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
 
@@ -168,9 +160,9 @@ See [nightly-descriptions.md](nightly-descriptions.md) for the detailed descript
 ### Workflow
 
 - The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
-- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
-- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
-- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
+- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
+- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
 
 ### Nightly tests
 
@@ -180,6 +172,6 @@ In [nightly-tests.json](tests/nightly-tests.json), we include the command line a
 
 The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
 
-WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
 
 WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).

.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/genai-perf-tests.json

@@ -12,7 +12,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

.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/nightly-tests.json

@@ -36,7 +36,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -90,7 +89,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -144,7 +142,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -195,7 +192,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -248,7 +244,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -301,7 +296,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

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

@@ -128,7 +128,7 @@ run_and_track_test() {
 
 # --- Actual Test Execution ---
 run_and_track_test 1 "test_struct_output_generate.py" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
 run_and_track_test 2 "test_moe_pallas.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
 run_and_track_test 3 "test_lora.py" \
@@ -139,6 +139,8 @@ run_and_track_test 5 "test_spmd_model_weight_loading.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
 run_and_track_test 6 "test_kv_cache_update_kernel.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
+run_and_track_test 7 "test_tpu_int8.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_int8.py"
 
 # After all tests have been attempted, exit with the overall status.
 if [ "$overall_script_exit_code" -ne 0 ]; then

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

@@ -134,7 +134,7 @@ run_and_track_test 1 "test_compilation.py" \
 run_and_track_test 2 "test_basic.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
 run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
+    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
 run_and_track_test 5 "examples/offline_inference/tpu.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
@@ -57,20 +47,20 @@ steps:
   - vllm/
   - tests/mq_llm_engine
   - tests/async_engine
-  - tests/test_inputs
+  - tests/test_inputs.py
+  - tests/test_outputs.py
   - tests/multimodal
-  - tests/test_utils
+  - tests/utils_
   - tests/worker
   - tests/standalone_tests/lazy_imports.py
   commands:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
   - pytest -v -s async_engine # AsyncLLMEngine
-  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s multimodal
-  - pytest -v -s test_utils.py # Utils
+  - pytest -v -s utils_ # Utils
   - pytest -v -s worker # Worker
 
 - label: Python-only Installation Test
@@ -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/
@@ -426,7 +418,6 @@ steps:
 
 - label: Tensorizer Test # 11min
   mirror_hardwares: [amdexperimental]
-  soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
   - tests/tensorizer_loader
@@ -535,8 +526,6 @@ steps:
   - vllm/
   - tests/models/language
   commands:
-    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
-    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/language -m core_model
 
@@ -547,8 +536,10 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
-    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
+    # Install fast path packages for testing against transformers
+    # Note: also needed to run plamo2 model in vLLM
+    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
     - pytest -v -s models/language/generation -m hybrid_model
 
 - label: Language Models Test (Extended Generation) # 1hr20min
@@ -670,6 +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
@@ -773,27 +765,6 @@ steps:
   - pytest -v -s models/test_oot_registration.py # it needs a clean process
   - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins
 
-- label: Multi-step Tests (4 GPUs) # 36min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/model_executor/layers/sampler.py
-  - vllm/sequence.py
-  - vllm/worker/worker_base.py
-  - vllm/worker/worker.py
-  - vllm/worker/multi_step_worker.py
-  - vllm/worker/model_runner_base.py
-  - vllm/worker/model_runner.py
-  - vllm/worker/multi_step_model_runner.py
-  - vllm/engine
-  - tests/multi_step
-  commands:
-  # this test is quite flaky
-  # TODO: investigate and fix.
-  # - pytest -v -s multi_step/test_correctness_async_llm.py
-  - pytest -v -s multi_step/test_correctness_llm.py
-
 - label: Pipeline Parallelism Test # 45min
   mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"

.github/CODEOWNERS

@@ -9,7 +9,7 @@
 /vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
@@ -20,7 +20,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
-/vllm/config.py @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor
+/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
 
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
@@ -34,16 +34,15 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
-/tests/kernels @tlrmchlsmth @WoosukKwon
+/tests/kernels @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/models @DarkLight1337 @ywang96
-/tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
-/tests/quantization @mgoin @robertgshaw2-redhat
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
-/tests/weight_loading @mgoin @youkaichao
+/tests/weight_loading @mgoin @youkaichao @yewentao256
 /tests/lora @jeejeelee
 
 # Docs

.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/mergify.yml

@@ -118,6 +118,20 @@ pull_request_rules:
       add:
         - qwen
 
+- name: label-gpt-oss
+  description: Automatically apply gpt-oss label
+  conditions:
+    - or:
+      - files~=^examples/.*gpt[-_]?oss.*\.py
+      - files~=^tests/.*gpt[-_]?oss.*\.py
+      - files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
+      - files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
+      - title~=(?i)gpt[-_]?oss
+  actions:
+    label:
+      add:
+        - gpt-oss
+
 - name: label-rocm
   description: Automatically apply rocm label
   conditions:

.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

@@ -4,6 +4,9 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*
 
+# triton jit 
+.triton
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -147,7 +150,8 @@ venv.bak/
 # mkdocs documentation
 /site
 docs/argparse
-docs/examples
+docs/examples/*
+!docs/examples/README.md
 
 # mypy
 .mypy_cache/
@@ -203,3 +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: ${MARLIN_ARCHS}")
   else()
     message(STATUS "Not building Marlin kernels as no compatible archs found"
@@ -427,6 +434,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(SRCS
       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
     )
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -853,6 +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})
 

README.md

@@ -18,14 +18,15 @@ Easy, fast, and cheap LLM serving for everyone
 
 *Latest News* 🔥
 
+- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
 - [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
 - [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
-- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 
 <details>
 <summary>Previous News</summary>
 
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
@@ -121,6 +122,7 @@ Cash Donations:
 
 Compute Resources:
 
+- Alibaba Cloud
 - AMD
 - Anyscale
 - AWS
@@ -160,7 +162,7 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 ## Contact Us
 
 <!-- --8<-- [start:contact-us] -->
-- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
+- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues)
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature

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/backend_request_func.py

@@ -31,7 +31,7 @@ class RequestFuncInput:
     model_name: Optional[str] = None
     logprobs: Optional[int] = None
     extra_body: Optional[dict] = None
-    multi_modal_content: Optional[dict] = None
+    multi_modal_content: Optional[dict | list[dict]] = None
     ignore_eos: bool = False
     language: Optional[str] = None
 
@@ -364,7 +364,15 @@ async def async_request_openai_chat_completions(
     ) as session:
         content = [{"type": "text", "text": request_func_input.prompt}]
         if request_func_input.multi_modal_content:
-            content.append(request_func_input.multi_modal_content)
+            mm_content = request_func_input.multi_modal_content
+            if isinstance(mm_content, list):
+                content.extend(mm_content)
+            elif isinstance(mm_content, dict):
+                content.append(mm_content)
+            else:
+                raise TypeError(
+                    "multi_modal_content must be a dict or list[dict] for openai-chat"
+                )
         payload = {
             "model": request_func_input.model_name
             if request_func_input.model_name
@@ -491,7 +499,10 @@ async def async_request_openai_audio(
             buffer.seek(0)
             return buffer
 
-        with to_bytes(*request_func_input.multi_modal_content["audio"]) as f:
+        mm_audio = request_func_input.multi_modal_content
+        if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
+            raise TypeError("multi_modal_content must be a dict containing 'audio'")
+        with to_bytes(*mm_audio["audio"]) as f:
             form = aiohttp.FormData()
             form.add_field("file", f, content_type="audio/wav")
             for key, value in payload.items():

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

@@ -52,7 +52,7 @@ class SampleRequest:
     prompt: Union[str, Any]
     prompt_len: int
     expected_output_len: int
-    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
+    multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
     lora_request: Optional[LoRARequest] = None
 
 
@@ -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_serving.py

@@ -263,7 +263,14 @@ async def benchmark(
         input_requests[0].multi_modal_data,
     )
 
-    assert test_mm_content is None or isinstance(test_mm_content, dict)
+    assert (
+        test_mm_content is None
+        or isinstance(test_mm_content, dict)
+        or (
+            isinstance(test_mm_content, list)
+            and all(isinstance(item, dict) for item in test_mm_content)
+        )
+    ), "multi_modal_data must be a dict or list[dict]"
     test_input = RequestFuncInput(
         model=model_id,
         model_name=model_name,

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_bitblas.py

@@ -3,6 +3,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+from packaging import version
+
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
     MINIMUM_BITBLAS_VERSION,
 )
@@ -10,7 +12,7 @@ from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
 try:
     import bitblas
 
-    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+    if version.parse(bitblas.__version__) < version.parse(MINIMUM_BITBLAS_VERSION):
         raise ImportError(
             "bitblas version is wrong. Please "
             f"install bitblas>={MINIMUM_BITBLAS_VERSION}"

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
@@ -22,10 +23,10 @@ from vllm.utils import FlexibleArgumentParser
 FP8_DTYPE = current_platform.fp8_dtype()
 
 
-def ensure_divisibility(numerator, denominator):
+def ensure_divisibility(numerator, denominator, text):
     """Ensure that numerator is divisible by the denominator."""
-    assert numerator % denominator == 0, (
-        "intermediate_size {} is not divisible by tp {}.".format(numerator, denominator)
+    assert numerator % denominator == 0, "{} {} is not divisible by tp {}.".format(
+        text, numerator, denominator
     )
 
 
@@ -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)
@@ -577,12 +580,10 @@ def main(args: argparse.Namespace):
         E = config.ffn_config.moe_num_experts
         topk = config.ffn_config.moe_top_k
         intermediate_size = config.ffn_config.ffn_hidden_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] == "JambaForCausalLM":
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] in (
         "DeepseekV3ForCausalLM",
         "DeepseekV2ForCausalLM",
@@ -591,17 +592,14 @@ def main(args: argparse.Namespace):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
         E = config.num_experts
         topk = config.moe_topk[0]
         intermediate_size = config.moe_intermediate_size[0]
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
         # Support for llama4
         config = config.get_text_config()
@@ -609,8 +607,14 @@ def main(args: argparse.Namespace):
         E = config.num_local_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
+    enable_ep = bool(args.enable_expert_parallel)
+    if enable_ep:
+        ensure_divisibility(E, args.tp_size, "Number of experts")
+        E = E // args.tp_size
+        shard_intermediate_size = 2 * intermediate_size
+    else:
+        ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
-    ensure_divisibility(intermediate_size, args.tp_size)
     hidden_size = config.hidden_size
     dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
     use_fp8_w8a8 = args.dtype == "fp8_w8a8"
@@ -706,6 +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")
@@ -742,10 +747,14 @@ if __name__ == "__main__":
     parser.add_argument(
         "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
     )
+    parser.add_argument("--enable-expert-parallel", "-enable-ep", action="store_true")
     parser.add_argument(
         "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
     )
     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/kernels/benchmark_mrope.py

