+                frames.append(serving_df[col])
+        # only show test name under debug mode
+        if debug is True:
             serving_df = serving_df.rename(columns={name_column: file + "_name"})
             frames.append(serving_df[file + "_name"])
+
+        file = "/".join(file.split("/")[:-1])
         serving_df = serving_df.rename(columns={data_column: file})
         frames.append(serving_df[file])
+        raw_data_cols.append(file)
         compare_frames.append(serving_df[file])
         if len(compare_frames) >= 2:
             # Compare numbers among two files
@@ -27,7 +41,68 @@ def compare_data_columns(
             compare_frames.pop(1)
 
     concat_df = pd.concat(frames, axis=1)
-    return concat_df
+    print(raw_data_cols)
+    return concat_df, raw_data_cols
+
+
+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):
+                data = data[key]
+                break
+
+    df = pd.DataFrame(data)
+
+    # Handle alias column names
+    rename_map = {
+        "tp_size": "TP Size",
+        "tensor_parallel_size": "TP Size",
+        "pp_size": "PP Size",
+        "pipeline_parallel_size": "PP Size",
+    }
+    df.rename(
+        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)
+    )
+
+    # 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)}")
+        os.makedirs(folder_name, exist_ok=True)
+        filepath = os.path.join(folder_name, "benchmark_results.json")
+        group_df.to_json(filepath, orient="records", indent=2, force_ascii=False)
+        print(f"Saved: {filepath}")
+        saved_paths.append(filepath)
+
+    return saved_paths
 
 
 if __name__ == "__main__":
@@ -36,31 +111,105 @@ if __name__ == "__main__":
         "-f", "--file", action="append", type=str, help="input file name"
     )
     parser.add_argument(
-        "--ignore_test_name", action="store_true", help="ignore_test_name or not"
+        "--debug", action="store_true", help="show all information for debugging"
+    )
+    parser.add_argument(
+        "--plot",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="plot perf diagrams or not --no-plot --plot",
+    )
+    parser.add_argument(
+        "-x",
+        "--xaxis",
+        type=str,
+        default="# of max concurrency.",
+        help="column name to use as X Axis in comparision graph",
     )
     args = parser.parse_args()
-    files = args.file
-    print("comparing : " + ", ".join(files))
 
     drop_column = "P99"
     name_column = "Test name"
+    info_cols = [
+        "Model",
+        "Dataset Name",
+        "Input Len",
+        "Output Len",
+        "TP Size",
+        "PP Size",
+        "# of max concurrency.",
+        "qps",
+    ]
     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",
     ]
-    ignore_test_name = args.ignore_test_name
+
+    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
+    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 = compare_data_columns(
+            output_df, raw_data_cols = compare_data_columns(
                 files,
                 name_column,
                 data_cols_to_compare[i],
+                info_cols,
                 drop_column,
-                ignore_test_name=ignore_test_name,
+                debug=debug,
             )
-            print(output_df)
-            html = output_df.to_html()
-            text_file.write(html_msgs_for_data_cols[i])
-            text_file.write(html)
+
+            # For Plot feature, insert y axis from one of info_cols
+            raw_data_cols.insert(0, info_cols[y_axis_index])
+
+            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)}"
+                )
+
+            output_df_sorted = output_df.sort_values(by=existing_group_cols)
+            output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
+            for name, group in output_groups:
+                html = group.to_html()
+                text_file.write(html_msgs_for_data_cols[i])
+                text_file.write(html)
+
+                if plot is True:
+                    import pandas as pd
+                    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,
+                    )
+                    # Export to HTML
+                    text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -1,17 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import argparse
 import json
 import os
+import shlex
 from importlib import util
 from pathlib import Path
+from typing import Any
 
 import pandas as pd
 import psutil
+import regex as re
 from tabulate import tabulate
 
-results_folder = Path("results/")
-
 # latency results and the keys that will be printed into markdown
 latency_results = []
 latency_column_mapping = {
@@ -42,14 +44,22 @@ throughput_results_column_mapping = {
 serving_results = []
 serving_column_mapping = {
     "test_name": "Test name",
+    "model_id": "Model",
+    "dataset_name": "Dataset Name",
+    "input_len": "Input Len",
+    "output_len": "Output Len",
+    "tp_size": "TP Size",
+    "pp_size": "PP Size",
+    "dtype": "dtype",
     "gpu_type": "GPU",
     "completed": "# of req.",
+    "qps": "qps",
     "max_concurrency": "# of max concurrency.",
     "request_throughput": "Tput (req/s)",
     "total_token_throughput": "Total Token Tput (tok/s)",
     "output_throughput": "Output Tput (tok/s)",
-    "total_input_tokens": "Total input tokens",
-    "total_output_tokens": "Total output tokens",
+    # "total_input_tokens": "Total input tokens",
+    # "total_output_tokens": "Total output tokens",
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
@@ -94,7 +104,104 @@ def get_size_with_unit(bytes, suffix="B"):
         bytes /= factor
 
 
+def _coerce(val: str) -> Any:
+    """Best-effort type coercion from string to Python types."""
+    low = val.lower()
+    if low == "null":
+        return None
+    if low == "true":
+        return True
+    if low == "false":
+        return False
+    # integers
+    if re.fullmatch(r"[+-]?\d+", val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+    # floats (keep 'inf'/'-inf'/'nan' as strings)
+    if re.fullmatch(r"[+-]?\d*\.\d+", val):
+        try:
+            return float(val)
+        except ValueError:
+            pass
+    return val
+
+
+def parse_client_command(cmd: str) -> dict[str, Any]:
+    """Parse the client_command shell string into {executable, script, args}."""
+    toks = shlex.split(cmd)
+    if len(toks) < 2:
+        raise ValueError("client_command must include an executable and a script")
+    executable, script = toks[0], toks[1]
+    args: dict[str, Any] = {}
+
+    i = 2
+    while i < len(toks):
+        t = toks[i]
+        if t.startswith("--"):
+            # --key=value or --key (value) or boolean flag
+            if "=" in t:
+                key, val = t.split("=", 1)
+                if key == "--metadata":
+                    md = {}
+                    if val:
+                        if "=" in val:
+                            k, v = val.split("=", 1)
+                            md[k] = _coerce(v)
+                        else:
+                            md[val] = True
+                    args[key] = md
+                else:
+                    args[key] = _coerce(val)
+                i += 1
+                continue
+
+            key = t
+
+            # Special: consume metadata k=v pairs until next --flag
+            if key == "--metadata":
+                i += 1
+                md = {}
+                while i < len(toks) and not toks[i].startswith("--"):
+                    pair = toks[i]
+                    if "=" in pair:
+                        k, v = pair.split("=", 1)
+                        md[k] = _coerce(v)
+                    else:
+                        md[pair] = True
+                    i += 1
+                args[key] = md
+                continue
+
+            # Standard: check if next token is a value (not a flag)
+            if i + 1 < len(toks) and not toks[i + 1].startswith("--"):
+                args[key] = _coerce(toks[i + 1])
+                i += 2
+            else:
+                # lone flag -> True
+                args[key] = True
+                i += 1
+        else:
+            # unexpected positional; skip
+            i += 1
+
+    return {"executable": executable, "script": script, "args": args}
+
+
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--result",
+        type=str,
+        default="results",
+        help="Folder name for benchmark output results.",
+    )
+    args = parser.parse_args()
+    results_folder = Path(args.result)
+    if not results_folder.exists():
+        raise FileNotFoundError(f"results folder does not exist: {results_folder}")
     # collect results
     for test_file in results_folder.glob("*.json"):
         with open(test_file) as f:
@@ -102,7 +209,6 @@ if __name__ == "__main__":
 
         if "serving" in str(test_file):
             # this result is generated via `vllm bench serve` command
-
             # attach the benchmarking command to raw_result
             try:
                 with open(test_file.with_suffix(".commands")) as f:
@@ -110,12 +216,44 @@ if __name__ == "__main__":
             except OSError as e:
                 print(e)
                 continue
+            # Parse Server Command Arg
+            out: dict[str, Any] = {
+                "server_command": parse_client_command(command["server_command"])
+            }
+            parse_args = [
+                "--tensor-parallel-size",
+                "--pipeline-parallel-size",
+                "--dtype",
+            ]
+            col_mapping = ["tp_size", "pp_size", "dtype"]
+            for index, arg in enumerate(parse_args):
+                if arg in out["server_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["server_command"]["args"][arg]}
+                    )
 
+            # Parse Client Command Arg
+            out: dict[str, Any] = {
+                "client_command": parse_client_command(command["client_command"])
+            }
+            parse_args = [
+                "--dataset-name",
+                "--random-input-len",
+                "--random-output-len",
+                "--request-rate",
+            ]
+            col_mapping = ["dataset_name", "input_len", "output_len", "qps"]
+
+            for index, arg in enumerate(parse_args):
+                if arg in out["client_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["client_command"]["args"][arg]}
+                    )
+            # Add Server, Client command
             raw_result.update(command)
 
             # update the test name of this result
             raw_result.update({"test_name": test_file.stem})
-
             # add the result to raw_result
             serving_results.append(raw_result)
             continue
@@ -205,7 +343,10 @@ if __name__ == "__main__":
             columns=latency_column_mapping
         )
     if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+        valid_columns = [
+            col for col in serving_column_mapping if col in serving_results.columns
+        ]
+        serving_results = serving_results[valid_columns].rename(
             columns=serving_column_mapping
         )
     if not throughput_results.empty:
@@ -245,7 +386,9 @@ if __name__ == "__main__":
     )
 
     # document the result
-    with open(results_folder / "benchmark_results.md", "w") as f:
+    md_file = "benchmark_results.md"
+    json_file = "benchmark_results.json"
+    with open(results_folder / md_file, "w") as f:
         results = read_markdown(
             "../.buildkite/nightly-benchmarks/"
             + "performance-benchmarks-descriptions.md"
@@ -260,7 +403,7 @@ if __name__ == "__main__":
         f.write(results)
 
     # document benchmarking results in json
-    with open(results_folder / "benchmark_results.json", "w") as f:
+    with open(results_folder / json_file, "w") as f:
         results = (
             latency_results.to_dict(orient="records")
             + throughput_results.to_dict(orient="records")

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

@@ -194,9 +194,11 @@ 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
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
+      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."
         continue
       fi
     else
@@ -261,9 +263,11 @@ 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
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
+      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 $test_name."
         continue
       fi
     else
@@ -329,12 +333,21 @@ run_serving_tests() {
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
     echo "Running over qps list $qps_list"
+    max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
+    if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
+        num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
+        max_concurrency_list="[$num_prompts]"
+    fi
+    max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
+    echo "Running over max concurrency list $max_concurrency_list"
 
     # check if there is enough resources to run the test
     tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
+      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."
         continue
       fi
     else
@@ -390,35 +403,39 @@ run_serving_tests() {
         echo "now qps is $qps"
       fi
 
-      new_test_name=$test_name"_qps_"$qps
+      # iterate over different max_concurrency
+      for max_concurrency in $max_concurrency_list; do
+        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
+        echo " new test name $new_test_name"
+        # pass the tensor parallel size to the client so that it can be displayed
+        # on the benchmark dashboard
+        client_command="vllm bench serve \
+          --save-result \
+          --result-dir $RESULTS_FOLDER \
+          --result-filename ${new_test_name}.json \
+          --request-rate $qps \
+          --max-concurrency $max_concurrency \
+          --metadata "tensor_parallel_size=$tp" \
+          $client_args $client_remote_args "
 
-      # pass the tensor parallel size to the client so that it can be displayed
-      # on the benchmark dashboard
-      client_command="vllm bench serve \
-        --save-result \
-        --result-dir $RESULTS_FOLDER \
-        --result-filename ${new_test_name}.json \
-        --request-rate $qps \
-        --metadata "tensor_parallel_size=$tp" \
-        $client_args $client_remote_args "
+        echo "Running test case $test_name with qps $qps"
+        echo "Client command: $client_command"
 
-      echo "Running test case $test_name with qps $qps"
-      echo "Client command: $client_command"
+        bash -c "$client_command"
 
-      bash -c "$client_command"
-
-      # record the benchmarking commands
-      jq_output=$(jq -n \
-        --arg server "$server_command" \
-        --arg client "$client_command" \
-        --arg gpu "$gpu_type" \
-        '{
-          server_command: $server,
-          client_command: $client,
-          gpu_type: $gpu
-        }')
-      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
+        # record the benchmarking commands
+        jq_output=$(jq -n \
+          --arg server "$server_command" \
+          --arg client "$client_command" \
+          --arg gpu "$gpu_type" \
+          '{
+            server_command: $server,
+            client_command: $client,
+            gpu_type: $gpu
+          }')
+        echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
 
+      done
     done
 
     # clean up

.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json

@@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
             "load_format": "dummy",
             "num_iters_warmup": 5,
@@ -20,7 +20,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
             "load_format": "dummy",
             "num_iters_warmup": 5,

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc2.json

@@ -1,7 +1,8 @@
 [
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "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,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "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,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "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,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -87,17 +88,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -120,19 +121,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp2_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -155,19 +156,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp4_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -176,7 +177,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -190,13 +191,11 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     }

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json

@@ -1,7 +1,8 @@
 [
     {
         "test_name": "serving_llama8B_pp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "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,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "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,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
-        "test_name": "serving_llama8B_tp2pp6_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_tp2pp3_sharegpt",
+        "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,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@@ -88,17 +89,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_pp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -107,7 +108,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -121,28 +122,28 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "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_SGL_KERNEL": 1,
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -156,19 +157,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp2pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -177,7 +178,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@@ -192,13 +193,12 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     }

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

@@ -2,6 +2,7 @@
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
         "qps_list": [1, 4, 16, "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,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
         "qps_list": [1, 4, 16, "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,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_sharegpt",
         "qps_list": [1, 4, 16, "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,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -87,17 +88,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_random_1024_128",
         "qps_list": [1, 4, 16, "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,
@@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -120,19 +121,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
             "num_prompts": 100
         }
     },
     {
         "test_name": "serving_llama8B_pp6_random_1024_128",
         "qps_list": [1, 4, 16, "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,
@@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 6,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -155,13 +156,12 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
             "num_prompts": 100
         }
     }

.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json

@@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
             "load_format": "dummy",
             "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
@@ -21,7 +21,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
             "load_format": "dummy",
             "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",

.buildkite/release-pipeline.yaml

@@ -1,4 +1,20 @@
 steps:
+  # aarch64 + CUDA builds
+  - label: "Build arm64 wheel - CUDA 12.8"
+    id: build-wheel-arm64-cuda-12-8
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
+      # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
+      - "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=12.8.1 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX' --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"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # x86 + CUDA builds
   - label: "Build wheel - CUDA 12.8"
     id: build-wheel-cuda-12-8
     agents:

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

@@ -121,7 +121,6 @@ fi
 if [[ $commands == *" kernels/quantization"* ]]; then
   commands="${commands} \
   --ignore=kernels/quantization/test_int8_quant.py \
-  --ignore=kernels/quantization/test_aqlm.py \
   --ignore=kernels/quantization/test_machete_mm.py \
   --ignore=kernels/quantization/test_block_fp8.py \
   --ignore=kernels/quantization/test_block_int8.py \

.buildkite/test-pipeline.yaml

@@ -31,16 +31,6 @@
 steps:
 ##### fast check tests  #####
 
-- label: Documentation Build # 2min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/test_docs"
-  fast_check: true
-  no_gpu: True
-  commands:
-  - pip install -r ../requirements/docs.txt
-  # TODO: add `--strict` once warnings in docstrings are fixed
-  - mkdocs build
-
 - label: Pytorch Nightly Dependency Override Check # 2min
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
@@ -263,6 +253,7 @@ steps:
     - pytest -v -s v1/engine
     - pytest -v -s v1/entrypoints
     - pytest -v -s v1/sample
+    - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
     - pytest -v -s v1/structured_output
     - pytest -v -s v1/spec_decode
@@ -409,6 +400,7 @@ steps:
 - label: Kernels MoE Test %N
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
   - csrc/moe/
   - tests/kernels/moe
   - vllm/model_executor/layers/fused_moe/
@@ -669,6 +661,7 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
     - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
+    - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,11 +1,5 @@
-# Essential Elements of an Effective PR Description Checklist
-
-- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
-- [ ] The test plan, such as providing test command.
-- [ ] The test results, such as pasting the results comparison before and after, or e2e results
-- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-
-PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE BEEN CONSIDERED.
+<!-- markdownlint-disable -->
+PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.
 
 ## Purpose
 
@@ -15,4 +9,14 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE B
 
 ## (Optional) Documentation Update
 
+---
+<details>
+<summary> Essential Elements of an Effective PR Description Checklist </summary>
+
+- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
+- [ ] The test plan, such as providing test command.
+- [ ] The test results, such as pasting the results comparison before and after, or e2e results
+- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
+</details>
+
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)

.github/scripts/cleanup_pr_body.sh

@@ -15,11 +15,11 @@ NEW=/tmp/new_pr_body.txt
 gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
 cp "${OLD}" "${NEW}"
 
-# Remove "FIX #xxxx (*link existing issues this PR will resolve*)"
-sed -i '/FIX #xxxx.*$/d' "${NEW}"
+# Remove markdown comments (like the <!-- markdownlint-disable --> at the start)
+sed -i '/<!--.*-->$/d' "${NEW}"
 
-# Remove "FILL IN THE PR DESCRIPTION HERE"
-sed -i '/FILL IN THE PR DESCRIPTION HERE/d' "${NEW}"
+# Remove "PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED."
+sed -i '/PLEASE FILL IN THE PR DESCRIPTION HERE.*$/d' "${NEW}"
 
 # Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
 sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"

.gitignore

@@ -207,3 +207,6 @@ shellcheck*/
 
 # Ignore moe/marlin_moe gen code
 csrc/moe/marlin_moe_wna16/kernel_*
+
+# Ignore ep_kernels_workspace folder
+ep_kernels_workspace/

CMakeLists.txt

@@ -249,7 +249,6 @@ set(VLLM_EXT_SRC
   "csrc/quantization/gguf/gguf_kernel.cu"
   "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
-  "csrc/prepare_inputs/advance_step.cu"
   "csrc/custom_all_reduce.cu"
   "csrc/torch_bindings.cpp")
 
@@ -287,7 +286,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   FetchContent_MakeAvailable(cutlass)
 
   list(APPEND VLLM_EXT_SRC
-    "csrc/quantization/aqlm/gemm_kernels.cu"
     "csrc/quantization/awq/gemm_kernels.cu"
     "csrc/permute_cols.cu"
     "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
@@ -351,6 +349,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set_gencode_flags_for_srcs(
       SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
       CUDA_ARCHS "${MARLIN_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties(${MARLIN_TEMPLATE_KERNEL_SRC}
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()
 
     list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
 
@@ -364,7 +366,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set_gencode_flags_for_srcs(
       SRCS "${MARLIN_SRCS}"
       CUDA_ARCHS "${MARLIN_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties("csrc/quantization/gptq_marlin/gptq_marlin.cu"
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()
     list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")
+
     message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
   else()
     message(STATUS "Not building Marlin kernels as no compatible archs found"
@@ -854,6 +861,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set_gencode_flags_for_srcs(
       SRCS "${MOE_WNAA16_MARLIN_SRC}"
       CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties(${MOE_WNAA16_MARLIN_SRC}
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()
 
     list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
 

benchmarks/README.md

@@ -22,6 +22,17 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
     </tr>
+    <tr>
+      <td><strong>ShareGPT4V (Image)</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>
+        <code>wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json</code>
+        <br>
+        <div>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:</div>
+        <code>wget http://images.cocodataset.org/zips/train2017.zip</code>
+      </td>
+    </tr>
     <tr>
       <td><strong>BurstGPT</strong></td>
       <td style="text-align: center;">✅</td>
@@ -29,7 +40,7 @@ become available.
       <td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
     </tr>
     <tr>
-      <td><strong>Sonnet</strong></td>
+      <td><strong>Sonnet (deprecated)</strong></td>
       <td style="text-align: center;">✅</td>
       <td style="text-align: center;">✅</td>
       <td>Local file: <code>benchmarks/sonnet.txt</code></td>
@@ -40,6 +51,12 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>synthetic</code></td>
     </tr>
+    <tr>
+      <td><strong>Prefix Repetition</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>synthetic</code></td>
+    </tr>
     <tr>
       <td><strong>HuggingFace-VisionArena</strong></td>
       <td style="text-align: center;">✅</td>
@@ -581,6 +598,20 @@ python3 benchmarks/benchmark_prefix_caching.py \
   --input-length-range 128:256
 ```
 
+### Prefix Repetition Dataset
+
+```bash
+vllm bench serve \
+  --backend openai \
+  --model meta-llama/Llama-2-7b-chat-hf \
+  --dataset-name prefix_repetition \
+  --num-prompts 100 \
+  --prefix-repetition-prefix-len 512 \
+  --prefix-repetition-suffix-len 128 \
+  --prefix-repetition-num-prefixes 5 \
+  --prefix-repetition-output-len 128 
+```
+
 </details>
 
 ## ⚡ Example - Request Prioritization Benchmark
@@ -616,3 +647,41 @@ python3 benchmarks/benchmark_prioritization.py \
 ```
 
 </details>
+
+## 👁️ Example - Multi-Modal Benchmark
+
+<details>
+<summary>Show more</summary>
+
+<br/>
+
+Benchmark the performance of multi-modal requests in vLLM.
+
+### Images (ShareGPT4V)
+
+Start vLLM:
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dtype bfloat16 \
+  --limit-mm-per-prompt '{"image": 1}' \
+  --allowed-local-media-path /path/to/sharegpt4v/images
+```
+
+Send requests with images:
+
+```bash
+python benchmarks/benchmark_serving.py \
+  --backend openai-chat \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dataset-name sharegpt \
+  --dataset-path /path/to/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json \
+  --num-prompts 100 \
+  --save-result \
+  --result-dir ~/vllm_benchmark_results \
+  --save-detailed \
+  --endpoint /v1/chat/completion
+```
+
+</details>

benchmarks/benchmark_block_pool.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
+
+from tabulate import tabulate
+
+from benchmark_utils import TimeCollector
+from vllm.utils import FlexibleArgumentParser
+from vllm.v1.core.block_pool import BlockPool
+
+
+def main(args):
+    rows = []
+    for allocate_block in args.allocate_blocks:
+        # Enforce a GC collect ahead to minimize the impact among runs
+        gc.collect()
+        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
+
+        get_blocks_times = TimeCollector(TimeCollector.US)
+        free_blocks_times = TimeCollector(TimeCollector.US)
+        for _ in range(args.num_iteration):
+            with get_blocks_times:
+                blocks = block_pool.get_new_blocks(allocate_block)
+            with free_blocks_times:
+                block_pool.free_blocks(blocks)
+
+        rows.append(
+            [get_blocks_times.cnt, args.num_gpu_blocks, allocate_block]
+            + get_blocks_times.dump_avg_max()
+            + free_blocks_times.dump_avg_max()
+        )
+
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "Iterations",
+                "Total\nBlocks",
+                "Allocated\nBlocks",
+                "Get Blocks\nAvg (us)",
+                "Get Blocks\nMax (us)",
+                "Free Blocks\nAvg (us)",
+                "Free Blocks\nMax (us)",
+            ],
+            tablefmt="grid",
+            floatfmt=".3f",
+        )
+    )
+
+
+def invoke_main() -> None:
+    parser = FlexibleArgumentParser(
+        description="Benchmark the performance of BlockPool for KV Cache."
+    )
+    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
+    parser.add_argument(
+        "--num-iteration",
+        type=int,
+        default=1000,
+        help="Number of iterations to run to stablize final data readings",
+    )
+    parser.add_argument(
+        "--allocate-blocks",
+        type=int,
+        nargs="*",
+        default=[10, 50, 100, 500, 1000],
+        help="Number of blocks to allocate",
+    )
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    invoke_main()  # pragma: no cover

benchmarks/benchmark_dataset.py

@@ -430,14 +430,20 @@ class ShareGPTDataset(BenchmarkDataset):
                 skip_min_output_len_check=output_len is not None,
             ):
                 continue
+            # TODO: Also support ShareGPT4Video.
+            if image_path := entry.get("image"):
+                mm_content = process_image(image_path)
+            else:
+                mm_content = None
             if enable_multimodal_chat:
-                prompt = self.apply_multimodal_chat_transformation(prompt, None)
+                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
             samples.append(
                 SampleRequest(
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=new_output_len,
                     lora_request=lora_request,
+                    multi_modal_data=mm_content,
                 )
             )
         self.maybe_oversample_requests(samples, num_requests)

benchmarks/benchmark_ngram_proposer.py

@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
+
+import numpy as np
+from tabulate import tabulate
+
+from benchmark_utils import TimeCollector
+from vllm.config import ModelConfig, SpeculativeConfig, VllmConfig
+from vllm.utils import FlexibleArgumentParser
+from vllm.v1.spec_decode.ngram_proposer import NgramProposer
+
+
+def main(args):
+    rows = []
+    for max_ngram in args.max_ngram:
+        collector = TimeCollector(TimeCollector.US)
+
+        model_config = ModelConfig(
+            model="facebook/opt-125m",
+            task="generate",
+            max_model_len=args.num_token + args.num_spec_token,
+            tokenizer="facebook/opt-125m",
+            tokenizer_mode="auto",
+            dtype="auto",
+            seed=None,
+            trust_remote_code=False,
+        )
+        proposer = NgramProposer(
+            vllm_config=VllmConfig(
+                model_config=model_config,
+                speculative_config=SpeculativeConfig(
+                    prompt_lookup_min=args.min_ngram,
+                    prompt_lookup_max=max_ngram,
+                    num_speculative_tokens=args.num_spec_token,
+                    method="ngram",
+                ),
+            )
+        )
+
+        # Warm up
+        proposer.propose(np.random.randint(0, 20, (args.num_token,)))
+
+        gc.collect()
+        for _ in range(args.num_iteration):
+            tokens = np.random.randint(0, 20, (args.num_req, args.num_token))
+            with collector:
+                for i in range(args.num_req):
+                    proposer.propose(tokens[i, :])
+        rows.append(
+            [args.num_req, args.num_token, args.min_ngram, max_ngram]
+            + collector.dump_avg_max()
+        )
+
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "# Request",
+                "# Token",
+                "Min Ngram",
+                "Max Ngram",
+                "Avg (us)",
+                "Max (us)",
+            ],
+            tablefmt="grid",
+            floatfmt=".3f",
+        )
+    )
+
+
+def invoke_main() -> None:
+    parser = FlexibleArgumentParser(
+        description="Benchmark the performance of N-gram speculative decode drafting"
+    )
+    parser.add_argument(
+        "--num-iteration",
+        type=int,
+        default=100,
+        help="Number of iterations to run to stablize final data readings",
+    )
+    parser.add_argument(
+        "--num-req", type=int, default=128, help="Number of requests in the batch"
+    )
+    parser.add_argument(
+        "--num-token", type=int, default=1500, help="Number of tokens for each request"
+    )
+    parser.add_argument(
+        "--min-ngram",
+        type=int,
+        default=3,
+        help="Minimum n-gram to match",
+    )
+    parser.add_argument(
+        "--max-ngram",
+        type=int,
+        nargs="*",
+        default=[5, 7, 10, 15, 20],
+        help="Maximum n-gram to match",
+    )
+    parser.add_argument(
+        "--num-spec-token",
+        type=int,
+        default=3,
+        help="Number of speculative tokens to generate",
+    )
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    invoke_main()  # pragma: no cover

benchmarks/benchmark_utils.py

@@ -1,11 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
 import argparse
 import json
 import math
 import os
-from typing import Any
+import time
+from types import TracebackType
+from typing import Any, Optional, Union
 
 
 def convert_to_pytorch_benchmark_format(
@@ -72,3 +73,53 @@ def write_to_json(filename: str, records: list) -> None:
             cls=InfEncoder,
             default=lambda o: f"<{type(o).__name__} object is not JSON serializable>",
         )
+
+
+# Collect time and generate time metrics
+#
+# Example Usage:
+#   collector = TimeCollector(TimeCollector.US)
+#   for _ in range(total_iteration):
+#      with collector:
+#          ...
+#   collector.dump_avg_max()
+class TimeCollector:
+    NS: int = 1
+    US: int = NS * 1000
+    MS: int = US * 1000
+    S: int = MS * 1000
+
+    def __init__(self, scale: int) -> None:
+        self.cnt: int = 0
+        self._sum: int = 0
+        self._max: Optional[int] = None
+        self.scale = scale
+        self.start_time: int = time.monotonic_ns()
+
+    def collect(self, v: int) -> None:
+        self.cnt += 1
+        self._sum += v
+        if self._max is None:
+            self._max = v
+        else:
+            self._max = max(self._max, v)
+
+    def avg(self) -> Union[float, str]:
+        return self._sum * 1.0 / self.cnt / self.scale if self.cnt > 0 else "N/A"
+
+    def max(self) -> Union[float, str]:
+        return self._max / self.scale if self._max else "N/A"
+
+    def dump_avg_max(self) -> list[Union[float, str]]:
+        return [self.avg(), self.max()]
+
+    def __enter__(self) -> None:
+        self.start_time = time.monotonic_ns()
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        exc_traceback: Optional[TracebackType],
+    ) -> None:
+        self.collect(time.monotonic_ns() - self.start_time)

benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py

@@ -1,63 +1,199 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import argparse
+import asyncio
+import logging
 import os
 
 import aiohttp
-from quart import Quart, make_response, request
+from quart import Quart, Response, make_response, request
+from rate_limiter import RateLimiter
+from request_queue import RequestQueue
 
-AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
-
-app = Quart(__name__)
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 
-async def forward_request(url, data):
-    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
+def parse_args():
+    """parse command line arguments"""
+    parser = argparse.ArgumentParser(description="vLLM P/D disaggregation proxy server")
+
+    # Add args
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=300,
+        help="Timeout for backend service requests in seconds (default: 300)",
+    )
+    parser.add_argument(
+        "--max-concurrent",
+        type=int,
+        default=100,
+        help="Maximum concurrent requests to backend services (default: 100)",
+    )
+    parser.add_argument(
+        "--queue-size",
+        type=int,
+        default=500,
+        help="Maximum number of requests in the queue (default: 500)",
+    )
+    parser.add_argument(
+        "--rate-limit",
+        type=int,
+        default=40,
+        help="Maximum requests per second (default: 40)",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="Port to run the server on (default: 8000)",
+    )
+    parser.add_argument(
+        "--prefill-url",
+        type=str,
+        default="http://localhost:8100/v1/completions",
+        help="Prefill service endpoint URL",
+    )
+    parser.add_argument(
+        "--decode-url",
+        type=str,
+        default="http://localhost:8200/v1/completions",
+        help="Decode service endpoint URL",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    """parse command line arguments"""
+    args = parse_args()
+
+    # Initialize configuration using command line parameters
+    AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=args.timeout)
+    MAX_CONCURRENT_REQUESTS = args.max_concurrent
+    REQUEST_QUEUE_SIZE = args.queue_size
+    RATE_LIMIT = args.rate_limit
+    PREFILL_SERVICE_URL = args.prefill_url
+    DECODE_SERVICE_URL = args.decode_url
+    PORT = args.port
+
+    app = Quart(__name__)
+
+    # Initialize the rate limiter and request queue
+    rate_limiter = RateLimiter(RATE_LIMIT)
+    request_queue = RequestQueue(MAX_CONCURRENT_REQUESTS, REQUEST_QUEUE_SIZE)
+
+    # Attach the configuration object to the application instance
+    app.config.update(
+        {
+            "AIOHTTP_TIMEOUT": AIOHTTP_TIMEOUT,
+            "rate_limiter": rate_limiter,
+            "request_queue": request_queue,
+            "PREFILL_SERVICE_URL": PREFILL_SERVICE_URL,
+            "DECODE_SERVICE_URL": DECODE_SERVICE_URL,
+        }
+    )
+
+    # Start queue processing on app startup
+    @app.before_serving
+    async def startup():
+        """Start request processing task when app starts serving"""
+        asyncio.create_task(request_queue.process())
+
+    async def forward_request(url, data):
+        """Forward request to backend service with rate limiting and error handling"""
         headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
-        async with session.post(url=url, json=data, headers=headers) as response:
-            if response.status == 200:
-                # if response.headers.get('Transfer-Encoding') == 'chunked':
-                if True:
-                    async for chunk_bytes in response.content.iter_chunked(1024):
-                        yield chunk_bytes
-                else:
-                    content = await response.read()
-                    yield content
 
-
-@app.route("/v1/completions", methods=["POST"])
-async def handle_request():
-    try:
-        original_request_data = await request.get_json()
-
-        prefill_request = original_request_data.copy()
-        # change max_tokens = 1 to let it only do prefill
-        prefill_request["max_tokens"] = 1
-
-        # finish prefill
-        async for _ in forward_request(
-            "http://localhost:8100/v1/completions", prefill_request
+        # Use rate limiter as context manager
+        async with (
+            rate_limiter,
+            aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session,
         ):
-            continue
+            try:
+                async with session.post(
+                    url=url, json=data, headers=headers
+                ) as response:
+                    if response.status == 200:
+                        # Stream response chunks
+                        async for chunk_bytes in response.content.iter_chunked(1024):
+                            yield chunk_bytes
+                    else:
+                        # Handle backend service errors
+                        error_text = await response.text()
+                        logger.error(
+                            "Backend service error: %s - %s",
+                            response.status,
+                            error_text,
+                        )
+                        yield b'{"error": "Backend service error"}'
+            except aiohttp.ClientError as e:
+                # Handle connection errors
+                logger.error("Connection error to %s: %s", url, str(e))
+                yield b'{"error": "Service unavailable"}'
+            except asyncio.TimeoutError:
+                # Handle timeout errors
+                logger.error("Timeout connecting to %s", url)
+                yield b'{"error": "Service timeout"}'
 
-        # return decode
-        generator = forward_request(
-            "http://localhost:8200/v1/completions", original_request_data
-        )
-        response = await make_response(generator)
-        response.timeout = None
+    async def process_request():
+        """Process a single request through prefill and decode stages"""
+        try:
+            original_request_data = await request.get_json()
 
-        return response
+            # Create prefill request (max_tokens=1)
+            prefill_request = original_request_data.copy()
+            prefill_request["max_tokens"] = 1
 
-    except Exception as e:
-        import sys
-        import traceback
+            # Execute prefill stage
+            async for _ in forward_request(PREFILL_SERVICE_URL, prefill_request):
+                continue
 
-        exc_info = sys.exc_info()
-        print("Error occurred in disagg prefill proxy server")
-        print(e)
-        print("".join(traceback.format_exception(*exc_info)))
+            # Execute decode stage and stream response
+            generator = forward_request(DECODE_SERVICE_URL, original_request_data)
+            response = await make_response(generator)
+            response.timeout = None  # Disable timeout for streaming response
+            return response
+
+        except Exception:
+            logger.exception("Error processing request")
+            return Response(
+                response=b'{"error": "Internal server error"}',
+                status=500,
+                content_type="application/json",
+            )
+
+    @app.route("/v1/completions", methods=["POST"])
+    async def handle_request():
+        """Handle incoming API requests with concurrency and rate limiting"""
+        # Create task for request processing
+        task = asyncio.create_task(process_request())
+
+        # Enqueue request or reject if queue is full
+        if not await request_queue.enqueue(task):
+            return Response(
+                response=b'{"error": "Server busy, try again later"}',
+                status=503,
+                content_type="application/json",
+            )
+
+        try:
+            # Return the response from the processing task
+            return await task
+        except asyncio.CancelledError:
+            # Handle task cancellation (timeout or queue full)
+            logger.warning("Request cancelled due to timeout or queue full")
+            return Response(
+                response=b'{"error": "Request cancelled"}',
+                status=503,
+                content_type="application/json",
+            )
+
+    # Start the Quart server with host can be set to 0.0.0.0
+    app.run(port=PORT)
 
 
 if __name__ == "__main__":
-    app.run(port=8000)
+    main()

benchmarks/disagg_benchmarks/rate_limiter.py

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import time
+
+
+class RateLimiter:
+    """Token bucket rate limiter implementation"""
+
+    def __init__(self, rate_limit):
+        self.rate_limit = rate_limit  # Requests per second
+        self.num_available_tokens = rate_limit  # Available tokens
+        self.last_refill = time.monotonic()  # Last token refill time
+        self.lock = asyncio.Lock()  # Synchronization lock
+
+    async def acquire(self):
+        """Acquire a token from the rate limiter"""
+        while True:
+            async with self.lock:
+                current_time = time.monotonic()
+                elapsed = current_time - self.last_refill
+
+                # Refill num_available_tokens if more than 1 second has passed
+                if elapsed > 1.0:
+                    self.num_available_tokens = self.rate_limit
+                    self.last_refill = current_time
+
+                # Check if num_available_tokens are available
+                if self.num_available_tokens > 0:
+                    self.num_available_tokens -= 1
+                    return True
+
+                # Calculate wait time if no num_available_tokens available
+                wait_time = 1.0 - elapsed
+            await asyncio.sleep(wait_time)
+
+    async def __aenter__(self):
+        """Enter async context manager - acquire token"""
+        await self.acquire()
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback):
+        """Exit async context manager - no cleanup needed"""
+        pass

benchmarks/disagg_benchmarks/request_queue.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+from collections import deque
+
+
+class RequestQueue:
+    """Request queue manager with concurrency control"""
+
+    def __init__(self, max_concurrent, max_queue_size):
+        # Maximum concurrent requests
+        self.max_concurrent = max_concurrent
+        self.max_queue_size = max_queue_size  # Maximum queue size
+        # Concurrency control
+        self.semaphore = asyncio.Semaphore(max_concurrent)
+        self.queue = deque()  # Request queue
+        self.queue_size = 0  # Current queue size
+        self.lock = asyncio.Lock()  # Sync queue Lock
+
+    async def enqueue(self, task):
+        """Add a request task to the queue"""
+        async with self.lock:
+            if self.queue_size >= self.max_queue_size:
+                return False
+
+            self.queue.append(task)
+            self.queue_size += 1
+            return True
+
+    async def process(self):
+        """Process queued requests using semaphore for concurrency control"""
+        while True:
+            if self.queue:
+                async with self.semaphore, self.lock:
+                    task = self.queue.popleft()
+                    self.queue_size -= 1
+                    await task
+            await asyncio.sleep(0.01)  # Yield control to event loop

benchmarks/kernels/benchmark_aqlm.py

@@ -1,345 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import os
-import sys
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.aqlm import (
-    dequantize_weight,
-    generic_dequantize_gemm,
-    get_int_dtype,
-    optimized_dequantize_gemm,
-)
-from vllm.utils import FlexibleArgumentParser
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-
-def torch_mult(
-    # [..., in_features]
-    input: torch.Tensor,
-    weights: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-) -> torch.Tensor:
-    output = F.linear(input, weights)
-    return output
-
-
-def dequant_out_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    if bias is None:
-        output = F.linear(input, weights, bias)
-        orig_shape = output.shape
-        flattened_output = output.view(-1, output.size(-1))
-        f_scales = scales.view(-1, scales.shape[0])
-        b_scales = f_scales.expand(flattened_output.shape[0], -1)
-        flattened_output *= b_scales
-        return flattened_output.view(orig_shape)
-    else:
-        b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
-        weights *= b_scales
-        return F.linear(input, weights, bias)
-
-
-def dequant_weight_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
-    weights *= b_scales
-    return F.linear(input, weights, bias)
-
-
-def dequant_no_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    return F.linear(input, weights, bias)
-
-
-# Compare the optimized 1x16 and 2x8 cuda decompression/dequant kernels against
-# the generic pytorch version.
-# Just visual comparison.
-def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:
-    n = int(parts.sum().item())
-
-    device = torch.device("cuda:0")
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(
-        -code_range,
-        code_range,
-        size=(n, k // ingroups, nbooks),
-        dtype=get_int_dtype(bits),
-        device=device,
-    )
-
-    codebooks = torch.randn(
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        dtype=torch.float16,
-        device=device,
-    )
-
-    count = 0
-    for index in range(16):
-        for i in range(8):
-            for book in range(nbooks):
-                codebooks[book, index, 0, i] = count * (10**book)
-            count += 1
-
-    print("codes shape", codes.shape)
-
-    for i in range(16):
-        for book in range(nbooks):
-            codes[0, i, book] = i
-            codes[0, -i, book] = i
-
-    weights = dequantize_weight(codes, codebooks, None)
-    weights2 = ops.aqlm_dequant(codes, codebooks, parts)
-
-    print("weights shape:", weights.shape)
-    print("weights2 shape:", weights2.shape)
-
-    print("weights are:", weights)
-    print("weights2 are:", weights2)
-
-    print("first 128 weights are", weights[0, 0:128].to(torch.int32))
-    print("first 128 weights2 are:", weights2[0, 0:128].to(torch.int32))
-
-    print("last 128 weights are", weights[0, -128:])
-    print("last 128 weights2 are:", weights2[0, -128:])
-
-
-def main():
-    parser = FlexibleArgumentParser(description="Benchmark aqlm performance.")
-
-    # Add arguments
-    parser.add_argument(
-        "--nbooks", type=int, default=1, help="Number of codebooks (default: 1)"
-    )
-    parser.add_argument(
-        "--bits",
-        type=int,
-        default=16,
-        help="Number of bits per code element (default: 16)",
-    )
-    parser.add_argument(
-        "--test",
-        type=bool,
-        default=False,
-        help="Run the decompression/dequant tester rather than benchmarking "
-        "(default: False)",
-    )
-
-    # Parse the arguments
-    args = parser.parse_args()
-
-    # Extract values
-    nbooks = args.nbooks
-    bits = args.bits
-
-    if args.test:
-        dequant_test(4096, torch.tensor((4096,)), nbooks, bits)
-        return
-
-    # Otherwise, benchmark.
-    methods = [
-        ops.aqlm_gemm,
-        dequant_out_scale,
-        generic_dequantize_gemm,
-        optimized_dequantize_gemm,
-        dequant_weight_scale,
-        torch_mult,
-        dequant_no_scale,
-    ]
-
-    filename = f"./aqlm_benchmark_{nbooks}x{bits}.csv"
-    print(f"writing benchmarks to file {filename}")
-    with open(filename, "w") as f:
-        sys.stdout = f
-
-        print("m | k | n | n parts", end="")
-        for method in methods:
-            print(f" | {method.__name__.replace('_', ' ')} (µs)", end="")
-        print("")
-
-        # These are reasonable prefill sizes.
-        ksandpartions = (
-            (4096, (4096, 4096, 4096)),
-            (4096, (4096,)),
-            (4096, (11008, 11008)),
-            (11008, (4096,)),
-        )
-
-        # reasonable ranges for m.
-        for m in [
-            1,
-            2,
-            4,
-            8,
-            10,
-            12,
-            14,
-            16,
-            24,
-            32,
-            48,
-            52,
-            56,
-            64,
-            96,
-            112,
-            128,
-            256,
-            512,
-            1024,
-            1536,
-            2048,
-            3072,
-            4096,
-        ]:
-            print(f"{m}", file=sys.__stdout__)
-            for ksp in ksandpartions:
-                run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits, methods)
-
-        sys.stdout = sys.__stdout__
-
-
-def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, methods):
-    # I didn't see visible improvements from increasing these, but feel free :)
-    num_warmup_trials = 1
-    num_trials = 1
-
-    num_calls = 100
-
-    # warmup.
-    for method in methods:
-        for _ in range(num_warmup_trials):
-            run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-    n = parts.sum().item()
-    print(f"{m} | {k} | {n} | {parts.tolist()}", end="")
-
-    for method in methods:
-        best_time_us = 1e20
-        for _ in range(num_trials):
-            kernel_dur_ms = run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-            kernel_dur_us = 1000 * kernel_dur_ms
-
-            if kernel_dur_us < best_time_us:
-                best_time_us = kernel_dur_us
-
-        print(f" | {kernel_dur_us:.0f}", end="")
-
-    print("")
-
-
-def run_timing(
-    num_calls: int, m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
-) -> float:
-    n = int(parts.sum().item())
-
-    device = torch.device("cuda:0")
-
-    input = torch.randn((1, m, k), dtype=torch.float16, device=device)
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(
-        -code_range,
-        code_range,
-        size=(n, k // ingroups, nbooks),
-        dtype=get_int_dtype(bits),
-        device=device,
-    )
-
-    codebooks = torch.randn(
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        dtype=torch.float16,
-        device=device,
-    )
-
-    scales = torch.randn(size=(n, 1, 1, 1), dtype=torch.float16, device=device)
-
-    # for comparison to just a pytorch mult.
-    weights = torch.randn((n, k), dtype=torch.float16, device=device)
-
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-
-    start_event.record()
-
-    if method is torch_mult:
-        for i in range(num_calls):
-            torch_mult(input, weights, scales)
-    else:
-        for i in range(num_calls):
-            method(input, codes, codebooks, scales, parts, None)
-
-    end_event.record()
-    end_event.synchronize()
-
-    dur_ms = start_event.elapsed_time(end_event) / num_calls
-    return dur_ms
-
-
-if __name__ == "__main__":
-    sys.exit(main())

