Merge remote-tracking branch 'origin/main' into fp8_ep_dp

[Bugfix][Model] Attempt to fix eagle in V0. (#18978 )
Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>
2025-06-02 14:53:27 -04:00 · 2025-06-02 08:15:53 -07:00 · 2025-06-02 15:04:23 +00:00 · 2025-06-02 19:22:54 +08:00 · 2025-06-02 11:07:26 +00:00 · 2025-06-02 17:38:50 +08:00
1188 changed files with 57817 additions and 26721 deletions
@@ -8,12 +8,12 @@ import zipfile
 # Note that we have 400 MiB quota, please use it wisely.
 # See https://github.com/pypi/support/issues/3792 .
 # Please also sync the value with the one in Dockerfile.
-VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 400))
+VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 400))


 def print_top_10_largest_files(zip_file):
    """Print the top 10 largest files in the given zip file."""
-    with zipfile.ZipFile(zip_file, 'r') as z:
+    with zipfile.ZipFile(zip_file, "r") as z:
        file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
        file_sizes.sort(key=lambda x: x[1], reverse=True)
        for f, size in file_sizes[:10]:
@@ -28,14 +28,18 @@ def check_wheel_size(directory):
                wheel_path = os.path.join(root, file_name)
                wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
                if wheel_size_mb > VLLM_MAX_SIZE_MB:
-                    print(f"Not allowed: Wheel {wheel_path} is larger "
-                          f"({wheel_size_mb:.2f} MB) than the limit "
-                          f"({VLLM_MAX_SIZE_MB} MB).")
+                    print(
+                        f"Not allowed: Wheel {wheel_path} is larger "
+                        f"({wheel_size_mb:.2f} MB) than the limit "
+                        f"({VLLM_MAX_SIZE_MB} MB)."
+                    )
                    print_top_10_largest_files(wheel_path)
                    return 1
                else:
-                    print(f"Wheel {wheel_path} is within the allowed size "
-                          f"({wheel_size_mb:.2f} MB).")
+                    print(
+                        f"Wheel {wheel_path} is within the allowed size "
+                        f"({wheel_size_mb:.2f} MB)."
+                    )
    return 0


@@ -45,4 +49,4 @@ if __name__ == "__main__":
        sys.exit(1)

    directory = sys.argv[1]
-    sys.exit(check_wheel_size(directory))
+    sys.exit(check_wheel_size(directory))
@@ -22,5 +22,5 @@ with open("index.html", "w") as f:
    print(f"Generated index.html for {args.wheel}")
    # cloudfront requires escaping the '+' character
    f.write(
-        template.format(wheel=filename,
-                        wheel_html_escaped=filename.replace("+", "%2B")))
+        template.format(wheel=filename, wheel_html_escaped=filename.replace("+", "%2B"))
+    )
@@ -8,11 +8,14 @@ def pytest_addoption(parser):
    parser.addoption(
        "--config-list-file",
        action="store",
-        help="Path to the file listing model config YAMLs (one per line)")
-    parser.addoption("--tp-size",
-                     action="store",
-                     default="1",
-                     help="Tensor parallel size to use for evaluation")
+        help="Path to the file listing model config YAMLs (one per line)",
+    )
+    parser.addoption(
+        "--tp-size",
+        action="store",
+        default="1",
+        help="Tensor parallel size to use for evaluation",
+    )


@pytest.fixture(scope="session")
@@ -33,7 +36,8 @@ def pytest_generate_tests(metafunc):
        config_dir = config_list_file.parent
        with open(config_list_file, encoding="utf-8") as f:
            configs = [
-                config_dir / line.strip() for line in f
+                config_dir / line.strip()
+                for line in f
                if line.strip() and not line.startswith("#")
            ]
        metafunc.parametrize("config_filename", configs)
@@ -16,19 +16,22 @@ RTOL = 0.08


 def launch_lm_eval(eval_config, tp_size):
-    trust_remote_code = eval_config.get('trust_remote_code', False)
-    model_args = f"pretrained={eval_config['model_name']}," \
-                 f"tensor_parallel_size={tp_size}," \
-                 f"enforce_eager=true," \
-                 f"add_bos_token=true," \
-                 f"trust_remote_code={trust_remote_code}"
+    trust_remote_code = eval_config.get("trust_remote_code", False)
+    model_args = (
+        f"pretrained={eval_config['model_name']},"
+        f"tensor_parallel_size={tp_size},"
+        f"enforce_eager=true,"
+        f"add_bos_token=true,"
+        f"trust_remote_code={trust_remote_code}"
+    )
    results = lm_eval.simple_evaluate(
        model="vllm",
        model_args=model_args,
        tasks=[task["name"] for task in eval_config["tasks"]],
        num_fewshot=eval_config["num_fewshot"],
        limit=eval_config["limit"],
-        batch_size="auto")
+        batch_size="auto",
+    )
    return results


@@ -42,9 +45,10 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
        for metric in task["metrics"]:
            ground_truth = metric["value"]
            measured_value = results["results"][task["name"]][metric["name"]]
-            print(f'{task["name"]} | {metric["name"]}: '
-                  f'ground_truth={ground_truth} | measured={measured_value}')
-            success = success and np.isclose(
-                ground_truth, measured_value, rtol=RTOL)
+            print(
+                f"{task['name']} | {metric['name']}: "
+                f"ground_truth={ground_truth} | measured={measured_value}"
+            )
+            success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)

    assert success
@@ -113,7 +113,7 @@ WARNING: The benchmarking script will save json results by itself, so please do

 ### Visualizing the results

-The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/descriptions.md) with real benchmarking results.
+The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.
 You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
 If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
@@ -65,18 +65,18 @@ def read_markdown(file):


 def results_to_json(latency, throughput, serving):
-    return json.dumps({
-        'latency': latency.to_dict(),
-        'throughput': throughput.to_dict(),
-        'serving': serving.to_dict()
-    })
+    return json.dumps(
+        {
+            "latency": latency.to_dict(),
+            "throughput": throughput.to_dict(),
+            "serving": serving.to_dict(),
+        }
+    )


 if __name__ == "__main__":
-
    # collect results
    for test_file in results_folder.glob("*.json"):
-
        with open(test_file) as f:
            raw_result = json.loads(f.read())

@@ -120,7 +120,8 @@ if __name__ == "__main__":
            for perc in [10, 25, 50, 75, 90, 99]:
                # Multiply 1000 to convert the time unit from s to ms
                raw_result.update(
-                    {f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
+                    {f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]}
+                )
            raw_result["avg_latency"] = raw_result["avg_latency"] * 1000

            # add the result to raw_result
@@ -153,26 +154,27 @@ if __name__ == "__main__":
    serving_results = pd.DataFrame.from_dict(serving_results)
    throughput_results = pd.DataFrame.from_dict(throughput_results)

-    raw_results_json = results_to_json(latency_results, throughput_results,
-                                       serving_results)
+    raw_results_json = results_to_json(
+        latency_results, throughput_results, serving_results
+    )

    # remapping the key, for visualization purpose
    if not latency_results.empty:
-        latency_results = latency_results[list(
-            latency_column_mapping.keys())].rename(
-                columns=latency_column_mapping)
+        latency_results = latency_results[list(latency_column_mapping.keys())].rename(
+            columns=latency_column_mapping
+        )
    if not serving_results.empty:
-        serving_results = serving_results[list(
-            serving_column_mapping.keys())].rename(
-                columns=serving_column_mapping)
+        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+            columns=serving_column_mapping
+        )
    if not throughput_results.empty:
-        throughput_results = throughput_results[list(
-            throughput_results_column_mapping.keys())].rename(
-                columns=throughput_results_column_mapping)
+        throughput_results = throughput_results[
+            list(throughput_results_column_mapping.keys())
+        ].rename(columns=throughput_results_column_mapping)

-    processed_results_json = results_to_json(latency_results,
-                                             throughput_results,
-                                             serving_results)
+    processed_results_json = results_to_json(
+        latency_results, throughput_results, serving_results
+    )

    for df in [latency_results, serving_results, throughput_results]:
        if df.empty:
@@ -184,38 +186,39 @@ if __name__ == "__main__":
        # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
        # we want to turn it into "8xGPUTYPE"
        df["GPU"] = df["GPU"].apply(
-            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")
+            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
+        )

    # get markdown tables
-    latency_md_table = tabulate(latency_results,
-                                headers='keys',
-                                tablefmt='pipe',
-                                showindex=False)
-    serving_md_table = tabulate(serving_results,
-                                headers='keys',
-                                tablefmt='pipe',
-                                showindex=False)
-    throughput_md_table = tabulate(throughput_results,
-                                   headers='keys',
-                                   tablefmt='pipe',
-                                   showindex=False)
+    latency_md_table = tabulate(
+        latency_results, headers="keys", tablefmt="pipe", showindex=False
+    )
+    serving_md_table = tabulate(
+        serving_results, headers="keys", tablefmt="pipe", showindex=False
+    )
+    throughput_md_table = tabulate(
+        throughput_results, headers="keys", tablefmt="pipe", showindex=False
+    )

    # document the result
    with open(results_folder / "benchmark_results.md", "w") as f:
-
-        results = read_markdown("../.buildkite/nightly-benchmarks/" +
-                                "performance-benchmarks-descriptions.md")
+        results = read_markdown(
+            "../.buildkite/nightly-benchmarks/"
+            + "performance-benchmarks-descriptions.md"
+        )
        results = results.format(
            latency_tests_markdown_table=latency_md_table,
            throughput_tests_markdown_table=throughput_md_table,
            serving_tests_markdown_table=serving_md_table,
-            benchmarking_results_in_json_string=processed_results_json)
+            benchmarking_results_in_json_string=processed_results_json,
+        )
        f.write(results)

    # document benchmarking results in json
    with open(results_folder / "benchmark_results.json", "w") as f:
-
-        results = latency_results.to_dict(
-            orient='records') + throughput_results.to_dict(
-                orient='records') + serving_results.to_dict(orient='records')
+        results = (
+            latency_results.to_dict(orient="records")
+            + throughput_results.to_dict(orient="records")
+            + serving_results.to_dict(orient="records")
+        )
        f.write(json.dumps(results))
@@ -14,15 +14,12 @@ def main(model, cachedir):

 if __name__ == "__main__":
    parser = argparse.ArgumentParser(
-        description="Download and save Hugging Face tokenizer")
-    parser.add_argument("--model",
-                        type=str,
-                        required=True,
-                        help="Name of the model")
-    parser.add_argument("--cachedir",
-                        type=str,
-                        required=True,
-                        help="Directory to save the tokenizer")
+        description="Download and save Hugging Face tokenizer"
+    )
+    parser.add_argument("--model", type=str, required=True, help="Name of the model")
+    parser.add_argument(
+        "--cachedir", type=str, required=True, help="Directory to save the tokenizer"
+    )

    args = parser.parse_args()
    main(args.model, args.cachedir)
@@ -11,33 +11,33 @@ from tabulate import tabulate

 def parse_arguments():
    parser = argparse.ArgumentParser(
-        description=
-        'Parse command line arguments for summary-nightly-results script.')
-    parser.add_argument('--results-folder',
-                        type=str,
-                        required=True,
-                        help='The folder where the results are stored.')
-    parser.add_argument('--description',
-                        type=str,
-                        required=True,
-                        help='Description of the results.')
+        description="Parse command line arguments for summary-nightly-results script."
+    )
+    parser.add_argument(
+        "--results-folder",
+        type=str,
+        required=True,
+        help="The folder where the results are stored.",
+    )
+    parser.add_argument(
+        "--description", type=str, required=True, help="Description of the results."
+    )

    args = parser.parse_args()
    return args


 def get_perf(df, method, model, metric):
-
    means = []

    for qps in [2, 4, 8, 16, "inf"]:
-        target = df['Test name'].str.contains(model)
-        target = target & df['Engine'].str.contains(method)
-        target = target & df['Test name'].str.contains("qps_" + str(qps))
+        target = df["Test name"].str.contains(model)
+        target = target & df["Engine"].str.contains(method)
+        target = target & df["Test name"].str.contains("qps_" + str(qps))
        filtered_df = df[target]

        if filtered_df.empty:
-            means.append(0.)
+            means.append(0.0)
        else:
            means.append(filtered_df[metric].values[0])

@@ -45,7 +45,6 @@ def get_perf(df, method, model, metric):


 def get_perf_w_std(df, method, model, metric):
-
    if metric in ["TTFT", "ITL"]:
        mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
        mean = mean.tolist()
@@ -60,7 +59,8 @@ def get_perf_w_std(df, method, model, metric):
    else:
        assert metric == "Tput"
        mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
-            df, method, model, "Output Tput (tok/s)")
+            df, method, model, "Output Tput (tok/s)"
+        )
        mean = mean.tolist()
        std = None

@@ -80,18 +80,17 @@ def main(args):
    # generate markdown table
    df = pd.DataFrame.from_dict(results)

-    md_table = tabulate(df, headers='keys', tablefmt='pipe', showindex=False)
+    md_table = tabulate(df, headers="keys", tablefmt="pipe", showindex=False)

    with open(args.description) as f:
        description = f.read()

-    description = description.format(
-        nightly_results_benchmarking_table=md_table)
+    description = description.format(nightly_results_benchmarking_table=md_table)

    with open("nightly_results.md", "w") as f:
        f.write(description)


-if __name__ == '__main__':
+if __name__ == "__main__":
    args = parse_arguments()
    main(args)
@@ -34,10 +34,8 @@ serving_column_mapping = {
 }

 if __name__ == "__main__":
-
    # collect results
    for test_file in results_folder.glob("*.json"):
-
        with open(test_file) as f:
            raw_result = json.loads(f.read())

@@ -56,17 +54,16 @@ if __name__ == "__main__":
    serving_results = pd.DataFrame.from_dict(serving_results)

    if not serving_results.empty:
-        serving_results = serving_results[list(
-            serving_column_mapping.keys())].rename(
-                columns=serving_column_mapping)
+        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+            columns=serving_column_mapping
+        )

-    serving_md_table_with_headers = tabulate(serving_results,
-                                             headers='keys',
-                                             tablefmt='pipe',
-                                             showindex=False)
+    serving_md_table_with_headers = tabulate(
+        serving_results, headers="keys", tablefmt="pipe", showindex=False
+    )
    # remove the first line of header
-    serving_md_table_lines = serving_md_table_with_headers.split('\n')
-    serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
+    serving_md_table_lines = serving_md_table_with_headers.split("\n")
+    serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])

    prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
@@ -76,10 +73,9 @@ if __name__ == "__main__":
        # document results with header.
        # for those who wants to reproduce our benchmark.
        f.write(serving_md_table_with_headers)
-        f.write('\n')
+        f.write("\n")

    # document benchmarking results in json
    with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
-
-        results = serving_results.to_dict(orient='records')
+        results = serving_results.to_dict(orient="records")
        f.write(json.dumps(results))
@@ -0,0 +1,46 @@
+# This local pyproject file is part of the migration from yapf to ruff format.
+# It uses the same core rules as the main pyproject.toml file, but with the
+# following differences:
+# - ruff line length is overridden to 88
+# - deprecated typing ignores (UP006, UP035) have been removed
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint.per-file-ignores]
+"vllm/third_party/**" = ["ALL"]
+"vllm/version.py" = ["F401"]
+"vllm/_version.py" = ["ALL"]
+
+[tool.ruff.lint]
+select = [
+    # pycodestyle
+    "E",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # flake8-logging-format
+    "G",
+]
+ignore = [
+    # star imports
+    "F405", "F403",
+    # lambda expression assignment
+    "E731",
+    # Loop control variable not used within loop body
+    "B007",
+    # f-string format
+    "UP032",
+    # Can remove once 3.10+ is the minimum Python version
+    "UP007",
+]
+
+[tool.ruff.format]
+docstring-code-format = true
@@ -14,7 +14,7 @@ steps:
    agents:
      queue: cpu_queue_postmerge
    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "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=12.6.3 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+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"
@@ -31,7 +31,7 @@ steps:
    agents:
      queue: cpu_queue_postmerge
    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "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=11.8.0 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
      - "bash .buildkite/scripts/upload-wheels.sh"
@@ -64,7 +64,7 @@ steps:
      - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
    plugins:
      - docker-login#v3.0.0:
-          username: vllm
+          username: vllmbot
          password-env: DOCKERHUB_TOKEN
    env:
      DOCKER_BUILDKIT: "1"
@@ -3,6 +3,9 @@
 # This script runs test inside the corresponding ROCm docker container.
 set -o pipefail

+# Export Python path
+export PYTHONPATH=".."
+
 # Print ROCm version
 echo "--- Confirming Clean Initial State"
 while true; do
@@ -74,6 +77,23 @@ HF_MOUNT="/root/.cache/huggingface"

 commands=$@
 echo "Commands:$commands"
+
+if [[ $commands == *"pytest -v -s basic_correctness/test_basic_correctness.py"* ]]; then
+  commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s basic_correctness/test_basic_correctness.py"}
+fi
+
+if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
+  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+fi
+
+if [[ $commands == *"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"* ]]; then
+  commands=${commands//"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"/"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2 and not BambaForCausalLM and not Gemma2ForCausalLM and not Grok1ModelForCausalLM and not Zamba2ForCausalLM and not Gemma2Model and not GritLM'"}
+fi
+
+if [[ $commands == *"pytest -v -s compile/test_basic_correctness.py"* ]]; then
+  commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s compile/test_basic_correctness.py"}
+fi
+
 #ignore certain kernels tests
 if [[ $commands == *" kernels/core"* ]]; then
  commands="${commands} \
@@ -161,6 +181,8 @@ fi


 PARALLEL_JOB_COUNT=8
+MYPYTHONPATH=".."
+
 # check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs. 
 if [[ $commands == *"--shard-id="* ]]; then
  # assign job count as the number of shards used   
@@ -181,6 +203,7 @@ if [[ $commands == *"--shard-id="* ]]; then
        -e AWS_SECRET_ACCESS_KEY \
        -v "${HF_CACHE}:${HF_MOUNT}" \
        -e "HF_HOME=${HF_MOUNT}" \
+        -e "PYTHONPATH=${MYPYTHONPATH}" \
        --name "${container_name}_${GPU}" \
        "${image_name}" \
        /bin/bash -c "${commands_gpu}" \
@@ -211,6 +234,7 @@ else
          -e AWS_SECRET_ACCESS_KEY \
          -v "${HF_CACHE}:${HF_MOUNT}" \
          -e "HF_HOME=${HF_MOUNT}" \
+          -e "PYTHONPATH=${MYPYTHONPATH}" \
          --name "${container_name}" \
          "${image_name}" \
          /bin/bash -c "${commands}"
@@ -32,9 +32,12 @@ function cpu_tests() {
    set -e
    pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
    pip install sentence-transformers datamodel_code_generator
-    pytest -v -s tests/models/embedding/language/test_cls_models.py::test_classification_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    pytest -v -s tests/models/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
-    pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
+    pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
+    pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
+    pytest -v -s tests/models/language/pooling/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]"
 }

 # All of CPU tests are expected to be finished less than 40 mins.
@@ -10,15 +10,17 @@ docker build -t hpu-test-env -f docker/Dockerfile.hpu .
 # Setup cleanup
 # certain versions of HPU software stack have a bug that can
 # override the exit code of the script, so we need to use
-# separate remove_docker_container and remove_docker_container_and_exit
+# separate remove_docker_containers and remove_docker_containers_and_exit
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-remove_docker_container() { docker rm -f hpu-test || true; }
-remove_docker_container_and_exit() { remove_docker_container; exit $EXITCODE; }
-trap remove_docker_container_and_exit EXIT
-remove_docker_container
+remove_docker_containers() { docker rm -f hpu-test || true; docker rm -f hpu-test-tp2 || true; }
+remove_docker_containers_and_exit() { remove_docker_containers; exit $EXITCODE; }
+trap remove_docker_containers_and_exit EXIT
+remove_docker_containers

 # Run the image and launch offline inference
 docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+docker run --runtime=habana --name=hpu-test-tp2 --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --tensor-parallel-size 2
+
 EXITCODE=$?
@@ -11,13 +11,14 @@ container_name="neuron_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
+HF_TOKEN=$(aws secretsmanager get-secret-value  --secret-id "ci/vllm-neuron/hf-token" --region us-west-2 --query 'SecretString' --output text | jq -r .VLLM_NEURON_CI_HF_TOKEN)

 NEURON_COMPILE_CACHE_URL="$(realpath ~)/neuron_compile_cache"
 mkdir -p "${NEURON_COMPILE_CACHE_URL}"
 NEURON_COMPILE_CACHE_MOUNT="/root/.cache/neuron_compile_cache"

 # Try building the docker image
-aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-west-2.amazonaws.com
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws

 # prune old image and containers to save disk space, and only once a day
 # by using a timestamp file in tmp.
@@ -47,8 +48,16 @@ trap remove_docker_container EXIT
 docker run --rm -it --device=/dev/neuron0 --network bridge \
       -v "${HF_CACHE}:${HF_MOUNT}" \
       -e "HF_HOME=${HF_MOUNT}" \
+       -e "HF_TOKEN=${HF_TOKEN}" \
       -v "${NEURON_COMPILE_CACHE_URL}:${NEURON_COMPILE_CACHE_MOUNT}" \
       -e "NEURON_COMPILE_CACHE_URL=${NEURON_COMPILE_CACHE_MOUNT}" \
       --name "${container_name}" \
       ${image_name} \
-       /bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys && python3 -m pytest /workspace/vllm/tests/neuron/2_core/ -v --capture=tee-sys"
+       /bin/bash -c "
+            python3 /workspace/vllm/examples/offline_inference/neuron.py;
+            python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
+            for f in /workspace/vllm/tests/neuron/2_core/*.py; do
+                echo 'Running test file: '$f;
+                python3 -m pytest \$f -v --capture=tee-sys;
+            done
+       "
@@ -2,102 +2,180 @@

 set -xu

+
+remove_docker_container() { 
+    docker rm -f tpu-test || true; 
+    docker rm -f vllm-tpu || true;
+}
+
+trap remove_docker_container EXIT
+
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
+
 # Build the docker image.
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .

 # Set up cleanup.
-remove_docker_container() { docker rm -f tpu-test || true; }
-trap remove_docker_container EXIT
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
+cleanup_docker() {
+  # Get Docker's root directory
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory."
+    exit 1
+  fi
+  echo "Docker root directory: $docker_root"
+  # Check disk usage of the filesystem where Docker's root directory is located
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  # Define the threshold
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    # Remove dangling images (those that are not tagged and not used by any container)
+    docker image prune -f
+    # Remove unused volumes / force the system prune for old images as well.
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
+}
+cleanup_docker

 # For HF_TOKEN.
 source /etc/environment
-# Run a simple end-to-end example.
+
 docker run --privileged --net host --shm-size=16G -it \
    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install pytest pytest-asyncio tpu-info \
-    && python3 -m pip install lm_eval[api]==0.4.4 \
-    && export VLLM_XLA_CACHE_PATH= \
-    && export VLLM_USE_V1=1 \
-    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
-    && echo HARDWARE \
-    && tpu-info \
-    && { \
-        echo TEST_0: Running test_perf.py; \
-        pytest -s -v /workspace/vllm/tests/tpu/test_perf.py; \
-        echo TEST_0_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_1: Running test_compilation.py; \
-        pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py; \
-        echo TEST_1_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_2: Running test_basic.py; \
-        pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py; \
-        echo TEST_2_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_3: Running test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
-        pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
-        echo TEST_3_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_4: Running test_quantization_accuracy.py; \
-        pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
-        echo TEST_4_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_5: Running examples/offline_inference/tpu.py; \
-        python3 /workspace/vllm/examples/offline_inference/tpu.py; \
-        echo TEST_5_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_6: Running test_tpu_model_runner.py; \
-        pytest -s -v /workspace/vllm/tests/tpu/worker/test_tpu_model_runner.py; \
-        echo TEST_6_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_7: Running test_sampler.py; \
-        pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py; \
-        echo TEST_7_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_8: Running test_topk_topp_sampler.py; \
-        pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py; \
-        echo TEST_8_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_9: Running test_multimodal.py; \
-        pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py; \
-        echo TEST_9_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_10: Running test_pallas.py; \
-        pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py; \
-        echo TEST_10_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_11: Running test_struct_output_generate.py; \
-        pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py; \
-        echo TEST_11_EXIT_CODE: \$?; \
-    } & \
-    && { \
-        echo TEST_12: Running test_moe_pallas.py; \
-        pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py; \
-        echo TEST_12_EXIT_CODE: \$?; \
-    } & \
-    # Disable the TPU LoRA tests until the feature is activated
-    # && { \
-    #     echo TEST_13: Running test_moe_pallas.py; \
-    #     pytest -s -v /workspace/vllm/tests/tpu/lora/; \
-    #     echo TEST_13_EXIT_CODE: \$?; \
-    # } & \
-    wait \
-    && echo 'All tests have attempted to run. Check logs for individual test statuses and exit codes.' \
-"
+    vllm-tpu /bin/bash -c '
+set -e # Exit immediately if a command exits with a non-zero status.
+set -u # Treat unset variables as an error.

+echo "--- Starting script inside Docker container ---"
+
+# Create results directory
+RESULTS_DIR=$(mktemp -d)
+# If mktemp fails, set -e will cause the script to exit.
+echo "Results will be stored in: $RESULTS_DIR"
+
+# Install dependencies
+echo "--- Installing Python dependencies ---"
+python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
+    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4
+echo "--- Python dependencies installed ---"
+export VLLM_USE_V1=1
+export VLLM_XLA_CHECK_RECOMPILATION=1
+export VLLM_XLA_CACHE_PATH=
+echo "Using VLLM V1"
+
+echo "--- Hardware Information ---"
+tpu-info
+echo "--- Starting Tests ---"
+set +e
+overall_script_exit_code=0
+
+# --- Test Definitions ---
+# If a test fails, this function will print logs and will not cause the main script to exit.
+run_test() {
+    local test_num=$1
+    local test_name=$2
+    local test_command=$3
+    local log_file="$RESULTS_DIR/test_${test_num}.log"
+    local actual_exit_code
+
+    echo "--- TEST_$test_num: Running $test_name ---"
+    
+    # Execute the test command.
+    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
+    actual_exit_code=$?
+
+    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
+    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
+
+    if [ "$actual_exit_code" -ne 0 ]; then
+        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
+        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
+        if [ -f "$log_file" ]; then
+            cat "$log_file" >&2
+        else
+            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
+        fi
+        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
+        return "$actual_exit_code" # Return the failure code
+    else
+        echo "TEST_$test_num ($test_name) PASSED."
+        return 0 # Return success
+    fi
+}
+
+# Helper function to call run_test and update the overall script exit code
+run_and_track_test() {
+    local test_num_arg="$1"
+    local test_name_arg="$2"
+    local test_command_arg="$3"
+
+    # Run the test
+    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
+    local test_specific_exit_code=$?
+
+    # If the test failed, set the overall script exit code to 1
+    if [ "$test_specific_exit_code" -ne 0 ]; then
+        # No need for extra echo here, run_test already logged the failure.
+        overall_script_exit_code=1
+    fi
+}
+
+# --- Actual Test Execution ---
+run_and_track_test 0 "test_perf.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_perf.py"
+run_and_track_test 1 "test_compilation.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/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" \
+    "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" \
+    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
+run_and_track_test 6 "test_tpu_model_runner.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
+run_and_track_test 7 "test_sampler.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py"
+run_and_track_test 8 "test_topk_topp_sampler.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py"
+run_and_track_test 9 "test_multimodal.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
+run_and_track_test 10 "test_pallas.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
+run_and_track_test 11 "test_struct_output_generate.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py"
+run_and_track_test 12 "test_moe_pallas.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
+run_and_track_test 13 "test_lora.py" \
+    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
+
+# After all tests have been attempted, exit with the overall status.
+if [ "$overall_script_exit_code" -ne 0 ]; then
+    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
+else
+    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
+fi
+exit "$overall_script_exit_code"
+' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
+
+# Capture the exit code of the docker run command
+DOCKER_RUN_EXIT_CODE=$?
+
+# The trap will run for cleanup.
+# Exit the main script with the Docker run command's exit code.
+if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
+    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
+    exit "$DOCKER_RUN_EXIT_CODE"
+else
+    echo "Docker run command completed successfully."
+    exit 0
+fi
 # TODO: This test fails because it uses RANDOM_SEED sampling
-# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
@@ -75,3 +75,4 @@ else
 fi

 aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
+aws s3 cp index.html "s3://vllm-wheels/$version/vllm/index.html"
@@ -32,16 +32,17 @@ steps:
 ##### fast check tests  #####

 - label: Documentation Build # 2min
-  working_dir: "/vllm-workspace/test_docs/docs"
+  mirror_hardwares: [amdexperimental]
+  working_dir: "/vllm-workspace/test_docs"
  fast_check: true
  no_gpu: True
  commands:
-  - pip install -r ../../requirements/docs.txt
-  - SPHINXOPTS=\"-W\" make html
-  # Check API reference (if it fails, you may have missing mock imports)
-  - grep \"sig sig-object py\" build/html/api/vllm/vllm.sampling_params.html
+  - pip install -r ../requirements/docs.txt
+  # TODO: add `--strict` once warnings in docstrings are fixed
+  - mkdocs build

 - label: Async Engine, Inputs, Utils, Worker Test # 24min
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/mq_llm_engine
@@ -57,11 +58,13 @@ steps:
  - 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 worker # Worker

 - label: Python-only Installation Test
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
  - setup.py
@@ -69,7 +72,7 @@ steps:
  - bash standalone_tests/python_only_compile.sh

 - label: Basic Correctness Test # 30min
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  fast_check: true
  torch_nightly: true
  source_file_dependencies:
@@ -86,6 +89,7 @@ steps:
  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py

 - label: Chunked Prefill Test
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/basic_correctness/test_chunked_prefill
@@ -94,7 +98,7 @@ steps:
  - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py

 - label: Core Test # 10min
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  fast_check: true
  source_file_dependencies:
  - vllm/core
@@ -104,10 +108,10 @@ steps:
  - pytest -v -s core

 - label: Entrypoints Test # 40min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  torch_nightly: true
-  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/llm
@@ -121,11 +125,12 @@ steps:
  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
  - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py  --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_openai_schema.py
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/
  - pytest -v -s entrypoints/test_chat_utils.py
  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests

 - label: Distributed Tests (4 GPUs) # 10min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@@ -133,6 +138,7 @@ steps:
  - vllm/core/
  - tests/distributed/test_utils
  - tests/distributed/test_pynccl
+  - tests/distributed/test_events
  - tests/spec_decode/e2e/test_integration_dist_tp4
  - tests/compile/test_basic_correctness
  - examples/offline_inference/rlhf.py
@@ -143,22 +149,25 @@ steps:
  # test with tp=2 and external_dp=2
  - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  # test with tp=2 and pp=2
+  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  # test with internal dp
  - python3 ../examples/offline_inference/data_parallel.py
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
  - pytest -v -s distributed/test_utils.py
  - pytest -v -s compile/test_basic_correctness.py
  - pytest -v -s distributed/test_pynccl.py
+  - pytest -v -s distributed/test_events.py
  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
  # TODO: create a dedicated test section for multi-GPU example tests
  # when we have multiple distributed example tests
  - pushd ../examples/offline_inference
-  - python3 rlhf.py
-  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
  - popd

 - label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  num_gpus: 2
  source_file_dependencies:
  - vllm/
@@ -172,7 +181,7 @@ steps:
 #####  1 GPU test  #####

 - label: Regression Test # 5min
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - vllm/
  - tests/test_regression
@@ -182,7 +191,7 @@ steps:
  working_dir: "/vllm-workspace/tests" # optional

 - label: Engine Test # 10min
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - vllm/
  - tests/engine
@@ -190,13 +199,14 @@ steps:
  - tests/test_sequence
  - tests/test_config
  - tests/test_logger
+  - tests/test_vllm_port
  commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
  # OOM in the CI unless we run this separately
  - pytest -v -s tokenization

 - label: V1 Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
    - vllm/
    - tests/v1
@@ -209,10 +219,11 @@ steps:
    - pytest -v -s v1/worker
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/spec_decode
+    - pytest -v -s v1/kv_connector/unit
    - pytest -v -s v1/test_serial_utils.py
-    - pytest -v -s v1/test_stats.py
    - pytest -v -s v1/test_utils.py
    - pytest -v -s v1/test_oracle.py
+    - pytest -v -s v1/test_metrics_reader.py
    # TODO: accuracy does not match, whether setting
    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
    - pytest -v -s v1/e2e
@@ -221,8 +232,8 @@ steps:
    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

 - label: Examples Test # 25min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/examples"
-  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/entrypoints
  - examples/
@@ -237,7 +248,7 @@ steps:
    - python3 offline_inference/vision_language.py --seed 0
    - python3 offline_inference/vision_language_embedding.py --seed 0
    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - VLLM_USE_V1=0 python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - VLLM_USE_V1=0 python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
    - python3 offline_inference/encoder_decoder.py
    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
    - python3 offline_inference/basic/classify.py
@@ -246,7 +257,7 @@ steps:
    - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2

 - label: Prefix Caching Test # 9min
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - vllm/
  - tests/prefix_caching
@@ -254,6 +265,7 @@ steps:
    - pytest -v -s prefix_caching

 - label: Samplers Test # 36min
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/sampling_metadata.py
@@ -263,18 +275,8 @@ steps:
    - pytest -v -s samplers
    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers

- label: LogitsProcessor Test # 5min
-  mirror_hardwares: [amd]
-  source_file_dependencies:
-  - vllm/model_executor/layers
-  - vllm/model_executor/guided_decoding
-  - tests/test_logits_processor
-  - tests/model_executor/test_guided_processors
-  commands:
-    - pytest -v -s test_logits_processor.py
-    - pytest -v -s model_executor/test_guided_processors.py
-
 - label: Speculative decoding tests # 40min
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/spec_decode
  - tests/spec_decode
@@ -285,7 +287,7 @@ steps:
    - pytest -v -s spec_decode/e2e/test_eagle_correctness.py

 - label: LoRA Test %N # 15min each
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/lora
  - tests/lora
@@ -293,6 +295,7 @@ steps:
  parallelism: 4

 - label: PyTorch Compilation Unit Tests
+  mirror_hardwares: [amdexperimental, amdproduction]
  torch_nightly: true
  source_file_dependencies:
    - vllm/
@@ -300,9 +303,12 @@ steps:
  commands:
    - pytest -v -s compile/test_pass_manager.py
    - pytest -v -s compile/test_fusion.py
+    - pytest -v -s compile/test_silu_mul_quant_fusion.py
    - pytest -v -s compile/test_sequence_parallelism.py
+    - pytest -v -s compile/test_async_tp.py

 - label: PyTorch Fullgraph Smoke Test # 9min
+  mirror_hardwares: [amdexperimental, amdproduction]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
@@ -314,6 +320,7 @@ steps:
  - pytest -v -s compile/piecewise/test_toy_llama.py

 - label: PyTorch Fullgraph Test # 18min
+  mirror_hardwares: [amdexperimental, amdproduction]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
@@ -322,7 +329,7 @@ steps:
  - pytest -v -s compile/test_full_graph.py

 - label: Kernels Core Operation Test
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - csrc/
  - tests/kernels/core
@@ -330,7 +337,7 @@ steps:
    - pytest -v -s kernels/core

 - label: Kernels Attention Test %N
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - csrc/attention/
  - vllm/attention
@@ -341,7 +348,7 @@ steps:
  parallelism: 2

 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - csrc/quantization/
  - vllm/model_executor/layers/quantization
@@ -351,7 +358,7 @@ steps:
  parallelism: 2

 - label: Kernels MoE Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/moe/
  - tests/kernels/moe
@@ -360,7 +367,7 @@ steps:
    - pytest -v -s kernels/moe

 - label: Kernels Mamba Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/mamba/
  - tests/kernels/mamba
@@ -368,25 +375,39 @@ steps:
    - pytest -v -s kernels/mamba

 - label: Tensorizer Test # 11min
-  # mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  soft_fail: true
  source_file_dependencies:
  - vllm/model_executor/model_loader
  - tests/tensorizer_loader
+  - tests/entrypoints/openai/test_tensorizer_entrypoint.py
  commands:
    - apt-get update && apt-get install -y curl libsodium23
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - pytest -v -s tensorizer_loader
+    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
+
+- label: Model Executor Test
+  mirror_hardwares: [amdexperimental, amdproduction]
+  soft_fail: true
+  source_file_dependencies:
+  - vllm/model_executor
+  - tests/model_executor
+  commands:
+    - apt-get update && apt-get install -y curl libsodium23
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -v -s model_executor

 - label: Benchmarks # 9min
+  mirror_hardwares: [amdexperimental, amdproduction]
  working_dir: "/vllm-workspace/.buildkite"
-  mirror_hardwares: [amd]
  source_file_dependencies:
  - benchmarks/
  commands:
  - bash scripts/run-benchmarks.sh

 - label: Benchmarks CLI Test # 10min
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - vllm/
  - tests/benchmarks/
@@ -394,6 +415,7 @@ steps:
  - pytest -v -s benchmarks/

 - label: Quantization Test
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
@@ -402,6 +424,7 @@ steps:
  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization

 - label: LM Eval Small Models # 53min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
@@ -411,6 +434,7 @@ steps:
  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small.txt --tp-size=1

 - label: OpenAI API correctness
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - vllm/entrypoints/openai/
@@ -419,6 +443,7 @@ steps:
  - pytest -s entrypoints/openai/correctness/