@@ -0,0 +1,328 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# This script benchmarks the mrope kernel (mainly for Qwen2VL and Qwen2.5VL models).
+# It generates test data, runs benchmarks, and saves results to a CSV file.
+#
+# The CSV file (named with current date/time) contains these columns:
+# model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
+# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
+# torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
+# speedup
+#
+# == Usage Examples ==
+#
+# Single model benchmark:
+# python3 benchmark_mrope.py --model-name Qwen/Qwen2-VL-7B-Instruct --tp-size 1 \
+#   --warmup-iter 10 --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models benchmark:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different TP sizes:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 2 4 8 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different token counts:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024 4096 16384
+import csv
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import numpy as np
+import torch
+
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
+from vllm.utils import FlexibleArgumentParser
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_test_data(
+    num_tokens: int,
+    num_q_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    max_position_embeddings: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    """Generate test data for given configuration."""
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(
+        0, max_position_embeddings // 4, (3, num_tokens), device=device
+    )
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
+    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
+
+    return positions, query, key
+
+
+def calculate_stats(times: list[float]) -> dict[str, float]:
+    """Calculate statistics from a list of times."""
+    times_array = np.array(times)
+    return {
+        "mean": np.mean(times_array),
+        "median": np.median(times_array),
+        "p99": np.percentile(times_array, 99),
+        "min": np.min(times_array),
+        "max": np.max(times_array),
+    }
+
+
+def benchmark_mrope(
+    model_name: str,
+    num_tokens: int,
+    head_dim: int,
+    tp_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    max_position: int = 8192,
+    rope_theta: float = 10000,
+    is_neox_style: bool = True,
+    rope_scaling: dict[str, Any] = None,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 0,
+    warmup_iter: int = 10,
+    benchmark_iter: int = 100,
+    csv_writer=None,
+):
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    # the parameters to compute the q k v size based on tp_size
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    print(80 * "=")
+    print(
+        f"Evaluating model: {model_name} "
+        f"with tp_size: {tp_size} "
+        f"and num_tokens: {num_tokens}, "
+        f"dtype: {dtype}"
+    )
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(
+        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
+    )
+
+    # Warm up
+    for _ in range(warmup_iter):
+        mrope_helper_class.forward_native(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+        mrope_helper_class.forward_cuda(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+    torch.cuda.synchronize()
+
+    # Time reference implementation
+    torch_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+
+        mrope_helper_class.forward_native(
+            positions,
+            query_clone,
+            key_clone,
+        )
+
+        torch.cuda.synchronize()
+        torch_times.append(time.time() - start_time)
+
+    # Time triton kernel implementation
+    triton_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+        mrope_helper_class.forward_cuda(
+            positions,
+            query_clone,
+            key_clone,
+        )
+        torch.cuda.synchronize()
+        triton_times.append(time.time() - start_time)
+
+    # Calculate statistics
+    torch_stats = calculate_stats(torch_times)
+    triton_stats = calculate_stats(triton_times)
+    print(f"\nPerformance for config ({num_tokens}, {num_heads}, {num_kv_heads}):")
+
+    print(
+        f"Torch implementation: "
+        f"mean={torch_stats['mean']:.8f}s, "
+        f"median={torch_stats['median']:.8f}s, "
+        f"p99={torch_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton implementation: "
+        f"mean={triton_stats['mean']:.8f}s, "
+        f"median={triton_stats['median']:.8f}s, "
+        f"p99={triton_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton Speedup over Torch: {torch_stats['mean'] / triton_stats['mean']:.8f}x"
+    )
+
+    # Write to CSV
+    if csv_writer:
+        row = [
+            model_name,
+            tp_size,
+            num_tokens,
+            num_heads,
+            num_kv_heads,
+            head_dim,
+            max_position,
+            rope_theta,
+            is_neox_style,
+            str(rope_scaling),
+            str(dtype).split(".")[-1],
+            torch_stats["mean"],
+            torch_stats["median"],
+            torch_stats["p99"],
+            torch_stats["min"],
+            torch_stats["max"],
+            triton_stats["mean"],
+            triton_stats["median"],
+            triton_stats["p99"],
+            triton_stats["min"],
+            triton_stats["max"],
+            torch_stats["mean"] / triton_stats["mean"],  # speedup
+        ]
+        csv_writer.writerow(row)
+
+    return torch_stats, triton_stats
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark the rotary embedding kernels."
+    )
+    parser.add_argument("--model-name", type=str, default="")
+    parser.add_argument("--tp-size", type=int, default=1)
+    parser.add_argument("--warmup-iter", type=int, default=10)
+    parser.add_argument("--benchmark-iter", type=int, default=100)
+    parser.add_argument("--dtype", type=str, choices=["bfloat16"], default="bfloat16")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num-tokens", type=int, nargs="+", required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--output-csv", type=str, default="mrope_benchmark_results.csv")
+    args = parser.parse_args()
+    print(args)
+
+    # Create CSV file for results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    csv_filename = f"{os.path.splitext(args.output_csv)[0]}_{timestamp}.csv"
+
+    with open(csv_filename, "w", newline="") as csvfile:
+        csv_writer = csv.writer(csvfile)
+        # Write header
+        header = [
+            "model_name",
+            "tp_size",
+            "num_tokens",
+            "num_heads",
+            "num_kv_heads",
+            "head_dim",
+            "max_position",
+            "rope_theta",
+            "is_neox_style",
+            "rope_scaling",
+            "dtype",
+            "torch_mean",
+            "torch_median",
+            "torch_p99",
+            "torch_min",
+            "torch_max",
+            "triton_mean",
+            "triton_median",
+            "triton_p99",
+            "triton_min",
+            "triton_max",
+            "speedup",
+        ]
+        csv_writer.writerow(header)
+
+        model_tp_dict = {}
+        if args.model_name == "":
+            model_tp_dict = {
+                "Qwen/Qwen2-VL-2B-Instruct": [1],
+                "Qwen/Qwen2-VL-7B-Instruct": [1],
+                "Qwen/Qwen2-VL-72B-Instruct": [2, 4, 8],
+                "Qwen/Qwen2.5-VL-3B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-7B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-72B-Instruct": [2, 4, 8],
+            }
+        else:
+            model_tp_dict[args.model_name] = [args.tp_size]
+
+        if args.num_tokens is None:
+            num_tokens_list = [2**i for i in range(0, 18)]
+        else:
+            num_tokens_list = args.num_tokens
+
+        for model_name, tp_list in model_tp_dict.items():
+            config = get_config(model_name, trust_remote_code=args.trust_remote_code)
+            for tp_size in tp_list:
+                # get the model config
+                total_num_kv_heads = config.num_key_value_heads
+                total_num_heads = config.num_attention_heads
+                num_heads = total_num_heads // tp_size
+                num_kv_heads = max(1, total_num_kv_heads // tp_size)
+                head_dim = config.hidden_size // total_num_heads
+                q_size = num_heads * head_dim
+                kv_size = num_kv_heads * head_dim
+                is_neox_style = True
+                rope_theta = config.rope_theta
+                max_position = config.max_position_embeddings
+
+                for num_tokens in num_tokens_list:
+                    benchmark_mrope(
+                        model_name=model_name,
+                        num_tokens=num_tokens,
+                        head_dim=head_dim,
+                        tp_size=tp_size,
+                        num_heads=num_heads,
+                        num_kv_heads=num_kv_heads,
+                        max_position=max_position,
+                        rope_theta=rope_theta,
+                        is_neox_style=is_neox_style,
+                        rope_scaling=config.rope_scaling,
+                        dtype=getattr(torch, args.dtype),
+                        seed=args.seed,
+                        warmup_iter=args.warmup_iter,
+                        benchmark_iter=args.benchmark_iter,
+                        csv_writer=csv_writer,
+                    )
+
+    print(f"Benchmark results saved to {csv_filename}")

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

benchmarks/multi_turn/README.md

@@ -0,0 +1,71 @@
+# Benchmark KV Cache Offloading with Multi-Turn Conversations
+
+The requirements (pip) for `benchmark_serving_multi_turn.py` can be found in `requirements.txt`
+
+First start serving your model
+
+```bash
+export MODEL_NAME=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
+
+vllm serve $MODEL_NAME --disable-log-requests
+```
+
+## Synthetic Multi-Turn Conversations
+
+Download the following text file (used for generation of synthetic conversations)
+
+```bash
+wget https://www.gutenberg.org/ebooks/1184.txt.utf-8
+mv 1184.txt.utf-8 pg1184.txt
+```
+
+The filename `pg1184.txt` is used in `generate_multi_turn.json` (see `"text_files"`).
+
+But you may use other text files if you prefer (using this specific file is not required).
+
+Then run the benchmarking script
+
+```bash
+export MODEL_NAME=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
+
+python benchmark_serving_multi_turn.py --model $MODEL_NAME --input-file generate_multi_turn.json \
+--num-clients 2 --max-active-conversations 6
+```
+
+You can edit the file `generate_multi_turn.json` to change the conversation parameters (number of turns, etc.).
+
+If successful, you will see the following output
+
+```bash
+----------------------------------------------------------------------------------------------------
+Statistics summary:
+runtime_sec = 215.810
+requests_per_sec = 0.769
+----------------------------------------------------------------------------------------------------
+                   count     mean     std      min      25%      50%      75%      90%      99%      max
+ttft_ms            166.0    78.22   67.63    45.91    59.94    62.26    64.43    69.66   353.18   567.54
+tpot_ms            166.0    25.37    0.57    24.40    25.07    25.31    25.50    25.84    27.50    28.05
+latency_ms         166.0  2591.07  326.90  1998.53  2341.62  2573.01  2860.10  3003.50  3268.46  3862.94
+input_num_turns    166.0     7.43    4.57     1.00     3.00     7.00    11.00    13.00    17.00    17.00
+input_num_tokens   166.0  2006.20  893.56   522.00  1247.75  2019.00  2718.00  3233.00  3736.45  3899.00
+output_num_tokens  166.0   100.01   11.80    80.00    91.00    99.00   109.75   116.00   120.00   120.00
+output_num_chunks  166.0    99.01   11.80    79.00    90.00    98.00   108.75   115.00   119.00   119.00
+----------------------------------------------------------------------------------------------------
+```
+
+## ShareGPT Conversations
+
+To run with the ShareGPT data, download the following ShareGPT dataset:
+`https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json`
+
+Use the `convert_sharegpt_to_openai.py` script to convert the dataset to a format supported by `benchmark_serving_multi_turn.py`
+
+```bash
+python convert_sharegpt_to_openai.py sharegpt_20230401_clean_lang_split.json sharegpt_conv_128.json --seed=99 --max-items=128
+```
+
+The script will convert the ShareGPT dataset to a dataset with the standard user/assistant roles.
+
+The flag `--max-items=128` is used to sample 128 conversations from the original dataset (change as needed).
+
+Use the output JSON file `sharegpt_conv_128.json` as the `--input-file` for `benchmark_serving_multi_turn.py`.