benchmarks/kernels/benchmark_machete.py

@@ -236,6 +236,7 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
             a=bt.a,
             c=None,
             b_q_weight=w_q,
+            b_bias=None,
             b_scales=w_s,
             global_scale=None,
             b_zeros=w_zp,

benchmarks/kernels/benchmark_moe.py

@@ -3,6 +3,7 @@
 
 import argparse
 import json
+import os
 import time
 from contextlib import nullcontext
 from datetime import datetime
@@ -542,6 +543,7 @@ def save_configs(
     use_fp8_w8a8: bool,
     use_int8_w8a16: bool,
     block_quant_shape: list[int],
+    save_dir: str,
 ) -> None:
     dtype_str = get_config_dtype_str(
         dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
@@ -552,7 +554,8 @@ def save_configs(
     filename = get_config_file_name(
         num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape
     )
-
+    os.makedirs(save_dir, exist_ok=True)
+    filename = os.path.join(save_dir, filename)
     print(f"Writing best config to {filename}...")
     with open(filename, "w") as f:
         json.dump(configs, f, indent=4)
@@ -707,6 +710,7 @@ def main(args: argparse.Namespace):
             use_fp8_w8a8,
             use_int8_w8a16,
             block_quant_shape,
+            args.save_dir,
         )
         end = time.time()
         print(f"Tuning took {end - start:.2f} seconds")
@@ -748,6 +752,9 @@ if __name__ == "__main__":
         "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
     )
     parser.add_argument("--use-deep-gemm", action="store_true")
+    parser.add_argument(
+        "--save-dir", type=str, default="./", help="Directory to save tuned results"
+    )
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--batch-size", type=int, nargs="+", required=False)
     parser.add_argument("--tune", action="store_true")

benchmarks/kv_cache/benchmark_block_pool.py

@@ -1,108 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import gc
-import time
-from typing import Optional
-
-from tabulate import tabulate
-
-from vllm.utils import FlexibleArgumentParser
-from vllm.v1.core.block_pool import BlockPool
-
-
-class Metric:
-    def __init__(self) -> None:
-        self.cnt: int = 0
-        self.sum_v: int = 0
-        self.max_v: Optional[int] = None
-
-    def update(self, v: int) -> None:
-        self.cnt += 1
-        self.sum_v += v
-        if self.max_v is None:
-            self.max_v = v
-        else:
-            self.max_v = max(self.max_v, v)
-
-    def avg_v(self) -> float:
-        return self.sum_v * 1.0 / self.cnt
-
-
-def main(args):
-    rows = []
-    for allocate_block in args.allocate_blocks:
-        # Enforce a GC collect ahead to minimize the impact among runs
-        gc.collect()
-        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
-
-        get_blocks_metric: Metric = Metric()
-        free_blocks_metric: Metric = Metric()
-        for _ in range(args.num_iteration):
-            t1 = time.monotonic_ns()
-            blocks = block_pool.get_new_blocks(allocate_block)
-            t2 = time.monotonic_ns()
-            block_pool.free_blocks(blocks)
-            t3 = time.monotonic_ns()
-            get_blocks_metric.update(t2 - t1)
-            free_blocks_metric.update(t3 - t2)
-
-        if get_blocks_metric.max_v is not None and free_blocks_metric.max_v is not None:
-            rows.append(
-                [
-                    get_blocks_metric.cnt,
-                    args.num_gpu_blocks,
-                    allocate_block,
-                    get_blocks_metric.avg_v() / 1000000,
-                    get_blocks_metric.max_v / 1000000.0,
-                    free_blocks_metric.avg_v() / 1000000,
-                    free_blocks_metric.max_v / 1000000.0,
-                ]
-            )
-        else:
-            print(
-                "No valid metrics found."
-                f" {get_blocks_metric.max_v=} {free_blocks_metric.max_v=}"
-            )
-
-    print(
-        tabulate(
-            rows,
-            headers=[
-                "Iterations",
-                "Total\nBlocks",
-                "Allocated\nBlocks",
-                "Get Blocks\nAvg (ms)",
-                "Get Blocks\nMax (ms)",
-                "Free Blocks\nAvg (ms)",
-                "Free Blocks\nMax (ms)",
-            ],
-            tablefmt="grid",
-            floatfmt=".6f",
-        )
-    )
-
-
-def invoke_main() -> None:
-    parser = FlexibleArgumentParser(
-        description="Benchmark the performance of BlockPool for KV Cache."
-    )
-    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
-    parser.add_argument(
-        "--num-iteration",
-        type=int,
-        default=1000,
-        help="Number of iterations to run to stablize final data readings",
-    )
-    parser.add_argument(
-        "--allocate-blocks",
-        type=int,
-        nargs="*",
-        default=[10, 50, 100, 500, 1000],
-        help="Number of blocks to allocate",
-    )
-    args = parser.parse_args()
-    main(args)
-
-
-if __name__ == "__main__":
-    invoke_main()  # pragma: no cover

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 93cf5a08f421a3efd0c4a7e005ef8f742b578ce0
+          GIT_TAG 57b4e68b9f9d94750b46de8f8dbd2bfcc86edd4f
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/activation_kernels.cu

@@ -128,6 +128,45 @@ __global__ void act_and_mul_kernel_with_param(
   }
 }
 
+template <typename T>
+__device__ __forceinline__ T swigluoai_and_mul(const T& gate, const T& up,
+                                               float alpha, float limit) {
+  // clamp gate: min=None, max=limit
+  const float gate_f = (float)gate;
+  const float clamped_gate = gate_f > limit ? limit : gate_f;
+
+  // clamp up: min=-limit, max=limit
+  const float up_f = (float)up;
+  const float clamped_up =
+      up_f > limit ? limit : (up_f < -limit ? -limit : up_f);
+
+  // glu = gate * sigmoid(gate * alpha)
+  const float sigmoid_val = 1.0f / (1.0f + expf(-clamped_gate * alpha));
+  const float glu = clamped_gate * sigmoid_val;
+
+  // (up + 1) * glu
+  return (T)((clamped_up + 1.0f) * glu);
+}
+
+template <typename scalar_t,
+          scalar_t (*ACT_FN)(const scalar_t&, const scalar_t&, const float,
+                             const float)>
+__global__ void swigluoai_and_mul_kernel(
+    scalar_t* __restrict__ out,          // [..., d]
+    const scalar_t* __restrict__ input,  // [..., 2, d]
+    const int d, const float alpha, const float limit) {
+  const int64_t token_idx = blockIdx.x;
+  // TODO: Vectorize loads and stores.
+  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
+    // gate = x[..., ::2]  (even indices)
+    const scalar_t gate = VLLM_LDG(&input[token_idx * 2 * d + 2 * idx]);
+    // up = x[..., 1::2]   (odd indices)
+    const scalar_t up = VLLM_LDG(&input[token_idx * 2 * d + 2 * idx + 1]);
+
+    out[token_idx * d + idx] = ACT_FN(gate, up, alpha, limit);
+  }
+}
+
 }  // namespace vllm
 
 #define LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(KERNEL, PARAM)         \
@@ -145,11 +184,31 @@ __global__ void act_and_mul_kernel_with_param(
                                          PARAM);                        \
       });
 
+#define LAUNCH_SIGLUOAI_AND_MUL(KERNEL, ALPHA, LIMIT)                          \
+  int d = input.size(-1) / 2;                                                  \
+  int64_t num_tokens = input.numel() / input.size(-1);                         \
+  dim3 grid(num_tokens);                                                       \
+  dim3 block(std::min(d, 1024));                                               \
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                \
+      input.scalar_type(), "clamp_swiglu_kernel_with_params", [&] {            \
+        vllm::swigluoai_and_mul_kernel<scalar_t, KERNEL<scalar_t>>             \
+            <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),             \
+                                         input.data_ptr<scalar_t>(), d, ALPHA, \
+                                         LIMIT);                               \
+      });
+
 void fatrelu_and_mul(torch::Tensor& out,    // [..., d],
                      torch::Tensor& input,  // [..., 2 * d]
                      double threshold) {
   LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(vllm::fatrelu_kernel, threshold);
 }
+void swigluoai_and_mul(torch::Tensor& out,    // [..., d]
+                       torch::Tensor& input,  // [..., 2 * d]
+                       double alpha, double limit) {
+  LAUNCH_SIGLUOAI_AND_MUL(vllm::swigluoai_and_mul, alpha, limit);
+}
 namespace vllm {
 
 // Element-wise activation kernel template.

csrc/core/scalar_type.hpp

@@ -321,6 +321,8 @@ static inline constexpr auto kFE3M2f =
     ScalarType::float_(3, 2, true, ScalarType::NAN_NONE);
 static inline constexpr auto kFE4M3fn =
     ScalarType::float_(4, 3, true, ScalarType::NAN_EXTD_RANGE_MAX_MIN);
+static inline constexpr auto kFE8M0fnu =
+    ScalarType(8, 0, false, 0, true, ScalarType::NAN_EXTD_RANGE_MAX_MIN);
 static inline constexpr auto kFE5M2 = ScalarType::float_IEEE754(5, 2);
 static inline constexpr auto kFE8M7 = ScalarType::float_IEEE754(8, 7);
 static inline constexpr auto kFE5M10 = ScalarType::float_IEEE754(5, 10);

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -20,6 +20,7 @@ namespace MARLIN_NAMESPACE_NAME {
 TEMPLATE = ("template __global__ void Marlin<"
             "{{scalar_t}}, "
             "{{w_type_id}}, "
+            "{{s_type_id}}, "
             "{{threads}}, "
             "{{thread_m_blocks}}, "
             "{{thread_n_blocks}}, "
@@ -77,6 +78,7 @@ def generate_new_kernels():
             if scalar_type == "vllm::kFE4M3fn" and group_blocks not in [-1, 8]:
                 continue
             # nvfp4 only supports group_size == 16
+            # mxfp4 only supports group_size == 32
             if scalar_type == "vllm::kFE2M1f" and group_blocks not in [1, 2]:
                 continue
             # other quantization methods don't support group_size = 16
@@ -89,9 +91,22 @@ def generate_new_kernels():
 
             c_dtype = "half" if dtype == "fp16" else "nv_bfloat16"
 
+            if scalar_type == "vllm::kFE2M1f" and group_blocks == 1:
+                s_type = "vllm::kFE4M3fn"
+            elif scalar_type == "vllm::kFE2M1f" and group_blocks == 2:
+                s_type = "vllm::kFE8M0fnu"
+                if dtype == "fp16":
+                    # we cannot safely dequantize e8m0 to fp16, so skip this
+                    continue
+            elif dtype == "fp16":
+                s_type = "vllm::kFloat16"
+            elif dtype == "bf16":
+                s_type = "vllm::kBFloat16"
+
             template_str = jinja2.Template(TEMPLATE).render(
                 scalar_t=c_dtype,
                 w_type_id=scalar_type + ".id()",
+                s_type_id=s_type + ".id()",
                 threads=threads,
                 thread_m_blocks=max(m_blocks, 1),
                 thread_n_blocks=n_blocks,

csrc/moe/marlin_moe_wna16/kernel.h

@@ -7,23 +7,25 @@
 #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__ scales_ptr,                            \
-      const uint16_t *__restrict__ scale2_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 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 int4 *__restrict__ scales_ptr,                                    \
+      const uint16_t *__restrict__ scale2_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, \
       bool use_fp32_reduce, int max_shared_mem
 
 namespace MARLIN_NAMESPACE_NAME {
 template <typename scalar_t,  // compute dtype, half or nv_float16
           const vllm::ScalarTypeId w_type_id,  // weight ScalarType id
+          const vllm::ScalarTypeId s_type_id,  // weight scale ScalarType id
           const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the

csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -280,6 +280,7 @@ __device__ inline void wait_negative_and_add(int* lock) {
 
 template <typename scalar_t,  // compute dtype, half or nv_float16
           const vllm::ScalarTypeId w_type_id,  // weight ScalarType id
+          const vllm::ScalarTypeId s_type_id,  // weight scale ScalarType id
           const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the
@@ -299,6 +300,7 @@ __global__ void Marlin(
     const int4* __restrict__ B,  // 4bit quantized weight matrix of shape kxn
     int4* __restrict__ C,        // fp16 output buffer of shape mxn
     int4* __restrict__ C_tmp,    // fp32 tmp output buffer (for reduce)
+    const int4* __restrict__ b_bias_ptr,
     const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                           // (k/groupsize)xn
     const uint16_t* __restrict__ scale2_ptr,  // fp16 global scale (for nvfp4
@@ -318,8 +320,9 @@ __global__ void Marlin(
     int prob_n,             // output dimension n
     int prob_k,             // reduction dimension k
     int* locks,             // extra global storage for barrier synchronization
-    bool use_atomic_add,    // whether to use atomic add to reduce
-    bool use_fp32_reduce,   // whether to use fp32 global reduce
+    bool has_bias,
+    bool use_atomic_add,   // whether to use atomic add to reduce
+    bool use_fp32_reduce,  // whether to use fp32 global reduce
     int max_shared_mem) {
   // Each threadblock processes one "stripe" of the B matrix with (roughly) the
   // same size, which might involve multiple column "slices" (of width 16 *
@@ -342,12 +345,23 @@ __global__ void Marlin(
 
   extern __shared__ int4 sh[];
   static constexpr auto w_type = vllm::ScalarType::from_id(w_type_id);
+  static constexpr auto s_type = vllm::ScalarType::from_id(s_type_id);
+  if constexpr (w_type == vllm::kFE2M1f) {
+    static_assert(s_type == vllm::kFE4M3fn && group_blocks == 1 ||
+                  s_type == vllm::kFE8M0fnu && group_blocks == 2);
+  } else if constexpr (std::is_same<scalar_t, nv_bfloat16>::value) {
+    static_assert(s_type == vllm::kBFloat16);
+  } else if constexpr (std::is_same<scalar_t, half>::value) {
+    static_assert(s_type == vllm::kFloat16);
+  }
+
   constexpr bool has_zp = w_type == vllm::kU4 || w_type == vllm::kU8;
   constexpr bool is_int_type = w_type == vllm::kU4 || w_type == vllm::kU8 ||
                                w_type == vllm::kU4B8 || w_type == vllm::kU8B128;
   // see comments of dequant.h for more details
   constexpr bool dequant_skip_flop =
-      !is_int_type ||
+      w_type == vllm::kFE4M3fn ||
+      w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn ||
       has_zp && !is_zp_float && !std::is_same<scalar_t, nv_bfloat16>::value ||
       has_zp && !is_zp_float && !(w_type == vllm::kU8);
 
@@ -365,6 +379,7 @@ __global__ void Marlin(
   const int zp_expert_stride =
       is_zp_float ? prob_n * prob_k / group_size / 8
                   : prob_n * prob_k / group_size / (pack_factor * 4);
+  const int b_bias_expert_stride = prob_n / 8;
 
   // parallel: num valid moe blocks
   int num_tokens_past_padded = num_tokens_past_padded_ptr[0];
@@ -475,7 +490,7 @@ __global__ void Marlin(
         for (int i = 0; i < 4; i++) {
           int idx = tid4 * 4 + i;
           idx = idx < block_num_valid_tokens ? idx : 0;
-          if constexpr (w_type == vllm::kFE2M1f) {
+          if constexpr (w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
             sh_block_topk_weights[idx] = __hmul2(
                 global_scale, Dtype::num2num2(Dtype::float2num(
                                   topk_weights_ptr[sh_block_sorted_ids[idx]])));
@@ -513,7 +528,7 @@ __global__ void Marlin(
       expert_id = expert_ids_ptr[block_id];
     }
 
-    if constexpr (w_type == vllm::kFE2M1f) {
+    if constexpr (w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
       uint16_t val = scale2_ptr[expert_id];
       global_scale = Dtype::num2num2(*reinterpret_cast<scalar_t*>(&val));
     }
@@ -526,6 +541,9 @@ __global__ void Marlin(
     if constexpr (has_act_order) {
       g_idx += (expert_id - old_expert_id) * prob_k;
     }
+    if (has_bias) {
+      b_bias_ptr += (expert_id - old_expert_id) * b_bias_expert_stride;
+    }
 
     read_moe_block_data(block_id);
   };
@@ -721,7 +739,7 @@ __global__ void Marlin(
 
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) / 4;
-    s_sh_rd = s_sh_rd * 2 + warp_row % 2;
+    s_sh_rd = s_sh_rd * 2 + (warp_row / group_blocks) % 2;
 
   } else if constexpr (group_blocks != -1)
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
@@ -734,6 +752,18 @@ __global__ void Marlin(
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) % 4;
 
+  int bias_sh_rd;
+  if constexpr (m_block_size_8) {
+    bias_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+                 (threadIdx.x % 32) / 8;
+  } else {
+    bias_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+                 (threadIdx.x % 32) % 4;
+  }
+
+  int bias_sh_wr = threadIdx.x;
+  int bias_gl_rd = (thread_n_blocks * 16 / 8) * slice_col + threadIdx.x;
+
   // Zero-points have the same read layout as the scales
   // (without column-wise case)
   constexpr int num_col_threads = 8;
@@ -793,7 +823,19 @@ __global__ void Marlin(
   constexpr int sh_b_size = stages * b_sh_stage;
   int4* sh_b = sh_new;
   int4* sh_red = sh_new;
-  int4* sh_g_idx = sh_b + (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
+
+  constexpr int sh_size_b_red_min =
+      (sh_red_size < sh_b_size ? sh_red_size : sh_b_size);
+  constexpr int sh_size_b_red_max =
+      (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
+  constexpr int sh_bias_size = (thread_n_blocks * 16 / 8);
+  constexpr int sh_b_red_bias_size =
+      sh_size_b_red_max > (sh_size_b_red_min + sh_bias_size)
+          ? sh_size_b_red_max
+          : (sh_size_b_red_min + sh_bias_size);
+
+  int4* sh_bias = sh_new + sh_size_b_red_min;
+  int4* sh_g_idx = sh_new + sh_b_red_bias_size;
   int4* sh_zp = sh_g_idx + (stages * g_idx_stage);
   constexpr int sh_s_size = has_act_order ? (act_s_max_num_groups * s_sh_stride)
                                           : (stages * s_sh_stage);
@@ -803,9 +845,9 @@ __global__ void Marlin(
   static_assert(thread_m_blocks * 16 * thread_n_blocks * 16 / 8 <=
                 stages * b_sh_stage);
   int4* sh_a = sh_s + sh_s_size;
-  constexpr int shm_size_used =
-      moe_block_size + stages * (g_idx_stage + zp_sh_stage) + sh_s_size +
-      (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
+  constexpr int shm_size_used = moe_block_size +
+                                stages * (g_idx_stage + zp_sh_stage) +
+                                sh_s_size + sh_b_red_bias_size;
 
   // all remaining shared memory is used to cache A (input)
   // sh_a_max_row is at least ` stages * 16 * thread_m_blocks `
@@ -816,7 +858,8 @@ __global__ void Marlin(
   FragA frag_a[2][thread_m_blocks];
   I4 frag_b_quant[2][b_thread_vecs];
   FragC frag_c[thread_m_blocks][4][2];
-  FragS frag_s[2][4];                    // No act-order
+  FragS frag_s[2][4];  // No act-order
+  FragS frag_bias[2][4];
   FragS act_frag_s[2][4][4];             // For act-order
   int frag_qzp[2][num_ints_per_thread];  // Zero-points
   FragZP frag_zp;                        // Zero-points in fp16
@@ -1065,10 +1108,15 @@ __global__ void Marlin(
           if constexpr (w_type_id != vllm::kFE2M1f.id()) {
             reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
                 sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
-          } else {
+          } else if constexpr (group_blocks == 1 || thread_k_blocks > 4) {
             reinterpret_cast<int2*>(&frag_s[k % 2])[0] =
                 reinterpret_cast<int2*>(
                     sh_s_stage)[s_sh_rd + cur_group_id * (2 * s_sh_stride)];
+          } else {
+            reinterpret_cast<int2*>(&frag_s[k % 2])[0] =
+                reinterpret_cast<int2*>(
+                    sh_s_stage)[s_sh_rd + cur_group_id * (2 * s_sh_stride) +
+                                k % 2];
           }
         }
       }
@@ -1281,9 +1329,9 @@ __global__ void Marlin(
       int s_quant_0 = reinterpret_cast<int*>(frag_s[k2])[0];
       int s_quant_1 = reinterpret_cast<int*>(frag_s[k2])[1];
 
-      dequant_fp8_scales<scalar_t2>(s_quant_0,
-                                    reinterpret_cast<scalar_t2*>(&frag_s[k2]));
-      dequant_fp8_scales<scalar_t2>(
+      dequant_fp8_scales<scalar_t2, s_type_id>(
+          s_quant_0, reinterpret_cast<scalar_t2*>(&frag_s[k2]));
+      dequant_fp8_scales<scalar_t2, s_type_id>(
           s_quant_1, reinterpret_cast<scalar_t2*>(&frag_s[k2]) + 2);
     }
 
@@ -1566,7 +1614,7 @@ __global__ void Marlin(
   // Write out the reduce final result in the correct layout. We only actually
   // reshuffle matrix fragments in this step, the reduction above is performed
   // in fragment layout.
-  auto write_result = [&]() {
+  auto write_result = [&](bool last) {
     int c_gl_stride = prob_n / 8;
     constexpr int c_sh_stride = 2 * thread_n_blocks + 1;
     int c_gl_wr_delta = c_gl_stride * (threads / (2 * thread_n_blocks));
@@ -1592,7 +1640,7 @@ __global__ void Marlin(
 
     // We first reorder in shared memory to guarantee the most efficient final
     // global write patterns
-    auto write = [&](int idx, float c0, float c1, FragS& s) {
+    auto write = [&](int idx, float c0, float c1, FragS& s, FragS& b_bias) {
       scalar_t2 res =
           Dtype::nums2num2(Dtype::float2num(c0), Dtype::float2num(c1));
 
@@ -1601,14 +1649,27 @@ __global__ void Marlin(
       if constexpr (!has_act_order && group_blocks == -1 &&
                     w_type.size_bits() == 4 &&
                     (has_zp && dequant_skip_flop || !has_zp)) {
-        res = __hmul2(res, s[0]);
+        scalar_t2 tmp_scale = s[0];
+        if constexpr (m_block_size_8) {
+          tmp_scale = Dtype::num2num2(
+              reinterpret_cast<scalar_t*>(&s[0])[(threadIdx.x % 8) / 4]);
+        }
+        res = __hmul2(res, tmp_scale);
       }
 
-      if constexpr (w_type == vllm::kFE2M1f) {
+      if constexpr (w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
         if (!mul_topk_weights) {
           res = __hmul2(res, global_scale);
         }
       }
+      if (has_bias && last) {
+        scalar_t2 tmp_bias = b_bias[0];
+        if constexpr (m_block_size_8) {
+          tmp_bias = Dtype::num2num2(
+              reinterpret_cast<scalar_t*>(&b_bias[0])[(threadIdx.x % 8) / 4]);
+        }
+        res = __hadd2(res, tmp_bias);
+      }
 
       if constexpr (m_block_size_8) {
         ((scalar_t*)sh_red)[idx] = res.x;
@@ -1626,19 +1687,25 @@ __global__ void Marlin(
           if constexpr (m_block_size_8) {
             int wr = c_sh_wr + 16 * j;
             write(wr, frag_c[i][j][0][0], frag_c[i][j][0][1],
-                  frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + 8, frag_c[i][j][0][2], frag_c[i][j][0][3],
-                  frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
           } else {
             int wr = c_sh_wr + 8 * j;
             write(wr + (4 * c_sh_stride) * 0 + 0, frag_c[i][j][0][0],
-                  frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + (4 * c_sh_stride) * 8 + 0, frag_c[i][j][0][2],
-                  frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + (4 * c_sh_stride) * 0 + 4, frag_c[i][j][1][0],
-                  frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
             write(wr + (4 * c_sh_stride) * 8 + 4, frag_c[i][j][1][2],
-                  frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
           }
         }
         c_sh_wr += 16 * (4 * c_sh_stride);
@@ -1805,6 +1872,14 @@ __global__ void Marlin(
       }
 
       thread_block_reduce();
+
+      if (has_bias && last) {
+        __syncthreads();
+        cp_async4_pred(&sh_bias[bias_sh_wr], &b_bias_ptr[bias_gl_rd],
+                       threadIdx.x < 16 * thread_n_blocks / 8);
+        cp_async_fence();
+      }
+
       if constexpr (!has_act_order && group_blocks == -1 &&
                     (has_zp && dequant_skip_flop || !has_zp)) {
         if (w_type.size_bits() == 8 || (last || use_atomic_add)) {
@@ -1867,11 +1942,20 @@ __global__ void Marlin(
         }
         barrier_release(&locks[locks_off], last);
       }
+
+      if (has_bias && last) {
+        cp_async_wait<0>();
+        __syncthreads();
+        reinterpret_cast<int4*>(&frag_bias)[0] = sh_bias[bias_sh_rd];
+        reinterpret_cast<int4*>(&frag_bias)[1] = sh_bias[bias_sh_rd + 4];
+        __syncthreads();
+      }
+
       if (use_atomic_add && slice_count > 1 && slice_idx != 0)
         wait_negative_and_add(&locks[locks_off]);
       if (last || use_atomic_add)
         // only the last block in a slice actually writes the result
-        write_result();
+        write_result(last);
       int old_slice_row = slice_row;
       slice_row = 0;
       slice_col_par++;
@@ -1904,6 +1988,7 @@ __global__ void Marlin(
           for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] -= b_gl_stride;
         }
 
+        bias_gl_rd = (thread_n_blocks * 16 / 8) * slice_col + threadIdx.x;
         // Update slice k/n for scales loading
         if constexpr (has_act_order) {
           slice_k_start = tb_k * slice_row;

csrc/moe/marlin_moe_wna16/ops.cu

@@ -51,8 +51,9 @@ __global__ void permute_cols_kernel(
 }  // namespace marlin
 
 torch::Tensor moe_wna16_marlin_gemm(
-    torch::Tensor& a, std::optional<torch::Tensor> const& c_or_none,
-    torch::Tensor& b_q_weight, torch::Tensor& b_scales,
+    torch::Tensor& a, std::optional<torch::Tensor> c_or_none,
+    torch::Tensor& b_q_weight,
+    std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
     std::optional<torch::Tensor> const& b_zeros_or_none,
     std::optional<torch::Tensor> const& g_idx_or_none,
     std::optional<torch::Tensor> const& perm_or_none, torch::Tensor& workspace,
@@ -212,7 +213,7 @@ int get_kernel_cache_size(thread_config_t const& th_config, bool m_block_size_8,
   // Get B size
   int tb_k = th_config.thread_k;
   int tb_n = th_config.thread_n;
-  int tb_m = thread_m_blocks * (m_block_size_8 ? 8 : 16);
+  int tb_m = thread_m_blocks * 16;
 
   // shm size for block_sorted_ids/rd_block_sorted_ids/block_topk_weights
   // both of them requires tb_m * 4 bytes (tb_m * int32 or tb_m * float32)
@@ -220,6 +221,11 @@ int get_kernel_cache_size(thread_config_t const& th_config, bool m_block_size_8,
   int sh_a_size = pipe_stages * (tb_m * tb_k) * 2;
   int sh_b_size = pipe_stages * (tb_k * tb_n / pack_factor) * 4;
   int sh_red_size = tb_m * (tb_n + 8) * 2;
+  int sh_bias_size = tb_n * 2;
+  int tmp_size =
+      (sh_b_size > sh_red_size ? sh_red_size : sh_b_size) + sh_bias_size;
+  tmp_size = max(max(sh_b_size, sh_red_size), tmp_size);
+
   int sh_s_size =
       get_scales_cache_size(th_config, prob_m, prob_n, prob_k, num_bits,
                             group_size, has_act_order, is_k_full);
@@ -234,8 +240,8 @@ int get_kernel_cache_size(thread_config_t const& th_config, bool m_block_size_8,
       sh_zp_size = sh_s_size / 2;
   }
 
-  int total_size = max(sh_b_size, sh_red_size) + sh_a_size + sh_s_size +
-                   sh_zp_size + sh_g_idx_size + sh_block_meta_size;
+  int total_size = tmp_size + sh_a_size + sh_s_size + sh_zp_size +
+                   sh_g_idx_size + sh_block_meta_size;
 
   return total_size;
 }
@@ -270,20 +276,25 @@ bool is_valid_config(thread_config_t const& th_config, bool m_block_size_8,
   int cache_size = get_kernel_cache_size(
       th_config, m_block_size_8, thread_m_blocks, prob_m, prob_n, prob_k,
       num_bits, group_size, has_act_order, is_k_full, has_zp, is_zp_float);
-  return cache_size <= max_shared_mem;
+  return cache_size + 512 <= max_shared_mem;
 }
 
-  #define _GET_IF(W_TYPE, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, \
-                  M_BLOCK_SIZE_8, GROUP_BLOCKS, NUM_THREADS, IS_ZP_FLOAT)    \
-    else if (q_type == W_TYPE && thread_m_blocks == THREAD_M_BLOCKS &&       \
-             thread_n_blocks == THREAD_N_BLOCKS &&                           \
-             thread_k_blocks == THREAD_K_BLOCKS &&                           \
-             m_block_size_8 == M_BLOCK_SIZE_8 &&                             \
-             group_blocks == GROUP_BLOCKS && num_threads == NUM_THREADS &&   \
-             is_zp_float == IS_ZP_FLOAT) {                                   \
-      kernel = Marlin<scalar_t, W_TYPE.id(), NUM_THREADS, THREAD_M_BLOCKS,   \
-                      THREAD_N_BLOCKS, THREAD_K_BLOCKS, M_BLOCK_SIZE_8,      \
-                      pipe_stages, GROUP_BLOCKS, IS_ZP_FLOAT>;               \
+  #define _GET_IF(W_TYPE, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,   \
+                  M_BLOCK_SIZE_8, GROUP_BLOCKS, NUM_THREADS, IS_ZP_FLOAT)      \
+    else if (q_type == W_TYPE && thread_m_blocks == THREAD_M_BLOCKS &&         \
+             thread_n_blocks == THREAD_N_BLOCKS &&                             \
+             thread_k_blocks == THREAD_K_BLOCKS &&                             \
+             m_block_size_8 == M_BLOCK_SIZE_8 &&                               \
+             group_blocks == GROUP_BLOCKS && num_threads == NUM_THREADS &&     \
+             is_zp_float == IS_ZP_FLOAT) {                                     \
+      constexpr auto S_TYPE =                                                  \
+          W_TYPE == vllm::kFE2M1f                                              \
+              ? (GROUP_BLOCKS == 1 ? vllm::kFE4M3fn : vllm::kFE8M0fnu)         \
+              : (std::is_same<scalar_t, half>::value ? vllm::kFloat16          \
+                                                     : vllm::kBFloat16);       \
+      kernel = Marlin<scalar_t, W_TYPE.id(), S_TYPE.id(), NUM_THREADS,         \
+                      THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,       \
+                      M_BLOCK_SIZE_8, pipe_stages, GROUP_BLOCKS, IS_ZP_FLOAT>; \
     }
 
   // COMMON: cases for (group_blocks in [-1, 2, 4, 8] and is_zp_float == false)
@@ -335,31 +346,45 @@ bool is_valid_config(thread_config_t const& th_config, bool m_block_size_8,
     _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS, false)  \
     _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, -1, NUM_THREADS, false) \
     _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS, false)  \
-                                                                          \
     _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, -1, NUM_THREADS, false) \
     _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS, false)
 
-  #define FP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)        \
-    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 1, NUM_THREADS, false) \
-    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
-
-  #define FP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)       \
-    _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
-    _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
-    _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
-
-  #define FP4_GET_IF(W_TYPE)            \
-    FP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
-    FP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
-    FP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
-    FP4_GET_IF_M234(W_TYPE, 8, 4, 128)
-
   #define BIGGROUP_GET_IF(W_TYPE)            \
     BIGGROUP_GET_IF_M1(W_TYPE, 8, 8, 256)    \
     BIGGROUP_GET_IF_M1(W_TYPE, 8, 4, 128)    \
     BIGGROUP_GET_IF_M234(W_TYPE, 16, 4, 256) \
     BIGGROUP_GET_IF_M234(W_TYPE, 8, 4, 128)
 
+  #define NVFP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)      \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 1, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
+
+  #define NVFP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)     \
+    _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
+
+  #define NVFP4_GET_IF(W_TYPE)            \
+    NVFP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
+    NVFP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
+    NVFP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
+    NVFP4_GET_IF_M234(W_TYPE, 8, 4, 128)
+
+  #define MXFP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)      \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false)
+
+  #define MXFP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)     \
+    _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false)
+
+  #define MXFP4_GET_IF(W_TYPE)            \
+    MXFP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
+    MXFP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
+    MXFP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
+    MXFP4_GET_IF_M234(W_TYPE, 8, 4, 128)
+
   // We currently have 4-bit models only with group_blocks == 4
   #define FZP_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)       \
     _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 4, NUM_THREADS, true) \
@@ -408,12 +433,17 @@ MarlinFuncPtr get_marlin_kernel(const vllm::ScalarType q_type,
   COMMON_GET_IF(vllm::kU4B8)
   COMMON_GET_IF(vllm::kU8B128)
 
-  BIGGROUP_GET_IF(vllm::kFE4M3fn)
+  NVFP4_GET_IF(vllm::kFE2M1f)
 
-  FP4_GET_IF(vllm::kFE2M1f)
+  BIGGROUP_GET_IF(vllm::kFE4M3fn)
 
   ACT_GET_IF(vllm::kU4B8)
   ACT_GET_IF(vllm::kU8B128)
+  if (std::is_same<scalar_t, nv_bfloat16>::value) {
+    if (false) {
+    }
+    MXFP4_GET_IF(vllm::kFE2M1f)
+  }
 
   return kernel;
 }
@@ -482,16 +512,16 @@ exec_config_t determine_exec_config(const vllm::ScalarType& q_type, int prob_m,
 }
 
 template <typename scalar_t>
-void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
-               void* s2, void* zp, void* g_idx, void* perm, void* a_tmp,
-               void* sorted_token_ids, void* expert_ids,
+void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
+               void* s, void* s2, void* zp, void* g_idx, 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 top_k, bool mul_topk_weights, bool is_ep,
                int prob_m, int prob_n, int prob_k, void* workspace,
-               vllm::ScalarType const& q_type, 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, bool use_atomic_add, bool use_fp32_reduce,
+               vllm::ScalarType const& q_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, 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;
@@ -538,6 +568,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
   const int4* B_ptr = (const int4*)B;
   int4* C_ptr = (int4*)C;
   int4* C_tmp_ptr = (int4*)C_tmp;
+  const int4* bias_ptr = (const int4*)b_bias;
   const int4* s_ptr = (const int4*)s;
   const uint16_t* s2_ptr = (const uint16_t*)s2;
   const int4* zp_ptr = (const int4*)zp;
@@ -648,10 +679,10 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
   // avoid ">>>" being formatted to "> > >"
   // clang-format off
   kernel<<<blocks, num_threads, max_shared_mem, stream>>>(
-      A_ptr, B_ptr, C_ptr, C_tmp_ptr, s_ptr, s2_ptr, zp_ptr, g_idx_ptr,
+      A_ptr, B_ptr, C_ptr, C_tmp_ptr, bias_ptr, s_ptr, s2_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,
-      prob_n, prob_k, locks, use_atomic_add, use_fp32_reduce, max_shared_mem);
+      prob_n, prob_k, locks, has_bias, use_atomic_add, use_fp32_reduce, max_shared_mem);
   // clang-format on
 }
 
@@ -659,7 +690,8 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
 
 torch::Tensor moe_wna16_marlin_gemm(
     torch::Tensor& a, std::optional<torch::Tensor> const& c_or_none,
-    torch::Tensor& b_q_weight, torch::Tensor& b_scales,
+    torch::Tensor& b_q_weight,
+    std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
     std::optional<torch::Tensor> const& global_scale_or_none,
     std::optional<torch::Tensor> const& b_zeros_or_none,
     std::optional<torch::Tensor> const& g_idx_or_none,
@@ -766,7 +798,6 @@ torch::Tensor moe_wna16_marlin_gemm(
   num_groups = b_scales.size(1);
 
   torch::Tensor g_idx, perm, a_tmp;
-  ;
   if (g_idx_or_none.has_value() && perm_or_none.has_value()) {
     g_idx = g_idx_or_none.value();
     perm = perm_or_none.value();
@@ -815,12 +846,24 @@ torch::Tensor moe_wna16_marlin_gemm(
   torch::Tensor global_scale;
   if (global_scale_or_none.has_value()) {
     global_scale = global_scale_or_none.value();
-    TORCH_CHECK(b_q_type == vllm::kFE2M1f,
-                "global_scale can only be used for float4_e2m1f.");
+    TORCH_CHECK(b_q_type == vllm::kFE2M1f && group_size == 16,
+                "global_scale can only be used for nvfp4 format.");
   } else {
     global_scale = torch::empty({0}, options);
-    TORCH_CHECK(!(b_q_type == vllm::kFE2M1f),
-                "the global_scale parameter must be passed for float4_e2m1f.");
+    TORCH_CHECK(!(b_q_type == vllm::kFE2M1f && group_size == 16),
+                "the global_scale parameter must be passed for nvfp4 format.");
+  }
+
+  bool has_bias = b_bias_or_none.has_value();
+  torch::Tensor b_bias;
+  if (has_bias) {
+    b_bias = b_bias_or_none.value();
+    TORCH_CHECK(b_bias.device().is_cuda(), "b_bias is not on GPU");
+    TORCH_CHECK(b_bias.is_contiguous(), "b_bias is not contiguous");
+    TORCH_CHECK(b_bias.size(1) == size_n, "b_bias.size(0) != size_n");
+    TORCH_CHECK(b_bias.stride(1) == 1, "b_bias.stride(1) != 1");
+  } else {
+    b_bias = torch::empty({0}, options);
   }
 
   torch::Tensor b_zeros;
@@ -832,7 +875,6 @@ torch::Tensor moe_wna16_marlin_gemm(
     b_zeros = torch::empty({0}, options);
   }
   bool has_zp = b_zeros.size(-1) > 0;
-
   if (has_zp) {
     TORCH_CHECK(
         b_q_type == vllm::kU4 || b_q_type == vllm::kU8,
@@ -890,41 +932,58 @@ torch::Tensor moe_wna16_marlin_gemm(
   if (a.scalar_type() == at::ScalarType::Half) {
     void* scales_ptr;
     if (b_q_type == vllm::kFE2M1f) {
-      scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      if (group_size == 16)
+        scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      else if (group_size == 32)
+        scales_ptr = b_scales.data_ptr<at::Float8_e8m0fnu>();
+      else
+        TORCH_CHECK(false,
+                    "float4_e2m1f only supports group_size == 16 (NVFP4) ",
+                    "and group_size == 32 (MXFP4)");
     } else {
       scales_ptr = b_scales.data_ptr<at::Half>();
     }
 