 - label: Encoder Decoder tests # 5min
+  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/encoder_decoder
@@ -426,8 +451,8 @@ steps:
    - pytest -v -s encoder_decoder

 - label: OpenAI-Compatible Tool Use # 20 min
+  mirror_hardwares: [amdexperimental]
  fast_check: false
-  #mirror_hardwares: [ amd ]
  source_file_dependencies:
    - vllm/
    - tests/tool_use
@@ -439,6 +464,7 @@ steps:
 #####  models test  #####

 - label: Basic Models Test # 24min
+  mirror_hardwares: [amdexperimental, amdproduction]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
@@ -448,43 +474,55 @@ steps:
    - pytest -v -s models/test_registry.py
    - pytest -v -s models/test_utils.py
    - pytest -v -s models/test_vision.py
-    # V1 Test: https://github.com/vllm-project/vllm/issues/14531
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'plamo2'
+    - pytest -v -s models/test_initialization.py

 - label: Language Models Test (Standard)
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
+  torch_nightly: true
  source_file_dependencies:
  - 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

- label: Language Models Test (Extended)
+- label: Language Models Test (Extended Generation) # 1hr20min
+  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
-  - tests/models/language
+  - 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'
-    - pytest -v -s models/language -m 'not core_model'
+    - pytest -v -s models/language/generation -m 'not core_model'
+
+- label: Language Models Test (Extended Pooling)  # 36min
+  mirror_hardwares: [amdexperimental]
+  optional: true
+  source_file_dependencies:
+  - vllm/
+  - tests/models/language/pooling
+  commands:
+    - pytest -v -s models/language/pooling -m 'not core_model'

 - label: Multi-Modal Models Test (Standard)
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
+  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pip freeze | grep -E 'torch'
    - pytest -v -s models/multimodal/processing
    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
    - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work

 - label: Multi-Modal Models Test (Extended) 1
+  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
@@ -494,6 +532,7 @@ steps:
    - pytest -v -s --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing models/multimodal -m 'not core_model'

 - label: Multi-Modal Models Test (Extended) 2
+  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
@@ -503,6 +542,7 @@ steps:
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'

 - label: Multi-Modal Models Test (Extended) 3
+  mirror_hardwares: [amdexperimental, amdproduction]
  optional: true
  source_file_dependencies:
  - vllm/
@@ -512,7 +552,7 @@ steps:
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'

 - label: Quantized Models Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  source_file_dependencies:
  - vllm/model_executor/layers/quantization
  - tests/models/quantization
@@ -521,7 +561,7 @@ steps:

 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  optional: true
  commands:
    - echo 'Testing custom models...'
@@ -533,7 +573,7 @@ steps:
 #####  multi gpus test  #####

 - label: Distributed Comm Ops Test # 7min
-  mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental, amdproduction]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
@@ -544,6 +584,7 @@ steps:
  - pytest -v -s distributed/test_shm_broadcast.py

 - label: 2 Node Tests (4 GPUs in total) # 16min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  num_nodes: 2
@@ -562,7 +603,7 @@ steps:
    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'

 - label: Distributed Tests (2 GPUs) # 40min
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
@@ -577,9 +618,11 @@ steps:
  - vllm/worker/model_runner.py
  - entrypoints/llm/test_collective_rpc.py
  - tests/v1/test_async_llm_dp.py
+  - tests/v1/entrypoints/openai/test_multi_api_servers.py
  - vllm/v1/engine/
  commands:
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
+  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
  - pytest -v -s entrypoints/llm/test_collective_rpc.py
  - pytest -v -s ./compile/test_basic_correctness.py
  - pytest -v -s ./compile/test_wrapper.py
@@ -599,13 +642,14 @@ steps:
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown

 - label: Plugin Tests (2 GPUs) # 40min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/plugins/
  - tests/plugins/
  commands:
-  # begin platform plugin tests, all the code in-between runs on dummy platform
+  # begin platform plugin and general plugin tests, all the code in-between runs on dummy platform
  - pip install -e ./plugins/vllm_add_dummy_platform
  - pytest -v -s plugins_tests/test_platform_plugins.py
  - pip uninstall vllm_add_dummy_platform -y
@@ -616,8 +660,10 @@ steps:
  - pytest -v -s distributed/test_distributed_oot.py
  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
  - 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:
@@ -638,6 +684,7 @@ steps:
  - pytest -v -s multi_step/test_correctness_llm.py

 - label: Pipeline Parallelism Test # 45min
+  mirror_hardwares: [amdexperimental, amdproduction]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@@ -651,6 +698,7 @@ steps:
  - pytest -v -s distributed/test_pipeline_parallel.py

 - label: LoRA TP Test (Distributed)
+  mirror_hardwares: [amdexperimental, amdproduction]
  num_gpus: 4
  source_file_dependencies:
  - vllm/lora
@@ -666,6 +714,7 @@ steps:


 - label: Weight Loading Multiple GPU Test  # 33min
+  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
@@ -675,6 +724,7 @@ steps:
    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt

 - label: Weight Loading Multiple GPU Test - Large Models # optional
+  mirror_hardwares: [amdexperimental] 
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  gpu: a100
@@ -13,6 +13,7 @@
 /vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
+/vllm/lora @jeejeelee
 CMakeLists.txt @tlrmchlsmth

 # vLLM V1
@@ -40,3 +41,8 @@ CMakeLists.txt @tlrmchlsmth
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb
 /tests/v1/structured_output @mgoin @russellb
 /tests/weight_loading @mgoin @youkaichao
+/tests/lora @jeejeelee
+
+# Docs
+/docs @hmellor
+mkdocs.yaml @hmellor
@@ -81,14 +81,14 @@ body:
    required: true
 - type: markdown
  attributes:
-    value: >
-      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the models' output:
+    value: |
+      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the model's output:

      - Try the counterpart of `transformers` first. If the error appears, please go to [their issues](https://github.com/huggingface/transformers/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc).

      - If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.

-      Thanks for contributing 🎉!
+      Thanks for reporting 🙏!
 - type: checkboxes
  id: askllm
  attributes:
@@ -0,0 +1,69 @@
+name: 🧪 CI failure report
+description: Report a failing test.
+title: "[CI Failure]: "
+labels: ["ci-failure"]
+
+body:
+- type: markdown
+  attributes:
+    value: >
+      #### Include the name of the failing Buildkite step and test file in the title.
+- type: input
+  attributes:
+    label: Name of failing test
+    description: |
+      Paste in the fully-qualified name of the failing test from the logs.
+    placeholder: |
+      `path/to/test_file.py::test_name[params]`
+  validations:
+    required: true
+- type: checkboxes
+  attributes:
+    label: Basic information
+    description: Select all items that apply to the failing test.
+    options:
+      - label: Flaky test
+      - label: Can reproduce locally
+      - label: Caused by external libraries (e.g. bug in `transformers`)
+- type: textarea
+  attributes:
+    label: 🧪 Describe the failing test
+    description: |
+      Please provide a clear and concise description of the failing test.
+    placeholder: |
+      A clear and concise description of the failing test.
+  
+      ```
+      The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
+      ```
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 📝 History of failing test
+    description: |
+      Since when did the test start to fail?
+      You can look up its history via [Buildkite Test Suites](https://buildkite.com/organizations/vllm/analytics/suites/ci-1/tests?branch=main).
+
+      If you have time, identify the PR that caused the test to fail on main. You can do so via the following methods:
+
+      - Use Buildkite Test Suites to find the PR where the test failure first occurred, and reproduce the failure locally.
+
+      - Run [`git bisect`](https://git-scm.com/docs/git-bisect) locally.
+
+      - Manually unblock Buildkite steps for suspected PRs on main and check the results. (authorized users only)
+    placeholder: |
+      Approximate timeline and/or problematic PRs
+
+      A link to the Buildkite analytics of the failing test (if available)
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: CC List.
+    description: >
+      The list of people you want to CC. Usually, this includes those who worked on the PR that failed the test.
+- type: markdown
+  attributes:
+    value: >
+      Thanks for reporting 🙏!
@@ -3,4 +3,4 @@ FILL IN THE PR DESCRIPTION HERE
 FIX #xxxx (*link existing issues this PR will resolve*)

 <!--- pyml disable-next-line no-emphasis-as-heading -->
-**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)
@@ -58,7 +58,7 @@ pull_request_rules:
      - files~=^benchmarks/structured_schemas/
      - files=benchmarks/benchmark_serving_structured_output.py
      - files=benchmarks/run_structured_output_benchmark.sh
-      - files=docs/source/features/structured_outputs.md
+      - files=docs/features/structured_outputs.md
      - files=examples/offline_inference/structured_outputs.py
      - files=examples/online_serving/openai_chat_completion_structured_outputs.py
      - files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
@@ -135,9 +135,7 @@ pull_request_rules:
      - files~=^tests/entrypoints/openai/tool_parsers/
      - files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py
      - files~=^vllm/entrypoints/openai/tool_parsers/
-      - files=docs/source/features/tool_calling.md
-      - files=docs/source/getting_started/examples/openai_chat_completion_client_with_tools.md
-      - files=docs/source/getting_started/examples/chat_with_tools.md
+      - files=docs/features/tool_calling.md
      - files~=^examples/tool_chat_*
      - files=examples/offline_inference/chat_with_tools.py
      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
@@ -163,6 +161,17 @@ pull_request_rules:

       https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

+- name: assign reviewer for tensorizer changes
+  conditions:
+      - files~=^vllm/model_executor/model_loader/tensorizer.py
+      - files~=^vllm/model_executor/model_loader/tensorizer_loader.py
+      - files~=^tests/entrypoints/openai/test_tensorizer_entrypoint.py
+      - files~=^tests/tensorizer_loader/
+  actions:
+    assign:
+      users:
+        - "sangstar"
+
 - name: remove 'needs-rebase' label when conflict is resolved
  conditions:
      - -conflict
@@ -26,7 +26,7 @@ sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"

 # Remove HTML <details> section that includes <summary> text of "PR Checklist (Click to Expand)"
 python3 - <<EOF
-import re
+import regex as re

 with open("${NEW}", "r") as file:
    content = file.read()
@@ -1,4 +1,6 @@
 name: Add label on auto-merge enabled
+permissions:
+    pull-requests: write
 on:
    pull_request_target:
        types:
@@ -20,7 +20,12 @@ jobs:
        with:
          python-version: '3.12'

+      - name: Install Python dependencies
+        run: |
+          python3 -m pip install --upgrade pip
+          python3 -m pip install regex
+
      - name: Update PR description
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
+        run: bash .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
@@ -2,6 +2,9 @@ name: Lint and Deploy Charts

 on: pull_request

+permissions:
+  contents: read
+
 jobs:
  lint-and-deploy:
    runs-on: ubuntu-latest
@@ -5,6 +5,9 @@ on:
  push:
    branches: [main]

+permissions:
+  contents: read
+
 jobs:
  pre-commit:
    runs-on: ubuntu-latest
@@ -1,4 +1,6 @@
 name: PR Reminder Comment Bot
+permissions:
+  pull-requests: write
 on:
  pull_request_target:
    types: [opened]
@@ -77,11 +77,6 @@ instance/
 # Scrapy stuff:
 .scrapy

-# Sphinx documentation
-docs/_build/
-docs/source/getting_started/examples/
-docs/source/api/vllm
-
 # PyBuilder
 .pybuilder/
 target/
@@ -151,6 +146,7 @@ venv.bak/

 # mkdocs documentation
 /site
+docs/examples

 # mypy
 .mypy_cache/
@@ -16,6 +16,8 @@ repos:
  hooks:
  - id: ruff
    args: [--output-format, github, --fix]
+  - id: ruff-format
+    files: ^(.buildkite|benchmarks|examples)/.*
 - repo: https://github.com/codespell-project/codespell
  rev: v2.4.1
  hooks:
@@ -37,6 +39,7 @@ repos:
  rev: v0.9.29
  hooks:
  - id: pymarkdown
+    exclude: '.*\.inc\.md'
    args: [fix]
 - repo: https://github.com/rhysd/actionlint
  rev: v1.7.7
@@ -55,7 +58,7 @@ repos:
    entry: tools/mypy.sh 0 "local"
    language: python
    types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
+    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
    stages: [pre-commit] # Don't run in CI
  - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
    name: Run mypy for Python 3.9
@@ -125,6 +128,21 @@ repos:
    name: Update Dockerfile dependency graph
    entry: tools/update-dockerfile-graph.sh
    language: script
+  - id: enforce-import-regex-instead-of-re
+    name: Enforce import regex as re
+    entry: python tools/enforce_regex_import.py
+    language: python
+    types: [python]
+    pass_filenames: false
+    additional_dependencies: [regex]
+  # forbid directly import triton
+  - id: forbid-direct-triton-import
+    name: "Forbid direct 'import triton'"
+    entry: python tools/check_triton_import.py
+    language: python
+    types: [python]
+    pass_filenames: false
+    additional_dependencies: [regex]
  # Keep `suggestion` last
  - id: suggestion
    name: Suggestion
@@ -8,12 +8,8 @@ build:
  tools:
    python: "3.12"

-sphinx:
-  configuration: docs/source/conf.py
-  fail_on_warning: true
-
-# If using Sphinx, optionally build your docs in additional formats such as PDF
-formats: []
+mkdocs:
+  configuration: mkdocs.yaml

 # Optionally declare the Python requirements required to build your docs
 python:
@@ -23,15 +23,15 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")

+# Prevent installation of dependencies (cutlass) by default.
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
+
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
 set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")

-# Supported NVIDIA architectures.
-set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
-
 # Supported AMD GPU architectures.
 set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")

@@ -79,6 +79,15 @@ endif()
 #
 find_package(Torch REQUIRED)

+# Supported NVIDIA architectures.
+# This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
+if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
+   CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
+else()
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
+endif()
+
 #
 # Forward the non-CUDA device extensions to external CMake scripts.
 #
@@ -226,10 +235,13 @@ endif()
 #

 set(VLLM_EXT_SRC
+  "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
+  "csrc/mamba/causal_conv1d/causal_conv1d.cu"
  "csrc/cache_kernels.cu"
  "csrc/attention/paged_attention_v1.cu"
  "csrc/attention/paged_attention_v2.cu"
  "csrc/attention/merge_attn_states.cu"
+  "csrc/attention/vertical_slash_index.cu"
  "csrc/pos_encoding_kernels.cu"
  "csrc/activation_kernels.cu"
  "csrc/layernorm_kernels.cu"
@@ -280,14 +292,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  FetchContent_MakeAvailable(cutlass)

  list(APPEND VLLM_EXT_SRC
-    "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
-    "csrc/mamba/causal_conv1d/causal_conv1d.cu"
    "csrc/quantization/aqlm/gemm_kernels.cu"
    "csrc/quantization/awq/gemm_kernels.cu"
    "csrc/permute_cols.cu"
    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
    "csrc/quantization/fp4/nvfp4_quant_entry.cu"
    "csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
+    "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
    "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
    "csrc/cutlass_extensions/common.cpp"
    "csrc/attention/mla/cutlass_mla_entry.cu")
@@ -299,7 +310,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  # Only build Marlin kernels if we are building for at least some compatible archs.
  # Keep building Marlin for 9.0 as there are some group sizes and shapes that
  # are not supported by Machete yet.
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
+  # 9.0 for latest bf16 atomicAdd PTX
+  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
  if (MARLIN_ARCHS)

    #
@@ -443,8 +455,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  #
  # For the cutlass_scaled_mm kernels we want to build the c2x (CUTLASS 2.x)
  # kernels for the remaining archs that are not already built for 3x.
+  # (Build 8.9 for FP8)
  cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
-    "7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
+    "7.5;8.0;8.9+PTX" "${CUDA_ARCHS}")
  # subtract out the archs that are already built for 3x
  list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
  if (SCALED_MM_2X_ARCHS)
@@ -495,7 +508,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
    set(SRCS
      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu")
+      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
+      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
+      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
      CUDA_ARCHS "${FP4_ARCHS}")
@@ -533,7 +548,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
  # to compile MoE kernels that use its output.
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
             "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
@@ -671,7 +686,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    CUDA_ARCHS "${CUDA_ARCHS}")

  list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
+  # 9.0 for latest bf16 atomicAdd PTX
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
  if (MARLIN_MOE_ARCHS)

    #
@@ -772,5 +788,7 @@ endif()
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
    include(cmake/external_projects/flashmla.cmake)
+
+    # vllm-flash-attn should be last as it overwrites some CMake functions
    include(cmake/external_projects/vllm_flash_attn.cmake)
 endif ()
@@ -1,3 +1,3 @@
 # Contributing to vLLM

-You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing/overview.html).
+You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing).
@@ -1,7 +1,7 @@
 <p align="center">
  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png">
-    <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-dark.png">
+    <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-light.png" width=55%>
  </picture>
 </p>

@@ -58,7 +58,7 @@ vLLM is fast with:
 - Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.
+- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [AutoRound](https://arxiv.org/abs/2309.05516),INT4, INT8, and FP8.
 - Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
 - Speculative decoding
 - Chunked prefill
@@ -74,7 +74,7 @@ vLLM is flexible and easy to use with:
 - OpenAI-compatible API server
 - Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
 - Prefix caching support
- Multi-lora support
+- Multi-LoRA support

 vLLM seamlessly supports most popular open-source models on HuggingFace, including:
 - Transformer-like LLMs (e.g., Llama)
@@ -100,14 +100,14 @@ Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
 ## Contributing

 We welcome and value any contributions and collaborations.
-Please check out [Contributing to vLLM](https://docs.vllm.ai/en/stable/contributing/overview.html) for how to get involved.
+Please check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.

 ## Sponsors

 vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!

 <!-- Note: Please sort them in alphabetical order. -->
-<!-- Note: Please keep these consistent with docs/source/community/sponsors.md -->
+<!-- Note: Please keep these consistent with docs/community/sponsors.md -->
 Cash Donations:
 - a16z
 - Dropbox
@@ -8,4 +8,6 @@ Please report security issues privately using [the vulnerability submission form

 ---

+Please see the [Security Guide in the vLLM documentation](https://docs.vllm.ai/en/latest/usage/security.html) for more information on vLLM's security assumptions and recommendations.
+
 Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.
@@ -64,6 +64,12 @@ become available.
      <td style="text-align: center;">✅</td>
      <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
    </tr>
+    <tr>
+      <td><strong>Custom</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>Local file: <code>data.jsonl</code></td>
+    </tr>
  </tbody>
 </table>

@@ -124,6 +130,38 @@ P99 ITL (ms):                            8.39
 ==================================================
 ```

+### Custom Dataset
+If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
+
+```
+{"prompt": "What is the capital of India?"}
+{"prompt": "What is the capital of Iran?"}
+{"prompt": "What is the capital of China?"}
+``` 
+
+```bash
+# start server
+VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
+```
+
+```bash
+# run benchmarking script
+python3 benchmarks/benchmark_serving.py --port 9001 --save-result --save-detailed \
+  --backend vllm \
+  --model meta-llama/Llama-3.1-8B-Instruct \
+  --endpoint /v1/completions \
+  --dataset-name custom \
+  --dataset-path <path-to-your-data-jsonl> \
+  --custom-skip-chat-template \
+  --num-prompts 80 \
+  --max-concurrency 1 \
+  --temperature=0.3 \
+  --top-p=0.75 \
+  --result-dir "./log/"
+```
+
+You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`.
+
 ### VisionArena Benchmark for Vision Language Models

 ```bash
@@ -146,10 +184,9 @@ python3 vllm/benchmarks/benchmark_serving.py \

 ``` bash
 VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
-    --speculative-model "[ngram]" \
-    --ngram_prompt_lookup_min 2 \
-    --ngram-prompt-lookup-max 5 \
-    --num_speculative_tokens 5
+    --speculative-config $'{"method": "ngram",
+    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
+    "prompt_lookup_min": 2}'
 ```

 ``` bash
@@ -204,6 +241,16 @@ python3 vllm/benchmarks/benchmark_serving.py \
    --seed 42
 ```

+**`philschmid/mt-bench`**
+
+``` bash
+python3 vllm/benchmarks/benchmark_serving.py \
+    --model Qwen/QwQ-32B \
+    --dataset-name hf \
+    --dataset-path philschmid/mt-bench \
+    --num-prompts 80
+```
+
 ### Running With Sampling Parameters

 When using OpenAI-compatible backends such as `vllm`, optional sampling
@@ -274,10 +321,9 @@ python3 vllm/benchmarks/benchmark_throughput.py \
    --output-len=100 \
    --num-prompts=2048 \
    --async-engine \
-    --speculative-model="[ngram]" \
-    --ngram_prompt_lookup_min=2 \
-    --ngram-prompt-lookup-max=5 \
-    --num_speculative_tokens=5
+    --speculative-config $'{"method": "ngram",
+    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
+    "prompt_lookup_min": 2}'
 ```

 ```
@@ -12,8 +12,7 @@ from typing import Optional, Union
 import aiohttp
 import huggingface_hub.constants
 from tqdm.asyncio import tqdm
-from transformers import (AutoTokenizer, PreTrainedTokenizer,
-                          PreTrainedTokenizerFast)
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast

 # NOTE(simon): do not import vLLM here so the benchmark script
 # can run without vLLM installed.
@@ -43,8 +42,7 @@ class RequestFuncOutput:
    latency: float = 0.0
    output_tokens: int = 0
    ttft: float = 0.0  # Time to first token
-    itl: list[float] = field(
-        default_factory=list)  # list of inter-token latencies
+    itl: list[float] = field(default_factory=list)  # list of inter-token latencies
    tpot: float = 0.0  # avg next-token latencies
    prompt_len: int = 0
    error: str = ""
@@ -57,8 +55,9 @@ async def async_request_tgi(
    api_url = request_func_input.api_url
    assert api_url.endswith("generate_stream")

-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) as session:
        params = {
            "max_new_tokens": request_func_input.output_len,
            "do_sample": True,
@@ -105,8 +104,7 @@ async def async_request_tgi(

                        # Decoding phase
                        else:
-                            output.itl.append(timestamp -
-                                              most_recent_timestamp)
+                            output.itl.append(timestamp - most_recent_timestamp)

                        most_recent_timestamp = timestamp

@@ -133,8 +131,9 @@ async def async_request_trt_llm(
    api_url = request_func_input.api_url
    assert api_url.endswith("generate_stream")

-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) as session:
        payload = {
            "accumulate_tokens": True,
            "text_input": request_func_input.prompt,
@@ -159,8 +158,7 @@ async def async_request_trt_llm(
                        if not chunk_bytes:
                            continue

-                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                            "data:")
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data:")

                        data = json.loads(chunk)
                        output.generated_text += data["text_output"]
@@ -172,8 +170,7 @@ async def async_request_trt_llm(

                        # Decoding phase
                        else:
-                            output.itl.append(timestamp -
-                                              most_recent_timestamp)
+                            output.itl.append(timestamp - most_recent_timestamp)

                        most_recent_timestamp = timestamp

@@ -197,9 +194,14 @@ async def async_request_deepspeed_mii(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    api_url = request_func_input.api_url
+    assert api_url.endswith(("completions", "profile")), (
+        "OpenAI Completions API URL must end with 'completions' or 'profile'."
+    )

+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) as session:
        payload = {
            "model": request_func_input.model,
            "prompt": request_func_input.prompt,
@@ -207,6 +209,8 @@ async def async_request_deepspeed_mii(
            "temperature": 0.01,  # deepspeed-mii does not accept 0.0 temp.
            "top_p": 1.0,
        }
+        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
+
        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

@@ -217,19 +221,21 @@ async def async_request_deepspeed_mii(

        st = time.perf_counter()
        try:
-            async with session.post(url=request_func_input.api_url,
-                                    json=payload) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                if response.status == 200:
                    parsed_resp = await response.json()
                    output.latency = time.perf_counter() - st
                    if "choices" in parsed_resp:
-                        output.generated_text = parsed_resp["choices"][0][
-                            "text"]
+                        output.generated_text = parsed_resp["choices"][0]["text"]
                    elif "text" in parsed_resp:
                        output.generated_text = parsed_resp["text"][0]
                    else:
-                        output.error = ("Unexpected response format: "
-                                        "neither 'choices' nor 'text' found")
+                        output.error = (
+                            "Unexpected response format: "
+                            "neither 'choices' nor 'text' found"
+                        )
                        output.success = False
                    output.success = True
                else:
@@ -250,15 +256,17 @@ async def async_request_openai_completions(
    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
    api_url = request_func_input.api_url
-    assert api_url.endswith(
-        ("completions", "profile")
-    ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
+    assert api_url.endswith(("completions", "profile")), (
+        "OpenAI Completions API URL must end with 'completions' or 'profile'."
+    )

-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) as session:
        payload = {
-            "model": request_func_input.model_name \
-                if request_func_input.model_name else request_func_input.model,
+            "model": request_func_input.model_name
+            if request_func_input.model_name
+            else request_func_input.model,
            "prompt": request_func_input.prompt,
            "temperature": 0.0,
            "repetition_penalty": 1.0,
@@ -273,9 +281,7 @@ async def async_request_openai_completions(
            payload["ignore_eos"] = request_func_input.ignore_eos
        if request_func_input.extra_body:
            payload.update(request_func_input.extra_body)
-        headers = {
-            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
-        }
+        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
@@ -284,8 +290,9 @@ async def async_request_openai_completions(
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
-            async with session.post(url=api_url, json=payload,
-                                    headers=headers) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                if response.status == 200:
                    first_chunk_received = False
                    async for chunk_bytes in response.content:
@@ -293,8 +300,7 @@ async def async_request_openai_completions(
                        if not chunk_bytes:
                            continue

-                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                            "data: ")
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
                        if chunk != "[DONE]":
                            data = json.loads(chunk)

@@ -314,21 +320,20 @@ async def async_request_openai_completions(

                                # Decoding phase
                                else:
-                                    output.itl.append(timestamp -
-                                                      most_recent_timestamp)
+                                    output.itl.append(timestamp - most_recent_timestamp)

                                most_recent_timestamp = timestamp
                                generated_text += text or ""
-                            elif usage := data.get("usage"):
-                                output.output_tokens = usage.get(
-                                    "completion_tokens")
+                            if usage := data.get("usage"):
+                                output.output_tokens = usage.get("completion_tokens")
                    if first_chunk_received:
                        output.success = True
                    else:
                        output.success = False
                        output.error = (
                            "Never received a valid chunk to calculate TTFT."
-                            "This response will be marked as failed!")
+                            "This response will be marked as failed!"
+                        )
                    output.generated_text = generated_text
                    output.latency = most_recent_timestamp - st
                else:
@@ -349,23 +354,22 @@ async def async_request_openai_chat_completions(
    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
    api_url = request_func_input.api_url
-    assert api_url.endswith(
-        ("chat/completions", "profile")
-    ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
+    assert api_url.endswith(("chat/completions", "profile")), (
+        "OpenAI Chat Completions API URL must end with 'chat/completions'."
+    )

-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) 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)
        payload = {
-            "model": request_func_input.model_name \
-                if request_func_input.model_name else request_func_input.model,
+            "model": request_func_input.model_name
+            if request_func_input.model_name
+            else request_func_input.model,
            "messages": [
-                {
-                    "role": "user",
-                    "content": content
-                },
+                {"role": "user", "content": content},
            ],
            "temperature": 0.0,
            "max_completion_tokens": request_func_input.output_len,
@@ -391,16 +395,16 @@ async def async_request_openai_chat_completions(
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
-            async with session.post(url=api_url, json=payload,
-                                    headers=headers) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

-                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                            "data: ")
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
                        if chunk != "[DONE]":
                            timestamp = time.perf_counter()
                            data = json.loads(chunk)
@@ -414,13 +418,11 @@ async def async_request_openai_chat_completions(

                                # Decoding phase
                                else:
-                                    output.itl.append(timestamp -
-                                                      most_recent_timestamp)
+                                    output.itl.append(timestamp - most_recent_timestamp)

                                generated_text += content or ""
                            elif usage := data.get("usage"):
-                                output.output_tokens = usage.get(
-                                    "completion_tokens")
+                                output.output_tokens = usage.get("completion_tokens")

                            most_recent_timestamp = timestamp

@@ -446,25 +448,28 @@ async def async_request_openai_audio(
 ) -> RequestFuncOutput:
    # Lazy import without PlaceholderModule to avoid vllm dep.
    import soundfile
+
    api_url = request_func_input.api_url
-    assert api_url.endswith(
-        ("transcriptions", "translations"
-         )), "OpenAI Chat Completions API URL must end with 'transcriptions' "
+    assert api_url.endswith(("transcriptions", "translations")), (
+        "OpenAI Chat Completions API URL must end with 'transcriptions' "
+    )
    "or `translations`."

-    async with aiohttp.ClientSession(trust_env=True,
-                                     timeout=AIOHTTP_TIMEOUT) as session:
+    async with aiohttp.ClientSession(
+        trust_env=True, timeout=AIOHTTP_TIMEOUT
+    ) as session:
        content = [{"type": "text", "text": request_func_input.prompt}]
        payload = {
-            "model": request_func_input.model_name \
-                if request_func_input.model_name else request_func_input.model,
+            "model": request_func_input.model_name
+            if request_func_input.model_name
+            else request_func_input.model,
            "temperature": 0.0,
            "max_completion_tokens": request_func_input.output_len,
            "stream": True,
            "language": "en",
            # Flattened due to multipart/form-data
            "stream_include_usage": True,
-            "stream_continuous_usage_stats": True
+            "stream_continuous_usage_stats": True,
        }
        if request_func_input.extra_body:
            payload.update(request_func_input.extra_body)
@@ -479,9 +484,9 @@ async def async_request_openai_audio(
            buffer.seek(0)
            return buffer

-        with to_bytes(*request_func_input.multi_modal_content['audio']) as f:
+        with to_bytes(*request_func_input.multi_modal_content["audio"]) as f:
            form = aiohttp.FormData()
-            form.add_field('file', f, content_type='audio/wav')
+            form.add_field("file", f, content_type="audio/wav")
            for key, value in payload.items():
                form.add_field(key, str(value))

@@ -493,24 +498,22 @@ async def async_request_openai_audio(
            st = time.perf_counter()
            most_recent_timestamp = st
            try:
-                async with session.post(url=api_url,
-                                        data=form,
-                                        headers=headers) as response:
+                async with session.post(
+                    url=api_url, data=form, headers=headers
+                ) as response:
                    if response.status == 200:
                        async for chunk_bytes in response.content:
                            chunk_bytes = chunk_bytes.strip()
                            if not chunk_bytes:
                                continue

-                            chunk = chunk_bytes.decode("utf-8").removeprefix(
-                                "data: ")
+                            chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
                            if chunk != "[DONE]":
                                timestamp = time.perf_counter()
                                data = json.loads(chunk)

                                if choices := data.get("choices"):
-                                    content = choices[0]["delta"].get(
-                                        "content")
+                                    content = choices[0]["delta"].get("content")
                                    # First token
                                    if ttft == 0.0:
                                        ttft = timestamp - st
@@ -519,12 +522,14 @@ async def async_request_openai_audio(
                                    # Decoding phase
                                    else:
                                        output.itl.append(
-                                            timestamp - most_recent_timestamp)
+                                            timestamp - most_recent_timestamp
+                                        )

                                    generated_text += content or ""
                                elif usage := data.get("usage"):
                                    output.output_tokens = usage.get(
-                                        "completion_tokens")
+                                        "completion_tokens"
+                                    )

                                most_recent_timestamp = timestamp

@@ -545,7 +550,7 @@ async def async_request_openai_audio(


 def get_model(pretrained_model_name_or_path: str) -> str:
-    if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
+    if os.getenv("VLLM_USE_MODELSCOPE", "False").lower() == "true":
        from modelscope import snapshot_download

        from vllm.model_executor.model_loader.weight_utils import get_lock
@@ -556,7 +561,8 @@ def get_model(pretrained_model_name_or_path: str) -> str:
            model_path = snapshot_download(
                model_id=pretrained_model_name_or_path,
                local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"],
+            )

            return model_path
    return pretrained_model_name_or_path
@@ -569,23 +575,23 @@ def get_tokenizer(
    **kwargs,
 ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
    if pretrained_model_name_or_path is not None and not os.path.exists(
-            pretrained_model_name_or_path):
-        pretrained_model_name_or_path = get_model(
-            pretrained_model_name_or_path)
+        pretrained_model_name_or_path
+    ):
+        pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
    if tokenizer_mode == "slow":
        if kwargs.get("use_fast", False):
-            raise ValueError(
-                "Cannot use the fast tokenizer in slow tokenizer mode.")
+            raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
        kwargs["use_fast"] = False
    if tokenizer_mode == "mistral":
        try:
            from vllm.transformers_utils.tokenizer import MistralTokenizer
        except ImportError as e:
-            raise ImportError("MistralTokenizer requires vllm package.\n"
-                              "Please install it with `pip install vllm` "
-                              "to use mistral tokenizer mode.") from e
-        return MistralTokenizer.from_pretrained(
-            str(pretrained_model_name_or_path))
+            raise ImportError(
+                "MistralTokenizer requires vllm package.\n"
+                "Please install it with `pip install vllm` "
+                "to use mistral tokenizer mode."
+            ) from e
+        return MistralTokenizer.from_pretrained(str(pretrained_model_name_or_path))
    else:
        return AutoTokenizer.from_pretrained(
            pretrained_model_name_or_path,
@@ -605,10 +611,11 @@ ASYNC_REQUEST_FUNCS = {
    "tensorrt-llm": async_request_trt_llm,
    "scalellm": async_request_openai_completions,
    "sglang": async_request_openai_completions,
+    "llama.cpp": async_request_openai_completions,
 }

 OPENAI_COMPATIBLE_BACKENDS = [
-    k for k, v in ASYNC_REQUEST_FUNCS.items()
-    if v in (async_request_openai_completions,
-             async_request_openai_chat_completions)
+    k
+    for k, v in ASYNC_REQUEST_FUNCS.items()
+    if v in (async_request_openai_completions, async_request_openai_chat_completions)
 ]
@@ -9,9 +9,6 @@ generation. Supported dataset types include:
  - BurstGPT
  - HuggingFace
  - VisionArena
-
-TODO: Implement CustomDataset to parse a JSON file and convert its contents into
-SampleRequest instances, similar to the approach used in ShareGPT.
 """

 import base64
@@ -35,6 +32,7 @@ from transformers import PreTrainedTokenizerBase
 from vllm.lora.request import LoRARequest
 from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.image import convert_image_mode
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer

 logger = logging.getLogger(__name__)
@@ -82,14 +80,12 @@ class BenchmarkDataset(ABC):
        self.dataset_path = dataset_path
        # Set the random seed, ensuring that a None value is replaced with the
        # default seed.
-        self.random_seed = (random_seed
-                            if random_seed is not None else self.DEFAULT_SEED)
+        self.random_seed = random_seed if random_seed is not None else self.DEFAULT_SEED
        self.data = None

    def apply_multimodal_chat_transformation(
-            self,
-            prompt: str,
-            mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
+        self, prompt: str, mm_content: Optional[MultiModalDataDict] = None
+    ) -> list[dict]:
        """
        Transform a prompt and optional multimodal content into a chat format.
        This method is used for chat models that expect a specific conversation
@@ -111,8 +107,7 @@ class BenchmarkDataset(ABC):
            NotImplementedError: If a subclass does not implement this method.
        """
        # TODO (jenniferzhao): add support for downloading data
-        raise NotImplementedError(
-            "load_data must be implemented in subclasses.")
+        raise NotImplementedError("load_data must be implemented in subclasses.")

    def get_random_lora_request(
        self,
@@ -158,8 +153,9 @@ class BenchmarkDataset(ABC):
        return lora_request, lora_tokenizer_cache[lora_id] or tokenizer

    @abstractmethod
-    def sample(self, tokenizer: PreTrainedTokenizerBase,
-               num_requests: int) -> list[SampleRequest]:
+    def sample(
+        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
+    ) -> list[SampleRequest]:
        """
        Abstract method to generate sample requests from the dataset.

@@ -177,8 +173,9 @@ class BenchmarkDataset(ABC):
        """
        raise NotImplementedError("sample must be implemented in subclasses.")