benchmarks/multi_turn/bench_dataset.py

@@ -0,0 +1,493 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+from statistics import mean
+from typing import Any, NamedTuple, Optional, Union
+
+import numpy as np  # type: ignore
+import pandas as pd  # type: ignore
+from bench_utils import (
+    TEXT_SEPARATOR,
+    Color,
+    logger,
+)
+from transformers import AutoTokenizer  # type: ignore
+
+# Conversation ID is a string (e.g: "UzTK34D")
+ConvId = str
+
+# A list of dicts (dicts with keys "id" and "messages")
+ShareGptConversations = list[dict[str, Any]]
+
+# A list of dicts (dicts with keys "role" and "content")
+MessagesList = list[dict[str, str]]
+
+# Map conversation ID to conversation messages
+ConversationsMap = list[ConvId, MessagesList]
+
+
+class Distribution(ABC):
+    @abstractmethod
+    def sample(self, size: int = 1) -> np.ndarray:
+        pass
+
+
+class UniformDistribution(Distribution):
+    def __init__(
+        self,
+        min_val: Union[int, float],
+        max_val: Union[int, float],
+        is_integer: bool = True,
+    ) -> None:
+        self.min_val = min_val
+        self.max_val = max_val
+        self.is_integer = is_integer
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        if self.is_integer:
+            return np.random.randint(
+                int(self.min_val), int(self.max_val + 1), size=size
+            )
+        else:
+            return np.random.uniform(self.min_val, self.max_val, size=size)
+
+    def __repr__(self) -> str:
+        return f"UniformDistribution[{self.min_val}, {self.max_val}]"
+
+
+class ConstantDistribution(Distribution):
+    def __init__(self, value: Union[int, float]) -> None:
+        self.value = value
+        self.max_val = value
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        return np.full(shape=size, fill_value=self.value)
+
+    def __repr__(self) -> str:
+        return f"Constant[{self.value}]"
+
+
+class ZipfDistribution(Distribution):
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
+        self.alpha = alpha
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.zipf(self.alpha, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+        return samples
+
+    def __repr__(self) -> str:
+        return f"ZipfDistribution[{self.alpha}]"
+
+
+class PoissonDistribution(Distribution):
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
+        self.alpha = alpha
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.poisson(self.alpha, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+        return samples
+
+    def __repr__(self) -> str:
+        return f"PoissonDistribution[{self.alpha}]"
+
+
+class LognormalDistribution(Distribution):
+    def __init__(
+        self, mean: float, sigma: float, max_val: Optional[int] = None
+    ) -> None:
+        self.mean = mean
+        self.sigma = sigma
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.lognormal(mean=self.mean, sigma=self.sigma, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+
+        return np.round(samples).astype(int)
+
+    def __repr__(self) -> str:
+        return f"LognormalDistribution[{self.mean}, {self.sigma}]"
+
+
+class GenConvArgs(NamedTuple):
+    num_conversations: int
+    text_files: list[str]
+    input_num_turns: Distribution
+    input_common_prefix_num_tokens: Distribution
+    input_prefix_num_tokens: Distribution
+    input_num_tokens: Distribution
+    output_num_tokens: Distribution
+    print_stats: bool
+
+
+def verify_field_exists(
+    conf: dict, field_name: str, section: str, subsection: str
+) -> None:
+    if field_name not in conf:
+        raise ValueError(
+            f"Missing field '{field_name}' in {section=} and {subsection=}"
+        )
+
+
+def get_random_distribution(
+    conf: dict, section: str, subsection: str, optional: bool = False
+) -> Distribution:
+    # section can be "prompt_input" or "prompt_output" (both required)
+    conf = conf[section]
+
+    if optional and subsection not in conf:
+        # Optional subsection, if not found assume the value is always 0
+        return ConstantDistribution(0)
+
+    # subsection can be "num_turns", "num_tokens" or "prefix_num_tokens"
+    if subsection not in conf:
+        raise ValueError(f"Missing subsection {subsection} in section {section}")
+
+    conf = conf[subsection]
+
+    distribution = conf.get("distribution")
+    if distribution is None:
+        raise ValueError(
+            f"Missing field 'distribution' in {section=} and {subsection=}"
+        )
+
+    if distribution == "constant":
+        verify_field_exists(conf, "value", section, subsection)
+        return ConstantDistribution(conf["value"])
+
+    elif distribution == "zipf":
+        verify_field_exists(conf, "alpha", section, subsection)
+        max_val = conf.get("max", None)
+        return ZipfDistribution(conf["alpha"], max_val=max_val)
+
+    elif distribution == "poisson":
+        verify_field_exists(conf, "alpha", section, subsection)
+        max_val = conf.get("max", None)
+        return PoissonDistribution(conf["alpha"], max_val=max_val)
+
+    elif distribution == "lognormal":
+        verify_field_exists(conf, "mean", section, subsection)
+        verify_field_exists(conf, "sigma", section, subsection)
+        max_val = conf.get("max", None)
+        return LognormalDistribution(conf["mean"], conf["sigma"], max_val=max_val)
+
+    elif distribution == "uniform":
+        verify_field_exists(conf, "min", section, subsection)
+        verify_field_exists(conf, "max", section, subsection)
+
+        min_value = conf["min"]
+        max_value = conf["max"]
+
+        assert min_value > 0
+        assert min_value <= max_value
+
+        is_integer = isinstance(min_value, int) and isinstance(max_value, int)
+        return UniformDistribution(min_value, max_value, is_integer)
+    else:
+        raise ValueError(f"Unknown distribution: {distribution}")
+
+
+def parse_input_json_file(conf: dict) -> GenConvArgs:
+    # Validate the input file
+    assert isinstance(conf, dict)
+    required_fields = [
+        "filetype",
+        "num_conversations",
+        "text_files",
+        "prompt_input",
+        "prompt_output",
+    ]
+    for field in required_fields:
+        assert field in conf, f"Missing field {field} in input {conf}"
+
+    assert conf["filetype"] == "generate_conversations"
+
+    assert conf["num_conversations"] > 0, "num_conversations should be larger than zero"
+
+    text_files = conf["text_files"]
+
+    assert isinstance(text_files, list), "Field 'text_files' should be a list"
+    assert len(text_files) > 0, (
+        "Field 'text_files' should be a list with at least one file"
+    )
+
+    # Parse the parameters for the prompt input/output workload
+    input_num_turns = get_random_distribution(conf, "prompt_input", "num_turns")
+    input_num_tokens = get_random_distribution(conf, "prompt_input", "num_tokens")
+    input_common_prefix_num_tokens = get_random_distribution(
+        conf, "prompt_input", "common_prefix_num_tokens", optional=True
+    )
+    input_prefix_num_tokens = get_random_distribution(
+        conf, "prompt_input", "prefix_num_tokens"
+    )
+    output_num_tokens = get_random_distribution(conf, "prompt_output", "num_tokens")
+
+    print_stats: bool = conf.get("print_stats", False)
+    assert isinstance(print_stats, bool), (
+        "Field 'print_stats' should be either 'true' or 'false'"
+    )
+
+    args = GenConvArgs(
+        num_conversations=conf["num_conversations"],
+        text_files=text_files,
+        input_num_turns=input_num_turns,
+        input_common_prefix_num_tokens=input_common_prefix_num_tokens,
+        input_prefix_num_tokens=input_prefix_num_tokens,
+        input_num_tokens=input_num_tokens,
+        output_num_tokens=output_num_tokens,
+        print_stats=print_stats,
+    )
+    return args
+
+
+def print_conv_stats(conversations: ConversationsMap, tokenizer: AutoTokenizer) -> None:
+    # Collect statistics
+    conv_stats: list[dict[Any, Any]] = []
+    req_stats: list[int] = []
+
+    print("\nCollecting statistics...")
+    for messages in conversations.values():
+        # messages is a list of dicts
+        user_tokens: list[int] = []
+        assistant_tokens: list[int] = []
+        request_tokens: list[int] = []
+
+        req_tokens = 0
+        for m in messages:
+            content = m["content"]
+            num_tokens = len(tokenizer(content).input_ids)
+
+            if m["role"] == "user":
+                user_tokens.append(num_tokens)
+                # New user prompt including all chat history
+                req_tokens += num_tokens
+                request_tokens.append(req_tokens)
+
+            elif m["role"] == "assistant":
+                assistant_tokens.append(num_tokens)
+                # Update assistant answer
+                # (will be part of chat history for the next user prompt)
+                req_tokens += num_tokens
+
+        item_stats = {
+            "conversation_turns": len(messages),
+            "user_tokens": mean(user_tokens),
+            "assistant_tokens": mean(assistant_tokens),
+        }
+
+        conv_stats.append(item_stats)
+        req_stats.extend(request_tokens)
+
+    # Print statistics
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99]
+
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    df = pd.DataFrame(conv_stats)
+    print(df.describe(percentiles=percentiles).transpose())
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Request statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    df = pd.DataFrame(req_stats, columns=["request_tokens"])
+    print(df.describe(percentiles=percentiles).transpose())
+    print(TEXT_SEPARATOR)
+
+
+def generate_conversations(
+    args: GenConvArgs, tokenizer: AutoTokenizer
+) -> ConversationsMap:
+    # Text for all user prompts
+    # (text from the input text files will be appended to this line)
+    base_prompt_text = "Please rewrite the following text and add more content: "
+    base_prompt_token_count = len(
+        tokenizer.encode(base_prompt_text, add_special_tokens=False)
+    )
+
+    logger.info(f"{Color.PURPLE}Generating conversations...{Color.RESET}")
+    logger.info(args)
+
+    list_of_tokens = []
+
+    for filename in args.text_files:
+        # Load text file that will be used to generate prompts
+        with open(filename) as file:
+            data = file.read()
+            tokens_in_file = tokenizer.encode(data, add_special_tokens=False)
+            list_of_tokens.extend(tokens_in_file)
+
+    conversations: ConversationsMap = {}
+    conv_id = 0
+
+    # Generate number of turns for every conversation
+    turn_count: np.ndarray = args.input_num_turns.sample(args.num_conversations)
+
+    # Turn count should be at least 2 (one user prompt and one assistant answer)
+    turn_count = np.maximum(turn_count, 2)
+
+    # Round up to an even number (every user prompt should have an answer)
+    turn_count = turn_count + (turn_count % 2)
+
+    # Generate number of prefix tokens for every conversation
+    conv_prefix_tokens: np.ndarray = args.input_prefix_num_tokens.sample(
+        args.num_conversations
+    )
+
+    # Used to reduce shared text between conversations
+    # (jump/skip over text sections between conversations)
+    base_offset = 0
+
+    # Common prefix size for all conversations (only 1 sample required)
+    common_prefix_text = ""
+    common_prefix_tokens: int = args.input_common_prefix_num_tokens.sample(1)[0]
+    if common_prefix_tokens > 0:
+        # Using "." at the end to separate sentences
+        common_prefix_text = (
+            tokenizer.decode(list_of_tokens[: common_prefix_tokens - 2]) + "."
+        )
+        base_offset += common_prefix_tokens
+
+    for conv_id in range(args.num_conversations):
+        # Generate a single conversation
+        messages: MessagesList = []
+
+        nturns = turn_count[conv_id]
+
+        # User prompt token count per turn (with lower limit)
+        input_token_count: np.ndarray = args.input_num_tokens.sample(nturns)
+        input_token_count = np.maximum(input_token_count, base_prompt_token_count)
+
+        # Assistant answer token count per turn (with lower limit)
+        output_token_count: np.ndarray = args.output_num_tokens.sample(nturns)
+        output_token_count = np.maximum(output_token_count, 1)
+
+        user_turn = True
+        for turn_id in range(nturns):
+            if user_turn:
+                role = "user"
+                num_tokens = input_token_count[turn_id]
+
+                # Generate the user prompt,
+                # use a unique prefix (the conv_id) for each conversation
+                # (to avoid shared prefix between conversations)
+                content = f"{conv_id} is a nice number... "
+
+                if len(common_prefix_text) > 0 and turn_id == 0:
+                    content = common_prefix_text + content
+
+                # Update the number of tokens left for the content
+                num_tokens -= len(tokenizer.encode(content, add_special_tokens=False))
+
+                if turn_id == 0:
+                    prefix_num_tokens = conv_prefix_tokens[conv_id]
+                    if prefix_num_tokens > 0:
+                        # Add prefix text (context) to the first turn
+                        start_offset = base_offset
+                        end_offset = start_offset + prefix_num_tokens
+                        assert len(list_of_tokens) > end_offset, (
+                            "Not enough input text to generate "
+                            f"{prefix_num_tokens} tokens for the "
+                            f"prefix text ({start_offset=}, {end_offset=})"
+                        )
+
+                        content += f"{conv_id}, " + tokenizer.decode(
+                            list_of_tokens[start_offset:end_offset]
+                        )
+                        base_offset += prefix_num_tokens
+
+                # Add the actual user prompt/question after the prefix text
+                content += base_prompt_text
+                num_tokens -= base_prompt_token_count
+
+                if num_tokens > 0:
+                    # Add text from the input file (to reach the desired token count)
+                    start_offset = base_offset + turn_id * input_token_count.max()
+                    end_offset = start_offset + num_tokens
+                    assert len(list_of_tokens) > end_offset, (
+                        f"Not enough input text to generate {num_tokens} tokens "
+                        f"for the prompt ({start_offset=}, {end_offset=})"
+                    )
+
+                    # Convert tokens back to text
+                    content += tokenizer.decode(list_of_tokens[start_offset:end_offset])
+            else:
+                role = "assistant"
+                # This content will not be used as input to the LLM server
+                # (actual answers will be used instead).
+                # Content is only required to determine the min_tokens/max_tokens
+                # (inputs to the LLM server).
+                num_tokens = output_token_count[turn_id]
+                assert len(list_of_tokens) > num_tokens, (
+                    f"Not enough input text to generate {num_tokens} "
+                    "tokens for assistant content"
+                )
+                content = tokenizer.decode(list_of_tokens[:num_tokens])
+
+            # Append the user/assistant message to the list of messages
+            messages.append({"role": role, "content": content})
+            user_turn = not user_turn
+
+        # Add the new conversation
+        conversations[f"CONV_ID_{conv_id}"] = messages
+
+        # Increase base offset for the next conversation
+        base_offset += nturns
+
+    if args.print_stats:
+        print_conv_stats(conversations, tokenizer)
+
+    return conversations
+
+
+def conversations_list_to_dict(input_list: ShareGptConversations) -> ConversationsMap:
+    conversations: ConversationsMap = {}
+
+    for item in input_list:
+        conv_id: str = item["id"]
+        assert isinstance(conv_id, str)
+
+        assert conv_id not in conversations, (
+            f"Conversation ID {conv_id} found more than once in the input"
+        )
+
+        messages: MessagesList = item["messages"]
+        assert isinstance(messages, list), (
+            f"Conversation messages should be a list (ID: {conv_id})"
+        )
+        assert len(messages) > 0, f"Conversation with no messages (ID: {conv_id})"
+
+        conversations[conv_id] = messages
+
+    logger.info(f"Using {len(conversations)} unique conversations (IDs)")
+    assert len(conversations) == len(input_list)
+
+    # Print statistics about the selected conversations
+    stats: list[dict[str, Any]] = []
+    for conv_data in conversations.values():
+        stats.append({"num_turns": len(conv_data)})
+
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
+    conv_stats = pd.DataFrame(stats).describe(percentiles=percentiles)
+    print(conv_stats.transpose())
+    print(TEXT_SEPARATOR)
+
+    return conversations
+
+
+def conversations_dict_to_list(input_dict: ConversationsMap) -> ShareGptConversations:
+    output: ShareGptConversations = []
+    for conv_id, conv_data in input_dict.items():
+        new_item = {"id": conv_id, "messages": conv_data}
+        output.append(new_item)
+
+    return output

benchmarks/multi_turn/bench_utils.py

@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import logging
+from enum import Enum
+
+
+class Color(Enum):
+    RED = "\033[91m"
+    GREEN = "\033[92m"
+    BLUE = "\033[94m"
+    PURPLE = "\033[95m"
+    CYAN = "\033[96m"
+    YELLOW = "\033[93m"
+    RESET = "\033[0m"
+
+    def __str__(self):
+        return self.value
+
+
+TEXT_SEPARATOR = "-" * 100
+
+# Configure the logger
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] - %(message)s",
+    datefmt="%d-%m-%Y %H:%M:%S",
+)
+logger = logging.getLogger(__name__)