     MARLIN_NAMESPACE_NAME::marlin_mm<half>(
         a.data_ptr<at::Half>(), b_q_weight.data_ptr(), c.data_ptr<at::Half>(),
-        c_tmp.data_ptr<float>(), scales_ptr, global_scale.data_ptr<at::Half>(),
-        b_zeros.data_ptr(), g_idx.data_ptr(), perm.data_ptr(),
-        a_tmp.data_ptr<at::Half>(), sorted_token_ids.data_ptr(),
-        expert_ids.data_ptr(), num_tokens_past_padded.data_ptr(),
-        topk_weights.data_ptr(), moe_block_size, top_k, mul_topk_weights, is_ep,
-        size_m, size_n, size_k, workspace.data_ptr(), b_q_type, has_act_order,
-        is_k_full, has_zp, num_groups, group_size, dev,
+        c_tmp.data_ptr<float>(), b_bias.data_ptr<at::Half>(), scales_ptr,
+        global_scale.data_ptr<at::Half>(), b_zeros.data_ptr(), g_idx.data_ptr(),
+        perm.data_ptr(), a_tmp.data_ptr<at::Half>(),
+        sorted_token_ids.data_ptr(), expert_ids.data_ptr(),
+        num_tokens_past_padded.data_ptr(), topk_weights.data_ptr(),
+        moe_block_size, top_k, mul_topk_weights, is_ep, size_m, size_n, size_k,
+        workspace.data_ptr(), b_q_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,
         use_atomic_add, use_fp32_reduce, is_zp_float);
   } else if (a.scalar_type() == at::ScalarType::BFloat16) {
     void* scales_ptr;
     if (b_q_type == vllm::kFE2M1f) {
-      scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      if (group_size == 16)
+        scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      else if (group_size == 32)
+        scales_ptr = b_scales.data_ptr<at::Float8_e8m0fnu>();
+      else
+        TORCH_CHECK(false,
+                    "float4_e2m1f only supports group_size == 16 (NVFP4) ",
+                    "and group_size == 32 (MXFP4)");
     } else {
       scales_ptr = b_scales.data_ptr<at::BFloat16>();
     }
 
     MARLIN_NAMESPACE_NAME::marlin_mm<nv_bfloat16>(
         a.data_ptr<at::BFloat16>(), b_q_weight.data_ptr(),
-        c.data_ptr<at::BFloat16>(), c_tmp.data_ptr<float>(), scales_ptr,
+        c.data_ptr<at::BFloat16>(), c_tmp.data_ptr<float>(),
+        b_bias.data_ptr<at::BFloat16>(), scales_ptr,
         global_scale.data_ptr<at::BFloat16>(), b_zeros.data_ptr(),
         g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(),
         sorted_token_ids.data_ptr(), expert_ids.data_ptr(),
         num_tokens_past_padded.data_ptr(), topk_weights.data_ptr(),
         moe_block_size, top_k, mul_topk_weights, is_ep, size_m, size_n, size_k,
-        workspace.data_ptr(), b_q_type, has_act_order, is_k_full, has_zp,
-        num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
-        thread_k, thread_n, sms, use_atomic_add, use_fp32_reduce, is_zp_float);
+        workspace.data_ptr(), b_q_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,
+        use_atomic_add, use_fp32_reduce, is_zp_float);
   } else {
     TORCH_CHECK(false,
                 "moe_wna16_marlin_gemm only supports bfloat16 and float16");

csrc/moe/torch_bindings.cpp

@@ -35,7 +35,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
 
   m.def(
       "moe_wna16_marlin_gemm(Tensor! a, Tensor? c_or_none,"
-      "Tensor! b_q_weight, Tensor! b_scales, Tensor? global_scale, Tensor? "
+      "Tensor! b_q_weight, Tensor? b_bias_or_none,"
+      "Tensor! b_scales, Tensor? global_scale, Tensor? "
       "b_zeros_or_none,"
       "Tensor? g_idx_or_none, Tensor? perm_or_none, Tensor! workspace,"
       "Tensor sorted_token_ids,"

csrc/ops.h

@@ -138,6 +138,8 @@ void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);
 
 void fatrelu_and_mul(torch::Tensor& out, torch::Tensor& input,
                      double threshold);
+void swigluoai_and_mul(torch::Tensor& out, torch::Tensor& input,
+                       double alpha = 1.702, double limit = 7.0);
 
 void gelu_new(torch::Tensor& out, torch::Tensor& input);
 
@@ -145,22 +147,6 @@ void gelu_fast(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_quick(torch::Tensor& out, torch::Tensor& input);
 
-void advance_step_flashattn(int64_t num_seqs, int64_t num_queries,
-                            int64_t block_size, torch::Tensor& input_tokens,
-                            torch::Tensor& sampled_token_ids,
-                            torch::Tensor& input_positions,
-                            torch::Tensor& seq_lens,
-                            torch::Tensor& slot_mapping,
-                            torch::Tensor& block_tables);
-
-void advance_step_flashinfer(
-    int64_t num_seqs, int64_t num_queries, int64_t block_size,
-    torch::Tensor& input_tokens, torch::Tensor& sampled_token_ids,
-    torch::Tensor& input_positions, torch::Tensor& seq_lens,
-    torch::Tensor& slot_mapping, torch::Tensor& block_tables,
-    torch::Tensor& paged_kv_indices, torch::Tensor& paged_kv_indptr,
-    torch::Tensor& paged_kv_last_page_len, torch::Tensor& block_table_bounds);
-
 void cutlass_mla_decode(torch::Tensor const& out, torch::Tensor const& q_nope,
                         torch::Tensor const& q_pe,
                         torch::Tensor const& kv_c_and_k_pe_cache,
@@ -170,15 +156,6 @@ void cutlass_mla_decode(torch::Tensor const& out, torch::Tensor const& q_nope,
 torch::Tensor get_cuda_view_from_cpu_tensor(torch::Tensor& cpu_tensor);
 
 #ifndef USE_ROCM
-torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
-                        const torch::Tensor& codebooks,
-                        const torch::Tensor& scales,
-                        const std::vector<int64_t>& codebook_partition_sizes,
-                        const std::optional<torch::Tensor>& bias);
-
-torch::Tensor aqlm_dequant(
-    const torch::Tensor& codes, const torch::Tensor& codebooks,
-    const std::vector<int64_t>& codebook_partition_sizes);
 
 torch::Tensor awq_gemm(torch::Tensor _in_feats, torch::Tensor _kernel,
                        torch::Tensor _scaling_factors, torch::Tensor _zeros,

csrc/prepare_inputs/advance_step.cu

@@ -1,336 +0,0 @@
-/*
- * The goal of this GPU kernel is to advance input tensors on the GPU directly
- * PR: https://github.com/vllm-project/vllm/pull/6338
- * Current restrictions:
- *     1. Specialized for DraftModelRunner
- *     2. Supports flash_attn only
- */
-
-#include "advance_step.cuh"
-
-namespace prepare_inputs {
-
-//
-template <int const num_threads>
-__global__ void advance_step_flashattn_kernel(
-    int num_seqs, int num_queries, int block_size, long* input_tokens_ptr,
-    long const* sampled_token_ids_ptr, long* input_positions_ptr,
-    int* seq_lens_ptr, long* slot_mapping_ptr, int const* block_tables_ptr,
-    int64_t const block_tables_stride) {
-  int const n_pad = num_seqs - num_queries;
-  if (n_pad && blockIdx.x == 0) {
-    // Handle cuda graph padding
-    int const offset = num_queries;
-    for (int i = threadIdx.x; i < n_pad; i += blockDim.x) {
-      input_tokens_ptr[offset + i] = 0;
-      input_positions_ptr[offset + i] = 0;
-      slot_mapping_ptr[offset + i] = -1;
-    }
-  }
-
-  int num_query_blocks = div_ceil(num_queries, num_threads);
-
-  if (blockIdx.x >= num_query_blocks) {
-    return;
-  }
-
-  int cur_query_id = blockIdx.x * num_threads + threadIdx.x;
-
-  if (cur_query_id >= num_queries) {
-    return;
-  }
-
-  // Update input_tokens
-  input_tokens_ptr[cur_query_id] = sampled_token_ids_ptr[cur_query_id];
-
-  int seq_len = seq_lens_ptr[cur_query_id];
-  int next_seq_len = seq_len + 1;
-  int next_input_pos = next_seq_len - 1;
-
-  // Update seq_lens
-  seq_lens_ptr[cur_query_id] = next_seq_len;
-  // Update input_positions
-  input_positions_ptr[cur_query_id] = next_input_pos;
-
-  int const* seq_block_tables_ptr =
-      block_tables_ptr + block_tables_stride * cur_query_id;
-
-  int block_index = next_input_pos / block_size;
-  int block_offset = next_input_pos % block_size;
-
-  int slot_num = seq_block_tables_ptr[block_index] * block_size + block_offset;
-  // Update slot_mapping
-  slot_mapping_ptr[cur_query_id] = slot_num;
-}
-
-inline void verify_tensor(std::string const& name, torch::Tensor const& t,
-                          int64_t const size_0, int64_t const size_1,
-                          c10::ScalarType const type) {
-  bool size_0_cond = true;
-  if (size_0 != -1) {
-    size_0_cond = t.size(0) == size_0;
-  }
-
-  bool size_1_cond = true;
-  if (size_1 != -1) {
-    size_1_cond = t.size(1) == size_1;
-  }
-
-  bool is_contiguous = t.is_contiguous();
-  bool same_type = t.dtype() == type;
-
-  bool pass = size_0_cond && size_1_cond && is_contiguous && same_type;
-  if (!pass) {
-    TORCH_CHECK(false, "tensor: name = ", name, ", shape = ", t.sizes(),
-                " is_cont = ", t.is_contiguous(), ", type = ", t.dtype(),
-                " is not as expected: shape = [", size_0, ", ", size_1,
-                "], type = ", type);
-  }
-}
-
-/// each thread processes a block per query
-__global__ void advance_step_flashinfer_kernel(
-    int num_threads, int num_seqs, int num_queries, int block_size,
-    long* input_tokens_ptr, long const* sampled_token_ids_ptr,
-    long* input_positions_ptr, int* seq_lens_ptr, long* slot_mapping_ptr,
-    int const* block_tables_ptr, int64_t const block_tables_stride,
-    int* paged_kv_last_page_len_ptr, int* block_table_bound_ptr) {
-  int const n_pad = num_seqs - num_queries;
-  if (n_pad && blockIdx.x == 0) {
-    // Handle cuda graph padding
-    int const offset = num_queries;
-    for (int i = threadIdx.x; i < n_pad; i += blockDim.x) {
-      input_tokens_ptr[offset + i] = 0;
-      input_positions_ptr[offset + i] = 0;
-      slot_mapping_ptr[offset + i] = -1;
-    }
-  }
-  int num_query_blocks = div_ceil(num_queries, num_threads);
-
-  if (blockIdx.x < num_query_blocks) {
-    int cur_query_id = blockIdx.x * num_threads + threadIdx.x;
-
-    if (cur_query_id < num_queries) {
-      // Update input_tokens
-      input_tokens_ptr[cur_query_id] = sampled_token_ids_ptr[cur_query_id];
-
-      int seq_len = seq_lens_ptr[cur_query_id];
-      int next_seq_len = seq_len + 1;
-      int next_input_pos = next_seq_len - 1;
-
-      // Update seq_lens
-      seq_lens_ptr[cur_query_id] = next_seq_len;
-      // Update input_positions
-      input_positions_ptr[cur_query_id] = next_input_pos;
-
-      int const* seq_block_tables_ptr =
-          block_tables_ptr + block_tables_stride * cur_query_id;
-
-      int block_index = next_input_pos / block_size;
-      int block_offset = next_input_pos % block_size;
-
-      // Update paged_kv_last_page_len
-      paged_kv_last_page_len_ptr[cur_query_id] = block_offset + 1;
-
-      int slot_num =
-          seq_block_tables_ptr[block_index] * block_size + block_offset;
-      // Update slot_mapping
-      slot_mapping_ptr[cur_query_id] = slot_num;
-      block_table_bound_ptr[cur_query_id] = div_ceil(next_seq_len, block_size);
-    }
-  }
-}
-
-__global__ void advance_step_flashinfer_indptr_kernel(
-    int num_threads, int num_seqs, int num_queries, int* paged_kv_indptr_ptr,
-    int* block_table_bound_ptr) {
-  int idx = blockIdx.x * num_threads + threadIdx.x;
-  // Update paged_kv_indptr
-  if (idx == 0) {
-    paged_kv_indptr_ptr[idx] = 0;
-  }
-  if (idx < num_queries) {
-    int sum = 0;
-    for (int i = 0; i <= idx; ++i) {
-      sum += block_table_bound_ptr[i];
-    }
-    paged_kv_indptr_ptr[idx + 1] = sum;
-  }
-}
-
-__global__ void advance_step_flashinfer_indices_kernel(
-    int num_seqs, int num_queries, int const* block_tables_ptr,
-    int64_t const max_num_blocks_per_seq, int* paged_kv_indices_ptr,
-    int* paged_kv_indptr_ptr, int* block_table_bound_ptr) {
-  // note: max_num_blocks_per_seq = block_tables.stride(0)
-  int tid = blockIdx.x * blockDim.x + threadIdx.x;
-
-  // when cuda graphs are enabled, paged_kv_indptr tensor
-  // has to be updated for the padded queries
-  // tid represents a query# for paged_kv_indptr tensor
-  if (num_queries < tid && tid <= num_seqs) {
-    paged_kv_indptr_ptr[tid] = paged_kv_indptr_ptr[num_queries];
-  }
-
-  // each thread processes a block_ptr in block_tables
-  // block_tables shape: [num_queries, max_num_blocks_per_seq]
-  // paged_kv_indices is flattened block_tables.
-  for (int idx = tid; idx < (num_seqs * max_num_blocks_per_seq);
-       idx += (gridDim.x * blockDim.x)) {
-    // block_tables-row = paged_kv_indptr[queryNum]
-    int queryNum = idx / max_num_blocks_per_seq;
-    int col = idx % max_num_blocks_per_seq;
-    if (queryNum < num_queries && col < block_table_bound_ptr[queryNum]) {
-      int indices_arr_idx = paged_kv_indptr_ptr[queryNum] + col;
-      int block_tables_idx = queryNum * max_num_blocks_per_seq + col;
-      paged_kv_indices_ptr[indices_arr_idx] =
-          block_tables_ptr[block_tables_idx];
-    }
-  }
-}
-
-void advance_step_flashattn(int num_seqs, int num_queries, int block_size,
-                            torch::Tensor& input_tokens,       // type: long
-                            torch::Tensor& sampled_token_ids,  // type: long
-                            torch::Tensor& input_positions,    // type: long
-                            torch::Tensor& seq_lens,           // type: int
-                            torch::Tensor& slot_mapping,       // type: long
-                            torch::Tensor& block_tables) {     // type: int
-
-  if (logging) {
-    printf("advance_step_flashattn:\n");
-    printf("  num_seqs = %d\n", num_seqs);
-    printf("  num_queries = %d\n", num_queries);
-    printf("  block_size = %d\n", block_size);
-  }
-  // Verify all tensors
-  verify_tensor("input_tokens", input_tokens, num_seqs, -1, at::kLong);
-  verify_tensor("sampled_token_ids", sampled_token_ids, num_queries, 1,
-                at::kLong);
-  verify_tensor("input_positions", input_positions, num_seqs, -1, at::kLong);
-  verify_tensor("seq_lens", seq_lens, num_seqs, -1, at::kInt);
-  verify_tensor("slot_mapping", slot_mapping, num_seqs, -1, at::kLong);
-  verify_tensor("block_tables", block_tables, num_seqs, -1, at::kInt);
-
-  int dev = sampled_token_ids.get_device();
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream(dev);
-
-  int blocks;
-  cudaDeviceGetAttribute(&blocks, cudaDevAttrMultiProcessorCount, dev);
-
-  advance_step_flashattn_kernel<max_threads>
-      <<<blocks, max_threads, 0, stream>>>(
-          num_seqs, num_queries, block_size,
-          reinterpret_cast<long*>(input_tokens.data_ptr()),
-          reinterpret_cast<long const*>(sampled_token_ids.data_ptr()),
-          reinterpret_cast<long*>(input_positions.data_ptr()),
-          reinterpret_cast<int*>(seq_lens.data_ptr()),
-          reinterpret_cast<long*>(slot_mapping.data_ptr()),
-          reinterpret_cast<int const*>(block_tables.data_ptr()),
-          block_tables.stride(0));
-}
-
-void advance_step_flashinfer(
-    int num_seqs, int num_queries, int block_size,
-    torch::Tensor& input_tokens,            // type: long
-    torch::Tensor& sampled_token_ids,       // type: long
-    torch::Tensor& input_positions,         // type: long
-    torch::Tensor& seq_lens,                // type: int
-    torch::Tensor& slot_mapping,            // type: long
-    torch::Tensor& block_tables,            // type: int
-    torch::Tensor& paged_kv_indices,        // type: int
-    torch::Tensor& paged_kv_indptr,         // type: int
-    torch::Tensor& paged_kv_last_page_len,  // type: int
-    torch::Tensor& block_table_bound) {     // type: int
-
-  if (logging) {
-    printf("advance_step_flashinfer:\n");
-    printf("  num_seqs = %d\n", num_seqs);
-    printf("  num_queries = %d\n", num_queries);
-    printf("  block_size = %d\n", block_size);
-    printf("  block_tables.stride(0) = %zu\n", block_tables.stride(0));
-  }
-  // Verify all tensors
-  verify_tensor("input_tokens", input_tokens, num_seqs, -1, at::kLong);
-  // verify_tensor("sampled_token_ids", sampled_token_ids, num_queries, 1,
-  //               at::kLong);
-  verify_tensor("input_positions", input_positions, num_seqs, -1, at::kLong);
-  verify_tensor("seq_lens", seq_lens, num_seqs, -1, at::kInt);
-  verify_tensor("slot_mapping", slot_mapping, num_seqs, -1, at::kLong);
-  verify_tensor("block_tables", block_tables, num_seqs, -1, at::kInt);
-
-  verify_tensor("paged_kv_indices", paged_kv_indices, -1, -1, at::kInt);
-  verify_tensor("paged_kv_indptr", paged_kv_indptr, num_seqs + 1, -1, at::kInt);
-  verify_tensor("paged_kv_last_page_len", paged_kv_last_page_len, num_seqs, -1,
-                at::kInt);
-
-  verify_tensor("block_table_bound", block_table_bound, num_seqs, -1, at::kInt);
-
-  int dev = sampled_token_ids.get_device();
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream(dev);
-
-  int blocks;
-  int threads;
-  cudaDeviceGetAttribute(&blocks, cudaDevAttrMultiProcessorCount, dev);
-  cudaDeviceGetAttribute(&threads, cudaDevAttrMaxThreadsPerBlock, dev);
-
-  TORCH_CHECK((blocks * threads > num_queries),
-              "multi-step: not enough threads to map to num_queries = ",
-              num_queries, " block_tables.stride(0) = ", block_tables.stride(0),
-              " blocks = ", blocks, " max_threads = ", threads);
-  if (logging) {
-    printf("launching kernels with %d blocks and %d threads\n", blocks,
-           threads);
-  }
-  advance_step_flashinfer_kernel<<<blocks, threads, 0, stream>>>(
-      threads, num_seqs, num_queries, block_size,
-      reinterpret_cast<long*>(input_tokens.data_ptr()),
-      reinterpret_cast<long const*>(sampled_token_ids.data_ptr()),
-      reinterpret_cast<long*>(input_positions.data_ptr()),
-      reinterpret_cast<int*>(seq_lens.data_ptr()),
-      reinterpret_cast<long*>(slot_mapping.data_ptr()),
-      reinterpret_cast<int const*>(block_tables.data_ptr()),
-      block_tables.stride(0),
-      reinterpret_cast<int*>(paged_kv_last_page_len.data_ptr()),
-      reinterpret_cast<int*>(block_table_bound.data_ptr()));
-
-  advance_step_flashinfer_indptr_kernel<<<blocks, threads, 0, stream>>>(
-      threads, num_seqs, num_queries,
-      reinterpret_cast<int*>(paged_kv_indptr.data_ptr()),
-      reinterpret_cast<int*>(block_table_bound.data_ptr()));
-
-  advance_step_flashinfer_indices_kernel<<<blocks, threads, 0, stream>>>(
-      num_seqs, num_queries,
-      reinterpret_cast<int const*>(block_tables.data_ptr()),
-      block_tables.stride(0),
-      reinterpret_cast<int*>(paged_kv_indices.data_ptr()),
-      reinterpret_cast<int*>(paged_kv_indptr.data_ptr()),
-      reinterpret_cast<int*>(block_table_bound.data_ptr()));
-}
-
-}  // namespace prepare_inputs
-
-void advance_step_flashattn(int64_t num_seqs, int64_t num_queries,
-                            int64_t block_size, torch::Tensor& input_tokens,
-                            torch::Tensor& sampled_token_ids,
-                            torch::Tensor& input_positions,
-                            torch::Tensor& seq_lens,
-                            torch::Tensor& slot_mapping,
-                            torch::Tensor& block_tables) {
-  prepare_inputs::advance_step_flashattn(
-      num_seqs, num_queries, block_size, input_tokens, sampled_token_ids,
-      input_positions, seq_lens, slot_mapping, block_tables);
-}
-
-void advance_step_flashinfer(
-    int64_t num_seqs, int64_t num_queries, int64_t block_size,
-    torch::Tensor& input_tokens, torch::Tensor& sampled_token_ids,
-    torch::Tensor& input_positions, torch::Tensor& seq_lens,
-    torch::Tensor& slot_mapping, torch::Tensor& block_tables,
-    torch::Tensor& paged_kv_indices, torch::Tensor& paged_kv_indptr,
-    torch::Tensor& paged_kv_last_page_len, torch::Tensor& block_table_bound) {
-  prepare_inputs::advance_step_flashinfer(
-      num_seqs, num_queries, block_size, input_tokens, sampled_token_ids,
-      input_positions, seq_lens, slot_mapping, block_tables, paged_kv_indices,
-      paged_kv_indptr, paged_kv_last_page_len, block_table_bound);
-}

csrc/prepare_inputs/advance_step.cuh

@@ -1,19 +0,0 @@
-#pragma once
-
-#include <torch/all.h>
-
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <cuda.h>
-#include <cuda_fp16.h>
-#include <cuda_runtime.h>
-#include <iostream>
-
-namespace prepare_inputs {
-
-static constexpr int max_threads = 256;
-static constexpr bool logging = false;
-
-constexpr int div_ceil(int a, int b) { return (a + b - 1) / b; }
-
-}  // namespace prepare_inputs

csrc/quantization/aqlm/gemm_kernels.cu

@@ -1,597 +0,0 @@
-/*
- * Modified by Neural Magic
- * Adapted from https://github.com/Vahe1994/AQLM
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *         http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuda.h>
-#include <cuda_fp16.h>
-#include <cuda_runtime.h>
-#include <torch/all.h>
-#include <c10/cuda/CUDAStream.h>
-#include <c10/cuda/CUDAGuard.h>
-
-#include <iostream>
-#include <cstdlib>
-
-namespace vllm {
-namespace aqlm {
-
-__global__ void Code1x16MatVec(
-    const int4* __restrict__ A, const int4* __restrict__ B,
-    int4* __restrict__ C, const int4* __restrict__ codebook, const int prob_m,
-    const int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  int b_gl_rd = 0;
-  int c_gl_wr = a_gl_rd;
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-
-  __shared__ int4 sh_b[32 * 9];
-  float res = 0;
-
-  int iters = (prob_k / 8 + 8 * 32 - 1) / (8 * 32);
-  while (iters--) {
-    // We pad shared memory to avoid bank conflicts during reads
-    __syncthreads();
-    for (int i = threadIdx.x; i < 32 * 8; i += blockDim.x) {
-      if (b_gl_rd + i < prob_k / 8) sh_b[9 * (i / 8) + i % 8] = B[b_gl_rd + i];
-    }
-    __syncthreads();
-    b_gl_rd += 32 * 8;
-
-    int b_sh_rd = 9 * (threadIdx.x % 32);
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint16_t* enc = reinterpret_cast<const uint16_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        uint32_t dec[4];
-        // We bypass the L1 cache to avoid massive amounts of memory streaming
-        // that doesn't actually help us; this brings > 2x speedup.
-        asm volatile("ld.cg.global.v4.u32 {%0, %1, %2, %3}, [%4];"
-                     : "=r"(dec[0]), "=r"(dec[1]), "=r"(dec[2]), "=r"(dec[3])
-                     : "l"((void*)&codebook[enc[i]]));
-        half2* a = reinterpret_cast<half2*>(&dec);
-        half2* b = reinterpret_cast<half2*>(&sh_b[b_sh_rd]);
-        half2 res2 = {};
-#pragma unroll
-        for (int j = 0; j < 4; j++) res2 = __hfma2(a[j], b[j], res2);
-        res += __half2float(res2.x) + __half2float(res2.y);
-        b_sh_rd++;
-      }
-      a_gl_rd += 32;
-    }
-  }
-
-  if (pred) {
-#pragma unroll
-    for (int i = 16; i > 0; i /= 2) res += __shfl_down_sync(0xffffffff, res, i);
-    if (threadIdx.x % 32 == 0)
-      reinterpret_cast<__half*>(C)[c_gl_wr] = __float2half(res);
-  }
-}
-
-__global__ void Code2x8MatVec(
-    const int4* __restrict__ A, const int4* __restrict__ B,
-    int4* __restrict__ C, const int4* __restrict__ codebook, int prob_m,
-    int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  int b_gl_rd = 0;
-  int c_gl_wr = a_gl_rd;
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-  int lane = threadIdx.x % 8;
-
-  extern __shared__ int4 sh[];
-  int4* sh_b = sh;
-  int4* sh_code = sh_b + 32 * 9;
-  int4* sh_code0 = sh_code;
-  int4* sh_code1 = sh_code + 256 * 8;
-
-  for (int i = threadIdx.x; i < 2 * 256; i += blockDim.x) {
-    int4 dec = codebook[i];
-#pragma unroll
-    for (int j = 0; j < 8; j++) sh_code[8 * i + (j + lane) % 8] = dec;
-  }
-  __syncthreads();
-
-  float res = 0;
-
-  int iters = (prob_k / 8 + 8 * 32 - 1) / (8 * 32);
-  while (iters--) {
-    // We pad shared memory to avoid bank conflicts during reads
-    __syncthreads();
-    for (int i = threadIdx.x; i < 32 * 8; i += blockDim.x) {
-      if (b_gl_rd + i < prob_k / 8) sh_b[9 * (i / 8) + i % 8] = B[b_gl_rd + i];
-    }
-    __syncthreads();
-    b_gl_rd += 32 * 8;
-
-    int b_sh_rd = 9 * (threadIdx.x % 32);
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint8_t* enc = reinterpret_cast<const uint8_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        half2* a0 =
-            reinterpret_cast<half2*>(&sh_code0[8 * enc[2 * i + 0] + lane]);
-        half2* a1 =
-            reinterpret_cast<half2*>(&sh_code1[8 * enc[2 * i + 1] + lane]);
-        half2* b = reinterpret_cast<half2*>(&sh_b[b_sh_rd]);
-        half2 res2 = {};
-#pragma unroll
-        for (int j = 0; j < 4; j++)
-          res2 = __hfma2(__hadd2(a0[j], a1[j]), b[j], res2);
-        res += __half2float(res2.x) + __half2float(res2.y);
-        b_sh_rd++;
-      }
-      a_gl_rd += 32;
-    }
-  }
-
-  if (pred) {
-#pragma unroll
-    for (int i = 16; i > 0; i /= 2) res += __shfl_down_sync(0xffffffff, res, i);
-    if (threadIdx.x % 32 == 0)
-      reinterpret_cast<__half*>(C)[c_gl_wr] = __float2half(res);
-  }
-}
-
-__global__ void Code1x16Dequant(
-    const int4* __restrict__ A, int4* __restrict__ C,
-    const int4* __restrict__ codebook, int prob_m, int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long, sums to m.
-    const int codebook_stride     // as int4
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-
-  int c_gl_stride = prob_k / 8;
-  int c_gl_wr = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  c_gl_wr = c_gl_stride * c_gl_wr + (threadIdx.x % 32) * 8;
-
-  int iters = (prob_k / 8 - 1) / (8 * 32) + 1;
-  while (iters--) {
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint16_t* enc = reinterpret_cast<const uint16_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        int4 chunk;
-        auto dec = reinterpret_cast<uint32_t*>(&chunk);
-        // We bypass the L1 cache to avoid massive amounts of memory streaming
-        // that doesn't actually help us; this brings > 2x speedup.
-        asm volatile("ld.cg.global.v4.u32 {%0, %1, %2, %3}, [%4];"
-                     : "=r"(dec[0]), "=r"(dec[1]), "=r"(dec[2]), "=r"(dec[3])
-                     : "l"((void*)&codebook[enc[i]]));
-
-        C[a_gl_rd * 8 + i] = chunk;
-      }
-    }
-    a_gl_rd += 32;
-  }
-}
-
-__global__ void Code2x8Dequant(
-    const int4* __restrict__ A, int4* __restrict__ C,
-    const int4* __restrict__ codebook, int prob_m, int prob_k,
-    const int4
-        codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                           // most 3 long, corresponds to cols.
-    const int codebook_stride  // as int4
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-  int lane = threadIdx.x % 8;
-
-  int c_gl_stride = prob_k / 8;
-  int c_gl_wr = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  c_gl_wr = c_gl_stride * c_gl_wr + (threadIdx.x % 32) * 8;
-
-  extern __shared__ int4 sh[];
-  int4* sh_code = sh;
-  int4* sh_code0 = sh_code;
-  int4* sh_code1 = sh_code + 256 * 8;
-
-  for (int i = threadIdx.x; i < 2 * 256; i += blockDim.x) {
-    int4 dec = codebook[i];
-#pragma unroll
-    for (int j = 0; j < 8; j++) sh_code[8 * i + (j + lane) % 8] = dec;
-  }
-  __syncthreads();
-
-  int iters = (prob_k / 8 - 1) / (8 * 32) + 1;
-  while (iters--) {
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint8_t* enc = reinterpret_cast<const uint8_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        int4 chunk;
-        half2* a0 =
-            reinterpret_cast<half2*>(&sh_code0[8 * enc[2 * i + 0] + lane]);
-        half2* a1 =
-            reinterpret_cast<half2*>(&sh_code1[8 * enc[2 * i + 1] + lane]);
-#pragma unroll
-        for (int j = 0; j < 4; j++)
-          reinterpret_cast<half2*>(&chunk)[j] = __hadd2(a0[j], a1[j]);
-        C[a_gl_rd * 8 + i] = chunk;
-      }
-    }
-    a_gl_rd += 32;
-  }
-}
-
-inline int ceildiv(int a, int b) { return (a + b - 1) / b; }
-
-const int THREAD_M = 16;
-
-void code1x16_matvec_cuda(const void* __restrict__ A,
-                          const void* __restrict__ B, void* __restrict__ C,
-                          const void* __restrict__ codebook, int prob_m,
-                          int prob_k, const int4 codebook_a_sizes,
-                          const int codebook_stride) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code1x16MatVec<<<blocks, threads, 16 * 32 * 9, stream>>>(
-      (const int4*)A, (const int4*)B, (int4*)C, (const int4*)codebook, prob_m,
-      prob_k, codebook_a_sizes, codebook_stride);
-}
-
-void code2x8_matvec_cuda(const void* __restrict__ A, const void* __restrict__ B,
-                         void* __restrict__ C,
-                         const void* __restrict__ codebook, int prob_m,
-                         int prob_k, const int4 codebook_a_sizes,
-                         const int codebook_stride) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  int shared = 16 * (2 * 256 * 8 + 32 * 9);
-  cudaFuncSetAttribute(Code2x8MatVec,
-                       cudaFuncAttributeMaxDynamicSharedMemorySize, shared);
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code2x8MatVec<<<blocks, threads, shared, stream>>>(
-      (const int4*)A, (const int4*)B, (int4*)C, (const int4*)codebook, prob_m,
-      prob_k, codebook_a_sizes, codebook_stride);
-}
-
-void code1x16_dequant_cuda(
-    const void* __restrict__ A, void* __restrict__ C,
-    const void* __restrict__ codebook, int prob_m, int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code1x16Dequant<<<blocks, threads, 0, stream>>>(
-      (const int4*)A, (int4*)C, (const int4*)codebook, prob_m, prob_k,
-      codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                         // most 3 long.
-      codebook_stride    // as int4.
-  );
-}
-
-// Dequantizes the code and codebook into weights.
-void code2x8_dequant_cuda(
-    const void* __restrict__ A, void* __restrict__ C,
-    const void* __restrict__ codebook, int prob_m, int prob_k,
-    const int4
-        codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                           // most 3 long, corresponds to cols.
-    const int codebook_stride  // as int4
-) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  int shared = 16 * (2 * 256 * 8 + 32 * 9);
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-
-  cudaFuncSetAttribute(Code2x8Dequant,
-                       cudaFuncAttributeMaxDynamicSharedMemorySize, shared);
-  Code2x8Dequant<<<blocks, threads, shared, stream>>>(
-      (const int4*)A, (int4*)C, (const int4*)codebook, prob_m, prob_k,
-      codebook_a_sizes, codebook_stride);
-}
-
-int codebook_stride(const torch::Tensor& codebooks) {
-  return codebooks.stride(0) * codebooks.element_size() / sizeof(int4);
-}
-
-void code1x16_matvec(
-    const torch::Tensor& A, const torch::Tensor& B, torch::Tensor& C,
-    const torch::Tensor& codebook,
-    const int4 codebook_a_sizes  // cumulative sizes of A spanning each
-                                 // codebook, at most 3 long.
-) {
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(A));
-  int prob_m = C.size(0);
-  int prob_k = B.size(0);
-
-  code1x16_matvec_cuda(A.data_ptr(), B.data_ptr(), C.data_ptr(),
-                       codebook.data_ptr(), prob_m, prob_k, codebook_a_sizes,
-                       codebook_stride(codebook));
-}
-
-torch::Tensor code1x16_matmat(const torch::Tensor& input,
-                              const torch::Tensor& codes,
-                              const torch::Tensor& codebooks,
-                              const torch::Tensor& scales,
-                              const int4 codebook_a_sizes,
-                              const std::optional<torch::Tensor>& bias) {
-  auto input_sizes = input.sizes();
-  auto out_features = codes.size(0) * codebooks.size(2);
-  auto flat_input = input.reshape({-1, input.size(-1)});
-  auto flat_output = torch::empty(
-      {flat_input.size(0), out_features},
-      torch::TensorOptions().dtype(input.dtype()).device(input.device()));
-
-  for (int i = 0; i < flat_input.size(0); ++i) {
-    auto input_vec = flat_input.index({i});
-    auto output_vec = flat_output.index({i});
-    code1x16_matvec(codes.squeeze(2), input_vec, output_vec, codebooks,
-                    codebook_a_sizes);
-  }
-  flat_output *= scales.flatten().unsqueeze(0);
-
-  if (bias.has_value()) {
-    flat_output += bias->unsqueeze(0);
-  }
-
-  auto output_sizes = input_sizes.vec();
-  output_sizes.pop_back();
-  output_sizes.push_back(-1);
-  auto output = flat_output.reshape(output_sizes);
-  return output;
-}
-
-void code2x8_matvec(const torch::Tensor& A, const torch::Tensor& B,
-                    torch::Tensor& C, const torch::Tensor& codebook,
-                    const int4 codebook_a_sizes) {
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(A));
-  int prob_m = C.size(0);
-  int prob_k = B.size(0);
-  code2x8_matvec_cuda(A.data_ptr(), B.data_ptr(), C.data_ptr(),
-                      codebook.data_ptr(), prob_m, prob_k, codebook_a_sizes,
-                      2 * codebook_stride(codebook));
-}
-
-torch::Tensor code2x8_matmat(const torch::Tensor& input,
-                             const torch::Tensor& codes,
-                             const torch::Tensor& codebooks,
-                             const torch::Tensor& scales,
-                             const int4 codebook_a_sizes,
-                             const std::optional<torch::Tensor>& bias) {
-  auto input_sizes = input.sizes();
-  auto out_features = codes.size(0) * codebooks.size(2);
-  auto flat_input = input.reshape({-1, input.size(-1)});
-  auto flat_output = torch::empty(
-      {flat_input.size(0), out_features},
-      torch::TensorOptions().dtype(input.dtype()).device(input.device()));
-
-  for (int i = 0; i < flat_input.size(0); ++i) {
-    auto input_vec = flat_input.index({i});
-    auto output_vec = flat_output.index({i});
-    code2x8_matvec(codes.squeeze(2), input_vec, output_vec, codebooks,
-                   codebook_a_sizes);
-  }
-  flat_output *= scales.flatten().unsqueeze(0);
-  if (bias.has_value()) {
-    flat_output += bias->unsqueeze(0);
-  }
-
-  auto output_sizes = input_sizes.vec();
-  output_sizes.pop_back();
-  output_sizes.push_back(-1);
-  auto output = flat_output.reshape(output_sizes);
-  return output;
-}
-
-// Accumulate the partition sizes.
-int4 accumulate_sizes(const std::vector<int64_t>& codebook_partition_sizes) {
-  int4 cumulative_sizes;
-  auto cumulative_size = &cumulative_sizes.x;
-  size_t i = 0;
-  int last = 0;
-  assert(codebook_partition_sizes.size() <= 4);
-  for (; i < codebook_partition_sizes.size(); ++i, ++cumulative_size) {
-    *cumulative_size = codebook_partition_sizes[i] + last;
-    last = *cumulative_size;
-  }
-  // fill in the rest with unreachable.
-  for (; i < 4; ++i, ++cumulative_size) {
-    *cumulative_size = last * 10;
-  }
-  return cumulative_sizes;
-}
-
-}  // namespace aqlm
-}  // namespace vllm
-
-torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
-                        const torch::Tensor& codebooks,
-                        const torch::Tensor& scales,
-                        const std::vector<int64_t>& codebook_partition_sizes,
-                        const std::optional<torch::Tensor>& bias) {
-  int4 cumulative_sizes =
-      vllm::aqlm::accumulate_sizes(codebook_partition_sizes);
-
-  int const nbooks = codebooks.size(0) / codebook_partition_sizes.size();
-  int const entries = codebooks.size(1);
-
-  if (nbooks == 1 && entries == (1 << 16)) {
-    return vllm::aqlm::code1x16_matmat(input, codes, codebooks, scales,
-                                       cumulative_sizes, bias);
-  }
-  if (nbooks == 2 && entries == (1 << 8)) {
-    return vllm::aqlm::code2x8_matmat(input, codes, codebooks, scales,
-                                      cumulative_sizes, bias);
-  }
-
-  TORCH_CHECK(false, "AQLM with ", nbooks, " codebooks and ", entries,
-              " entries is not currently supported.")
-  return {};
-}
-
-torch::Tensor aqlm_dequant(
-    const torch::Tensor& codes, const torch::Tensor& codebooks,
-    const std::vector<int64_t>& codebook_partition_sizes) {
-  int4 cumulative_sizes =
-      vllm::aqlm::accumulate_sizes(codebook_partition_sizes);
-
-  int const nbooks = codebooks.size(0) / codebook_partition_sizes.size();
-  int const entries = codebooks.size(1);
-
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(codes));
-  int rows = codes.size(1);
-  int cols = codes.size(0);
-
-  auto in_features = codes.size(1) * 8;
-  auto out_features = codes.size(0);
-
-  assert(out_features == std::accumulate(codebook_partition_sizes.begin(),
-                                         codebook_partition_sizes.end(), 0));
-
-  auto weights = torch::empty({out_features, in_features},
-                              torch::TensorOptions()
-                                  .dtype(codebooks.dtype())
-                                  .device(codebooks.device()));
-
-  if (nbooks == 1 && entries == (1 << 16)) {
-    vllm::aqlm::code1x16_dequant_cuda(codes.data_ptr(), weights.data_ptr(),
-                                      codebooks.data_ptr(), out_features,
-                                      in_features, cumulative_sizes,
-                                      vllm::aqlm::codebook_stride(codebooks));
-
-    // if you wanted to flip to scaling the weights, (though it's 30%-ish slower
-    // and not consistent with gemv implementation.) weights *=
-    // scales.index({"...", 0, 0});
-
-    return weights;
-  }
-
-  if (nbooks == 2 && entries == (1 << 8)) {
-    vllm::aqlm::code2x8_dequant_cuda(codes.data_ptr(), weights.data_ptr(),
-                                     codebooks.data_ptr(), out_features,
-                                     in_features, cumulative_sizes,
-                                     vllm::aqlm::codebook_stride(codebooks));
-
-    // if you wanted to flip to scaling the weights, (though it's 30%-ish slower
-    // and not consistent with gemv implementation) weights *=
-    // scales.index({"...", 0, 0});
-
-    return weights;
-  }
-
-  TORCH_CHECK(false, "AQLM with ", nbooks, " codebooks and ", entries,
-              " entries is not currently supported.")
-  return {};
-}

csrc/quantization/cutlass_w8a8/moe/moe_data.cu

@@ -161,6 +161,7 @@ void get_cutlass_moe_mm_data_caller(
       topk_ids.size(1));
 }
 
+template <bool SWAP_AB>
 __global__ void compute_pplx_data(int32_t* expert_offsets,
                                   int32_t* problem_sizes1,
                                   int32_t* problem_sizes2,
@@ -168,14 +169,23 @@ __global__ void compute_pplx_data(int32_t* expert_offsets,
                                   const int padded_m, const int n,
                                   const int k) {
   int expert_idx = threadIdx.x;
-
   expert_offsets[expert_idx] = expert_idx * padded_m;
-  problem_sizes1[expert_idx * 3] = expert_num_tokens[expert_idx];
-  problem_sizes1[expert_idx * 3 + 1] = 2 * n;
-  problem_sizes1[expert_idx * 3 + 2] = k;
-  problem_sizes2[expert_idx * 3] = expert_num_tokens[expert_idx];
-  problem_sizes2[expert_idx * 3 + 1] = k;
-  problem_sizes2[expert_idx * 3 + 2] = n;
+
+  if constexpr (!SWAP_AB) {
+    problem_sizes1[expert_idx * 3] = expert_num_tokens[expert_idx];
+    problem_sizes1[expert_idx * 3 + 1] = 2 * n;
+    problem_sizes1[expert_idx * 3 + 2] = k;
+    problem_sizes2[expert_idx * 3] = expert_num_tokens[expert_idx];
+    problem_sizes2[expert_idx * 3 + 1] = k;
+    problem_sizes2[expert_idx * 3 + 2] = n;
+  } else {
+    problem_sizes1[expert_idx * 3] = 2 * n;
+    problem_sizes1[expert_idx * 3 + 1] = expert_num_tokens[expert_idx];
+    problem_sizes1[expert_idx * 3 + 2] = k;
+    problem_sizes2[expert_idx * 3] = k;
+    problem_sizes2[expert_idx * 3 + 1] = expert_num_tokens[expert_idx];
+    problem_sizes2[expert_idx * 3 + 2] = n;
+  }
 }
 
 void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
@@ -187,10 +197,19 @@ void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
                                          const int64_t n, const int64_t k) {
   auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
 