-    def maybe_oversample_requests(self, requests: list[SampleRequest],
-                                  num_requests: int) -> None:
+    def maybe_oversample_requests(
+        self, requests: list[SampleRequest], num_requests: int
+    ) -> None:
        """
        Oversamples the list of requests if its size is less than the desired
        number.
@@ -189,11 +186,9 @@ class BenchmarkDataset(ABC):
        """
        if len(requests) < num_requests:
            random.seed(self.random_seed)
-            additional = random.choices(requests,
-                                        k=num_requests - len(requests))
+            additional = random.choices(requests, k=num_requests - len(requests))
            requests.extend(additional)
-            logger.info("Oversampled requests to reach %d total samples.",
-                        num_requests)
+            logger.info("Oversampled requests to reach %d total samples.", num_requests)


 # -----------------------------------------------------------------------------
@@ -218,14 +213,14 @@ def is_valid_sequence(
    """
    # Check for invalid conditions
    prompt_too_short = prompt_len < min_len
-    output_too_short = (not skip_min_output_len_check) and (output_len
-                                                            < min_len)
+    output_too_short = (not skip_min_output_len_check) and (output_len < min_len)
    prompt_too_long = prompt_len > max_prompt_len
    combined_too_long = (prompt_len + output_len) > max_total_len

    # Return True if none of the invalid conditions are met
-    return not (prompt_too_short or output_too_short or prompt_too_long
-                or combined_too_long)
+    return not (
+        prompt_too_short or output_too_short or prompt_too_long or combined_too_long
+    )


@cache
@@ -257,28 +252,28 @@ def process_image(image: Any) -> Mapping[str, Any]:
    Raises:
        ValueError: If the input is not a supported type.
    """
-    if isinstance(image, dict) and 'bytes' in image:
-        image = Image.open(BytesIO(image['bytes']))
+    if isinstance(image, dict) and "bytes" in image:
+        image = Image.open(BytesIO(image["bytes"]))
    if isinstance(image, Image.Image):
-        image = image.convert("RGB")
+        image = convert_image_mode(image, "RGB")
        with io.BytesIO() as image_data:
            image.save(image_data, format="JPEG")
-            image_base64 = base64.b64encode(
-                image_data.getvalue()).decode("utf-8")
+            image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
        return {
            "type": "image_url",
-            "image_url": {
-                "url": f"data:image/jpeg;base64,{image_base64}"
-            },
+            "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
        }

    if isinstance(image, str):
-        image_url = (image if image.startswith(
-            ("http://", "file://")) else f"file://{image}")
+        image_url = (
+            image if image.startswith(("http://", "file://")) else f"file://{image}"
+        )
        return {"type": "image_url", "image_url": {"url": image_url}}

-    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
-                     " or str or dictionary with raw image bytes.")
+    raise ValueError(
+        f"Invalid image input {image}. Must be a PIL.Image.Image"
+        " or str or dictionary with raw image bytes."
+    )


 # -----------------------------------------------------------------------------
@@ -318,8 +313,11 @@ class RandomDataset(BenchmarkDataset):
        num_special_tokens = tokenizer.num_special_tokens_to_add()
        real_input_len = input_len - num_special_tokens

-        prefix_token_ids = (np.random.randint(
-            0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
+        prefix_token_ids = (
+            np.random.randint(0, vocab_size, size=prefix_len).tolist()
+            if prefix_len > 0
+            else []
+        )

        # New sampling logic: [X * (1 - b), X * (1 + b)]
        input_low = int(real_input_len * (1 - range_ratio))
@@ -329,21 +327,17 @@ class RandomDataset(BenchmarkDataset):

        # Add logging for debugging
        logger.info("Sampling input_len from [%s, %s]", input_low, input_high)
-        logger.info("Sampling output_len from [%s, %s]", output_low,
-                    output_high)
+        logger.info("Sampling output_len from [%s, %s]", output_low, output_high)

-        input_lens = np.random.randint(input_low,
-                                       input_high + 1,
-                                       size=num_requests)
-        output_lens = np.random.randint(output_low,
-                                        output_high + 1,
-                                        size=num_requests)
+        input_lens = np.random.randint(input_low, input_high + 1, size=num_requests)
+        output_lens = np.random.randint(output_low, output_high + 1, size=num_requests)
        offsets = np.random.randint(0, vocab_size, size=num_requests)

        requests = []
        for i in range(num_requests):
-            inner_seq = ((offsets[i] + i + np.arange(input_lens[i])) %
-                         vocab_size).tolist()
+            inner_seq = (
+                (offsets[i] + i + np.arange(input_lens[i])) % vocab_size
+            ).tolist()
            token_sequence = prefix_token_ids + inner_seq
            prompt = tokenizer.decode(token_sequence)
            # After decoding the prompt we have to encode and decode it again.
@@ -354,8 +348,9 @@ class RandomDataset(BenchmarkDataset):
            # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
            # To avoid uncontrolled change of the prompt length,
            # the encoded sequence is truncated before being decode again.
-            re_encoded_sequence = tokenizer.encode(
-                prompt, add_special_tokens=False)[:input_lens[i]]
+            re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
+                : input_lens[i]
+            ]
            prompt = tokenizer.decode(re_encoded_sequence)
            total_input_len = prefix_len + int(input_lens[i])
            requests.append(
@@ -363,7 +358,8 @@ class RandomDataset(BenchmarkDataset):
                    prompt=prompt,
                    prompt_len=total_input_len,
                    expected_output_len=int(output_lens[i]),
-                ))
+                )
+            )
        return requests


@@ -390,7 +386,8 @@ class ShareGPTDataset(BenchmarkDataset):
            self.data = json.load(f)
        # Filter entries with at least two conversation turns.
        self.data = [
-            entry for entry in self.data
+            entry
+            for entry in self.data
            if "conversations" in entry and len(entry["conversations"]) >= 2
        ]
        random.seed(self.random_seed)
@@ -416,31 +413,123 @@ class ShareGPTDataset(BenchmarkDataset):
            )

            lora_request, tokenizer = self.get_random_lora_request(
-                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
+            )
            prompt_ids = tokenizer(prompt).input_ids
            completion_ids = tokenizer(completion).input_ids
            prompt_len = len(prompt_ids)
-            new_output_len = (len(completion_ids)
-                              if output_len is None else output_len)
-            if not is_valid_sequence(prompt_len,
-                                     new_output_len,
-                                     skip_min_output_len_check=output_len
-                                     is not None):
+            new_output_len = len(completion_ids) if output_len is None else output_len
+            if not is_valid_sequence(
+                prompt_len,
+                new_output_len,
+                skip_min_output_len_check=output_len is not None,
+            ):
                continue
            if enable_multimodal_chat:
-                prompt = self.apply_multimodal_chat_transformation(
-                    prompt, None)
+                prompt = self.apply_multimodal_chat_transformation(prompt, None)
            samples.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=new_output_len,
                    lora_request=lora_request,
-                ))
+                )
+            )
        self.maybe_oversample_requests(samples, num_requests)
        return samples


+# -----------------------------------------------------------------------------
+# Custom Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class CustomDataset(BenchmarkDataset):
+    """
+    Implements the Custom dataset.  Loads data from a JSONL file and generates
+    sample requests based on conversation turns. E.g.,
+    ```
+    {"prompt": "What is the capital of India?"}
+    {"prompt": "What is the capital of Iran?"}
+    {"prompt": "What is the capital of China?"}
+    ```
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        # self.data will be a list of dictionaries
+        # e.g., [{"prompt": "What is the capital of India?"}, ...]
+        # This will be the standardized format which load_data()
+        # has to convert into depending on the filetype of dataset_path.
+        # sample() will assume this standardized format of self.data
+        self.data = []
+
+        # Load the JSONL file
+        if self.dataset_path.endswith(".jsonl"):
+            jsonl_data = pd.read_json(path_or_buf=self.dataset_path, lines=True)
+
+            # check if the JSONL file has a 'prompt' column
+            if "prompt" not in jsonl_data.columns:
+                raise ValueError("JSONL file must contain a 'prompt' column.")
+
+            # Convert each row to a dictionary and append to self.data
+            # This will convert the DataFrame to a list of dictionaries
+            # where each dictionary corresponds to a row in the DataFrame.
+            # This is the standardized format we want for self.data
+            for _, row in jsonl_data.iterrows():
+                self.data.append(row.to_dict())
+        else:
+            raise NotImplementedError(
+                "Only JSONL format is supported for CustomDataset."
+            )
+
+        random.seed(self.random_seed)
+        random.shuffle(self.data)
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        lora_path: Optional[str] = None,
+        max_loras: Optional[int] = None,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        skip_chat_template: bool = False,
+        **kwargs,
+    ) -> list:
+        sampled_requests = []
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item["prompt"]
+
+            # apply template
+            if not skip_chat_template:
+                prompt = tokenizer.apply_chat_template(
+                    [{"role": "user", "content": prompt}],
+                    add_generation_prompt=True,
+                    tokenize=False,
+                )
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                )
+            )
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+
+        return sampled_requests
+
+
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------
@@ -482,20 +571,20 @@ class SonnetDataset(BenchmarkDataset):
    ) -> list:
        # Calculate average token length for a poem line.
        tokenized_lines = [tokenizer(line).input_ids for line in self.data]
-        avg_len = sum(len(tokens)
-                      for tokens in tokenized_lines) / len(tokenized_lines)
+        avg_len = sum(len(tokens) for tokens in tokenized_lines) / len(tokenized_lines)

        # Build the base prompt.
        base_prompt = "Pick as many lines as you can from these poem lines:\n"
        base_msg = [{"role": "user", "content": base_prompt}]
-        base_fmt = tokenizer.apply_chat_template(base_msg,
-                                                 add_generation_prompt=True,
-                                                 tokenize=False)
+        base_fmt = tokenizer.apply_chat_template(
+            base_msg, add_generation_prompt=True, tokenize=False
+        )
        base_offset = len(tokenizer(base_fmt).input_ids)
        if input_len <= base_offset:
            raise ValueError(
                f"'input_len' must be higher than the base prompt length "
-                f"({base_offset}).")
+                f"({base_offset})."
+            )

        # Determine how many poem lines to use.
        num_input_lines = round((input_len - base_offset) / avg_len)
@@ -504,21 +593,23 @@ class SonnetDataset(BenchmarkDataset):

        samples = []
        while len(samples) < num_requests:
-            extra_lines = random.choices(self.data,
-                                         k=num_input_lines - num_prefix_lines)
+            extra_lines = random.choices(
+                self.data, k=num_input_lines - num_prefix_lines
+            )
            prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
            msg = [{"role": "user", "content": prompt}]
            prompt_formatted = tokenizer.apply_chat_template(
-                msg, add_generation_prompt=True, tokenize=False)
+                msg, add_generation_prompt=True, tokenize=False
+            )
            prompt_len = len(tokenizer(prompt_formatted).input_ids)
            if prompt_len <= input_len:
                samples.append(
                    SampleRequest(
-                        prompt=prompt_formatted
-                        if return_prompt_formatted else prompt,
+                        prompt=prompt_formatted if return_prompt_formatted else prompt,
                        prompt_len=prompt_len,
                        expected_output_len=output_len,
-                    ))
+                    )
+                )
        return samples


@@ -538,7 +629,9 @@ class BurstGPTDataset(BenchmarkDataset):
        super().__init__(**kwargs)
        self.load_data()

-    def load_data(self, ):
+    def load_data(
+        self,
+    ):
        if self.dataset_path is None:
            raise ValueError("dataset_path must be provided for loading data.")

@@ -552,8 +645,7 @@ class BurstGPTDataset(BenchmarkDataset):

    def _sample_loaded_data(self, num_requests: int) -> list:
        if num_requests <= len(self.data):
-            data = self.data.sample(n=num_requests,
-                                    random_state=self.random_seed)
+            data = self.data.sample(n=num_requests, random_state=self.random_seed)
        else:
            data = self.data.sample(
                n=num_requests,
@@ -577,7 +669,8 @@ class BurstGPTDataset(BenchmarkDataset):
            input_len = int(data[i][2])
            output_len = int(data[i][3])
            lora_req, tokenizer = self.get_random_lora_request(
-                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
+            )
            vocab_size = tokenizer.vocab_size
            # Generate a synthetic prompt: a list of token IDs computed as (i +
            # j) modulo vocab_size.
@@ -589,7 +682,8 @@ class BurstGPTDataset(BenchmarkDataset):
                    prompt_len=input_len,
                    expected_output_len=output_len,
                    lora_request=lora_req,
-                ))
+                )
+            )
        return samples


@@ -632,20 +726,23 @@ class HuggingFaceDataset(BenchmarkDataset):

 class ConversationDataset(HuggingFaceDataset):
    """Dataset for conversation data with multimodal support."""
+
    SUPPORTED_DATASET_PATHS = {
-        'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
+        "lmms-lab/LLaVA-OneVision-Data",
+        "Aeala/ShareGPT_Vicuna_unfiltered",
    }
    IS_MULTIMODAL = True

-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        **kwargs,
+    ) -> list:
        # Filter examples with at least 2 conversations
-        filtered_data = self.data.filter(
-            lambda x: len(x["conversations"]) >= 2)
+        filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
        sampled_requests = []
        dynamic_output = output_len is None

@@ -661,24 +758,22 @@ class ConversationDataset(HuggingFaceDataset):
            completion_len = len(completion_ids)
            output_len = completion_len if dynamic_output else output_len
            assert isinstance(output_len, int) and output_len > 0
-            if dynamic_output and not is_valid_sequence(
-                    prompt_len, completion_len):
+            if dynamic_output and not is_valid_sequence(prompt_len, completion_len):
                continue
-            mm_content = process_image(
-                item["image"]) if "image" in item else None
+            mm_content = process_image(item["image"]) if "image" in item else None
            if enable_multimodal_chat:
                # Note: when chat is enabled the request prompt_len is no longer
                # accurate and we will be using request output to count the
                # actual prompt len and output len
-                prompt = self.apply_multimodal_chat_transformation(
-                    prompt, mm_content)
+                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
-                ))
+                )
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests

@@ -695,10 +790,8 @@ class VisionArenaDataset(HuggingFaceDataset):

    DEFAULT_OUTPUT_LEN = 128
    SUPPORTED_DATASET_PATHS = {
-        "lmarena-ai/VisionArena-Chat":
-        lambda x: x["conversation"][0][0]["content"],
-        "lmarena-ai/vision-arena-bench-v0.1":
-        lambda x: x["turns"][0][0]["content"]
+        "lmarena-ai/VisionArena-Chat": lambda x: x["conversation"][0][0]["content"],
+        "lmarena-ai/vision-arena-bench-v0.1": lambda x: x["turns"][0][0]["content"],
    }
    IS_MULTIMODAL = True

@@ -710,16 +803,14 @@ class VisionArenaDataset(HuggingFaceDataset):
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
-        output_len = (output_len
-                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
            if parser_fn is None:
-                raise ValueError(
-                    f"Unsupported dataset path: {self.dataset_path}")
+                raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
            prompt = parser_fn(item)
            mm_content = process_image(item["images"][0])
            prompt_len = len(tokenizer(prompt).input_ids)
@@ -727,15 +818,15 @@ class VisionArenaDataset(HuggingFaceDataset):
                # Note: when chat is enabled the request prompt_len is no longer
                # accurate and we will be using request output to count the
                # actual prompt len
-                prompt = self.apply_multimodal_chat_transformation(
-                    prompt, mm_content)
+                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
-                ))
+                )
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests

@@ -760,14 +851,15 @@ class InstructCoderDataset(HuggingFaceDataset):
        "likaixin/InstructCoder",
    }

-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
-        output_len = (output_len
-                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        **kwargs,
+    ) -> list:
+        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
        for item in self.data:
            if len(sampled_requests) >= num_requests:
@@ -779,7 +871,8 @@ class InstructCoderDataset(HuggingFaceDataset):
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
-                ))
+                )
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests

@@ -794,38 +887,38 @@ class MTBenchDataset(HuggingFaceDataset):
    MT-Bench Dataset.
    https://huggingface.co/datasets/philschmid/mt-bench

-    We create a single turn dataset for MT-Bench. 
+    We create a single turn dataset for MT-Bench.
    This is similar to Spec decoding benchmark setup in vLLM
    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
-    """ # noqa: E501
+    """  # noqa: E501

    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
    SUPPORTED_DATASET_PATHS = {
        "philschmid/mt-bench",
    }

-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
-        output_len = (output_len
-                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        **kwargs,
+    ) -> list:
+        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []

        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
-            prompt = item['turns'][0]
+            prompt = item["turns"][0]

            # apply template
-            prompt = tokenizer.apply_chat_template([{
-                "role": "user",
-                "content": prompt
-            }],
-                                                   add_generation_prompt=True,
-                                                   tokenize=False)
+            prompt = tokenizer.apply_chat_template(
+                [{"role": "user", "content": prompt}],
+                add_generation_prompt=True,
+                tokenize=False,
+            )

            prompt_len = len(tokenizer(prompt).input_ids)
            sampled_requests.append(
@@ -833,7 +926,8 @@ class MTBenchDataset(HuggingFaceDataset):
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
-                ))
+                )
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests

@@ -847,23 +941,27 @@ class AIMODataset(HuggingFaceDataset):
    """
    Dataset class for processing a AIMO dataset with reasoning questions.
    """
+
    SUPPORTED_DATASET_PATHS = {
-        "AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
-        "AI-MO/NuminaMath-CoT"
+        "AI-MO/aimo-validation-aime",
+        "AI-MO/NuminaMath-1.5",
+        "AI-MO/NuminaMath-CoT",
    }

-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               **kwargs) -> list:
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        **kwargs,
+    ) -> list:
        sampled_requests = []
        dynamic_output = output_len is None

        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
-            prompt, completion = item['problem'], item["solution"]
+            prompt, completion = item["problem"], item["solution"]

            prompt_ids = tokenizer(prompt).input_ids
            completion_ids = tokenizer(completion).input_ids
@@ -871,10 +969,9 @@ class AIMODataset(HuggingFaceDataset):
            completion_len = len(completion_ids)
            output_len = completion_len if dynamic_output else output_len
            assert isinstance(output_len, int) and output_len > 0
-            if dynamic_output and not is_valid_sequence(prompt_len,
-                                                        completion_len,
-                                                        max_prompt_len=2048,
-                                                        max_total_len=32000):
+            if dynamic_output and not is_valid_sequence(
+                prompt_len, completion_len, max_prompt_len=2048, max_total_len=32000
+            ):
                continue
            sampled_requests.append(
                SampleRequest(
@@ -882,7 +979,8 @@ class AIMODataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=None,
-                ))
+                )
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests

@@ -905,25 +1003,25 @@ You are a code completion assistant and your task is to analyze user edits and t

 ### Response:

-""" # noqa: E501
+"""  # noqa: E501


 def _format_zeta_prompt(
-        sample: dict,
-        original_start_marker: str = "<|editable_region_start|>") -> dict:
+    sample: dict, original_start_marker: str = "<|editable_region_start|>"
+) -> dict:
    """Format the zeta prompt for the Next Edit Prediction (NEP) dataset.
-    
-    This function formats examples from the NEP dataset 
-    into prompts and expected outputs. It could be 
+
+    This function formats examples from the NEP dataset
+    into prompts and expected outputs. It could be
    further extended to support more NEP datasets.
-    
+
    Args:
-        sample: The dataset sample containing events, 
+        sample: The dataset sample containing events,
            inputs, and outputs.
-        original_start_marker: The marker indicating the 
-            start of the editable region. Defaults to 
+        original_start_marker: The marker indicating the
+            start of the editable region. Defaults to
            "<|editable_region_start|>".
-            
+
    Returns:
        A dictionary with the formatted prompts and expected outputs.
    """
@@ -953,10 +1051,8 @@ class NextEditPredictionDataset(HuggingFaceDataset):
        "zed-industries/zeta": _format_zeta_prompt,
    }

-    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
-               **kwargs):
-        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
-            self.dataset_path)
+    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
+        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
        if formatting_prompt_func is None:
            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
        samples = []
@@ -967,8 +1063,10 @@ class NextEditPredictionDataset(HuggingFaceDataset):
                    prompt=sample["prompt"],
                    prompt_len=len(tokenizer(sample["prompt"]).input_ids),
                    expected_output_len=len(
-                        tokenizer(sample["expected_output"]).input_ids),
-                ))
+                        tokenizer(sample["expected_output"]).input_ids
+                    ),
+                )
+            )
            if len(samples) >= num_requests:
                break
        self.maybe_oversample_requests(samples, num_requests)
@@ -997,18 +1095,22 @@ class ASRDataset(HuggingFaceDataset):
    | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
    +----------------+----------------------------------------+--------------------------+-----------------------------+

-    """ # noqa: E501
+    """  # noqa: E501
+
    SUPPORTED_DATASET_PATHS = {
-        "openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
-        "edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
+        "openslr/librispeech_asr",
+        "facebook/voxpopuli",
+        "LIUM/tedlium",
+        "edinburghcstr/ami",
+        "speechcolab/gigaspeech",
+        "kensho/spgispeech",
    }

    DEFAULT_OUTPUT_LEN = 128
    IS_MULTIMODAL = True

    # TODO Whisper-specific. Abstract interface when more models are supported.
-    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|>"\
-                              "<|notimestamps|>"
+    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
    skip_long_audios: bool = True

    def sample(
@@ -1019,8 +1121,8 @@ class ASRDataset(HuggingFaceDataset):
        **kwargs,
    ) -> list:
        import librosa
-        output_len = (output_len
-                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+
+        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
        prompt_len = len(tokenizer(prompt).input_ids)
        sampled_requests = []
@@ -1043,10 +1145,14 @@ class ASRDataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
-                ))
+                )
+            )
        if skipped:
-            logger.warning("%d samples discarded from dataset due to" \
-                           " their length being greater than" \
-                           " what Whisper supports.", skipped)
+            logger.warning(
+                "%d samples discarded from dataset due to"
+                " their length being greater than"
+                " what Whisper supports.",
+                skipped,
+            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests
@@ -6,14 +6,13 @@ import dataclasses
 import json
 import os
 import time
-from pathlib import Path
 from typing import Any, Optional

 import numpy as np
-import torch
-from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from tqdm import tqdm

+import vllm.envs as envs
+from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.inputs import PromptType
@@ -21,13 +20,14 @@ from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser


-def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                     results: dict[str, Any]) -> None:
+def save_to_pytorch_benchmark_format(
+    args: argparse.Namespace, results: dict[str, Any]
+) -> None:
    pt_records = convert_to_pytorch_benchmark_format(
        args=args,
        metrics={"latency": results["latencies"]},
-        extra_info={k: results[k]
-                    for k in ["avg_latency", "percentiles"]})
+        extra_info={k: results[k] for k in ["avg_latency", "percentiles"]},
+    )
    if pt_records:
        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
        write_to_json(pt_file, pt_records)
@@ -42,9 +42,11 @@ def main(args: argparse.Namespace):
    # the engine will automatically process the request in multiple batches.
    llm = LLM(**dataclasses.asdict(engine_args))
    assert llm.llm_engine.model_config.max_model_len >= (
-        args.input_len +
-        args.output_len), ("Please ensure that max_model_len is greater than"
-                           " the sum of input_len and output_len.")
+        args.input_len + args.output_len
+    ), (
+        "Please ensure that max_model_len is greater than"
+        " the sum of input_len and output_len."
+    )

    sampling_params = SamplingParams(
        n=args.n,
@@ -55,18 +57,16 @@ def main(args: argparse.Namespace):
        detokenize=not args.disable_detokenize,
    )
    print(sampling_params)
-    dummy_prompt_token_ids = np.random.randint(10000,
-                                               size=(args.batch_size,
-                                                     args.input_len))
-    dummy_prompts: list[PromptType] = [{
-        "prompt_token_ids": batch
-    } for batch in dummy_prompt_token_ids.tolist()]
+    dummy_prompt_token_ids = np.random.randint(
+        10000, size=(args.batch_size, args.input_len)
+    )
+    dummy_prompts: list[PromptType] = [
+        {"prompt_token_ids": batch} for batch in dummy_prompt_token_ids.tolist()
+    ]

    def llm_generate():
        if not args.use_beam_search:
-            llm.generate(dummy_prompts,
-                         sampling_params=sampling_params,
-                         use_tqdm=False)
+            llm.generate(dummy_prompts, sampling_params=sampling_params, use_tqdm=False)
        else:
            llm.beam_search(
                dummy_prompts,
@@ -79,16 +79,9 @@ def main(args: argparse.Namespace):

    def run_to_completion(profile_dir: Optional[str] = None):
        if profile_dir:
-            with torch.profiler.profile(
-                    activities=[
-                        torch.profiler.ProfilerActivity.CPU,
-                        torch.profiler.ProfilerActivity.CUDA,
-                    ],
-                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                        str(profile_dir)),
-            ) as p:
-                llm_generate()
-            print(p.key_averages().table(sort_by="self_cuda_time_total"))
+            llm.start_profile()
+            llm_generate()
+            llm.stop_profile()
        else:
            start_time = time.perf_counter()
            llm_generate()
@@ -101,10 +94,7 @@ def main(args: argparse.Namespace):
        run_to_completion(profile_dir=None)

    if args.profile:
-        profile_dir = args.profile_result_dir
-        if not profile_dir:
-            profile_dir = (Path(".") / "vllm_benchmark_result" /
-                           f"latency_result_{time.time()}")
+        profile_dir = envs.VLLM_TORCH_PROFILER_DIR
        print(f"Profiling (results will be saved to '{profile_dir}')...")
        run_to_completion(profile_dir=profile_dir)
        return
@@ -135,7 +125,8 @@ def main(args: argparse.Namespace):
 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
        description="Benchmark the latency of processing a single batch of "
-        "requests till completion.")
+        "requests till completion."
+    )
    parser.add_argument("--input-len", type=int, default=32)
    parser.add_argument("--output-len", type=int, default=128)
    parser.add_argument("--batch-size", type=int, default=8)
@@ -152,22 +143,14 @@ if __name__ == "__main__":
        default=10,
        help="Number of iterations to run for warmup.",
    )
-    parser.add_argument("--num-iters",
-                        type=int,
-                        default=30,
-                        help="Number of iterations to run.")
+    parser.add_argument(
+        "--num-iters", type=int, default=30, help="Number of iterations to run."
+    )
    parser.add_argument(
        "--profile",
        action="store_true",
        help="profile the generation process of a single batch",
    )
-    parser.add_argument(
-        "--profile-result-dir",
-        type=str,
-        default=None,
-        help=("path to save the pytorch profiler output. Can be visualized "
-              "with ui.perfetto.dev or Tensorboard."),
-    )
    parser.add_argument(
        "--output-json",
        type=str,
@@ -177,10 +160,20 @@ if __name__ == "__main__":
    parser.add_argument(
        "--disable-detokenize",
        action="store_true",
-        help=("Do not detokenize responses (i.e. do not include "
-              "detokenization time in the latency measurement)"),
+        help=(
+            "Do not detokenize responses (i.e. do not include "
+            "detokenization time in the latency measurement)"
+        ),
    )

    parser = EngineArgs.add_cli_args(parser)
+    # V1 enables prefix caching by default which skews the latency
+    # numbers. We need to disable prefix caching by default.
+    parser.set_defaults(enable_prefix_caching=False)
    args = parser.parse_args()
+    if args.profile and not envs.VLLM_TORCH_PROFILER_DIR:
+        raise OSError(
+            "The environment variable 'VLLM_TORCH_PROFILER_DIR' is not set. "
+            "Please set it to a valid path to use torch profiler."
+        )
    main(args)
@@ -76,7 +76,7 @@ def repeat_prompts(prompts, repeat_count, mode: str):
            - 'random': Shuffle the prompts randomly after repetition.
            - 'tile': Repeat the entire prompt list in sequence.
              Example: [1, 2, 3] -> [1, 2, 3, 1, 2, 3].
-            - 'interleave': Repeat each prompt consecutively before moving to 
+            - 'interleave': Repeat each prompt consecutively before moving to
              the next. Example: [1, 2, 3] -> [1, 1, 2, 2, 3, 3].

    Returns:
@@ -86,20 +86,21 @@ def repeat_prompts(prompts, repeat_count, mode: str):
        ValueError: If an invalid mode is provided.
    """
    print("Repeat mode: ", mode)
-    if mode == 'random':
+    if mode == "random":
        repeated_prompts = prompts * repeat_count
        random.shuffle(repeated_prompts)
        return repeated_prompts
-    elif mode == 'tile':
+    elif mode == "tile":
        return prompts * repeat_count
-    elif mode == 'interleave':
+    elif mode == "interleave":
        repeated_prompts = []
        for prompt in prompts:
            repeated_prompts.extend([prompt] * repeat_count)
        return repeated_prompts
    else:
-        raise ValueError(f"Invalid mode: {mode}, only support "
-                         "'random', 'tile', 'interleave'")
+        raise ValueError(
+            f"Invalid mode: {mode}, only support 'random', 'tile', 'interleave'"
+        )


 def main(args):
@@ -109,16 +110,16 @@ def main(args):
    # we append the document id at the beginning to avoid any of the document
    # being the prefix of other documents
    prompts = [
-        str(i) + ' '.join(['hi'] * args.document_length)
+        str(i) + " ".join(["hi"] * args.document_length)
        for i in range(args.num_documents)
    ]

    prompts = repeat_prompts(prompts, args.repeat_count, mode=args.repeat_mode)

    warmup_prompts = [
-        "This is warm up request " + str(i) + \
-                ' '.join(['hi'] * args.document_length)
-        for i in range(args.num_documents)]
+        "This is warm up request " + str(i) + " ".join(["hi"] * args.document_length)
+        for i in range(args.num_documents)
+    ]

    # Create the LLM engine
    engine_args = EngineArgs.from_cli_args(args)
@@ -142,42 +143,52 @@ def main(args):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description=
-        'Benchmark the performance with or without automatic prefix caching.')
+        description="Benchmark the performance with or "
+        "without automatic prefix caching."
+    )

    parser.add_argument(
-        '--document-length',
+        "--document-length",
        type=int,
        # Roughly the number of tokens for a system paper,
        # excluding images
        default=20000,
-        help='Range of input lengths for sampling prompts,'
-        'specified as "min:max" (e.g., "128:256").')
+        help="Range of input lengths for sampling prompts, "
+        'specified as "min:max" (e.g., "128:256").',
+    )

-    parser.add_argument('--num-documents',
-                        type=int,
-                        default=8,
-                        help='Range of input lengths for sampling prompts,'
-                        'specified as "min:max" (e.g., "128:256").')
+    parser.add_argument(
+        "--num-documents",
+        type=int,
+        default=8,
+        help="Range of input lengths for sampling prompts, "
+        'specified as "min:max" (e.g., "128:256").',
+    )

-    parser.add_argument('--output-len', type=int, default=10)
+    parser.add_argument("--output-len", type=int, default=10)

-    parser.add_argument('--repeat-count',
-                        type=int,
-                        default=2,
-                        help='Number of times to repeat each prompt')
+    parser.add_argument(
+        "--repeat-count",
+        type=int,
+        default=2,
+        help="Number of times to repeat each prompt",
+    )

-    parser.add_argument("--repeat-mode",
-                        type=str,
-                        default='random',
-                        help='The mode to repeat prompts. The supported '
-                        'modes are "random", "tile", and "interleave". '
-                        'See repeat_prompts() in the source code for details.')
+    parser.add_argument(
+        "--repeat-mode",
+        type=str,
+        default="random",
+        help="The mode to repeat prompts. The supported "
+        'modes are "random", "tile", and "interleave". '
+        "See repeat_prompts() in the source code for details.",
+    )

-    parser.add_argument("--shuffle-seed",
-                        type=int,
-                        default=0,
-                        help='Random seed when the repeat mode is "random"')
+    parser.add_argument(
+        "--shuffle-seed",
+        type=int,
+        default=0,
+        help='Random seed when the repeat mode is "random"',
+    )

    parser = EngineArgs.add_cli_args(parser)
    args = parser.parse_args()
@@ -63,8 +63,7 @@ class Request:
    output_len: int


-def sample_tokens(tokenizer: PreTrainedTokenizerBase,
-                  length: int) -> list[int]:
+def sample_tokens(tokenizer: PreTrainedTokenizerBase, length: int) -> list[int]:
    vocab = tokenizer.get_vocab()
    all_special_ids = set(tokenizer.all_special_ids)

@@ -91,8 +90,10 @@ def sample_requests_from_dataset(
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
-    dataset = [(data["conversations"][0]["value"],
-                data["conversations"][1]["value"]) for data in dataset]
+    dataset = [
+        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+        for data in dataset
+    ]

    # Shuffle the dataset.
    random.shuffle(dataset)
@@ -113,8 +114,9 @@ def sample_requests_from_dataset(
        completion = dataset[i][1]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
-        output_len = (len(completion_token_ids)
-                      if fixed_output_len is None else fixed_output_len)
+        output_len = (
+            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
+        )
        if min_len <= prompt_len <= max_len:
            filtered_requests.append(Request(prompt, prompt_len, output_len))

@@ -128,27 +130,27 @@ def sample_requests_from_random(
    fixed_output_len: Optional[int],
    prefix_len: int,
 ) -> list[Request]:
-
    requests = []
    prefix_token_ids = sample_tokens(tokenizer, prefix_len)
    min_len, max_len = input_length_range

    for i in range(num_requests):
        unique_part_token_ids = sample_tokens(
-            tokenizer,
-            random.randint(min_len - prefix_len, max_len - prefix_len))
+            tokenizer, random.randint(min_len - prefix_len, max_len - prefix_len)
+        )
        prompt_token_ids = prefix_token_ids + unique_part_token_ids
        prompt = tokenizer.decode(prompt_token_ids)
        prompt_len = len(prompt_token_ids)
-        assert (min_len <= prompt_len <= max_len
-                ), f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
+        assert min_len <= prompt_len <= max_len, (
+            f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
+        )
        requests.append(Request(prompt, prompt_len, fixed_output_len))
    return requests


-def repeat_and_sort_requests(requests: list[Request],
-                             repeat_count: int,
-                             sort: bool = False) -> list[str]:
+def repeat_and_sort_requests(
+    requests: list[Request], repeat_count: int, sort: bool = False
+) -> list[str]:
    repeated_requests = requests * repeat_count
    if sort:
        repeated_requests.sort(key=lambda x: x[1])
@@ -159,14 +161,14 @@ def repeat_and_sort_requests(requests: list[Request],

 def main(args):
    tokenizer = get_tokenizer(args.model, trust_remote_code=True)
-    input_length_range = tuple(map(int, args.input_length_range.split(':')))
+    input_length_range = tuple(map(int, args.input_length_range.split(":")))
    random.seed(args.seed)
    if args.dataset_path is not None:
        if args.prefix_len > 0:
-            raise ValueError("prefix-len is not supported when "
-                             "dataset-path is provided.")
-        print(f"Start to sample {args.num_prompts} prompts "
-              f"from {args.dataset_path}")
+            raise ValueError(
+                "prefix-len is not supported when dataset-path is provided."
+            )
+        print(f"Start to sample {args.num_prompts} prompts from {args.dataset_path}")
        filtered_requests = sample_requests_from_dataset(
            dataset_path=args.dataset_path,
            num_requests=args.num_prompts,
@@ -196,14 +198,16 @@ def main(args):

    llm = LLM(**dataclasses.asdict(engine_args))

-    sampling_params = SamplingParams(temperature=0,
-                                     max_tokens=args.output_len,
-                                     detokenize=not args.disable_detokenize)
+    sampling_params = SamplingParams(
+        temperature=0,
+        max_tokens=args.output_len,
+        detokenize=not args.disable_detokenize,
+    )

    print("Testing filtered requests")
-    prompts = repeat_and_sort_requests(filtered_requests,
-                                       repeat_count=args.repeat_count,
-                                       sort=args.sort)
+    prompts = repeat_and_sort_requests(
+        filtered_requests, repeat_count=args.repeat_count, sort=args.sort
+    )

    print("------start generating------")
    test_prefix(
@@ -215,29 +219,35 @@ def main(args):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description=
-        'Benchmark the performance with or without automatic prefix caching.')
-    parser.add_argument("--dataset-path",
-                        type=str,
-                        default=None,
-                        help="Path to the dataset.")
-    parser.add_argument('--output-len', type=int, default=10)
-    parser.add_argument('--num-prompts',
-                        type=int,
-                        required=True,
-                        help="Number of the prompts sampled from dataset")
-    parser.add_argument('--repeat-count',
-                        type=int,
-                        default=1,
-                        help='Number of times to repeat each prompt')
-    parser.add_argument('--sort',
-                        action='store_true',
-                        help='Sort prompts by input length')
-    parser.add_argument('--input-length-range',
-                        type=str,
-                        required=True,
-                        help='Range of input lengths for sampling prompts,'
-                        'specified as "min:max" (e.g., "128:256").')
+        description="Benchmark the performance with or without "
+        "automatic prefix caching."
+    )
+    parser.add_argument(
+        "--dataset-path", type=str, default=None, help="Path to the dataset."
+    )
+    parser.add_argument("--output-len", type=int, default=10)
+    parser.add_argument(
+        "--num-prompts",
+        type=int,
+        required=True,
+        help="Number of the prompts sampled from dataset",
+    )
+    parser.add_argument(
+        "--repeat-count",
+        type=int,
+        default=1,
+        help="Number of times to repeat each prompt",
+    )
+    parser.add_argument(
+        "--sort", action="store_true", help="Sort prompts by input length"
+    )
+    parser.add_argument(
+        "--input-length-range",
+        type=str,
+        required=True,
+        help="Range of input lengths for sampling prompts,"
+        'specified as "min:max" (e.g., "128:256").',
+    )
    parser.add_argument(
        "--prefix-len",
        type=int,
@@ -248,10 +258,12 @@ if __name__ == "__main__":
        "when dataset-path is not provided.",
    )
    parser.add_argument(
-        '--disable-detokenize',
-        action='store_true',
-        help=("Do not detokenize responses (i.e. do not include "
-              "detokenization time in the latency measurement)"),
+        "--disable-detokenize",
+        action="store_true",
+        help=(
+            "Do not detokenize responses (i.e. do not include "
+            "detokenization time in the latency measurement)"
+        ),
    )

    parser = EngineArgs.add_cli_args(parser)
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 """Benchmark offline prioritization."""
+
 import argparse
 import dataclasses
 import json
@@ -13,7 +14,7 @@ from vllm.engine.arg_utils import EngineArgs
 from vllm.utils import FlexibleArgumentParser


-#Select a equi-probable random priority
+# Select a equi-probable random priority
 def get_random_flag():
    return 0 if random.random() < 0.5 else 1

@@ -33,8 +34,10 @@ def sample_requests(
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
-    dataset = [(data["conversations"][0]["value"],
-                data["conversations"][1]["value"]) for data in dataset]
+    dataset = [
+        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+        for data in dataset
+    ]