benchmarks/multi_turn/convert_sharegpt_to_openai.py

@@ -0,0 +1,354 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Download dataset from:
+https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json
+
+Convert to OpenAI API:
+export INPUT_FILE=sharegpt_20230401_clean_lang_split.json
+python convert_sharegpt_to_openai.py $INPUT_FILE sharegpt_conv_128.json --max-items=128
+"""
+
+import argparse
+import json
+import random
+from statistics import mean
+from typing import Any, Optional
+
+import pandas as pd  # type: ignore
+import tqdm  # type: ignore
+from transformers import AutoTokenizer  # type: ignore
+
+
+def has_non_english_chars(text: str) -> bool:
+    return not text.isascii()
+
+
+def content_is_valid(
+    content: str, min_content_len: Optional[int], max_content_len: Optional[int]
+) -> bool:
+    if min_content_len and len(content) < min_content_len:
+        return False
+
+    if max_content_len and len(content) > max_content_len:
+        return False
+
+    return has_non_english_chars(content)
+
+
+def print_stats(
+    conversations: "list[dict[Any, Any]]", tokenizer: Optional[AutoTokenizer] = None
+) -> None:
+    # Collect statistics
+    stats = []
+
+    print("\nCollecting statistics...")
+    for item in tqdm.tqdm(conversations):
+        # item has "id" and "messages"
+        messages = item["messages"]
+
+        user_turns = 0
+        assistant_turns = 0
+        user_words = 0
+        assistant_words = 0
+        conv_chars = 0
+
+        user_tokens: list[int] = []
+        assistant_tokens: list[int] = []
+
+        for m in messages:
+            content = m["content"]
+            conv_chars += len(content)
+            content_num_words = content.count(" ") + 1
+
+            num_tokens = 0
+            if tokenizer:
+                num_tokens = len(tokenizer(m["content"]).input_ids)
+
+            if m["role"] == "user":
+                user_turns += 1
+                user_words += content_num_words
+                if tokenizer:
+                    user_tokens.append(num_tokens)
+
+            elif m["role"] == "assistant":
+                assistant_turns += 1
+                assistant_words += content_num_words
+                if tokenizer:
+                    assistant_tokens.append(num_tokens)
+
+        # assert user_turns == assistant_turns, \
+        # f"Invalid conversation ID {item['id']}"
+
+        conv_words = user_words + assistant_words
+        item_stats = {
+            "user_turns": user_turns,
+            "assistant_turns": assistant_turns,
+            "user_words": user_words,
+            "assistant_words": assistant_words,
+            "conv_turns": len(messages),
+            "conv_words": conv_words,
+            "conv_characters": conv_chars,
+        }
+
+        if len(user_tokens) > 0:
+            item_stats["user_tokens"] = int(mean(user_tokens))
+
+        if len(assistant_tokens) > 0:
+            item_stats["assistant_tokens"] = int(mean(assistant_tokens))
+
+        stats.append(item_stats)
+
+    print("\nStatistics:")
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
+    df = pd.DataFrame(stats)
+    print(df.describe(percentiles=percentiles).transpose())
+
+
+def convert_sharegpt_to_openai(
+    seed: int,
+    input_file: str,
+    output_file: str,
+    max_items: Optional[int],
+    min_content_len: Optional[int] = None,
+    max_content_len: Optional[int] = None,
+    min_turns: Optional[int] = None,
+    max_turns: Optional[int] = None,
+    model: Optional[str] = None,
+) -> None:
+    if min_turns and max_turns:
+        assert min_turns <= max_turns
+
+    if min_content_len and max_content_len:
+        # Verify that min is not larger than max if both were given
+        assert min_content_len <= max_content_len
+
+    print(
+        f"Input parameters:\n{seed=}, {max_items=}, {min_content_len=},"
+        f" {max_content_len=}, {min_turns=}, {max_turns=}\n"
+    )
+
+    random.seed(seed)
+
+    tokenizer = None
+    if model is not None:
+        print(f"Loading tokenizer from: {model}")
+        tokenizer = AutoTokenizer.from_pretrained(model)
+
+    # Read the ShareGPT JSON file
+    print(f"Reading file: {input_file}")
+    with open(input_file, encoding="utf-8") as f:
+        # Should be a list of dicts
+        # Each dict should have "id" (string) and "conversations" (list of dicts)
+        sharegpt_data = json.load(f)
+
+    assert isinstance(sharegpt_data, list), "Input file should contain a list of dicts"
+
+    print(f"Total items in input file: {len(sharegpt_data):,}")
+
+    print(f"Shuffling dataset with seed {seed}")
+    random.shuffle(sharegpt_data)
+
+    # Map conversation ID to the all the messages
+    conversation_parts: dict[str, list[Any]] = {}
+
+    for item in tqdm.tqdm(sharegpt_data):
+        assert "id" in item, "Missing key 'id'"
+        assert "conversations" in item, "Missing key 'conversations'"
+
+        # Conversation ID (e.g: "hiWPlMD") and part/session (0, 1, 2, etc.)
+        conv_id, _ = item["id"].split("_")
+        new_turns = item["conversations"]
+
+        if conv_id not in conversation_parts:
+            # Start new conversation
+            conversation_parts[conv_id] = []
+        elif len(conversation_parts[conv_id]) > 0 and len(new_turns) > 0:
+            prev_turns = conversation_parts[conv_id][-1]
+            if prev_turns[-1]["from"] == new_turns[0]["from"]:
+                new_turns = new_turns[1:]
+
+        if len(new_turns) > 0:
+            # We assume that parts are in order in the ShareGPT dataset
+            conversation_parts[conv_id].append(new_turns)
+
+    dataset: list[dict[str, Any]] = []
+    for conv_id, conv_parts in conversation_parts.items():
+        new_item = {"id": conv_id}
+
+        conversations: list[dict[str, str]] = []
+
+        # Merge all parts
+        for conv_part in conv_parts:
+            conversations.extend(conv_part)
+
+        if len(conversations) > 0:
+            new_item["conversations"] = conversations
+            dataset.append(new_item)
+
+    print(f"Total unique conversations (IDs) in input file: {len(dataset):,}")
+
+    # Final output data
+    final_openai_dataset: list[dict] = []
+
+    # Filter conversations from the ShareGPT dataset and convert to OpenAI format
+    for item in tqdm.tqdm(dataset):
+        messages: list[dict] = []
+
+        assert "id" in item, "Missing key 'id'"
+        assert "conversations" in item, "Missing key 'conversations'"
+
+        conv_id = item["id"]
+        conversations = item["conversations"]
+
+        if min_turns is not None and len(conversations) < min_turns:
+            # Skip short conversations
+            continue
+
+        # Convert each message in the conversation, up to max_turns if specified
+        for i, turn in enumerate(conversations):
+            assert "from" in turn and "value" in turn, (
+                f"Invalid conversation ID {conv_id} - missing 'from' or 'value'"
+            )
+
+            role = None
+            turn_from = turn["from"]
+
+            if turn_from in {"human", "user"}:
+                role = "user"
+            elif turn_from in {"gpt", "bing", "chatgpt", "bard"}:
+                role = "assistant"
+            elif turn_from == "system":
+                role = "system"
+
+            assert role is not None, (
+                f"Invalid conversation ID {conv_id} - 'from'='{turn_from}' is invalid"
+            )
+
+            if i == 0 and role != "user":
+                # If the first message is from assistant (gpt), skip it.
+                # this happens when the conversation is a follow-up
+                # to a previous conversation (from the same user).
+                continue
+
+            if max_turns is not None and i >= max_turns:
+                break
+
+            # Convert message to OpenAI format (with "role" and "content")
+            content = turn["value"]
+            messages.append({"role": role, "content": content})
+
+        # Add the converted conversation to the OpenAI format
+        if len(messages) > 0:
+            valid_messages = True
+
+            # First turn should always be from the user
+            user_turn = True
+
+            for m in messages:
+                # Make sure that turns alternate between user and assistant
+                if (user_turn and m["role"] != "user") or (
+                    not user_turn and m["role"] != "assistant"
+                ):
+                    valid_messages = False
+                    break
+
+                user_turn = not user_turn
+
+                content = m["content"]
+                valid_messages = content_is_valid(
+                    content, min_content_len, max_content_len
+                )
+                if not valid_messages:
+                    break
+
+            if valid_messages is True:
+                final_openai_dataset.append({"id": conv_id, "messages": messages})
+
+    assert len(final_openai_dataset) > 0, "Final number of conversations is zero"
+
+    print_stats(final_openai_dataset)
+
+    print_stats_again = False
+    if max_items is not None and len(final_openai_dataset) > max_items:
+        print(f"\n\nSampling {max_items} items from the dataset...")
+        print_stats_again = True
+        final_openai_dataset = random.sample(final_openai_dataset, max_items)
+
+    if print_stats_again:
+        # Print stats after the dataset changed
+        print_stats(final_openai_dataset, tokenizer)
+
+    # Write the converted data to a new JSON file
+    final_size = len(final_openai_dataset)
+    print(f"\nTotal conversations converted (after filtering): {final_size:,}")
+    print(f"\nWriting file: {output_file}")
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(final_openai_dataset, f, ensure_ascii=False, indent=2)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert ShareGPT dataset to OpenAI API format"
+    )
+    parser.add_argument("input_file", help="Path to the input ShareGPT JSON file")
+    parser.add_argument(
+        "output_file", help="Path to the output OpenAI format JSON file"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=0, help="Seed for random number generators"
+    )
+    parser.add_argument(
+        "--max-items",
+        type=int,
+        default=None,
+        help="Maximum number of items in the output file",
+    )
+    parser.add_argument(
+        "--min-turns",
+        type=int,
+        default=None,
+        help="Minimum number of turns per conversation",
+    )
+    parser.add_argument(
+        "--max-turns",
+        type=int,
+        default=None,
+        help="Maximum number of turns per conversation",
+    )
+    parser.add_argument(
+        "--min-content-len",
+        type=int,
+        default=None,
+        help="Min number of characters in the messages' content",
+    )
+    parser.add_argument(
+        "--max-content-len",
+        type=int,
+        default=None,
+        help="Max number of characters in the messages' content",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="LLM model, only the tokenizer will be used",
+    )
+
+    args = parser.parse_args()
+
+    convert_sharegpt_to_openai(
+        args.seed,
+        args.input_file,
+        args.output_file,
+        args.max_items,
+        args.min_content_len,
+        args.max_content_len,
+        args.min_turns,
+        args.max_turns,
+        args.model,
+    )
+
+
+if __name__ == "__main__":
+    main()

benchmarks/multi_turn/generate_multi_turn.json

@@ -0,0 +1,35 @@
+{
+    "filetype": "generate_conversations",
+    "num_conversations": 24,
+    "text_files": ["pg1184.txt"],
+    "print_stats": false,
+    "prompt_input": {
+        "num_turns": {
+            "distribution": "uniform",
+            "min": 12,
+            "max": 18
+        },
+        "common_prefix_num_tokens": {
+            "distribution": "constant",
+            "value": 500
+        },
+        "prefix_num_tokens": {
+            "distribution": "lognormal",
+            "mean": 6,
+            "sigma": 4,
+            "max": 1500
+        },
+        "num_tokens": {
+            "distribution": "uniform",
+            "min": 120,
+            "max": 160
+        }
+    },
+    "prompt_output": {
+        "num_tokens": {
+            "distribution": "uniform",
+            "min": 80,
+            "max": 120
+        }
+    }
+}

benchmarks/multi_turn/requirements.txt

@@ -0,0 +1,5 @@
+numpy>=1.24
+pandas>=2.0.0
+aiohttp>=3.10
+transformers>=4.46
+xlsxwriter>=3.2.1

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA.git
-        GIT_TAG 575f7724b9762f265bbee5889df9c7d630801845
+        GIT_TAG 0e43e774597682284358ff2c54530757b654b8d1
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -37,9 +37,9 @@ cuda_archs_loose_intersection(FLASH_MLA_ARCHS "9.0a" "${CUDA_ARCHS}")
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3 AND FLASH_MLA_ARCHS)
     set(FlashMLA_SOURCES
         ${flashmla_SOURCE_DIR}/csrc/flash_api.cpp
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_bf16_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_fp16_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_metadata.cu)
+        ${flashmla_SOURCE_DIR}/csrc/kernels/splitkv_mla.cu
+        ${flashmla_SOURCE_DIR}/csrc/kernels/mla_combine.cu
+        ${flashmla_SOURCE_DIR}/csrc/kernels/get_mla_metadata.cu)
 
     set(FlashMLA_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include

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 6dbc6e011a3ebe9349eeb74578940dd7095436ba
+          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/cutlass_extensions/common.hpp

@@ -60,3 +60,13 @@ struct enable_sm100_only : Kernel {
 #endif
   }
 };
+
+template <typename Kernel>
+struct enable_sm120_only : Kernel {
+  template <typename... Args>
+  CUTLASS_DEVICE void operator()(Args&&... args) {
+#if defined __CUDA_ARCH__ && __CUDA_ARCH__ == 1200
+    Kernel::operator()(std::forward<Args>(args)...);
+#endif
+  }
+};

csrc/mamba/mamba_ssm/selective_scan.h

@@ -45,6 +45,9 @@ struct SSMParamsBase {
     index_t out_d_stride;
     index_t out_z_batch_stride;
     index_t out_z_d_stride;
+    index_t ssm_states_batch_stride;
+    index_t ssm_states_dim_stride;  
+    index_t ssm_states_dstate_stride;
 
     // Common data pointers.
     void *__restrict__ A_ptr;

csrc/mamba/mamba_ssm/selective_scan_fwd.cu

@@ -132,8 +132,10 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
     input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + sequence_start_index * params.B_batch_stride + group_id * params.B_group_stride;
     weight_t *C = reinterpret_cast<weight_t *>(params.C_ptr) + dim_id * kNRows * params.C_d_stride;
     input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + sequence_start_index * params.C_batch_stride + group_id * params.C_group_stride;
-    input_t *ssm_states = reinterpret_cast<input_t *>(params.ssm_states_ptr) + (cache_index * params.dim + dim_id * kNRows) * params.dstate;
-
+    input_t *ssm_states = reinterpret_cast<input_t *>(params.ssm_states_ptr) + 
+    cache_index * params.ssm_states_batch_stride + 
+    dim_id * kNRows * params.ssm_states_dim_stride;
+    
     float D_val[kNRows] = {0};
     if (params.D_ptr != nullptr) {
         #pragma unroll
@@ -248,7 +250,7 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
                 }
                 // Initialize running total
 
-                scan_t running_prefix = chunk > 0 ? smem_running_prefix[state_idx + r * MAX_DSTATE] : make_float2(1.0, has_initial_state ? float(ssm_states[state_idx]): 0.0);
+                scan_t running_prefix = chunk > 0 ? smem_running_prefix[state_idx + r * MAX_DSTATE] : make_float2(1.0, has_initial_state ? float(ssm_states[state_idx * params.ssm_states_dstate_stride]): 0.0);
 
                 SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
                 typename Ktraits::BlockScanT(smem_scan).InclusiveScan(
@@ -259,7 +261,7 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
                 if (threadIdx.x == 0) {
                     smem_running_prefix[state_idx] = prefix_op.running_prefix;
                     if (chunk == n_chunks - 1) {
-                        ssm_states[state_idx] = input_t(prefix_op.running_prefix.y);
+                        ssm_states[state_idx * params.ssm_states_dstate_stride] = input_t(prefix_op.running_prefix.y);
                     }
                 }
                 #pragma unroll
@@ -481,6 +483,10 @@ void set_ssm_params_fwd(SSMParamsBase &params,
         params.out_batch_stride = out.stride(1);
         params.out_d_stride = out.stride(0);
 
+        params.ssm_states_batch_stride = ssm_states.stride(0);
+        params.ssm_states_dim_stride = ssm_states.stride(1);  
+        params.ssm_states_dstate_stride = ssm_states.stride(2);
+
     }
     else{
         if (!is_variable_B) {
@@ -509,6 +515,10 @@ void set_ssm_params_fwd(SSMParamsBase &params,
         }
         params.out_batch_stride = out.stride(0);
         params.out_d_stride = out.stride(1);
+        
+        params.ssm_states_batch_stride = ssm_states.stride(0);
+        params.ssm_states_dim_stride = ssm_states.stride(1);  
+        params.ssm_states_dstate_stride = ssm_states.stride(2);
     }
 }
 

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/topk_softmax_kernels.cu

@@ -188,7 +188,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   It fuses the softmax, max and argmax into a single kernel.
 
   Limitations:
-  1) This implementation is intended for when the number of experts is a small power of 2.
+  1) This implementation is optimized for when the number of experts is a small power of 2.
+     Additionally it also supports when number of experts is multiple of 64 which is still
+     faster than the computing softmax and topK separately (only tested on CUDA yet).
   2) This implementation assumes k is small, but will work for any k.
 */
 
@@ -198,8 +200,6 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
         int* source_rows, const int k, const int start_expert, const int end_expert)
 {
     // We begin by enforcing compile time assertions and setting up compile time constants.
-    static_assert(VPT == (VPT & -VPT), "VPT must be power of 2");
-    static_assert(NUM_EXPERTS == (NUM_EXPERTS & -NUM_EXPERTS), "NUM_EXPERTS must be power of 2");
     static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG), "BYTES_PER_LDG must be power of 2");
     static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
 
@@ -407,12 +407,10 @@ struct TopkConstants
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, typename IndType>
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType>
 void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
     int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, cudaStream_t stream)
 {
-    static constexpr std::size_t MAX_BYTES_PER_LDG = 16;
-
     static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(float) * EXPERTS);
     using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
@@ -425,21 +423,27 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
         input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert);
 }
 
-#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB)                                \
-    switch (warpSize) {                                                          \
-        case 32:                                                                 \
-            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32>(      \
-                gating_output, nullptr, topk_weights, topk_indices,              \
-                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
-            break;                                                               \
-        case 64:                                                                 \
-            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64>(      \
-                gating_output, nullptr, topk_weights, topk_indices,              \
-                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
-            break;                                                               \
-        default:                                                                 \
-            TORCH_CHECK(false, "Unsupported warp size: ", warpSize);             \
+#ifndef USE_ROCM
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                          \
+    static_assert(WARP_SIZE == 32,                                                    \
+                  "Unsupported warp size. Only 32 is supported for CUDA");            \
+    topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES>( \
+        gating_output, nullptr, topk_weights, topk_indices,                           \
+        token_expert_indices, num_tokens, topk, 0, num_experts, stream);
+#else
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                             \
+    if (WARP_SIZE == 64) {                                                               \
+        topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64, MAX_BYTES>(       \
+            gating_output, nullptr, topk_weights, topk_indices,                          \
+            token_expert_indices, num_tokens, topk, 0, num_experts, stream);             \
+    } else if (WARP_SIZE == 32) {                                                        \
+        topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES>(       \
+            gating_output, nullptr, topk_weights, topk_indices,                          \
+            token_expert_indices, num_tokens, topk, 0, num_experts, stream);             \
+    } else {                                                                             \
+        assert(false && "Unsupported warp size. Only 32 and 64 are supported for ROCm"); \
     }
+#endif
 
 template <typename IndType>
 void topkGatingSoftmaxKernelLauncher(
@@ -453,38 +457,64 @@ void topkGatingSoftmaxKernelLauncher(
     const int topk,
     cudaStream_t stream) {
     static constexpr int WARPS_PER_TB = 4;
-    auto warpSize = WARP_SIZE;
+    static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
+#ifndef USE_ROCM
+    static constexpr int BYTES_PER_LDG_MULTIPLE_64 = 8;
+#endif
     switch (num_experts) {
         case 1:
-            LAUNCH_SOFTMAX(1, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 2:
-            LAUNCH_SOFTMAX(2, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 4:
-            LAUNCH_SOFTMAX(4, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 8:
-            LAUNCH_SOFTMAX(8, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 16:
-            LAUNCH_SOFTMAX(16, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 32:
-            LAUNCH_SOFTMAX(32, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 64:
-            LAUNCH_SOFTMAX(64, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 128:
-            LAUNCH_SOFTMAX(128, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 256:
-            LAUNCH_SOFTMAX(256, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
+        case 512:
+            LAUNCH_SOFTMAX(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            break;
+        // (CUDA only) support multiples of 64 when num_experts is not power of 2.
+        // ROCm uses WARP_SIZE 64 so 8 bytes loading won't fit for some of num_experts,
+        // alternatively we can test 4 bytes loading and enable it in future.
+#ifndef USE_ROCM
+        case 192:
+            LAUNCH_SOFTMAX(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 320:
+            LAUNCH_SOFTMAX(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 384:
+            LAUNCH_SOFTMAX(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 448:
+            LAUNCH_SOFTMAX(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 576:
+            LAUNCH_SOFTMAX(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+#endif
         default: {
             TORCH_CHECK(softmax_workspace != nullptr,
-                "softmax_workspace must be provided for num_experts that are not a power of 2.");
+                "softmax_workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
             static constexpr int TPB = 256;
             moeSoftmax<TPB><<<num_tokens, TPB, 0, stream>>>(
                 gating_output, nullptr, softmax_workspace, num_experts);