-  compute_pplx_data<<<1, num_local_experts, 0, stream>>>(
-      static_cast<int32_t*>(expert_offsets.data_ptr()),
-      static_cast<int32_t*>(problem_sizes1.data_ptr()),
-      static_cast<int32_t*>(problem_sizes2.data_ptr()),
-      static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
-      k);
+  if (num_local_experts * padded_m > SWAP_AB_THRESHOLD) {
+    compute_pplx_data<false><<<1, num_local_experts, 0, stream>>>(
+        static_cast<int32_t*>(expert_offsets.data_ptr()),
+        static_cast<int32_t*>(problem_sizes1.data_ptr()),
+        static_cast<int32_t*>(problem_sizes2.data_ptr()),
+        static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
+        k);
+  } else {
+    compute_pplx_data<true><<<1, num_local_experts, 0, stream>>>(
+        static_cast<int32_t*>(expert_offsets.data_ptr()),
+        static_cast<int32_t*>(problem_sizes1.data_ptr()),
+        static_cast<int32_t*>(problem_sizes2.data_ptr()),
+        static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
+        k);
+  }
 }

csrc/quantization/gptq_marlin/dequant.h

@@ -470,11 +470,12 @@ __device__ inline void dequant<nv_bfloat162, vllm::kFE2M1f.id(), false>(
   frag_b[0] = __hmul2(frag_b[0], bias_reg);
 }
 
-template <typename scalar_t2>
+template <typename scalar_t2, vllm::ScalarTypeId s_type_id>
 __device__ inline void dequant_fp8_scales(int q, scalar_t2* frag_b);
 
 template <>
-__device__ inline void dequant_fp8_scales<half2>(int q, half2* frag_b) {
+__device__ inline void dequant_fp8_scales<half2, vllm::kFE4M3fn.id()>(
+    int q, half2* frag_b) {
   int Out1 = (q & 0xFF00FF00) >> 1;
   ;
   q <<= 8;
@@ -486,8 +487,8 @@ __device__ inline void dequant_fp8_scales<half2>(int q, half2* frag_b) {
 };
 
 template <>
-__device__ inline void dequant_fp8_scales<nv_bfloat162>(int q,
-                                                        nv_bfloat162* frag_b) {
+__device__ inline void dequant_fp8_scales<nv_bfloat162, vllm::kFE4M3fn.id()>(
+    int q, nv_bfloat162* frag_b) {
   constexpr int FP8_EXPONENT = 4, BF16_EXPONENT = 8;
   constexpr int RIGHT_SHIFT = BF16_EXPONENT - FP8_EXPONENT;
   constexpr int MASK = 0x7F007F00;
@@ -502,6 +503,20 @@ __device__ inline void dequant_fp8_scales<nv_bfloat162>(int q,
   frag_b[0] = *reinterpret_cast<const nv_bfloat162*>(&Out2);
 }
 
+template <>
+__device__ inline void dequant_fp8_scales<nv_bfloat162, vllm::kFE8M0fnu.id()>(
+    int q, nv_bfloat162* frag_b) {
+  // In this conversion, 2 ** -127 in FP8E8M0 would become 0 in BF16,
+  // but we assume that such a extreme value would not occur in real models.
+  int Out1 = (q & 0xFF00FF00) >> 1;
+  q <<= 7;
+  int Out2 = q & 0x7F807F80;
+
+  // Note: reverse indexing is intentional because weights are permuted
+  frag_b[1] = *reinterpret_cast<const nv_bfloat162*>(&Out1);
+  frag_b[0] = *reinterpret_cast<const nv_bfloat162*>(&Out2);
+}
+
 #endif
 
 }  // namespace MARLIN_NAMESPACE_NAME

csrc/quantization/gptq_marlin/generate_kernels.py

@@ -20,6 +20,7 @@ namespace MARLIN_NAMESPACE_NAME {
 TEMPLATE = ("template __global__ void Marlin<"
             "{{scalar_t}}, "
             "{{w_type_id}}, "
+            "{{s_type_id}}, "
             "{{threads}}, "
             "{{thread_m_blocks}}, "
             "{{thread_n_blocks}}, "
@@ -78,7 +79,8 @@ def generate_new_kernels():
             if scalar_type == "vllm::kFE4M3fn" and group_blocks not in [-1, 8]:
                 continue
             # nvfp4 only supports group_size == 16
-            if scalar_type == "vllm::kFE2M1f" and group_blocks != 1:
+            # mxfp4 only supports group_size == 32
+            if scalar_type == "vllm::kFE2M1f" and group_blocks not in [1, 2]:
                 continue
             # other quantization methods don't support group_size = 16
             if scalar_type != "vllm::kFE2M1f" and group_blocks == 1:
@@ -97,10 +99,23 @@ def generate_new_kernels():
                 # 4bit quantization and fp16
                 is_zp_float_list.append(True)
 
+            if scalar_type == "vllm::kFE2M1f" and group_blocks == 1:
+                s_type = "vllm::kFE4M3fn"
+            elif scalar_type == "vllm::kFE2M1f" and group_blocks == 2:
+                s_type = "vllm::kFE8M0fnu"
+                if dtype == "fp16":
+                    # we cannot safely dequantize e8m0 to fp16, so skip this
+                    continue
+            elif dtype == "fp16":
+                s_type = "vllm::kFloat16"
+            elif dtype == "bf16":
+                s_type = "vllm::kBFloat16"
+
             for is_zp_float in is_zp_float_list:
                 template_str = jinja2.Template(TEMPLATE).render(
                     scalar_t=c_dtype,
                     w_type_id=scalar_type + ".id()",
+                    s_type_id=s_type + ".id()",
                     threads=threads,
                     thread_m_blocks=max(m_blocks, 1),
                     thread_n_blocks=n_blocks,

csrc/quantization/gptq_marlin/gptq_marlin.cu

@@ -48,7 +48,8 @@ __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
 
 torch::Tensor gptq_marlin_gemm(
     torch::Tensor& a, std::optional<torch::Tensor> c_or_none,
-    torch::Tensor& b_q_weight, torch::Tensor& b_scales,
+    torch::Tensor& b_q_weight,
+    std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
     std::optional<torch::Tensor> const& b_zeros_or_none,
     std::optional<torch::Tensor> const& g_idx_or_none,
     std::optional<torch::Tensor> const& perm_or_none, torch::Tensor& workspace,
@@ -187,7 +188,12 @@ int get_kernel_cache_size(thread_config_t const& th_config, int thread_m_blocks,
   int tb_m = thread_m_blocks * 16;
   int sh_a_size = pipe_stages * (tb_m * tb_k) * 2;
   int sh_b_size = pipe_stages * (tb_k * tb_n / pack_factor) * 4;
-  int sh_red_size = tb_m * (tb_n + 8);
+  int sh_red_size = tb_m * (tb_n + 8) * 2;
+  int sh_bias_size = tb_n * 2;
+  int tmp_size =
+      (sh_b_size > sh_red_size ? sh_red_size : sh_b_size) + sh_bias_size;
+  tmp_size = max(max(sh_b_size, sh_red_size), tmp_size);
+
   int sh_s_size =
       get_scales_cache_size(th_config, prob_m, prob_n, prob_k, num_bits,
                             group_size, has_act_order, is_k_full);
@@ -202,8 +208,8 @@ int get_kernel_cache_size(thread_config_t const& th_config, int thread_m_blocks,
       sh_zp_size = sh_s_size / 2;
   }
 
-  int total_size = max(sh_b_size, sh_red_size) + sh_a_size + sh_s_size +
-                   sh_zp_size + sh_g_idx_size;
+  int total_size =
+      tmp_size + sh_a_size + sh_s_size + sh_zp_size + sh_g_idx_size;
 
   return total_size;
 }
@@ -237,20 +243,25 @@ bool is_valid_config(thread_config_t const& th_config, int thread_m_blocks,
   int cache_size = get_kernel_cache_size(
       th_config, thread_m_blocks, prob_m, prob_n, prob_k, num_bits, group_size,
       has_act_order, is_k_full, has_zp, is_zp_float);
-  return cache_size <= max_shared_mem;
+  return cache_size + 512 <= max_shared_mem;
 }
 
-  #define _GET_IF(W_TYPE, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, \
-                  M_BLOCK_SIZE_8, GROUP_BLOCKS, NUM_THREADS, IS_ZP_FLOAT)    \
-    else if (q_type == W_TYPE && thread_m_blocks == THREAD_M_BLOCKS &&       \
-             thread_n_blocks == THREAD_N_BLOCKS &&                           \
-             thread_k_blocks == THREAD_K_BLOCKS &&                           \
-             m_block_size_8 == M_BLOCK_SIZE_8 &&                             \
-             group_blocks == GROUP_BLOCKS && num_threads == NUM_THREADS &&   \
-             is_zp_float == IS_ZP_FLOAT) {                                   \
-      kernel = Marlin<scalar_t, W_TYPE.id(), NUM_THREADS, THREAD_M_BLOCKS,   \
-                      THREAD_N_BLOCKS, THREAD_K_BLOCKS, M_BLOCK_SIZE_8,      \
-                      pipe_stages, GROUP_BLOCKS, IS_ZP_FLOAT>;               \
+  #define _GET_IF(W_TYPE, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,   \
+                  M_BLOCK_SIZE_8, GROUP_BLOCKS, NUM_THREADS, IS_ZP_FLOAT)      \
+    else if (q_type == W_TYPE && thread_m_blocks == THREAD_M_BLOCKS &&         \
+             thread_n_blocks == THREAD_N_BLOCKS &&                             \
+             thread_k_blocks == THREAD_K_BLOCKS &&                             \
+             m_block_size_8 == M_BLOCK_SIZE_8 &&                               \
+             group_blocks == GROUP_BLOCKS && num_threads == NUM_THREADS &&     \
+             is_zp_float == IS_ZP_FLOAT) {                                     \
+      constexpr auto S_TYPE =                                                  \
+          W_TYPE == vllm::kFE2M1f                                              \
+              ? (GROUP_BLOCKS == 1 ? vllm::kFE4M3fn : vllm::kFE8M0fnu)         \
+              : (std::is_same<scalar_t, half>::value ? vllm::kFloat16          \
+                                                     : vllm::kBFloat16);       \
+      kernel = Marlin<scalar_t, W_TYPE.id(), S_TYPE.id(), NUM_THREADS,         \
+                      THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,       \
+                      M_BLOCK_SIZE_8, pipe_stages, GROUP_BLOCKS, IS_ZP_FLOAT>; \
     }
 
   // COMMON: cases for (group_blocks in [-1, 2, 4, 8] and is_zp_float == false)
@@ -315,22 +326,39 @@ bool is_valid_config(thread_config_t const& th_config, int thread_m_blocks,
     BIGGROUP_GET_IF_M234(W_TYPE, 8, 4, 128)  \
     BIGGROUP_GET_IF_M234(W_TYPE, 4, 8, 128)
 
-  #define FP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)        \
+  #define NVFP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)      \
     _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 1, NUM_THREADS, false) \
     _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
 
-  #define FP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)       \
+  #define NVFP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)     \
     _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
     _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false) \
     _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 1, NUM_THREADS, false)
 
-  #define FP4_GET_IF(W_TYPE)            \
-    FP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
-    FP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
-    FP4_GET_IF_M1(W_TYPE, 4, 8, 128)    \
-    FP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
-    FP4_GET_IF_M234(W_TYPE, 8, 4, 128)  \
-    FP4_GET_IF_M234(W_TYPE, 4, 8, 128)
+  #define NVFP4_GET_IF(W_TYPE)            \
+    NVFP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
+    NVFP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
+    NVFP4_GET_IF_M1(W_TYPE, 4, 8, 128)    \
+    NVFP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
+    NVFP4_GET_IF_M234(W_TYPE, 8, 4, 128)  \
+    NVFP4_GET_IF_M234(W_TYPE, 4, 8, 128)
+
+  #define MXFP4_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)      \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, true, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 1, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false)
+
+  #define MXFP4_GET_IF_M234(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)     \
+    _GET_IF(W_TYPE, 2, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 3, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false) \
+    _GET_IF(W_TYPE, 4, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS, false)
+
+  #define MXFP4_GET_IF(W_TYPE)            \
+    MXFP4_GET_IF_M1(W_TYPE, 8, 8, 256)    \
+    MXFP4_GET_IF_M1(W_TYPE, 8, 4, 128)    \
+    MXFP4_GET_IF_M1(W_TYPE, 4, 8, 128)    \
+    MXFP4_GET_IF_M234(W_TYPE, 16, 4, 256) \
+    MXFP4_GET_IF_M234(W_TYPE, 8, 4, 128)  \
+    MXFP4_GET_IF_M234(W_TYPE, 4, 8, 128)
 
   // We currently have 4-bit models only with group_blocks == 4
   #define FZP_GET_IF_M1(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)       \
@@ -384,7 +412,7 @@ MarlinFuncPtr get_marlin_kernel(const vllm::ScalarType q_type,
   COMMON_GET_IF(vllm::kU4B8)
   COMMON_GET_IF(vllm::kU8B128)
 
-  FP4_GET_IF(vllm::kFE2M1f)
+  NVFP4_GET_IF(vllm::kFE2M1f)
 
   BIGGROUP_GET_IF(vllm::kFE4M3fn)
 
@@ -396,6 +424,11 @@ MarlinFuncPtr get_marlin_kernel(const vllm::ScalarType q_type,
     }
     FZP_GET_IF(vllm::kU4)
   }
+  if (std::is_same<scalar_t, nv_bfloat16>::value) {
+    if (false) {
+    }
+    MXFP4_GET_IF(vllm::kFE2M1f)
+  }
 
   return kernel;
 }
@@ -453,12 +486,12 @@ exec_config_t determine_exec_config(const vllm::ScalarType& q_type, int prob_m,
 }
 
 template <typename scalar_t>
-void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
-               void* s2, void* zp, void* g_idx, void* perm, void* a_tmp,
-               int prob_m, int prob_n, int prob_k, int lda, void* workspace,
-               vllm::ScalarType const& q_type, 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_init,
+void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
+               void* s, void* s2, void* zp, void* g_idx, void* perm,
+               void* a_tmp, int prob_m, int prob_n, int prob_k, int lda,
+               void* workspace, vllm::ScalarType const& q_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_init,
                int thread_n_init, int sms, bool use_atomic_add,
                bool use_fp32_reduce, bool is_zp_float) {
   if (has_zp) {
@@ -503,6 +536,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
   const int4* B_ptr = (const int4*)B;
   int4* C_ptr = (int4*)C;
   int4* C_tmp_ptr = (int4*)C_tmp;
+  const int4* bias_ptr = (const int4*)b_bias;
   const int4* s_ptr = (const int4*)s;
   const uint16_t* s2_ptr = (const uint16_t*)s2;
   const int4* zp_ptr = (const int4*)zp;
@@ -623,8 +657,9 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
     // avoid ">>>" being formatted to "> > >"
     // clang-format off
     kernel<<<blocks, num_threads, max_shared_mem_new, stream>>>(
-        A_ptr, B_ptr, C_ptr, C_tmp_ptr, s_ptr, s2_ptr, zp_ptr, g_idx_ptr, num_groups,
-        prob_m_split, prob_n, prob_k, lda, locks, part_use_atomic_add,
+        A_ptr, B_ptr, C_ptr, C_tmp_ptr, bias_ptr, s_ptr, s2_ptr, zp_ptr,
+        g_idx_ptr, num_groups,
+        prob_m_split, prob_n, prob_k, lda, locks, has_bias, part_use_atomic_add,
         use_fp32_reduce, max_shared_mem_new);
     // clang-format on
 
@@ -638,7 +673,8 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
 
 torch::Tensor gptq_marlin_gemm(
     torch::Tensor& a, std::optional<torch::Tensor> c_or_none,
-    torch::Tensor& b_q_weight, torch::Tensor& b_scales,
+    torch::Tensor& b_q_weight,
+    std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
     std::optional<torch::Tensor> const& global_scale_or_none,
     std::optional<torch::Tensor> const& b_zeros_or_none,
     std::optional<torch::Tensor> const& g_idx_or_none,
@@ -785,12 +821,24 @@ torch::Tensor gptq_marlin_gemm(
   torch::Tensor global_scale;
   if (global_scale_or_none.has_value()) {
     global_scale = global_scale_or_none.value();
-    TORCH_CHECK(b_q_type == vllm::kFE2M1f,
-                "global_scale can only be used for float4_e2m1f.");
+    TORCH_CHECK(b_q_type == vllm::kFE2M1f && group_size == 16,
+                "global_scale can only be used for nvfp4 format.");
   } else {
     global_scale = torch::empty({0}, options);
-    TORCH_CHECK(!(b_q_type == vllm::kFE2M1f),
-                "the global_scale parameter must be passed for float4_e2m1f.");
+    TORCH_CHECK(!(b_q_type == vllm::kFE2M1f && group_size == 16),
+                "the global_scale parameter must be passed for nvfp4 format.");
+  }
+
+  bool has_bias = b_bias_or_none.has_value();
+  torch::Tensor b_bias;
+  if (has_bias) {
+    b_bias = b_bias_or_none.value();
+    TORCH_CHECK(b_bias.device().is_cuda(), "b_bias is not on GPU");
+    TORCH_CHECK(b_bias.is_contiguous(), "b_bias is not contiguous");
+    TORCH_CHECK(b_bias.size(0) == size_n, "b_bias.size(0) != size_n");
+    TORCH_CHECK(b_bias.stride(0) == 1, "b_bias.stride(0) != 1");
+  } else {
+    b_bias = torch::empty({0}, options);
   }
 
   torch::Tensor b_zeros;
@@ -857,34 +905,50 @@ torch::Tensor gptq_marlin_gemm(
   if (a.scalar_type() == at::ScalarType::Half) {
     void* scales_ptr;
     if (b_q_type == vllm::kFE2M1f) {
-      scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      if (group_size == 16)
+        scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      else if (group_size == 32)
+        scales_ptr = b_scales.data_ptr<at::Float8_e8m0fnu>();
+      else
+        TORCH_CHECK(false,
+                    "float4_e2m1f only supports group_size == 16 (NVFP4) ",
+                    "and group_size == 32 (MXFP4)");
     } else {
       scales_ptr = b_scales.data_ptr<at::Half>();
     }
 
     marlin::marlin_mm<half>(
         a.data_ptr<at::Half>(), b_q_weight.data_ptr(), c.data_ptr<at::Half>(),
-        c_tmp.data_ptr<float>(), scales_ptr, global_scale.data_ptr<at::Half>(),
-        b_zeros.data_ptr(), g_idx.data_ptr(), perm.data_ptr(),
-        a_tmp.data_ptr<at::Half>(), size_m, size_n, size_k, a.stride(0),
-        workspace.data_ptr(), b_q_type, has_act_order, is_k_full, has_zp,
-        num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
-        thread_k, thread_n, sms, use_atomic_add, use_fp32_reduce, is_zp_float);
+        c_tmp.data_ptr<float>(), b_bias.data_ptr<at::Half>(), scales_ptr,
+        global_scale.data_ptr<at::Half>(), b_zeros.data_ptr(), g_idx.data_ptr(),
+        perm.data_ptr(), a_tmp.data_ptr<at::Half>(), size_m, size_n, size_k,
+        a.stride(0), workspace.data_ptr(), b_q_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,
+        use_atomic_add, use_fp32_reduce, is_zp_float);
   } else if (a.scalar_type() == at::ScalarType::BFloat16) {
     void* scales_ptr;
     if (b_q_type == vllm::kFE2M1f) {
-      scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      if (group_size == 16)
+        scales_ptr = b_scales.data_ptr<at::Float8_e4m3fn>();
+      else if (group_size == 32)
+        scales_ptr = b_scales.data_ptr<at::Float8_e8m0fnu>();
+      else
+        TORCH_CHECK(false,
+                    "float4_e2m1f only supports group_size == 16 (NVFP4) ",
+                    "and group_size == 32 (MXFP4)");
     } else {
       scales_ptr = b_scales.data_ptr<at::BFloat16>();
     }
 
     marlin::marlin_mm<nv_bfloat16>(
         a.data_ptr<at::BFloat16>(), b_q_weight.data_ptr(),
-        c.data_ptr<at::BFloat16>(), c_tmp.data_ptr<float>(), scales_ptr,
+        c.data_ptr<at::BFloat16>(), c_tmp.data_ptr<float>(),
+        b_bias.data_ptr<at::BFloat16>(), scales_ptr,
         global_scale.data_ptr<at::BFloat16>(), b_zeros.data_ptr(),
         g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(),
         size_m, size_n, size_k, a.stride(0), workspace.data_ptr(), b_q_type,
-        has_act_order, is_k_full, has_zp, num_groups, group_size, dev,
+        has_bias, has_act_order, is_k_full, has_zp, num_groups, group_size, dev,
         at::cuda::getCurrentCUDAStream(dev), thread_k, thread_n, sms,
         use_atomic_add, use_fp32_reduce, is_zp_float);
   } else {

csrc/quantization/gptq_marlin/kernel.h

@@ -10,15 +10,18 @@
 #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 int4 *__restrict__ scales_ptr,                                     \
       const uint16_t *__restrict__ scale2_ptr,                                 \
       const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx,          \
       int num_groups, int prob_m, int prob_n, int prob_k, int lda, int *locks, \
-      bool use_atomic_add, bool use_fp32_reduce, int max_shared_mem
+      bool has_bias, bool use_atomic_add, bool use_fp32_reduce,                \
+      int max_shared_mem
 
 namespace MARLIN_NAMESPACE_NAME {
 template <typename scalar_t,  // compute dtype, half or nv_float16
           const vllm::ScalarTypeId w_type_id,  // weight ScalarType id
+          const vllm::ScalarTypeId s_type_id,  // weight ScalarType id
           const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the

csrc/quantization/gptq_marlin/marlin_template.h

@@ -39,6 +39,7 @@ namespace MARLIN_NAMESPACE_NAME {
 
 template <typename scalar_t,  // compute dtype, half or nv_float16
           const vllm::ScalarTypeId w_type_id,  // weight ScalarType id
+          const vllm::ScalarTypeId s_type_id,  // weight scale ScalarType id
           const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the
@@ -271,6 +272,7 @@ __device__ inline void wait_negative_and_add(int* lock) {
 
 template <typename scalar_t,  // compute dtype, half or nv_float16
           const vllm::ScalarTypeId w_type_id,  // weight ScalarType id
+          const vllm::ScalarTypeId s_type_id,  // weight scale ScalarType id
           const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the
@@ -290,6 +292,7 @@ __global__ void Marlin(
     const int4* __restrict__ B,  // 4bit quantized weight matrix of shape kxn
     int4* __restrict__ C,        // fp16 output buffer of shape mxn
     int4* __restrict__ C_tmp,    // fp32 tmp output buffer (for reduce)
+    const int4* __restrict__ b_bias_ptr,
     const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                           // (k/groupsize)xn
     const uint16_t* __restrict__ scale2_ptr,  // fp16 global scale (for nvfp4
@@ -297,12 +300,13 @@ __global__ void Marlin(
     const int4* __restrict__ zp_ptr,  // 4bit packed zero-points of shape
                                       // (k/groupsize)x(n/pack_factor)
     const int* __restrict__ g_idx,    // int32 group indices of shape k
-    int num_groups,        // number of scale groups per output channel
-    int prob_m,            // batch dimension m
-    int prob_n,            // output dimension n
-    int prob_k,            // reduction dimension k
-    int lda,               // A.stride(0), equal to prob_k is A is contiguous
-    int* locks,            // extra global storage for barrier synchronization
+    int num_groups,  // number of scale groups per output channel
+    int prob_m,      // batch dimension m
+    int prob_n,      // output dimension n
+    int prob_k,      // reduction dimension k
+    int lda,         // A.stride(0), equal to prob_k is A is contiguous
+    int* locks,      // extra global storage for barrier synchronization
+    bool has_bias,
     bool use_atomic_add,   // whether to use atomic add to reduce
     bool use_fp32_reduce,  // whether to use fp32 global reduce
     int max_shared_mem) {
@@ -326,18 +330,29 @@ __global__ void Marlin(
   using FragZP = typename ScalarType<scalar_t>::FragZP;
 
   static constexpr auto w_type = vllm::ScalarType::from_id(w_type_id);
+  static constexpr auto s_type = vllm::ScalarType::from_id(s_type_id);
+  if constexpr (w_type == vllm::kFE2M1f) {
+    static_assert(s_type == vllm::kFE4M3fn && group_blocks == 1 ||
+                  s_type == vllm::kFE8M0fnu && group_blocks == 2);
+  } else if constexpr (std::is_same<scalar_t, nv_bfloat16>::value) {
+    static_assert(s_type == vllm::kBFloat16);
+  } else if constexpr (std::is_same<scalar_t, half>::value) {
+    static_assert(s_type == vllm::kFloat16);
+  }
+
   constexpr bool has_zp = w_type == vllm::kU4 || w_type == vllm::kU8;
   constexpr bool is_int_type = w_type == vllm::kU4 || w_type == vllm::kU8 ||
                                w_type == vllm::kU4B8 || w_type == vllm::kU8B128;
   // see comments of dequant.h for more details
   constexpr bool dequant_skip_flop =
-      !is_int_type ||
+      w_type == vllm::kFE4M3fn ||
+      w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn ||
       has_zp && !is_zp_float && !std::is_same<scalar_t, nv_bfloat16>::value ||
       has_zp && !is_zp_float && !(w_type == vllm::kU8);
 
   scalar_t2 global_scale;
-
-  if constexpr (w_type == vllm::kFE2M1f) {
+  if constexpr (w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
+    // NVFP4 format requires global scale
     uint16_t val = scale2_ptr[0];
     global_scale = Dtype::num2num2(*reinterpret_cast<scalar_t*>(&val));
   }
@@ -589,7 +604,7 @@ __global__ void Marlin(
 
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) / 4;
-    s_sh_rd = s_sh_rd * 2 + warp_row % 2;
+    s_sh_rd = s_sh_rd * 2 + (warp_row / group_blocks) % 2;
 
   } else if constexpr (group_blocks != -1)
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
@@ -602,6 +617,18 @@ __global__ void Marlin(
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) % 4;
 
+  int bias_sh_rd;
+  if constexpr (m_block_size_8) {
+    bias_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+                 (threadIdx.x % 32) / 8;
+  } else {
+    bias_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+                 (threadIdx.x % 32) % 4;
+  }
+
+  int bias_sh_wr = threadIdx.x;
+  int bias_gl_rd = (thread_n_blocks * 16 / 8) * slice_col + threadIdx.x;
+
   // Zero-points have the same read layout as the scales
   // (without column-wise case)
   constexpr int num_col_threads = 8;
@@ -670,7 +697,19 @@ __global__ void Marlin(
   constexpr int sh_b_size = stages * b_sh_stage;
   int4* sh_b = sh;
   int4* sh_red = sh;
-  int4* sh_g_idx = sh_b + (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
+
+  constexpr int sh_size_b_red_min =
+      (sh_red_size < sh_b_size ? sh_red_size : sh_b_size);
+  constexpr int sh_size_b_red_max =
+      (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
+  constexpr int sh_bias_size = (thread_n_blocks * 16 / 8);
+  constexpr int sh_b_red_bias_size =
+      sh_size_b_red_max > (sh_size_b_red_min + sh_bias_size)
+          ? sh_size_b_red_max
+          : (sh_size_b_red_min + sh_bias_size);
+
+  int4* sh_bias = sh + sh_size_b_red_min;
+  int4* sh_g_idx = sh + sh_b_red_bias_size;
   int4* sh_zp = sh_g_idx + (stages * g_idx_stage);
   constexpr int sh_s_size = has_act_order ? (act_s_max_num_groups * s_sh_stride)
                                           : (stages * s_sh_stage);
@@ -680,15 +719,13 @@ __global__ void Marlin(
   static_assert(thread_m_blocks * 16 * thread_n_blocks * 16 / 8 <=
                 stages * b_sh_stage);
   int4* sh_a = sh_s + sh_s_size;
-  // constexpr int shm_size_used =
-  //     stages * (g_idx_stage + zp_sh_stage) + sh_s_size +
-  //     (sh_red_size > sh_b_size ? sh_red_size : sh_b_size);
 
   // Register storage for double buffer of shared memory reads.
   FragA frag_a[2][thread_m_blocks];
   I4 frag_b_quant[2][b_thread_vecs];
   FragC frag_c[thread_m_blocks][4][2];
-  FragS frag_s[2][4];                    // No act-order
+  FragS frag_s[2][4];  // No act-order
+  FragS frag_bias[2][4];
   FragS act_frag_s[2][4][4];             // For act-order
   int frag_qzp[2][num_ints_per_thread];  // Zero-points
   FragZP frag_zp;                        // Zero-points in fp16
@@ -923,10 +960,15 @@ __global__ void Marlin(
           if constexpr (w_type_id != vllm::kFE2M1f.id()) {
             reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
                 sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
-          } else {
+          } else if constexpr (group_blocks == 1 || thread_k_blocks > 4) {
             reinterpret_cast<int2*>(&frag_s[k % 2])[0] =
                 reinterpret_cast<int2*>(
                     sh_s_stage)[s_sh_rd + cur_group_id * (2 * s_sh_stride)];
+          } else {
+            reinterpret_cast<int2*>(&frag_s[k % 2])[0] =
+                reinterpret_cast<int2*>(
+                    sh_s_stage)[s_sh_rd + cur_group_id * (2 * s_sh_stride) +
+                                k % 2];
           }
         }
       }
@@ -1139,9 +1181,9 @@ __global__ void Marlin(
       int s_quant_0 = reinterpret_cast<int*>(frag_s[k2])[0];
       int s_quant_1 = reinterpret_cast<int*>(frag_s[k2])[1];
 
-      dequant_fp8_scales<scalar_t2>(s_quant_0,
-                                    reinterpret_cast<scalar_t2*>(&frag_s[k2]));
-      dequant_fp8_scales<scalar_t2>(
+      dequant_fp8_scales<scalar_t2, s_type_id>(
+          s_quant_0, reinterpret_cast<scalar_t2*>(&frag_s[k2]));
+      dequant_fp8_scales<scalar_t2, s_type_id>(
           s_quant_1, reinterpret_cast<scalar_t2*>(&frag_s[k2]) + 2);
     }
 
@@ -1411,7 +1453,7 @@ __global__ void Marlin(
   // Write out the reduce final result in the correct layout. We only actually
   // reshuffle matrix fragments in this step, the reduction above is performed
   // in fragment layout.
-  auto write_result = [&]() {
+  auto write_result = [&](bool last) {
     int c_gl_stride = prob_n / 8;
     constexpr int c_sh_stride = 2 * thread_n_blocks + 1;
     int c_gl_wr_delta = c_gl_stride * (threads / (2 * thread_n_blocks));
@@ -1438,7 +1480,7 @@ __global__ void Marlin(
     int c_gl_wr_end = c_gl_stride * prob_m;
     // We first reorder in shared memory to guarantee the most efficient final
     // global write patterns
-    auto write = [&](int idx, float c0, float c1, FragS& s) {
+    auto write = [&](int idx, float c0, float c1, FragS& s, FragS& b_bias) {
       scalar_t2 res =
           Dtype::nums2num2(Dtype::float2num(c0), Dtype::float2num(c1));
 
@@ -1447,12 +1489,25 @@ __global__ void Marlin(
       if constexpr (!has_act_order && group_blocks == -1 &&
                     w_type.size_bits() == 4 &&
                     (has_zp && dequant_skip_flop || !has_zp)) {
-        res = __hmul2(res, s[0]);
+        scalar_t2 tmp_scale = s[0];
+        if constexpr (m_block_size_8) {
+          tmp_scale = Dtype::num2num2(
+              reinterpret_cast<scalar_t*>(&s[0])[(threadIdx.x % 8) / 4]);
+        }
+        res = __hmul2(res, tmp_scale);
       }
 
-      if constexpr (w_type == vllm::kFE2M1f) {
+      if constexpr (w_type == vllm::kFE2M1f && s_type == vllm::kFE4M3fn) {
         res = __hmul2(res, global_scale);
       }
+      if (has_bias && last) {
+        scalar_t2 tmp_bias = b_bias[0];
+        if constexpr (m_block_size_8) {
+          tmp_bias = Dtype::num2num2(
+              reinterpret_cast<scalar_t*>(&b_bias[0])[(threadIdx.x % 8) / 4]);
+        }
+        res = __hadd2(res, tmp_bias);
+      }
 
       if constexpr (m_block_size_8) {
         ((scalar_t*)sh_red)[idx] = res.x;
@@ -1470,19 +1525,25 @@ __global__ void Marlin(
           if constexpr (m_block_size_8) {
             int wr = c_sh_wr + 16 * j;
             write(wr, frag_c[i][j][0][0], frag_c[i][j][0][1],
-                  frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + 8, frag_c[i][j][0][2], frag_c[i][j][0][3],
-                  frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
           } else {
             int wr = c_sh_wr + 8 * j;
             write(wr + (4 * c_sh_stride) * 0 + 0, frag_c[i][j][0][0],
-                  frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + (4 * c_sh_stride) * 8 + 0, frag_c[i][j][0][2],
-                  frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0]);
+                  frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0],
+                  frag_bias[j / 2][2 * (j % 2) + 0]);
             write(wr + (4 * c_sh_stride) * 0 + 4, frag_c[i][j][1][0],
-                  frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
             write(wr + (4 * c_sh_stride) * 8 + 4, frag_c[i][j][1][2],
-                  frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1]);
+                  frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1],
+                  frag_bias[j / 2][2 * (j % 2) + 1]);
           }
         }
         c_sh_wr += 16 * (4 * c_sh_stride);
@@ -1622,6 +1683,14 @@ __global__ void Marlin(
       }
 
       thread_block_reduce();
+
+      if (has_bias && last) {
+        __syncthreads();
+        cp_async4_pred(&sh_bias[bias_sh_wr], &b_bias_ptr[bias_gl_rd],
+                       threadIdx.x < 16 * thread_n_blocks / 8);
+        cp_async_fence();
+      }
+
       if constexpr (!has_act_order && group_blocks == -1 &&
                     (has_zp && dequant_skip_flop || !has_zp)) {
         if (w_type.size_bits() == 8 || (last || use_atomic_add)) {
@@ -1684,11 +1753,20 @@ __global__ void Marlin(
         }
         barrier_release(&locks[locks_off], last);
       }
+
+      if (has_bias && last) {
+        cp_async_wait<0>();
+        __syncthreads();
+        reinterpret_cast<int4*>(&frag_bias)[0] = sh_bias[bias_sh_rd];
+        reinterpret_cast<int4*>(&frag_bias)[1] = sh_bias[bias_sh_rd + 4];
+        __syncthreads();
+      }
+
       if (use_atomic_add && slice_count > 1 && slice_idx != 0)
         wait_negative_and_add(&locks[locks_off]);
       if (last || use_atomic_add)
         // only the last block in a slice actually writes the result
-        write_result();
+        write_result(last);
       slice_row = 0;
       slice_col_par++;
       slice_col++;
@@ -1706,6 +1784,7 @@ __global__ void Marlin(
           for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] -= b_gl_stride;
         }
 
+        bias_gl_rd = (thread_n_blocks * 16 / 8) * slice_col + threadIdx.x;
         // Update slice k/n for scales loading
         if constexpr (has_act_order) {
           slice_k_start = tb_k * slice_row;

csrc/torch_bindings.cpp

@@ -130,6 +130,12 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("fatrelu_and_mul(Tensor! out, Tensor input, float threshold) -> ()");
   ops.impl("fatrelu_and_mul", torch::kCUDA, &fatrelu_and_mul);
 
+  ops.def(
+      "swigluoai_and_mul(Tensor! out, Tensor input, float alpha=1.702, float "
+      "limit=7.0) "
+      "-> ()");
+  ops.impl("swigluoai_and_mul", torch::kCUDA, &swigluoai_and_mul);
+
   // GELU implementation used in GPT-2.
   ops.def("gelu_new(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_new", torch::kCUDA, &gelu_new);
@@ -142,25 +148,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_quick", torch::kCUDA, &gelu_quick);
 
-  // prepare_inputs advance_step
-  ops.def(
-      "advance_step_flashattn(int num_seqs, int num_queries, int block_size, "
-      "Tensor! input_tokens, Tensor sampled_token_ids, "
-      "Tensor! input_positions, Tensor! seq_lens, Tensor! slot_mapping, "
-      "Tensor block_tables) -> ()");
-  ops.impl("advance_step_flashattn", torch::kCUDA, &advance_step_flashattn);
-
-  ops.def(
-      "advance_step_flashinfer("
-      "    int num_seqs, int num_queries, int block_size,"
-      "    Tensor! input_tokens, Tensor sampled_token_ids,"
-      "    Tensor! input_positions, Tensor! seq_lens, Tensor! slot_mapping,"
-      "    Tensor block_tables, Tensor! paged_kv_indices,"
-      "    Tensor! paged_kv_indptr, Tensor! paged_kv_last_page_len,"
-      "    Tensor! block_table_bounds"
-      ") -> ()");
-  ops.impl("advance_step_flashinfer", torch::kCUDA, &advance_step_flashinfer);
-
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(
@@ -226,21 +213,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 
   // Quantization ops
 #ifndef USE_ROCM
-  // Quantized GEMM for AQLM.
-  ops.def(
-      "aqlm_gemm(Tensor input, Tensor codes, Tensor codebooks, "
-      "Tensor scales, int[] codebook_partition_sizes, Tensor? bias) "
-      "-> Tensor",
-      {stride_tag});
-  ops.impl("aqlm_gemm", torch::kCUDA, &aqlm_gemm);
-
-  // Decompression method for AQLM.
-  ops.def(
-      "aqlm_dequant(Tensor codes, Tensor codebooks, "
-      "int[] codebook_partition_sizes) -> Tensor",
-      {stride_tag});
-  ops.impl("aqlm_dequant", torch::kCUDA, &aqlm_dequant);
-
   // Quantized GEMM for AWQ.
   ops.def(
       "awq_gemm(Tensor _in_feats, Tensor _kernel, Tensor _scaling_factors, "
@@ -326,6 +298,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // gptq_marlin Optimized Quantized GEMM for GPTQ.
   ops.def(
       "gptq_marlin_gemm(Tensor a, Tensor? c_or_none, Tensor b_q_weight, "
+      "Tensor? b_bias_or_none,"
       "Tensor b_scales, Tensor? global_scale, Tensor? b_zeros_or_none, Tensor? "
       "g_idx_or_none, Tensor? perm_or_none, Tensor workspace, int b_q_type, "
       "SymInt size_m, SymInt size_n, SymInt size_k, bool is_k_full, "

docker/Dockerfile

@@ -139,21 +139,6 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 WORKDIR /workspace
 
 # install build and runtime dependencies
-
-# arm64 (GH200) build follows the practice of "use existing pytorch" build,
-# we need to install torch and torchvision from the nightly builds first,
-# pytorch will not appear as a vLLM dependency in all of the following steps
-# after this step
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system \
-            --index-url ${PYTORCH_CUDA_NIGHTLY_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-            "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319";  \
-        uv pip install --system \
-            --index-url ${PYTORCH_CUDA_NIGHTLY_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-            --pre pytorch_triton==3.3.0+gitab727c40; \
-    fi
-
 COPY requirements/common.txt requirements/common.txt
 COPY requirements/cuda.txt requirements/cuda.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
@@ -234,6 +219,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         && sccache --show-stats; \
     fi
 
+ARG vllm_target_device="cuda"
+ENV VLLM_TARGET_DEVICE=${vllm_target_device}
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
@@ -497,14 +484,11 @@ ENV HF_HUB_ENABLE_HF_TRANSFER 1
 # Copy in the v1 package for testing (it isn't distributed yet)
 COPY vllm/v1 /usr/local/lib/python${PYTHON_VERSION}/dist-packages/vllm/v1
 
-# doc requires source code
-# we hide them inside `test_docs/` , so that this source code
+# Source code is used in the `python_only_compile.sh` test
+# We hide it inside `src/` so that this source code
 # will not be imported by other tests
-RUN mkdir test_docs
-RUN mv docs test_docs/
-RUN cp -r examples test_docs/
-RUN mv vllm test_docs/
-RUN mv mkdocs.yaml test_docs/
+RUN mkdir src
+RUN mv vllm src/vllm
 #################### TEST IMAGE ####################
 
 #################### OPENAI API SERVER ####################

docs/contributing/benchmarks.md

@@ -11,7 +11,7 @@ vLLM contains two sets of benchmarks:
 
 The performance benchmarks are used for development to confirm whether new changes improve performance under various workloads. They are triggered on every commit with both the `perf-benchmarks` and `ready` labels, and when a PR is merged into vLLM.
 