    # Shuffle the dataset.
    random.shuffle(dataset)
@@ -51,8 +54,9 @@ def sample_requests(
        completion = dataset[i][1]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
+        output_len = (
+            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
+        )
        if prompt_len < 4 or output_len < 4:
            # Prune too short sequences.
            continue
@@ -74,13 +78,16 @@ def run_vllm(
    disable_detokenize: bool = False,
 ) -> float:
    from vllm import LLM, SamplingParams
+
    llm = LLM(**dataclasses.asdict(engine_args))

    assert all(
        llm.llm_engine.model_config.max_model_len >= (request[1] + request[2])
-        for request in requests), (
-            "Please ensure that max_model_len is greater than the sum of"
-            " input_len and output_len for all requests.")
+        for request in requests
+    ), (
+        "Please ensure that max_model_len is greater than the sum of"
+        " input_len and output_len for all requests."
+    )

    # Add the requests to the engine.
    prompts = []
@@ -97,7 +104,8 @@ def run_vllm(
                ignore_eos=True,
                max_tokens=output_len,
                detokenize=not disable_detokenize,
-            ))
+            )
+        )

    start = time.perf_counter()
    llm.generate(prompts, sampling_params, priority=priority, use_tqdm=True)
@@ -111,26 +119,33 @@ def main(args: argparse.Namespace):

    # Sample the requests.
    tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer, trust_remote_code=args.trust_remote_code)
+        args.tokenizer, trust_remote_code=args.trust_remote_code
+    )
    if args.dataset is None:
        # Synthesize a prompt with the given input length.
        prompt = "hi" * (args.input_len - 1)
-        requests = [(prompt, args.input_len, args.output_len,
-                     get_random_flag()) for _ in range(args.num_prompts)]
+        requests = [
+            (prompt, args.input_len, args.output_len, get_random_flag())
+            for _ in range(args.num_prompts)
+        ]
    else:
-        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
-                                   args.output_len)
+        requests = sample_requests(
+            args.dataset, args.num_prompts, tokenizer, args.output_len
+        )

    if args.backend == "vllm":
-        elapsed_time = run_vllm(requests, args.n,
-                                EngineArgs.from_cli_args(args),
-                                args.disable_detokenize)
+        elapsed_time = run_vllm(
+            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
+        )
    else:
        raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(prompt_len + output_len
-                           for _, prompt_len, output_len, priority in requests)
-    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
+    total_num_tokens = sum(
+        prompt_len + output_len for _, prompt_len, output_len, priority in requests
+    )
+    print(
+        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+        f"{total_num_tokens / elapsed_time:.2f} tokens/s"
+    )

    # Output JSON results if specified
    if args.output_json:
@@ -147,41 +162,44 @@ def main(args: argparse.Namespace):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
-    parser.add_argument("--backend",
-                        type=str,
-                        choices=["vllm", "hf", "mii"],
-                        default="vllm")
-    parser.add_argument("--dataset",
-                        type=str,
-                        default=None,
-                        help="Path to the dataset.")
-    parser.add_argument("--input-len",
-                        type=int,
-                        default=None,
-                        help="Input prompt length for each request")
-    parser.add_argument("--output-len",
-                        type=int,
-                        default=None,
-                        help="Output length for each request. Overrides the "
-                        "output length from the dataset.")
-    parser.add_argument("--n",
-                        type=int,
-                        default=1,
-                        help="Number of generated sequences per prompt.")
-    parser.add_argument("--num-prompts",
-                        type=int,
-                        default=200,
-                        help="Number of prompts to process.")
    parser.add_argument(
-        '--output-json',
+        "--backend", type=str, choices=["vllm", "hf", "mii"], default="vllm"
+    )
+    parser.add_argument(
+        "--dataset", type=str, default=None, help="Path to the dataset."
+    )
+    parser.add_argument(
+        "--input-len",
+        type=int,
+        default=None,
+        help="Input prompt length for each request",
+    )
+    parser.add_argument(
+        "--output-len",
+        type=int,
+        default=None,
+        help="Output length for each request. Overrides the "
+        "output length from the dataset.",
+    )
+    parser.add_argument(
+        "--n", type=int, default=1, help="Number of generated sequences per prompt."
+    )
+    parser.add_argument(
+        "--num-prompts", type=int, default=200, help="Number of prompts to process."
+    )
+    parser.add_argument(
+        "--output-json",
        type=str,
        default=None,
-        help='Path to save the throughput results in JSON format.')
+        help="Path to save the throughput results in JSON format.",
+    )
    parser.add_argument(
-        '--disable-detokenize',
-        action='store_true',
-        help=("Do not detokenize responses (i.e. do not include "
-              "detokenization time in the latency measurement)"),
+        "--disable-detokenize",
+        action="store_true",
+        help=(
+            "Do not detokenize responses (i.e. do not include "
+            "detokenization time in the latency measurement)"
+        ),
    )

    parser = EngineArgs.add_cli_args(parser)
@@ -19,6 +19,7 @@ On the client side, run:
        --endpoint /generate_stream
    to the end of the command above.
 """
+
 import argparse
 import asyncio
 import copy
@@ -36,11 +37,15 @@ from typing import Optional
 import datasets
 import numpy as np
 import pandas as pd
-from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
-                                  RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase

+from backend_request_func import (
+    ASYNC_REQUEST_FUNCS,
+    RequestFuncInput,
+    RequestFuncOutput,
+)
+
 try:
    from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
@@ -52,7 +57,8 @@ except ImportError:
    from argparse import ArgumentParser as FlexibleArgumentParser

 from vllm.v1.structured_output.backend_xgrammar import (
-    has_xgrammar_unsupported_json_features)
+    has_xgrammar_unsupported_json_features,
+)

 MILLISECONDS_TO_SECONDS_CONVERSION = 1000

@@ -98,6 +104,7 @@ class SampleRequest:
        prompt_len: The length of the prompt in tokens.
        expected_output_len: The expected length of the output in tokens.
    """
+
    prompt: str
    prompt_len: int
    expected_output_len: int
@@ -106,32 +113,28 @@ class SampleRequest:
    completion: str = None


-def sample_requests(tokenizer: PreTrainedTokenizerBase,
-                    args: argparse.Namespace) -> list[SampleRequest]:
-    if args.dataset == 'json' or args.dataset == 'json-unique':
+def sample_requests(
+    tokenizer: PreTrainedTokenizerBase, args: argparse.Namespace
+) -> list[SampleRequest]:
+    if args.dataset == "json" or args.dataset == "json-unique":
        if args.json_schema_path is None:
            dir_path = os.path.dirname(os.path.realpath(__file__))
-            args.json_schema_path = os.path.join(dir_path,
-                                                 "structured_schemas",
-                                                 "structured_schema_1.json")
+            args.json_schema_path = os.path.join(
+                dir_path, "structured_schemas", "structured_schema_1.json"
+            )
        json_schemas = []
        with open(args.json_schema_path) as f:
            schema = json.load(f)

-        if args.dataset == 'json-unique':
-            json_schemas = [
-                copy.deepcopy(schema) for _ in range(args.num_prompts)
-            ]
+        if args.dataset == "json-unique":
+            json_schemas = [copy.deepcopy(schema) for _ in range(args.num_prompts)]
            for i in range(len(json_schemas)):
                if "properties" not in json_schemas[i]:
                    json_schemas[i]["properties"] = {}
-                json_schemas[i]["properties"][
-                    f"__optional_field_{uuid.uuid4()}"] = {
-                        "type":
-                        "string",
-                        "description":
-                        "An unique optional field to avoid cached schemas"
-                    }
+                json_schemas[i]["properties"][f"__optional_field_{uuid.uuid4()}"] = {
+                    "type": "string",
+                    "description": "An unique optional field to avoid cached schemas",
+                }
        else:
            json_schemas = [schema] * args.num_prompts

@@ -142,11 +145,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
            return json_schemas[index % len(json_schemas)]

        requests = [
-            SampleRequest(prompt=gen_prompt(i),
-                          prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
-                          expected_output_len=args.output_len,
-                          schema=get_schema(i),
-                          structure_type=args.structure_type)
+            SampleRequest(
+                prompt=gen_prompt(i),
+                prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
+                expected_output_len=args.output_len,
+                schema=get_schema(i),
+                structure_type=args.structure_type,
+            )
            for i in range(args.num_prompts)
        ]

@@ -170,11 +175,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=schema,
-                          structure_type=args.structure_type)
+            SampleRequest(
+                prompt=prompt,
+                prompt_len=input_len,
+                expected_output_len=args.output_len,
+                schema=schema,
+                structure_type=args.structure_type,
+            )
            for _ in range(args.num_prompts)
        ]

@@ -188,11 +195,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=regex,
-                          structure_type=args.structure_type)
+            SampleRequest(
+                prompt=prompt,
+                prompt_len=input_len,
+                expected_output_len=args.output_len,
+                schema=regex,
+                structure_type=args.structure_type,
+            )
            for _ in range(args.num_prompts)
        ]

@@ -203,48 +212,55 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=choice,
-                          structure_type=args.structure_type)
+            SampleRequest(
+                prompt=prompt,
+                prompt_len=input_len,
+                expected_output_len=args.output_len,
+                schema=choice,
+                structure_type=args.structure_type,
+            )
            for _ in range(args.num_prompts)
        ]

    elif args.dataset == "xgrammar_bench":
        requests: list[SampleRequest] = []
-        dataset = datasets.load_dataset("NousResearch/json-mode-eval",
-                                        split="train")
+        dataset = datasets.load_dataset("NousResearch/json-mode-eval", split="train")
        full_dataset_len = len(dataset)

        def _filter_func(item):
            import json
+
            schema = json.loads(item["schema"])
            return not has_xgrammar_unsupported_json_features(schema)

        dataset = dataset.filter(_filter_func)
        num_filtered_out = full_dataset_len - len(dataset)
-        print(f"dataset has {len(dataset)} entries after filtering "
-              f"out {num_filtered_out} entries with unsupported features")
+        print(
+            f"dataset has {len(dataset)} entries after filtering "
+            f"out {num_filtered_out} entries with unsupported features"
+        )
        len_dataset = len(dataset)
        for data_point_idx in range(args.num_prompts):
            idx = data_point_idx
            while idx >= len_dataset:
                idx -= len_dataset
            schema = dataset["schema"][idx]
-            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
-                                                   tokenize=False,
-                                                   add_generation_prompt=True)
+            prompt = tokenizer.apply_chat_template(
+                dataset["prompt"][idx], tokenize=False, add_generation_prompt=True
+            )
            input_len = len(tokenizer(prompt).input_ids)
            completion = dataset["completion"][idx]

            requests.append(
-                SampleRequest(prompt=prompt,
-                              prompt_len=input_len,
-                              expected_output_len=args.output_len,
-                              schema=schema,
-                              structure_type=args.structure_type,
-                              completion=completion))
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=input_len,
+                    expected_output_len=args.output_len,
+                    schema=schema,
+                    structure_type=args.structure_type,
+                    completion=completion,
+                )
+            )

    return requests

@@ -276,7 +292,8 @@ async def get_request(

    # Calculate scale parameter theta to maintain the desired request_rate.
    assert burstiness > 0, (
-        f"A positive burstiness factor is expected, but given {burstiness}.")
+        f"A positive burstiness factor is expected, but given {burstiness}."
+    )
    theta = 1.0 / (request_rate * burstiness)

    for i, request in enumerate(input_requests):
@@ -318,8 +335,8 @@ def calculate_metrics(
            # multiple output tokens may be bundled together
            # Note : this may inflate the output token count slightly
            output_len = len(
-                tokenizer(outputs[i].generated_text,
-                          add_special_tokens=False).input_ids)
+                tokenizer(outputs[i].generated_text, add_special_tokens=False).input_ids
+            )
            actual_output_lens.append(output_len)
            total_input += input_requests[i].prompt_len
            tpot = 0
@@ -343,16 +360,19 @@ def calculate_metrics(

        if "ttft" in goodput_config_dict:
            valid_metrics.append(ttfts)
-            slo_values.append(goodput_config_dict["ttft"] /
-                              MILLISECONDS_TO_SECONDS_CONVERSION)
+            slo_values.append(
+                goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
+            )
        if "tpot" in goodput_config_dict:
            valid_metrics.append(all_tpots)
-            slo_values.append(goodput_config_dict["tpot"] /
-                              MILLISECONDS_TO_SECONDS_CONVERSION)
+            slo_values.append(
+                goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
+            )
        if "e2el" in goodput_config_dict:
            valid_metrics.append(e2els)
-            slo_values.append(goodput_config_dict["e2el"] /
-                              MILLISECONDS_TO_SECONDS_CONVERSION)
+            slo_values.append(
+                goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
+            )

        for req_metric in zip(*valid_metrics):
            is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
@@ -363,7 +383,8 @@ def calculate_metrics(
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
            "on the benchmark arguments.",
-            stacklevel=2)
+            stacklevel=2,
+        )
    metrics = BenchmarkMetrics(
        completed=completed,
        total_input=total_input,
@@ -372,27 +393,31 @@ def calculate_metrics(
        request_goodput=good_completed / dur_s,
        output_throughput=sum(actual_output_lens) / dur_s,
        total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
-        mean_ttft_ms=np.mean(ttfts or 0) *
-        1000,  # ttfts is empty if streaming is not supported by backend
+        mean_ttft_ms=np.mean(ttfts or 0)
+        * 1000,  # ttfts is empty if streaming is not supported by backend
        std_ttft_ms=np.std(ttfts or 0) * 1000,
        median_ttft_ms=np.median(ttfts or 0) * 1000,
-        percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
-                             for p in selected_percentiles],
+        percentiles_ttft_ms=[
+            (p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
+        ],
        mean_tpot_ms=np.mean(tpots or 0) * 1000,
        std_tpot_ms=np.std(tpots or 0) * 1000,
        median_tpot_ms=np.median(tpots or 0) * 1000,
-        percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
-                             for p in selected_percentiles],
+        percentiles_tpot_ms=[
+            (p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
+        ],
        mean_itl_ms=np.mean(itls or 0) * 1000,
        std_itl_ms=np.std(itls or 0) * 1000,
        median_itl_ms=np.median(itls or 0) * 1000,
-        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
-                            for p in selected_percentiles],
+        percentiles_itl_ms=[
+            (p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
+        ],
        mean_e2el_ms=np.mean(e2els or 0) * 1000,
        std_e2el_ms=np.std(e2els or 0) * 1000,
        median_e2el_ms=np.median(e2els or 0) * 1000,
-        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
-                             for p in selected_percentiles],
+        percentiles_e2el_ms=[
+            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
+        ],
    )

    return metrics, actual_output_lens
@@ -429,12 +454,13 @@ async def benchmark(

    print("Starting initial single prompt test run...")
    structured_output_req_idx = random.sample(
-        range(len(input_requests)),
-        int(len(input_requests) * structured_output_ratio))
+        range(len(input_requests)), int(len(input_requests) * structured_output_ratio)
+    )

    test_request = input_requests[0]
-    test_req_extra_body = (prepare_extra_body(test_request)
-                           if 0 in structured_output_req_idx else None)
+    test_req_extra_body = (
+        prepare_extra_body(test_request) if 0 in structured_output_req_idx else None
+    )
    test_input = RequestFuncInput(
        model=model_id,
        prompt=test_request.prompt,
@@ -448,7 +474,8 @@ async def benchmark(
    if not test_output.success:
        raise ValueError(
            "Initial test run failed - Please make sure benchmark arguments "
-            f"are correctly specified. Error: {test_output.error}")
+            f"are correctly specified. Error: {test_output.error}"
+        )
    else:
        print("Initial test run completed. Starting main benchmark run...")

@@ -467,10 +494,7 @@ async def benchmark(
        if profile_output.success:
            print("Profiler started")

-    if burstiness == 1.0:
-        distribution = "Poisson process"
-    else:
-        distribution = "Gamma distribution"
+    distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"

    print(f"Traffic request rate: {request_rate}")
    print(f"Burstiness factor: {burstiness} ({distribution})")
@@ -482,24 +506,21 @@ async def benchmark(
    # and it will simplify the code in limited_request_func.
    #    semaphore = (asyncio.Semaphore(max_concurrency)
    #                 if max_concurrency else contextlib.nullcontext())
-    semaphore = (asyncio.Semaphore(max_concurrency)
-                 if max_concurrency else None)
+    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None

    async def limited_request_func(request_func_input, pbar):
        if semaphore is None:
-            return await request_func(request_func_input=request_func_input,
-                                      pbar=pbar)
+            return await request_func(request_func_input=request_func_input, pbar=pbar)
        async with semaphore:
-            return await request_func(request_func_input=request_func_input,
-                                      pbar=pbar)
+            return await request_func(request_func_input=request_func_input, pbar=pbar)

    benchmark_start_time = time.perf_counter()
    tasks: list[asyncio.Task] = []
    expected: list[str] = []
-    async for i, request in get_request(input_requests, request_rate,
-                                        burstiness):
-        extra_body = prepare_extra_body(
-            request) if i in structured_output_req_idx else None
+    async for i, request in get_request(input_requests, request_rate, burstiness):
+        extra_body = (
+            prepare_extra_body(request) if i in structured_output_req_idx else None
+        )
        request_func_input = RequestFuncInput(
            model=model_id,
            prompt=request.prompt,
@@ -512,8 +533,9 @@ async def benchmark(
        expected.append(request.completion)
        tasks.append(
            asyncio.create_task(
-                limited_request_func(request_func_input=request_func_input,
-                                     pbar=pbar)))
+                limited_request_func(request_func_input=request_func_input, pbar=pbar)
+            )
+        )
    outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)

    if profile:
@@ -545,54 +567,58 @@ async def benchmark(
        goodput_config_dict=goodput_config_dict,
    )

-    print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
+    print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
-    print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
-                                    benchmark_duration))
+    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
-    print("{:<40} {:<10}".format("Total generated tokens:",
-                                 metrics.total_output))
-    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
-                                    metrics.request_throughput))
+    print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
+    print(
+        "{:<40} {:<10.2f}".format(
+            "Request throughput (req/s):", metrics.request_throughput
+        )
+    )
    if goodput_config_dict:
-        print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
-                                        metrics.request_goodput))
-    print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
-                                    metrics.output_throughput))
-    print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
-                                    metrics.total_token_throughput))
+        print(
+            "{:<40} {:<10.2f}".format(
+                "Request goodput (req/s):", metrics.request_goodput
+            )
+        )
+    print(
+        "{:<40} {:<10.2f}".format(
+            "Output token throughput (tok/s):", metrics.output_throughput
+        )
+    )
+    print(
+        "{:<40} {:<10.2f}".format(
+            "Total Token throughput (tok/s):", metrics.total_token_throughput
+        )
+    )

    result = {
-        "duration":
-        benchmark_duration,
-        "completed":
-        metrics.completed,
-        "total_input_tokens":
-        metrics.total_input,
-        "total_output_tokens":
-        metrics.total_output,
-        "request_throughput":
-        metrics.request_throughput,
-        "output_throughput":
-        metrics.output_throughput,
-        "total_token_throughput":
-        metrics.total_token_throughput,
-        "ttft_description":
-        pd.Series([output.ttft for output in outputs]).describe().to_dict(),
-        "tpot_description":
-        pd.Series([output.tpot for output in outputs]).describe().to_dict(),
+        "duration": benchmark_duration,
+        "completed": metrics.completed,
+        "total_input_tokens": metrics.total_input,
+        "total_output_tokens": metrics.total_output,
+        "request_throughput": metrics.request_throughput,
+        "output_throughput": metrics.output_throughput,
+        "total_token_throughput": metrics.total_token_throughput,
+        "ttft_description": pd.Series([output.ttft for output in outputs])
+        .describe()
+        .to_dict(),
+        "tpot_description": pd.Series([output.tpot for output in outputs])
+        .describe()
+        .to_dict(),
        "input_lens": [output.prompt_len for output in outputs],
-        "output_lens":
-        actual_output_lens,
+        "output_lens": actual_output_lens,
        "ttfts": [output.ttft for output in outputs],
        "itls": [output.itl for output in outputs],
        "errors": [output.error for output in outputs],
    }

-    ret = [{
-        'generated': output.generated_text,
-        'expected': gt
-    } for output, gt in zip(outputs, expected)]
+    ret = [
+        {"generated": output.generated_text, "expected": gt}
+        for output, gt in zip(outputs, expected)
+    ]

    def process_one_metric(
        # E.g., "ttft"
@@ -606,29 +632,35 @@ async def benchmark(
        # metric.
        if metric_attribute_name not in selected_percentile_metrics:
            return
-        print("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
-        print("{:<40} {:<10.2f}".format(
-            f"Mean {metric_name} (ms):",
-            getattr(metrics, f"mean_{metric_attribute_name}_ms")))
-        print("{:<40} {:<10.2f}".format(
-            f"Median {metric_name} (ms):",
-            getattr(metrics, f"median_{metric_attribute_name}_ms")))
+        print("{s:{c}^{n}}".format(s=metric_header, n=50, c="-"))
+        print(
+            "{:<40} {:<10.2f}".format(
+                f"Mean {metric_name} (ms):",
+                getattr(metrics, f"mean_{metric_attribute_name}_ms"),
+            )
+        )
+        print(
+            "{:<40} {:<10.2f}".format(
+                f"Median {metric_name} (ms):",
+                getattr(metrics, f"median_{metric_attribute_name}_ms"),
+            )
+        )
        result[f"mean_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"mean_{metric_attribute_name}_ms")
+            metrics, f"mean_{metric_attribute_name}_ms"
+        )
        result[f"median_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"median_{metric_attribute_name}_ms")
+            metrics, f"median_{metric_attribute_name}_ms"
+        )
        result[f"std_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"std_{metric_attribute_name}_ms")
-        for p, value in getattr(metrics,
-                                f"percentiles_{metric_attribute_name}_ms"):
+            metrics, f"std_{metric_attribute_name}_ms"
+        )
+        for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
            p_word = str(int(p)) if int(p) == p else str(p)
-            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
-                                            value))
+            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
            result[f"p{p_word}_{metric_attribute_name}_ms"] = value

    process_one_metric("ttft", "TTFT", "Time to First Token")
-    process_one_metric("tpot", "TPOT",
-                       "Time per Output Token (excl. 1st token)")
+    process_one_metric("tpot", "TPOT", "Time per Output Token (excl. 1st token)")
    process_one_metric("itl", "ITL", "Inter-token Latency")
    process_one_metric("e2el", "E2EL", "End-to-end Latency")

@@ -638,13 +670,13 @@ async def benchmark(


 def evaluate(ret, args):
-
    def _eval_correctness_json(expected, actual):
        # extract json string from string using regex
-        import re
-        actual = actual.replace('\n', '').replace(' ', '').strip()
+        import regex as re
+
+        actual = actual.replace("\n", "").replace(" ", "").strip()
        try:
-            actual = re.search(r'\{.*\}', actual).group()
+            actual = re.search(r"\{.*\}", actual).group()
            actual = json.loads(actual)
        except Exception:
            return False
@@ -655,29 +687,33 @@ def evaluate(ret, args):
        return actual in args.choice

    def _eval_correctness_regex(expected, actual):
-        import re
+        import regex as re
+
        return re.match(args.regex, actual) is not None

    def _eval_correctness(expected, actual):
-        if args.structure_type == 'guided_json':
+        if args.structure_type == "guided_json":
            return _eval_correctness_json(expected, actual)
-        elif args.structure_type == 'guided_regex':
+        elif args.structure_type == "guided_regex":
            return _eval_correctness_regex(expected, actual)
-        elif args.structure_type == 'guided_choice':
+        elif args.structure_type == "guided_choice":
            return _eval_correctness_choice(expected, actual)
        else:
            return None

    scores = []
    for res in ret:
-        score = _eval_correctness(res['expected'], res['generated'])
-        res['correctness'] = score
+        score = _eval_correctness(res["expected"], res["generated"])
+        res["correctness"] = score
        scores.append(score)

    not_none_scores = [score for score in scores if score is not None]

-    return (sum(not_none_scores) / len(not_none_scores) *
-            100) if len(not_none_scores) > 0 else None
+    return (
+        (sum(not_none_scores) / len(not_none_scores) * 100)
+        if len(not_none_scores) > 0
+        else None
+    )


 def parse_goodput(slo_pairs):
@@ -689,9 +725,10 @@ def parse_goodput(slo_pairs):
    except ValueError as err:
        raise argparse.ArgumentTypeError(
            "Invalid format found for service level objectives. "
-            "Specify service level objectives for goodput as \"KEY:VALUE\" "
+            'Specify service level objectives for goodput as "KEY:VALUE" '
            "pairs, where the key is a metric name, and the value is a "
-            "number in milliseconds.") from err
+            "number in milliseconds."
+        ) from err
    return goodput_config_dict


@@ -705,12 +742,14 @@ def check_goodput_args(args):
                raise ValueError(
                    f"Invalid metric name found, {slo_name}: {slo_val}. "
                    "The service level objective name should be one of "
-                    f"{str(VALID_NAMES)}. ")
+                    f"{str(VALID_NAMES)}. "
+                )
            if slo_val < 0:
                raise ValueError(
                    f"Invalid value found, {slo_name}: {slo_val}. "
                    "The service level objective value should be "
-                    "non-negative.")
+                    "non-negative."
+                )
    return goodput_config_dict


@@ -736,19 +775,19 @@ def main(args: argparse.Namespace):
        tokenizer_mode=args.tokenizer_mode,
    )

-    if args.dataset == 'grammar':
-        args.structure_type = 'guided_grammar'
-    elif args.dataset == 'regex':
-        args.structure_type = 'guided_regex'
-    elif args.dataset == 'choice':
-        args.structure_type = 'guided_choice'
+    if args.dataset == "grammar":
+        args.structure_type = "guided_grammar"
+    elif args.dataset == "regex":
+        args.structure_type = "guided_regex"
+    elif args.dataset == "choice":
+        args.structure_type = "guided_choice"
    else:
-        args.structure_type = 'guided_json'
+        args.structure_type = "guided_json"

    if args.no_structured_output:
        args.structured_output_ratio = 0
    if args.save_results:
-        result_file_name = f'{args.structured_output_ratio}guided'
+        result_file_name = f"{args.structured_output_ratio}guided"
        result_file_name += f"_{backend}"
        result_file_name += f"_{args.request_rate}qps"
        result_file_name += f"_{args.model.split('/')[-1]}"
@@ -776,36 +815,29 @@ def main(args: argparse.Namespace):
            disable_tqdm=args.disable_tqdm,
            profile=args.profile,
            selected_percentile_metrics=args.percentile_metrics.split(","),
-            selected_percentiles=[
-                float(p) for p in args.metric_percentiles.split(",")
-            ],
+            selected_percentiles=[float(p) for p in args.metric_percentiles.split(",")],
            ignore_eos=args.ignore_eos,
            max_concurrency=args.max_concurrency,
            structured_output_ratio=args.structured_output_ratio,
            goodput_config_dict=goodput_config_dict,
-        ))
+        )
+    )

    # Save config and results to json
    score = evaluate(ret, args)
-    print("correct_rate(%)", score, '\n')
+    print("correct_rate(%)", score, "\n")
    if args.save_results:
        results = {
-            "backend":
-            backend,
-            "model_id":
-            model_id,
-            "tokenizer_id":
-            tokenizer_id,
-            "num_prompts":
-            args.num_prompts,
-            "request_rate":
-            args.request_rate if args.request_rate < float("inf") else "inf",
-            "burstiness":
-            args.burstiness,
-            "max_concurrency":
-            args.max_concurrency,
-            "correct_rate(%)":
-            score
+            "backend": backend,
+            "model_id": model_id,
+            "tokenizer_id": tokenizer_id,
+            "num_prompts": args.num_prompts,
+            "request_rate": args.request_rate
+            if args.request_rate < float("inf")
+            else "inf",
+            "burstiness": args.burstiness,
+            "max_concurrency": args.max_concurrency,
+            "correct_rate(%)": score,
        }
        results = {"outputs": ret, **results, **benchmark_result}

@@ -814,13 +846,14 @@ def main(args: argparse.Namespace):
            result_file_name = args.result_filename
        if args.result_dir:
            result_file_name = os.path.join(args.result_dir, result_file_name)
-        with open(result_file_name, "w", encoding='utf-8') as outfile:
+        with open(result_file_name, "w", encoding="utf-8") as outfile:
            json.dump(results, outfile, indent=4)


 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description="Benchmark the online serving throughput.")
+        description="Benchmark the online serving throughput."
+    )
    parser.add_argument(
        "--backend",
        type=str,
@@ -842,16 +875,14 @@ if __name__ == "__main__":
        default="/v1/completions",
        help="API endpoint.",
    )
-    parser.add_argument("--dataset",
-                        default='json',
-                        choices=[
-                            'json', 'json-unique', 'grammar', 'regex',
-                            'choice', 'xgrammar_bench'
-                        ])
-    parser.add_argument("--json-schema-path",
-                        type=str,
-                        default=None,
-                        help="Path to json schema.")
+    parser.add_argument(
+        "--dataset",
+        default="json",
+        choices=["json", "json-unique", "grammar", "regex", "choice", "xgrammar_bench"],
+    )
+    parser.add_argument(
+        "--json-schema-path", type=str, default=None, help="Path to json schema."
+    )
    parser.add_argument(
        "--max-concurrency",
        type=int,
@@ -863,7 +894,8 @@ if __name__ == "__main__":
        "initiated, this argument will control how many are actually allowed "
        "to execute at a time. This means that when used in combination, the "
        "actual request rate may be lower than specified with --request-rate, "
-        "if the server is not processing requests fast enough to keep up.")
+        "if the server is not processing requests fast enough to keep up.",
+    )
    parser.add_argument(
        "--model",
        type=str,
@@ -873,15 +905,13 @@ if __name__ == "__main__":
    parser.add_argument(
        "--tokenizer",
        type=str,
-        help=
-        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
+        help="Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
    )
    parser.add_argument(
        "--tokenizer-mode",
        type=str,
        default="auto",
-        help=
-        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
+        help="Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
    )
    parser.add_argument(
        "--num-prompts",
@@ -958,44 +988,51 @@ if __name__ == "__main__":
        "--ignore-eos",
        action="store_true",
        help="Set ignore_eos flag when sending the benchmark request."
-        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
+        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.",
+    )
    parser.add_argument(
        "--percentile-metrics",
        type=str,
        default="ttft,tpot,itl",
        help="Comma-separated list of selected metrics to report percentils. "
        "This argument specifies the metrics to report percentiles. "
-        "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
-        "Default value is \"ttft,tpot,itl\".")
+        'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
+        'Default value is "ttft,tpot,itl".',
+    )
    parser.add_argument(
        "--metric-percentiles",
        type=str,
        default="99",
        help="Comma-separated list of percentiles for selected metrics. "
-        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
-        "Default value is \"99\". "
-        "Use \"--percentile-metrics\" to select metrics.",
+        'To report 25-th, 50-th, and 75-th percentiles, use "25,50,75". '
+        'Default value is "99". '
+        'Use "--percentile-metrics" to select metrics.',
    )
    parser.add_argument(
        "--goodput",
        nargs="+",
        required=False,
-        help="Specify service level objectives for goodput as \"KEY:VALUE\" "
+        help='Specify service level objectives for goodput as "KEY:VALUE" '
        "pairs, where the key is a metric name, and the value is in "
-        "milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, "
+        'milliseconds. Multiple "KEY:VALUE" pairs can be provided, '
        "separated by spaces. Allowed request level metric names are "
-        "\"ttft\", \"tpot\", \"e2el\". For more context on the definition of "
+        '"ttft", "tpot", "e2el". For more context on the definition of '
        "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
-        "and the blog: https://hao-ai-lab.github.io/blogs/distserve")
+        "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
+    )

-    parser.add_argument("--no-structured-output",
-                        action='store_true',
-                        default=False,
-                        help="Whether to disable JSON decoding or not.")
-    parser.add_argument("--structured-output-ratio",
-                        type=float,
-                        default=1.0,
-                        help="Ratio of Structured Outputs requests")
+    parser.add_argument(
+        "--no-structured-output",
+        action="store_true",
+        default=False,
+        help="Whether to disable JSON decoding or not.",
+    )
+    parser.add_argument(
+        "--structured-output-ratio",
+        type=float,
+        default=1.0,
+        help="Ratio of Structured Outputs requests",
+    )

    args = parser.parse_args()
    main(args)
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 """Benchmark offline inference throughput."""
+
 import argparse
 import dataclasses
 import json
@@ -11,18 +12,25 @@ from typing import Any, Optional, Union

 import torch
 import uvloop
-from benchmark_dataset import (AIMODataset, BurstGPTDataset,
-                               ConversationDataset, InstructCoderDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
-from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from tqdm import tqdm
-from transformers import (AutoModelForCausalLM, AutoTokenizer,
-                          PreTrainedTokenizerBase)
+from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase

+from benchmark_dataset import (
+    AIMODataset,
+    BurstGPTDataset,
+    ConversationDataset,
+    InstructCoderDataset,
+    RandomDataset,
+    SampleRequest,
+    ShareGPTDataset,
+    SonnetDataset,
+    VisionArenaDataset,
+)
+from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
-    build_async_engine_client_from_engine_args)
+    build_async_engine_client_from_engine_args,
+)
 from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
@@ -37,23 +45,30 @@ def run_vllm(
    disable_detokenize: bool = False,
 ) -> tuple[float, Optional[list[RequestOutput]]]:
    from vllm import LLM, SamplingParams
+
    llm = LLM(**dataclasses.asdict(engine_args))
    assert all(
-        llm.llm_engine.model_config.max_model_len >= (
-            request.prompt_len + request.expected_output_len)
-        for request in requests), (
-            "Please ensure that max_model_len is greater than the sum of"
-            " prompt_len and expected_output_len for all requests.")
+        llm.llm_engine.model_config.max_model_len
+        >= (request.prompt_len + request.expected_output_len)
+        for request in requests
+    ), (
+        "Please ensure that max_model_len is greater than the sum of"
+        " prompt_len and expected_output_len for all requests."
+    )
    # Add the requests to the engine.
    prompts: list[Union[TextPrompt, TokensPrompt]] = []
    sampling_params: list[SamplingParams] = []
    for request in requests:
        prompts.append(
-            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
-                       multi_modal_data=request.multi_modal_data)
-            if "prompt_token_ids" in request.prompt else \
-            TextPrompt(prompt=request.prompt,
-                       multi_modal_data=request.multi_modal_data))
+            TokensPrompt(
+                prompt_token_ids=request.prompt["prompt_token_ids"],
+                multi_modal_data=request.multi_modal_data,
+            )
+            if "prompt_token_ids" in request.prompt
+            else TextPrompt(
+                prompt=request.prompt, multi_modal_data=request.multi_modal_data
+            )
+        )
        sampling_params.append(
            SamplingParams(
                n=n,
@@ -62,7 +77,8 @@ def run_vllm(
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                detokenize=not disable_detokenize,
-            ))
+            )
+        )
    lora_requests: Optional[list[LoRARequest]] = None
    if engine_args.enable_lora:
        lora_requests = [request.lora_request for request in requests]
@@ -72,10 +88,9 @@ def run_vllm(
    outputs = None
    if not use_beam_search:
        start = time.perf_counter()
-        outputs = llm.generate(prompts,
-                               sampling_params,
-                               lora_request=lora_requests,
-                               use_tqdm=True)
+        outputs = llm.generate(
+            prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
+        )
        end = time.perf_counter()
    else:
        assert lora_requests is None, "BeamSearch API does not support LoRA"
@@ -91,30 +106,35 @@ def run_vllm(
                beam_width=n,
                max_tokens=output_len,
                ignore_eos=True,
-            ))
+            ),
+        )
        end = time.perf_counter()
    return end - start, outputs


 def run_vllm_chat(
-        requests: list[SampleRequest],
-        n: int,
-        engine_args: EngineArgs,
-        disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
+    requests: list[SampleRequest],
+    n: int,
+    engine_args: EngineArgs,
+    disable_detokenize: bool = False,
+) -> tuple[float, list[RequestOutput]]:
    """
    Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
    multimodal models as it properly handles multimodal inputs and chat
    formatting. For non-multimodal models, use run_vllm() instead.
    """
    from vllm import LLM, SamplingParams
+
    llm = LLM(**dataclasses.asdict(engine_args))

    assert all(
-        llm.llm_engine.model_config.max_model_len >= (
-            request.prompt_len + request.expected_output_len)
-        for request in requests), (
-            "Please ensure that max_model_len is greater than the sum of "
-            "prompt_len and expected_output_len for all requests.")
+        llm.llm_engine.model_config.max_model_len
+        >= (request.prompt_len + request.expected_output_len)
+        for request in requests
+    ), (
+        "Please ensure that max_model_len is greater than the sum of "
+        "prompt_len and expected_output_len for all requests."
+    )

    prompts = []
    sampling_params: list[SamplingParams] = []
@@ -128,7 +148,8 @@ def run_vllm_chat(
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                detokenize=not disable_detokenize,
-            ))
+            )
+        )
    start = time.perf_counter()
    outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
    end = time.perf_counter()
@@ -145,13 +166,17 @@ async def run_vllm_async(
    from vllm import SamplingParams

    async with build_async_engine_client_from_engine_args(
-            engine_args, disable_frontend_multiprocessing) as llm:
+        engine_args, disable_frontend_multiprocessing
+    ) as llm:
+        model_config = await llm.get_model_config()
        assert all(
-            llm.model_config.max_model_len >= (request.prompt_len +
-                                               request.expected_output_len)
-            for request in requests), (
-                "Please ensure that max_model_len is greater than the sum of"
-                " prompt_len and expected_output_len for all requests.")
+            model_config.max_model_len
+            >= (request.prompt_len + request.expected_output_len)
+            for request in requests
+        ), (
+            "Please ensure that max_model_len is greater than the sum of"
+            " prompt_len and expected_output_len for all requests."
+        )