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/c3x/scaled_mm_blockwise_sm120_fp8.cu

@@ -0,0 +1,23 @@
+#include "scaled_mm_kernels.hpp"
+#include "scaled_mm_blockwise_sm120_fp8_dispatch.cuh"
+#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
+
+namespace vllm {
+
+void cutlass_scaled_mm_blockwise_sm120_fp8(torch::Tensor& out,
+                                           torch::Tensor const& a,
+                                           torch::Tensor const& b,
+                                           torch::Tensor const& a_scales,
+                                           torch::Tensor const& b_scales) {
+  if (out.dtype() == torch::kBFloat16) {
+    cutlass_gemm_blockwise_sm120_fp8_dispatch<cutlass::bfloat16_t>(
+        out, a, b, a_scales, b_scales);
+
+  } else {
+    TORCH_CHECK(out.dtype() == torch::kFloat16);
+    cutlass_gemm_blockwise_sm120_fp8_dispatch<cutlass::half_t>(
+        out, a, b, a_scales, b_scales);
+  }
+}
+
+}  // namespace vllm

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8_dispatch.cuh

@@ -0,0 +1,183 @@
+#pragma once
+
+#include "cuda_utils.h"
+#include "cutlass/cutlass.h"
+#include "cutlass/numeric_types.h"
+
+#include "cute/tensor.hpp"
+#include "cutlass/tensor_ref.h"
+#include "cutlass/gemm/dispatch_policy.hpp"
+#include "cutlass/gemm/collective/collective_builder.hpp"
+#include "cutlass/gemm/device/gemm_universal_adapter.h"
+#include "cutlass/gemm/kernel/gemm_universal.hpp"
+#include "cutlass/gemm/kernel/tile_scheduler_params.h"
+#include "cutlass/epilogue/dispatch_policy.hpp"
+#include "cutlass/epilogue/collective/collective_builder.hpp"
+
+#include "cutlass_extensions/gemm/dispatch_policy.hpp"
+#include "cutlass_extensions/gemm/collective/collective_builder.hpp"
+
+#include "cutlass_gemm_caller.cuh"
+
+namespace vllm {
+
+using namespace cute;
+
+// clang-format off
+template <class OutType, int ScaleGranularityM,
+          int ScaleGranularityN, int ScaleGranularityK,
+          class MmaTileShape, class ClusterShape,
+          class EpilogueScheduler, class MainloopScheduler>
+struct cutlass_3x_gemm_fp8_blockwise {
+  using ElementAB = cutlass::float_e4m3_t;
+
+  using ElementA = ElementAB;
+  using LayoutA = cutlass::layout::RowMajor;
+  using LayoutA_Transpose = typename cutlass::layout::LayoutTranspose<LayoutA>::type;
+  static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
+
+  using ElementB = ElementAB;
+  // ColumnMajor is used for B to match the CUTLASS convention.
+  using LayoutB = cutlass::layout::ColumnMajor;
+  using LayoutB_Transpose = typename cutlass::layout::LayoutTranspose<LayoutB>::type;
+  static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
+
+  using ElementD = OutType;
+  using LayoutD = cutlass::layout::RowMajor;
+  using LayoutD_Transpose = typename cutlass::layout::LayoutTranspose<LayoutD>::type;
+  static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
+
+  using ElementC = void; // TODO: support bias
+  using LayoutC = LayoutD;
+  using LayoutC_Transpose = LayoutD_Transpose;
+  static constexpr int AlignmentC = AlignmentD;
+
+  using ElementAccumulator = float;
+  using ElementCompute = float;
+  using ElementBlockScale = float; 
+
+  using ScaleConfig = cutlass::detail::Sm120BlockwiseScaleConfig<
+        ScaleGranularityM, ScaleGranularityN, ScaleGranularityK,
+        cute::UMMA::Major::MN, cute::UMMA::Major::K>;
+
+  // layout_SFA and layout_SFB cannot be swapped since they are deduced.
+  using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
+  using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
+
+  using ArchTag = cutlass::arch::Sm120;
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  static constexpr auto RoundStyle = cutlass::FloatRoundStyle::round_to_nearest;
+  using ElementScalar = float;
+  using DefaultOperation = cutlass::epilogue::fusion::LinearCombination<ElementD, ElementCompute, ElementC, ElementScalar, RoundStyle>;
+  using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
+      ArchTag,
+      OperatorClass,
+      MmaTileShape,
+      ClusterShape,
+      cutlass::epilogue::collective::EpilogueTileAuto,
+      ElementAccumulator,
+      ElementCompute,
+      ElementC,
+      LayoutC,
+      AlignmentC,
+      ElementD,
+      LayoutD,
+      AlignmentD,
+      EpilogueScheduler,
+      DefaultOperation
+  >::CollectiveOp;
+ 
+  using StageCountType = cutlass::gemm::collective::StageCountAuto; 
+  using CollectiveMainloop = 
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          ArchTag,
+          OperatorClass,
+          ElementA,
+          cute::tuple<LayoutA, LayoutSFA>,
+          AlignmentA,
+          ElementB,
+          cute::tuple<LayoutB, LayoutSFB>,
+          AlignmentB,
+          ElementAccumulator,
+          MmaTileShape,
+          ClusterShape,
+          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
+          MainloopScheduler
+      >::CollectiveOp;
+
+  using KernelType = enable_sm120_only<cutlass::gemm::kernel::GemmUniversal<
+      Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue>>;
+
+  struct GemmKernel : public KernelType {};
+};
+
+template <typename Gemm>
+void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
+                                   torch::Tensor const& b,
+                                   torch::Tensor const& a_scales,
+                                   torch::Tensor const& b_scales) {
+  using GemmKernel = typename Gemm::GemmKernel;
+  using StrideA = typename Gemm::GemmKernel::StrideA;
+  using StrideB = typename Gemm::GemmKernel::StrideB;
+  using StrideD = typename Gemm::GemmKernel::StrideD;
+  using StrideC = typename Gemm::GemmKernel::StrideC;
+  using LayoutSFA = typename Gemm::LayoutSFA;
+  using LayoutSFB = typename Gemm::LayoutSFB;
+  using ScaleConfig = typename Gemm::ScaleConfig;
+
+  using ElementAB = typename Gemm::ElementAB;
+  using ElementD = typename Gemm::ElementD;
+
+  int32_t m = a.size(0), n = b.size(1), k = a.size(1);
+
+  StrideA a_stride;
+  StrideB b_stride;
+  StrideC c_stride;
+  a_stride =
+      cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(m, k, 1));
+  b_stride =
+      cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(n, k, 1));
+  c_stride =
+      cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(m, n, 1));
+
+  LayoutSFA layout_SFA = 
+      ScaleConfig::tile_atom_to_shape_SFA(make_shape(m, n, k, 1));
+  LayoutSFB layout_SFB = 
+      ScaleConfig::tile_atom_to_shape_SFB(make_shape(m, n, k, 1));
+
+  auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
+  auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
+  auto a_scales_ptr = static_cast<float*>(a_scales.data_ptr());
+  auto b_scales_ptr = static_cast<float*>(b_scales.data_ptr());
+
+  auto mainloop_args = [&](){
+    return typename GemmKernel::MainloopArguments{
+        a_ptr,        a_stride,   b_ptr,        b_stride,
+        a_scales_ptr, layout_SFA, b_scales_ptr, layout_SFB
+    };
+  }();
+  auto prob_shape = cute::make_shape(m, n, k, 1);
+
+  auto c_ptr = static_cast<ElementD*>(out.data_ptr());
+  typename GemmKernel::EpilogueArguments epilogue_args{
+      {}, c_ptr, c_stride, c_ptr, c_stride};
+  c3x::cutlass_gemm_caller<GemmKernel>(a.device(), prob_shape, mainloop_args,
+                                       epilogue_args);
+}
+
+template <typename OutType>
+void cutlass_gemm_blockwise_sm120_fp8_dispatch(torch::Tensor& out,
+                                               torch::Tensor const& a,
+                                               torch::Tensor const& b,
+                                               torch::Tensor const& a_scales,
+                                               torch::Tensor const& b_scales) {
+  // TODO: better heuristics
+  cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
+      OutType, 1, 128, 128, Shape<_128, _128, _128>,
+      Shape<_1, _1, _1>, cutlass::epilogue::collective::EpilogueScheduleAuto,
+      cutlass::gemm::collective::KernelScheduleAuto>>(
+      out, a, b, a_scales, b_scales);
+}
+
+}  // namespace vllm

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_kernels.hpp

@@ -47,4 +47,10 @@ void cutlass_scaled_mm_blockwise_sm100_fp8(torch::Tensor& out,
                                            torch::Tensor const& b,
                                            torch::Tensor const& a_scales,
                                            torch::Tensor const& b_scales);
+
+void cutlass_scaled_mm_blockwise_sm120_fp8(torch::Tensor& out,
+                                           torch::Tensor const& a,
+                                           torch::Tensor const& b,
+                                           torch::Tensor const& a_scales,
+                                           torch::Tensor const& b_scales);
 }  // namespace vllm

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/cutlass_w8a8/scaled_mm_c3x_sm120.cu

@@ -1,11 +1,9 @@
-#include <cudaTypedefs.h>
+#include "c3x/scaled_mm_helper.hpp"
 #include "c3x/scaled_mm_kernels.hpp"
 
-#include "cuda_utils.h"
-
 /*
    This file defines quantized GEMM operations using the CUTLASS 3.x API, for
-   NVIDIA GPUs with sm120 (Blackwell Geforce).
+   NVIDIA GPUs with sm120 (Blackwell).
 */
 
 #if defined ENABLE_SCALED_MM_SM120 && ENABLE_SCALED_MM_SM120
@@ -15,20 +13,10 @@ void cutlass_scaled_mm_sm120(torch::Tensor& c, torch::Tensor const& a,
                              torch::Tensor const& a_scales,
                              torch::Tensor const& b_scales,
                              std::optional<torch::Tensor> const& bias) {
-  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
-
-  int M = a.size(0), N = b.size(1), K = a.size(1);
-  TORCH_CHECK(
-      (a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
-          (b_scales.numel() == 1 || b_scales.numel() == b.size(1)),
-      "Currently, block scaled fp8 gemm is not implemented for Blackwell");
-
-  // Standard per-tensor/per-token/per-channel scaling
-  TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
-  TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn,
-              "Currently, only fp8 gemm is implemented for Blackwell");
-  vllm::cutlass_scaled_mm_sm120_fp8(c, a, b, a_scales, b_scales, bias);
+  dispatch_scaled_mm(c, a, b, a_scales, b_scales, bias,
+                     vllm::cutlass_scaled_mm_sm120_fp8,
+                     nullptr,  // int8 not supported on SM120
+                     vllm::cutlass_scaled_mm_blockwise_sm120_fp8);
 }
 
 #endif

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/rocm/attention.cu

@@ -270,7 +270,7 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int num_kv_heads,   
     const float scale,    
     const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,   // [num_seqs]
+    const int* __restrict__ seq_lens,   // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes, // [num_heads]
@@ -304,12 +304,12 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
 
   const auto max_num_partitions = gridDim.y;
 
-  const int context_len = context_lens[seq_idx];
+  const int seq_len = seq_lens[seq_idx];
 
   const int partition_start_token_idx =
       partition_idx * T_PAR_SIZE;  // partition_size;
   // exit if partition is out of context for seq
-  if (partition_start_token_idx >= context_len) {
+  if (partition_start_token_idx >= seq_len) {
     return;
   }
 
@@ -361,8 +361,8 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   // output layout from QKmfma : QH16xT4x4 16 qheads across 16 lanes, 16 tokens
   // across 4 rows x 4 tokens per lane
 
-  const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
-  const int last_ctx_block = num_context_blocks - 1;
+  const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
+  const int last_seq_block = num_seq_blocks - 1;
 
   const int* block_table_seq = block_tables + seq_idx * max_num_blocks_per_seq;
 
@@ -373,9 +373,9 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int klocal_token_idx =
         TOKENS_PER_WARP * warpid + token_depth * 16 + lane16id;
     const int kglobal_token_idx = partition_start_token_idx + klocal_token_idx;
-    const int kblock_idx = (kglobal_token_idx < context_len)
+    const int kblock_idx = (kglobal_token_idx < seq_len)
                                ? kglobal_token_idx / BLOCK_SIZE
-                               : last_ctx_block;
+                               : last_seq_block;
     kphysical_block_number[token_depth] = block_table_seq[kblock_idx];
   }
 