-The latest performance results are hosted on the public [vLLM Performance Dashboard](https://perf.vllm.ai).
+The latest performance results are hosted on the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
 More information on the performance benchmarks and their parameters can be found [here](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
 

docs/design/fused_moe_modular_kernel.md

@@ -175,11 +175,19 @@ implementations that input `FusedMoEActivationFormat.Standard` support chunking
 
 ### FusedMoEModularKernel Initialization
 
-`FusedMoEMethodBase` class has 2 methods that are collectively responsible in creating the `FusedMoEModularKernel` object. They are,
+`FusedMoEMethodBase` class has 3 methods that are collectively responsible in creating the `FusedMoEModularKernel` object. They are,
 
+* maybe_make_prepare_finalize,
 * select_gemm_impl, and
 * init_prepare_finalize
 
+#### maybe_make_prepare_finalize
+
+The `maybe_make_prepare_finalize` method is responsbile for constructing an instance of `FusedMoEPrepareAndFinalize` when appropriate based on the current all2all backend, e.g. when EP + DP is enabled.  The base class method currently constructs all the `FusedMoEPrepareAndFinalize` objects for the EP+DP case.  Derived classes can override this method to construct prepare/finalize objects for different scenarios, e.g. `ModelOptNvFp4FusedMoE` can construct a `FlashInferCutlassMoEPrepareAndFinalize` for the EP+TP case.
+Please refer to the implementations in,
+
+* `ModelOptNvFp4FusedMoE`
+
 #### select_gemm_impl
 
 The `select_gemm_impl` method is undefined in the base class. It is the responsibility of the derived class to implement a method that constructs a valid/appropriate `FusedMoEPermuteExpertsUnpermute` object.

docs/features/multimodal_inputs.md

@@ -216,7 +216,7 @@ Instead of NumPy arrays, you can also pass `'torch.Tensor'` instances, as shown
     from vllm import LLM, SamplingParams
     from qwen_vl_utils import process_vision_info
 
-    model_path = "Qwen/Qwen2.5-VL-3B-Instruct/"
+    model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
     video_path = "https://content.pexels.com/videos/free-videos.mp4"
 
     llm = LLM(

docs/features/quantization/supported_hardware.md

@@ -17,7 +17,6 @@ th {
 | INT8 (W8A8)           | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ✅︎        | ✅︎          | ✅︎           |
 | FP8 (W8A8)            | ❌      | ❌       | ❌       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌         | ❌        | ✅︎          | ❌           |
 | BitBLAS (GPTQ)        | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
-| AQLM                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
 | bitsandbytes          | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
 | DeepSpeedFP           | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
 | GGUF                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌         | ❌        | ❌          | ❌           |

docs/getting_started/installation/README.md

@@ -18,7 +18,7 @@ vLLM supports the following hardware platforms:
 ## Hardware Plugins
 
 The backends below live **outside** the main `vllm` repository and follow the
-[Hardware-Pluggable RFC](../design/plugin_system.md).
+[Hardware-Pluggable RFC](../../design/plugin_system.md).
 
 | Accelerator | PyPI / package | Repository |
 |-------------|----------------|------------|

docs/mkdocs/hooks/generate_examples.py

@@ -24,7 +24,6 @@ def fix_case(text: str) -> str:
         "llm": "LLM",
         "mae": "MAE",
         "tpu": "TPU",
-        "aqlm": "AQLM",
         "gguf": "GGUF",
         "lora": "LoRA",
         "rlhf": "RLHF",

docs/models/extensions/fastsafetensor.md

@@ -2,4 +2,5 @@ Loading Model weights with fastsafetensors
 ===================================================================
 
 Using fastsafetensors library enables loading model weights to GPU memory by leveraging GPU direct storage. See [their GitHub repository](https://github.com/foundation-model-stack/fastsafetensors) for more details.
-For enabling this feature, set the environment variable ``USE_FASTSAFETENSOR`` to ``true``
+
+To enable this feature, use the ``--load-format fastsafetensors`` command-line argument

docs/models/supported_models.md

@@ -330,6 +330,7 @@ th {
 | `BambaForCausalLM` | Bamba | `ibm-ai-platform/Bamba-9B-fp8`, `ibm-ai-platform/Bamba-9B` | ✅︎ | ✅︎ | ✅︎ |
 | `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ | |
 | `BartForConditionalGeneration` | BART | `facebook/bart-base`, `facebook/bart-large-cnn`, etc. | | | |
+| `MBartForConditionalGeneration` | mBART | `facebook/mbart-large-en-ro`, `facebook/mbart-large-50`, etc. | | | |
 | `ChatGLMModel`, `ChatGLMForConditionalGeneration` | ChatGLM | `zai-org/chatglm2-6b`, `zai-org/chatglm3-6b`, `ShieldLM-6B-chatglm3`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `CohereForCausalLM`, `Cohere2ForCausalLM` | Command-R | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `DbrxForCausalLM` | DBRX | `databricks/dbrx-base`, `databricks/dbrx-instruct`, etc. | | ✅︎ | ✅︎ |
@@ -418,6 +419,9 @@ Some models are supported only via the [Transformers backend](#transformers). Th
 !!! note
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
 
+!!! note
+    Some mBART models' config files do not have an `architecture` defined. Therefore, you need to use `--hf-overrides '{"architectures": ["MBartForConditionalGeneration"]}'` to explicitly specify the use of the `MBartForConditionalGeneration` architecture.
+
 ### Pooling Models
 
 See [this page](./pooling_models.md) for more information on how to use pooling models.
@@ -615,7 +619,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Gemma3nForConditionalGeneration` | Gemma 3n | T + I + A | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | | ✅︎ |
 | `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | | ✅︎ | ✅︎ |
+| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ | ✅︎ |
 | `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ | ✅︎ |
 | `Idefics3ForConditionalGeneration` | Idefics3 | T + I | `HuggingFaceM4/Idefics3-8B-Llama3`, etc. | ✅︎ | | ✅︎ |

docs/usage/troubleshooting.md

@@ -35,6 +35,7 @@ You can check if this is happening by trying the old defaults with `--generation
 If other strategies don't solve the problem, it's likely that the vLLM instance is stuck somewhere. You can use the following environment variables to help debug the issue:
 
 - `export VLLM_LOGGING_LEVEL=DEBUG` to turn on more logging.
+- `export VLLM_LOG_STATS_INTERVAL=1.` to get log statistics more frequently for tracking running queue, waiting queue and cache hit states.
 - `export CUDA_LAUNCH_BLOCKING=1` to identify which CUDA kernel is causing the problem.
 - `export NCCL_DEBUG=TRACE` to turn on more logging for NCCL.
 - `export VLLM_TRACE_FUNCTION=1` to record all function calls for inspection in the log files to tell which function crashes or hangs. Do not use this flag unless absolutely needed for debugging, it will cause significant delays in startup time.

examples/offline_inference/basic/README.md

@@ -52,20 +52,6 @@ Try it yourself with the following argument:
 
 ### Quantization
 
-#### AQLM
-
-vLLM supports models that are quantized using AQLM.
-
-Try one yourself by passing one of the following models to the `--model` argument:
-
-- `ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf`
-- `ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf`
-- `ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf`
-- `ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf`
-- `BlackSamorez/TinyLlama-1_1B-Chat-v1_0-AQLM-2Bit-1x16-hf`
-
-> Some of these models are likely to be too large for a single GPU. You can split them across multiple GPUs by setting `--tensor-parallel-size` to the number of required GPUs.
-
 #### GGUF
 
 vLLM supports models that are quantized using GGUF.

examples/offline_inference/data_parallel.py

@@ -70,12 +70,27 @@ def parse_args():
         default=64,
         help=("Maximum number of sequences to be processed in a single iteration."),
     )
+    parser.add_argument(
+        "--max-model-len",
+        type=int,
+        help=("Maximum number of tokens to be processed in a single iteration."),
+    )
+    parser.add_argument(
+        "--timeout",
+        type=int,
+        default=300,
+        help=("Number of seconds before unresponsive process is killed."),
+    )
     parser.add_argument(
         "--gpu-memory-utilization",
         type=float,
         default=0.8,
         help=("Fraction of GPU memory vLLM is allowed to allocate (0.0, 1.0]."),
     )
+    parser.add_argument(
+        "--quantization",
+        type=str,
+    )
     return parser.parse_args()
 
 
@@ -90,7 +105,9 @@ def main(
     enforce_eager,
     trust_remote_code,
     max_num_seqs,
+    max_model_len,
     gpu_memory_utilization,
+    quantization,
 ):
     os.environ["VLLM_DP_RANK"] = str(global_dp_rank)
     os.environ["VLLM_DP_RANK_LOCAL"] = str(local_dp_rank)
@@ -142,7 +159,9 @@ def main(
         enable_expert_parallel=True,
         trust_remote_code=trust_remote_code,
         max_num_seqs=max_num_seqs,
+        max_model_len=max_model_len,
         gpu_memory_utilization=gpu_memory_utilization,
+        quantization=quantization,
     )
     outputs = llm.generate(prompts, sampling_params)
     # Print the outputs.
@@ -198,14 +217,16 @@ if __name__ == "__main__":
                 args.enforce_eager,
                 args.trust_remote_code,
                 args.max_num_seqs,
+                args.max_model_len,
                 args.gpu_memory_utilization,
+                args.quantization,
             ),
         )
         proc.start()
         procs.append(proc)
     exit_code = 0
     for proc in procs:
-        proc.join(timeout=300)
+        proc.join(timeout=args.timeout)
         if proc.exitcode is None:
             print(f"Killing process {proc.pid} that didn't stop within 5 minutes.")
             proc.kill()

examples/offline_inference/encoder_decoder.py

@@ -2,9 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Demonstrate prompting of text-to-text
-encoder/decoder models, specifically BART
+encoder/decoder models, specifically BART and mBART.
+
+This script is refactored to allow model selection via command-line arguments.
 """
 
+import argparse
+from typing import NamedTuple, Optional
+
 from vllm import LLM, SamplingParams
 from vllm.inputs import (
     ExplicitEncoderDecoderPrompt,
@@ -14,119 +19,175 @@ from vllm.inputs import (
 )
 
 
-def create_prompts(tokenizer):
-    # Test prompts
-    #
-    # This section shows all of the valid ways to prompt an
-    # encoder/decoder model.
-    #
-    # - Helpers for building prompts
-    text_prompt_raw = "Hello, my name is"
-    text_prompt = TextPrompt(prompt="The president of the United States is")
+class ModelRequestData(NamedTuple):
+    """
+    Holds the configuration for a specific model, including its
+    HuggingFace ID and the prompts to use for the demo.
+    """
+
+    model_id: str
+    encoder_prompts: list
+    decoder_prompts: list
+    hf_overrides: Optional[dict] = None
+
+
+def get_bart_config() -> ModelRequestData:
+    """
+    Returns the configuration for facebook/bart-large-cnn.
+    This uses the exact test cases from the original script.
+    """
+    encoder_prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "An encoder prompt",
+    ]
+    decoder_prompts = [
+        "A decoder prompt",
+        "Another decoder prompt",
+    ]
+    return ModelRequestData(
+        model_id="facebook/bart-large-cnn",
+        encoder_prompts=encoder_prompts,
+        decoder_prompts=decoder_prompts,
+    )
+
+
+def get_mbart_config() -> ModelRequestData:
+    """
+    Returns the configuration for facebook/mbart-large-en-ro.
+    This uses prompts suitable for an English-to-Romanian translation task.
+    """
+    encoder_prompts = [
+        "The quick brown fox jumps over the lazy dog.",
+        "How are you today?",
+    ]
+    decoder_prompts = ["", ""]
+    hf_overrides = {"architectures": ["MBartForConditionalGeneration"]}
+    return ModelRequestData(
+        model_id="facebook/mbart-large-en-ro",
+        encoder_prompts=encoder_prompts,
+        decoder_prompts=decoder_prompts,
+        hf_overrides=hf_overrides,
+    )
+
+
+MODEL_GETTERS = {
+    "bart": get_bart_config,
+    "mbart": get_mbart_config,
+}
+
+
+def create_all_prompt_types(
+    encoder_prompts_raw: list,
+    decoder_prompts_raw: list,
+    tokenizer,
+) -> list:
+    """
+    Generates a list of diverse prompt types for demonstration.
+    This function is generic and uses the provided raw prompts
+    to create various vLLM input objects.
+    """
+    text_prompt_raw = encoder_prompts_raw[0]
+    text_prompt = TextPrompt(prompt=encoder_prompts_raw[1 % len(encoder_prompts_raw)])
     tokens_prompt = TokensPrompt(
-        prompt_token_ids=tokenizer.encode(prompt="The capital of France is")
-    )
-    # - Pass a single prompt to encoder/decoder model
-    #   (implicitly encoder input prompt);
-    #   decoder input prompt is assumed to be None
-
-    single_text_prompt_raw = text_prompt_raw  # Pass a string directly
-    single_text_prompt = text_prompt  # Pass a TextPrompt
-    single_tokens_prompt = tokens_prompt  # Pass a TokensPrompt
-
-    # ruff: noqa: E501
-    # - Pass explicit encoder and decoder input prompts within one data structure.
-    #   Encoder and decoder prompts can both independently be text or tokens, with
-    #   no requirement that they be the same prompt type. Some example prompt-type
-    #   combinations are shown below, note that these are not exhaustive.
-
-    enc_dec_prompt1 = ExplicitEncoderDecoderPrompt(
-        # Pass encoder prompt string directly, &
-        # pass decoder prompt tokens
-        encoder_prompt=single_text_prompt_raw,
-        decoder_prompt=single_tokens_prompt,
-    )
-    enc_dec_prompt2 = ExplicitEncoderDecoderPrompt(
-        # Pass TextPrompt to encoder, and
-        # pass decoder prompt string directly
-        encoder_prompt=single_text_prompt,
-        decoder_prompt=single_text_prompt_raw,
-    )
-    enc_dec_prompt3 = ExplicitEncoderDecoderPrompt(
-        # Pass encoder prompt tokens directly, and
-        # pass TextPrompt to decoder
-        encoder_prompt=single_tokens_prompt,
-        decoder_prompt=single_text_prompt,
+        prompt_token_ids=tokenizer.encode(
+            encoder_prompts_raw[2 % len(encoder_prompts_raw)]
+        )
     )
 
-    # - Finally, here's a useful helper function for zipping encoder and
-    #   decoder prompts together into a list of ExplicitEncoderDecoderPrompt
-    #   instances
+    decoder_tokens_prompt = TokensPrompt(
+        prompt_token_ids=tokenizer.encode(decoder_prompts_raw[0])
+    )
+    single_prompt_examples = [
+        text_prompt_raw,
+        text_prompt,
+        tokens_prompt,
+    ]
+    explicit_pair_examples = [
+        ExplicitEncoderDecoderPrompt(
+            encoder_prompt=text_prompt_raw,
+            decoder_prompt=decoder_tokens_prompt,
+        ),
+        ExplicitEncoderDecoderPrompt(
+            encoder_prompt=text_prompt,
+            decoder_prompt=decoder_prompts_raw[1 % len(decoder_prompts_raw)],
+        ),
+        ExplicitEncoderDecoderPrompt(
+            encoder_prompt=tokens_prompt,
+            decoder_prompt=text_prompt,
+        ),
+    ]
     zipped_prompt_list = zip_enc_dec_prompts(
-        ["An encoder prompt", "Another encoder prompt"],
-        ["A decoder prompt", "Another decoder prompt"],
+        encoder_prompts_raw,
+        decoder_prompts_raw,
     )
-
-    # - Let's put all of the above example prompts together into one list
-    #   which we will pass to the encoder/decoder LLM.
-    return [
-        single_text_prompt_raw,
-        single_text_prompt,
-        single_tokens_prompt,
-        enc_dec_prompt1,
-        enc_dec_prompt2,
-        enc_dec_prompt3,
-    ] + zipped_prompt_list
+    return single_prompt_examples + explicit_pair_examples + zipped_prompt_list
 
 
-# Create a sampling params object.
-def create_sampling_params():
+def create_sampling_params() -> SamplingParams:
+    """Create a sampling params object."""
     return SamplingParams(
         temperature=0,
         top_p=1.0,
         min_tokens=0,
-        max_tokens=20,
+        max_tokens=30,
     )
 
 
-# Print the outputs.
-def print_outputs(outputs):
-    print("-" * 50)
+def print_outputs(outputs: list):
+    """Formats and prints the generation outputs."""
+    print("-" * 80)
     for i, output in enumerate(outputs):
         prompt = output.prompt
         encoder_prompt = output.encoder_prompt
         generated_text = output.outputs[0].text
         print(f"Output {i + 1}:")
-        print(
-            f"Encoder prompt: {encoder_prompt!r}\n"
-            f"Decoder prompt: {prompt!r}\n"
-            f"Generated text: {generated_text!r}"
+        print(f"Encoder Prompt: {encoder_prompt!r}")
+        print(f"Decoder Prompt: {prompt!r}")
+        print(f"Generated Text: {generated_text!r}")
+        print("-" * 80)
+
+
+def main(args):
+    """Main execution function."""
+    model_key = args.model
+    if model_key not in MODEL_GETTERS:
+        raise ValueError(
+            f"Unknown model: {model_key}. "
+            f"Available models: {list(MODEL_GETTERS.keys())}"
         )
-        print("-" * 50)
+    config_getter = MODEL_GETTERS[model_key]
+    model_config = config_getter()
 
-
-def main():
-    dtype = "float"
-
-    # Create a BART encoder/decoder model instance
+    print(f"🚀 Running demo for model: {model_config.model_id}")
     llm = LLM(
-        model="facebook/bart-large-cnn",
-        dtype=dtype,
+        model=model_config.model_id,
+        dtype="float",
+        hf_overrides=model_config.hf_overrides,
     )
-
-    # Get BART tokenizer
     tokenizer = llm.llm_engine.get_tokenizer_group()
-
-    prompts = create_prompts(tokenizer)
+    prompts = create_all_prompt_types(
+        encoder_prompts_raw=model_config.encoder_prompts,
+        decoder_prompts_raw=model_config.decoder_prompts,
+        tokenizer=tokenizer,
+    )
     sampling_params = create_sampling_params()
-
-    # Generate output tokens from the prompts. The output is a list of
-    # RequestOutput objects that contain the prompt, generated
-    # text, and other information.
     outputs = llm.generate(prompts, sampling_params)
-
     print_outputs(outputs)
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser(
+        description="A flexible demo for vLLM encoder-decoder models."
+    )
+    parser.add_argument(
+        "--model",
+        "-m",
+        type=str,
+        default="bart",
+        choices=MODEL_GETTERS.keys(),
+        help="The short name of the model to run.",
+    )
+    args = parser.parse_args()
+    main(args)

examples/offline_inference/logits_processor.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""This example demonstrates instantiating vLLM with a custom logits processor
+class object.
+
+For a basic example of implementing a custom logits processor, see
+the `DummyLogitsProcessor` implementation in `vllm/test_utils.py`.
+
+For testing purposes, a dummy logits processor is employed which, if
+`target_token` is passed as a keyword argument to `SamplingParams.extra_args`,
+will mask out all tokens except `target_token`.
+
+A batch is constructed with `temperature=0.0` and 50% of requests specifying
+`target_token`, and for these requests - and *only* these requests - we
+expect the `target_token` to be decoded in each step, yielding an output
+similar to that shown below:
+
+Generated Outputs:
+------------------------------------------------------------
+Prompt:    'Hello, my name is'
+Output:    " ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' '"
+------------------------------------------------------------
+Prompt:    'The president of the United States is'
+Output:    " not a racist. He is a racist.\nHe's a racist because he"
+------------------------------------------------------------
+Prompt:    'The capital of France is'
+Output:    ' also also also also also also also also also also also also also
+             also also also'
+------------------------------------------------------------
+Prompt:    'The future of AI is'
+Output:    ' in the hands of the people.\n\nThe future of AI is in the'
+------------------------------------------------------------
+"""
+
+from typing import Optional
+
+import torch
+
+from vllm import LLM, SamplingParams
+from vllm.config import VllmConfig
+from vllm.v1.sample.logits_processor import (
+    BatchUpdate,
+    LogitsProcessor,
+    MoveDirectionality,
+)
+
+
+# Hypothetical custom logits processor
+class DummyLogitsProcessor(LogitsProcessor):
+    """Fake logit processor to support unit testing and examples"""
+
+    def __init__(
+        self, vllm_config: VllmConfig, device: torch.device, is_pin_memory: bool
+    ):
+        self.req_info: dict[int, SamplingParams] = {}
+
+    def is_argmax_invariant(self) -> bool:
+        """Never impacts greedy sampling"""
+        return False
+
+    def update_state(self, batch_update: Optional[BatchUpdate]):
+        if not batch_update:
+            return
+
+        # Process added requests.
+        for index, params, _, _ in batch_update.added:
+            assert params is not None
+            if params.extra_args and (
+                target_token := params.extra_args.get("target_token")
+            ):
+                self.req_info[index] = target_token
+
+        if self.req_info:
+            # Process removed requests.
+            for index in batch_update.removed:
+                self.req_info.pop(index, None)
+
+            # Process moved requests, unidirectional move (a->b) and swap
+            # (a<->b)
+            for adx, bdx, direct in batch_update.moved:
+                a_val = self.req_info.pop(adx, None)
+                b_val = self.req_info.pop(bdx, None)
+                if a_val is not None:
+                    self.req_info[bdx] = a_val
+                if direct == MoveDirectionality.SWAP and b_val is not None:
+                    self.req_info[adx] = b_val
+
+    def apply(self, logits: torch.Tensor) -> torch.Tensor:
+        if not self.req_info:
+            return logits
+
+        # Save target values before modification
+        rows_list = list(self.req_info.keys())
+        cols = torch.tensor(
+            [self.req_info[i] for i in rows_list],
+            dtype=torch.long,
+            device=logits.device,
+        )
+        rows = torch.tensor(rows_list, dtype=torch.long, device=logits.device)
+        values_to_keep = logits[rows, cols].clone()
+
+        # Mask all but target tokens
+        logits[rows] = float("-inf")
+        logits[rows, cols] = values_to_keep
+
+        return logits
+
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a mixture of requests which do and don't utilize the dummy logitproc
+sampling_params_list = [
+    SamplingParams(temperature=0.0, extra_args={"target_token": 128}),
+    SamplingParams(temperature=0.0),
+    SamplingParams(temperature=0.0, extra_args={"target_token": 67}),
+    SamplingParams(temperature=0.0),
+]
+
+
+def main():
+    # Create an LLM.
+    llm = LLM(
+        model="facebook/opt-125m",
+        logits_processors=[DummyLogitsProcessor],
+    )
+    # Generate texts from the prompts.
+    # The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params_list)
+    # Print the outputs.
+    print("\nGenerated Outputs:\n" + "-" * 60)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt:    {prompt!r}")
+        print(f"Output:    {generated_text!r}")
+        print("-" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/structured_outputs.py

@@ -15,6 +15,8 @@ from pydantic import BaseModel
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams
 
+MAX_TOKENS = 50
+
 # Guided decoding by Choice (list of possible options)
 guided_decoding_params_choice = GuidedDecodingParams(choice=["Positive", "Negative"])
 sampling_params_choice = SamplingParams(guided_decoding=guided_decoding_params_choice)
@@ -23,7 +25,9 @@ prompt_choice = "Classify this sentiment: vLLM is wonderful!"
 # Guided decoding by Regex
 guided_decoding_params_regex = GuidedDecodingParams(regex=r"\w+@\w+\.com\n")
 sampling_params_regex = SamplingParams(
-    guided_decoding=guided_decoding_params_regex, stop=["\n"]
+    guided_decoding=guided_decoding_params_regex,
+    stop=["\n"],
+    max_tokens=MAX_TOKENS,
 )
 prompt_regex = (
     "Generate an email address for Alan Turing, who works in Enigma."
@@ -48,7 +52,10 @@ class CarDescription(BaseModel):
 
 json_schema = CarDescription.model_json_schema()
 guided_decoding_params_json = GuidedDecodingParams(json=json_schema)
-sampling_params_json = SamplingParams(guided_decoding=guided_decoding_params_json)
+sampling_params_json = SamplingParams(
+    guided_decoding=guided_decoding_params_json,
+    max_tokens=MAX_TOKENS,
+)
 prompt_json = (
     "Generate a JSON with the brand, model and car_type of"
     "the most iconic car from the 90's"
@@ -64,7 +71,10 @@ condition ::= column "= " number
 number ::= "1 " | "2 "
 """
 guided_decoding_params_grammar = GuidedDecodingParams(grammar=simplified_sql_grammar)
-sampling_params_grammar = SamplingParams(guided_decoding=guided_decoding_params_grammar)
+sampling_params_grammar = SamplingParams(
+    guided_decoding=guided_decoding_params_grammar,
+    max_tokens=MAX_TOKENS,
+)
 prompt_grammar = (
     "Generate an SQL query to show the 'username' and 'email'from the 'users' table."
 )

setup.py

@@ -60,7 +60,8 @@ MAIN_CUDA_VERSION = "12.8"
 
 
 def is_sccache_available() -> bool:
-    return which("sccache") is not None
+    return which("sccache") is not None and \
+        not bool(int(os.getenv("VLLM_DISABLE_SCCACHE", "0")))
 
 
 def is_ccache_available() -> bool:

tests/compile/piecewise/test_full_cudagraph.py

@@ -3,7 +3,8 @@
 import contextlib
 import os
 import weakref
-from contextlib import ExitStack
+from dataclasses import dataclass
+from typing import Optional
 
 import pytest
 
@@ -32,27 +33,130 @@ def temporary_environ(env_vars):
                 os.environ[k] = v
 
 
+@dataclass
+class BackendConfig:
+    name: str
+    env_vars: dict
+    comp_config: dict
+    specific_gpu_arch: Optional[tuple] = None
+
+
+# Define all backend configurations of full cudagraph to be tested
+backend_configs = {
+    # FA3 on Hopper
+    "FA3":
+    BackendConfig(name="FA3",
+                  env_vars={"VLLM_FLASH_ATTN_VERSION": "3"},
+                  comp_config={
+                      "cudagraph_mode": "FULL",
+                  },
+                  specific_gpu_arch=(9, 0)),
+    # FlashMLA on Hopper
+    "FlashMLA":
+    BackendConfig(name="FlashMLA",
+                  env_vars={
+                      "VLLM_ATTENTION_BACKEND": "FLASHMLA",
+                  },
+                  comp_config={
+                      "cudagraph_mode": "FULL_AND_PIECEWISE",
+                  },
+                  specific_gpu_arch=(9, 0)),
+    # Cutlass MLA on Blackwell
+    "CutlassMLA":
+    BackendConfig(
+        name="CutlassMLA",
+        env_vars={
+            "VLLM_USE_V1": "1",
+            "VLLM_ATTENTION_BACKEND": "CUTLASS_MLA",
+            "FORCE_NUM_KV_SPLITS":
+            "1",  # TODO: remove this when hang issue is fixed
+        },
+        comp_config={
+            "cudagraph_mode": "FULL_AND_PIECEWISE",
+            "cudagraph_capture_sizes": [16, 32, 64, 128, 256, 512],
+        },
+        specific_gpu_arch=(10, 0)),
+    # FA2
+    "FA2":
+    BackendConfig(name="FA2",
+                  env_vars={"VLLM_FLASH_ATTN_VERSION": "2"},
+                  comp_config={
+                      "cudagraph_mode": "FULL",
+                  }),
+    # Triton Attention
+    "TritonAttn":
+    BackendConfig(name="TritonAttn",
+                  env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN_VLLM_V1"},
+                  comp_config={
+                      "cudagraph_mode": "FULL",
+                  }),
+    # FlashInfer
+    "FlashInfer":
+    BackendConfig(name="FlashInfer",
+                  env_vars={"VLLM_ATTENTION_BACKEND": "FLASHINFER"},
+                  comp_config={
+                      "cudagraph_mode": "FULL_AND_PIECEWISE",
+                  }),
+}
+
+test_params_full_cudagraph = []
+
+# deepseek-ai/DeepSeek-V2-Lite with MLA
+MLA_backends = ["FlashMLA", "CutlassMLA"]
+for mla_backend in MLA_backends:
+    test_params_full_cudagraph.append(
+        pytest.param(
+            ("deepseek-ai/DeepSeek-V2-Lite", backend_configs[mla_backend])))
+
+# Qwen/Qwen2-1.5B-Instruct with other backends
+other_backend_configs = [
+    backend_configs[c] for c in backend_configs if c not in MLA_backends
+]
+for backend_config in other_backend_configs:
+    test_params_full_cudagraph.append(
+        pytest.param(("Qwen/Qwen2-1.5B-Instruct", backend_config)))
+
+
 @pytest.fixture(scope="class")
 def llm_pair(request):
-    model = request.param
+    model, backend_config = request.param
 
-    with temporary_environ({
-            "VLLM_USE_V1": "1",
-            "VLLM_FLASH_ATTN_VERSION": "3"
-    }):
+    # Dynamically skip test if GPU capability is not met
+    if backend_config.specific_gpu_arch and backend_config.specific_gpu_arch\
+        != current_platform.get_device_capability():
+        if backend_config.specific_gpu_arch == (9, 0):
+            pytest.skip("Only Hopper GPUs support FA3 and FlashMLA")
+        elif backend_config.specific_gpu_arch == (10, 0):
+            pytest.skip("Only Blackwell GPUs support Cutlass MLA")
+
+    env_vars = {
+        "VLLM_USE_V1": "1",
+        # Force native sampler to avoid potential nondeterminism in FlashInfer
+        # when per-request generators are not used in V1.
+        "VLLM_USE_FLASHINFER_SAMPLER": "0",
+        **backend_config.env_vars,
+    }
+    with temporary_environ(env_vars):
         full = LLM(
             model=model,
-            gpu_memory_utilization=0.45,
+            gpu_memory_utilization=0.43,
             trust_remote_code=True,
             max_model_len=1024,
-            compilation_config=CompilationConfig(full_cuda_graph=True),
+            max_num_seqs=128,
+            compilation_config=\
+                CompilationConfig(**backend_config.comp_config),
+            generation_config="vllm",
+            seed=42,
         )
         piecewise = LLM(
             model=model,
-            gpu_memory_utilization=0.45,
+            gpu_memory_utilization=0.43,
             trust_remote_code=True,
             max_model_len=1024,
-            compilation_config=CompilationConfig(),
+            max_num_seqs=128,
+            compilation_config=CompilationConfig(cudagraph_mode="PIECEWISE"),
+            generation_config="vllm",
+            seed=42,
         )
 
     # PyTest caches the fixture values so we use weakref.proxy to enable GC
@@ -66,16 +170,7 @@ def llm_pair(request):
     )
 
 
-@pytest.mark.parametrize(
-    "llm_pair",
-    [
-        # Model names for the llm_pair fixture
-        "deepseek-ai/DeepSeek-V2-Lite",
-        "Qwen/Qwen2-1.5B-Instruct"
-    ],
-    indirect=True)
-@pytest.mark.skipif(current_platform.get_device_capability() != (9, 0),
-                    reason="Only Hopper GPUs support FA3 and FlashMLA")
+@pytest.mark.parametrize("llm_pair", test_params_full_cudagraph, indirect=True)
 class TestFullCUDAGraph:
     """
     Use a class such that an llm pair is constructed once for all
@@ -104,12 +199,14 @@ class TestFullCUDAGraph:
         full cudagraph compilation works for padded cases too.
         """
 
-        piecewise_llm, full_cudagraph_llm = llm_pair
+        full_cudagraph_llm, piecewise_llm = llm_pair
 
-        prompts = ["Hello, my name is"] * batch_size
+        prompts = ["the quick brown fox"] * batch_size
+        # Use purely greedy decoding to avoid top-p truncation sensitivity
+        # that can amplify tiny numeric differences across runtimes.
         sampling_params = SamplingParams(temperature=0.0,
                                          max_tokens=max_tokens,
-                                         top_p=0.95)
+                                         top_p=1.0)
 
         piecewise_responses = piecewise_llm.generate(prompts, sampling_params)
         full_responses = full_cudagraph_llm.generate(prompts, sampling_params)
@@ -117,42 +214,16 @@ class TestFullCUDAGraph:
         # Check that all responses are the same
         for piecewise_res, full_res in zip(piecewise_responses,
                                            full_responses):
-            assert piecewise_res.outputs[0].text == full_res.outputs[0].text
-
-
-@pytest.mark.parametrize(
-    "model, supported",
-    [
-        ("Qwen/Qwen2-1.5B-Instruct", True),
-        # MLA does not support capturing CUDA Graphs with size > max_num_seqs
-        ("deepseek-ai/DeepSeek-V2-Lite", False),
-    ])
-@pytest.mark.skipif(current_platform.get_device_capability() != (9, 0),
-                    reason="Only Hopper GPUs support FA3 and FlashMLA")
-def test_lower_max_num_seqs(model, supported):
-    with temporary_environ({
-            "VLLM_USE_V1": "1",
-            "VLLM_FLASH_ATTN_VERSION": "3"
-    }), ExitStack() as stack:
-        if not supported:
-            stack.enter_context(pytest.raises(RuntimeError))
-
-        llm = LLM(model=model,
-                  max_num_seqs=256,
-                  trust_remote_code=True,
-                  max_model_len=1024,
-                  compilation_config=CompilationConfig(
-                      full_cuda_graph=True,
-                      cudagraph_capture_sizes=[64, 256, 512]))
-        llm.generate(["Hello, my name is"] * 10)
+            assert piecewise_res.outputs[0].text.lower() == \
+                full_res.outputs[0].text.lower()
 
 
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
 def test_full_cudagraph_with_invalid_backend():
     with temporary_environ({
             "VLLM_USE_V1": "1",
-            "VLLM_FLASH_ATTN_VERSION":
-            "2"  #FA2 not supported with full_cuda_graph
+            "VLLM_ATTENTION_BACKEND": "FLEX_ATTENTION"
+            # Flex_Attention is not supported with full cuda graph
     }), pytest.raises(RuntimeError):
         LLM(model="Qwen/Qwen2-1.5B-Instruct",
-            compilation_config=CompilationConfig(full_cuda_graph=True))
+            compilation_config=CompilationConfig(cudagraph_mode="FULL"))

tests/compile/piecewise/test_simple.py

@@ -11,10 +11,10 @@ from torch.library import Library
 
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import (CompilationConfig, CompilationLevel, VllmConfig,
-                         set_current_vllm_config)
+from vllm.config import (CompilationConfig, CompilationLevel, CUDAGraphMode,
+                         VllmConfig, set_current_vllm_config)
 from vllm.envs import VLLM_USE_V1
-from vllm.forward_context import set_forward_context
+from vllm.forward_context import BatchDescriptor, set_forward_context
 from vllm.utils import direct_register_custom_op
 
 global_counter = 0
@@ -101,16 +101,33 @@ def test_simple_piecewise_compile(use_inductor):
             num_backend_compilations=3,  # num_piecewise_capturable_graphs_seen
             num_cudagraph_captured=
             6,  # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
-    ), set_forward_context({}, vllm_config=vllm_config):
-
+    ), set_forward_context(None,
+                           vllm_config=vllm_config):  # background context
+        # warm up with background context
         model(inputs)
 
-        model(torch.randn(2).cuda())
-        model(torch.randn(1).cuda())
+        # capturing/replaying should under context of cudagraph dispatching
+        with set_forward_context(
+                None,
+                vllm_config=vllm_config,
+                cudagraph_runtime_mode=CUDAGraphMode.PIECEWISE,
+                batch_descriptor=BatchDescriptor(num_tokens=2, )):
+            model(torch.randn(2).cuda())
+        with set_forward_context(
+                None,
+                vllm_config=vllm_config,
+                cudagraph_runtime_mode=CUDAGraphMode.PIECEWISE,
+                batch_descriptor=BatchDescriptor(num_tokens=1, )):
+            model(torch.randn(1).cuda())
 
         input = torch.zeros(2).cuda()
         global global_counter
         global_counter = 0
-        output = model(input)
+        with set_forward_context(
+                None,
+                vllm_config=vllm_config,
+                cudagraph_runtime_mode=CUDAGraphMode.PIECEWISE,
+                batch_descriptor=BatchDescriptor(num_tokens=2, )):
+            output = model(input)
         assert global_counter == 2
         assert torch.allclose(output.cpu(), torch.tensor([3., 1.]))

tests/compile/piecewise/test_toy_llama.py

@@ -18,9 +18,9 @@ from torch.library import Library
 
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import (CompilationConfig, CompilationLevel, VllmConfig,
-                         set_current_vllm_config)
-from vllm.forward_context import set_forward_context
+from vllm.config import (CompilationConfig, CompilationLevel, CUDAGraphMode,
+                         VllmConfig, set_current_vllm_config)
+from vllm.forward_context import BatchDescriptor, set_forward_context
 from vllm.utils import direct_register_custom_op
 
 # create a library to hold the custom op
@@ -276,9 +276,11 @@ def run_model(llama_config,
         )
         if split_attn:
             compilation_config.splitting_ops = ["silly.attention"]
+        cudagraph_runtime_mode = CUDAGraphMode.PIECEWISE
     else:
         compilation_config = CompilationConfig(
             level=CompilationLevel.NO_COMPILATION, )
+        cudagraph_runtime_mode = CUDAGraphMode.NONE
 
     vllm_config = VllmConfig(compilation_config=compilation_config,
                              additional_config=llama_config)
@@ -287,17 +289,37 @@ def run_model(llama_config,
                            vllm_config=vllm_config,
                            prefix="").eval().cuda()
 
-    with set_forward_context({}, vllm_config=vllm_config):
+    with set_forward_context({},
+                             vllm_config=vllm_config):  # background context
         B = 16  # max batch size
         input_ids = torch.randint(0, llama_config.vocab_size, (B, )).cuda()
         positions = torch.arange(B).cuda()
 
+        # warmup for the model with cudagraph_mode NONE
         model(input_ids, positions)
-        model(input_ids[:2], positions[:2])
-        model(input_ids[:1], positions[:1])
+
+        # simulate cudagraphs capturing
+        with set_forward_context({},
+                                 vllm_config=vllm_config,
+                                 cudagraph_runtime_mode=cudagraph_runtime_mode,
+                                 batch_descriptor=BatchDescriptor(
+                                     num_tokens=2, )):
+            model(input_ids[:2], positions[:2])
+        with set_forward_context({},
+                                 vllm_config=vllm_config,
+                                 cudagraph_runtime_mode=cudagraph_runtime_mode,
+                                 batch_descriptor=BatchDescriptor(
+                                     num_tokens=1, )):
+            model(input_ids[:1], positions[:1])
 
         input_ids[:2].zero_()
-        output = model(input_ids[:2], positions[:2])
+        # simulate cudagraphs replay
+        with set_forward_context({},
+                                 vllm_config=vllm_config,
+                                 cudagraph_runtime_mode=cudagraph_runtime_mode,
+                                 batch_descriptor=BatchDescriptor(
+                                     num_tokens=2, )):
+            output = model(input_ids[:2], positions[:2])
 
         output = output.cpu()
 

tests/compile/test_full_graph.py

@@ -31,10 +31,6 @@ def models_list(*, all: bool = True, keywords: Optional[list[str]] = None):
     ]
 
     if all:
-        if is_quant_method_supported("aqlm"):
-            TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
-                "quantization": "aqlm"
-            }))
 
         # TODO: figure out why this fails.
         if False and is_quant_method_supported("gguf"):  # noqa: SIM223

tests/detokenizer/test_min_tokens.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm import SamplingParams
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.detokenizer import FastIncrementalDetokenizer
+
+PROMPT = "Hello, my name is Lee, and I'm a student in the " + \
+         "college of engineering"
+
+
+@pytest.mark.parametrize("min_tokens,stop,truth", [
+    (0, None, " is Lee, and I'm a student in the college of engineering"),
+    (0, "e", " is L"),
+    (5, "e", " is Lee, and I'm a stud"),
+])
+def test_min_tokens_with_stop(min_tokens: int, stop: str, truth: str):
+    """Test for a specific min_tokens and stop.
+
+    See https://github.com/vllm-project/vllm/pull/22014
+    """
+    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
+    all_prompt_ids = tokenizer(PROMPT, add_special_tokens=False).input_ids
+
+    # The prompt is "Hello, my name is"
+    prompt_token_ids = all_prompt_ids[:4]
+    params = SamplingParams(
+        stop=stop,
+        min_tokens=min_tokens,
+    )
+    request = EngineCoreRequest("",
+                                prompt_token_ids,
+                                None,
+                                None,
+                                None,
+                                params,
+                                None,
+                                None,
+                                0.0,
+                                None,
+                                cache_salt=None,
+                                data_parallel_rank=None)
+
+    detokenizer = FastIncrementalDetokenizer(tokenizer, request)
+
+    detokenizer.update(all_prompt_ids[4:], False)
+    assert detokenizer.output_text == truth

tests/entrypoints/openai/test_default_mm_loras.py

@@ -24,18 +24,7 @@ ACTIVE_MM_LORA_RESPONSE = "Spoken text: The first words I spoke in the original
 
 
 @pytest.fixture(scope="module")
-def monkeypatch_module():
-    from _pytest.monkeypatch import MonkeyPatch
-    mpatch = MonkeyPatch()
-    yield mpatch
-    mpatch.undo()
-
-
-@pytest.fixture(scope="module", params=[False, True])
-def multimodal_server(request, monkeypatch_module):  # noqa: F811
-
-    use_v1 = request.param
-    monkeypatch_module.setenv('VLLM_USE_V1', '1' if use_v1 else '0')
+def multimodal_server():  # noqa: F811
 
     args = [
         # use half precision for speed and memory savings in CI environment
@@ -59,7 +48,8 @@ def multimodal_server(request, monkeypatch_module):  # noqa: F811
         f"{{\"audio\": \"{AUDIO_LORA_PATH}\"}}",
     ]
 
-    with RemoteOpenAIServer(MULTIMODAL_MODEL_NAME, args) as remote_server:
+    with RemoteOpenAIServer(MULTIMODAL_MODEL_NAME, args,
+                            max_wait_seconds=480) as remote_server:
         yield remote_server
 
 

tests/entrypoints/openai/test_openai_schema.py

@@ -54,38 +54,54 @@ def before_generate_case(context: schemathesis.hooks.HookContext, strategy):
     op = context.operation
     assert op is not None
 
-    def no_file_type(case: schemathesis.models.Case):
+    def no_invalid_types(case: schemathesis.models.Case):
         """
-        This filter skips test cases for the `POST /tokenize` endpoint where the
-        HTTP request body uses `"type": "file"` in any message's content.
-        We expect these cases to fail because that type isn't implemented here
-        https://github.com/vllm-project/vllm/blob/0b34593017953051b3225b1483ce0f4670e3eb0e/vllm/entrypoints/chat_utils.py#L1038-L1095
+        This filter skips test cases with invalid data that schemathesis
+        incorrectly generates due to permissive schema configurations.
+        
+        1. Skips `POST /tokenize` endpoint cases with `"type": "file"` in 
+           message content, which isn't implemented.
+        
+        2. Skips tool_calls with `"type": "custom"` which schemathesis 
+           incorrectly generates instead of the valid `"type": "function"`.
 