        # Add the requests to the engine.
        prompts: list[Union[TextPrompt, TokensPrompt]] = []
@@ -159,11 +184,15 @@ async def run_vllm_async(
        lora_requests: list[Optional[LoRARequest]] = []
        for request in requests:
            prompts.append(
-                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
-                        multi_modal_data=request.multi_modal_data)
-                if "prompt_token_ids" in request.prompt else \
-                TextPrompt(prompt=request.prompt,
-                           multi_modal_data=request.multi_modal_data))
+                TokensPrompt(
+                    prompt_token_ids=request.prompt["prompt_token_ids"],
+                    multi_modal_data=request.multi_modal_data,
+                )
+                if "prompt_token_ids" in request.prompt
+                else TextPrompt(
+                    prompt=request.prompt, multi_modal_data=request.multi_modal_data
+                )
+            )
            sampling_params.append(
                SamplingParams(
                    n=n,
@@ -172,17 +201,16 @@ async def run_vllm_async(
                    ignore_eos=True,
                    max_tokens=request.expected_output_len,
                    detokenize=not disable_detokenize,
-                ))
+                )
+            )
            lora_requests.append(request.lora_request)

        generators = []
        start = time.perf_counter()
-        for i, (prompt, sp,
-                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
-            generator = llm.generate(prompt,
-                                     sp,
-                                     lora_request=lr,
-                                     request_id=f"test{i}")
+        for i, (prompt, sp, lr) in enumerate(
+            zip(prompts, sampling_params, lora_requests)
+        ):
+            generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
            generators.append(generator)
        all_gens = merge_async_iterators(*generators)
        async for i, res in all_gens:
@@ -201,7 +229,8 @@ def run_hf(
    disable_detokenize: bool = False,
 ) -> float:
    llm = AutoModelForCausalLM.from_pretrained(
-        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
+        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code
+    )
    if llm.config.model_type == "llama":
        # To enable padding in the HF backend.
        tokenizer.pad_token = tokenizer.eos_token
@@ -224,14 +253,15 @@ def run_hf(
            # Check if we can add more requests to the batch.
            next_prompt_len = requests[i + 1].prompt_len
            next_output_len = requests[i + 1].expected_output_len
-            if (max(max_prompt_len, next_prompt_len) +
-                    max(max_output_len, next_output_len)) <= 2048:
+            if (
+                max(max_prompt_len, next_prompt_len)
+                + max(max_output_len, next_output_len)
+            ) <= 2048:
                # We can add more requests to the batch.
                continue

        # Generate the sequences.
-        input_ids = tokenizer(batch, return_tensors="pt",
-                              padding=True).input_ids
+        input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
        llm_outputs = llm.generate(
            input_ids=input_ids.cuda(),
            do_sample=True,
@@ -261,6 +291,7 @@ def run_mii(
    output_len: int,
 ) -> float:
    from mii import client, serve
+
    llm = serve(model, tensor_parallel=tensor_parallel_size)
    prompts = [request.prompt for request in requests]

@@ -272,8 +303,9 @@ def run_mii(
    return end - start


-def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                     results: dict[str, Any]) -> None:
+def save_to_pytorch_benchmark_format(
+    args: argparse.Namespace, results: dict[str, Any]
+) -> None:
    pt_records = convert_to_pytorch_benchmark_format(
        args=args,
        metrics={
@@ -281,9 +313,9 @@ def save_to_pytorch_benchmark_format(args: argparse.Namespace,
            "tokens_per_second": [results["tokens_per_second"]],
        },
        extra_info={
-            k: results[k]
-            for k in ["elapsed_time", "num_requests", "total_num_tokens"]
-        })
+            k: results[k] for k in ["elapsed_time", "num_requests", "total_num_tokens"]
+        },
+    )
    if pt_records:
        # Don't use json suffix here as we don't want CI to pick it up
        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
@@ -315,7 +347,8 @@ def get_requests(args, tokenizer):
            sample_kwargs["enable_multimodal_chat"] = True
    elif args.dataset_name == "sonnet":
        assert tokenizer.chat_template or tokenizer.default_chat_template, (
-            "Tokenizer/model must have chat template for sonnet dataset.")
+            "Tokenizer/model must have chat template for sonnet dataset."
+        )
        dataset_cls = SonnetDataset
        sample_kwargs["prefix_len"] = args.prefix_len
        sample_kwargs["return_prompt_formatted"] = True
@@ -324,21 +357,21 @@ def get_requests(args, tokenizer):
    elif args.dataset_name == "hf":
        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = VisionArenaDataset
-            common_kwargs['dataset_subset'] = None
-            common_kwargs['dataset_split'] = "train"
+            common_kwargs["dataset_subset"] = None
+            common_kwargs["dataset_split"] = "train"
            sample_kwargs["enable_multimodal_chat"] = True
        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = InstructCoderDataset
-            common_kwargs['dataset_split'] = "train"
+            common_kwargs["dataset_split"] = "train"
        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = ConversationDataset
-            common_kwargs['dataset_subset'] = args.hf_subset
-            common_kwargs['dataset_split'] = args.hf_split
+            common_kwargs["dataset_subset"] = args.hf_subset
+            common_kwargs["dataset_split"] = args.hf_split
            sample_kwargs["enable_multimodal_chat"] = True
        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = AIMODataset
-            common_kwargs['dataset_subset'] = None
-            common_kwargs['dataset_split'] = "train"
+            common_kwargs["dataset_subset"] = None
+            common_kwargs["dataset_split"] = "train"
    else:
        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
    # Remove None values
@@ -353,10 +386,10 @@ def main(args: argparse.Namespace):
    random.seed(args.seed)
    # Sample the requests.
    tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer, trust_remote_code=args.trust_remote_code)
+        args.tokenizer, trust_remote_code=args.trust_remote_code
+    )
    requests = get_requests(args, tokenizer)
-    is_multi_modal = any(request.multi_modal_data is not None
-                         for request in requests)
+    is_multi_modal = any(request.multi_modal_data is not None for request in requests)
    request_outputs: Optional[list[RequestOutput]] = None
    if args.backend == "vllm":
        if args.async_engine:
@@ -367,23 +400,34 @@ def main(args: argparse.Namespace):
                    AsyncEngineArgs.from_cli_args(args),
                    args.disable_frontend_multiprocessing,
                    args.disable_detokenize,
-                ))
+                )
+            )
        else:
            elapsed_time, request_outputs = run_vllm(
-                requests, args.n, EngineArgs.from_cli_args(args),
-                args.disable_detokenize)
+                requests,
+                args.n,
+                EngineArgs.from_cli_args(args),
+                args.disable_detokenize,
+            )
    elif args.backend == "hf":
        assert args.tensor_parallel_size == 1
-        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
-                              args.hf_max_batch_size, args.trust_remote_code,
-                              args.disable_detokenize)
+        elapsed_time = run_hf(
+            requests,
+            args.model,
+            tokenizer,
+            args.n,
+            args.hf_max_batch_size,
+            args.trust_remote_code,
+            args.disable_detokenize,
+        )
    elif args.backend == "mii":
-        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
-                               args.output_len)
+        elapsed_time = run_mii(
+            requests, args.model, args.tensor_parallel_size, args.output_len
+        )
    elif args.backend == "vllm-chat":
        elapsed_time, request_outputs = run_vllm_chat(
-            requests, args.n, EngineArgs.from_cli_args(args),
-            args.disable_detokenize)
+            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
+        )
    else:
        raise ValueError(f"Unknown backend: {args.backend}")

@@ -395,28 +439,31 @@ def main(args: argparse.Namespace):
        for ro in request_outputs:
            if not isinstance(ro, RequestOutput):
                continue
-            total_prompt_tokens += len(
-                ro.prompt_token_ids) if ro.prompt_token_ids else 0
-            total_output_tokens += sum(
-                len(o.token_ids) for o in ro.outputs if o)
+            total_prompt_tokens += (
+                len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
+            )
+            total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
        total_num_tokens = total_prompt_tokens + total_output_tokens
    else:
-        total_num_tokens = sum(r.prompt_len + r.expected_output_len
-                               for r in requests)
+        total_num_tokens = sum(r.prompt_len + r.expected_output_len for r in requests)
        total_output_tokens = sum(r.expected_output_len for r in requests)
        total_prompt_tokens = total_num_tokens - total_output_tokens

    if is_multi_modal and args.backend != "vllm-chat":
-        print("\033[91mWARNING\033[0m: Multi-modal request with "
-              f"{args.backend} backend detected. The "
-              "following metrics are not accurate because image tokens are not"
-              " counted. See vllm-project/vllm/issues/9778 for details.")
+        print(
+            "\033[91mWARNING\033[0m: Multi-modal request with "
+            f"{args.backend} backend detected. The "
+            "following metrics are not accurate because image tokens are not"
+            " counted. See vllm-project/vllm/issues/9778 for details."
+        )
        # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
        # vllm-chat backend counts the image tokens now

-    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
-          f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
+    print(
+        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
+        f"{total_output_tokens / elapsed_time:.2f} output tokens/s"
+    )
    print(f"Total num prompt tokens:  {total_prompt_tokens}")
    print(f"Total num output tokens:  {total_output_tokens}")

@@ -444,7 +491,8 @@ def validate_args(args):
        warnings.warn(
            "The '--dataset' argument will be deprecated in the next release. "
            "Please use '--dataset-name' and '--dataset-path' instead.",
-            stacklevel=2)
+            stacklevel=2,
+        )
        args.dataset_path = args.dataset

    if not getattr(args, "tokenizer", None):
@@ -457,9 +505,8 @@ def validate_args(args):

    # === Dataset Configuration ===
    if not args.dataset and not args.dataset_path:
-        print(
-            "When dataset path is not set, it will default to random dataset")
-        args.dataset_name = 'random'
+        print("When dataset path is not set, it will default to random dataset")
+        args.dataset_name = "random"
        if args.input_len is None:
            raise ValueError("input_len must be provided for a random dataset")

@@ -467,41 +514,55 @@ def validate_args(args):
    # --hf-subset and --hf-split: only used
    # when dataset_name is 'hf'
    if args.dataset_name != "hf" and (
-            getattr(args, "hf_subset", None) is not None
-            or getattr(args, "hf_split", None) is not None):
-        warnings.warn("--hf-subset and --hf-split will be ignored \
+        getattr(args, "hf_subset", None) is not None
+        or getattr(args, "hf_split", None) is not None
+    ):
+        warnings.warn(
+            "--hf-subset and --hf-split will be ignored \
                since --dataset-name is not 'hf'.",
-                      stacklevel=2)
+            stacklevel=2,
+        )
    elif args.dataset_name == "hf":
        if args.dataset_path in (
-                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
-                | ConversationDataset.SUPPORTED_DATASET_PATHS):
-            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
-        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
-                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
-            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
+            VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
+            | ConversationDataset.SUPPORTED_DATASET_PATHS
+        ):
+            assert args.backend == "vllm-chat", (
+                f"{args.dataset_path} needs to use vllm-chat as the backend."
+            )  # noqa: E501
+        elif args.dataset_path in (
+            InstructCoderDataset.SUPPORTED_DATASET_PATHS
+            | AIMODataset.SUPPORTED_DATASET_PATHS
+        ):
+            assert args.backend == "vllm", (
+                f"{args.dataset_path} needs to use vllm as the backend."
+            )  # noqa: E501
        else:
-            raise ValueError(
-                f"{args.dataset_path} is not supported by hf dataset.")
+            raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")

    # --random-range-ratio: only used when dataset_name is 'random'
-    if args.dataset_name != 'random' and args.random_range_ratio is not None:
-        warnings.warn("--random-range-ratio will be ignored since \
+    if args.dataset_name != "random" and args.random_range_ratio is not None:
+        warnings.warn(
+            "--random-range-ratio will be ignored since \
                --dataset-name is not 'random'.",
-                      stacklevel=2)
+            stacklevel=2,
+        )

    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
    # set.
-    if args.dataset_name not in {"random", "sonnet", None
-                                 } and args.prefix_len is not None:
-        warnings.warn("--prefix-len will be ignored since --dataset-name\
+    if (
+        args.dataset_name not in {"random", "sonnet", None}
+        and args.prefix_len is not None
+    ):
+        warnings.warn(
+            "--prefix-len will be ignored since --dataset-name\
                 is not 'random', 'sonnet', or not set.",
-                      stacklevel=2)
+            stacklevel=2,
+        )

    # === LoRA Settings ===
    if getattr(args, "enable_lora", False) and args.backend != "vllm":
-        raise ValueError(
-            "LoRA benchmarking is only supported for vLLM backend")
+        raise ValueError("LoRA benchmarking is only supported for vLLM backend")
    if getattr(args, "enable_lora", False) and args.lora_path is None:
        raise ValueError("LoRA path must be provided when enable_lora is True")

@@ -511,8 +572,10 @@ def validate_args(args):
    if args.backend != "hf" and args.hf_max_batch_size is not None:
        raise ValueError("HF max batch size is only for HF backend.")

-    if args.backend in {"hf", "mii"} and getattr(args, "quantization",
-                                                 None) is not None:
+    if (
+        args.backend in {"hf", "mii"}
+        and getattr(args, "quantization", None) is not None
+    ):
        raise ValueError("Quantization is only for vLLM backend.")

    if args.backend == "mii" and args.dtype != "auto":
@@ -520,29 +583,32 @@ def validate_args(args):
    if args.backend == "mii" and args.n != 1:
        raise ValueError("n must be 1 for MII backend.")
    if args.backend == "mii" and args.tokenizer != args.model:
-        raise ValueError(
-            "Tokenizer must be the same as the model for MII backend.")
+        raise ValueError("Tokenizer must be the same as the model for MII backend.")

    # --data-parallel is not supported currently.
    # https://github.com/vllm-project/vllm/issues/16222
    if args.data_parallel_size > 1:
        raise ValueError(
            "Data parallel is not supported in offline benchmark, \
-            please use benchmark serving instead")
+            please use benchmark serving instead"
+        )


 if __name__ == "__main__":
    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
-    parser.add_argument("--backend",
-                        type=str,
-                        choices=["vllm", "hf", "mii", "vllm-chat"],
-                        default="vllm")
+    parser.add_argument(
+        "--backend",
+        type=str,
+        choices=["vllm", "hf", "mii", "vllm-chat"],
+        default="vllm",
+    )
    parser.add_argument(
        "--dataset-name",
        type=str,
        choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
        help="Name of the dataset to benchmark on.",
-        default="sharegpt")
+        default="sharegpt",
+    )
    parser.add_argument(
        "--dataset",
        type=str,
@@ -550,57 +616,70 @@ if __name__ == "__main__":
        help="Path to the ShareGPT dataset, will be deprecated in\
            the next release. The dataset is expected to "
        "be a json in form of list[dict[..., conversations: "
-        "list[dict[..., value: <prompt_or_response>]]]]")
-    parser.add_argument("--dataset-path",
-                        type=str,
-                        default=None,
-                        help="Path to the dataset")
-    parser.add_argument("--input-len",
-                        type=int,
-                        default=None,
-                        help="Input prompt length for each request")
-    parser.add_argument("--output-len",
-                        type=int,
-                        default=None,
-                        help="Output length for each request. Overrides the "
-                        "output length from the dataset.")
-    parser.add_argument("--n",
-                        type=int,
-                        default=1,
-                        help="Number of generated sequences per prompt.")
-    parser.add_argument("--num-prompts",
-                        type=int,
-                        default=1000,
-                        help="Number of prompts to process.")
-    parser.add_argument("--hf-max-batch-size",
-                        type=int,
-                        default=None,
-                        help="Maximum batch size for HF backend.")
+        "list[dict[..., value: <prompt_or_response>]]]]",
+    )
    parser.add_argument(
-        '--output-json',
+        "--dataset-path", type=str, default=None, help="Path to the dataset"
+    )
+    parser.add_argument(
+        "--input-len",
+        type=int,
+        default=None,
+        help="Input prompt length for each request",
+    )
+    parser.add_argument(
+        "--output-len",
+        type=int,
+        default=None,
+        help="Output length for each request. Overrides the "
+        "output length from the dataset.",
+    )
+    parser.add_argument(
+        "--n", type=int, default=1, help="Number of generated sequences per prompt."
+    )
+    parser.add_argument(
+        "--num-prompts", type=int, default=1000, help="Number of prompts to process."
+    )
+    parser.add_argument(
+        "--hf-max-batch-size",
+        type=int,
+        default=None,
+        help="Maximum batch size for HF backend.",
+    )
+    parser.add_argument(
+        "--output-json",
        type=str,
        default=None,
-        help='Path to save the throughput results in JSON format.')
-    parser.add_argument("--async-engine",
-                        action='store_true',
-                        default=False,
-                        help="Use vLLM async engine rather than LLM class.")
-    parser.add_argument("--disable-frontend-multiprocessing",
-                        action='store_true',
-                        default=False,
-                        help="Disable decoupled async engine frontend.")
+        help="Path to save the throughput results in JSON format.",
+    )
+    parser.add_argument(
+        "--async-engine",
+        action="store_true",
+        default=False,
+        help="Use vLLM async engine rather than LLM class.",
+    )
+    parser.add_argument(
+        "--disable-frontend-multiprocessing",
+        action="store_true",
+        default=False,
+        help="Disable decoupled async engine frontend.",
+    )
    parser.add_argument(
        "--disable-detokenize",
        action="store_true",
-        help=("Do not detokenize the response (i.e. do not include "
-              "detokenization time in the measurement)"))
+        help=(
+            "Do not detokenize the response (i.e. do not include "
+            "detokenization time in the measurement)"
+        ),
+    )
    # LoRA
    parser.add_argument(
        "--lora-path",
        type=str,
        default=None,
-        help="Path to the lora adapters to use. This can be an absolute path, "
-        "a relative path, or a Hugging Face model identifier.")
+        help="Path to the LoRA adapters to use. This can be an absolute path, "
+        "a relative path, or a Hugging Face model identifier.",
+    )
    parser.add_argument(
        "--prefix-len",
        type=int,
@@ -614,7 +693,8 @@ if __name__ == "__main__":
        f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
        "controls how much of the input is fixed lines versus "
        "random lines, but the total input length remains approximately "
-        "input_len tokens.")
+        "input_len tokens.",
+    )
    # random dataset
    parser.add_argument(
        "--random-range-ratio",
@@ -628,14 +708,12 @@ if __name__ == "__main__":
    )

    # hf dtaset
-    parser.add_argument("--hf-subset",
-                        type=str,
-                        default=None,
-                        help="Subset of the HF dataset.")
-    parser.add_argument("--hf-split",
-                        type=str,
-                        default=None,
-                        help="Split of the HF dataset.")
+    parser.add_argument(
+        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
+    )
+    parser.add_argument(
+        "--hf-split", type=str, default=None, help="Split of the HF dataset."
+    )

    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()
@@ -7,9 +7,9 @@ import os
 from typing import Any


-def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                        metrics: dict[str, list],
-                                        extra_info: dict[str, Any]) -> list:
+def convert_to_pytorch_benchmark_format(
+    args: argparse.Namespace, metrics: dict[str, list], extra_info: dict[str, Any]
+) -> list:
    """
    Save the benchmark results in the format used by PyTorch OSS benchmark with
    on metric per record
@@ -37,12 +37,12 @@ def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
            },
        }

-        tp = record["benchmark"]["extra_info"]["args"].get(
-            "tensor_parallel_size")
+        tp = record["benchmark"]["extra_info"]["args"].get("tensor_parallel_size")
        # Save tensor_parallel_size parameter if it's part of the metadata
        if not tp and "tensor_parallel_size" in extra_info:
-            record["benchmark"]["extra_info"]["args"][
-                "tensor_parallel_size"] = extra_info["tensor_parallel_size"]
+            record["benchmark"]["extra_info"]["args"]["tensor_parallel_size"] = (
+                extra_info["tensor_parallel_size"]
+            )

        records.append(record)

@@ -50,7 +50,6 @@ def convert_to_pytorch_benchmark_format(args: argparse.Namespace,


 class InfEncoder(json.JSONEncoder):
-
    def clear_inf(self, o: Any):
        if isinstance(o, dict):
            return {k: self.clear_inf(v) for k, v in o.items()}
@@ -23,8 +23,9 @@ DEFAULT_TP_SIZES = [1]


 # bench
-def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
-             **kwargs) -> TMeasurement:
+def bench_fn(
+    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
+) -> TMeasurement:
    min_run_time = 1

    globals = {
@@ -41,16 +42,18 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
    ).blocked_autorange(min_run_time=min_run_time)


-def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-               sub_label: str) -> Iterable[TMeasurement]:
+def bench_int8(
+    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+) -> Iterable[TMeasurement]:
    assert dtype == torch.int8
    b_compressed, e, a, b = make_rand_sparse_tensors(torch.int8, m, n, k)
    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)

-    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
-                                       torch.bfloat16)
+    out = ops.cutlass_scaled_sparse_mm(
+        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
+    )
    out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)

    if not torch.allclose(out, out_ref):
@@ -63,54 +66,107 @@ def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
    timers = []
    # pytorch impl - bfloat16
    timers.append(
-        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
-                 torch.mm, a.to(dtype=torch.bfloat16),
-                 b.to(dtype=torch.bfloat16)))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_bf16_bf16_bf16_matmul-no-scales",
+            torch.mm,
+            a.to(dtype=torch.bfloat16),
+            b.to(dtype=torch.bfloat16),
+        )
+    )

    # pytorch impl - float16
    timers.append(
-        bench_fn(label, sub_label,
-                 "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
-                 a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_fp16_fp16_fp16_matmul-no-scales",
+            torch.mm,
+            a.to(dtype=torch.float16),
+            b.to(dtype=torch.float16),
+        )
+    )

    # cutlass impl
    timers.append(
-        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
-                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
-                 torch.bfloat16))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_i8_i8_bf16_scaled_mm",
+            ops.cutlass_scaled_mm,
+            a,
+            b,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+        )
+    )

    # cutlass with bias
    timers.append(
-        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
-                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
-                 bias))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_i8_i8_bf16_scaled_mm_bias",
+            ops.cutlass_scaled_mm,
+            a,
+            b,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+            bias,
+        )
+    )

    # cutlass sparse impl
    timers.append(
-        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.bfloat16))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_i8_i8_bf16_scaled_sparse_mm",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+        )
+    )

    # cutlass sparse with bias
    timers.append(
-        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.bfloat16, bias))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+            bias,
+        )
+    )

    return timers


-def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-              sub_label: str) -> Iterable[TMeasurement]:
+def bench_fp8(
+    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+) -> Iterable[TMeasurement]:
    assert dtype == torch.float8_e4m3fn
-    b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n,
-                                                     k)
+    b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n, k)
    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)

-    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
-                                       torch.bfloat16)
+    out = ops.cutlass_scaled_sparse_mm(
+        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
+    )
    out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)

    if not torch.allclose(out, out_ref):
@@ -124,97 +180,165 @@ def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,

    # pytorch impl w. bf16
    timers.append(
-        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
-                 torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
-                 b.to(dtype=torch.bfloat16, device="cuda")))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_bf16_bf16_bf16_matmul-no-scales",
+            torch.mm,
+            a.to(dtype=torch.bfloat16, device="cuda"),
+            b.to(dtype=torch.bfloat16, device="cuda"),
+        )
+    )

    # pytorch impl: bf16 output, without fp8 fast accum
    timers.append(
-        bench_fn(label,
-                 sub_label,
-                 "pytorch_fp8_fp8_bf16_scaled_mm",
-                 torch._scaled_mm,
-                 a,
-                 b,
-                 scale_a=scale_a,
-                 scale_b=scale_b,
-                 out_dtype=torch.bfloat16))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_fp8_fp8_bf16_scaled_mm",
+            torch._scaled_mm,
+            a,
+            b,
+            scale_a=scale_a,
+            scale_b=scale_b,
+            out_dtype=torch.bfloat16,
+        )
+    )

    # pytorch impl: bf16 output, with fp8 fast accum
    timers.append(
-        bench_fn(label,
-                 sub_label,
-                 "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
-                 torch._scaled_mm,
-                 a,
-                 b,
-                 scale_a=scale_a,
-                 scale_b=scale_b,
-                 out_dtype=torch.bfloat16,
-                 use_fast_accum=True))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
+            torch._scaled_mm,
+            a,
+            b,
+            scale_a=scale_a,
+            scale_b=scale_b,
+            out_dtype=torch.bfloat16,
+            use_fast_accum=True,
+        )
+    )

    # pytorch impl: fp16 output, without fp8 fast accum
    timers.append(
-        bench_fn(label,
-                 sub_label,
-                 "pytorch_fp8_fp8_fp16_scaled_mm",
-                 torch._scaled_mm,
-                 a,
-                 b,
-                 scale_a=scale_a,
-                 scale_b=scale_b,
-                 out_dtype=torch.float16))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_fp8_fp8_fp16_scaled_mm",
+            torch._scaled_mm,
+            a,
+            b,
+            scale_a=scale_a,
+            scale_b=scale_b,
+            out_dtype=torch.float16,
+        )
+    )

    # pytorch impl: fp16 output, with fp8 fast accum
    timers.append(
-        bench_fn(label,
-                 sub_label,
-                 "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
-                 torch._scaled_mm,
-                 a,
-                 b,
-                 scale_a=scale_a,
-                 scale_b=scale_b,
-                 out_dtype=torch.float16,
-                 use_fast_accum=True))
+        bench_fn(
+            label,
+            sub_label,
+            "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
+            torch._scaled_mm,
+            a,
+            b,
+            scale_a=scale_a,
+            scale_b=scale_b,
+            out_dtype=torch.float16,
+            use_fast_accum=True,
+        )
+    )

    # cutlass impl: bf16 output
    timers.append(
-        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
-                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
-                 torch.bfloat16))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_fp8_fp8_bf16_scaled_mm",
+            ops.cutlass_scaled_mm,
+            a,
+            b,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+        )
+    )

    # cutlass impl: bf16 output
    timers.append(
-        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.bfloat16))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+        )
+    )

    # cutlass impl: fp16 output
    timers.append(
-        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.float16))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.float16,
+        )
+    )

    # cutlass impl: bf16 output, with bias
    timers.append(
-        bench_fn(label, sub_label,
-                 "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.bfloat16, bias))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.bfloat16,
+            bias,
+        )
+    )

    # cutlass impl: fp16 output, with bias
    timers.append(
-        bench_fn(label, sub_label,
-                 "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
-                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-                 scale_b, torch.float16, bias.to(dtype=torch.float16)))
+        bench_fn(
+            label,
+            sub_label,
+            "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
+            ops.cutlass_scaled_sparse_mm,
+            a,
+            b_compressed,
+            e,
+            scale_a,
+            scale_b,
+            torch.float16,
+            bias.to(dtype=torch.float16),
+        )
+    )

    return timers


-def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-          sub_label: str) -> Iterable[TMeasurement]:
+def bench(
+    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+) -> Iterable[TMeasurement]:
    if dtype == torch.int8:
        return bench_int8(dtype, m, k, n, label, sub_label)
    if dtype == torch.float8_e4m3fn:
@@ -228,12 +352,12 @@ def print_timers(timers: Iterable[TMeasurement]):
    compare.print()


-def run(dtype: torch.dtype,
-        MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
+def run(
+    dtype: torch.dtype, MKNs: Iterable[tuple[int, int, int]]
+) -> Iterable[TMeasurement]:
    results = []
    for m, k, n in MKNs:
-        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
-                       f"MKN=({m}x{k}x{n})")
+        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm", f"MKN=({m}x{k}x{n})")
        print_timers(timers)
        results.extend(timers)

@@ -241,10 +365,12 @@ def run(dtype: torch.dtype,


 # output makers
-def make_output(data: Iterable[TMeasurement],
-                MKNs: Iterable[tuple[int, int, int]],
-                base_description: str,
-                timestamp=None):
+def make_output(
+    data: Iterable[TMeasurement],
+    MKNs: Iterable[tuple[int, int, int]],
+    base_description: str,
+    timestamp=None,
+):
    print(f"== All Results {base_description} ====")
    print_timers(data)

@@ -258,8 +384,7 @@ def make_output(data: Iterable[TMeasurement],


 def run_square_bench(args):
-    dim_sizes = list(
-        range(args.dim_start, args.dim_end + 1, args.dim_increment))
+    dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
    MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
    data = run(args.dtype, MKNs)

@@ -319,7 +444,7 @@ def run_model_bench(args):
        pkl.dump(all_data, f)


-if __name__ == '__main__':
+if __name__ == "__main__":

    def to_torch_dtype(dt):
        if dt == "int8":
@@ -344,12 +469,15 @@ Benchmark Cutlass GEMM.
    Output:
        - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
            """,  # noqa: E501
-        formatter_class=argparse.RawTextHelpFormatter)
+        formatter_class=argparse.RawTextHelpFormatter,
+    )

-    parser.add_argument("--dtype",
-                        type=to_torch_dtype,
-                        required=True,
-                        help="Available options are ['int8', 'fp8']")
+    parser.add_argument(
+        "--dtype",
+        type=to_torch_dtype,
+        required=True,
+        help="Available options are ['int8', 'fp8']",
+    )
    subparsers = parser.add_subparsers(dest="cmd")

    square_parser = subparsers.add_parser("square_bench")
@@ -368,19 +496,19 @@ Benchmark Cutlass GEMM.
    range_parser.set_defaults(func=run_range_bench)

    model_parser = subparsers.add_parser("model_bench")
-    model_parser.add_argument("--models",
-                              nargs="+",
-                              type=str,
-                              default=DEFAULT_MODELS,
-                              choices=WEIGHT_SHAPES.keys())
-    model_parser.add_argument("--tp-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_TP_SIZES)
-    model_parser.add_argument("--batch-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_BATCH_SIZES)
+    model_parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES.keys(),
+    )
+    model_parser.add_argument(
+        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+    )
+    model_parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
    model_parser.set_defaults(func=run_model_bench)

    args = parser.parse_args()
@@ -10,8 +10,9 @@ import vllm._custom_ops as ops

 def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
    finfo = torch.finfo(torch.float8_e4m3fn)
-    return torch.round(tensor.clamp(
-        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
+    return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
+        dtype=torch.float8_e4m3fn
+    )


 def to_int8(tensor: torch.Tensor) -> torch.Tensor:
@@ -26,10 +27,11 @@ def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
    return tensor.to(dtype=torch.float16)


-def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
-                      k: int) -> tuple[torch.Tensor, torch.Tensor]:
-    a = torch.randn((m, k), device='cuda') * 5
-    b = torch.randn((n, k), device='cuda').t() * 5
+def make_rand_tensors(
+    dtype: torch.dtype, m: int, n: int, k: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device="cuda") * 5
+    b = torch.randn((n, k), device="cuda").t() * 5

    if dtype == torch.int8:
        return to_int8(a), to_int8(b)
@@ -49,9 +51,7 @@ def prune_to_2_4(tensor):

    # Create binary mask
    mask = torch.zeros_like(reshaped)
-    mask.scatter_(dim=1,
-                  index=indices,
-                  src=torch.ones_like(indices, dtype=mask.dtype))
+    mask.scatter_(dim=1, index=indices, src=torch.ones_like(indices, dtype=mask.dtype))

    # Apply mask and reshape back
    pruned = reshaped * mask
@@ -62,10 +62,11 @@ def prune_to_2_4(tensor):
    return pruned.reshape(original_shape)


-def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
-                             k: int) -> tuple[torch.Tensor, torch.Tensor]:
-    a = torch.randn((m, k), device='cuda') * 5
-    b = torch.randn((n, k), device='cuda').t() * 5
+def make_rand_sparse_tensors(
+    dtype: torch.dtype, m: int, n: int, k: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device="cuda") * 5
+    b = torch.randn((n, k), device="cuda").t() * 5

    b = prune_to_2_4(b.t()).t()

@@ -86,9 +87,9 @@ def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
    return b_compressed, e, a, b


-def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype,
-                        m: int, n: int, k: int) -> \
-                        tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
+def make_n_rand_sparse_tensors(
+    num_tensors: int, dtype: torch.dtype, m: int, n: int, k: int
+) -> tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
    ABs = []
    for _ in range(num_tensors):
        b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)
@@ -16,7 +16,8 @@ from weight_shapes import WEIGHT_SHAPES

 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    w8a8_block_fp8_matmul)
+    w8a8_block_fp8_matmul,
+)
 from vllm.utils import FlexibleArgumentParser

 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -25,8 +26,9 @@ DEFAULT_TP_SIZES = [1]


 # bench
-def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
-             **kwargs) -> TMeasurement:
+def bench_fn(
+    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
+) -> TMeasurement:
    min_run_time = 1

    globals = {
@@ -44,45 +46,48 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,


 def bench_int8(
-        dtype: torch.dtype,
-        m: int,
-        k: int,
-        n: int,
-        label: str,
-        sub_label: str,
-        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
+    dtype: torch.dtype,
+    m: int,
+    k: int,
+    n: int,
+    label: str,
+    sub_label: str,
+    bench_kernels: Optional[list[str]] = None,
+) -> Iterable[TMeasurement]:
    """Benchmark INT8-based kernels."""
    assert dtype == torch.int8
    a, b = make_rand_tensors(torch.int8, m, n, k)
    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
-    azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
-    azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
+    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
+    azp = torch.zeros((m,), device="cuda", dtype=torch.int32)
+    azp_adj = torch.zeros((n,), device="cuda", dtype=torch.int32)

    bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        "cutlass_i8_i8_bf16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
-        "cutlass_i8_i8_bf16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
-                                      bias),
-        "cutlass_i8_i8_bf16_scaled_mm_azp":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_bias":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, None, bias),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, azp),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, azp, bias),
+        "pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
+            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+        ),
+        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
+            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.bfloat16
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.bfloat16, bias
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm_azp": lambda: ops.cutlass_scaled_mm_azp(
+            a, b, scale_a, scale_b, torch.bfloat16, azp_adj
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_bias": lambda: ops.cutlass_scaled_mm_azp(
+            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, None, bias
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_pt": lambda: ops.cutlass_scaled_mm_azp(
+            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp
+        ),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias": lambda: ops.cutlass_scaled_mm_azp(
+            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp, bias
+        ),
    }

    timers = []
@@ -96,73 +101,73 @@ def bench_int8(


 def bench_fp8(
-        dtype: torch.dtype,
-        m: int,
-        k: int,
-        n: int,
-        label: str,
-        sub_label: str,
-        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
+    dtype: torch.dtype,
+    m: int,
+    k: int,
+    n: int,
+    label: str,
+    sub_label: str,
+    bench_kernels: Optional[list[str]] = None,
+) -> Iterable[TMeasurement]:
    """Benchmark FP8-based kernels."""
    assert dtype == torch.float8_e4m3fn
    a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
    a_cont = a.contiguous()
    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    block_scale_a = torch.rand((m, k // 128),
-                               device="cuda",
-                               dtype=torch.float32)
-    block_scale_b = torch.rand((k // 128, n // 128),
-                               device="cuda",
-                               dtype=torch.float32)
+
+    def ceil_div(x: int, y: int) -> int:
+        return (x + y - 1) // y
+
+    block_scale_a = torch.rand(
+        (m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
+    )
+    block_scale_b = torch.rand(
+        ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
+    )
    block_scale_a_M_major = block_scale_a.t().contiguous().t()
    block_scale_b_K_major = block_scale_b.t().contiguous().t()
-    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)

    print(m, k, n)

    bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        "pytorch_fp8_fp8_fp16_scaled_mm":
-        lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.float16),
-        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
-        lambda: torch._scaled_mm(a,
-                                 b,
-                                 scale_a,
-                                 scale_b,
-                                 out_dtype=torch.float16,
-                                 use_fast_accum=True),
-        "pytorch_fp8_fp8_bf16_scaled_mm":
-        lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
-        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
-        lambda: torch._scaled_mm(a,
-                                 b,
-                                 scale_a,
-                                 scale_b,
-                                 out_dtype=torch.bfloat16,
-                                 use_fast_accum=True),
-        "cutlass_fp8_fp8_bf16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
-        "cutlass_fp8_fp8_fp16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
-        "cutlass_fp8_fp8_bf16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
-                                      bias),
-        "cutlass_fp8_fp8_fp16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
-                                      bias.to(dtype=torch.float16)),
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise":
-        lambda: w8a8_block_fp8_matmul(a_cont, b.t(), block_scale_a,
-                                      block_scale_b.t(), (128, 128)),
-        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise":
-        lambda: ops.cutlass_scaled_mm(a, b, block_scale_a_M_major,
-                                      block_scale_b_K_major, torch.float16),
+        "pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
+            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+        ),
+        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
+            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
+        ),
+        "pytorch_fp8_fp8_fp16_scaled_mm": lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.float16
+        ),
+        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.float16, use_fast_accum=True
+        ),
+        "pytorch_fp8_fp8_bf16_scaled_mm": lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.bfloat16
+        ),
+        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.bfloat16, use_fast_accum=True
+        ),
+        "cutlass_fp8_fp8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.bfloat16
+        ),
+        "cutlass_fp8_fp8_fp16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.float16
+        ),
+        "cutlass_fp8_fp8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.bfloat16, bias
+        ),
+        "cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
+            a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
+        ),
+        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
+            a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
+        ),
+        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(
+            a, b, block_scale_a_M_major, block_scale_b_K_major, torch.float16
+        ),
    }

    timers = []
@@ -175,13 +180,15 @@ def bench_fp8(
    return timers


-def bench(dtype: torch.dtype,
-          m: int,
-          k: int,
-          n: int,
-          label: str,
-          sub_label: str,
-          bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
+def bench(
+    dtype: torch.dtype,
+    m: int,
+    k: int,
+    n: int,
+    label: str,
+    sub_label: str,
+    bench_kernels: Optional[list[str]] = None,
+) -> Iterable[TMeasurement]:
    if dtype == torch.int8:
        return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
    if dtype == torch.float8_e4m3fn:
@@ -195,27 +202,33 @@ def print_timers(timers: Iterable[TMeasurement]):
    compare.print()