@@ -476,9 +476,9 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
       // tokens
       const int vglobal_token_idx =
           partition_start_token_idx + vlocal_token_idx;
-      const int vblock_idx = (vglobal_token_idx < context_len)
+      const int vblock_idx = (vglobal_token_idx < seq_len)
                                  ? vglobal_token_idx / BLOCK_SIZE
-                                 : last_ctx_block;
+                                 : last_seq_block;
       vphysical_block_number[vtoken_depth][vblock_depth] =
           block_table_seq[vblock_idx];
     }
@@ -554,7 +554,7 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   if constexpr (ALIBI_ENABLED) {
     for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
       const int local_token_idx = qkout_token_idx + token_depth * 16;
-      const int alibi_offset = local_token_idx - context_len + 1;
+      const int alibi_offset = local_token_idx - seq_len + 1;
       for (int i = 0; i < 4; i++) {
         d_out[token_depth][i] += alibi_slope * (alibi_offset + i);
       }
@@ -568,9 +568,8 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 4; i++) {
-      const float tmp = (local_token_idx + i < context_len)
-                            ? d_out[token_depth][i]
-                            : -FLT_MAX;
+      const float tmp =
+          (local_token_idx + i < seq_len) ? d_out[token_depth][i] : -FLT_MAX;
       qk_max = fmaxf(qk_max, tmp);
     }
   }
@@ -582,7 +581,7 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 4; i++) {
-      const float tmp = (local_token_idx + i < context_len)
+      const float tmp = (local_token_idx + i < seq_len)
                             ? __expf(d_out[token_depth][i] - qk_max)
                             : 0.0f;
       d_out[token_depth][i] = tmp;
@@ -780,7 +779,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const int num_kv_heads,
     const float scale,
     const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,   // [num_seqs]
+    const int* __restrict__ seq_lens,   // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes, // [num_heads]
@@ -809,10 +808,10 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
   const auto partition_size = blockDim.x;
   const auto max_num_partitions = gridDim.y;
 
-  const int context_len = context_lens[seq_idx];
+  const int seq_len = seq_lens[seq_idx];
   const int partition_start_token_idx = partition_idx * partition_size;
   // exit if partition is out of context for seq
-  if (partition_start_token_idx >= context_len) {
+  if (partition_start_token_idx >= seq_len) {
     return;
   }
   // every 4 lanes fetch 4 different qheads
@@ -855,7 +854,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
   const int warp_start_token_idx =
       partition_start_token_idx + warpid * WARP_SIZE;
 
-  if (warp_start_token_idx >= context_len) {  // warp out of context
+  if (warp_start_token_idx >= seq_len) {  // warp out of context
   #pragma unroll
     for (int h = 0; h < GQA_RATIO4; h++) {
       shared_qk_max[warpid][h] = -FLT_MAX;
@@ -863,8 +862,8 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     }
   } else {  // warp within context
 
-    const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
-    const int last_ctx_block = num_context_blocks - 1;
+    const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
+    const int last_seq_block = num_seq_blocks - 1;
 
     const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
     // token id within partition
@@ -873,9 +872,9 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const int global_token_idx = partition_start_token_idx + local_token_idx;
 
     // fetch block number for k
-    const int block_idx = (global_token_idx < context_len)
+    const int block_idx = (global_token_idx < seq_len)
                               ? global_token_idx / BLOCK_SIZE
-                              : last_ctx_block;
+                              : last_seq_block;
 
     // fetch k physical block number
     //  int32 physical_block_number leads to overflow when multiplied with
@@ -888,7 +887,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     for (int b = 0; b < VBLOCKS; b++) {
       const int vblock_idx = warp_start_block_idx + b;
       const int vblock_idx_ctx =
-          (vblock_idx <= last_ctx_block) ? vblock_idx : last_ctx_block;
+          (vblock_idx <= last_seq_block) ? vblock_idx : last_seq_block;
       vphysical_blocks[b] = block_table[vblock_idx_ctx];
     }
 
@@ -1057,7 +1056,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const int lane4_token_idx = 4 * (global_token_idx >> 2);
 
     if constexpr (ALIBI_ENABLED) {
-      const int alibi_offset = lane4_token_idx - context_len + 1;
+      const int alibi_offset = lane4_token_idx - seq_len + 1;
       for (int h = 0; h < QHLOOP; h++) {
         for (int i = 0; i < 4; i++) {
           d_out[h][i] += alibi_slope[h] * (alibi_offset + i);
@@ -1070,7 +1069,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     for (int h = 0; h < QHLOOP; h++) {
       qk_max[h] = -FLT_MAX;
       for (int i = 0; i < 4; i++) {
-        qk_max[h] = (lane4_token_idx + i < context_len)
+        qk_max[h] = (lane4_token_idx + i < seq_len)
                         ? fmaxf(qk_max[h], d_out[h][i])
                         : qk_max[h];
       }
@@ -1101,7 +1100,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     for (int h = 0; h < QHLOOP; h++) {
       exp_sum[h] = 0.0f;
       for (int i = 0; i < 4; i++) {
-        d_out[h][i] = (lane4_token_idx + i < context_len)
+        d_out[h][i] = (lane4_token_idx + i < seq_len)
                           ? __expf(d_out[h][i] - qk_max[h])
                           : 0.0f;
         exp_sum[h] += d_out[h][i];
@@ -1181,7 +1180,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     }
   }
 
-  if (warp_start_token_idx >= context_len) {  // warp out of context
+  if (warp_start_token_idx >= seq_len) {  // warp out of context
     for (int qh = 0; qh < QHLOOP; qh++) {
       for (int vh = 0; vh < VHELOOP; vh++) {
         vout_shared[qh][vh][laneid][warpid] = {0};
@@ -1279,7 +1278,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
                                            // max_num_partitions]
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads,
                                            // max_num_partitions, head_size]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,      // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions, const float* __restrict__ fp8_out_scale_ptr) {
   const auto num_heads = gridDim.x;
@@ -1293,8 +1292,8 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     return;
   }
 
-  const int context_len = context_lens[seq_idx];
-  const int num_partitions = DIVIDE_ROUND_UP(context_len, PARTITION_SIZE);
+  const int seq_len = seq_lens[seq_idx];
+  const int num_partitions = DIVIDE_ROUND_UP(seq_len, PARTITION_SIZE);
   const auto warpid = threadIdx.x / WARP_SIZE;
 
   __shared__ float shared_global_exp_sum;
@@ -1581,7 +1580,7 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
                                           // head_size, block_size]
     const int num_kv_heads, const float scale,
     const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,  // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -1615,11 +1614,11 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
 
   const int max_num_partitions = gridDim.y;
 
-  const int context_len = context_lens[seq_idx];  // length of a seq
+  const int seq_len = seq_lens[seq_idx];  // length of a seq
 
   const int partition_start_token_idx = partition_idx * T_PAR_SIZE;
   // exit if partition is out of context for seq
-  if (partition_start_token_idx >= context_len) {
+  if (partition_start_token_idx >= seq_len) {
     return;
   }
 
@@ -1715,8 +1714,8 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
     }
   }
 
-  const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
-  const int last_ctx_block = num_context_blocks - 1;
+  const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
+  const int last_seq_block = num_seq_blocks - 1;
 
   const int* block_table_seq = block_tables + seq_idx * max_num_blocks_per_seq;
 
@@ -1727,9 +1726,9 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int klocal_token_idx =
         TOKENS_PER_WARP * warpid + token_depth * 16 + lane16id;
     const int kglobal_token_idx = partition_start_token_idx + klocal_token_idx;
-    const int kblock_idx = (kglobal_token_idx < context_len)
+    const int kblock_idx = (kglobal_token_idx < seq_len)
                                ? kglobal_token_idx / BLOCK_SIZE
-                               : last_ctx_block;
+                               : last_seq_block;
     kphysical_block_number[token_depth] = block_table_seq[kblock_idx];
   }
 
@@ -1781,9 +1780,9 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
           vblock_depth * BLOCK_SIZE;
       const int vglobal_token_idx =
           partition_start_token_idx + vlocal_token_idx;
-      const int vblock_idx = (vglobal_token_idx < context_len)
+      const int vblock_idx = (vglobal_token_idx < seq_len)
                                  ? vglobal_token_idx / BLOCK_SIZE
-                                 : last_ctx_block;
+                                 : last_seq_block;
       vphysical_block_number[vtoken_depth][vblock_depth] =
           block_table_seq[vblock_idx];
     }
@@ -1836,9 +1835,8 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 8; i++) {
-      const float tmp = (local_token_idx + 2 * i < context_len)
-                            ? dout[token_depth][i]
-                            : -FLT_MAX;
+      const float tmp =
+          (local_token_idx + 2 * i < seq_len) ? dout[token_depth][i] : -FLT_MAX;
       qk_max = fmaxf(qk_max, tmp);
     }
   }
@@ -1848,7 +1846,7 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 8; i++) {
-      const float tmp = (local_token_idx + 2 * i < context_len)
+      const float tmp = (local_token_idx + 2 * i < seq_len)
                             ? __expf(dout[token_depth][i] - qk_max)
                             : 0.0f;
       dout[token_depth][i] = tmp;
@@ -2019,7 +2017,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
                                           // head_size, block_size]
     const int num_kv_heads, const float scale,
     const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,      // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -2046,7 +2044,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
                                            // max_num_partitions]
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads,
                                            // max_num_partitions, head_size]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,      // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions, const float* __restrict__ fp8_out_scale_ptr) {
   const auto num_heads = gridDim.x;
@@ -2060,8 +2058,8 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     return;
   }
 
-  const int context_len = context_lens[seq_idx];
-  const int num_partitions = DIVIDE_ROUND_UP(context_len, PARTITION_SIZE);
+  const int seq_len = seq_lens[seq_idx];
+  const int num_partitions = DIVIDE_ROUND_UP(seq_len, PARTITION_SIZE);
   const int warpid = threadIdx.x / WARP_SIZE;
 
   __shared__ float shared_global_exp_sum;
@@ -2349,7 +2347,7 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
                                           // head_size, block_size]
     const int num_kv_heads, const float scale,
     const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,  // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -2382,11 +2380,11 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
 
   const int max_num_partitions = gridDim.y;
 
-  const int context_len = context_lens[seq_idx];  // length of a seq
+  const int seq_len = seq_lens[seq_idx];  // length of a seq
 
   const int partition_start_token_idx = partition_idx * T_PAR_SIZE;
   // exit if partition is out of context for seq
-  if (partition_start_token_idx >= context_len) {
+  if (partition_start_token_idx >= seq_len) {
     return;
   }
 
@@ -2482,8 +2480,8 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
     }
   }
 
-  const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
-  const int last_ctx_block = num_context_blocks - 1;
+  const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
+  const int last_seq_block = num_seq_blocks - 1;
 
   const int* block_table_seq = block_tables + seq_idx * max_num_blocks_per_seq;
 
@@ -2494,9 +2492,9 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int klocal_token_idx =
         TOKENS_PER_WARP * warpid + token_depth * 16 + lane16id;
     const int kglobal_token_idx = partition_start_token_idx + klocal_token_idx;
-    const int kblock_idx = (kglobal_token_idx < context_len)
+    const int kblock_idx = (kglobal_token_idx < seq_len)
                                ? kglobal_token_idx / BLOCK_SIZE
-                               : last_ctx_block;
+                               : last_seq_block;
     kphysical_block_number[token_depth] = block_table_seq[kblock_idx];
   }
 
@@ -2548,9 +2546,9 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
           rowid * VTOKENS_PER_LANE + vblock_depth * BLOCK_SIZE;
       const int vglobal_token_idx =
           partition_start_token_idx + vlocal_token_idx;
-      const int vblock_idx = (vglobal_token_idx < context_len)
+      const int vblock_idx = (vglobal_token_idx < seq_len)
                                  ? vglobal_token_idx / BLOCK_SIZE
-                                 : last_ctx_block;
+                                 : last_seq_block;
       vphysical_block_number[vtoken_depth][vblock_depth] =
           block_table_seq[vblock_idx];
     }
@@ -2604,7 +2602,7 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 8; i++) {
       const float tmp =
-          (local_token_idx + i < context_len) ? dout[token_depth][i] : -FLT_MAX;
+          (local_token_idx + i < seq_len) ? dout[token_depth][i] : -FLT_MAX;
       qk_max = fmaxf(qk_max, tmp);
     }
   }
@@ -2614,7 +2612,7 @@ __launch_bounds__(NUM_THREADS, 3) void paged_attention_ll4mi_QKV_mfma16_kernel(
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     const int local_token_idx = qkout_token_idx + token_depth * 16;
     for (int i = 0; i < 8; i++) {
-      const float tmp = (local_token_idx + i < context_len)
+      const float tmp = (local_token_idx + i < seq_len)
                             ? __expf(dout[token_depth][i] - qk_max)
                             : 0.0f;
       dout[token_depth][i] = tmp;
@@ -2751,7 +2749,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
                                           // head_size, block_size]
     const int num_kv_heads, const float scale,
     const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,      // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -2778,7 +2776,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
                                            // max_num_partitions]
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads,
                                            // max_num_partitions, head_size]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,      // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions, const float* __restrict__ fp8_out_scale_ptr) {
   const auto num_heads = gridDim.x;
@@ -2792,8 +2790,8 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     return;
   }
 
-  const int context_len = context_lens[seq_idx];
-  const int num_partitions = DIVIDE_ROUND_UP(context_len, PARTITION_SIZE);
+  const int seq_len = seq_lens[seq_idx];
+  const int num_partitions = DIVIDE_ROUND_UP(seq_len, PARTITION_SIZE);
   const int warpid = threadIdx.x / WARP_SIZE;
 
   __shared__ float shared_global_exp_sum;
@@ -2980,7 +2978,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const int num_kv_heads,
     const float scale,
     const int* __restrict__ block_tables,    // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,    // [num_seqs]
+    const int* __restrict__ seq_lens,    // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -3007,7 +3005,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const int num_kv_heads,
     const float scale,
     const int* __restrict__ block_tables,    // [num_seqs, max_num_blocks_per_seq]
-    const int* __restrict__ context_lens,    // [num_seqs]
+    const int* __restrict__ seq_lens,    // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
@@ -3031,7 +3029,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     const float* __restrict__ exp_sums,    // [num_seqs, num_heads, max_num_partitions]
     const float* __restrict__ max_logits,  // [num_seqs, num_heads, max_num_partitions]
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads, max_num_partitions, head_size]
-    const int* __restrict__ context_lens,  // [num_seqs]
+    const int* __restrict__ seq_lens,  // [num_seqs]
     const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions, const float* __restrict__ fp8_out_scale_ptr) {
   UNREACHABLE_CODE
@@ -3046,7 +3044,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
                                           GQA_RATIO>                           \
       <<<grid, block, 0, stream>>>(                                            \
           query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,      \
-          block_tables_ptr, context_lens_ptr, query_start_loc_ptr,             \
+          block_tables_ptr, seq_lens_ptr, query_start_loc_ptr,                 \
           max_num_blocks_per_seq, alibi_slopes_ptr, q_stride, kv_block_stride, \
           kv_head_stride, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr,  \
           max_ctx_blocks, k_scale_ptr, v_scale_ptr);
@@ -3057,18 +3055,17 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
                                          GQA_RATIO>                            \
       <<<grid, block, 0, stream>>>(                                            \
           query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,      \
-          block_tables_ptr, context_lens_ptr, query_start_loc_ptr,             \
+          block_tables_ptr, seq_lens_ptr, query_start_loc_ptr,                 \
           max_num_blocks_per_seq, alibi_slopes_ptr, q_stride, kv_block_stride, \
           kv_head_stride, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr,  \
           max_ctx_blocks, k_scale_ptr, v_scale_ptr);
 