         Example test cases that are skipped:
         curl -X POST -H 'Content-Type: application/json' \
-            -d '{"messages": [{"role": "assistant"}, {"content": [{"file": {}, "type": "file"}], "role": "user"}]}' \
+            -d '{"messages": [{"content": [{"file": {}, "type": "file"}], "role": "user"}]}' \
             http://localhost:8000/tokenize
 
         curl -X POST -H 'Content-Type: application/json' \
-            -d '{"messages": [{"content": [{"file": {}, "type": "file"}], "role": "user"}]}' \
-            http://localhost:8000/tokenize
+            -d '{"messages": [{"role": "assistant", "tool_calls": [{"custom": {"input": "", "name": ""}, "id": "", "type": "custom"}]}]}' \
+            http://localhost:8000/v1/chat/completions
         """  # noqa: E501
-        if (op.method.lower() == "post" and op.path == "/tokenize"
-                and hasattr(case, "body") and isinstance(case.body, dict)
+        if (hasattr(case, "body") and isinstance(case.body, dict)
                 and "messages" in case.body
                 and isinstance(case.body["messages"], list)
                 and len(case.body["messages"]) > 0):
+
             for message in case.body["messages"]:
                 if not isinstance(message, dict):
                     continue
-                content = message.get("content", [])
-                if not isinstance(content, list) or len(content) == 0:
-                    continue
-                if any(item.get("type") == "file" for item in content):
-                    return False
+
+                # Check for invalid file type in tokenize endpoint
+                if op.method.lower() == "post" and op.path == "/tokenize":
+                    content = message.get("content", [])
+                    if (isinstance(content, list) and len(content) > 0 and any(
+                            item.get("type") == "file" for item in content)):
+                        return False
+
+                # Check for invalid tool_calls with non-function types
+                tool_calls = message.get("tool_calls", [])
+                if isinstance(tool_calls, list):
+                    for tool_call in tool_calls:
+                        if isinstance(tool_call, dict):
+                            if tool_call.get("type") != "function":
+                                return False
+                            if "custom" in tool_call:
+                                return False
         return True
 
-    return strategy.filter(no_file_type)
+    return strategy.filter(no_invalid_types)
 
 
 @schema.parametrize()

tests/entrypoints/openai/test_prompt_validation.py

@@ -1,10 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import io
+
 # imports for guided decoding tests
 import openai
+import pybase64
 import pytest
 import regex as re
+import torch
+
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
 
 from ...utils import RemoteOpenAIServer
 
@@ -42,3 +48,46 @@ async def test_out_of_vocab_token_ids():
                                             prompt=[999999],
                                             max_tokens=5,
                                             temperature=0.0)
+
+
+@pytest.mark.parametrize("dtype",
+                         [torch.float32, torch.bfloat16, torch.float16])
+@pytest.mark.parametrize(
+    "layout",
+    [torch.strided, torch.sparse_coo, torch.sparse_csc, torch.sparse_csr])
+@pytest.mark.parametrize("seq_len", [2, 10])
+@pytest.mark.parametrize("hidden_size", [2, 10])
+def test_load_prompt_embeds(dtype: torch.dtype, layout: torch.layout,
+                            seq_len: int, hidden_size: int):
+    # construct arbitrary tensors of various dtypes, layouts, and sizes.
+    # We need to check against different layouts to make sure that if a user
+    # uses sparse tensors to reduce the transmission size of prompt embeddings,
+    # we must cast them to dense/strided before passing them into the engine.
+    # We don't use non-CPU tensors in this test to avoid preemptively
+    # initializing cuda and break other tests in the suite that fork processes.
+    # We also need to make sure that we only use devices that are actually
+    # available in the environment the test is running on. For simplicity,
+    # we just test against CPU.
+    tensor = torch.randn((seq_len, hidden_size), dtype=dtype)
+    if layout == torch.strided:
+        tensor = tensor.contiguous()
+    elif layout == torch.sparse_coo:
+        tensor = tensor.to_sparse_coo()
+    elif layout == torch.sparse_csc:
+        tensor = tensor.to_sparse_csc()
+    elif layout == torch.sparse_csr:
+        tensor = tensor.to_sparse_csr()
+
+    buffer = io.BytesIO()
+    torch.save(tensor, buffer)
+    buffer.seek(0)
+    encoded_tensor = pybase64.b64encode(buffer.getvalue())
+
+    loaded_prompt_embeds = OpenAIServing._load_prompt_embeds(encoded_tensor)
+    assert len(loaded_prompt_embeds) == 1
+    loaded_tensor = loaded_prompt_embeds[0]["prompt_embeds"]
+    assert loaded_tensor.device.type == "cpu"
+    assert loaded_tensor.layout == torch.strided
+    torch.testing.assert_close(loaded_tensor,
+                               tensor.to("cpu").to_dense(),
+                               equal_nan=True)

tests/entrypoints/openai/test_transcription_validation.py

@@ -4,19 +4,20 @@
 # imports for guided decoding tests
 import io
 import json
-from unittest.mock import patch
 
 import librosa
 import numpy as np
 import openai
 import pytest
+import pytest_asyncio
 import soundfile as sf
-from openai._base_client import AsyncAPIClient
 
 from vllm.assets.audio import AudioAsset
 
 from ...utils import RemoteOpenAIServer
 
+MODEL_NAME = "openai/whisper-large-v3-turbo"
+SERVER_ARGS = ["--enforce-eager"]
 MISTRAL_FORMAT_ARGS = [
     "--tokenizer_mode", "mistral", "--config_format", "mistral",
     "--load_format", "mistral"
@@ -37,6 +38,18 @@ def winning_call():
         yield f
 
 
+@pytest.fixture(scope="module")
+def server():
+    with RemoteOpenAIServer(MODEL_NAME, SERVER_ARGS) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name",
@@ -60,57 +73,11 @@ async def test_basic_audio(mary_had_lamb, model_name):
         assert "Mary had a little lamb," in out
 
 
-@pytest.mark.asyncio
-async def test_bad_requests(mary_had_lamb):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-
-        # invalid language
-        with pytest.raises(openai.BadRequestError):
-            await client.audio.transcriptions.create(model=model_name,
-                                                     file=mary_had_lamb,
-                                                     language="hh",
-                                                     temperature=0.0)
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", ["openai/whisper-large-v3-turbo"])
-async def test_long_audio_request(mary_had_lamb, model_name):
-    server_args = ["--enforce-eager"]
-
-    if model_name.startswith("openai"):
-        return
-
-    mary_had_lamb.seek(0)
-    audio, sr = librosa.load(mary_had_lamb)
-    # Add small silence after each audio for repeatability in the split process
-    audio = np.pad(audio, (0, 1600))
-    repeated_audio = np.tile(audio, 10)
-    # Repeated audio to buffer
-    buffer = io.BytesIO()
-    sf.write(buffer, repeated_audio, sr, format='WAV')
-    buffer.seek(0)
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        transcription = await client.audio.transcriptions.create(
-            model=model_name,
-            file=buffer,
-            language="en",
-            response_format="text",
-            temperature=0.0)
-        out = json.loads(transcription)['text']
-        counts = out.count("Mary had a little lamb")
-        assert counts == 10, counts
-
-
 @pytest.mark.asyncio
 async def test_non_asr_model(winning_call):
     # text to text model
     model_name = "JackFram/llama-68m"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
+    with RemoteOpenAIServer(model_name, SERVER_ARGS) as remote_server:
         client = remote_server.get_async_client()
         res = await client.audio.transcriptions.create(model=model_name,
                                                        file=winning_call,
@@ -123,157 +90,149 @@ async def test_non_asr_model(winning_call):
 
 
 @pytest.mark.asyncio
-async def test_completion_endpoints():
+async def test_bad_requests(mary_had_lamb, client):
+    # invalid language
+    with pytest.raises(openai.BadRequestError):
+        await client.audio.transcriptions.create(model=MODEL_NAME,
+                                                 file=mary_had_lamb,
+                                                 language="hh",
+                                                 temperature=0.0)
+
+
+@pytest.mark.asyncio
+async def test_long_audio_request(mary_had_lamb, client):
+    mary_had_lamb.seek(0)
+    audio, sr = librosa.load(mary_had_lamb)
+    # Add small silence after each audio for repeatability in the split process
+    audio = np.pad(audio, (0, 1600))
+    repeated_audio = np.tile(audio, 10)
+    # Repeated audio to buffer
+    buffer = io.BytesIO()
+    sf.write(buffer, repeated_audio, sr, format='WAV')
+    buffer.seek(0)
+    transcription = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=buffer,
+        language="en",
+        response_format="text",
+        temperature=0.0)
+    out = json.loads(transcription)['text']
+    counts = out.count("Mary had a little lamb")
+    assert counts == 10, counts
+
+
+@pytest.mark.asyncio
+async def test_completion_endpoints(client):
     # text to text model
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        res = await client.chat.completions.create(
-            model=model_name,
-            messages=[{
-                "role": "system",
-                "content": "You are a helpful assistant."
-            }])
-        err = res.error
-        assert err["code"] == 400
-        assert err[
-            "message"] == "The model does not support Chat Completions API"
+    res = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "system",
+            "content": "You are a helpful assistant."
+        }])
+    err = res.error
+    assert err["code"] == 400
+    assert err["message"] == "The model does not support Chat Completions API"
 
-        res = await client.completions.create(model=model_name, prompt="Hello")
-        err = res.error
-        assert err["code"] == 400
-        assert err["message"] == "The model does not support Completions API"
+    res = await client.completions.create(model=MODEL_NAME, prompt="Hello")
+    err = res.error
+    assert err["code"] == 400
+    assert err["message"] == "The model does not support Completions API"
 
 
 @pytest.mark.asyncio
-async def test_streaming_response(winning_call):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
+async def test_streaming_response(winning_call, client):
     transcription = ""
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        res_no_stream = await client.audio.transcriptions.create(
-            model=model_name,
-            file=winning_call,
-            response_format="json",
-            language="en",
-            temperature=0.0)
-        # Unfortunately this only works when the openai client is patched
-        # to use streaming mode, not exposed in the transcription api.
-        original_post = AsyncAPIClient.post
+    res_no_stream = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=winning_call,
+        response_format="json",
+        language="en",
+        temperature=0.0)
+    res = await client.audio.transcriptions.create(model=MODEL_NAME,
+                                                   file=winning_call,
+                                                   language="en",
+                                                   temperature=0.0,
+                                                   stream=True,
+                                                   timeout=30)
+    # Reconstruct from chunks and validate
+    async for chunk in res:
+        text = chunk.choices[0]['delta']['content']
+        transcription += text
 
-        async def post_with_stream(*args, **kwargs):
-            kwargs['stream'] = True
-            return await original_post(*args, **kwargs)
-
-        with patch.object(AsyncAPIClient, "post", new=post_with_stream):
-            client = remote_server.get_async_client()
-            res = await client.audio.transcriptions.create(
-                model=model_name,
-                file=winning_call,
-                language="en",
-                temperature=0.0,
-                extra_body=dict(stream=True),
-                timeout=30)
-            # Reconstruct from chunks and validate
-            async for chunk in res:
-                # just a chunk
-                text = chunk.choices[0]['delta']['content']
-                transcription += text
-
-        assert transcription == res_no_stream.text
+    assert transcription == res_no_stream.text
 
 
 @pytest.mark.asyncio
-async def test_stream_options(winning_call):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        original_post = AsyncAPIClient.post
-
-        async def post_with_stream(*args, **kwargs):
-            kwargs['stream'] = True
-            return await original_post(*args, **kwargs)
-
-        with patch.object(AsyncAPIClient, "post", new=post_with_stream):
-            client = remote_server.get_async_client()
-            res = await client.audio.transcriptions.create(
-                model=model_name,
-                file=winning_call,
-                language="en",
-                temperature=0.0,
-                extra_body=dict(stream=True,
-                                stream_include_usage=True,
-                                stream_continuous_usage_stats=True),
-                timeout=30)
-            final = False
-            continuous = True
-            async for chunk in res:
-                if not len(chunk.choices):
-                    # final usage sent
-                    final = True
-                else:
-                    continuous = continuous and hasattr(chunk, 'usage')
-            assert final and continuous
+async def test_stream_options(winning_call, client):
+    res = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=winning_call,
+        language="en",
+        temperature=0.0,
+        stream=True,
+        extra_body=dict(stream_include_usage=True,
+                        stream_continuous_usage_stats=True),
+        timeout=30)
+    final = False
+    continuous = True
+    async for chunk in res:
+        if not len(chunk.choices):
+            # final usage sent
+            final = True
+        else:
+            continuous = continuous and hasattr(chunk, 'usage')
+    assert final and continuous
 
 
 @pytest.mark.asyncio
-async def test_sampling_params(mary_had_lamb):
+async def test_sampling_params(mary_had_lamb, client):
     """
     Compare sampling with params and greedy sampling to assert results
     are different when extreme sampling parameters values are picked. 
     """
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        transcription = await client.audio.transcriptions.create(
-            model=model_name,
-            file=mary_had_lamb,
-            language="en",
-            temperature=0.8,
-            extra_body=dict(seed=42,
-                            repetition_penalty=1.9,
-                            top_k=12,
-                            top_p=0.4,
-                            min_p=0.5,
-                            frequency_penalty=1.8,
-                            presence_penalty=2.0))
+    transcription = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        temperature=0.8,
+        extra_body=dict(seed=42,
+                        repetition_penalty=1.9,
+                        top_k=12,
+                        top_p=0.4,
+                        min_p=0.5,
+                        frequency_penalty=1.8,
+                        presence_penalty=2.0))
 
-        greedy_transcription = await client.audio.transcriptions.create(
-            model=model_name,
-            file=mary_had_lamb,
-            language="en",
-            temperature=0.0,
-            extra_body=dict(seed=42))
+    greedy_transcription = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        temperature=0.0,
+        extra_body=dict(seed=42))
 
-        assert greedy_transcription.text != transcription.text
+    assert greedy_transcription.text != transcription.text
 
 
 @pytest.mark.asyncio
-async def test_audio_prompt(mary_had_lamb):
-    model_name = "openai/whisper-large-v3-turbo"
-    server_args = ["--enforce-eager"]
+async def test_audio_prompt(mary_had_lamb, client):
     prompt = "This is a speech, recorded in a phonograph."
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        #Prompts should not omit the part of original prompt while transcribing.
-        prefix = "The first words I spoke in the original phonograph"
-        client = remote_server.get_async_client()
-        transcription = await client.audio.transcriptions.create(
-            model=model_name,
-            file=mary_had_lamb,
-            language="en",
-            response_format="text",
-            temperature=0.0)
-        out = json.loads(transcription)['text']
-        assert prefix in out
-        transcription_wprompt = await client.audio.transcriptions.create(
-            model=model_name,
-            file=mary_had_lamb,
-            language="en",
-            response_format="text",
-            prompt=prompt,
-            temperature=0.0)
-        out_prompt = json.loads(transcription_wprompt)['text']
-        assert prefix in out_prompt
+    #Prompts should not omit the part of original prompt while transcribing.
+    prefix = "The first words I spoke in the original phonograph"
+    transcription = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        response_format="text",
+        temperature=0.0)
+    out = json.loads(transcription)['text']
+    assert prefix in out
+    transcription_wprompt = await client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        response_format="text",
+        prompt=prompt,
+        temperature=0.0)
+    out_prompt = json.loads(transcription_wprompt)['text']
+    assert prefix in out_prompt

tests/entrypoints/openai/test_translation_validation.py

@@ -4,18 +4,21 @@
 import io
 # imports for guided decoding tests
 import json
-from unittest.mock import patch
 
+import httpx
 import librosa
 import numpy as np
 import pytest
+import pytest_asyncio
 import soundfile as sf
-from openai._base_client import AsyncAPIClient
 
 from vllm.assets.audio import AudioAsset
 
 from ...utils import RemoteOpenAIServer
 
+MODEL_NAME = "openai/whisper-small"
+SERVER_ARGS = ["--enforce-eager"]
+
 
 @pytest.fixture
 def foscolo():
@@ -25,50 +28,23 @@ def foscolo():
         yield f
 
 
-# NOTE: (NickLucche) the large-v3-turbo model was not trained on translation!
-@pytest.mark.asyncio
-async def test_basic_audio(foscolo):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        translation = await client.audio.translations.create(
-            model=model_name,
-            file=foscolo,
-            response_format="text",
-            # TODO remove once language detection is implemented
-            extra_body=dict(language="it"),
-            temperature=0.0)
-        out = json.loads(translation)['text'].strip().lower()
-        assert "greek sea" in out
+@pytest.fixture(scope="module")
+def server():
+    with RemoteOpenAIServer(MODEL_NAME, SERVER_ARGS) as remote_server:
+        yield remote_server
 
 
-@pytest.mark.asyncio
-async def test_audio_prompt(foscolo):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    # Condition whisper on starting text
-    prompt = "Nor have I ever"
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        transcription = await client.audio.translations.create(
-            model=model_name,
-            file=foscolo,
-            prompt=prompt,
-            extra_body=dict(language="it"),
-            response_format="text",
-            temperature=0.0)
-        out = json.loads(transcription)['text']
-        assert "Nor will I ever touch the sacred" not in out
-        assert prompt not in out
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
 
 
 @pytest.mark.asyncio
 async def test_non_asr_model(foscolo):
     # text to text model
     model_name = "JackFram/llama-68m"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
+    with RemoteOpenAIServer(model_name, SERVER_ARGS) as remote_server:
         client = remote_server.get_async_client()
         res = await client.audio.translations.create(model=model_name,
                                                      file=foscolo,
@@ -78,81 +54,117 @@ async def test_non_asr_model(foscolo):
         assert err["message"] == "The model does not support Translations API"
 
 
+# NOTE: (NickLucche) the large-v3-turbo model was not trained on translation!
 @pytest.mark.asyncio
-async def test_streaming_response(foscolo):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
+async def test_basic_audio(foscolo, client):
+    translation = await client.audio.translations.create(
+        model=MODEL_NAME,
+        file=foscolo,
+        response_format="text",
+        # TODO remove once language detection is implemented
+        extra_body=dict(language="it"),
+        temperature=0.0)
+    out = json.loads(translation)['text'].strip().lower()
+    assert "greek sea" in out
+
+
+@pytest.mark.asyncio
+async def test_audio_prompt(foscolo, client):
+    # Condition whisper on starting text
+    prompt = "Nor have I ever"
+    transcription = await client.audio.translations.create(
+        model=MODEL_NAME,
+        file=foscolo,
+        prompt=prompt,
+        extra_body=dict(language="it"),
+        response_format="text",
+        temperature=0.0)
+    out = json.loads(transcription)['text']
+    assert "Nor will I ever touch the sacred" not in out
+    assert prompt not in out
+
+
+@pytest.mark.asyncio
+async def test_streaming_response(foscolo, client, server):
     translation = ""
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        res_no_stream = await client.audio.translations.create(
-            model=model_name,
-            file=foscolo,
-            response_format="json",
-            extra_body=dict(language="it"),
-            temperature=0.0)
-        # Unfortunately this only works when the openai client is patched
-        # to use streaming mode, not exposed in the translation api.
-        original_post = AsyncAPIClient.post
+    res_no_stream = await client.audio.translations.create(
+        model=MODEL_NAME,
+        file=foscolo,
+        response_format="json",
+        extra_body=dict(language="it"),
+        temperature=0.0)
+    # Stream via HTTPX since OpenAI translation client doesn't expose streaming
+    url = server.url_for("v1/audio/translations")
+    headers = {"Authorization": f"Bearer {server.DUMMY_API_KEY}"}
+    data = {
+        "model": MODEL_NAME,
+        "language": "it",
+        "stream": True,
+        "temperature": 0.0,
+    }
+    foscolo.seek(0)
+    async with httpx.AsyncClient() as http_client:
+        files = {"file": foscolo}
+        async with http_client.stream("POST",
+                                      url,
+                                      headers=headers,
+                                      data=data,
+                                      files=files) as response:
+            async for line in response.aiter_lines():
+                if not line:
+                    continue
+                if line.startswith("data: "):
+                    line = line[len("data: "):]
+                if line.strip() == "[DONE]":
+                    break
+                chunk = json.loads(line)
+                text = chunk["choices"][0].get("delta", {}).get("content")
+                translation += text or ""
 
-        async def post_with_stream(*args, **kwargs):
-            kwargs['stream'] = True
-            return await original_post(*args, **kwargs)
-
-        with patch.object(AsyncAPIClient, "post", new=post_with_stream):
-            client = remote_server.get_async_client()
-            res = await client.audio.translations.create(model=model_name,
-                                                         file=foscolo,
-                                                         temperature=0.0,
-                                                         extra_body=dict(
-                                                             stream=True,
-                                                             language="it"))
-            # Reconstruct from chunks and validate
-            async for chunk in res:
-                # just a chunk
-                text = chunk.choices[0]['delta']['content']
-                translation += text
-
-        assert translation == res_no_stream.text
+    assert translation == res_no_stream.text
 
 
 @pytest.mark.asyncio
-async def test_stream_options(foscolo):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        original_post = AsyncAPIClient.post
-
-        async def post_with_stream(*args, **kwargs):
-            kwargs['stream'] = True
-            return await original_post(*args, **kwargs)
-
-        with patch.object(AsyncAPIClient, "post", new=post_with_stream):
-            client = remote_server.get_async_client()
-            res = await client.audio.translations.create(
-                model=model_name,
-                file=foscolo,
-                temperature=0.0,
-                extra_body=dict(language="it",
-                                stream=True,
-                                stream_include_usage=True,
-                                stream_continuous_usage_stats=True))
-            final = False
-            continuous = True
-            async for chunk in res:
-                if not len(chunk.choices):
+async def test_stream_options(foscolo, client, server):
+    url = server.url_for("v1/audio/translations")
+    headers = {"Authorization": f"Bearer {server.DUMMY_API_KEY}"}
+    data = {
+        "model": MODEL_NAME,
+        "language": "it",
+        "stream": True,
+        "stream_include_usage": True,
+        "stream_continuous_usage_stats": True,
+        "temperature": 0.0,
+    }
+    foscolo.seek(0)
+    final = False
+    continuous = True
+    async with httpx.AsyncClient() as http_client:
+        files = {"file": foscolo}
+        async with http_client.stream("POST",
+                                      url,
+                                      headers=headers,
+                                      data=data,
+                                      files=files) as response:
+            async for line in response.aiter_lines():
+                if not line:
+                    continue
+                if line.startswith("data: "):
+                    line = line[len("data: "):]
+                if line.strip() == "[DONE]":
+                    break
+                chunk = json.loads(line)
+                choices = chunk.get("choices", [])
+                if not choices:
                     # final usage sent
                     final = True
                 else:
-                    continuous = continuous and hasattr(chunk, 'usage')
-            assert final and continuous
+                    continuous = continuous and ("usage" in chunk)
+    assert final and continuous
 
 
 @pytest.mark.asyncio
-async def test_long_audio_request(foscolo):
-    model_name = "openai/whisper-small"
-    server_args = ["--enforce-eager"]
-
+async def test_long_audio_request(foscolo, client):
     foscolo.seek(0)
     audio, sr = librosa.load(foscolo)
     repeated_audio = np.tile(audio, 2)
@@ -160,13 +172,11 @@ async def test_long_audio_request(foscolo):
     buffer = io.BytesIO()
     sf.write(buffer, repeated_audio, sr, format='WAV')
     buffer.seek(0)
-    with RemoteOpenAIServer(model_name, server_args) as remote_server:
-        client = remote_server.get_async_client()
-        translation = await client.audio.translations.create(
-            model=model_name,
-            file=buffer,
-            extra_body=dict(language="it"),
-            response_format="text",
-            temperature=0.0)
-        out = json.loads(translation)['text'].strip().lower()
-        assert out.count("greek sea") == 2
+    translation = await client.audio.translations.create(
+        model=MODEL_NAME,
+        file=buffer,
+        extra_body=dict(language="it"),
+        response_format="text",
+        temperature=0.0)
+    out = json.loads(translation)['text'].strip().lower()
+    assert out.count("greek sea") == 2

tests/entrypoints/openai/tool_parsers/test_hermes_tool_parser.py

@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+
+import pytest
+
+from ....utils import RemoteOpenAIServer
+
+MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+LORA_MODEL = "minpeter/LoRA-Llama-3.2-1B-tool-vllm-ci"
+
+SERVER_ARGS = [
+    "--enforce-eager",
+    "--enable-auto-tool-choice",
+    "--tool-call-parser",
+    "hermes",
+    "--enable-lora",
+    "--lora-modules",
+    f"{LORA_MODEL}={LORA_MODEL}",
+]
+
+TOOLS = [{
+    "type": "function",
+    "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description":
+                    "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"]
+                },
+            },
+            "required": ["location"],
+        },
+    },
+}]
+
+MESSAGES = [{"role": "user", "content": "What's the weather like in Boston?"}]
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_tool_call():
+    """Test tool call in non-streaming mode."""
+    with RemoteOpenAIServer(MODEL_NAME, SERVER_ARGS) as server:
+        client = server.get_async_client()
+
+        response = await client.chat.completions.create(
+            model=LORA_MODEL,
+            messages=MESSAGES,
+            tools=TOOLS,
+            tool_choice="auto",
+            temperature=0.0,
+        )
+
+        assert response.choices
+        choice = response.choices[0]
+        message = choice.message
+
+        assert choice.finish_reason == "tool_calls"
+        assert message.tool_calls is not None
+
+        tool_call = message.tool_calls[0]
+        assert tool_call.type == "function"
+        assert tool_call.function.name == "get_current_weather"
+
+        arguments = json.loads(tool_call.function.arguments)
+        assert "location" in arguments
+        assert "Boston" in arguments["location"]
+        print("\n[Non-Streaming Test Passed]")
+        print(f"Tool Call: {tool_call.function.name}")
+        print(f"Arguments: {arguments}")
+
+
+@pytest.mark.asyncio
+async def test_streaming_tool_call():
+    """Test tool call in streaming mode."""
+    with RemoteOpenAIServer(MODEL_NAME, SERVER_ARGS) as server:
+        client = server.get_async_client()
+
+        stream = await client.chat.completions.create(
+            model=LORA_MODEL,
+            messages=MESSAGES,
+            tools=TOOLS,
+            tool_choice="auto",
+            temperature=0.0,
+            stream=True,
+        )
+
+        tool_call_chunks = {}
+        async for chunk in stream:
+            if not chunk.choices:
+                continue
+
+            delta = chunk.choices[0].delta
+            if not delta or not delta.tool_calls:
+                continue
+
+            for tool_chunk in delta.tool_calls:
+                index = tool_chunk.index
+                if index not in tool_call_chunks:
+                    tool_call_chunks[index] = {"name": "", "arguments": ""}
+
+                if tool_chunk.function.name:
+                    tool_call_chunks[index]["name"] += tool_chunk.function.name
+                if tool_chunk.function.arguments:
+                    tool_call_chunks[index][
+                        "arguments"] += tool_chunk.function.arguments
+
+        assert len(tool_call_chunks) == 1
+        reconstructed_tool_call = tool_call_chunks[0]
+
+        assert reconstructed_tool_call["name"] == "get_current_weather"
+
+        arguments = json.loads(reconstructed_tool_call["arguments"])
+        assert "location" in arguments
+        assert "Boston" in arguments["location"]
+        print("\n[Streaming Test Passed]")
+        print(f"Reconstructed Tool Call: {reconstructed_tool_call['name']}")
+        print(f"Reconstructed Arguments: {arguments}")

tests/kernels/attention/test_aiter_flash_attn.py

@@ -9,10 +9,10 @@ import torch
 import vllm.v1.attention.backends.rocm_aiter_fa  # noqa: F401
 from vllm.platforms import current_platform
 
-NUM_HEADS = [(4, 4), (8, 2), (16, 2)]
+NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]
-BLOCK_SIZES = [16, 32]
-DTYPES = [torch.float16, torch.bfloat16]
+BLOCK_SIZES = [16]
+DTYPES = [torch.bfloat16]
 QDTYPES = [None]
 # one value large enough to test overflow in index calculation.
 # one value small enough to test the schema op check

tests/kernels/attention/test_attention.py

@@ -29,17 +29,14 @@ MAX_SEQ_LEN = get_max_shared_memory_bytes() // FLOAT32_BYTES - 512
 NUM_BLOCKS = 4321  # Arbitrary values for testing
 PARTITION_SIZE = 512
 PARTITION_SIZE_ROCM = 256
-# flshattF and tritonflashattF supported: {torch.float16, torch.bfloat16}
-DTYPES = [
-    torch.half, torch.bfloat16, torch.float
-] if not current_platform.is_rocm() else [torch.half, torch.bfloat16]
+DTYPES = [torch.bfloat16]
 NUM_GEN_SEQS = [7]  # Arbitrary values for testing
 NUM_PREFILL_SEQS = [3]  # Arbitrary values for testing
 NUM_HEADS = [(40, 40), (64, 8)]  # Arbitrary values for testing
 
 # This should be sync with get_supported_head_sizes() in
 # vllm.attention.ops.paged_attn.PagedAttention
-HEAD_SIZES = [32, 64, 80, 96, 112, 120, 128, 192, 256]
+HEAD_SIZES = [32, 80, 128, 256]
 
 BLOCK_SIZES = [16, 32]
 USE_ALIBI = [False, True]

tests/kernels/attention/test_cache.py

@@ -11,11 +11,11 @@ from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
 
 COPYING_DIRECTION = [('cuda', 'cpu'), ('cuda', 'cuda'), ('cpu', 'cuda')]
-DTYPES = [torch.half, torch.bfloat16, torch.float]
+DTYPES = [torch.bfloat16, torch.float]
 NUM_TOKENS = [42]  # Arbitrary values for testing
 NUM_LAYERS = [1]  # Arbitrary values for testing
 NUM_HEADS = [8]  # Arbitrary values for testing
-HEAD_SIZES = [64, 80, 120, 256]
+HEAD_SIZES = [64, 80, 256]
 BLOCK_SIZES = [8, 16, 32]
 CACHE_LAYOUTS = ["NHD", "HND"]
 

tests/kernels/attention/test_flash_attn.py

@@ -12,14 +12,16 @@ from vllm.vllm_flash_attn import (fa_version_unsupported_reason,
                                   flash_attn_with_kvcache,
                                   is_fa_version_supported)
 
-NUM_HEADS = [(4, 4), (8, 2), (16, 2)]
+NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]
-BLOCK_SIZES = [16, 32]
-DTYPES = [torch.float16, torch.bfloat16]
+BLOCK_SIZES = [16]
+DTYPES = [torch.bfloat16]
 QDTYPES = [None, torch.float8_e4m3fn]
 # one value large enough to test overflow in index calculation.
 # one value small enough to test the schema op check
 NUM_BLOCKS = [32768, 2048]
+SOFT_CAPS = [None, 50.0]
+SLIDING_WINDOWS = [None, 256]
 
 
 def ref_paged_attn(
@@ -83,9 +85,9 @@ def ref_paged_attn(
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
 @pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
-@pytest.mark.parametrize("sliding_window", [None, 256])
+@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
 @pytest.mark.parametrize("fa_version", [2, 3])
 @pytest.mark.parametrize("q_dtype", QDTYPES)
 @torch.inference_mode()
@@ -198,9 +200,9 @@ def test_flash_attn_with_paged_kv(
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
-@pytest.mark.parametrize("sliding_window", [None, 256])
+@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
 @pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
 @pytest.mark.parametrize("fa_version", [2, 3])
 @pytest.mark.parametrize("q_dtype", QDTYPES)

tests/kernels/attention/test_flashinfer.py

@@ -9,11 +9,13 @@ import torch
 
 from vllm.platforms import current_platform
 
-NUM_HEADS = [(16, 16), (32, 8), (64, 8), (6, 1)]
+NUM_HEADS = [(32, 8), (6, 1)]
 HEAD_SIZES = [128, 256]
 BLOCK_SIZES = [16, 32]
-DTYPES = [torch.float16, torch.bfloat16]
+DTYPES = [torch.bfloat16]
 NUM_BLOCKS = 32768  # Large enough to test overflow in index calculation.
+SOFT_CAPS = [None, 30.0]
+SLIDING_WINDOWS = [None, 64]
 
 
 def ref_paged_attn(
@@ -76,8 +78,8 @@ def ref_paged_attn(
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
-@pytest.mark.parametrize("sliding_window", [None, 64])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
+@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
 @torch.inference_mode
 def test_flashinfer_decode_with_paged_kv(
     kv_lens: list[int],
@@ -173,8 +175,8 @@ def test_flashinfer_decode_with_paged_kv(
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
-@pytest.mark.parametrize("sliding_window", [None, 64])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
+@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
 @torch.inference_mode
 def test_flashinfer_prefill_with_paged_kv(
     seq_lens: list[tuple[int, int]],
@@ -278,11 +280,11 @@ def test_flashinfer_prefill_with_paged_kv(
 
 
 @pytest.mark.parametrize("seq_lens", [[(1, 132), (5, 18)]])
-@pytest.mark.parametrize("num_heads", [(32, 8), (6, 1)])
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
 def test_flashinfer_prefill_with_paged_fp8_kv(
         seq_lens: list[tuple[int, int]], num_heads: tuple[int, int],
         head_size: int, dtype: torch.dtype, block_size: int,
@@ -385,11 +387,12 @@ def test_flashinfer_prefill_with_paged_fp8_kv(
 
 
 @pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
-@pytest.mark.parametrize("num_heads", [(32, 8), (64, 8), (6, 1)])
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
+@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
+@pytest.mark.skip(reason="TODO: fix the accuracy issue")
 @torch.inference_mode
 def test_flashinfer_decode_with_paged_fp8_kv(
     kv_lens: list[int],
@@ -399,7 +402,6 @@ def test_flashinfer_decode_with_paged_fp8_kv(
     block_size: int,
     soft_cap: Optional[float],
 ) -> None:
-    pytest.skip("TODO: fix the accuracy issue")
     # test doesn't work for num_heads = (16,16)
     torch.set_default_device("cuda")
     current_platform.seed_everything(0)

tests/kernels/attention/test_flashinfer_trtllm_attention.py

@@ -20,11 +20,11 @@ FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 MAX_Q_LEN = 1024
 MAX_KV_LEN = 4096
 BATCH_SIZES = [4, 12]
-NUM_HEADS = [(64, 8), (16, 16), (40, 8), (32, 8)]
+NUM_HEADS = [(16, 16), (40, 8)]
 HEAD_SIZES = [128]
-BLOCK_SIZES = [16, 32]
+BLOCK_SIZES = [16]
 KV_LAYOUTS = ["HND"]
-DTYPES = [torch.float16, torch.bfloat16]
+DTYPES = [torch.bfloat16]
 KV_CACHE_DTYPES = [None, current_platform.fp8_dtype()]
 NUM_BLOCKS = 32768  # Large enough to test overflow in index calculation.
 SOFT_CAPS = [None, 50.0]
@@ -113,7 +113,7 @@ def test_flashinfer_trtllm_decode_with_baseline(
     kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
     kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
 
-    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
+    workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
     wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
         workspace_buffer,
         kv_layout,
@@ -247,7 +247,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
     kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
 
-    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
+    workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
     wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
         workspace_buffer, kv_layout)
     wrapper.plan(q_indptr,

tests/kernels/attention/test_prefix_prefill.py

@@ -19,13 +19,13 @@ from vllm.platforms import current_platform
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 
 NUM_HEADS = [64]
-NUM_QUERIES_PER_KV = [1, 8, 64]
-HEAD_SIZES = [128, 96, 24]
+NUM_QUERIES_PER_KV = [1, 64]
+HEAD_SIZES = [24, 128]
 DTYPES = [torch.float16]
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ]
-SLIDING_WINDOW = [0, 16, 64, 128, 256, 512, 2048]
+SLIDING_WINDOW = [0, 16, 2048]
 KV_CACHE_DTYPES = ["auto", "fp8", "fp8_e5m2"]
 
 OPS = [chunked_prefill_paged_decode, context_attention_fwd]

tests/kernels/attention/test_triton_unified_attention.py

@@ -9,11 +9,11 @@ import torch
 from vllm.attention.ops.triton_unified_attention import unified_attention
 from vllm.platforms import current_platform
 
-NUM_HEADS = [(4, 4), (8, 2), (16, 2)]
+NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]
-BLOCK_SIZES = [16, 32]
+BLOCK_SIZES = [16]
 
-DTYPES = [torch.float16, torch.bfloat16]
+DTYPES = [torch.bfloat16]
 QDTYPES = [None, torch.float8_e4m3fn] if not current_platform.is_rocm() else [
     None, torch.float8_e4m3fnuz
 ]
@@ -85,7 +85,7 @@ def ref_paged_attn(
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("sliding_window", [None, 256])
 @pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
+@pytest.mark.parametrize("soft_cap", [None, 50.0])
 @pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
 @pytest.mark.parametrize("q_dtype", QDTYPES)
 @torch.inference_mode()

tests/kernels/core/test_activation.py

@@ -11,7 +11,7 @@ from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul,
                                                    GeluAndMul, MulAndSilu,
                                                    NewGELU, QuickGELU,
-                                                   SiluAndMul)
+                                                   SiluAndMul, SwigluOAIAndMul)
 from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
@@ -25,7 +25,15 @@ CUDA_DEVICES = [
 
 @pytest.mark.parametrize(
     "activation",
-    ["silu_and_mul", "mul_and_silu", "gelu", "gelu_tanh", "fatrelu"])
+    [
+        "silu_and_mul",
+        "mul_and_silu",
+        "gelu",
+        "gelu_tanh",
+        "fatrelu",
+        "swigluoai_and_mul",
+    ],
+)
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("d", D)
 @pytest.mark.parametrize("dtype", DTYPES)
@@ -59,18 +67,43 @@ def test_act_and_mul(
         threshold = random.uniform(0, 1)
         layer = FatreluAndMul(threshold)
         fn = torch.ops._C.fatrelu_and_mul
+    elif activation == "swigluoai_and_mul":
+        layer = SwigluOAIAndMul()
+        fn = torch.ops._C.swigluoai_and_mul
     out = layer(x)
     ref_out = layer.forward_native(x)
-    # The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
-    # equivalent to the native PyTorch implementations, so we can do exact
-    # comparison.
-    torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
+    if activation == "swigluoai_and_mul":
+
+        rtol = {
+            #For fp16, change the relative tolerance from 1e-3 to 2e-3
+            torch.float16:
+            2e-3,
+            torch.bfloat16:
+            2e-2,
+            torch.float:
+            1.3e-6
+        }
+
+        def _get_rtol(output) -> float:
+            return rtol[output.dtype]
+
+        torch.testing.assert_close(out,
+                                   ref_out,
+                                   atol=get_default_atol(out),
+                                   rtol=_get_rtol(out))
+    else:
+        # The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
+        # equivalent to the native PyTorch implementations, so we can do exact
+        # comparison.
+        torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
 
     d = x.shape[-1] // 2
     output_shape = (x.shape[:-1] + (d, ))
     out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
     if activation == "fatrelu":
         opcheck(fn, (out, x, threshold))
+    elif activation == "swigluoai_and_mul":
+        opcheck(fn, (out, x, layer.alpha, layer.limit))
     else:
         opcheck(fn, (out, x))
 

tests/kernels/mamba/test_mamba_ssm_ssd.py

@@ -9,7 +9,7 @@ from einops import rearrange, repeat
 from vllm.model_executor.layers.mamba.ops.ssd_combined import (
     mamba_chunk_scan_combined)
 from vllm.platforms import current_platform
-from vllm.v1.attention.backends.mamba_attn import (
+from vllm.v1.attention.backends.mamba2_attn import (
     _query_start_loc_to_chunk_indices_offsets)
 
 # Added by the IBM Team, 2024

tests/kernels/moe/modular_kernel_tools/common.py

@@ -7,41 +7,22 @@ import torch
 
 import vllm._custom_ops as ops
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from tests.kernels.moe.utils import make_test_weights, per_token_cast_to_fp8
+from tests.kernels.quantization.nvfp4_utils import (FLOAT4_E2M1_MAX,
+                                                    FLOAT8_E4M3_MAX,
+                                                    dequantize_nvfp4_to_dtype)
 from tests.kernels.utils import torch_experts
 from vllm.config import VllmConfig
 from vllm.distributed import get_dp_group, get_tensor_model_parallel_world_size
-# Fused experts and PrepareFinalize imports
-from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
-    BatchedDeepGemmExperts)
-from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
-    BatchedTritonOrDeepGemmExperts)
+from vllm.forward_context import set_forward_context
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig, FusedMoEParallelConfig, FusedMoEQuantConfig)
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
-from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
-from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
-    BatchedTritonExperts, NaiveBatchedExperts)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
-from vllm.model_executor.layers.fused_moe.layer import (FusedMoEMethodBase,
-                                                        TritonExperts)
-from vllm.model_executor.layers.fused_moe.prepare_finalize import (
-    MoEPrepareAndFinalizeNoEP)
-from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
-    TritonOrDeepGemmExperts)
 from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx
 