-def run(dtype: torch.dtype,
-        MKNs: Iterable[tuple[int, int, int]],
-        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
+def run(
+    dtype: torch.dtype,
+    MKNs: Iterable[tuple[int, int, int]],
+    bench_kernels: Optional[list[str]] = None,
+) -> Iterable[TMeasurement]:
    results = []
    for m, k, n in MKNs:
-        timers = bench(dtype,
-                       m,
-                       k,
-                       n,
-                       f"scaled-{dtype}-gemm",
-                       f"MKN=({m}x{k}x{n})",
-                       bench_kernels=bench_kernels)
+        timers = bench(
+            dtype,
+            m,
+            k,
+            n,
+            f"scaled-{dtype}-gemm",
+            f"MKN=({m}x{k}x{n})",
+            bench_kernels=bench_kernels,
+        )
        print_timers(timers)
        results.extend(timers)
    return results


-def make_output(data: Iterable[TMeasurement],
-                MKNs: Iterable[tuple[int, int, int]],
-                base_description: str,
-                timestamp=None):
+def make_output(
+    data: Iterable[TMeasurement],
+    MKNs: Iterable[tuple[int, int, int]],
+    base_description: str,
+    timestamp=None,
+):
    print(f"== All Results {base_description} ====")
    print_timers(data)

@@ -226,8 +239,7 @@ def make_output(data: Iterable[TMeasurement],


 def run_square_bench(args):
-    dim_sizes = list(
-        range(args.dim_start, args.dim_end + 1, args.dim_increment))
+    dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
    MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
    data = run(args.dtype, MKNs, bench_kernels=args.kernels)
    make_output(data, MKNs, f"square_bench-{args.dtype}")
@@ -285,7 +297,7 @@ def run_model_bench(args):
        pkl.dump(all_data, f)


-if __name__ == '__main__':
+if __name__ == "__main__":

    def to_torch_dtype(dt):
        if dt == "int8":
@@ -310,19 +322,21 @@ Benchmark Cutlass GEMM.
    Output:
        - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
            """,  # noqa: E501
-        formatter_class=argparse.RawTextHelpFormatter)
+        formatter_class=argparse.RawTextHelpFormatter,
+    )

-    parser.add_argument("--dtype",
-                        type=to_torch_dtype,
-                        required=True,
-                        help="Available options are ['int8', 'fp8']")
+    parser.add_argument(
+        "--dtype",
+        type=to_torch_dtype,
+        required=True,
+        help="Available options are ['int8', 'fp8']",
+    )
    parser.add_argument(
        "--kernels",
        nargs="+",
        type=str,
        default=None,
-        help=
-        "Exact names of the kernels to benchmark. If not set, runs all kernels."
+        help="Exact names of the kernels to benchmark. If not set, runs all kernels.",
    )

    subparsers = parser.add_subparsers(dest="cmd")
@@ -343,19 +357,19 @@ Benchmark Cutlass GEMM.
    range_parser.set_defaults(func=run_range_bench)

    model_parser = subparsers.add_parser("model_bench")
-    model_parser.add_argument("--models",
-                              nargs="+",
-                              type=str,
-                              default=DEFAULT_MODELS,
-                              choices=WEIGHT_SHAPES.keys())
-    model_parser.add_argument("--tp-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_TP_SIZES)
-    model_parser.add_argument("--batch-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_BATCH_SIZES)
+    model_parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES.keys(),
+    )
+    model_parser.add_argument(
+        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+    )
+    model_parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
    model_parser.set_defaults(func=run_model_bench)

    args = parser.parse_args()
@@ -42,4 +42,4 @@ WEIGHT_SHAPES = {
        ([8192, 57344], 1),
        ([28672, 8192], 0),
    ],
-}
+}
@@ -12,39 +12,37 @@ app = Quart(__name__)

 async def forward_request(url, data):
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        headers = {
-            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
-        }
-        async with session.post(url=url, json=data,
-                                headers=headers) as response:
+        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):
+                    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'])
+@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
+        prefill_request["max_tokens"] = 1

        # finish prefill
-        async for _ in forward_request('http://localhost:8100/v1/completions',
-                                       prefill_request):
+        async for _ in forward_request(
+            "http://localhost:8100/v1/completions", prefill_request
+        ):
            continue

        # return decode
-        generator = forward_request('http://localhost:8200/v1/completions',
-                                    original_request_data)
+        generator = forward_request(
+            "http://localhost:8200/v1/completions", original_request_data
+        )
        response = await make_response(generator)
        response.timeout = None

@@ -53,11 +51,12 @@ async def handle_request():
    except Exception as e:
        import sys
        import traceback
+
        exc_info = sys.exc_info()
        print("Error occurred in disagg prefill proxy server")
        print(e)
        print("".join(traceback.format_exception(*exc_info)))


-if __name__ == '__main__':
+if __name__ == "__main__":
    app.run(port=8000)
@@ -8,7 +8,6 @@ from aiohttp import web


 class RoundRobinProxy:
-
    def __init__(self, target_ports):
        self.target_ports = target_ports
        self.port_cycle = itertools.cycle(self.target_ports)
@@ -21,14 +20,15 @@ class RoundRobinProxy:
            try:
                # Forward the request
                async with session.request(
-                        method=request.method,
-                        url=target_url,
-                        headers=request.headers,
-                        data=request.content,
+                    method=request.method,
+                    url=target_url,
+                    headers=request.headers,
+                    data=request.content,
                ) as response:
                    # Start sending the response
-                    resp = web.StreamResponse(status=response.status,
-                                              headers=response.headers)
+                    resp = web.StreamResponse(
+                        status=response.status, headers=response.headers
+                    )
                    await resp.prepare(request)

                    # Stream the response content
@@ -45,11 +45,11 @@ class RoundRobinProxy:
 async def main():
    proxy = RoundRobinProxy([8100, 8200])
    app = web.Application()
-    app.router.add_route('*', '/{path:.*}', proxy.handle_request)
+    app.router.add_route("*", "/{path:.*}", proxy.handle_request)

    runner = web.AppRunner(app)
    await runner.setup()
-    site = web.TCPSite(runner, 'localhost', 8000)
+    site = web.TCPSite(runner, "localhost", 8000)
    await site.start()

    print("Proxy server started on http://localhost:8000")
@@ -58,5 +58,5 @@ async def main():
    await asyncio.Event().wait()


-if __name__ == '__main__':
+if __name__ == "__main__":
    asyncio.run(main())
@@ -6,43 +6,41 @@ import matplotlib.pyplot as plt
 import pandas as pd

 if __name__ == "__main__":
-
    data = []
-    for name in ['disagg_prefill', 'chunked_prefill']:
+    for name in ["disagg_prefill", "chunked_prefill"]:
        for qps in [2, 4, 6, 8]:
            with open(f"results/{name}-qps-{qps}.json") as f:
                x = json.load(f)
-                x['name'] = name
-                x['qps'] = qps
+                x["name"] = name
+                x["qps"] = qps
                data.append(x)

    df = pd.DataFrame.from_dict(data)
-    dis_df = df[df['name'] == 'disagg_prefill']
-    chu_df = df[df['name'] == 'chunked_prefill']
+    dis_df = df[df["name"] == "disagg_prefill"]
+    chu_df = df[df["name"] == "chunked_prefill"]

-    plt.style.use('bmh')
-    plt.rcParams['font.size'] = 20
+    plt.style.use("bmh")
+    plt.rcParams["font.size"] = 20

    for key in [
-            'mean_ttft_ms', 'median_ttft_ms', 'p99_ttft_ms', 'mean_itl_ms',
-            'median_itl_ms', 'p99_itl_ms'
+        "mean_ttft_ms",
+        "median_ttft_ms",
+        "p99_ttft_ms",
+        "mean_itl_ms",
+        "median_itl_ms",
+        "p99_itl_ms",
    ]:
-
        fig, ax = plt.subplots(figsize=(11, 7))
-        plt.plot(dis_df['qps'],
-                 dis_df[key],
-                 label='disagg_prefill',
-                 marker='o',
-                 linewidth=4)
-        plt.plot(chu_df['qps'],
-                 chu_df[key],
-                 label='chunked_prefill',
-                 marker='o',
-                 linewidth=4)
+        plt.plot(
+            dis_df["qps"], dis_df[key], label="disagg_prefill", marker="o", linewidth=4
+        )
+        plt.plot(
+            chu_df["qps"], chu_df[key], label="chunked_prefill", marker="o", linewidth=4
+        )
        ax.legend()

-        ax.set_xlabel('QPS')
+        ax.set_xlabel("QPS")
        ax.set_ylabel(key)
        ax.set_ylim(bottom=0)
-        fig.savefig(f'results/{key}.png')
+        fig.savefig(f"results/{key}.png")
        plt.close(fig)
@@ -24,10 +24,12 @@ class bench_params_t:
    dtype: torch.dtype

    def description(self):
-        return (f'N {self.num_tokens} '
-                f'x D {self.hidden_size} '
-                f'x R {self.add_residual} '
-                f'x DT {self.dtype}')
+        return (
+            f"N {self.num_tokens} "
+            f"x D {self.hidden_size} "
+            f"x R {self.add_residual} "
+            f"x DT {self.dtype}"
+        )


 def get_bench_params() -> list[bench_params_t]:
@@ -38,15 +40,19 @@ def get_bench_params() -> list[bench_params_t]:
    DTYPES = [torch.bfloat16, torch.float]

    combinations = product(NUM_TOKENS, HIDDEN_SIZES, ADD_RESIDUAL, DTYPES)
-    bench_params = list(map(lambda x: \
-        bench_params_t(x[0], x[1], x[2], x[3]), combinations))
+    bench_params = list(
+        map(lambda x: bench_params_t(x[0], x[1], x[2], x[3]), combinations)
+    )
    return bench_params


 # Reference impls
-def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
-                      residual: Optional[torch.Tensor],
-                      quant_dtype: torch.dtype):
+def unfused_int8_impl(
+    rms_norm_layer: RMSNorm,
+    x: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    quant_dtype: torch.dtype,
+):
    # Norm
    torch_out = None
    if residual is None:
@@ -58,9 +64,12 @@ def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
    torch_out, _, _ = ops.scaled_int8_quant(torch_out)


-def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
-                     residual: Optional[torch.Tensor],
-                     quant_dtype: torch.dtype):
+def unfused_fp8_impl(
+    rms_norm_layer: RMSNorm,
+    x: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    quant_dtype: torch.dtype,
+):
    # Norm
    torch_out = None
    if residual is None:
@@ -73,22 +82,27 @@ def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,


 def fused_impl(
-        rms_norm_layer: RMSNorm,  # this stores the weights
-        x: torch.Tensor,
-        residual: Optional[torch.Tensor],
-        quant_dtype: torch.dtype):
-    out, _ = ops.rms_norm_dynamic_per_token_quant(x,
-                                                  rms_norm_layer.weight,
-                                                  1e-6,
-                                                  quant_dtype,
-                                                  residual=residual)
+    rms_norm_layer: RMSNorm,  # this stores the weights
+    x: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    quant_dtype: torch.dtype,
+):
+    out, _ = ops.rms_norm_dynamic_per_token_quant(
+        x, rms_norm_layer.weight, 1e-6, quant_dtype, residual=residual
+    )


 # Bench functions
-def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
-             quant_dtype: torch.dtype, label: str, sub_label: str,
-             fn: Callable, description: str) -> TMeasurement:
-
+def bench_fn(
+    rms_norm_layer: RMSNorm,
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    quant_dtype: torch.dtype,
+    label: str,
+    sub_label: str,
+    fn: Callable,
+    description: str,
+) -> TMeasurement:
    min_run_time = 1

    globals = {
@@ -106,43 +120,81 @@ def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
        description=description,
    ).blocked_autorange(min_run_time=min_run_time)

-def bench(params: bench_params_t, label: str, sub_label: str) \
-        -> Iterable[TMeasurement]:

+def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasurement]:
    # Make inputs
    layer = RMSNorm(params.hidden_size, 1e-6).to(dtype=params.dtype)
    # Make weights
    layer.weight.data.normal_(mean=1.0, std=0.1)
    # Make inputs
    scale = 1 / params.hidden_size
-    x = torch.randn(params.num_tokens,
-                    params.hidden_size,
-                    dtype=params.dtype,
-                    device='cuda') * scale
-    residual = (torch.randn_like(x) * scale).to(device='cuda') \
-            if params.add_residual else None
+    x = (
+        torch.randn(
+            params.num_tokens, params.hidden_size, dtype=params.dtype, device="cuda"
+        )
+        * scale
+    )
+    residual = (
+        (torch.randn_like(x) * scale).to(device="cuda") if params.add_residual else None
+    )

    timers = []

    # unfused int8 impl.
    timers.append(
-        bench_fn(layer, x, residual, torch.int8, label, sub_label,
-                 unfused_int8_impl, "unfused_int8_impl"))
+        bench_fn(
+            layer,
+            x,
+            residual,
+            torch.int8,
+            label,
+            sub_label,
+            unfused_int8_impl,
+            "unfused_int8_impl",
+        )
+    )

    # unfused fp8 impl.
    timers.append(
-        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
-                 unfused_fp8_impl, "unfused_fp8_impl"))
+        bench_fn(
+            layer,
+            x,
+            residual,
+            torch.float8_e4m3fn,
+            label,
+            sub_label,
+            unfused_fp8_impl,
+            "unfused_fp8_impl",
+        )
+    )

    # fused int8 impl.
    timers.append(
-        bench_fn(layer, x, residual, torch.int8, label, sub_label, fused_impl,
-                 "fused_int8_impl"))
+        bench_fn(
+            layer,
+            x,
+            residual,
+            torch.int8,
+            label,
+            sub_label,
+            fused_impl,
+            "fused_int8_impl",
+        )
+    )

    # fused fp8 impl.
    timers.append(
-        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
-                 fused_impl, "fused_fp8_impl"))
+        bench_fn(
+            layer,
+            x,
+            residual,
+            torch.float8_e4m3fn,
+            label,
+            sub_label,
+            fused_impl,
+            "fused_fp8_impl",
+        )
+    )

    print_timers(timers)

@@ -157,13 +209,12 @@ def print_timers(timers: Iterable[TMeasurement]):


 def main():
-    torch.set_default_device('cuda')
+    torch.set_default_device("cuda")
    bench_params = get_bench_params()

    timers = []
    for bp in tqdm(bench_params):
-        timers.extend(
-            bench(bp, "rms-norm-dynamic-per-token-quant", bp.description()))
+        timers.extend(bench(bp, "rms-norm-dynamic-per-token-quant", bp.description()))
    print_timers(timers)

    # pickle all the results
@@ -172,5 +223,5 @@ def main():
        pkl.dump(timers, f)


-if __name__ == '__main__':
+if __name__ == "__main__":
    main()
@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import copy
+import itertools
+
+import torch
+import triton
+from weight_shapes import WEIGHT_SHAPES
+
+from vllm._custom_ops import cutlass_scaled_mm as vllm_scaled_mm
+from vllm._custom_ops import scaled_fp8_quant as vllm_scaled_fp8_quant
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
+        x_log=False,
+        line_arg="provider",
+        line_vals=[
+            "torch-bf16",
+            # "fp8-tensor-w-token-a",
+            "fp8-tensor-w-tensor-a",
+            "fp8-channel-w-token-a",
+            # "fp8-channel-w-tensor-a",
+            # "fp8-tensor-w-token-a-noquant",
+            "fp8-tensor-w-tensor-a-noquant",
+            "fp8-channel-w-token-a-noquant",
+            # "fp8-channel-w-tensor-a-noquant",
+        ],
+        line_names=[
+            "torch-bf16",
+            # "fp8-tensor-w-token-a",
+            "fp8-tensor-w-tensor-a",
+            "fp8-channel-w-token-a",
+            # "fp8-channel-w-tensor-a",
+            # "fp8-tensor-w-token-a-noquant",
+            "fp8-tensor-w-tensor-a-noquant",
+            "fp8-channel-w-token-a-noquant",
+            # "fp8-channel-w-tensor-a-noquant",
+        ],
+        ylabel="TFLOP/s (larger is better)",
+        plot_name="BF16 vs FP8 GEMMs",
+        args={},
+    )
+)
+def benchmark(batch_size, provider, N, K):
+    M = batch_size
+    device = "cuda"
+    dtype = torch.bfloat16
+
+    # Create input tensors
+    a = torch.randn((M, K), device=device, dtype=dtype)
+    b = torch.randn((N, K), device=device, dtype=dtype)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if "torch-bf16" in provider:
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
+        )
+
+    elif "fp8" in provider:
+        # Weights are always quantized ahead of time
+        if "noquant" in provider:
+            # For no quantization, we just measure the GEMM
+            if "tensor-w-token-a" in provider:
+                # Dynamic per-token quant for A, per-tensor quant for B
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b)
+                assert scale_b_fp8.numel() == 1
+                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
+                    a, use_per_token_if_dynamic=True
+                )
+
+                def run_quant():
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "tensor-w-tensor-a" in provider:
+                # Static per-tensor quantization with fixed scales
+                # for both A and B
+                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
+                scale_b = torch.tensor([1.0], device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                assert scale_b_fp8.numel() == 1
+                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
+
+                def run_quant():
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "channel-w-token-a" in provider:
+                # Static per-channel quantization for weights, per-token
+                # quant for A
+                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
+                assert scale_b_fp8.numel() == N
+                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
+                    a, use_per_token_if_dynamic=True
+                )
+
+                def run_quant():
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "channel-w-tensor-a" in provider:
+                # Static per-channel quantization for weights, per-tensor
+                # quant for A
+                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
+                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
+                assert scale_b_fp8.numel() == N
+                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
+
+                def run_quant():
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+        else:
+            # In these cases, we quantize the activations during the GEMM call
+            if "tensor-w-token-a" in provider:
+                # Dynamic per-token quant for A, per-tensor quant for B
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b)
+                assert scale_b_fp8.numel() == 1
+
+                def run_quant():
+                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
+                        a, use_per_token_if_dynamic=True
+                    )
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "tensor-w-tensor-a" in provider:
+                # Static per-tensor quantization with fixed scales
+                # for both A and B
+                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
+                scale_b = torch.tensor([1.0], device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                assert scale_b_fp8.numel() == 1
+
+                def run_quant():
+                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "channel-w-token-a" in provider:
+                # Static per-channel quantization for weights, per-token
+                # quant for A
+                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
+                assert scale_b_fp8.numel() == N
+
+                def run_quant():
+                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
+                        a, use_per_token_if_dynamic=True
+                    )
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+            elif "channel-w-tensor-a" in provider:
+                # Static per-channel quantization for weights, per-tensor
+                # quant for A
+                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
+                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
+                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
+                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
+                assert scale_b_fp8.numel() == N
+
+                def run_quant():
+                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
+                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
+
+        b_fp8 = b_fp8.t()
+
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: run_quant(), quantiles=quantiles
+        )
+
+    # Calculate TFLOP/s, two flops per multiply-add
+    tflops = lambda ms: (2 * M * N * K) * 1e-12 / (ms * 1e-3)
+    return tflops(ms), tflops(max_ms), tflops(min_ms)
+
+
+def prepare_shapes(args):
+    KN_model_names = []
+    models_tps = list(itertools.product(args.models, args.tp_sizes))
+    for model, tp_size in models_tps:
+        assert model in WEIGHT_SHAPES
+        for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
+            KN[tp_split_dim] = KN[tp_split_dim] // tp_size
+            KN.append(model)
+            KN_model_names.append(KN)
+    return KN_model_names
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=["meta-llama/Llama-3.1-8B-Instruct"],
+        choices=[*WEIGHT_SHAPES.keys()],
+        help="List of models to benchmark",
+    )
+    parser.add_argument(
+        "--tp-sizes",
+        nargs="+",
+        type=int,
+        default=[1],
+        help="List of tensor parallel sizes",
+    )
+    args = parser.parse_args()
+
+    KN_model_names = prepare_shapes(args)
+    for K, N, model_name in KN_model_names:
+        print(f"{model_name}, N={N} K={K}, BF16 vs FP8 GEMMs TFLOP/s:")
+        benchmark.run(
+            print_data=True,
+            show_plots=True,
+            save_path=f"bench_fp8_res_n{N}_k{K}",
+            N=N,
+            K=K,
+        )
+
+    print("Benchmark finished!")
@@ -9,32 +9,39 @@ 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)
+    dequantize_weight,
+    generic_dequantize_gemm,
+    get_int_dtype,
+    optimized_dequantize_gemm,
+)
 from vllm.utils import FlexibleArgumentParser

-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"


 def torch_mult(
-        input: torch.Tensor,  #  [..., in_features]
-        weights: torch.Tensor,
-        scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    # [..., 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(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    # [..., 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:
@@ -46,40 +53,42 @@ def dequant_out_scale(
        flattened_output *= b_scales
        return flattened_output.view(orig_shape)
    else:
-        b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-            -1, weights.shape[1])
+        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(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    # [..., 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])
+    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(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    # [..., 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)
@@ -89,23 +98,26 @@ def dequant_no_scale(
 # 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')
+    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)
+    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)
+    codebooks = torch.randn(
+        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
+        dtype=torch.float16,
+        device=device,
+    )

    count = 0
    for index in range(16):
@@ -138,24 +150,25 @@ def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:


 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(
+        "--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)")
+        "(default: False)",
+    )

    # Parse the arguments
    args = parser.parse_args()
@@ -165,7 +178,7 @@ def main():
    bits = args.bits

    if args.test:
-        dequant_test(4096, torch.tensor((4096, )), nbooks, bits)
+        dequant_test(4096, torch.tensor((4096,)), nbooks, bits)
        return

    # Otherwise, benchmark.
@@ -184,31 +197,54 @@ def main():
    with open(filename, "w") as f:
        sys.stdout = f

-        print('m | k | n | n parts', end='')
+        print("m | k | n | n parts", end="")
        for method in methods:
-            print(f" | {method.__name__.replace('_', ' ')} (µs)", end='')
-        print('')
+            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, )))
+        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
+            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__)
+            print(f"{m}", file=sys.__stdout__)
            for ksp in ksandpartions:
-                run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits,
-                         methods)
+                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):
-
+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
@@ -229,7 +265,7 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
            )

    n = parts.sum().item()
-    print(f'{m} | {k} | {n} | {parts.tolist()}', end='')
+    print(f"{m} | {k} | {n} | {parts.tolist()}", end="")

    for method in methods:
        best_time_us = 1e20
@@ -249,32 +285,36 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
            if kernel_dur_us < best_time_us:
                best_time_us = kernel_dur_us

-        print(f' | {kernel_dur_us:.0f}', end='')
+        print(f" | {kernel_dur_us:.0f}", end="")

-    print('')
+    print("")


-def run_timing(num_calls: int, m: int, k: int, parts: torch.Tensor,
-               nbooks: int, bits: int, method) -> float:
-
+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')
+    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)
+    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)
+    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)

@@ -3,27 +3,33 @@
 # Licensed under the MIT License.

 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
-    MINIMUM_BITBLAS_VERSION)
+    MINIMUM_BITBLAS_VERSION,
+)

 try:
    import bitblas
+
    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
-        raise ImportError("bitblas version is wrong. Please "
-                          f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+        raise ImportError(
+            "bitblas version is wrong. Please "
+            f"install bitblas>={MINIMUM_BITBLAS_VERSION}"
+        )
 except ImportError as e:
    bitblas_import_exception = e
-    raise ValueError("Trying to use the bitblas backend, but could not import"
-                     f"with the following error: {bitblas_import_exception}. "
-                     "Please install bitblas through the following command: "
-                     f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
-                     ) from bitblas_import_exception
+    raise ValueError(
+        "Trying to use the bitblas backend, but could not import"
+        f"with the following error: {bitblas_import_exception}. "
+        "Please install bitblas through the following command: "
+        f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+    ) from bitblas_import_exception

 from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target

 from vllm.utils import FlexibleArgumentParser

 parser = FlexibleArgumentParser(
-    description="Benchmark BitBLAS int4 on a specific target.")
+    description="Benchmark BitBLAS int4 on a specific target."
+)

 # Add arguments to the parser
 parser.add_argument(
@@ -32,10 +38,9 @@ parser.add_argument(
    default=auto_detect_nvidia_target(),
    help="Specify the target device for benchmarking.",
 )
-parser.add_argument("--group_size",
-                    type=int,
-                    default=None,
-                    help="Group size for grouped quantization.")
+parser.add_argument(
+    "--group_size", type=int, default=None, help="Group size for grouped quantization."
+)
 parser.add_argument(
    "--A_dtype",
    type=str,
@@ -82,17 +87,17 @@ parser.add_argument(
    choices=["nt", "nn"],
    help="Matrix layout, 'nt' for non-transpose A and transpose W.",
 )
-parser.add_argument("--with_bias",
-                    action="store_true",
-                    help="Include bias in the benchmark.")
+parser.add_argument(
+    "--with_bias", action="store_true", help="Include bias in the benchmark."
+)
 parser.add_argument(
    "--with_scaling",
    action="store_true",
    help="Include scaling factor in the quantization.",
 )
-parser.add_argument("--with_zeros",
-                    action="store_true",
-                    help="Include zeros in the quantization.")
+parser.add_argument(
+    "--with_zeros", action="store_true", help="Include zeros in the quantization."
+)
 parser.add_argument(
    "--zeros_mode",
    type=str,
@@ -170,8 +175,7 @@ shapes = [
 ]

 # Build test shapes with all the shared arguments
-test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args))
-               for shape in shapes]
+test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args)) for shape in shapes]

 benchmark_sets = []
 benchmark_sets.extend(test_shapes)
@@ -206,12 +210,12 @@ for config_key, values in benchmark_results.items():
    func_name = args_split[0]
    input_args_str = "-".join(args_split[1:])
    col_widths[0] = max(col_widths[0], len(func_name) + 2, len(headers[0]) + 2)
-    col_widths[1] = max(col_widths[1],
-                        len(input_args_str) + 2,
-                        len(headers[1]) + 2)
-    col_widths[2] = max(col_widths[2],
-                        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
-                        len(headers[2]) + 2)
+    col_widths[1] = max(col_widths[1], len(input_args_str) + 2, len(headers[1]) + 2)
+    col_widths[2] = max(
+        col_widths[2],
+        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
+        len(headers[2]) + 2,
+    )
    # break only if you want to measure widths from a single example;
    # otherwise, let it loop over all items.

@@ -232,5 +236,6 @@ for config_key, values in benchmark_results.items():
        f"{values['BitBLAS_top20_latency']:.3f} ms",
    ]
    row_str = "".join(
-        [str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)])
+        [str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)]
+    )
    print(row_str)
@@ -0,0 +1,489 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Benchmark the performance of the cutlass_moe_fp4 kernel vs the triton_moe
+kernel. The cutlass_moe_fp4 kernel takes in fp4 quantized weights and 16-bit
+activations. The triton_moe kernel takes in fp8 weights(tensor scaled to fp8)
+and 16-bit activations.
+"""
+
+import nvtx
+import torch
+import torch.utils.benchmark as benchmark
+
+from vllm import _custom_ops as ops
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.scalar_type import scalar_types
+from vllm.utils import FlexibleArgumentParser
+
+WEIGHT_SHAPES_MOE = {
+    "nvidia/DeepSeek-R1-FP4": [
+        [256, 8, 2048, 7168],
+    ],
+}
+
+DEFAULT_MODELS = [
+    "nvidia/DeepSeek-R1-FP4",
+]
+
+DEFAULT_BATCH_SIZES = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
+DEFAULT_TP_SIZES = [1]
+
+PER_ACT_TOKEN_OPTS = [False]
+PER_OUT_CH_OPTS = [False]
+FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
+FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
+
+
+def to_fp8(tensor: torch.Tensor):
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
+        dtype=torch.float8_e4m3fn
+    )
+
+
+def bench_run(
+    results: list[benchmark.Measurement],
+    model: str,
+    num_experts: int,
+    topk: int,
+    per_act_token: bool,
+    per_out_ch: bool,
+    mkn: tuple[int, int, int],
+):
+    label = "NVFP4 Blockscaled CUTLASS MOE vs FP8 Tensor Scaled Triton"
+
+    sub_label = (
+        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, MKN=({})".format(
+            model, num_experts, topk, per_act_token, per_out_ch, mkn
+        )
+    )
+
+    print(f"Testing: {sub_label}")
+
+    (m, k, n) = mkn
+
+    dtype = torch.half
+    device = "cuda"
+    a = torch.randn((m, k), device=device, dtype=dtype) / 10
+    w1 = torch.randn((num_experts, 2 * n, k), device=device, dtype=dtype) / 10
+    w2 = torch.randn((num_experts, k, n), device=device, dtype=dtype) / 10
+
+    _, a_fp8_scale = ops.scaled_fp8_quant(a)
+
+    w1_fp8q = torch.empty(
+        (num_experts, 2 * n, k), device=device, dtype=torch.float8_e4m3fn
+    )
+    w2_fp8q = torch.empty((num_experts, k, n), device=device, dtype=torch.float8_e4m3fn)
+    w1_fp8scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+    w2_fp8scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+
+    for expert in range(num_experts):
+        w1_fp8q[expert], w1_fp8scale[expert] = ops.scaled_fp8_quant(w1[expert])
+        w2_fp8q[expert], w2_fp8scale[expert] = ops.scaled_fp8_quant(w2[expert])
+
+    w1_fp8q_notransp = w1_fp8q.clone()
+    w2_fp8q_notransp = w2_fp8q.clone()
+    w1_fp8q = w1_fp8q.transpose(1, 2)
+    w2_fp8q = w2_fp8q.transpose(1, 2)
+
+    score = torch.randn((m, num_experts), device=device, dtype=dtype)
+
+    topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
+
+    quant_blocksize = 16
+    w1_blockscale = torch.empty(
+        (num_experts, 2 * n, k // quant_blocksize),
+        device=device,
+        dtype=torch.float8_e4m3fn,
+    )
+    w2_blockscale = torch.empty(
+        (num_experts, k, n // quant_blocksize), device=device, dtype=torch.float8_e4m3fn
+    )
+
+    # n_b_scales = 2 * n if per_out_ch else 1
+    # k_b_scales = k if per_out_ch else 1
+    w1_fp4 = torch.empty((num_experts, 2 * n, k // 2), device=device, dtype=torch.uint8)
+    w2_fp4 = torch.empty((num_experts, k, n // 2), device=device, dtype=torch.uint8)
+
+    w1_gs = torch.empty((num_experts,), device=device, dtype=torch.float32)
+    w2_gs = torch.empty((num_experts,), device=device, dtype=torch.float32)
+    a1_gs = torch.ones((num_experts,), device=device, dtype=torch.float32)
+    a2_gs = torch.ones((num_experts,), device=device, dtype=torch.float32)
+
+    for expert in range(num_experts):
+        w1_e = w1[expert]
+        w2_e = w2[expert]
+        w1_amax = torch.abs(w1_e).max().to(torch.float32)
+        w2_amax = torch.abs(w2_e).max().to(torch.float32)
+        w1_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
+        w2_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
+
+        w1_fp4[expert], w1_blockscale[expert] = ops.scaled_fp4_quant(
+            w1_e, w1_gs[expert]
+        )
+
+        w2_fp4[expert], w2_blockscale[expert] = ops.scaled_fp4_quant(
+            w2_e, w2_gs[expert]
+        )
+
+    def run_triton_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a_fp8_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        for _ in range(num_repeats):
+            fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                use_fp8_w8a8=True,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a_fp8_scale,
+            )
+
+    def run_cutlass_moe_fp4(
+        a: torch.Tensor,
+        w1_fp4: torch.Tensor,
+        w2_fp4: torch.Tensor,
+        w1_blockscale: torch.Tensor,
+        w2_blockscale: torch.Tensor,
+        w1_gs: torch.Tensor,
+        w2_gs: torch.Tensor,
+        a1_gs: torch.Tensor,
+        a2_gs: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        m: int,
+        n: int,
+        k: int,
+        e: int,
+        device: torch.device,
+        num_repeats: int,
+    ):
+        for _ in range(num_repeats):
+            with nvtx.annotate("cutlass_moe_fp4", color="green"):
+                cutlass_moe_fp4(
+                    a=a,
+                    a1_gscale=a1_gs,
+                    a2_gscale=a2_gs,
+                    w1_fp4=w1_fp4,
+                    w1_blockscale=w1_blockscale,
+                    w1_alphas=w1_gs,
+                    w2_fp4=w2_fp4,
+                    w2_blockscale=w2_blockscale,
+                    w2_alphas=w2_gs,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    m=m,
+                    n=n,
+                    k=k,
+                    e=num_experts,
+                    device=device,
+                )
+
+    def run_cutlass_from_graph(
+        a: torch.Tensor,
+        a1_gscale: torch.Tensor,
+        w1_fp4: torch.Tensor,
+        w1_blockscale: torch.Tensor,
+        w1_alphas: torch.Tensor,
+        a2_gscale: torch.Tensor,
+        w2_fp4: torch.Tensor,
+        w2_blockscale: torch.Tensor,
+        w2_alphas: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        m: int,
+        n: int,
+        k: int,
+        e: int,
+        device: torch.device,
+    ):
+        with set_current_vllm_config(
+            VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
+        ):
+            return cutlass_moe_fp4(
+                a=a,
+                a1_gscale=a1_gs,
+                w1_fp4=w1_fp4,
+                w1_blockscale=w1_blockscale,
+                w1_alphas=w1_alphas,
+                a2_gscale=a2_gs,
+                w2_fp4=w2_fp4,
+                w2_blockscale=w2_blockscale,
+                w2_alphas=w2_alphas,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                m=m,
+                n=n,
+                k=k,
+                e=num_experts,
+                device=device,
+            )
+
+    def run_triton_from_graph(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a_fp8_scale: torch.Tensor,
+    ):
+        with set_current_vllm_config(
+            VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
+        ):
+            return fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                use_fp8_w8a8=True,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a_fp8_scale,
+            )
+
+    def replay_graph(graph, num_repeats):
+        for _ in range(num_repeats):
+            graph.replay()
+        torch.cuda.synchronize()
+
+    cutlass_stream = torch.cuda.Stream()
+    cutlass_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
+        run_cutlass_from_graph(
+            a=a,
+            a1_gscale=a1_gs,
+            w1_fp4=w1_fp4,
+            w1_blockscale=w1_blockscale,
+            w1_alphas=w1_gs,
+            a2_gscale=a2_gs,
+            w2_fp4=w2_fp4,
+            w2_blockscale=w2_blockscale,
+            w2_alphas=w2_gs,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            m=m,
+            n=n,
+            k=k,
+            e=num_experts,
+            device=device,
+        )
+    torch.cuda.synchronize()
+
+    triton_stream = torch.cuda.Stream()
+    triton_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(triton_graph, stream=triton_stream):
+        run_triton_from_graph(
+            a,
+            w1_fp8q_notransp,
+            w2_fp8q_notransp,
+            topk_weights,
+            topk_ids,
+            w1_fp8scale,
+            w2_fp8scale,
+            a_fp8_scale,
+        )
+    torch.cuda.synchronize()
+
+    min_run_time = 5
+    num_warmup = 5
+    num_runs = 25
+
+    globals = {
+        # Baseline params
+        "w1": w1,
+        "w2": w2,
+        "score": score,
+        "topk": topk,
+        "w1_fp8q_notransp": w1_fp8q_notransp,
+        "w2_fp8q_notransp": w2_fp8q_notransp,
+        "w1_fp8scale": w1_fp8scale,
+        "w2_fp8scale": w2_fp8scale,
+        "a_fp8_scale": a_fp8_scale,
+        # Cutlass params
+        "a": a,
+        "a1_gscale": a1_gs,
+        "w1_fp4": w1_fp4,
+        "w1_blockscale": w1_blockscale,
+        "w1_alphas": w1_gs,
+        "a2_gscale": a2_gs,
+        "w2_fp4": w2_fp4,
+        "w2_blockscale": w2_blockscale,
+        "w2_alphas": w2_gs,
+        "topk_weights": topk_weights,
+        "topk_ids": topk_ids,
+        "m": m,
+        "n": n,
+        "k": k,
+        "e": num_experts,
+        "device": device,
+        # cuda graph params
+        "cutlass_graph": cutlass_graph,
+        "triton_graph": triton_graph,
+        # Gen params
+        "num_runs": num_runs,
+        # Kernels
+        "run_triton_moe": run_triton_moe,
+        "run_cutlass_moe_fp4": run_cutlass_moe_fp4,
+        "replay_graph": replay_graph,
+    }
+
+    # Warmup
+    run_triton_moe(
+        a,
+        w1_fp8q_notransp,
+        w2_fp8q_notransp,
+        topk_weights,
+        topk_ids,
+        w1_fp8scale,
+        w2_fp8scale,
+        a_fp8_scale,
+        num_warmup,
+    )
+
+    results.append(
+        benchmark.Timer(
+            stmt="run_triton_moe(a, w1_fp8q_notransp, w2_fp8q_notransp, topk_weights, topk_ids, w1_fp8scale, w2_fp8scale, a_fp8_scale, num_runs)",  # noqa: E501
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="triton_moe",
+        ).blocked_autorange(min_run_time=min_run_time)
+    )
+
+    # Warmup
+    replay_graph(triton_graph, num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt="replay_graph(triton_graph, num_runs)",
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="triton_moe_cuda_graphs",
+        ).blocked_autorange(min_run_time=min_run_time)
+    )
+
+    # Warmup
+
+    run_cutlass_moe_fp4(
+        a,
+        w1_fp4,
+        w2_fp4,
+        w1_blockscale,
+        w2_blockscale,
+        w1_gs,
+        w2_gs,
+        a1_gs,
+        a2_gs,
+        topk_weights,
+        topk_ids,
+        m,
+        n,
+        k,
+        num_experts,
+        device,
+        num_warmup,
+    )
+
+    results.append(
+        benchmark.Timer(
+            stmt="run_cutlass_moe_fp4(a, w1_fp4, w2_fp4, w1_blockscale, w2_blockscale, w1_alphas, w2_alphas, a1_gscale, a2_gscale, topk_weights, topk_ids, m, n, k, e, device, num_runs)",  # noqa: E501
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="cutlass_moe_fp4",
+        ).blocked_autorange(min_run_time=min_run_time)
+    )
+
+    # Warmup
+    replay_graph(cutlass_graph, num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt="replay_graph(cutlass_graph, num_runs)",
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="cutlass_moe_fp4_cuda_graphs",
+        ).blocked_autorange(min_run_time=min_run_time)
+    )
+
+
+def main(args):
+    print("Benchmarking models:")
+    for i, model in enumerate(args.models):
+        print(f"[{i}]  {model}")
+
+    results: list[benchmark.Measurement] = []
+
+    for model in args.models:
+        for tp in args.tp_sizes:
+            for layer in WEIGHT_SHAPES_MOE[model]:
+                num_experts = layer[0]
+                topk = layer[1]
+                size_k = layer[2]
+                size_n = layer[3] // tp
+
+                if len(args.limit_k) > 0 and size_k not in args.limit_k:
+                    continue
+
+                if len(args.limit_n) > 0 and size_n not in args.limit_n:
+                    continue
+
+                for per_act_token in PER_ACT_TOKEN_OPTS:
+                    for per_out_ch in PER_OUT_CH_OPTS:
+                        for size_m in args.batch_sizes:
+                            mkn = (size_m, size_k, size_n)
+                            bench_run(
+                                results,
+                                model,
+                                num_experts,
+                                topk,
+                                per_act_token,
+                                per_out_ch,
+                                mkn,
+                            )
+
+    compare = benchmark.Compare(results)
+    compare.print()
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark NVFP4 CUTLASS MOE across specified models/shapes/batches"
+    )
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES_MOE.keys(),
+    )
+    parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
+    parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
+    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-per-act-token", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])
+
+    args = parser.parse_args()
+    main(args)
@@ -6,14 +6,18 @@ from benchmark_shapes import WEIGHT_SHAPES_MOE