-#define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS)                          \
-  paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE, \
-                                      PARTITION_SIZE, NPAR_LOOPS>    \
-      <<<reduce_grid, reduce_block, 0, stream>>>(                    \
-          out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr,        \
-          context_lens_ptr, query_start_loc_ptr, max_num_partitions, \
-          fp8_out_scale_ptr);
+#define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS)                                 \
+  paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE,        \
+                                      PARTITION_SIZE, NPAR_LOOPS>           \
+      <<<reduce_grid, reduce_block, 0, stream>>>(                           \
+          out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, seq_lens_ptr, \
+          query_start_loc_ptr, max_num_partitions, fp8_out_scale_ptr);
 
 template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE,
           int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE_OLD,
@@ -3077,8 +3074,8 @@ void paged_attention_custom_launcher(
     torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
     torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
     torch::Tensor& value_cache, const int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& context_lens,
-    const std::optional<torch::Tensor>& query_start_loc, int max_context_len,
+    torch::Tensor& block_tables, torch::Tensor& seq_lens,
+    const std::optional<torch::Tensor>& query_start_loc, int max_seq_len,
     const std::optional<torch::Tensor>& alibi_slopes, torch::Tensor& k_scale,
     torch::Tensor& v_scale, const std::optional<torch::Tensor>& fp8_out_scale) {
   int num_seqs = block_tables.size(0);
@@ -3109,7 +3106,7 @@ void paged_attention_custom_launcher(
   KVT* key_cache_ptr = reinterpret_cast<KVT*>(key_cache.data_ptr());
   KVT* value_cache_ptr = reinterpret_cast<KVT*>(value_cache.data_ptr());
   int* block_tables_ptr = block_tables.data_ptr<int>();
-  int* context_lens_ptr = context_lens.data_ptr<int>();
+  int* seq_lens_ptr = seq_lens.data_ptr<int>();
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
   // NOTE: fp8_out_scale is optional.
@@ -3119,13 +3116,12 @@ void paged_attention_custom_launcher(
           : nullptr;
   OUTT* out_ptr = reinterpret_cast<OUTT*>(out.data_ptr());
 
-  const int max_ctx_blocks = DIVIDE_ROUND_UP(max_context_len, BLOCK_SIZE);
+  const int max_ctx_blocks = DIVIDE_ROUND_UP(max_seq_len, BLOCK_SIZE);
 
   // partition size is fixed at 256 since both mfma4 and mfma16 kernels support
   // it mfma4 kernel also supports partition size 512
   constexpr int PARTITION_SIZE = 256;
-  const int max_num_partitions =
-      DIVIDE_ROUND_UP(max_context_len, PARTITION_SIZE);
+  const int max_num_partitions = DIVIDE_ROUND_UP(max_seq_len, PARTITION_SIZE);
   const int gqa_ratio = num_heads / num_kv_heads;
   assert(num_heads % num_kv_heads == 0);
   assert(head_size == HEAD_SIZE);
@@ -3234,8 +3230,8 @@ void paged_attention_custom_launcher_navi(
     torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
     torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
     torch::Tensor& value_cache, const int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& context_lens,
-    const std::optional<torch::Tensor>& query_start_loc, int max_context_len,
+    torch::Tensor& block_tables, torch::Tensor& seq_lens,
+    const std::optional<torch::Tensor>& query_start_loc, int max_seq_len,
     const std::optional<torch::Tensor>& alibi_slopes, torch::Tensor& k_scale,
     torch::Tensor& v_scale) {
   int num_seqs = block_tables.size(0);
@@ -3263,7 +3259,7 @@ void paged_attention_custom_launcher_navi(
   KVT* key_cache_ptr = reinterpret_cast<KVT*>(key_cache.data_ptr());
   KVT* value_cache_ptr = reinterpret_cast<KVT*>(value_cache.data_ptr());
   int* block_tables_ptr = block_tables.data_ptr<int>();
-  int* context_lens_ptr = context_lens.data_ptr<int>();
+  int* seq_lens_ptr = seq_lens.data_ptr<int>();
 
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
@@ -3271,11 +3267,10 @@ void paged_attention_custom_launcher_navi(
   const auto fp8_out_scale_ptr = nullptr;
   OUTT* out_ptr = reinterpret_cast<OUTT*>(out.data_ptr());
 
-  const int max_ctx_blocks = DIVIDE_ROUND_UP(max_context_len, BLOCK_SIZE);
+  const int max_ctx_blocks = DIVIDE_ROUND_UP(max_seq_len, BLOCK_SIZE);
 
   constexpr int PARTITION_SIZE = 256;
-  const int max_num_partitions =
-      DIVIDE_ROUND_UP(max_context_len, PARTITION_SIZE);
+  const int max_num_partitions = DIVIDE_ROUND_UP(max_seq_len, PARTITION_SIZE);
   const int gqa_ratio = num_heads / num_kv_heads;
   assert(num_heads % num_kv_heads == 0);
   assert(head_size == HEAD_SIZE);
@@ -3407,14 +3402,14 @@ void paged_attention_custom_launcher_navi(
     paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE,  \
                                     OUTT, PSIZE, ALIBI_ENABLED>(            \
         out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache,  \
-        num_kv_heads, scale, block_tables, context_lens, query_start_loc,   \
-        max_context_len, alibi_slopes, k_scale, v_scale, fp8_out_scale);    \
+        num_kv_heads, scale, block_tables, seq_lens, query_start_loc,       \
+        max_seq_len, alibi_slopes, k_scale, v_scale, fp8_out_scale);        \
   } else {                                                                  \
     paged_attention_custom_launcher_navi<                                   \
         T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, PSIZE, ALIBI_ENABLED>( \
         out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache,  \
-        num_kv_heads, scale, block_tables, context_lens, query_start_loc,   \
-        max_context_len, alibi_slopes, k_scale, v_scale);                   \
+        num_kv_heads, scale, block_tables, seq_lens, query_start_loc,       \
+        max_seq_len, alibi_slopes, k_scale, v_scale);                       \
   }
 
 #define CALL_CUSTOM_LAUNCHER_ALIBI(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE,    \
@@ -3502,9 +3497,9 @@ void paged_attention(
     int64_t num_kv_heads, 
     double scale,
     torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
-    torch::Tensor& context_lens, // [num_seqs]
+    torch::Tensor& seq_lens, // [num_seqs]
     const std::optional<torch::Tensor>& query_start_loc, // [num_seqs]
-    int64_t block_size, int64_t max_context_len,
+    int64_t block_size, int64_t max_seq_len,
     const std::optional<torch::Tensor>& alibi_slopes,
     const std::string& kv_cache_dtype, torch::Tensor& k_scale,
     torch::Tensor& v_scale,

csrc/rocm/ops.h

@@ -15,8 +15,8 @@ void paged_attention(
     torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
     torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
     torch::Tensor& value_cache, int64_t num_kv_heads, double scale,
-    torch::Tensor& block_tables, torch::Tensor& context_lens,
+    torch::Tensor& block_tables, torch::Tensor& seq_lens,
     const std::optional<torch::Tensor>& query_start_loc, int64_t block_size,
-    int64_t max_context_len, const std::optional<torch::Tensor>& alibi_slopes,
+    int64_t max_seq_len, const std::optional<torch::Tensor>& alibi_slopes,
     const std::string& kv_cache_dtype, torch::Tensor& k_scale,
     torch::Tensor& v_scale, const std::optional<torch::Tensor>& fp8_out_scale);

csrc/rocm/torch_bindings.cpp

@@ -41,10 +41,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, rocm_ops) {
       "                Tensor query, Tensor key_cache,"
       "                Tensor value_cache, int num_kv_heads,"
       "                float scale, Tensor block_tables,"
-      "                Tensor context_lens,"
+      "                Tensor seq_lens,"
       "                Tensor? query_start_loc,"
       "                int block_size,"
-      "                int max_context_len,"
+      "                int max_seq_len,"
       "                Tensor? alibi_slopes,"
       "                str kv_cache_dtype,"
       "                Tensor k_scale, Tensor v_scale,"

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 \
@@ -210,16 +195,7 @@ ARG SCCACHE_REGION_NAME=us-west-2
 ARG SCCACHE_S3_NO_CREDENTIALS=0
 
 # Flag to control whether to use pre-built vLLM wheels
-ARG VLLM_USE_PRECOMPILED
-# TODO: in setup.py VLLM_USE_PRECOMPILED is sensitive to truthiness, it will take =0 as "true", this should be fixed
-ENV VLLM_USE_PRECOMPILED=""
-RUN if [ "${VLLM_USE_PRECOMPILED}" = "1" ]; then \
-        export VLLM_USE_PRECOMPILED=1 && \
-        echo "Using precompiled wheels"; \
-    else \
-        unset VLLM_USE_PRECOMPILED && \
-        echo "Leaving VLLM_USE_PRECOMPILED unset to build wheels from source"; \
-    fi
+ARG VLLM_USE_PRECOMPILED=""
 
 # if USE_SCCACHE is set, use sccache to speed up compilation
 RUN --mount=type=cache,target=/root/.cache/uv \
@@ -236,11 +212,15 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
         && export SCCACHE_IDLE_TIMEOUT=0 \
         && export CMAKE_BUILD_TYPE=Release \
+        && export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" \
+        && export VLLM_DOCKER_BUILD_CONTEXT=1 \
         && sccache --show-stats \
         && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
         && sccache --show-stats; \
     fi
 
+ARG vllm_target_device="cuda"
+ENV VLLM_TARGET_DEVICE=${vllm_target_device}
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
@@ -249,6 +229,8 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
         # Clean any existing CMake artifacts
         rm -rf .deps && \
         mkdir -p .deps && \
+        export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" && \
+        export VLLM_DOCKER_BUILD_CONTEXT=1 && \
         python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
     fi
 
@@ -392,7 +374,7 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
 ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
 # Keep this in sync with https://github.com/vllm-project/vllm/blob/main/requirements/cuda.txt
 # We use `--force-reinstall --no-deps` to avoid issues with the existing FlashInfer wheel.
-ARG FLASHINFER_GIT_REF="v0.2.9"
+ARG FLASHINFER_GIT_REF="v0.2.11"
 RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
   . /etc/environment
     git clone --depth 1 --recursive --shallow-submodules \
@@ -437,7 +419,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 
 # Install DeepGEMM from source
 ARG DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git"
-ARG DEEPGEMM_GIT_REF="187656694f7f69e3e7975617a68bc3387680a7e1"
+ARG DEEPGEMM_GIT_REF="7b6b5563b9d4c1ae07ffbce7f78ad3ac9204827c"
 RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
   . /etc/environment
     CUDA_MAJOR="${CUDA_VERSION%%.*}"
@@ -502,14 +484,11 @@ ENV HF_HUB_ENABLE_HF_TRANSFER 1
 # Copy in the v1 package for testing (it isn't distributed yet)
 COPY vllm/v1 /usr/local/lib/python${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 ####################

docker/Dockerfile.xpu

@@ -1,9 +1,12 @@
-# oneapi 2025.0.2 docker base image use rolling 2448 package. https://dgpu-docs.intel.com/releases/packages.html?release=Rolling+2448.13&os=Ubuntu+22.04, and we don't need install driver manually.
-FROM intel/deep-learning-essentials:2025.0.2-0-devel-ubuntu22.04 AS vllm-base
+FROM intel/deep-learning-essentials:2025.1.3-0-devel-ubuntu24.04 AS vllm-base
 
 RUN rm /etc/apt/sources.list.d/intel-graphics.list
 
-RUN apt-get update -y && \
+RUN apt clean && apt-get update -y && \
+    apt-get install -y software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get install -y python3.10 python3.10-distutils && \
+    curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10 && \
     apt-get install -y --no-install-recommends --fix-missing \
     curl \
     ffmpeg \
@@ -14,11 +17,13 @@ RUN apt-get update -y && \
     libgl1 \
     lsb-release \
     numactl \
-    python3 \
-    python3-dev \
-    python3-pip \
+    python3.10-dev \
     wget
 
+
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1
+
 WORKDIR /workspace/vllm
 COPY requirements/xpu.txt /workspace/vllm/requirements/xpu.txt
 COPY requirements/common.txt /workspace/vllm/requirements/common.txt

docs/.nav.yml

@@ -1,25 +1,17 @@
 nav:
-  - Home: 
-    - vLLM: README.md
+  - Home: README.md
+  - User Guide:
+    - usage/README.md
     - Getting Started:
       - getting_started/quickstart.md
       - getting_started/installation
     - Examples:
+      - examples/README.md
       - Offline Inference: examples/offline_inference
       - Online Serving: examples/online_serving
       - Others: examples/others
-    - Quick Links:
-      - User Guide: usage/README.md
-      - Developer Guide: contributing/README.md
-      - API Reference: api/README.md
-      - CLI Reference: cli/README.md
-    - Timeline:
-      - Roadmap: https://roadmap.vllm.ai
-      - Releases: https://github.com/vllm-project/vllm/releases
-  - User Guide:
-    - Summary: usage/README.md
-    - usage/v1_guide.md
     - General:
+      - usage/v1_guide.md
       - usage/*
     - Inference and Serving:
       - serving/offline_inference.md
@@ -32,7 +24,7 @@ nav:
       - deployment/integrations
     - Training: training
     - Configuration:
-      - Summary: configuration/README.md
+      - configuration/README.md
       - configuration/*
     - Models:
       - models/supported_models.md
@@ -45,11 +37,11 @@ nav:
       - features/*
       - features/quantization
   - Developer Guide:
-    - Summary: contributing/README.md
+    - contributing/README.md
     - General:
       - glob: contributing/*
         flatten_single_child_sections: true
-    - Model Implementation: 
+    - Model Implementation:
       - contributing/model/README.md
       - contributing/model/basic.md
       - contributing/model/registration.md
@@ -58,12 +50,9 @@ nav:
     - CI: contributing/ci
     - Design Documents: design
   - API Reference:
-    - Summary: api/README.md
-    - Contents:
-      - glob: api/vllm/*
-        preserve_directory_names: true
-  - CLI Reference:
-    - Summary: cli/README.md
+    - api/README.md
+    - api/vllm/*
+  - CLI Reference: cli
   - Community:
     - community/*
     - Blog: https://blog.vllm.ai

docs/README.md

@@ -1,3 +1,9 @@
+---
+hide:
+  - navigation
+  - toc
+---
+
 # Welcome to vLLM
 
 <figure markdown="span">
@@ -21,6 +27,17 @@ vLLM is a fast and easy-to-use library for LLM inference and serving.
 
 Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
 
+Where to get started with vLLM depends on the type of user. If you are looking to:
+
+- Run open-source models on vLLM, we recommend starting with the [Quickstart Guide](./getting_started/quickstart.md)
+- Build applications with vLLM, we recommend starting with the [User Guide](./usage)
+- Build vLLM, we recommend starting with [Developer Guide](./contributing)
+
+For information about the development of vLLM, see:
+
+- [Roadmap](https://roadmap.vllm.ai)
+- [Releases](https://github.com/vllm-project/vllm/releases)
+
 vLLM is fast with:
 
 - State-of-the-art serving throughput

docs/api/README.md

@@ -1,7 +1,5 @@
 # Summary
 
-[](){ #configuration }
-
 ## Configuration
 
 API documentation for vLLM's configuration classes.

docs/cli/.meta.yml

@@ -0,0 +1 @@
+toc_depth: 3

docs/cli/.nav.yml

@@ -0,0 +1,8 @@
+nav:
+  - README.md
+  - serve.md
+  - chat.md
+  - complete.md
+  - run-batch.md
+  - vllm bench:
+    - bench/*.md

docs/cli/README.md

@@ -1,7 +1,3 @@
----
-toc_depth: 4
----
-
 # vLLM CLI Guide
 
 The vllm command-line tool is used to run and manage vLLM models. You can start by viewing the help message with:
@@ -18,37 +14,46 @@ vllm {chat,complete,serve,bench,collect-env,run-batch}
 
 ## serve
 
-Start the vLLM OpenAI Compatible API server.
+Starts the vLLM OpenAI Compatible API server.
 
-??? console "Examples"
+Start with a model:
 
-    ```bash
-    # Start with a model
-    vllm serve meta-llama/Llama-2-7b-hf
+```bash
+vllm serve meta-llama/Llama-2-7b-hf
+```
 
-    # Specify the port
-    vllm serve meta-llama/Llama-2-7b-hf --port 8100
+Specify the port:
 
-    # Check with --help for more options
-    # To list all groups
-    vllm serve --help=listgroup
+```bash
+vllm serve meta-llama/Llama-2-7b-hf --port 8100
+```
 
-    # To view a argument group
-    vllm serve --help=ModelConfig
+Serve over a Unix domain socket:
 
-    # To view a single argument
-    vllm serve --help=max-num-seqs
+```bash
+vllm serve meta-llama/Llama-2-7b-hf --uds /tmp/vllm.sock
+```
 
-    # To search by keyword
-    vllm serve --help=max
+Check with --help for more options:
 
-    # To view full help with pager (less/more)
-    vllm serve --help=page
-    ```
+```bash
+# To list all groups
+vllm serve --help=listgroup
 
-### Options
+# To view a argument group
+vllm serve --help=ModelConfig
 
---8<-- "docs/argparse/serve.md"
+# To view a single argument
+vllm serve --help=max-num-seqs
+
+# To search by keyword
+vllm serve --help=max
+
+# To view full help with pager (less/more)
+vllm serve --help=page
+```
+
+See [vllm serve](./serve.md) for the full reference of all available arguments.
 
 ## chat
 
@@ -65,6 +70,8 @@ vllm chat --url http://{vllm-serve-host}:{vllm-serve-port}/v1
 vllm chat --quick "hi"
 ```
 
+See [vllm chat](./chat.md) for the full reference of all available arguments.
+
 ## complete
 
 Generate text completions based on the given prompt via the running API server.
@@ -80,7 +87,7 @@ vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1
 vllm complete --quick "The future of AI is"
 ```
 
-</details>
+See [vllm complete](./complete.md) for the full reference of all available arguments.
 
 ## bench
 
@@ -107,6 +114,8 @@ vllm bench latency \
     --load-format dummy
 ```
 
+See [vllm bench latency](./bench/latency.md) for the full reference of all available arguments.
+
 ### serve
 
 Benchmark the online serving throughput.
@@ -121,6 +130,8 @@ vllm bench serve \
     --num-prompts  5
 ```
 
+See [vllm bench serve](./bench/serve.md) for the full reference of all available arguments.
+
 ### throughput
 
 Benchmark offline inference throughput.
@@ -134,6 +145,8 @@ vllm bench throughput \
     --load-format dummy
 ```
 
+See [vllm bench throughput](./bench/throughput.md) for the full reference of all available arguments.
+
 ## collect-env
 
 Start collecting environment information.
@@ -146,24 +159,25 @@ vllm collect-env
 
 Run batch prompts and write results to file.
 
-<details>
-<summary>Examples</summary>
+Running with a local file:
 
 ```bash
-# Running with a local file
 vllm run-batch \
     -i offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
+```
 
-# Using remote file
+Using remote file:
+
+```bash
 vllm run-batch \
     -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
 
-</details>
+See [vllm run-batch](./run-batch.md) for the full reference of all available arguments.
 
 ## More Help
 

docs/cli/bench/latency.md

@@ -0,0 +1,9 @@
+# vllm bench latency
+
+## JSON CLI Arguments
+
+--8<-- "docs/cli/json_tip.inc.md"
+
+## Options
+
+--8<-- "docs/argparse/bench_latency.md"

docs/cli/bench/serve.md

@@ -0,0 +1,9 @@
+# vllm bench serve
+
+## JSON CLI Arguments
+
+--8<-- "docs/cli/json_tip.inc.md"
+
+## Options
+
+--8<-- "docs/argparse/bench_serve.md"

docs/cli/bench/throughput.md

@@ -0,0 +1,9 @@
+# vllm bench throughput
+
+## JSON CLI Arguments
+
+--8<-- "docs/cli/json_tip.inc.md"
+
+## Options
+
+--8<-- "docs/argparse/bench_throughput.md"

docs/cli/chat.md

@@ -0,0 +1,5 @@
+# vllm chat
+
+## Options
+
+--8<-- "docs/argparse/chat.md"

docs/cli/complete.md

@@ -0,0 +1,5 @@
+# vllm complete
+
+## Options
+
+--8<-- "docs/argparse/complete.md"

docs/cli/json_tip.inc.md

@@ -0,0 +1,9 @@
+When passing JSON CLI arguments, the following sets of arguments are equivalent:
+
+- `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`
+- `--json-arg.key1 value1 --json-arg.key2.key3 value2`
+
+Additionally, list elements can be passed individually using `+`:
+
+- `--json-arg '{"key4": ["value3", "value4", "value5"]}'`
+- `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`

docs/cli/run-batch.md

@@ -0,0 +1,9 @@
+# vllm run-batch
+
+## JSON CLI Arguments
+
+--8<-- "docs/cli/json_tip.inc.md"
+
+## Options
+
+--8<-- "docs/argparse/run-batch.md"

docs/cli/serve.md

@@ -0,0 +1,9 @@
+# vllm serve
+
+## JSON CLI Arguments
+
+--8<-- "docs/cli/json_tip.inc.md"
+
+## Options
+
+--8<-- "docs/argparse/serve.md"

docs/community/meetups.md

@@ -2,6 +2,7 @@
 
 We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
 
+- [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA), August 2nd 2025. [[Slides]](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) [[Recording]](https://www.chaspark.com/#/live/1166916873711665152).
 - [NYC vLLM Meetup](https://lu.ma/c1rqyf1f), May 7th, 2025. [[Slides]](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing)
 - [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day), April 3rd 2025. [[Slides]](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama), March 27th 2025. [[Slides]](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).

docs/community/sponsors.md

@@ -15,6 +15,7 @@ Cash Donations:
 
 Compute Resources:
 
+- Alibaba Cloud
 - AMD
 - Anyscale
 - AWS

docs/configuration/conserving_memory.md

@@ -86,7 +86,7 @@ llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct",
 
 If you run out of CPU RAM, try the following options:
 
-- (Multi-modal models only) you can set the size of multi-modal input cache using `VLLM_MM_INPUT_CACHE_GIB` environment variable (default 4 GiB).
+- (Multi-modal models only) you can set the size of multi-modal processor cache by setting `mm_processor_cache_gb` engine argument (default 4 GiB per API process + 4 GiB per engine core process)
 - (CPU backend only) you can set the size of KV cache using `VLLM_CPU_KVCACHE_SPACE` environment variable (default 4 GiB).
 
 ## Multi-modal input limits
@@ -129,20 +129,18 @@ reduce the size of the processed multi-modal inputs, which in turn saves memory.
 
 Here are some examples:
 
-??? code
+```python
+from vllm import LLM
 
-    ```python
-    from vllm import LLM
+# Available for Qwen2-VL series models
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          mm_processor_kwargs={
+              "max_pixels": 768 * 768,  # Default is 1280 * 28 * 28
+          })
 
-    # Available for Qwen2-VL series models
-    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
-            mm_processor_kwargs={
-                "max_pixels": 768 * 768,  # Default is 1280 * 28 * 28
-            })
-
-    # Available for InternVL series models
-    llm = LLM(model="OpenGVLab/InternVL2-2B",
-            mm_processor_kwargs={
-                "max_dynamic_patch": 4,  # Default is 12
-            })
-    ```
+# Available for InternVL series models
+llm = LLM(model="OpenGVLab/InternVL2-2B",
+          mm_processor_kwargs={
+              "max_dynamic_patch": 4,  # Default is 12
+          })
+```

docs/configuration/engine_args.md

@@ -11,6 +11,8 @@ Engine arguments control the behavior of the vLLM engine.
 
 The engine argument classes, [EngineArgs][vllm.engine.arg_utils.EngineArgs] and [AsyncEngineArgs][vllm.engine.arg_utils.AsyncEngineArgs], are a combination of the configuration classes defined in [vllm.config][]. Therefore, if you are interested in developer documentation, we recommend looking at these configuration classes as they are the source of truth for types, defaults and docstrings.
 
+--8<-- "docs/cli/json_tip.inc.md"
+
 ## `EngineArgs`
 
 --8<-- "docs/argparse/engine_args.md"

docs/configuration/optimization.md

@@ -2,6 +2,9 @@
 
 This guide covers optimization strategies and performance tuning for vLLM V1.
 
+!!! tip
+    Running out of memory? Consult [this guide](./conserving_memory.md) on how to conserve memory.
+
 ## Preemption
 
 Due to the auto-regressive nature of transformer architecture, there are times when KV cache space is insufficient to handle all batched requests.
@@ -126,62 +129,50 @@ Data parallelism replicates the entire model across multiple GPU sets and proces
 Data parallelism can be combined with the other parallelism strategies and is set by `data_parallel_size=N`.
 Note that MoE layers will be sharded according to the product of the tensor parallel size and data parallel size.
 
-## Reducing Memory Usage
+## Input Processing
 
-If you encounter out-of-memory issues, consider these strategies:
+### Parallel Processing
 
-### Context Length and Batch Size
+You can run input processing in parallel via [API server scale-out](../serving/data_parallel_deployment.md#internal-load-balancing).
+This is useful when input processing (which is run inside the API server)
+becomes a bottleneck compared to model execution (which is run inside engine core)
+and you have excess CPU capacity.
 
-You can reduce memory usage by limiting the context length and batch size:
+```console
+# Run 4 API processes and 1 engine core process
+vllm serve Qwen/Qwen2.5-VL-3B-Instruct --api-server-count 4
 
-```python
-from vllm import LLM
-
-llm = LLM(
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    max_model_len=2048,  # Limit context window
-    max_num_seqs=4       # Limit batch size
-)
+# Run 4 API processes and 2 engine core processes
+vllm serve Qwen/Qwen2.5-VL-3B-Instruct --api-server-count 4 -dp 2
 ```
 
-### Adjust CUDA Graph Compilation
+!!! note
+    API server scale-out is only available for online inference.
 
-CUDA graph compilation in V1 uses more memory than in V0. You can reduce memory usage by adjusting the compilation level:
+!!! note
+    [Multi-modal processor cache](#processor-cache) is disabled when API server scale-out is enabled
+    because it requires a one-to-one correspondance between API and engine core processes.
+
+## Multi-Modal Caching
+
+### Processor Cache
+
+By default, the multi-modal processor cache is enabled to avoid repeatedly processing
+the same multi-modal inputs via Hugging Face `AutoProcessor`,
+which commonly occurs in multi-turn conversations.
+
+You can adjust the size of the cache by setting the value of `mm_processor_cache_gb`
+(default 4 GiB per API process + 4 GiB per engine core process).
+If you do not benefit much from the cache, you can disable it completely via `mm_processor_cache_gb=0`.
+
+Examples:
 
 ```python
-from vllm import LLM
-from vllm.config import CompilationConfig, CompilationLevel
+# Use a larger cache
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          mm_processor_cache_gb=8)
 
-llm = LLM(
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    compilation_config=CompilationConfig(
-        level=CompilationLevel.PIECEWISE,
-        cudagraph_capture_sizes=[1, 2, 4, 8]  # Capture fewer batch sizes
-    )
-)
-```
-
-Or, if you are not concerned about latency or overall performance, disable CUDA graph compilation entirely with `enforce_eager=True`:
-
-```python
-from vllm import LLM
-
-llm = LLM(
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    enforce_eager=True  # Disable CUDA graph compilation
-)
-```
-
-### Multimodal Models
-
-For multi-modal models, you can reduce memory usage by limiting the number of images/videos per request:
-
-```python
-from vllm import LLM
-
-# Accept up to 2 images per prompt
-llm = LLM(
-    model="Qwen/Qwen2.5-VL-3B-Instruct",
-    limit_mm_per_prompt={"image": 2}
-)
+# Disable the cache
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          mm_processor_cache_gb=0)
 ```