+from .mk_objects import (expert_info, make_fused_experts,
+                         make_prepare_finalize, prepare_finalize_info)
 from .parallel_utils import ProcessGroupInfo
-from .utils import (make_block_quant_fp8_weights, make_non_quant_weights,
-                    make_quant_fp8_weights, per_token_cast_to_fp8)
-
-if has_pplx():
-    from vllm.model_executor.layers.fused_moe.pplx_prepare_finalize import (
-        PplxPrepareAndFinalize)
-if has_deep_ep():
-    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
-        DeepEPHTPrepareAndFinalize)
-    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
-        DeepEPLLPrepareAndFinalize)
 
 
 def _describe_tensor(t: Optional[torch.Tensor], name: str) -> str:
@@ -69,24 +50,31 @@ class Config:
 
     torch_trace_dir_path: Optional[str] = None
 
+    def __post_init__(self):
+        if self.quant_config is None:
+            self.quant_config = FusedMoEQuantConfig()
+
     def describe(self) -> str:
         s = ""
-        s += "== Config: \n"
-        s += f" world_size={self.world_size} \n"
-        s += f" PF={self.prepare_finalize_type.__name__} \n"
-        s += f" FE={self.fused_experts_type.__name__} \n"
-        s += f" topk={self.topks} \n"
-        s += f" dtype={self.dtype} \n"
-        s += f" fused_moe_chunk_size={self.fused_moe_chunk_size} \n"
-        s += " Quant: \n"
-        s += f" fused_moe_chunk_size={self.fused_moe_chunk_size} \n "
+        s += "== Config:\n"
+        s += f" world_size={self.world_size}\n"
+        s += f" PF={self.prepare_finalize_type.__name__}\n"
+        s += f" FE={self.fused_experts_type.__name__}\n"
+        s += f" E={self.E}\n"
+        s += f" Ms={self.Ms}\n"
+        s += f" N={self.N}\n"
+        s += f" K={self.K}\n"
+        s += f" topk={self.topks}\n"
+        s += f" dtype={self.dtype}\n"
+        s += f" fused_moe_chunk_size={self.fused_moe_chunk_size}\n"
+        s += " Quant:\n"
         if self.quant_config is not None:
-            s += f"     q_dtype={self.quant_dtype} \n"
-            s += f"     q_block_shape={self.quant_block_shape} \n"
-            s += f"     q_per_out_ch_quant={self.is_per_out_ch_quant} \n"
-            s += f"     q_per_act_token={self.is_per_act_token_quant} \n"
+            s += f"     q_dtype={self.quant_dtype}\n"
+            s += f"     q_block_shape={self.quant_block_shape}\n"
+            s += f"     q_per_out_ch_quant={self.is_per_out_ch_quant}\n"
+            s += f"     q_per_act_token={self.is_per_act_token_quant}\n"
         else:
-            s += "     quant=None \n"
+            s += "     quant=None\n"
         return s
 
     @property
@@ -95,34 +83,28 @@ class Config:
         return self.Ms
 
     @property
-    def quant_dtype(self) -> Optional[torch.dtype]:
-        if self.quant_config is None:
-            return None
+    def quant_dtype(self) -> Union[torch.dtype, str, None]:
+        assert self.quant_config is not None
         return self.quant_config.quant_dtype
 
     @property
     def is_per_act_token_quant(self) -> bool:
-        if self.quant_config is None:
-            return False
+        assert self.quant_config is not None
         return self.quant_config.per_act_token_quant
 
     @property
     def is_per_tensor_act_quant(self) -> bool:
-        if self.quant_config is None:
-            return False
         return (not self.is_per_act_token_quant
                 and self.quant_block_shape is None)
 
     @property
     def is_per_out_ch_quant(self) -> bool:
-        if self.quant_config is None:
-            return False
+        assert self.quant_config is not None
         return self.quant_config.per_out_ch_quant
 
     @property
     def quant_block_shape(self) -> Optional[list[int]]:
-        if self.quant_config is None:
-            return None
+        assert self.quant_config is not None
         return self.quant_config.block_shape
 
     @property
@@ -130,36 +112,30 @@ class Config:
         assert isinstance(self.topks, int)
         return self.topks
 
-    @property
-    def topk_ids_dtype(self) -> Optional[torch.dtype]:
-        topk_ids_dtype = None
-        if self.prepare_finalize_type == PplxPrepareAndFinalize:
-            topk_ids_dtype = torch.uint32
-        elif self.prepare_finalize_type in [
-                DeepEPHTPrepareAndFinalize, DeepEPLLPrepareAndFinalize
-        ]:
-            topk_ids_dtype = torch.int64
-        return topk_ids_dtype
-
     @property
     def num_local_experts(self) -> int:
         return self.E // self.world_size
 
     def make_env_data(self) -> tuple[VllmConfig, dict[Any, Any]]:
         """
-        make env data for vllm launch. 
+        make env data for vllm launch.
         """
         vllm_config = VllmConfig()
         vllm_config.parallel_config.data_parallel_size = self.world_size
         vllm_config.parallel_config.enable_expert_parallel = True
 
         env_dict = {
-            "VLLM_ALL2ALL_BACKEND": self.all2all_backend(),
             "VLLM_USE_DEEP_GEMM": str(int(self.needs_deep_gemm())),
         }
+
+        backend = self.all2all_backend()
+        if backend is not None:
+            env_dict.update({"VLLM_ALL2ALL_BACKEND": backend})
+
         if self.fused_moe_chunk_size is not None:
             env_dict.update(
                 {"VLLM_FUSED_MOE_CHUNK_SIZE": str(self.fused_moe_chunk_size)})
+
         return vllm_config, env_dict
 
     def is_fp8_block_quantized(self):
@@ -167,85 +143,59 @@ class Config:
                 and self.quant_block_shape is not None)
 
     def is_batched_prepare_finalize(self):
-        return self.prepare_finalize_type in [
-            PplxPrepareAndFinalize, DeepEPLLPrepareAndFinalize
-        ]
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return (mk.FusedMoEActivationFormat.BatchedExperts ==
+                info.activation_format)
 
     def is_batched_fused_experts(self):
-        return self.fused_experts_type in [
-            CutlassExpertsFp8, BatchedDeepGemmExperts, BatchedTritonExperts,
-            NaiveBatchedExperts, BatchedTritonOrDeepGemmExperts
-        ]
+        info = expert_info(self.fused_experts_type)
+        return (mk.FusedMoEActivationFormat.BatchedExperts ==
+                info.activation_format)
 
     def is_standard_fused_experts(self):
-        return self.fused_experts_type in [
-            CutlassExpertsFp8, DeepGemmExperts, TritonOrDeepGemmExperts,
-            TritonExperts
-        ]
+        info = expert_info(self.fused_experts_type)
+        return mk.FusedMoEActivationFormat.Standard == info.activation_format
 
-    def is_fe_16bit_supported(self):
-        return self.fused_experts_type in [
-            BatchedTritonExperts, BatchedTritonOrDeepGemmExperts,
-            NaiveBatchedExperts, TritonExperts
-        ]
+    def fe_supported_types(self):
+        info = expert_info(self.fused_experts_type)
+        return info.supported_dtypes
 
-    def is_fe_fp8_supported(self):
-        return self.fused_experts_type in [
-            BatchedDeepGemmExperts,
-            BatchedTritonExperts,
-            BatchedTritonOrDeepGemmExperts,
-            CutlassExpertsFp8,
-            DeepGemmExperts,
-            TritonExperts,
-            TritonOrDeepGemmExperts,
-            NaiveBatchedExperts,
-        ]
+    def pf_supported_types(self):
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return info.supported_dtypes
 
-    def is_fe_block_fp8_supported(self):
-        return self.fused_experts_type in [
-            BatchedDeepGemmExperts,
-            BatchedTritonOrDeepGemmExperts,
-            DeepGemmExperts,
-            TritonExperts,
-            TritonOrDeepGemmExperts,
-            BatchedTritonExperts,
-            NaiveBatchedExperts,
-        ]
+    def is_block_quant_supported(self):
+        info = expert_info(self.fused_experts_type)
+        return info.blocked_quantization_support
 
     def is_fe_supports_chunking(self):
-        return self.fused_experts_type in [
-            CutlassExpertsFp8, DeepGemmExperts, TritonOrDeepGemmExperts,
-            TritonExperts
-        ]
+        info = expert_info(self.fused_experts_type)
+        return info.supports_chunking
+
+    def supports_expert_map(self):
+        info = expert_info(self.fused_experts_type)
+        return info.supports_expert_map
+
+    def supports_apply_weight_on_input(self):
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return info.supports_apply_weight_on_input
 
     def needs_deep_gemm(self):
-        return self.fused_experts_type in [
-            BatchedDeepGemmExperts,
-            DeepGemmExperts,
-        ]
+        info = expert_info(self.fused_experts_type)
+        return info.needs_deep_gemm
 
     def needs_pplx(self):
-        return self.prepare_finalize_type in [PplxPrepareAndFinalize]
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return info.backend == "pplx"
 
     def needs_deep_ep(self):
-        return self.prepare_finalize_type in [
-            DeepEPHTPrepareAndFinalize, DeepEPLLPrepareAndFinalize
-        ]
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return (info.backend == "deepep_high_throughput"
+                or info.backend == "deepep_low_latency")
 
     def all2all_backend(self):
-        if self.needs_pplx():
-            return "pplx"
-        if self.prepare_finalize_type == DeepEPHTPrepareAndFinalize:
-            return "deepep_high_throughput"
-        if self.prepare_finalize_type == DeepEPLLPrepareAndFinalize:
-            return "deepep_low_latency"
-        return "naive"
-
-    def needs_all2all(self):
-        return self.prepare_finalize_type in [
-            PplxPrepareAndFinalize, DeepEPHTPrepareAndFinalize,
-            DeepEPLLPrepareAndFinalize
-        ]
+        info = prepare_finalize_info(self.prepare_finalize_type)
+        return info.backend
 
     def is_valid(self):
         # Check prepare-finalize and fused-experts compatibility
@@ -267,28 +217,28 @@ class Config:
             # invalid quant config
             return False
 
-        # check bf16 / fp16 support
-        is_16bit = (self.dtype.itemsize == 2 and self.quant_dtype is None)
-        if is_16bit and not self.is_fe_16bit_supported():
-            return False
+        # check type support
+        if self.quant_dtype is None:
+            if (self.dtype not in self.pf_supported_types()
+                    or self.dtype not in self.fe_supported_types()):
+                return False
+        else:
+            if (self.quant_dtype not in self.pf_supported_types()
+                    or self.quant_dtype not in self.fe_supported_types()):
+                return False
 
-        # Check fp8 support
-        is_fp8 = self.quant_dtype == torch.float8_e4m3fn
-        if is_fp8 and not self.is_fe_fp8_supported():
-            return False
-
-        # Check fp8 block quanization support
+        # Check block quanization support
         is_block_quatized = self.quant_block_shape is not None
-        if is_block_quatized and not is_fp8:
+        if is_block_quatized and self.quant_dtype is None:
             return False
-        if is_block_quatized and not self.is_fe_block_fp8_supported():
+        if is_block_quatized and not self.is_block_quant_supported():
             return False
 
         # deep_gemm only works with block-quantized
         if self.needs_deep_gemm() and not is_block_quatized:
             return False
 
-        # Check dependencies
+        # Check dependencies (turn into asserts?)
         if self.needs_deep_ep() and not has_deep_ep():
             return False
         if self.needs_deep_gemm() and not has_deep_gemm():
@@ -305,6 +255,8 @@ class WeightTensors:
     w2: torch.Tensor
     w1_scale: Optional[torch.Tensor]
     w2_scale: Optional[torch.Tensor]
+    w1_gs: Optional[torch.Tensor] = None
+    w2_gs: Optional[torch.Tensor] = None
 
     def describe(self):
         s = ""
@@ -313,13 +265,20 @@ class WeightTensors:
         s += f' - {_describe_tensor(self.w2, "w2")} \n'
         s += f' - {_describe_tensor(self.w1_scale, "w1_scale")} \n'
         s += f' - {_describe_tensor(self.w2_scale, "w2_scale")} \n'
+        s += f' - {_describe_tensor(self.w1_gs, "w1_gs")} \n'
+        s += f' - {_describe_tensor(self.w2_gs, "w2_gs")} \n'
         return s
 
+    def is_quantized(self) -> bool:
+        # or w1_scale is not None?
+        return (self.w1.dtype == torch.float8_e4m3fn
+                or self.w1.dtype == torch.uint8 or self.w1.dtype == torch.int8)
+
     def to_current_device(self):
         self.w1 = self.w1.to(device=torch.cuda.current_device())
         self.w2 = self.w2.to(device=torch.cuda.current_device())
-        is_quantized = self.w1.dtype == torch.float8_e4m3fn
-        if is_quantized:
+
+        if self.is_quantized():
             assert self.w1_scale is not None
             assert self.w2_scale is not None
             self.w1_scale = self.w1_scale.to(
@@ -327,56 +286,51 @@ class WeightTensors:
             self.w2_scale = self.w2_scale.to(
                 device=torch.cuda.current_device())
 
+        if self.w1_gs is not None:
+            assert self.w2_gs is not None
+            self.w1_gs = self.w1_gs.to(device=torch.cuda.current_device())
+            self.w2_gs = self.w2_gs.to(device=torch.cuda.current_device())
+
     def slice_weights(self, rank: int,
                       num_local_experts: int) -> "WeightTensors":
         s = rank * num_local_experts
         e = s + num_local_experts
         w1 = self.w1[s:e, :, :]
         w2 = self.w2[s:e, :, :]
-        is_quantized = self.w1.dtype == torch.float8_e4m3fn
+
         w1_scale, w2_scale = (None, None)
-        if is_quantized:
+        if self.is_quantized():
             assert self.w1_scale is not None
             assert self.w2_scale is not None
             w1_scale = self.w1_scale[s:e, :, :]
             w2_scale = self.w2_scale[s:e, :, :]
-        return WeightTensors(w1, w2, w1_scale, w2_scale)
+
+        w1_gs = self.w1_gs
+        w2_gs = self.w2_gs
+        if w1_gs is not None:
+            assert w2_gs is not None
+            w1_gs = w1_gs[s:e]
+            w2_gs = w2_gs[s:e]
+
+        return WeightTensors(w1, w2, w1_scale, w2_scale, w1_gs, w2_gs)
 
     @staticmethod
     def make(config: Config) -> "WeightTensors":
-
-        if config.quant_dtype is None:
-            # just make normal dtype weights
-            w1, w2 = make_non_quant_weights(e=config.E,
-                                            n=config.N,
-                                            k=config.K,
-                                            dtype=config.dtype)
-            return WeightTensors(w1=w1, w2=w2, w1_scale=None, w2_scale=None)
-
-        assert config.quant_dtype == torch.float8_e4m3fn
-        if not config.is_fp8_block_quantized():
-            w1, w2, w1_scale, w2_scale = make_quant_fp8_weights(
-                e=config.E,
-                n=config.N,
-                k=config.K,
-                per_out_channel_quant=config.is_per_out_ch_quant,
-            )
-            return WeightTensors(w1=w1,
-                                 w2=w2,
-                                 w1_scale=w1_scale,
-                                 w2_scale=w2_scale)
-
-        assert config.quant_block_shape is not None
-        w1, w2, w1_scale, w2_scale = make_block_quant_fp8_weights(
+        (_, w1, w1_scale, w1_gs), (_, w2, w2_scale, w2_gs) = make_test_weights(
             e=config.E,
             n=config.N,
             k=config.K,
-            block_size=config.quant_block_shape,
+            in_dtype=config.dtype,
+            quant_dtype=config.quant_dtype,
+            block_shape=config.quant_block_shape,
+            per_act_token_quant=config.is_per_out_ch_quant,
         )
         return WeightTensors(w1=w1,
                              w2=w2,
                              w1_scale=w1_scale,
-                             w2_scale=w2_scale)
+                             w2_scale=w2_scale,
+                             w1_gs=w1_gs,
+                             w2_gs=w2_gs)
 
 
 @dataclass
@@ -449,7 +403,6 @@ class RankTensors:
                             dtype=dtype)
         topk_weights, topk_ids, _ = fused_topk(hidden_states, score, topk,
                                                False)
-        topk_ids = topk_ids.to(config.topk_ids_dtype)
 
         # distribute topk_ids evenly
         for mi in range(m):
@@ -457,7 +410,7 @@ class RankTensors:
         topk_ids = topk_ids.to(device=torch.cuda.current_device())
 
         expert_map = None
-        if config.world_size > 1:
+        if config.world_size > 1 and config.supports_expert_map():
             expert_map = torch.full((global_num_experts, ),
                                     fill_value=-1,
                                     dtype=torch.int32)
@@ -480,92 +433,100 @@ class RankTensors:
 def reference_moe_impl(config: Config, weights: WeightTensors,
                        rank_tensors: RankTensors) -> torch.Tensor:
 
-    return torch_experts(a=rank_tensors.hidden_states,
-                         w1=weights.w1,
-                         w2=weights.w2,
+    if config.quant_dtype == "nvfp4":
+        quant_blocksize = 16
+        dtype = config.dtype
+
+        w1_q = weights.w1
+        w1_blockscale = weights.w1_scale
+        w1_gs = weights.w1_gs
+
+        w2_q = weights.w2
+        w2_blockscale = weights.w2_scale
+        w2_gs = weights.w2_gs
+
+        a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(
+            rank_tensors.hidden_states.flatten(), dim=-1)).to(torch.float32)
+
+        assert w1_gs is not None
+        assert w2_gs is not None
+        assert w1_blockscale is not None
+        assert w2_blockscale is not None
+
+        assert w1_blockscale.shape[1] % 128 == 0
+        assert w1_blockscale.shape[2] % 4 == 0
+        assert w2_blockscale.shape[1] % 128 == 0
+        assert w2_blockscale.shape[2] % 4 == 0
+
+        a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(
+            rank_tensors.hidden_states, a_global_scale)
+
+        a = dequantize_nvfp4_to_dtype(a_fp4,
+                                      a_scale_interleaved,
+                                      a_global_scale,
+                                      dtype=dtype,
+                                      device=a_fp4.device,
+                                      block_size=quant_blocksize)
+
+        e = w1_q.shape[0]
+        n = w1_q.shape[1] // 2
+        k = w2_q.shape[1]
+
+        w1 = torch.zeros((e, 2 * n, k), device="cuda", dtype=dtype)
+        w2 = torch.zeros((e, k, n), device="cuda", dtype=dtype)
+
+        for idx in range(0, e):
+            w1[idx] = dequantize_nvfp4_to_dtype(w1_q[idx],
+                                                w1_blockscale[idx],
+                                                w1_gs[idx],
+                                                dtype=dtype,
+                                                device=w1_q.device,
+                                                block_size=quant_blocksize)
+            w2[idx] = dequantize_nvfp4_to_dtype(w2_q[idx],
+                                                w2_blockscale[idx],
+                                                w2_gs[idx],
+                                                dtype=dtype,
+                                                device=w2_q.device,
+                                                block_size=quant_blocksize)
+        a_scale = None
+        w1_scale = None
+        w2_scale = None
+        quant_dtype = None
+        per_act_token_quant = False
+        block_shape = None
+    else:
+        a = rank_tensors.hidden_states
+        a_scale = rank_tensors.hidden_states_scale
+        w1 = weights.w1
+        w1_scale = weights.w1_scale
+        w2 = weights.w2
+        w2_scale = weights.w2_scale
+        quant_dtype = config.quant_dtype
+        per_act_token_quant = config.is_per_act_token_quant
+        block_shape = config.quant_block_shape
+
+    return torch_experts(a=a,
+                         w1=w1,
+                         w2=w2,
                          topk_weight=rank_tensors.topk_weights,
                          topk_ids=rank_tensors.topk_ids,
                          global_num_experts=config.E,
                          expert_map=None,
-                         w1_scale=weights.w1_scale,
-                         w2_scale=weights.w2_scale,
-                         a1_scale=rank_tensors.hidden_states_scale,
-                         quant_dtype=config.quant_dtype,
-                         per_act_token_quant=config.is_per_act_token_quant,
-                         block_shape=config.quant_block_shape,
-                         apply_router_weights_on_input=config.topk == 1)
+                         w1_scale=w1_scale,
+                         w2_scale=w2_scale,
+                         a1_scale=a_scale,
+                         quant_dtype=quant_dtype,
+                         per_act_token_quant=per_act_token_quant,
+                         block_shape=block_shape,
+                         apply_router_weights_on_input=config.topk == 1
+                         and config.supports_apply_weight_on_input())
 
 
-def make_fused_experts(
-        config: Config, moe: FusedMoEConfig,
-        num_dispatchers: int) -> mk.FusedMoEPermuteExpertsUnpermute:
-
-    use_fp8 = config.quant_dtype == torch.float8_e4m3fn
-    batch_kwargs = {
-        "max_num_tokens": moe.max_num_tokens,
-        "num_dispatchers": num_dispatchers,
-    }
-    quant_kwargs = {
-        "use_fp8_w8a8": use_fp8,
-        "use_int8_w8a8": False,
-        "use_int8_w8a16": False,
-        "use_int4_w4a16": False,
-        "block_shape": config.quant_block_shape,
-        "per_act_token_quant": config.is_per_act_token_quant,
-    }
-    deepgemm_kwargs = {"allow_deep_gemm": has_deep_gemm()}
-
-    if config.fused_experts_type == BatchedDeepGemmExperts:
-        kwargs = batch_kwargs | {
-            "block_shape": config.quant_block_shape,
-            "per_act_token_quant": config.is_per_act_token_quant,
-        }
-        print(f"Making BatchedDeepGemmExperts {kwargs} ...")
-        experts = BatchedDeepGemmExperts(**kwargs)
-    elif config.fused_experts_type == BatchedTritonExperts:
-        kwargs = batch_kwargs | quant_kwargs
-        print(f"Making BatchedTritonExperts {kwargs} ...")
-        experts = BatchedTritonExperts(**kwargs)
-    elif config.fused_experts_type == BatchedTritonOrDeepGemmExperts:
-        kwargs = batch_kwargs | quant_kwargs | deepgemm_kwargs
-        print(f"Making BatchedTritonOrDeepGemmExperts {kwargs} ...")
-        experts = BatchedTritonOrDeepGemmExperts(**kwargs)
-    elif config.fused_experts_type == DeepGemmExperts:
-        print("Making DeepGemmExperts () ...")
-        experts = DeepGemmExperts()
-    elif config.fused_experts_type == TritonExperts:
-        kwargs = quant_kwargs
-        print(f"Making TritonExperts {kwargs} ...")
-        experts = TritonExperts(**kwargs)
-    elif config.fused_experts_type == TritonOrDeepGemmExperts:
-        kwargs = quant_kwargs | deepgemm_kwargs
-        print(f"Making TritonOrDeepGemmExperts {kwargs} ...")
-        experts = TritonOrDeepGemmExperts(**kwargs)
-    elif config.fused_experts_type == NaiveBatchedExperts:
-        kwargs = batch_kwargs | quant_kwargs
-        print(f"Making NaiveBatchedExperts {kwargs} ...")
-        experts = NaiveBatchedExperts(**kwargs)
-    elif config.fused_experts_type == CutlassExpertsFp8:
-        use_batched_format = config.is_batched_prepare_finalize()
-        num_experts = (moe.num_local_experts
-                       if use_batched_format else moe.num_experts)
-        kwargs = {
-            "max_experts_per_worker": num_experts,
-            "out_dtype": moe.in_dtype,
-            "per_act_token_quant": config.is_per_act_token_quant,
-            "per_out_ch_quant": config.is_per_out_ch_quant,
-            "block_shape": config.quant_block_shape,
-            "num_dispatchers": num_dispatchers,
-            "use_batched_format": use_batched_format
-        }
-        print(f"Making CutlassExpertsFp8 {kwargs} ...")
-        experts = CutlassExpertsFp8(**kwargs)
-
-    return experts
-
-
-def make_modular_kernel(config: Config,
-                        vllm_config: VllmConfig) -> mk.FusedMoEModularKernel:
+def make_modular_kernel(
+    config: Config,
+    vllm_config: VllmConfig,
+    weights: WeightTensors,
+) -> mk.FusedMoEModularKernel:
 
     def next_power_of_2(x):
         import math
@@ -579,6 +540,7 @@ def make_modular_kernel(config: Config,
         dp_size_=get_dp_group().world_size,
         vllm_parallel_config=vllm_config.parallel_config,
     )
+
     moe = FusedMoEConfig(
         num_experts=config.E,
         experts_per_token=config.topk,
@@ -591,15 +553,16 @@ def make_modular_kernel(config: Config,
     )
 
     # make modular kernel
-    prepare_finalize = None
-    if config.needs_all2all():
-        prepare_finalize = FusedMoEMethodBase.maybe_make_prepare_finalize(moe)
-        assert prepare_finalize is not None
-    else:
-        prepare_finalize = MoEPrepareAndFinalizeNoEP()
+    prepare_finalize = make_prepare_finalize(config.prepare_finalize_type,
+                                             config.all2all_backend(), moe)
 
-    fused_experts = make_fused_experts(config, moe,
-                                       prepare_finalize.num_dispatchers())
+    fused_experts = make_fused_experts(
+        config.fused_experts_type,
+        moe,
+        prepare_finalize.num_dispatchers(),
+        weights.w1_gs,
+        weights.w2_gs,
+    )
 
     modular_kernel = mk.FusedMoEModularKernel(
         prepare_finalize=prepare_finalize, fused_experts=fused_experts)
@@ -620,22 +583,45 @@ def run_modular_kernel(
     # weights for rank
     rank_weights = weights.slice_weights(pgi.rank, config.num_local_experts)
 
-    mk = make_modular_kernel(config, vllm_config)
+    mk = make_modular_kernel(config, vllm_config, weights)
 
     mk_kwargs = {
-        "hidden_states": rank_tensors.hidden_states.clone(
+        "hidden_states":
+        rank_tensors.hidden_states.clone(
         ),  # impls might update the tensor in place
-        "w1": rank_weights.w1,
-        "w2": rank_weights.w2,
-        "topk_weights": rank_tensors.topk_weights,
-        "topk_ids": rank_tensors.topk_ids,
-        "expert_map": rank_tensors.expert_map,
-        "w1_scale": rank_weights.w1_scale,
-        "w2_scale": rank_weights.w2_scale,
-        "a1_scale": rank_tensors.hidden_states_scale,
-        "global_num_experts": config.E,
-        "apply_router_weight_on_input": config.topk == 1,
+        "w1":
+        rank_weights.w1,
+        "w2":
+        rank_weights.w2,
+        "topk_weights":
+        rank_tensors.topk_weights,
+        "topk_ids":
+        rank_tensors.topk_ids.to(mk.prepare_finalize.topk_indices_dtype()),
+        "expert_map":
+        rank_tensors.expert_map,
+        "w1_scale":
+        rank_weights.w1_scale,
+        "w2_scale":
+        rank_weights.w2_scale,
+        "a1_scale":
+        rank_tensors.hidden_states_scale,
+        "global_num_experts":
+        config.E,
+        "apply_router_weight_on_input":
+        config.topk == 1 and config.supports_apply_weight_on_input(),
     }
-    out = mk.forward(**mk_kwargs)
+
+    num_tokens = rank_tensors.hidden_states.shape[0]
+    num_tokens_across_dp = torch.tensor([num_tokens] * config.world_size,
+                                        device="cuda",
+                                        dtype=torch.int)
+
+    with set_forward_context(
+            None,
+            vllm_config,
+            num_tokens=num_tokens,
+            num_tokens_across_dp=num_tokens_across_dp,
+    ):
+        out = mk.forward(**mk_kwargs)
 
     return out

tests/kernels/moe/modular_kernel_tools/mk_objects.py

@@ -1,58 +1,316 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Optional, Union
 
 import torch
 
 # Fused experts and PrepareFinalize imports
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
     BatchedDeepGemmExperts)
 from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
     BatchedTritonOrDeepGemmExperts)
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.config import (FusedMoEConfig,
+                                                         FusedMoEQuantConfig)
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
     BatchedTritonExperts, NaiveBatchedExperts)
-from vllm.model_executor.layers.fused_moe.layer import TritonExperts
+from vllm.model_executor.layers.fused_moe.layer import (FusedMoEMethodBase,
+                                                        TritonExperts)
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP)
 from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
     TritonOrDeepGemmExperts)
-from vllm.utils import has_deep_ep, has_pplx
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    cutlass_fp4_supported)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    cutlass_fp8_supported)
+from vllm.platforms import current_platform
+from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx
+from vllm.utils.deep_gemm import is_deep_gemm_supported
+from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
 
-if has_deep_ep():
+
+@dataclass
+class PrepareFinalizeInfo:
+    activation_format: mk.FusedMoEActivationFormat
+    supported_dtypes: list[Union[torch.dtype, str]]
+    blocked_quantization_support: bool
+    backend: Optional[str]
+    supports_apply_weight_on_input: bool = True
+
+
+@dataclass
+class ExpertInfo:
+    activation_format: mk.FusedMoEActivationFormat
+    supported_dtypes: list[Union[torch.dtype, str]]
+    blocked_quantization_support: bool
+    supports_chunking: bool
+    supports_expert_map: bool
+    needs_matching_quant: bool = False
+    needs_deep_gemm: bool = False
+
+
+PREPARE_FINALIZE_INFO: dict[mk.FusedMoEPrepareAndFinalize,
+                            PrepareFinalizeInfo] = {}
+EXPERT_INFO: dict[mk.FusedMoEPermuteExpertsUnpermute, ExpertInfo] = {}
+MK_ALL_PREPARE_FINALIZE_TYPES: list[mk.FusedMoEPrepareAndFinalize] = []
+MK_MULTI_GPU_PREPARE_FINALIZE_TYPES: list[mk.FusedMoEPrepareAndFinalize] = []
+MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES: list[mk.FusedMoEPrepareAndFinalize] = []
+MK_FUSED_EXPERT_TYPES: list[mk.FusedMoEPermuteExpertsUnpermute] = []
+
+standard_format = mk.FusedMoEActivationFormat.Standard
+batched_format = mk.FusedMoEActivationFormat.BatchedExperts
+common_float_types: list[Union[torch.dtype, str]] = [
+    torch.float8_e4m3fn, torch.bfloat16, torch.float16, torch.float32
+]
+common_float_and_int_types = common_float_types + [torch.int8]
+nv_fp4_types = ["nvfp4"]
+fp8_types = [torch.float8_e4m3fn]
+
+
+def register_prepare_and_finalize(
+    kind,
+    activation_format: mk.FusedMoEActivationFormat,
+    supported_dtypes: list[Union[torch.dtype, str]],
+    blocked_quantization_support: bool,
+    backend: Optional[str],
+    force_multigpu: bool = False,
+    supports_apply_weight_on_input: bool = True,
+):
+    global PREPARE_FINALIZE_INFO
+    global MK_ALL_PREPARE_FINALIZE_TYPES
+    global MK_MULTI_GPU_PREPARE_FINALIZE_TYPES
+    global MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES
+    assert kind not in PREPARE_FINALIZE_INFO
+
+    PREPARE_FINALIZE_INFO[kind] = PrepareFinalizeInfo(
+        activation_format,
+        supported_dtypes,
+        blocked_quantization_support,
+        backend,
+        supports_apply_weight_on_input,
+    )
+    MK_ALL_PREPARE_FINALIZE_TYPES.append(kind)
+    if backend is not None or force_multigpu:
+        MK_MULTI_GPU_PREPARE_FINALIZE_TYPES.append(kind)
+    else:
+        MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES.append(kind)
+
+
+def register_experts(
+    kind,
+    activation_format: mk.FusedMoEActivationFormat,
+    supported_dtypes: list[Union[torch.dtype, str]],
+    blocked_quantization_support: bool,
+    supports_chunking: bool,
+    supports_expert_map: bool,
+    needs_matching_quant: bool = False,
+    needs_deep_gemm: bool = False,
+):
+    global EXPERT_INFO
+    global MK_FUSED_EXPERT_TYPES
+    assert kind not in EXPERT_INFO
+
+    EXPERT_INFO[kind] = ExpertInfo(
+        activation_format,
+        supported_dtypes,
+        blocked_quantization_support,
+        supports_chunking,
+        supports_expert_map,
+        needs_matching_quant,
+        needs_deep_gemm,
+    )
+
+    MK_FUSED_EXPERT_TYPES.append(kind)
+
+
+def prepare_finalize_info(kind) -> PrepareFinalizeInfo:
+    info = PREPARE_FINALIZE_INFO.get(kind)
+    assert info is not None
+    return info
+
+
+def expert_info(kind) -> ExpertInfo:
+    info = EXPERT_INFO.get(kind)
+    assert info is not None
+    return info
+
+
+register_prepare_and_finalize(
+    MoEPrepareAndFinalizeNoEP,
+    standard_format,
+    common_float_types,
+    blocked_quantization_support=True,
+    backend=None,
+)
+
+register_experts(
+    BatchedTritonExperts,
+    batched_format,
+    common_float_types,
+    blocked_quantization_support=True,
+    supports_chunking=False,
+    supports_expert_map=False,
+    needs_matching_quant=True,
+)
+
+register_experts(
+    TritonExperts,
+    standard_format,
+    common_float_and_int_types,
+    blocked_quantization_support=True,
+    supports_chunking=True,
+    supports_expert_map=True,
+    needs_matching_quant=True,
+)
+
+register_experts(
+    NaiveBatchedExperts,
+    batched_format,
+    common_float_and_int_types,
+    blocked_quantization_support=True,
+    supports_chunking=False,
+    supports_expert_map=True,
+)
+
+# Disable on blackwell for now
+if has_deep_ep() and not current_platform.has_device_capability(100):
     from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
         DeepEPHTPrepareAndFinalize)
     from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
         DeepEPLLPrepareAndFinalize)
 
+    register_prepare_and_finalize(
+        DeepEPHTPrepareAndFinalize,
+        standard_format,
+        common_float_types,
+        blocked_quantization_support=True,
+        backend="deepep_high_throughput",
+    )
+
+    register_prepare_and_finalize(
+        DeepEPLLPrepareAndFinalize,
+        batched_format,
+        common_float_types,
+        blocked_quantization_support=True,
+        backend="deepep_low_latency",
+    )
+
 if has_pplx():
     from vllm.model_executor.layers.fused_moe.pplx_prepare_finalize import (
         PplxPrepareAndFinalize)
+    register_prepare_and_finalize(
+        PplxPrepareAndFinalize,
+        batched_format,
+        common_float_and_int_types,
+        blocked_quantization_support=True,
+        backend="pplx",
+    )
 
-MK_MULTI_GPU_PREPARE_FINALIZE_TYPES = []
-if has_pplx():
-    MK_MULTI_GPU_PREPARE_FINALIZE_TYPES += [PplxPrepareAndFinalize]
-if has_deep_ep():
-    MK_MULTI_GPU_PREPARE_FINALIZE_TYPES += [
-        DeepEPHTPrepareAndFinalize, DeepEPLLPrepareAndFinalize
-    ]
+if (has_flashinfer_cutlass_fused_moe()
+        and current_platform.has_device_capability(100)):
+    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
+        FlashInferExperts)
+    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501
+        FlashInferCutlassMoEPrepareAndFinalize)
 
-MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES = [MoEPrepareAndFinalizeNoEP]
+    register_prepare_and_finalize(
+        FlashInferCutlassMoEPrepareAndFinalize,
+        standard_format,
+        nv_fp4_types,
+        blocked_quantization_support=True,
+        backend=None,
+        force_multigpu=True,
+        supports_apply_weight_on_input=False,
+    )
 
-MK_ALL_PREPARE_FINALIZE_TYPES = (MK_MULTI_GPU_PREPARE_FINALIZE_TYPES +
-                                 MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES)
+    register_experts(
+        FlashInferExperts,
+        standard_format,
+        nv_fp4_types,
+        blocked_quantization_support=True,
+        supports_chunking=True,
+        # Note: this is a hack to get it to run for now
+        supports_expert_map=True,
+    )
+else:
+    FlashInferCutlassMoEPrepareAndFinalize = None
 
-MK_FUSED_EXPERT_TYPES = [
-    BatchedDeepGemmExperts,
-    BatchedTritonExperts,
-    NaiveBatchedExperts,
-    BatchedTritonOrDeepGemmExperts,
-    CutlassExpertsFp8,
-    DeepGemmExperts,
-    TritonOrDeepGemmExperts,
-    TritonExperts,
-]
+if has_deep_gemm() and is_deep_gemm_supported():
+    register_experts(
+        BatchedDeepGemmExperts,
+        batched_format,
+        fp8_types,
+        blocked_quantization_support=True,
+        supports_chunking=False,
+        supports_expert_map=False,
+        needs_matching_quant=False,
+        needs_deep_gemm=True,
+    )
+    register_experts(
+        DeepGemmExperts,
+        standard_format,
+        fp8_types,
+        blocked_quantization_support=True,
+        supports_chunking=True,
+        supports_expert_map=True,
+        needs_matching_quant=False,
+        needs_deep_gemm=True,
+    ),
+    register_experts(
+        BatchedTritonOrDeepGemmExperts,
+        batched_format,
+        common_float_and_int_types,
+        blocked_quantization_support=True,
+        supports_chunking=False,
+        supports_expert_map=False,
+        needs_matching_quant=True,
+        needs_deep_gemm=True,
+    )
+    register_experts(
+        TritonOrDeepGemmExperts,
+        standard_format,
+        common_float_and_int_types,
+        blocked_quantization_support=True,
+        supports_chunking=True,
+        supports_expert_map=True,
+        needs_matching_quant=True,
+        needs_deep_gemm=True,
+    )
+
+if cutlass_fp8_supported():
+    from vllm.model_executor.layers.fused_moe import (CutlassBatchedExpertsFp8,
+                                                      CutlassExpertsFp8)
+    register_experts(
+        CutlassExpertsFp8,
+        standard_format,
+        fp8_types,
+        blocked_quantization_support=False,
+        supports_chunking=True,
+        supports_expert_map=False,
+    )
+    register_experts(
+        CutlassBatchedExpertsFp8,
+        batched_format,
+        fp8_types,
+        blocked_quantization_support=False,
+        supports_chunking=False,
+        supports_expert_map=False,
+    )
+
+if cutlass_fp4_supported():
+    from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        CutlassExpertsFp4)
+    register_experts(
+        CutlassExpertsFp4,
+        standard_format,
+        nv_fp4_types,
+        blocked_quantization_support=True,
+        supports_chunking=True,
+        supports_expert_map=False,
+    )
 
 MK_QUANT_CONFIGS = [
     None,
@@ -85,3 +343,156 @@ MK_QUANT_CONFIGS = [
     # block-quantized weights and per-token activations
     # block-quantized weights and per-tensor activations
 ]
+
+if cutlass_fp4_supported() or has_flashinfer_cutlass_fused_moe():
+    MK_QUANT_CONFIGS += [
+        FusedMoEQuantConfig(quant_dtype="nvfp4",
+                            per_out_ch_quant=False,
+                            per_act_token_quant=False,
+                            block_shape=None),
+    ]
+
+
+def _make_gscale(num_experts: int) -> torch.Tensor:
+    return torch.ones((num_experts, ),
+                      device=torch.cuda.current_device(),
+                      dtype=torch.float32)
+
+
+def make_prepare_finalize(
+    prepare_finalize_type: mk.FusedMoEPrepareAndFinalize,
+    backend: Optional[str],
+    moe: FusedMoEConfig,
+) -> mk.FusedMoEPrepareAndFinalize:
+    if backend != "naive" and backend is not None:
+        prepare_finalize = FusedMoEMethodBase._maybe_make_prepare_finalize(moe)
+        assert prepare_finalize is not None
+        return prepare_finalize
+    elif prepare_finalize_type == FlashInferCutlassMoEPrepareAndFinalize:
+        return FlashInferCutlassMoEPrepareAndFinalize(
+            use_dp=moe.moe_parallel_config.dp_size > 1,
+            a1_gscale=_make_gscale(moe.num_local_experts),
+        )
+    else:
+        return MoEPrepareAndFinalizeNoEP()
+
+
+def _slice(rank: int, num_local_experts: int, t: torch.Tensor) -> torch.Tensor:
+    s = rank * num_local_experts
+    e = s + num_local_experts
+    return t[s:e]
+
+
+def make_fused_experts(
+    fused_experts_type: mk.FusedMoEPermuteExpertsUnpermute,
+    moe: FusedMoEConfig,
+    num_dispatchers: int,
+    w1_gs: Optional[torch.Tensor],
+    w2_gs: Optional[torch.Tensor],
+) -> mk.FusedMoEPermuteExpertsUnpermute:
+
+    use_fp8 = moe.quant_dtype == torch.float8_e4m3fn
+    batch_kwargs = {
+        "max_num_tokens": moe.max_num_tokens,
+        "num_dispatchers": num_dispatchers,
+    }
+    quant_kwargs = {
+        "use_fp8_w8a8": use_fp8,
+        "use_int8_w8a8": False,
+        "use_int8_w8a16": False,
+        "use_int4_w4a16": False,
+        "block_shape": moe.block_shape,
+        "per_act_token_quant": moe.per_act_token_quant,
+    }
+    deepgemm_kwargs = {"allow_deep_gemm": has_deep_gemm()}
+
+    if fused_experts_type == BatchedDeepGemmExperts:
+        kwargs = batch_kwargs | {
+            "block_shape": moe.block_shape,
+            "per_act_token_quant": moe.per_act_token_quant,
+        }
+        print(f"Making BatchedDeepGemmExperts {kwargs} ...")
+        experts = BatchedDeepGemmExperts(**kwargs)
+    elif fused_experts_type == BatchedTritonExperts:
+        kwargs = batch_kwargs | quant_kwargs
+        print(f"Making BatchedTritonExperts {kwargs} ...")
+        experts = BatchedTritonExperts(**kwargs)
+    elif fused_experts_type == BatchedTritonOrDeepGemmExperts:
+        kwargs = batch_kwargs | quant_kwargs | deepgemm_kwargs
+        print(f"Making BatchedTritonOrDeepGemmExperts {kwargs} ...")
+        experts = BatchedTritonOrDeepGemmExperts(**kwargs)
+    elif fused_experts_type == DeepGemmExperts:
+        print("Making DeepGemmExperts () ...")
+        experts = DeepGemmExperts()
+    elif fused_experts_type == TritonExperts:
+        kwargs = quant_kwargs
+        print(f"Making TritonExperts {kwargs} ...")
+        experts = TritonExperts(**kwargs)
+    elif fused_experts_type == TritonOrDeepGemmExperts:
+        kwargs = quant_kwargs | deepgemm_kwargs
+        print(f"Making TritonOrDeepGemmExperts {kwargs} ...")
+        experts = TritonOrDeepGemmExperts(**kwargs)
+    elif fused_experts_type == NaiveBatchedExperts:
+        kwargs = batch_kwargs | quant_kwargs
+        print(f"Making NaiveBatchedExperts {kwargs} ...")
+        experts = NaiveBatchedExperts(**kwargs)
+    elif fused_experts_type == CutlassExpertsFp8:
+        kwargs = {
+            "out_dtype": moe.in_dtype,
+            "per_act_token_quant": moe.per_act_token_quant,
+            "per_out_ch_quant": moe.per_out_ch_quant,
+            "block_shape": moe.block_shape,
+        }
+        print(f"Making CutlassExpertsFp8 {kwargs} ...")
+        experts = CutlassExpertsFp8(**kwargs)
+    elif fused_experts_type == CutlassBatchedExpertsFp8:
+        kwargs = {
+            "max_experts_per_worker": moe.num_local_experts,
+            "num_dispatchers": num_dispatchers,
+            "out_dtype": moe.in_dtype,
+            "per_act_token_quant": moe.per_act_token_quant,
+            "per_out_ch_quant": moe.per_out_ch_quant,
+            "block_shape": moe.block_shape,
+        }
+        print(f"Making CutlassBatchedExpertsFp8 {kwargs} ...")
+        experts = CutlassBatchedExpertsFp8(**kwargs)
+    elif fused_experts_type == CutlassExpertsFp4:
+        assert w1_gs is not None and w2_gs is not None
+        num_experts = moe.num_local_experts
+        rank = moe.moe_parallel_config.dp_rank
+        kwargs = {
+            "g1_alphas": _slice(rank, num_experts, (1 / w1_gs)),
+            "g2_alphas": _slice(rank, num_experts, (1 / w2_gs)),
+            "a1_gscale": _make_gscale(num_experts),
+            "a2_gscale": _make_gscale(num_experts),
+            "max_experts_per_worker": num_experts,
+            "out_dtype": moe.in_dtype,
+            "per_act_token_quant": moe.per_act_token_quant,
+            "per_out_ch_quant": moe.per_out_ch_quant,
+            "block_shape": moe.block_shape,
+            "num_dispatchers": num_dispatchers,
+        }
+        print(f"Making CutlassExpertsFp4 {kwargs} ...")
+        experts = CutlassExpertsFp4(**kwargs)
+    elif fused_experts_type == FlashInferExperts:
+        assert w1_gs is not None and w2_gs is not None
+        num_experts = moe.num_local_experts
+        rank = moe.moe_parallel_config.dp_rank
+        kwargs = {
+            "g1_alphas": _slice(rank, num_experts, (1 / w1_gs)),
+            "g2_alphas": _slice(rank, num_experts, (1 / w2_gs)),
+            "a1_gscale": _make_gscale(num_experts),
+            "a2_gscale": _make_gscale(num_experts),
+            "out_dtype": moe.in_dtype,
+            "quant_dtype": "nvfp4",
+            "ep_rank": moe.ep_rank,
+            "ep_size": moe.ep_size,
+            "tp_rank": moe.tp_rank,
+            "tp_size": moe.tp_size,
+        }
+        print(f"Making FlashInferExperts {kwargs} ...")
+        experts = FlashInferExperts(**kwargs)
+    else:
+        raise RuntimeError(f"Unknown fused experts type: {fused_experts_type}")
+
+    return experts

tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py

@@ -52,7 +52,7 @@ def profile_modular_kernel(
     rank_weights = weights.slice_weights(pgi.rank, config.num_local_experts)
 