 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.fused_moe import (cutlass_moe_fp8,
-                                                            fused_experts,
-                                                            fused_topk)
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    cutlass_moe_fp8,
+    fused_experts,
+    fused_topk,
+)
 from vllm.utils import FlexibleArgumentParser

 DEFAULT_MODELS = [
-    "nm-testing/Mixtral-8x7B-Instruct-v0.1", "nm-testing/deepseekv2-lite",
-    "ibm-granite/granite-3.0-1b-a400m", "ibm-granite/granite-3.0-3b-a800m"
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1",
+    "nm-testing/deepseekv2-lite",
+    "ibm-granite/granite-3.0-1b-a400m",
+    "ibm-granite/granite-3.0-3b-a800m",
 ]
 DEFAULT_BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512]
 DEFAULT_TP_SIZES = [1]
@@ -24,19 +28,27 @@ PER_OUT_CH_OPTS = [False]

 def to_fp8(tensor: torch.Tensor):
    finfo = torch.finfo(torch.float8_e4m3fn)
-    return torch.round(tensor.clamp(
-        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
+    return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
+        dtype=torch.float8_e4m3fn
+    )


-def bench_run(results: list[benchmark.Measurement], model: str,
-              num_experts: int, topk: int, per_act_token: bool,
-              per_out_ch: bool, mkn: tuple[int, int, int]):
+def bench_run(
+    results: list[benchmark.Measurement],
+    model: str,
+    num_experts: int,
+    topk: int,
+    per_act_token: bool,
+    per_out_ch: bool,
+    mkn: tuple[int, int, int],
+):
    label = "Quant Matmul"

    sub_label = (
-        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
-        "MKN=({})".format(model, num_experts, topk, per_act_token, per_out_ch,
-                          mkn))
+        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, MKN=({})".format(
+            model, num_experts, topk, per_act_token, per_out_ch, mkn
+        )
+    )

    print(f"Testing: {sub_label}")

@@ -50,35 +62,17 @@ def bench_run(results: list[benchmark.Measurement], model: str,

    _, a_scale = ops.scaled_fp8_quant(a)

-    w1_q = torch.empty((num_experts, 2 * n, k),
-                       device="cuda",
-                       dtype=torch.float8_e4m3fn)
-    w2_q = torch.empty((num_experts, k, n),
-                       device="cuda",
-                       dtype=torch.float8_e4m3fn)
-    w1_scale = torch.empty((num_experts, 1, 1),
-                           device="cuda",
-                           dtype=torch.float32)
-    w2_scale = torch.empty((num_experts, 1, 1),
-                           device="cuda",
-                           dtype=torch.float32)
+    w1_q = torch.empty(
+        (num_experts, 2 * n, k), device="cuda", dtype=torch.float8_e4m3fn
+    )
+    w2_q = torch.empty((num_experts, k, n), device="cuda", dtype=torch.float8_e4m3fn)
+    w1_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
+    w2_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)

-    ab_strides1 = torch.full((num_experts, ),
-                             k,
-                             device="cuda",
-                             dtype=torch.int64)
-    c_strides1 = torch.full((num_experts, ),
-                            2 * n,
-                            device="cuda",
-                            dtype=torch.int64)
-    ab_strides2 = torch.full((num_experts, ),
-                             n,
-                             device="cuda",
-                             dtype=torch.int64)
-    c_strides2 = torch.full((num_experts, ),
-                            k,
-                            device="cuda",
-                            dtype=torch.int64)
+    ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
+    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
+    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)

    for expert in range(num_experts):
        w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(w1[expert])
@@ -91,82 +85,120 @@ def bench_run(results: list[benchmark.Measurement], model: str,
    score = torch.randn((m, num_experts), device="cuda", dtype=dtype)

    topk_weights, topk_ids, token_expert_indices = fused_topk(
-        a, score, topk, renormalize=False)
+        a, score, topk, renormalize=False
+    )

-    def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
-                       topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-                       w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-                       a_scale: torch.Tensor, num_repeats: int):
+    def run_triton_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a_scale: torch.Tensor,
+        num_repeats: int,
+    ):
        for _ in range(num_repeats):
-            fused_experts(a,
-                          w1,
-                          w2,
-                          topk_weights,
-                          topk_ids,
-                          use_fp8_w8a8=True,
-                          w1_scale=w1_scale,
-                          w2_scale=w2_scale,
-                          a1_scale=a_scale)
+            fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                use_fp8_w8a8=True,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a_scale,
+            )

-    def run_cutlass_moe(a: torch.Tensor, a_scale: torch.Tensor,
-                        w1: torch.Tensor, w2: torch.Tensor,
-                        w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-                        topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-                        ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
-                        ab_strides2: torch.Tensor, c_strides2: torch.Tensor,
-                        num_repeats: int):
+    def run_cutlass_moe(
+        a: torch.Tensor,
+        a_scale: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        c_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides2: torch.Tensor,
+        num_repeats: int,
+    ):
        for _ in range(num_repeats):
-            cutlass_moe_fp8(a,
-                            w1,
-                            w2,
-                            w1_scale,
-                            w2_scale,
-                            topk_weights,
-                            topk_ids,
-                            ab_strides1,
-                            c_strides1,
-                            ab_strides2,
-                            c_strides2,
-                            a1_scale=a_scale)
+            cutlass_moe_fp8(
+                a,
+                w1,
+                w2,
+                w1_scale,
+                w2_scale,
+                topk_weights,
+                topk_ids,
+                ab_strides1,
+                c_strides1,
+                ab_strides2,
+                c_strides2,
+                a1_scale=a_scale,
+            )

    def run_cutlass_from_graph(
-            a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
-            w2_q: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-            topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-            ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
-            ab_strides2: torch.Tensor, c_strides2: torch.Tensor):
+        a: torch.Tensor,
+        a_scale: torch.Tensor,
+        w1_q: torch.Tensor,
+        w2_q: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        c_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides2: torch.Tensor,
+    ):
        with set_current_vllm_config(
-                VllmConfig(parallel_config=ParallelConfig(
-                    pipeline_parallel_size=1))):
-            return cutlass_moe_fp8(a,
-                                   w1_q,
-                                   w2_q,
-                                   w1_scale,
-                                   w2_scale,
-                                   topk_weights,
-                                   topk_ids,
-                                   ab_strides1,
-                                   c_strides1,
-                                   ab_strides2,
-                                   c_strides2,
-                                   a1_scale=a_scale)
+            VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
+        ):
+            return cutlass_moe_fp8(
+                a,
+                w1_q,
+                w2_q,
+                w1_scale,
+                w2_scale,
+                topk_weights,
+                topk_ids,
+                ab_strides1,
+                c_strides1,
+                ab_strides2,
+                c_strides2,
+                a1_scale=a_scale,
+            )

-    def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
-                              w2: torch.Tensor, topk_weights: torch.Tensor,
-                              topk_ids: torch.Tensor, w1_scale: torch.Tensor,
-                              w2_scale: torch.Tensor, a_scale: torch.Tensor):
+    def run_triton_from_graph(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a_scale: torch.Tensor,
+    ):
        with set_current_vllm_config(
-                VllmConfig(parallel_config=ParallelConfig(
-                    pipeline_parallel_size=1))):
-            return fused_experts(a,
-                                 w1,
-                                 w2,
-                                 topk_weights,
-                                 topk_ids,
-                                 use_fp8_w8a8=True,
-                                 w1_scale=w1_scale,
-                                 w2_scale=w2_scale,
-                                 a1_scale=a_scale)
+            VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
+        ):
+            return fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                use_fp8_w8a8=True,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a_scale,
+            )

    def replay_graph(graph, num_repeats):
        for _ in range(num_repeats):
@@ -176,16 +208,35 @@ def bench_run(results: list[benchmark.Measurement], model: str,
    cutlass_stream = torch.cuda.Stream()
    cutlass_graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
-        run_cutlass_from_graph(a, a_scale, w1_q, w2_q, w1_scale, w2_scale,
-                               topk_weights, topk_ids, ab_strides1, c_strides1,
-                               ab_strides2, c_strides2)
+        run_cutlass_from_graph(
+            a,
+            a_scale,
+            w1_q,
+            w2_q,
+            w1_scale,
+            w2_scale,
+            topk_weights,
+            topk_ids,
+            ab_strides1,
+            c_strides1,
+            ab_strides2,
+            c_strides2,
+        )
    torch.cuda.synchronize()

    triton_stream = torch.cuda.Stream()
    triton_graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(triton_graph, stream=triton_stream):
-        run_triton_from_graph(a, w1_q_notransp, w2_q_notransp, topk_weights,
-                              topk_ids, w1_scale, w2_scale, a_scale)
+        run_triton_from_graph(
+            a,
+            w1_q_notransp,
+            w2_q_notransp,
+            topk_weights,
+            topk_ids,
+            w1_scale,
+            w2_scale,
+            a_scale,
+        )
    torch.cuda.synchronize()

    min_run_time = 5
@@ -225,18 +276,27 @@ def bench_run(results: list[benchmark.Measurement], model: str,
    }

    # Warmup
-    run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids,
-                   w1_scale, w2_scale, a_scale, num_warmup)
+    run_triton_moe(
+        a,
+        w1_q_notransp,
+        w2_q_notransp,
+        topk_weights,
+        topk_ids,
+        w1_scale,
+        w2_scale,
+        a_scale,
+        num_warmup,
+    )

    results.append(
        benchmark.Timer(
-            stmt=
-            "run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)",  # noqa: E501
+            stmt="run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
            description="triton_moe",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    # Warmup
    replay_graph(triton_graph, num_warmup)
@@ -248,22 +308,35 @@ def bench_run(results: list[benchmark.Measurement], model: str,
            label=label,
            sub_label=sub_label,
            description="triton_moe_cuda_graphs",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    # Warmup
-    run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights,
-                    topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2,
-                    num_warmup)
+    run_cutlass_moe(
+        a,
+        a_scale,
+        w1_q,
+        w2_q,
+        w1_scale,
+        w2_scale,
+        topk_weights,
+        topk_ids,
+        ab_strides1,
+        c_strides1,
+        ab_strides2,
+        c_strides2,
+        num_warmup,
+    )

    results.append(
        benchmark.Timer(
-            stmt=
-            "run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
            description="grouped_gemm_moe",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    # Warmup
    replay_graph(cutlass_graph, num_warmup)
@@ -275,7 +348,8 @@ def bench_run(results: list[benchmark.Measurement], model: str,
            label=label,
            sub_label=sub_label,
            description="grouped_gemm_moe_cuda_graphs",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )


 def main(args):
@@ -303,8 +377,15 @@ def main(args):
                    for per_out_ch in PER_OUT_CH_OPTS:
                        for size_m in DEFAULT_BATCH_SIZES:
                            mkn = (size_m, size_k, size_n)
-                            bench_run(results, model, num_experts, topk,
-                                      per_act_token, per_out_ch, mkn)
+                            bench_run(
+                                results,
+                                model,
+                                num_experts,
+                                topk,
+                                per_act_token,
+                                per_out_ch,
+                                mkn,
+                            )

    compare = benchmark.Compare(results)
    compare.print()
@@ -312,7 +393,8 @@ def main(args):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description="Benchmark Marlin across specified models/shapes/batches")
+        description="Benchmark Marlin across specified models/shapes/batches"
+    )
    parser.add_argument(
        "--models",
        nargs="+",
@@ -320,21 +402,14 @@ if __name__ == "__main__":
        default=DEFAULT_MODELS,
        choices=WEIGHT_SHAPES_MOE.keys(),
    )
-    parser.add_argument("--tp-sizes",
-                        nargs="+",
-                        type=int,
-                        default=DEFAULT_TP_SIZES)
-    parser.add_argument("--batch-sizes",
-                        nargs="+",
-                        type=int,
-                        default=DEFAULT_BATCH_SIZES)
+    parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
+    parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
    parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-per-act-token",
-                        nargs="+",
-                        type=int,
-                        default=[])
+    parser.add_argument("--limit-per-act-token", nargs="+", type=int, default=[])
    parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])

    args = parser.parse_args()
@@ -10,14 +10,16 @@ from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser


@torch.inference_mode()
-def main(num_tokens: int,
-         hidden_size: int,
-         add_residual: bool,
-         dtype: torch.dtype,
-         seed: int = 0,
-         do_profile: bool = False,
-         num_warmup_iters: int = 5,
-         num_iters: int = 100) -> None:
+def main(
+    num_tokens: int,
+    hidden_size: int,
+    add_residual: bool,
+    dtype: torch.dtype,
+    seed: int = 0,
+    do_profile: bool = False,
+    num_warmup_iters: int = 5,
+    num_iters: int = 100,
+) -> None:
    current_platform.seed_everything(seed)
    torch.set_default_device("cuda")

@@ -56,33 +58,35 @@ def main(num_tokens: int,
    print(f"Kernel running time: {latency * 1000000:.3f} us")


-if __name__ == '__main__':
-    parser = FlexibleArgumentParser(
-        description="Benchmark the layernorm kernel.")
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the layernorm kernel.")
    parser.add_argument("--num-tokens", type=int, default=4096)
    parser.add_argument("--hidden-size", type=int, default=8192)
    parser.add_argument("--add-residual", action="store_true")
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["half", "bfloat16", "float"],
-                        default="half")
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--profile", action="store_true")
    parser.add_argument("--num-warmup-iters", type=int, default=5)
-    parser.add_argument("--num-iters",
-                        type=int,
-                        default=100,
-                        help="Number of benchmark iterations. "
-                        "If --profile is set, this number is ignored")
+    parser.add_argument(
+        "--num-iters",
+        type=int,
+        default=100,
+        help="Number of benchmark iterations. "
+        "If --profile is set, this number is ignored",
+    )

    args = parser.parse_args()
    print(args)

-    main(num_tokens=args.num_tokens,
-         hidden_size=args.hidden_size,
-         add_residual=args.add_residual,
-         dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-         seed=args.seed,
-         do_profile=args.profile,
-         num_warmup_iters=args.num_warmup_iters,
-         num_iters=args.num_iters)
+    main(
+        num_tokens=args.num_tokens,
+        hidden_size=args.hidden_size,
+        add_residual=args.add_residual,
+        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+        seed=args.seed,
+        do_profile=args.profile,
+        num_warmup_iters=args.num_warmup_iters,
+        num_iters=args.num_iters,
+    )
@@ -20,12 +20,18 @@ from weight_shapes import WEIGHT_SHAPES

 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, marlin_permute_scales,
-    marlin_zero_points)
+    GPTQ_MARLIN_MAX_PARALLEL,
+    GPTQ_MARLIN_MIN_THREAD_N,
+    marlin_permute_scales,
+    marlin_zero_points,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
-    MarlinWorkspace)
+    MarlinWorkspace,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    pack_rows, quantize_weights)
+    pack_rows,
+    quantize_weights,
+)
 from vllm.scalar_type import ScalarType, scalar_types
 from vllm.utils import FlexibleArgumentParser

@@ -82,12 +88,14 @@ def rand_data(shape, dtype=torch.float16, scale=1):
        return torch.randint(-15, 15, shape, dtype=dtype, device="cuda")


-def quantize_and_pack(atype: torch.dtype,
-                      w: torch.Tensor,
-                      wtype: ScalarType,
-                      stype: Optional[torch.dtype],
-                      group_size: Optional[int],
-                      zero_points: bool = False):
+def quantize_and_pack(
+    atype: torch.dtype,
+    w: torch.Tensor,
+    wtype: ScalarType,
+    stype: Optional[torch.dtype],
+    group_size: Optional[int],
+    zero_points: bool = False,
+):
    assert wtype.is_integer(), "TODO: support floating point weights"

    w_ref, w_q, w_s, w_zp = quantize_weights(
@@ -96,21 +104,24 @@ def quantize_and_pack(atype: torch.dtype,
        group_size=group_size,
        zero_points=zero_points,
        # to match how the kernel applies zps
-        ref_zero_points_after_scales=True)
+        ref_zero_points_after_scales=True,
+    )

    w_q = pack_rows(w_q, wtype.size_bits, *w_q.shape)
    return w_ref, w_q, w_s, w_zp


-def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
-                         group_size: Optional[int]) -> list[BenchmarkTensors]:
+def create_bench_tensors(
+    shape: tuple[int, int, int], types: TypeConfig, group_size: Optional[int]
+) -> list[BenchmarkTensors]:
    m, n, k = shape

    # we want to make sure that weights don't fit into L2 cache between runs so
    #  we construct enough weights to exceed L2 cache, which is 50mb on a H100
    #  so we target total weight size > 2*50mb
-    num_weights = math.ceil(2 * 50 * 1024**2 * 8 /
-                            (k * n * types.weight_type.size_bits))
+    num_weights = math.ceil(
+        2 * 50 * 1024**2 * 8 / (k * n * types.weight_type.size_bits)
+    )

    a = rand_data((m, k), types.act_type, scale=5)

@@ -124,8 +135,13 @@ def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
            w = w.to(torch.float16)

        w_ref, w_q_packed, w_s, w_zp = quantize_and_pack(
-            a.dtype, w, types.weight_type, types.group_scale_type, group_size,
-            types.group_zero_type is not None)
+            a.dtype,
+            w,
+            types.weight_type,
+            types.group_scale_type,
+            group_size,
+            types.group_zero_type is not None,
+        )

        if not a.dtype.is_floating_point:
            aiinfo = torch.iinfo(a.dtype)
@@ -133,21 +149,30 @@ def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,

        w_ref = w_ref.to(torch.float32)

-        w_ch_s = None if types.channel_scale_type is None else\
-            rand_data((n,), types.channel_scale_type)
-        w_tok_s = None if types.token_scale_type is None else\
-            rand_data((m,), types.token_scale_type)
+        w_ch_s = (
+            None
+            if types.channel_scale_type is None
+            else rand_data((n,), types.channel_scale_type)
+        )
+        w_tok_s = (
+            None
+            if types.token_scale_type is None
+            else rand_data((m,), types.token_scale_type)
+        )

        benchmark_tensors.append(
-            BenchmarkTensors(w_ref=w_ref,
-                             a=a,
-                             w_q=w_q_packed,
-                             wtype=types.weight_type,
-                             w_g_s=w_s,
-                             w_g_zp=w_zp,
-                             group_size=group_size,
-                             w_ch_s=w_ch_s,
-                             w_tok_s=w_tok_s))
+            BenchmarkTensors(
+                w_ref=w_ref,
+                a=a,
+                w_q=w_q_packed,
+                wtype=types.weight_type,
+                w_g_s=w_s,
+                w_g_zp=w_zp,
+                group_size=group_size,
+                w_ch_s=w_ch_s,
+                w_tok_s=w_tok_s,
+            )
+        )

    return benchmark_tensors

@@ -170,50 +195,57 @@ def cutlass_scaled_mm_create_bench_fn(bt: BenchmarkTensors) -> Callable:
        scale_b = torch.tensor(1.0, dtype=torch.float32, device=bt.a.device)
    w_col_major = bt.w_ref.to(bt.a.dtype).t().contiguous().t()
    return lambda: ops.cutlass_scaled_mm(
-        bt.a, w_col_major, scale_a, scale_b, out_dtype=torch.float16)
+        bt.a, w_col_major, scale_a, scale_b, out_dtype=torch.float16
+    )


 def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
    device = bt.a.device

-    workspace = MarlinWorkspace(bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N,
-                                GPTQ_MARLIN_MAX_PARALLEL)
+    workspace = MarlinWorkspace(
+        bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+    )

    if bt.w_g_zp is None:
        w_zp = torch.empty(0, dtype=torch.int, device=device)
    else:
-        w_zp = marlin_zero_points(bt.w_g_zp, bt.w_ref.shape[0],
-                                  bt.w_ref.shape[1], bt.wtype.size_bits)
+        w_zp = marlin_zero_points(
+            bt.w_g_zp, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
+        )

    if bt.group_size is None:
        w_s = torch.tensor([], device="cuda", dtype=torch.half)
    else:
-        w_s = marlin_permute_scales(bt.w_g_s, bt.w_ref.shape[0],
-                                    bt.w_ref.shape[1], bt.group_size)
+        w_s = marlin_permute_scales(
+            bt.w_g_s, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.group_size
+        )

    sort_indices = torch.empty(0, dtype=torch.int, device=device)
    g_idx = torch.empty(0, dtype=torch.int, device=device)
-    w_q = ops.gptq_marlin_repack(bt.w_q, sort_indices, bt.w_ref.shape[0],
-                                 bt.w_ref.shape[1], bt.wtype.size_bits)
+    w_q = ops.gptq_marlin_repack(
+        bt.w_q, sort_indices, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
+    )

    if bt.a.dtype.is_floating_point:
        assert bt.w_ch_s is None
        assert bt.w_tok_s is None
        assert bt.group_size is not None

-        fn = lambda: ops.gptq_marlin_gemm(a=bt.a,
-                                          b_q_weight=w_q,
-                                          b_scales=w_s,
-                                          b_zeros=w_zp,
-                                          g_idx=g_idx,
-                                          perm=sort_indices,
-                                          workspace=workspace.scratch,
-                                          b_q_type=bt.wtype,
-                                          size_m=bt.a.shape[0],
-                                          size_n=bt.w_ref.shape[1],
-                                          size_k=bt.w_ref.shape[0],
-                                          is_k_full=True,
-                                          is_zp_float=False)
+        fn = lambda: ops.gptq_marlin_gemm(
+            a=bt.a,
+            b_q_weight=w_q,
+            b_scales=w_s,
+            b_zeros=w_zp,
+            g_idx=g_idx,
+            perm=sort_indices,
+            workspace=workspace.scratch,
+            b_q_type=bt.wtype,
+            size_m=bt.a.shape[0],
+            size_n=bt.w_ref.shape[1],
+            size_k=bt.w_ref.shape[0],
+            is_k_full=True,
+            is_zp_float=False,
+        )
    else:
        assert bt.a.dtype == torch.int8
        assert bt.wtype == scalar_types.uint4b8
@@ -221,36 +253,35 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
        if bt.w_ch_s is not None:
            s_ch = bt.w_ch_s.to(torch.float32)
        else:
-            s_ch = torch.ones(bt.w_ref.shape[1],
-                              dtype=torch.float32,
-                              device=device)
+            s_ch = torch.ones(bt.w_ref.shape[1], dtype=torch.float32, device=device)

        if bt.w_tok_s is not None:
            s_tok = bt.w_tok_s.to(torch.float32)
        else:
-            s_tok = torch.ones(bt.a.shape[0],
-                               dtype=torch.float32,
-                               device=device)
+            s_tok = torch.ones(bt.a.shape[0], dtype=torch.float32, device=device)

-        fn = lambda: ops.marlin_qqq_gemm(a=bt.a,
-                                         b_q_weight=w_q,
-                                         s_group=w_s,
-                                         s_tok=s_tok,
-                                         s_ch=s_ch,
-                                         workspace=workspace.scratch,
-                                         size_m=bt.a.shape[0],
-                                         size_n=bt.w_ref.shape[1],
-                                         size_k=bt.w_ref.shape[0])
+        fn = lambda: ops.marlin_qqq_gemm(
+            a=bt.a,
+            b_q_weight=w_q,
+            s_group=w_s,
+            s_tok=s_tok,
+            s_ch=s_ch,
+            workspace=workspace.scratch,
+            size_m=bt.a.shape[0],
+            size_n=bt.w_ref.shape[1],
+            size_k=bt.w_ref.shape[0],
+        )

    return fn


-def machete_create_bench_fn(bt: BenchmarkTensors,
-                            out_type=torch.dtype,
-                            schedule=None) -> Callable:
+def machete_create_bench_fn(
+    bt: BenchmarkTensors, out_type=torch.dtype, schedule=None
+) -> Callable:
    w_q = bt.w_q.t().contiguous().t()  # make col major
-    w_q = ops.machete_prepack_B(w_q, bt.a.dtype, bt.wtype,
-                                None if bt.w_g_s is None else bt.w_g_s.dtype)
+    w_q = ops.machete_prepack_B(
+        w_q, bt.a.dtype, bt.wtype, None if bt.w_g_s is None else bt.w_g_s.dtype
+    )

    w_g_zp = bt.w_g_zp
    if w_g_zp is not None:
@@ -275,26 +306,24 @@ def machete_create_bench_fn(bt: BenchmarkTensors,
 # bench


-def bench_fns(label: str, sub_label: str, description: str,
-              fns: list[Callable]):
-
+def bench_fns(label: str, sub_label: str, description: str, fns: list[Callable]):
    min_run_time = 1 if not NVTX_PROFILE else 0.1
    res = TBenchmark.Timer(
        stmt="""
        for fn in fns:
            fn()
        """,
-        globals={
-            "fns": fns
-        },
+        globals={"fns": fns},
        label=label,
        sub_label=sub_label,
        description=description,
    ).blocked_autorange(min_run_time=min_run_time)

    if NVTX_PROFILE:
-        with nvtx.annotate("mm-bench"), nvtx.annotate(
-                f"{label}|{sub_label}|{description}"):
+        with (
+            nvtx.annotate("mm-bench"),
+            nvtx.annotate(f"{label}|{sub_label}|{description}"),
+        ):
            fns[0]()

    return res
@@ -304,19 +333,20 @@ _SWEEP_SCHEDULES_RESULTS: Optional[pd.DataFrame] = None
 _SWEEP_SCHEDULES_RESULTS_CSV: Optional[str] = None


-def bench(types: TypeConfig,
-          group_size: int,
-          m: int,
-          k: int,
-          n: int,
-          label: str,
-          sub_label: str,
-          sweep_schedules: bool = True) -> list[TMeasurement]:
+def bench(
+    types: TypeConfig,
+    group_size: int,
+    m: int,
+    k: int,
+    n: int,
+    label: str,
+    sub_label: str,
+    sweep_schedules: bool = True,
+) -> list[TMeasurement]:
    benchmark_tensors = create_bench_tensors((m, n, k), types, group_size)
    sub_label += f", L={len(benchmark_tensors)}"

-    name_type_string = f"W{types.weight_type}"+\
-                       f"-A{terse_type_name(types.act_type)}"
+    name_type_string = f"W{types.weight_type}" + f"-A{terse_type_name(types.act_type)}"
    if types.group_scale_type is not None:
        name_type_string += f"-GS{terse_type_name(types.group_scale_type)}"
    if types.group_zero_type is not None:
@@ -332,31 +362,45 @@ def bench(types: TypeConfig,
    # pytorch impl
    timers.append(
        bench_fns(
-            label, sub_label, "torch.matmul (fp16)",
-            [torch_matmul_f16_create_bench_fn(bt)
-             for bt in benchmark_tensors]))
+            label,
+            sub_label,
+            "torch.matmul (fp16)",
+            [torch_matmul_f16_create_bench_fn(bt) for bt in benchmark_tensors],
+        )
+    )

    if types.act_type == torch.int8 or types.act_type == torch.float8_e4m3fn:
        timers.append(
            bench_fns(
-                label, sub_label,
-                f"cutlass_scaled_mm ({terse_type_name(types.act_type)})", [
-                    cutlass_scaled_mm_create_bench_fn(bt)
-                    for bt in benchmark_tensors
-                ]))
+                label,
+                sub_label,
+                f"cutlass_scaled_mm ({terse_type_name(types.act_type)})",
+                [cutlass_scaled_mm_create_bench_fn(bt) for bt in benchmark_tensors],
+            )
+        )

    if types.act_type != torch.float8_e4m3fn:
        timers.append(
-            bench_fns(label, sub_label, f"marlin ({name_type_string})",
-                      [marlin_create_bench_fn(bt)
-                       for bt in benchmark_tensors]))
+            bench_fns(
+                label,
+                sub_label,
+                f"marlin ({name_type_string})",
+                [marlin_create_bench_fn(bt) for bt in benchmark_tensors],
+            )
+        )

    # machete
    timers.append(
-        bench_fns(label, sub_label, f"machete ({name_type_string})", [
-            machete_create_bench_fn(bt, out_type=types.output_type)
-            for bt in benchmark_tensors
-        ]))
+        bench_fns(
+            label,
+            sub_label,
+            f"machete ({name_type_string})",
+            [
+                machete_create_bench_fn(bt, out_type=types.output_type)
+                for bt in benchmark_tensors
+            ],
+        )
+    )

    if sweep_schedules:
        global _SWEEP_SCHEDULES_RESULTS
@@ -371,7 +415,8 @@ def bench(types: TypeConfig,
            group_zeros_type=types.group_zero_type,
            token_scales_type=types.token_scale_type,
            channel_scales_type=types.channel_scale_type,
-            out_type=types.output_type)
+            out_type=types.output_type,
+        )

        if schedules is None or len(schedules) == 0:
            raise ValueError("No schedules found to sweep")
@@ -383,11 +428,17 @@ def bench(types: TypeConfig,
            if schedule_M >= 2 * max(m, 16) or schedule_M < m // 4:
                continue

-            res = bench_fns(label, sub_label, "machete_best", [
-                machete_create_bench_fn(
-                    bt, out_type=types.output_type, schedule=schedule)
-                for bt in benchmark_tensors
-            ])
+            res = bench_fns(
+                label,
+                sub_label,
+                "machete_best",
+                [
+                    machete_create_bench_fn(
+                        bt, out_type=types.output_type, schedule=schedule
+                    )
+                    for bt in benchmark_tensors
+                ],
+            )

            results_row = {
                "M": m,
@@ -398,10 +449,8 @@ def bench(types: TypeConfig,
                "median": res.median,
            }
            if _SWEEP_SCHEDULES_RESULTS is None:
-                _SWEEP_SCHEDULES_RESULTS = pd.DataFrame(
-                    columns=results_row.keys())
-            _SWEEP_SCHEDULES_RESULTS.\
-                loc[len(_SWEEP_SCHEDULES_RESULTS)] = results_row
+                _SWEEP_SCHEDULES_RESULTS = pd.DataFrame(columns=results_row.keys())
+            _SWEEP_SCHEDULES_RESULTS.loc[len(_SWEEP_SCHEDULES_RESULTS)] = results_row

            print(f"  {res.median:5.5} ", schedule)
            if not best or res.median < best.median:
@@ -422,8 +471,9 @@ def print_timers(timers: list[TMeasurement]):
 def run(args, MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
    types = TypeConfig(
        act_type=args.act_type,
-        weight_type=scalar_types.uint4b8 if args.group_zero_type is None \
-            else scalar_types.uint4,
+        weight_type=scalar_types.uint4b8
+        if args.group_zero_type is None
+        else scalar_types.uint4,
        output_type=args.out_type,
        group_scale_type=args.group_scale_type,
        group_zero_type=args.group_zero_type,
@@ -433,14 +483,16 @@ def run(args, MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:

    results: list[TMeasurement] = []
    for m, k, n in MKNs:
-        timers = bench(types,
-                       args.group_size,
-                       m,
-                       k,
-                       n,
-                       f"{args.act_type}-gemm",
-                       f"MKN=({m}x{k}x{n})",
-                       sweep_schedules=args.sweep_schedules)
+        timers = bench(
+            types,
+            args.group_size,
+            m,
+            k,
+            n,
+            f"{args.act_type}-gemm",
+            f"MKN=({m}x{k}x{n})",
+            sweep_schedules=args.sweep_schedules,
+        )
        print_timers(timers)
        results.extend(timers)

@@ -454,7 +506,6 @@ def make_output(
    base_description: str,
    timestamp=None,
 ):
-
    print(f"== All Results {base_description} ====")
    print_timers(data)

@@ -468,8 +519,7 @@ def make_output(


 def run_square_bench(args):
-    dim_sizes = list(
-        range(args.dim_start, args.dim_end + 1, args.dim_increment))
+    dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
    MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
    data = run(args.dtype, args.sweep_schedules, MKNs)

@@ -479,8 +529,9 @@ def run_square_bench(args):
 def run_range_bench(args):
    m_start, k_start, n_start = (int(x) for x in args.dim_start.split(","))
    m_end, k_end, n_end = (int(x) for x in args.dim_end.split(","))
-    m_increment, k_increment, n_increment = \
-        (int(x) for x in args.dim_increment.split(","))
+    m_increment, k_increment, n_increment = (
+        int(x) for x in args.dim_increment.split(",")
+    )
    Ms = list(range(m_start, m_end + 1, m_increment))
    Ks = list(range(k_start, k_end + 1, k_increment))
    Ns = list(range(n_start, n_end + 1, n_increment))
@@ -492,7 +543,6 @@ def run_range_bench(args):


 def run_model_bench(args):
-
    print("Benchmarking models:")
    for i, model in enumerate(args.models):
        print(f"[{i}]  {model}")
@@ -535,10 +585,13 @@ def run_model_bench(args):
    with open(f"model_bench-{type_string}-{timestr}.pkl", "wb") as f:
        args_dict = vars(args)
        args_dict.pop("func")
-        pkl.dump({
-            "args": args_dict,
-            "results": all_results,
-        }, f)
+        pkl.dump(
+            {
+                "args": args_dict,
+                "results": all_results,
+            },
+            f,
+        )


 if __name__ == "__main__":
@@ -554,7 +607,6 @@ if __name__ == "__main__":
        }[dt]

    class ToTorchDtype(argparse.Action):
-
        def __call__(self, parser, namespace, values, option_string=None):
            setattr(namespace, self.dest, to_torch_dtype(values))

@@ -580,32 +632,32 @@ Benchmark Machete GEMM.
        "--act-type",
        action=ToTorchDtype,
        required=True,
-        choices=['bfloat16', 'float16', 'int8', 'float8_e4m3fn'],
+        choices=["bfloat16", "float16", "int8", "float8_e4m3fn"],
    )
    parser.add_argument(
        "--group-scale-type",
        action=ToTorchDtype,
-        choices=['bfloat16', 'float16'],
+        choices=["bfloat16", "float16"],
    )
    parser.add_argument(
        "--group-zero-type",
        type=to_torch_dtype,
-        choices=['bfloat16', 'float16'],
+        choices=["bfloat16", "float16"],
    )
    parser.add_argument(
        "--channel-scale-type",
        action=ToTorchDtype,
-        choices=['float'],
+        choices=["float"],
    )
    parser.add_argument(
        "--token-scale-type",
        action=ToTorchDtype,
-        choices=['float'],
+        choices=["float"],
    )
    parser.add_argument(
        "--out-type",
        action=ToTorchDtype,
-        choices=['bfloat16', 'float16'],
+        choices=["bfloat16", "float16"],
    )
    parser.add_argument(
        "--group-size",
@@ -618,9 +670,11 @@ Benchmark Machete GEMM.
        action="store_true",
        help="Run a sweep over all supported schedules",
    )
-    parser.add_argument("--sweep-csv-out",
-                        help="CSV to store sweep results",
-                        default="sch_sweep_results.csv")
+    parser.add_argument(
+        "--sweep-csv-out",
+        help="CSV to store sweep results",
+        default="sch_sweep_results.csv",
+    )
    subparsers = parser.add_subparsers(dest="cmd", required=True)

    square_parser = subparsers.add_parser("square_bench")
@@ -634,17 +688,20 @@ Benchmark Machete GEMM.
        "--dim-start",
        type=str,
        required=True,
-        help="Start value for M,K,N as common separated list")
+        help="Start value for M,K,N as common separated list",
+    )
    range_parser.add_argument(
        "--dim-end",
        type=str,
        required=True,
-        help="End value (inclusive) for M,K,N as common separated list")
+        help="End value (inclusive) for M,K,N as common separated list",
+    )
    range_parser.add_argument(
        "--dim-increment",
        type=str,
        required=True,
-        help="Increment value for M,K,N as common separated list")
+        help="Increment value for M,K,N as common separated list",
+    )
    range_parser.set_defaults(func=run_range_bench)

    model_parser = subparsers.add_parser("model_bench")
@@ -655,14 +712,12 @@ Benchmark Machete GEMM.
        default=DEFAULT_MODELS,
        choices=WEIGHT_SHAPES.keys(),
    )
-    model_parser.add_argument("--tp-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_TP_SIZES)
-    model_parser.add_argument("--batch-sizes",
-                              nargs="+",
-                              type=int,
-                              default=DEFAULT_BATCH_SIZES)
+    model_parser.add_argument(
+        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+    )
+    model_parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
    model_parser.set_defaults(func=run_model_bench)

    args = parser.parse_args()
@@ -6,19 +6,34 @@ from benchmark_shapes import WEIGHT_SHAPES