     # make modular kernel
-    mk = make_modular_kernel(config, vllm_config)
+    mk = make_modular_kernel(config, vllm_config, weights)
 
     mk_kwargs = {
         "hidden_states": rank_tensors.hidden_states,
@@ -83,7 +83,7 @@ def rank_worker(
     # sanity check
     from vllm import envs
     if config.fused_moe_chunk_size is not None:
-        assert (config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE)
+        assert config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE
 
     # get weights to this device
     weights.to_current_device()

tests/kernels/moe/modular_kernel_tools/utils.py

@@ -1,117 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-import vllm._custom_ops as ops
-from vllm.utils.deep_gemm import per_block_cast_to_fp8
-
-
-def per_token_cast_to_fp8(
-        x: torch.Tensor, block_size: int) -> tuple[torch.Tensor, torch.Tensor]:
-    assert x.dim() == 2
-    m, n = x.shape
-    pad_size = (block_size - (n % block_size)) % block_size
-    x = torch.nn.functional.pad(x,
-                                (0, pad_size), value=0) if pad_size > 0 else x
-    x_view = x.view(m, -1, block_size)
-    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
-    fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
-    return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
-
-
-def make_non_quant_weights(
-    e: int,
-    n: int,
-    k: int,
-    dtype: torch.dtype,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """
-    Return weights w1, w2
-    """
-    device = torch.cuda.current_device()
-    w1 = torch.randn((e, 2 * n, k), device=device, dtype=dtype) / 15
-    w2 = torch.randn((e, k, n), device=device, dtype=dtype) / 15
-    return w1, w2
-
-
-def make_block_quant_fp8_weights(
-    e: int,
-    n: int,
-    k: int,
-    block_size: list[int],
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Return weights w1, w2, w1_scale, w2_scale
-    """
-    dtype = torch.bfloat16
-    device = torch.cuda.current_device()
-
-    fp8_info = torch.finfo(torch.float8_e4m3fn)
-    fp8_max, fp8_min = fp8_info.max, fp8_info.min
-
-    w1_bf16, w2_bf16 = make_non_quant_weights(e, n, k, dtype)
-    w1_bf16 = w1_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
-    w2_bf16 = w2_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
-
-    block_n, block_k = block_size[0], block_size[1]
-    n_tiles_w1 = ((2 * n) + block_n - 1) // block_n
-    k_tiles_w1 = (k + block_k - 1) // block_k
-    n_tiles_w2 = (k + block_n - 1) // block_n
-    k_tiles_w2 = (n + block_k - 1) // block_k
-
-    w1 = torch.empty_like(w1_bf16, dtype=torch.float8_e4m3fn, device=device)
-    w2 = torch.empty_like(w2_bf16, dtype=torch.float8_e4m3fn, device=device)
-
-    w1_s = torch.empty((e, n_tiles_w1, k_tiles_w1),
-                       device=device,
-                       dtype=torch.float32)
-    w2_s = torch.empty((e, n_tiles_w2, k_tiles_w2),
-                       device=device,
-                       dtype=torch.float32)
-
-    assert w1_s.shape == (e, (2 * n + (block_n - 1)) // block_n,
-                          (k + (block_k - 1)) // block_k)
-    assert (w2.shape[-2] + block_n - 1) // block_n == w2_s.shape[-2]
-
-    for i in range(e):
-        w1[i], w1_s[i] = per_block_cast_to_fp8(w1_bf16[i],
-                                               block_size=[block_k, block_n])
-        w2[i], w2_s[i] = per_block_cast_to_fp8(w2_bf16[i],
-                                               block_size=[block_k, block_n])
-
-    return w1, w2, w1_s, w2_s
-
-
-def make_quant_fp8_weights(
-    e: int,
-    n: int,
-    k: int,
-    per_out_channel_quant: bool,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Return w1, w2, w1_scale, w2_scale
-    """
-    q_dtype = torch.float8_e4m3fn
-
-    w1, w2 = make_non_quant_weights(e, n, k, dtype=torch.bfloat16)
-
-    # w1 -> w1_q, w2 -> w2_q
-    w1_q = torch.empty((e, 2 * n, k), device="cuda", dtype=q_dtype)
-    w2_q = torch.empty((e, k, n), device="cuda", dtype=q_dtype)
-
-    n_b_scales = 2 * n if per_out_channel_quant else 1
-    k_b_scales = k if per_out_channel_quant else 1
-    w1_scale = torch.empty((e, n_b_scales, 1),
-                           device="cuda",
-                           dtype=torch.float32)
-    w2_scale = torch.empty((e, k_b_scales, 1),
-                           device="cuda",
-                           dtype=torch.float32)
-
-    for expert in range(e):
-        w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(
-            w1[expert], use_per_token_if_dynamic=per_out_channel_quant)
-        w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(
-            w2[expert], use_per_token_if_dynamic=per_out_channel_quant)
-    return w1_q, w2_q, w1_scale, w2_scale

tests/kernels/moe/test_batched_moe.py

@@ -89,14 +89,11 @@ class BatchedMMTensors:
         return BatchedMMTensors(A, B, C, num_expert_tokens)
 
 
-@pytest.mark.parametrize("num_experts", [8, 16, 32])
-@pytest.mark.parametrize("max_tokens_per_expert",
-                         [32, 64, 128, 192, 224, 256, 512])
-@pytest.mark.parametrize("K", [128, 256, 1024])
-@pytest.mark.parametrize("N", [128, 256, 1024])
-@pytest.mark.parametrize(
-    "dtype",
-    [torch.float8_e4m3fn, torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("num_experts", [8, 32])
+@pytest.mark.parametrize("max_tokens_per_expert", [32, 224, 512])
+@pytest.mark.parametrize("K", [128, 1024])
+@pytest.mark.parametrize("N", [128, 1024])
+@pytest.mark.parametrize("dtype", [torch.float8_e4m3fn, torch.bfloat16])
 @pytest.mark.parametrize("block_shape", [None, [128, 128]])
 @pytest.mark.parametrize("per_act_token_quant", [False, True])
 def test_batched_mm(num_experts: int, max_tokens_per_expert: int, K: int,
@@ -136,7 +133,7 @@ def test_batched_mm(num_experts: int, max_tokens_per_expert: int, K: int,
         per_act_token_quant=per_act_token_quant,
     )
 
-    B, B_q, B_scale, _, _, _ = make_test_weights(
+    (B, B_q, B_scale, _), _ = make_test_weights(
         num_experts,
         N // 2,
         K,
@@ -246,7 +243,7 @@ def test_fused_moe_batched_experts(
         act_dtype = dtype
         quant_dtype = None
 
-    w1_16, w1, w1_s, w2_16, w2, w2_s = make_test_weights(
+    (w1_16, w1, w1_s, _), (w2_16, w2, w2_s, _) = make_test_weights(
         e,
         n,
         k,

tests/kernels/moe/test_block_fp8.py

@@ -161,18 +161,20 @@ def test_w8a8_block_fp8_fused_moe(M, N, K, E, topk, block_size, dtype, seed,
     a = torch.randn((M, K), dtype=dtype) / 10
     score = torch.randn((M, E), dtype=dtype)
 
-    _, w1, w1_s, _, w2, w2_s = make_test_weights(E,
-                                                 N,
-                                                 K,
-                                                 dtype,
-                                                 torch.float8_e4m3fn,
-                                                 per_act_token_quant=False,
-                                                 block_shape=block_size)
+    (_, w1, w1_s, _), (_, w2, w2_s,
+                       _) = make_test_weights(E,
+                                              N,
+                                              K,
+                                              dtype,
+                                              torch.float8_e4m3fn,
+                                              per_act_token_quant=False,
+                                              block_shape=block_size)
 
     m_fused_moe = modular_triton_fused_moe(use_fp8_w8a8=True,
                                            use_int8_w8a8=False,
                                            use_int8_w8a16=False,
                                            use_int4_w4a16=False,
+                                           use_mxfp4_w4a4=False,
                                            per_act_token_quant=False,
                                            block_shape=block_size)
 
@@ -247,13 +249,14 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
     a = torch.randn((M, K), dtype=dtype) / 10
     score = torch.randn((M, E), dtype=dtype)
 
-    _, w1, w1_s, _, w2, w2_s = make_test_weights(E,
-                                                 N,
-                                                 K,
-                                                 dtype,
-                                                 torch.float8_e4m3fn,
-                                                 per_act_token_quant=False,
-                                                 block_shape=block_size)
+    (_, w1, w1_s, _), (_, w2, w2_s,
+                       _) = make_test_weights(E,
+                                              N,
+                                              K,
+                                              dtype,
+                                              torch.float8_e4m3fn,
+                                              per_act_token_quant=False,
+                                              block_shape=block_size)
 
     # Note: for now use_compile will error out if the problem size is
     # large enough to trigger chunking. I'm leaving the flag and

tests/kernels/moe/test_block_int8.py

@@ -118,13 +118,14 @@ def test_w8a8_block_int8_fused_moe(M, N, K, E, topk, block_size, dtype, seed):
     a = torch.randn((M, K), dtype=dtype) / 10
     score = torch.randn((M, E), dtype=dtype)
 
-    _, w1, w1_s, _, w2, w2_s = make_test_weights(E,
-                                                 N,
-                                                 K,
-                                                 dtype,
-                                                 torch.int8,
-                                                 per_act_token_quant=False,
-                                                 block_shape=block_size)
+    (_, w1, w1_s, _), (_, w2, w2_s,
+                       _) = make_test_weights(E,
+                                              N,
+                                              K,
+                                              dtype,
+                                              torch.int8,
+                                              per_act_token_quant=False,
+                                              block_shape=block_size)
 
     # Set the context to avoid lots of warning spam.
     with set_current_vllm_config(vllm_config):

tests/kernels/moe/test_count_expert_num_tokens.py

@@ -113,8 +113,7 @@ def do_test_compute_expert_num_tokens(num_tokens: int, num_topk: int,
                                    rtol=0)
 
 
-@pytest.mark.parametrize(
-    "num_tokens", [1, 4, 8, 11, 19, 128, 127, 405, 1024, 3333, 6666, 7317])
+@pytest.mark.parametrize("num_tokens", [1, 4, 8, 11, 127, 128, 3333, 7317])
 @pytest.mark.parametrize("num_topk", [2, 6, 8])
 @pytest.mark.parametrize("num_experts", [64])
 @pytest.mark.parametrize("ep_size", [1, 2, 4])
@@ -126,7 +125,7 @@ def test_compute_expert_num_tokens(num_tokens: int, num_topk: int,
                                       ep_size, topk_ids_dtype)
 
 
-@pytest.mark.parametrize("numel", list(range(1, 8192, 11)))
+@pytest.mark.parametrize("numel", list(range(1, 8192, 111)))
 @pytest.mark.parametrize("num_experts", [32])
 @pytest.mark.parametrize("ep_size", [2])
 @pytest.mark.parametrize("topk_ids_dtype", [torch.int64])

tests/kernels/moe/test_cutlass_grouped_gemm.py

@@ -9,6 +9,7 @@ import random
 import pytest
 import torch
 
+from tests.kernels.moe.utils import per_token_cast_to_fp8
 from tests.kernels.utils import baseline_scaled_mm
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
@@ -16,20 +17,6 @@ from vllm.utils import cdiv
 from vllm.utils.deep_gemm import per_block_cast_to_fp8
 
 
-def per_token_cast_to_fp8(
-        x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-    assert x.dim() == 2
-    m, n = x.shape
-    pad_size = (128 - (n % 128)) % 128
-    x = torch.nn.functional.pad(x,
-                                (0, pad_size), value=0) if pad_size > 0 else x
-    x_view = x.view(m, -1, 128)
-    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
-    fp8_data = (x_view *
-                (448.0 / x_amax.unsqueeze(2))).to(dtype=torch.float8_e4m3fn)
-    return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
-
-
 @pytest.mark.parametrize("num_groups, expected_m_per_group, k, n", [
     (4, 8192, 7168, 4096),
     (4, 8192, 2048, 7168),
@@ -76,7 +63,7 @@ def test_cutlass_grouped_gemm(
                          device=device,
                          dtype=torch.float))
     for i in range(num_groups):
-        y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
+        y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i], [128, 128])
 
     for i in range(num_groups):
         a = x_fp8[0][ep_offset[i]:ep_offset[i + 1]]

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -70,8 +70,10 @@ def make_block_quant_fp8_weights(
     """
     Return weights w1q, w2q, w1_scale, w2_scale
     """
-    w1, w1q, w1_scale, w2, w2q, w2_scale = make_test_weights(
-        e, n, k, torch.bfloat16, torch.float8_e4m3fn, block_size)
+    (_, w1q, w1_scale, _), (_, w2q, w2_scale,
+                            _) = make_test_weights(e, n, k, torch.bfloat16,
+                                                   torch.float8_e4m3fn,
+                                                   block_size)
     return w1q, w2q, w1_scale, w2_scale
 
 

tests/kernels/moe/test_deepgemm.py

@@ -132,9 +132,9 @@ def run_single_case(m, n, k, topk, num_experts, block_size):
 # Note: W1 has shape (E, 2N, K), so N = 512
 # can trigger the deepgemm path.
 MNKs = [
-    (1024, 512, 128),
-    (1024, 512, 512),
-    (2048, 512, 512),
+    (1024, 768, 128),
+    (1024, 768, 512),
+    (2048, 768, 512),
     (512, 1024, 1024),
     (512, 2048, 2048),
     (4096, 4096, 1024),

tests/kernels/moe/test_flashinfer_moe.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from tests.kernels.moe.utils import make_test_weights
+from tests.kernels.quantization.nvfp4_utils import (FLOAT4_E2M1_MAX,
+                                                    FLOAT8_E4M3_MAX,
+                                                    dequantize_nvfp4_to_dtype)
+from tests.kernels.utils import torch_moe
+from vllm import _custom_ops as ops
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
+    FlashInferExperts, is_valid_flashinfer_cutlass_fused_moe)
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEModularKernel)
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP)
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
+
+if not has_flashinfer_cutlass_fused_moe(
+) or not current_platform.has_device_capability(100):
+    pytest.skip("Requires flashinfer_cutlass_fused_moe and nvfp4 support",
+                allow_module_level=True)
+
+MNK_FACTORS = [
+    (2, 1024, 1024),
+    (2, 1024, 1536),
+    (2, 3072, 1024),
+    (2, 3072, 1536),
+    (64, 1024, 1024),
+    (64, 1024, 1536),
+    (64, 3072, 1024),
+    (64, 2048, 1536),
+    (224, 1024, 1024),
+    (224, 1024, 1536),
+]
+
+
+@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
+@pytest.mark.parametrize("e", [40, 64, 256])
+#@pytest.mark.parametrize("e", [128, 256])
+@pytest.mark.parametrize("topk", [1, 6, 8])
+@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16])
+@torch.inference_mode()
+def test_flashinfer_fp4_moe_no_graph(m: int, n: int, k: int, e: int, topk: int,
+                                     dtype: torch.dtype):
+    current_platform.seed_everything(7)
+    with set_current_vllm_config(
+            VllmConfig(parallel_config=ParallelConfig(
+                pipeline_parallel_size=1))):
+
+        a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+
+        quant_blocksize = 16
+
+        (_, w1_q, w1_blockscale,
+         w1_gs), (_, w2_q, w2_blockscale, w2_gs) = make_test_weights(
+             e,
+             n,
+             k,
+             in_dtype=dtype,
+             quant_dtype="nvfp4",
+             block_shape=None,  # use quant_blocksize?
+             per_act_token_quant=False,
+         )
+
+        score = torch.randn((m, e), device="cuda", dtype=dtype)
+        topk_weights, topk_ids, _ = fused_topk(a,
+                                               score,
+                                               topk,
+                                               renormalize=False)
+
+        a1_gs = torch.ones((e, ), device="cuda", dtype=torch.float32)
+        a2_gs = torch.ones((e, ), device="cuda", dtype=torch.float32)
+
+        assert is_valid_flashinfer_cutlass_fused_moe(a, w1_q, w2_q)
+
+        assert w1_gs is not None
+        assert w2_gs is not None
+        assert w1_blockscale is not None
+        assert w2_blockscale is not None
+
+        flashinfer_experts = FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
+            FlashInferExperts(
+                a1_gscale=a1_gs,
+                g1_alphas=(1 / w1_gs),
+                a2_gscale=a2_gs,
+                g2_alphas=(1 / w2_gs),
+                out_dtype=dtype,
+                quant_dtype="nvfp4",
+            ))
+
+        flashinfer_output = flashinfer_experts(
+            hidden_states=a,
+            w1=w1_q,
+            w1_scale=w1_blockscale,
+            w2=w2_q,
+            w2_scale=w2_blockscale,
+            a1_scale=a1_gs,
+            a2_scale=a2_gs,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+        )
+
+        # Reference check:
+        a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
+                          torch.amax(a.flatten(), dim=-1)).to(torch.float32)
+        a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a, a_global_scale)
+        _, m_k = a_fp4.shape
+        a_in_dtype = dequantize_nvfp4_to_dtype(a_fp4,
+                                               a_scale_interleaved,
+                                               a_global_scale,
+                                               dtype=a.dtype,
+                                               device=a.device,
+                                               block_size=quant_blocksize)
+
+        w1_d = torch.empty((e, 2 * n, k), device="cuda", dtype=dtype)
+        w2_d = torch.empty((e, k, n), device="cuda", dtype=dtype)
+
+        for idx in range(0, e):
+            w1_d[idx] = dequantize_nvfp4_to_dtype(w1_q[idx],
+                                                  w1_blockscale[idx],
+                                                  w1_gs[idx],
+                                                  dtype=dtype,
+                                                  device=w1_q.device,
+                                                  block_size=quant_blocksize)
+            w2_d[idx] = dequantize_nvfp4_to_dtype(w2_q[idx],
+                                                  w2_blockscale[idx],
+                                                  w2_gs[idx],
+                                                  dtype=dtype,
+                                                  device=w2_q.device,
+                                                  block_size=quant_blocksize)
+
+        torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk)
+
+        torch.testing.assert_close(torch_output,
+                                   flashinfer_output,
+                                   atol=1e-1,
+                                   rtol=1e-1)
+
+
+if __name__ == "__main__":
+    test_flashinfer_fp4_moe_no_graph((2, 1024, 1024), 40, 1, torch.half)

tests/kernels/moe/test_modular_kernel_combinations.py

@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import copy
+import textwrap
+import traceback
 from itertools import product
 from typing import Optional
 
@@ -10,41 +12,51 @@ import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.config import VllmConfig, current_platform, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
-    BatchedTritonOrDeepGemmExperts)
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
-from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
-    BatchedTritonExperts)
-from vllm.model_executor.layers.fused_moe.layer import TritonExperts
-from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
-    TritonOrDeepGemmExperts)
 from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx
+from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
 
 from .modular_kernel_tools.common import (Config, RankTensors, WeightTensors,
                                           reference_moe_impl,
                                           run_modular_kernel)
 from .modular_kernel_tools.mk_objects import (
     MK_FUSED_EXPERT_TYPES, MK_MULTI_GPU_PREPARE_FINALIZE_TYPES,
-    MK_QUANT_CONFIGS, MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES)
+    MK_QUANT_CONFIGS, MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES, expert_info)
 from .modular_kernel_tools.parallel_utils import (ProcessGroupInfo,
                                                   parallel_launch_with_config)
 
-# TODO (varun): These requirements are very strict and could be relaxed.
-has_all_packages = (has_deep_ep() and has_deep_gemm() and has_pplx())
+has_any_multi_gpu_package = (has_deep_ep() or has_deep_gemm() or has_pplx()
+                             or has_flashinfer_cutlass_fused_moe())
 
-meets_package_requirements = pytest.mark.skipif(
-    not has_all_packages,
-    reason="Requires deep_ep & deep_gemm & pplx packages",
+meets_multi_gpu_requirements = pytest.mark.skipif(
+    not has_any_multi_gpu_package,
+    reason="Requires deep_ep or deep_gemm or pplx or flashinfer packages",
 )
 
 
+def format_result(verbose, msg, ex=None):
+    if ex is not None:
+        x = str(ex)
+        newx = x.strip(" \n\t")[:16]
+        if len(newx) < len(x):
+            newx = newx + " ..."
+
+        prefix = "E\t"
+        print(f"{textwrap.indent(traceback.format_exc(), prefix)}")
+        print(f"FAILED {msg} - {newx}\n")
+    elif verbose:
+        print(f"PASSED {msg}")
+    else:
+        print(".", end="")
+
+
 def rank_worker(
     pgi: ProcessGroupInfo,
     vllm_config: VllmConfig,
     cpu_group,
     config: Config,
     weights: WeightTensors,
+    verbose: bool,
 ):
     current_platform.seed_everything(pgi.rank)
 
@@ -61,39 +73,64 @@ def rank_worker(
     TOPKs = config.topks
     assert isinstance(TOPKs, list)
 
+    exceptions = []
+    count = 0
+
     for m, topk in product(Ms, TOPKs):
-        print(f"Running m={m}, topk={topk} ...")
-        # override m and topk
-        cfgx = copy.deepcopy(config)
-        cfgx.Ms = m
-        cfgx.topks = topk
+        try:
+            print(f"Running[{pgi.rank}]: m={m}, topk={topk} ...")
+            count = count + 1
+            # override m and topk
+            cfgx = copy.deepcopy(config)
+            cfgx.Ms = m
+            cfgx.topks = topk
 
-        # inputs for rank
-        rank_tensors = RankTensors.make(cfgx, pgi)
+            # inputs for rank
+            rank_tensors = RankTensors.make(cfgx, pgi)
 
-        # modular kernel out
-        mk_out = run_modular_kernel(pgi, vllm_config, cfgx, weights,
-                                    rank_tensors)
+            # modular kernel out
+            mk_out = run_modular_kernel(pgi, vllm_config, cfgx, weights,
+                                        rank_tensors)
 
-        with set_current_vllm_config(vllm_config):
-            ref_out = reference_moe_impl(cfgx, weights, rank_tensors)
+            with set_current_vllm_config(vllm_config):
+                ref_out = reference_moe_impl(cfgx, weights, rank_tensors)
 
-        torch.testing.assert_close(ref_out, mk_out, atol=3e-2, rtol=3e-2)
+            if config.quant_dtype == "nvfp4":
+                atol = 1e-1
+                rtol = 1e-1
+            else:
+                atol = 3e-2
+                rtol = 3e-2
+
+            torch.testing.assert_close(ref_out, mk_out, atol=atol, rtol=rtol)
+            format_result(verbose, config.describe())
+        except Exception as ex:
+            format_result(verbose, config.describe(), ex)
+            exceptions.append(ex)
+
+    if len(exceptions) > 0:
+        raise RuntimeError(
+            f"{len(exceptions)} of {count} tests failed in child process, "
+            f"rank={pgi.rank}.")
+    else:
+        print(f"{count} of {count} tests passed in child process, "
+              f"rank={pgi.rank}.")
 
 
-def run(config: Config):
+def run(config: Config, verbose: bool):
     assert config.is_valid()
-    print(f"Testing config \n{config.describe()} ...")
 
     weights: WeightTensors = WeightTensors.make(config)
 
     vllm_config, env_dict = config.make_env_data()
     parallel_launch_with_config(config.world_size, rank_worker, vllm_config,
-                                env_dict, config, weights)
+                                env_dict, config, weights, verbose)
 
 
 Ms = [32, 64]
-Ks = [7168]  # hidden sizes
+# hidden sizes, making this too large will cause fp4 tests to fail.
+# Also needs to be a multiple of 1024 for deep_gemm.
+Ks = [2048]
 Ns = [2048]
 TOPKs = [4, 1]
 Es = [32]
@@ -103,19 +140,16 @@ FUSED_MOE_CHUNK_SIZEs = [None, 16]
 
 def is_nyi_config(config: Config) -> bool:
     # We know these configs to be legitimate. but still fail.
+    info = expert_info(config.fused_experts_type)
 
-    if (config.fused_experts_type in [
-            BatchedTritonExperts, BatchedTritonOrDeepGemmExperts,
-            TritonExperts, TritonOrDeepGemmExperts
-    ]):
+    if info.needs_matching_quant:
         # The triton kernels expect both per-act-token-quant and
         # per-out-ch-quant or neither.
         unsupported_quant_config = ((config.is_per_act_token_quant +
                                      config.is_per_out_ch_quant) == 1)
         return unsupported_quant_config
 
-    # cutlass kernels dont support expert_maps yet.
-    return config.fused_experts_type == CutlassExpertsFp8
+    return not info.supports_expert_map
 
 
 @pytest.mark.parametrize("k", Ks)
@@ -128,13 +162,13 @@ def is_nyi_config(config: Config) -> bool:
     product(MK_MULTI_GPU_PREPARE_FINALIZE_TYPES, MK_FUSED_EXPERT_TYPES))
 @pytest.mark.parametrize("fused_moe_chunk_size", FUSED_MOE_CHUNK_SIZEs)
 @pytest.mark.parametrize("world_size", [2])
-@meets_package_requirements
+@meets_multi_gpu_requirements
 def test_modular_kernel_combinations_multigpu(
         k: int, n: int, e: int, dtype: torch.dtype,
-        quant_config: FusedMoEQuantConfig,
+        quant_config: Optional[FusedMoEQuantConfig],
         combination: tuple[mk.FusedMoEPrepareAndFinalize,
                            mk.FusedMoEPermuteExpertsUnpermute],
-        fused_moe_chunk_size: Optional[int], world_size: int):
+        fused_moe_chunk_size: Optional[int], world_size: int, pytestconfig):
 
     config = Config(
         Ms=Ms,
@@ -149,14 +183,15 @@ def test_modular_kernel_combinations_multigpu(
         fused_moe_chunk_size=fused_moe_chunk_size,
         world_size=world_size,
     )
+
     if not config.is_valid():
         pytest.skip(f"Tests config {config} is not valid. Skipping ...")
 
     if is_nyi_config(config):
         pytest.skip(f"Tests config {config} is nyi. Skipping ...")
 
-    print(f"{config.describe()}")
-    run(config)
+    verbosity = pytestconfig.getoption('verbose')
+    run(config, verbosity > 0)
 
 
 @pytest.mark.parametrize("k", Ks)
@@ -169,13 +204,12 @@ def test_modular_kernel_combinations_multigpu(
     product(MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES, MK_FUSED_EXPERT_TYPES))
 @pytest.mark.parametrize("fused_moe_chunk_size", FUSED_MOE_CHUNK_SIZEs)
 @pytest.mark.parametrize("world_size", [1])
-@meets_package_requirements
 def test_modular_kernel_combinations_singlegpu(
         k: int, n: int, e: int, dtype: torch.dtype,
-        quant_config: FusedMoEQuantConfig,
+        quant_config: Optional[FusedMoEQuantConfig],
         combination: tuple[mk.FusedMoEPrepareAndFinalize,
                            mk.FusedMoEPermuteExpertsUnpermute],
-        fused_moe_chunk_size: Optional[int], world_size: int):
+        fused_moe_chunk_size: Optional[int], world_size: int, pytestconfig):
     config = Config(
         Ms=Ms,
         K=k,
@@ -196,7 +230,8 @@ def test_modular_kernel_combinations_singlegpu(
     if is_nyi_config(config):
         pytest.skip(f"Tests config {config} is nyi. Skipping ...")
 
-    run(config)
+    verbosity = pytestconfig.getoption('verbose')
+    run(config, verbosity > 0)
 
 
 if __name__ == '__main__':
@@ -211,4 +246,4 @@ if __name__ == '__main__':
     args = parser.parse_args()
     config = make_config(args)
 
-    run(config)
+    run(config, True)

tests/kernels/moe/test_moe.py

@@ -24,8 +24,10 @@ from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_topk, modular_triton_fused_moe)
 from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
     fused_moe as iterative_moe)
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    marlin_permute_bias)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
-    rand_marlin_weight_fp4_like)
+    rand_marlin_weight_mxfp4_like, rand_marlin_weight_nvfp4_like)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     marlin_quant_fp8_torch)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
@@ -40,6 +42,24 @@ NUM_EXPERTS = [8, 64, 192]
 EP_SIZE = [1, 4]
 TOP_KS = [2, 6]
 
+FUSED_MOE_MNK_FACTORS = [
+    (1, 128, 128),
+    (1, 2048, 128),
+    (33, 2048, 128),
+    (222, 1024, 1024),
+    (32768, 128, 128),
+    (32768, 2048, 511),
+    (40000, 1024, 1024),
+]
+
+FUSED_MOE_WN16_MNK_FACTORS = [
+    (1, 128, 128),
+    (1, 1024, 1024),
+    (32, 2048, 128),
+    (32, 1024, 1024),
+    (222, 2048, 1024),
+]
+
 vllm_config = VllmConfig()
 vllm_config.scheduler_config.max_num_seqs = 128
 vllm_config.scheduler_config.max_model_len = 8192
@@ -114,13 +134,11 @@ def run_moe_test(
     return baseline_output
 
 
-@pytest.mark.parametrize("m", [1, 33, 64, 222, 32768, 40000])
-@pytest.mark.parametrize("n", [128, 1024, 2048])
-@pytest.mark.parametrize("k", [128, 511, 1024])
+@pytest.mark.parametrize("m,n,k", FUSED_MOE_MNK_FACTORS)
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("ep_size", EP_SIZE)
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("padding", [True, False])
 @pytest.mark.parametrize("chunk_size", [8192])
 def test_fused_moe(
@@ -233,13 +251,11 @@ def test_fused_moe(
                use_cudagraph=use_cudagraph)
 
 
-@pytest.mark.parametrize("m", [1, 32, 222])
-@pytest.mark.parametrize("n", [128, 1024, 2048])
-@pytest.mark.parametrize("k", [128, 1024])
+@pytest.mark.parametrize("m,n,k", FUSED_MOE_WN16_MNK_FACTORS)
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("ep_size", EP_SIZE)
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("group_size", [64, 128])
 @pytest.mark.parametrize("has_zp", [True, False])
 @pytest.mark.parametrize("weight_bits", [4, 8])
@@ -350,8 +366,7 @@ def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
     torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
 
 
-@pytest.mark.parametrize("dtype",
-                         [torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("padding", [True, False])
 @pytest.mark.parametrize(
     "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
@@ -476,8 +491,11 @@ def marlin_moe_generate_valid_test_cases():
         if quant_type == scalar_types.float8_e4m3fn and \
                 group_size not in [-1, 128]:
             return False
-        if quant_type == scalar_types.float4_e2m1f and group_size != 16:
-            return False
+        if quant_type == scalar_types.float4_e2m1f:
+            if group_size not in [16, 32]:
+                return False
+            if dtype == torch.float16 and group_size == 32:
+                return False
         if quant_type != scalar_types.float4_e2m1f and group_size == 16:
             return False
 
@@ -520,31 +538,6 @@ def test_fused_marlin_moe(
     torch.cuda.manual_seed(0)
     has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
 
-    if quant_type == scalar_types.float8_e4m3fn:
-        if group_size not in [-1, 128]:
-            return
-        if act_order:
-            return
-
-    # Filter act_order
-    if act_order:
-        if quant_type == scalar_types.float8_e4m3fn:
-            return
-        if group_size == -1:
-            return
-        if group_size in (k, n):
-            return
-        if has_zp:
-            return
-    else:
-        if not is_k_full:
-            return
-
-    if quant_type == scalar_types.float4_e2m1f and group_size != 16:
-        return
-    if quant_type != scalar_types.float4_e2m1f and group_size == 16:
-        return
-
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 20
     w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 20
@@ -569,13 +562,19 @@ def test_fused_marlin_moe(
 
     for i in range(w1.shape[0]):
         if quant_type == scalar_types.float4_e2m1f:
-            w_ref1, qweight1, scales1, global_scale1 = \
-                rand_marlin_weight_fp4_like(w1[i], group_size)
+            if group_size == 16:
+                w_ref1, qweight1, scales1, global_scale1 = \
+                    rand_marlin_weight_nvfp4_like(w1[i], group_size)
+            else:
+                w_ref1, qweight1, scales1 = \
+                    rand_marlin_weight_mxfp4_like(w1[i], group_size)
+                global_scale1 = None
 
             w_ref1_l.append(w_ref1.T)
             qweight1_l.append(qweight1)
             scales1_l.append(scales1)
-            global_scale1_l.append(global_scale1)
+            if global_scale1 is not None:
+                global_scale1_l.append(global_scale1)
         elif quant_type == scalar_types.float8_e4m3fn:
             w_ref1, qweight1, scales1 = marlin_quant_fp8_torch(
                 w1[i], group_size)
@@ -620,13 +619,19 @@ def test_fused_marlin_moe(
 
     for i in range(w2.shape[0]):
         if quant_type == scalar_types.float4_e2m1f:
-            w_ref2, qweight2, scales2, global_scale2 = \
-                rand_marlin_weight_fp4_like(w2[i], group_size)
+            if group_size == 16:
+                w_ref2, qweight2, scales2, global_scale2 = \
+                    rand_marlin_weight_nvfp4_like(w2[i], group_size)
+            else:
+                w_ref2, qweight2, scales2 = \
+                    rand_marlin_weight_mxfp4_like(w2[i], group_size)
+                global_scale2 = None
 
             w_ref2_l.append(w_ref2.T)
             qweight2_l.append(qweight2)
             scales2_l.append(scales2)
-            global_scale2_l.append(global_scale2)
+            if global_scale2 is not None:
+                global_scale2_l.append(global_scale2)
         elif quant_type == scalar_types.float8_e4m3fn:
             w_ref2, qweight2, scales2 = marlin_quant_fp8_torch(
                 w2[i], group_size)
@@ -677,6 +682,8 @@ def test_fused_marlin_moe(
         a,
         qweight1,
         qweight2,
+        None,
+        None,
         scales1,
         scales2,
         score,
@@ -698,6 +705,119 @@ def test_fused_marlin_moe(
     torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)
 
 
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
+@pytest.mark.parametrize("m", [1, 256])
+def test_fused_marlin_moe_with_bias(m):
+    torch.cuda.manual_seed(0)
+
+    e, topk = 32, 4
+    n, k = 2048, 2048
+    group_size = 128
+    act_order = False
+    is_k_full = True
+    quant_type = scalar_types.uint4b8
+    dtype = torch.half
+
+    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
+    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
+    b_bias1 = torch.randn((e, 2 * n), device="cuda", dtype=dtype) / 10
+    b_bias2 = torch.randn((e, k), device="cuda", dtype=dtype) / 10
+
+    b_bias1_l = []
+    w_ref1_l = []
+    qweight1_l = []
+    scales1_l = []
+    g_idx1_l = []
+    sort_indices1_l = []
+
+    for i in range(w1.shape[0]):
+        test_perm = torch.randperm(k)
+        w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = \
+            marlin_quantize(w1[i].transpose(1, 0), quant_type,
+                            group_size, act_order, test_perm)
+
+        w_ref1_l.append(w_ref1.T)
+        qweight1_l.append(qweight1)
+        scales1_l.append(scales1)
+        g_idx1_l.append(g_idx1)
+        sort_indices1_l.append(sort_indices1)
+        b_bias1_l.append(marlin_permute_bias(b_bias1[i]))
+
+    w_ref1 = stack_and_dev(w_ref1_l)
+    qweight1 = stack_and_dev(qweight1_l).contiguous()
+    scales1 = stack_and_dev(scales1_l)
+    global_scale1 = None
+    g_idx1 = stack_and_dev(g_idx1_l) if g_idx1_l else None
+    zeros1 = None
+    sort_indices1 = stack_and_dev(sort_indices1_l) if sort_indices1_l else None
+    marlin_bias1 = stack_and_dev(b_bias1_l) if b_bias1_l else None
+
+    b_bias2_l = []
+    w_ref2_l = []
+    qweight2_l = []
+    scales2_l = []
+    g_idx2_l = []
+    sort_indices2_l = []
+
+    for i in range(w2.shape[0]):
+        test_perm = torch.randperm(n)
+        w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = \
+            marlin_quantize(w2[i].transpose(1, 0), quant_type,
+                            group_size, act_order, test_perm)
+
+        w_ref2_l.append(w_ref2.T)
+        qweight2_l.append(qweight2)
+        scales2_l.append(scales2)
+        g_idx2_l.append(g_idx2)
+        sort_indices2_l.append(sort_indices2)
+        b_bias2_l.append(marlin_permute_bias(b_bias2[i]))
+
+    w_ref2 = stack_and_dev(w_ref2_l)
+    qweight2 = stack_and_dev(qweight2_l).contiguous()
+    scales2 = stack_and_dev(scales2_l)
+    global_scale2 = None
+    g_idx2 = stack_and_dev(g_idx2_l) if g_idx2_l else None
+    zeros2 = None
+    sort_indices2 = stack_and_dev(sort_indices2_l) if sort_indices2_l else None
+    marlin_bias2 = stack_and_dev(b_bias2_l) if b_bias2_l else None
+
+    score = torch.randn((m, e), device="cuda", dtype=dtype)
+
+    topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
+
+    with set_current_vllm_config(vllm_config):
+        torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, b_bias1,
+                                 b_bias2)
+
+    marlin_output = torch.ops.vllm.fused_marlin_moe(
+        a,
+        qweight1,
+        qweight2,
+        marlin_bias1,
+        marlin_bias2,
+        scales1,
+        scales2,
+        score,
+        topk_weights,
+        topk_ids,
+        global_num_experts=e,
+        expert_map=None,
+        global_scale1=global_scale1,
+        global_scale2=global_scale2,
+        g_idx1=g_idx1,
+        g_idx2=g_idx2,
+        sort_indices1=sort_indices1,
+        sort_indices2=sort_indices2,
+        w1_zeros=zeros1,
+        w2_zeros=zeros2,
+        quant_type_id=quant_type.id,
+        is_k_full=is_k_full)
+
+    torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)
+
+
 def test_moe_align_block_size_opcheck():
     num_experts = 4
     block_size = 4

tests/kernels/moe/test_moe_align_block_size.py

@@ -15,10 +15,10 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
 from vllm.platforms import current_platform
 from vllm.utils import round_up
 
-NUM_TOKENS = [1, 3, 7, 16, 256, 2256, 4096]
-NUM_EXPERTS = [32, 160, 256, 257, 512]
+NUM_TOKENS = [1, 3, 256, 2256, 4096]
+NUM_EXPERTS = [32, 160, 256, 257]
 TOP_KS = [1, 2, 16, 32]
-BLOCK_SIZES = [32, 64, 128, 256]
+BLOCK_SIZES = [32, 128]
 current_platform.seed_everything(0)
 
 

tests/kernels/moe/test_moe_permute_unpermute.py

@@ -18,7 +18,7 @@ from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
 from vllm.platforms import current_platform
 
 NUM_EXPERTS = [16, 64, 256]
-TOP_KS = [2, 4, 6, 8]
+TOP_KS = [2, 6, 8]
 EP_SIZE = [1, 4, 16]
 current_platform.seed_everything(0)
 
@@ -177,11 +177,11 @@ def torch_unpermute(permuted_hidden_states: torch.Tensor,
     return output
 
 
-@pytest.mark.parametrize("n_token", [1, 33, 64, 222, 1024, 2048, 3000, 5000])
-@pytest.mark.parametrize("n_hidden", [2048, 4096, 7168])
+@pytest.mark.parametrize("n_token", [1, 33, 1024, 5000])
+@pytest.mark.parametrize("n_hidden", [2048, 7168])
 @pytest.mark.parametrize("n_expert", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("ep_size", EP_SIZE)
 @pytest.mark.parametrize("align_block_size", [None, 128])
 def test_moe_permute_unpermute(n_token: int, n_hidden: int, topk: int,