 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
-    GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
-    GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES)
+    GPTQ_MARLIN_24_MAX_PARALLEL,
+    GPTQ_MARLIN_24_MIN_THREAD_N,
+    GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES,
+    GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES,
+)
 from vllm.model_executor.layers.quantization.utils.allspark_utils import (
-    ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD, ALLSPARK_SUPPORTED_QUANT_TYPES)
+    ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD,
+    ALLSPARK_SUPPORTED_QUANT_TYPES,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
-    MARLIN_SUPPORTED_GROUP_SIZES, query_marlin_supported_quant_types)
+    GPTQ_MARLIN_MAX_PARALLEL,
+    GPTQ_MARLIN_MIN_THREAD_N,
+    MARLIN_SUPPORTED_GROUP_SIZES,
+    query_marlin_supported_quant_types,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
-    MarlinWorkspace, marlin_quantize)
+    MarlinWorkspace,
+    marlin_quantize,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
-    marlin_24_quantize)
+    marlin_24_quantize,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    gptq_pack, gptq_quantize_weights, quantize_weights, sort_weights)
+    gptq_pack,
+    gptq_quantize_weights,
+    quantize_weights,
+    sort_weights,
+)
 from vllm.scalar_type import ScalarType
 from vllm.utils import FlexibleArgumentParser

@@ -29,22 +44,29 @@ ACT_ORDER_OPTS = [False, True]
 K_FULL_OPTS = [False, True]


-def bench_run(results: list[benchmark.Measurement], model: str,
-              act_order: bool, is_k_full: bool, quant_type: ScalarType,
-              group_size: int, size_m: int, size_k: int, size_n: int):
+def bench_run(
+    results: list[benchmark.Measurement],
+    model: str,
+    act_order: bool,
+    is_k_full: bool,
+    quant_type: ScalarType,
+    group_size: int,
+    size_m: int,
+    size_k: int,
+    size_n: int,
+):
    label = "Quant Matmul"

-    sub_label = ("{}, act={} k_full={}, q={}, g={}, "
-                 "MKN=({}x{}x{})".format(model, act_order, is_k_full,
-                                         str(quant_type), group_size, size_m,
-                                         size_k, size_n))
+    sub_label = "{}, act={} k_full={}, q={}, g={}, MKN=({}x{}x{})".format(
+        model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
+    )

    print(f"Testing: {sub_label}")

    a = torch.randn(size_m, size_k).to(torch.half).cuda()
    b = torch.rand(size_k, size_n).to(torch.half).cuda()

-    a_tmp = (torch.zeros(size_m, size_k).to(torch.half).cuda())
+    a_tmp = torch.zeros(size_m, size_k).to(torch.half).cuda()

    # Marlin quant
    (
@@ -57,14 +79,16 @@ def bench_run(results: list[benchmark.Measurement], model: str,
    ) = marlin_quantize(b, quant_type, group_size, act_order)

    # Marlin_24 quant
-    (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta,
-     marlin_24_s) = marlin_24_quantize(b, quant_type, group_size)
+    (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
+        marlin_24_quantize(b, quant_type, group_size)
+    )

    marlin_zp = torch.empty(0, dtype=torch.int, device=b.device)

    # GPTQ quant
-    (w_ref, q_w, s, g_idx,
-     rand_perm) = gptq_quantize_weights(b, quant_type, group_size, act_order)
+    (w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
+        b, quant_type, group_size, act_order
+    )
    q_w_gptq = gptq_pack(q_w, quant_type.size_bits, size_k, size_n)

    # For act_order, sort the "weights" and "g_idx"
@@ -74,32 +98,37 @@ def bench_run(results: list[benchmark.Measurement], model: str,
        (q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)

    # Prepare
-    marlin_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
-                                       GPTQ_MARLIN_MAX_PARALLEL)
+    marlin_workspace = MarlinWorkspace(
+        size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+    )

-    marlin_24_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_24_MIN_THREAD_N,
-                                          GPTQ_MARLIN_24_MAX_PARALLEL)
+    marlin_24_workspace = MarlinWorkspace(
+        size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
+    )
    marlin_zp = torch.zeros_like(marlin_s, dtype=torch.int)

    # AllSpark W8A16 quant
-    as_supported_case = (quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
-                         and group_size == -1 and not act_order and is_k_full)
+    as_supported_case = (
+        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
+        and group_size == -1
+        and not act_order
+        and is_k_full
+    )
    if as_supported_case:
        properties = torch.cuda.get_device_properties(b.device.index)
        sm_count = properties.multi_processor_count
        sm_version = properties.major * 10 + properties.minor

-        supported_arch = (sm_version >= 80 and sm_version < 90)
+        supported_arch = sm_version >= 80 and sm_version < 90
        as_supported_case = as_supported_case and supported_arch
        if supported_arch:
            has_zp = False
-            w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size,
-                                                has_zp)
+            w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
            qw = qw.to(torch.uint8)

-            qw_reorder, s_reorder, zp_reorder = \
-                ops.allspark_repack_weight(
-                qw, s, zp, has_zp)
+            qw_reorder, s_reorder, zp_reorder = ops.allspark_repack_weight(
+                qw, s, zp, has_zp
+            )
            CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD

    globals = {
@@ -136,8 +165,7 @@ def bench_run(results: list[benchmark.Measurement], model: str,
        "zp_reorder": zp_reorder if as_supported_case else None,
        "sm_count": sm_count if as_supported_case else None,
        "sm_version": sm_version if as_supported_case else None,
-        "CUBLAS_M_THRESHOLD":
-        CUBLAS_M_THRESHOLD if as_supported_case else None,
+        "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD if as_supported_case else None,
        # Kernels
        "gptq_marlin_gemm": ops.gptq_marlin_gemm,
        "gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
@@ -158,60 +186,63 @@ def bench_run(results: list[benchmark.Measurement], model: str,
            label=label,
            sub_label=sub_label,
            description="pytorch_gemm",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    results.append(
        benchmark.Timer(
-            stmt=
-            "output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
            description="gptq_marlin_gemm_fp16",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    results.append(
        benchmark.Timer(
-            stmt=
-            "output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
            description="gptq_marlin_gemm_fp32",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

-    if (quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-            and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES):
+    if (
+        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
+        and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
+    ):
        results.append(
            benchmark.Timer(
-                stmt=
-                "output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)",  # noqa: E501
+                stmt="output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)",  # noqa: E501
                globals=globals,
                label=label,
                sub_label=sub_label,
                description="gptq_marlin_24_gemm",
-            ).blocked_autorange(min_run_time=min_run_time))
+            ).blocked_autorange(min_run_time=min_run_time)
+        )

    results.append(
        benchmark.Timer(
-            stmt=
-            "q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)",  # noqa: E501
+            stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
            description="gptq_marlin_repack",
-        ).blocked_autorange(min_run_time=min_run_time))
+        ).blocked_autorange(min_run_time=min_run_time)
+    )

    if as_supported_case:
        results.append(
            benchmark.Timer(
-                stmt=
-                "output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)",  # noqa: E501
+                stmt="output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)",  # noqa: E501
                globals=globals,
                label=label,
                sub_label=sub_label,
                description="allspark_w8a16_gemm_fp32",
-            ).blocked_autorange(min_run_time=min_run_time))
+            ).blocked_autorange(min_run_time=min_run_time)
+        )


 def main(args):
@@ -233,37 +264,50 @@ def main(args):
                continue

            for act_order in ACT_ORDER_OPTS:
-                if len(args.limit_act_order
-                       ) > 0 and act_order not in args.limit_act_order:
+                if (
+                    len(args.limit_act_order) > 0
+                    and act_order not in args.limit_act_order
+                ):
                    continue

                for is_k_full in K_FULL_OPTS:
-                    if len(args.limit_k_full
-                           ) > 0 and is_k_full not in args.limit_k_full:
+                    if (
+                        len(args.limit_k_full) > 0
+                        and is_k_full not in args.limit_k_full
+                    ):
                        continue

-                    for quant_type in query_marlin_supported_quant_types(
-                            False):
-                        if len(args.limit_num_bits) > 0 and \
-                            quant_type.size_bits not in args.limit_num_bits:
+                    for quant_type in query_marlin_supported_quant_types(False):
+                        if (
+                            len(args.limit_num_bits) > 0
+                            and quant_type.size_bits not in args.limit_num_bits
+                        ):
                            continue

                        for group_size in MARLIN_SUPPORTED_GROUP_SIZES:
-                            if len(
-                                    args.limit_group_size
-                            ) > 0 and group_size not in args.limit_group_size:
+                            if (
+                                len(args.limit_group_size) > 0
+                                and group_size not in args.limit_group_size
+                            ):
                                continue

                            # For act_order, the group_size must be less than
                            # size_k
-                            if act_order and (group_size == size_k
-                                              or group_size == -1):
+                            if act_order and (group_size == size_k or group_size == -1):
                                continue

                            for size_m in args.batch_sizes:
-                                bench_run(results, model, act_order, is_k_full,
-                                          quant_type, group_size, size_m,
-                                          size_k, size_n)
+                                bench_run(
+                                    results,
+                                    model,
+                                    act_order,
+                                    is_k_full,
+                                    quant_type,
+                                    group_size,
+                                    size_m,
+                                    size_k,
+                                    size_n,
+                                )

    compare = benchmark.Compare(results)
    compare.print()
@@ -274,7 +318,8 @@ def main(args):
 #
 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description="Benchmark Marlin across specified models/shapes/batches")
+        description="Benchmark Marlin across specified models/shapes/batches"
+    )
    parser.add_argument(
        "--models",
        nargs="+",
@@ -282,10 +327,9 @@ if __name__ == "__main__":
        default=DEFAULT_MODELS,
        choices=WEIGHT_SHAPES.keys(),
    )
-    parser.add_argument("--batch-sizes",
-                        nargs="+",
-                        type=int,
-                        default=DEFAULT_BATCH_SIZES)
+    parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
    parser.add_argument("--limit-group-size", nargs="+", type=int, default=[])
@@ -31,56 +31,60 @@ class BenchmarkConfig(TypedDict):
    num_stages: int


-def benchmark_config(config: BenchmarkConfig,
-                     num_tokens: int,
-                     num_experts: int,
-                     shard_intermediate_size: int,
-                     hidden_size: int,
-                     topk: int,
-                     dtype: torch.dtype,
-                     use_fp8_w8a8: bool,
-                     use_int8_w8a16: bool,
-                     num_iters: int = 100,
-                     block_quant_shape: List[int] = None,
-                     use_deep_gemm: bool = False) -> float:
+def benchmark_config(
+    config: BenchmarkConfig,
+    num_tokens: int,
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    num_iters: int = 100,
+    block_quant_shape: List[int] = None,
+    use_deep_gemm: bool = False,
+) -> float:
    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
    if use_int8_w8a16:
-        w1 = torch.randint(-127,
-                           127, (
-                               num_experts,
-                               shard_intermediate_size,
-                               hidden_size,
-                           ),
-                           dtype=torch.int8)
-        w2 = torch.randint(-127,
-                           127, (
-                               num_experts,
-                               hidden_size,
-                               shard_intermediate_size // 2,
-                           ),
-                           dtype=torch.int8)
+        w1 = torch.randint(
+            -127,
+            127,
+            (
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+            ),
+            dtype=torch.int8,
+        )
+        w2 = torch.randint(
+            -127,
+            127,
+            (
+                num_experts,
+                hidden_size,
+                shard_intermediate_size // 2,
+            ),
+            dtype=torch.int8,
+        )
    else:
-        w1 = torch.randn(num_experts,
-                         shard_intermediate_size,
-                         hidden_size,
-                         dtype=init_dtype)
-        w2 = torch.randn(num_experts,
-                         hidden_size,
-                         shard_intermediate_size // 2,
-                         dtype=init_dtype)
-    gating_output = torch.randn(num_iters,
-                                num_tokens,
-                                num_experts,
-                                dtype=torch.float32)
+        w1 = torch.randn(
+            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
+        )
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
+        )
+    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)

    w1_scale = None
    w2_scale = None
    a1_scale = None
    a2_scale = None
    if use_int8_w8a16:
-        w1_scale = torch.randn((num_experts, 2 * shard_intermediate_size),
-                               dtype=torch.float32)
+        w1_scale = torch.randn(
+            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
+        )
        w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
    if use_fp8_w8a8:
        if block_quant_shape:
@@ -93,10 +97,14 @@ def benchmark_config(config: BenchmarkConfig,
            n_tiles_w2 = (K + block_n - 1) // block_n
            k_tiles_w1 = (K + block_k - 1) // block_k
            k_tiles_w2 = (N + block_k - 1) // block_k
-            w1_scale = torch.rand((E, n_tiles_w1, k_tiles_w1),
-                                  dtype=torch.float32) * factor_for_scale
-            w2_scale = torch.rand((E, n_tiles_w2, k_tiles_w2),
-                                  dtype=torch.float32) * factor_for_scale
+            w1_scale = (
+                torch.rand((E, n_tiles_w1, k_tiles_w1), dtype=torch.float32)
+                * factor_for_scale
+            )
+            w2_scale = (
+                torch.rand((E, n_tiles_w2, k_tiles_w2), dtype=torch.float32)
+                * factor_for_scale
+            )
        else:
            w1_scale = torch.randn(num_experts, dtype=torch.float32)
            w2_scale = torch.randn(num_experts, dtype=torch.float32)
@@ -114,10 +122,12 @@ def benchmark_config(config: BenchmarkConfig,

    def run():
        from vllm.model_executor.layers.fused_moe import override_config
+
        with override_config(config):
            if use_deep_gemm:
                topk_weights, topk_ids, token_expert_indices = fused_topk(
-                    x, input_gating, topk, False)
+                    x, input_gating, topk, False
+                )
                return fused_experts(
                    x,
                    w1,
@@ -213,8 +223,7 @@ def get_rocm_tuning_space(use_fp16):
    return param_ranges


-def get_configs_compute_bound(use_fp16,
-                              block_quant_shape) -> list[dict[str, int]]:
+def get_configs_compute_bound(use_fp16, block_quant_shape) -> list[dict[str, int]]:
    configs: list[BenchmarkConfig] = []

    if current_platform.is_rocm():
@@ -250,20 +259,25 @@ def get_configs_compute_bound(use_fp16,
    if block_quant_shape is not None and not use_fp16:
        block_n, block_k = block_quant_shape[0], block_quant_shape[1]
        for config in configs[:]:
-            if config["BLOCK_SIZE_K"] % block_k != 0 or config[
-                    "BLOCK_SIZE_N"] % block_n != 0:
+            if (
+                config["BLOCK_SIZE_K"] % block_k != 0
+                or config["BLOCK_SIZE_N"] % block_n != 0
+            ):
                configs.remove(config)
    return configs


-def prune_rocm_search_space(num_tokens, shard_intermediate_size, hidden_size,
-                            search_space, is_fp16, topk):
+def prune_rocm_search_space(
+    num_tokens, shard_intermediate_size, hidden_size, search_space, is_fp16, topk
+):
    N1, K1 = shard_intermediate_size, hidden_size
    N2, K2 = hidden_size, shard_intermediate_size // 2
-    pruned_space_1 = prune_rocm_configs(num_tokens * topk, N1, K1,
-                                        search_space, is_fp16)
-    pruned_space_2 = prune_rocm_configs(num_tokens * topk, N2, K2,
-                                        search_space, is_fp16)
+    pruned_space_1 = prune_rocm_configs(
+        num_tokens * topk, N1, K1, search_space, is_fp16
+    )
+    pruned_space_2 = prune_rocm_configs(
+        num_tokens * topk, N2, K2, search_space, is_fp16
+    )
    search_space = merge_unique_dicts(pruned_space_1, pruned_space_2)
    return search_space

@@ -301,14 +315,14 @@ def prune_rocm_configs(M, N, K, configs, is_fp16=True):
        SPLIT_K = config.get("SPLIT_K", 1)
        GROUP_M = config.get("GROUP_SIZE_M")
        if is_fp16:
-            if (matrix_instr_nonkdim > BLOCK_SIZE_M
-                    or matrix_instr_nonkdim > BLOCK_SIZE_N):
+            if (
+                matrix_instr_nonkdim > BLOCK_SIZE_M
+                or matrix_instr_nonkdim > BLOCK_SIZE_N
+            ):
                continue
-            if (matrix_instr_nonkdim >= M
-                    and matrix_instr_nonkdim != BLOCK_SIZE_M):
+            if matrix_instr_nonkdim >= M and matrix_instr_nonkdim != BLOCK_SIZE_M:
                continue
-            if (matrix_instr_nonkdim >= N
-                    and matrix_instr_nonkdim != BLOCK_SIZE_N):
+            if matrix_instr_nonkdim >= N and matrix_instr_nonkdim != BLOCK_SIZE_N:
                continue
        # Skip BLOCK_SIZE that is too large compare to M/N
        # unless BLOCK_SIZE is already small enough
@@ -329,8 +343,10 @@ def prune_rocm_configs(M, N, K, configs, is_fp16=True):
            continue
        # out of shared memory resource
        # TODO (zhanglx): This does not consider the LDS usage in the epilogue
-        LDS = (BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a +
-               BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b)
+        LDS = (
+            BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a
+            + BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b
+        )
        if LDS > 65536:
            continue
        # Skip small block sizes and num_warps for large gemm
@@ -364,7 +380,6 @@ def merge_unique_dicts(list1, list2):

@ray.remote(num_gpus=1)
 class BenchmarkWorker:
-
    def __init__(self, seed: int) -> None:
        torch.set_default_device("cuda")
        current_platform.seed_everything(seed)
@@ -388,36 +403,40 @@ class BenchmarkWorker:
        use_deep_gemm: bool = False,
    ) -> tuple[dict[str, int], float]:
        current_platform.seed_everything(self.seed)
-        dtype_str = get_config_dtype_str(dtype,
-                                         use_int8_w8a16=use_int8_w8a16,
-                                         use_fp8_w8a8=use_fp8_w8a8)
+        dtype_str = get_config_dtype_str(
+            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+        )
        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
        # is the intermediate size after silu_and_mul.
-        op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
-                                    dtype_str)
+        op_config = get_moe_configs(
+            num_experts, shard_intermediate_size // 2, dtype_str
+        )
        if op_config is None:
-            config = get_default_config(num_tokens,
-                                        num_experts,
-                                        shard_intermediate_size,
-                                        hidden_size,
-                                        topk,
-                                        dtype_str,
-                                        is_marlin=False)
+            config = get_default_config(
+                num_tokens,
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+                topk,
+                dtype_str,
+                is_marlin=False,
+            )
        else:
-            config = op_config[min(op_config.keys(),
-                                   key=lambda x: abs(x - num_tokens))]
-        kernel_time = benchmark_config(config,
-                                       num_tokens,
-                                       num_experts,
-                                       shard_intermediate_size,
-                                       hidden_size,
-                                       topk,
-                                       dtype,
-                                       use_fp8_w8a8,
-                                       use_int8_w8a16,
-                                       num_iters=100,
-                                       block_quant_shape=block_quant_shape,
-                                       use_deep_gemm=use_deep_gemm)
+            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
+        kernel_time = benchmark_config(
+            config,
+            num_tokens,
+            num_experts,
+            shard_intermediate_size,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            block_quant_shape=block_quant_shape,
+            use_deep_gemm=use_deep_gemm,
+        )
        return config, kernel_time

    def tune(
@@ -438,10 +457,14 @@ class BenchmarkWorker:
        best_time = float("inf")
        if current_platform.is_rocm():
            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
-            search_space = prune_rocm_search_space(num_tokens,
-                                                   shard_intermediate_size,
-                                                   hidden_size, search_space,
-                                                   is_fp16, topk)
+            search_space = prune_rocm_search_space(
+                num_tokens,
+                shard_intermediate_size,
+                hidden_size,
+                search_space,
+                is_fp16,
+                topk,
+            )

        need_device_guard = False
        if current_platform.is_rocm():
@@ -449,8 +472,7 @@ class BenchmarkWorker:
            if visible_device != f"{self.device_id}":
                need_device_guard = True

-        with torch.cuda.device(
-                self.device_id) if need_device_guard else nullcontext():
+        with torch.cuda.device(self.device_id) if need_device_guard else nullcontext():
            for config in tqdm(search_space):
                try:
                    kernel_time = benchmark_config(
@@ -465,7 +487,8 @@ class BenchmarkWorker:
                        use_int8_w8a16,
                        num_iters=20,
                        block_quant_shape=block_quant_shape,
-                        use_deep_gemm=use_deep_gemm)
+                        use_deep_gemm=use_deep_gemm,
+                    )
                except triton.runtime.autotuner.OutOfResources:
                    # Some configurations may be invalid and fail to compile.
                    continue
@@ -481,42 +504,44 @@ class BenchmarkWorker:

 def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
    return {
-        "BLOCK_SIZE_M":
-        config["BLOCK_SIZE_M"],
-        "BLOCK_SIZE_N":
-        config["BLOCK_SIZE_N"],
-        "BLOCK_SIZE_K":
-        config["BLOCK_SIZE_K"],
-        "GROUP_SIZE_M":
-        config["GROUP_SIZE_M"],
-        "num_warps":
-        config["num_warps"],
-        "num_stages":
-        config["num_stages"],
-        **({
-            "waves_per_eu": config["waves_per_eu"]
-        } if "waves_per_eu" in config else {}),
-        **({
-            "matrix_instr_nonkdim": config["matrix_instr_nonkdim"]
-        } if "matrix_instr_nonkdim" in config else {}),
-        **({
-            "kpack": config["kpack"]
-        } if "kpack" in config else {}),
+        "BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
+        "BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
+        "BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
+        "GROUP_SIZE_M": config["GROUP_SIZE_M"],
+        "num_warps": config["num_warps"],
+        "num_stages": config["num_stages"],
+        **(
+            {"waves_per_eu": config["waves_per_eu"]} if "waves_per_eu" in config else {}
+        ),
+        **(
+            {"matrix_instr_nonkdim": config["matrix_instr_nonkdim"]}
+            if "matrix_instr_nonkdim" in config
+            else {}
+        ),
+        **({"kpack": config["kpack"]} if "kpack" in config else {}),
    }


-def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
-                 shard_intermediate_size: int, hidden_size: int, topk: int,
-                 dtype: torch.dtype, use_fp8_w8a8: bool, use_int8_w8a16: bool,
-                 block_quant_shape: List[int]) -> None:
-    dtype_str = get_config_dtype_str(dtype,
-                                     use_int8_w8a16=use_int8_w8a16,
-                                     use_fp8_w8a8=use_fp8_w8a8)
+def save_configs(
+    configs: dict[int, BenchmarkConfig],
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    block_quant_shape: List[int],
+) -> None:
+    dtype_str = get_config_dtype_str(
+        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+    )

    # NOTE(woosuk): The current naming convention uses w2.shape[2], which
    # is the intermediate size after silu_and_mul.
-    filename = get_config_file_name(num_experts, shard_intermediate_size // 2,
-                                    dtype_str, block_quant_shape)
+    filename = get_config_file_name(
+        num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape
+    )

    print(f"Writing best config to {filename}...")
    with open(filename, "w") as f:
@@ -525,18 +550,16 @@ def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,


 def get_weight_block_size_safety(config, default_value=None):
-
-    quantization_config = getattr(config, 'quantization_config', {})
+    quantization_config = getattr(config, "quantization_config", {})
    if isinstance(quantization_config, dict):
-        return quantization_config.get('weight_block_size', default_value)
+        return quantization_config.get("weight_block_size", default_value)
    return default_value


 def main(args: argparse.Namespace):
    print(args)

-    config = get_config(model=args.model,
-                        trust_remote_code=args.trust_remote_code)
+    config = get_config(model=args.model, trust_remote_code=args.trust_remote_code)
    if args.model_prefix:
        config = getattr(config, args.model_prefix)
    config = SimpleNamespace(**config)
@@ -551,14 +574,12 @@ def main(args: argparse.Namespace):
        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")):
+    elif config.architectures[0] in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM"):
        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"):
+    elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
@@ -573,16 +594,35 @@ def main(args: argparse.Namespace):
        shard_intermediate_size = 2 * intermediate_size // args.tp_size

    hidden_size = config.hidden_size
-    dtype = torch.float16 if current_platform.is_rocm() else getattr(
-        torch, config.torch_dtype)
+    dtype = (
+        torch.float16
+        if current_platform.is_rocm()
+        else getattr(torch, config.torch_dtype)
+    )
    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
    use_int8_w8a16 = args.dtype == "int8_w8a16"
    block_quant_shape = get_weight_block_size_safety(config)

    if args.batch_size is None:
        batch_sizes = [
-            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
-            2048, 3072, 4096
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            48,
+            64,
+            96,
+            128,
+            256,
+            512,
+            1024,
+            1536,
+            2048,
+            3072,
+            4096,
        ]
    else:
        batch_sizes = [args.batch_size]
@@ -593,7 +633,8 @@ def main(args: argparse.Namespace):
        # Ray will set ROCR_VISIBLE_DEVICES for device visibility
        logger.warning(
            "Ray uses ROCR_VISIBLE_DEVICES to control device accessibility."
-            "Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES.")
+            "Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES."
+        )
        val = os.environ["HIP_VISIBLE_DEVICES"]
        os.environ["ROCR_VISIBLE_DEVICES"] = val
        del os.environ["HIP_VISIBLE_DEVICES"]
@@ -620,25 +661,59 @@ def main(args: argparse.Namespace):

        start = time.time()
        configs = _distribute(
-            "tune", [(batch_size, E, shard_intermediate_size, hidden_size,
-                      topk, dtype, use_fp8_w8a8, use_int8_w8a16, search_space,
-                      block_quant_shape, use_deep_gemm)
-                     for batch_size in batch_sizes])
+            "tune",
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a16,
+                    search_space,
+                    block_quant_shape,
+                    use_deep_gemm,
+                )
+                for batch_size in batch_sizes
+            ],
+        )
        best_configs = {
-            M: sort_config(config)
-            for M, config in zip(batch_sizes, configs)
+            M: sort_config(config) for M, config in zip(batch_sizes, configs)
        }
-        save_configs(best_configs, E, shard_intermediate_size, hidden_size,
-                     topk, dtype, use_fp8_w8a8, use_int8_w8a16,
-                     block_quant_shape)
+        save_configs(
+            best_configs,
+            E,
+            shard_intermediate_size,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            block_quant_shape,
+        )
        end = time.time()
        print(f"Tuning took {end - start:.2f} seconds")
    else:
        outputs = _distribute(
            "benchmark",
-            [(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
-              use_fp8_w8a8, use_int8_w8a16, block_quant_shape, use_deep_gemm)
-             for batch_size in batch_sizes])
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a16,
+                    block_quant_shape,
+                    use_deep_gemm,
+                )
+                for batch_size in batch_sizes
+            ],
+        )

        for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
            print(f"Batch size: {batch_size}, config: {config}")
@@ -647,18 +722,15 @@ def main(args: argparse.Namespace):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser()
-    parser.add_argument("--model",
-                        type=str,
-                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    parser.add_argument("--tp-size",
-                        "-tp",
-                        "--tensor-parallel-size",
-                        type=int,
-                        default=2)
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["auto", "fp8_w8a8", "int8_w8a16"],
-                        default="auto")
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument(
+        "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
+    )
+    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("--seed", type=int, default=0)
    parser.add_argument("--batch-size", type=int, required=False)
@@ -8,7 +8,9 @@ import torch
 from transformers import AutoConfig

 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
-    _moe_permute, _moe_unpermute_and_reduce)
+    _moe_permute,
+    _moe_unpermute_and_reduce,
+)
 from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
@@ -27,15 +29,17 @@ class BenchmarkConfig(TypedDict):
    num_stages: int


-def benchmark_permute(num_tokens: int,
-                      num_experts: int,
-                      hidden_size: int,
-                      topk: int,
-                      dtype: torch.dtype,
-                      use_fp8_w8a8: bool,
-                      use_int8_w8a16: bool,
-                      num_iters: int = 100,
-                      use_customized_permute: bool = False) -> float:
+def benchmark_permute(
+    num_tokens: int,
+    num_experts: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    num_iters: int = 100,
+    use_customized_permute: bool = False,
+) -> float:
    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
    # output_hidden_states = torch.empty_like(hidden_states)
@@ -46,36 +50,41 @@ def benchmark_permute(num_tokens: int,
        align_block_size = None
        qhidden_states = hidden_states

-    gating_output = torch.randn(num_iters,
-                                num_tokens,
-                                num_experts,
-                                dtype=torch.float32)
+    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)

    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
    topk_weights, topk_ids, token_expert_indices = fused_topk(
-        qhidden_states, input_gating, topk, False)
+        qhidden_states, input_gating, topk, False
+    )

    def prepare(i: int):
        input_gating.copy_(gating_output[i])

    def run():
        if use_customized_permute:
-            (permuted_hidden_states, first_token_off, inv_perm_idx,
-             m_indices) = moe_permute(
-                 qhidden_states,
-                 topk_weights=topk_weights,
-                 topk_ids=topk_ids,
-                 token_expert_indices=token_expert_indices,
-                 topk=topk,
-                 n_expert=num_experts,
-                 n_local_expert=num_experts,
-                 expert_map=None,
-                 align_block_size=align_block_size,
-             )
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
+                moe_permute(
+                    qhidden_states,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    token_expert_indices=token_expert_indices,
+                    topk=topk,
+                    n_expert=num_experts,
+                    n_local_expert=num_experts,
+                    expert_map=None,
+                    align_block_size=align_block_size,
+                )
+            )
        else:
-            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
-             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
-                                      num_experts, None, align_block_size)
+            (
+                permuted_hidden_states,
+                a1q_scale,
+                sorted_token_ids,
+                expert_ids,
+                inv_perm,
+            ) = _moe_permute(
+                qhidden_states, None, topk_ids, num_experts, None, align_block_size
+            )

    # JIT compilation & warmup
    run()
@@ -111,15 +120,17 @@ def benchmark_permute(num_tokens: int,
    return avg


-def benchmark_unpermute(num_tokens: int,
-                        num_experts: int,
-                        hidden_size: int,
-                        topk: int,
-                        dtype: torch.dtype,
-                        use_fp8_w8a8: bool,
-                        use_int8_w8a16: bool,
-                        num_iters: int = 100,
-                        use_customized_permute: bool = False) -> float:
+def benchmark_unpermute(
+    num_tokens: int,
+    num_experts: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    num_iters: int = 100,
+    use_customized_permute: bool = False,
+) -> float:
    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
    output_hidden_states = torch.empty_like(hidden_states)
@@ -133,46 +144,74 @@ def benchmark_unpermute(num_tokens: int,
    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)

    topk_weights, topk_ids, token_expert_indices = fused_topk(
-        qhidden_states, input_gating, topk, False)
+        qhidden_states, input_gating, topk, False
+    )

    def prepare():
        if use_customized_permute:
-            (permuted_hidden_states, first_token_off, inv_perm_idx,
-             m_indices) = moe_permute(
-                 qhidden_states,
-                 topk_weights=topk_weights,
-                 topk_ids=topk_ids,
-                 token_expert_indices=token_expert_indices,
-                 topk=topk,
-                 n_expert=num_experts,
-                 n_local_expert=num_experts,
-                 expert_map=None,
-                 align_block_size=align_block_size,
-             )
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
+                moe_permute(
+                    qhidden_states,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    token_expert_indices=token_expert_indices,
+                    topk=topk,
+                    n_expert=num_experts,
+                    n_local_expert=num_experts,
+                    expert_map=None,
+                    align_block_size=align_block_size,
+                )
+            )
            # convert to fp16/bf16 as gemm output
-            return (permuted_hidden_states.to(dtype), first_token_off,
-                    inv_perm_idx, m_indices)
+            return (
+                permuted_hidden_states.to(dtype),
+                first_token_off,
+                inv_perm_idx,
+                m_indices,
+            )
        else:
-            (permuted_qhidden_states, a1q_scale, sorted_token_ids, expert_ids,
-             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
-                                      num_experts, None, align_block_size)
+            (
+                permuted_qhidden_states,
+                a1q_scale,
+                sorted_token_ids,
+                expert_ids,
+                inv_perm,
+            ) = _moe_permute(
+                qhidden_states, None, topk_ids, num_experts, None, align_block_size
+            )
            # convert to fp16/bf16 as gemm output
-            return (permuted_qhidden_states.to(dtype), a1q_scale,
-                    sorted_token_ids, expert_ids, inv_perm)
+            return (
+                permuted_qhidden_states.to(dtype),
+                a1q_scale,
+                sorted_token_ids,
+                expert_ids,
+                inv_perm,
+            )

    def run(input: tuple):
        if use_customized_permute:
-            (permuted_hidden_states, first_token_off, inv_perm_idx,
-             m_indices) = input
-            moe_unpermute(permuted_hidden_states, topk_weights, topk_ids,
-                          inv_perm_idx, first_token_off, topk, num_experts,
-                          num_experts)
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = input
+            moe_unpermute(
+                permuted_hidden_states,
+                topk_weights,
+                topk_ids,
+                inv_perm_idx,
+                first_token_off,
+                topk,
+                num_experts,
+                num_experts,
+            )
        else:
-            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
-             inv_perm) = input
-            _moe_unpermute_and_reduce(output_hidden_states,
-                                      permuted_hidden_states, inv_perm,
-                                      topk_weights)
+            (
+                permuted_hidden_states,
+                a1q_scale,
+                sorted_token_ids,
+                expert_ids,
+                inv_perm,
+            ) = input
+            _moe_unpermute_and_reduce(
+                output_hidden_states, permuted_hidden_states, inv_perm, topk_weights
+            )

    # JIT compilation & warmup
    input = prepare()
@@ -209,7 +248,6 @@ def benchmark_unpermute(num_tokens: int,

@ray.remote(num_gpus=1)
 class BenchmarkWorker:
-
    def __init__(self, seed: int) -> None:
        torch.set_default_device("cuda")
        current_platform.seed_everything(seed)
@@ -241,7 +279,8 @@ class BenchmarkWorker:
            use_fp8_w8a8,
            use_int8_w8a16,
            num_iters=100,
-            use_customized_permute=use_customized_permute)
+            use_customized_permute=use_customized_permute,
+        )
        unpermute_time = benchmark_unpermute(
            num_tokens,
            num_experts,
@@ -251,15 +290,15 @@ class BenchmarkWorker:
            use_fp8_w8a8,
            use_int8_w8a16,
            num_iters=100,
-            use_customized_permute=use_customized_permute)
+            use_customized_permute=use_customized_permute,
+        )
        return permute_time, unpermute_time


 def get_weight_block_size_safety(config, default_value=None):
-
-    quantization_config = getattr(config, 'quantization_config', {})
+    quantization_config = getattr(config, "quantization_config", {})
    if isinstance(quantization_config, dict):
-        return quantization_config.get('weight_block_size', default_value)
+        return quantization_config.get("weight_block_size", default_value)
    return default_value


@@ -267,20 +306,21 @@ def main(args: argparse.Namespace):
    print(args)

    config = AutoConfig.from_pretrained(
-        args.model, trust_remote_code=args.trust_remote_code)
+        args.model, trust_remote_code=args.trust_remote_code
+    )
    if config.architectures[0] == "DbrxForCausalLM":
        E = config.ffn_config.moe_num_experts
        topk = config.ffn_config.moe_top_k
    elif config.architectures[0] == "JambaForCausalLM":
        E = config.num_experts
        topk = config.num_experts_per_tok
-    elif (config.architectures[0] == "DeepseekV3ForCausalLM"
-          or config.architectures[0] == "DeepseekV2ForCausalLM"):
+    elif (
+        config.architectures[0] == "DeepseekV3ForCausalLM"
+        or config.architectures[0] == "DeepseekV2ForCausalLM"
+    ):
        E = config.n_routed_experts
        topk = config.num_experts_per_tok
-    elif config.architectures[0] in [
-            "Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"
-    ]:
+    elif config.architectures[0] in ["Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"]:
        E = config.num_experts
        topk = config.num_experts_per_tok

@@ -299,8 +339,24 @@ def main(args: argparse.Namespace):

    if args.batch_size is None:
        batch_sizes = [
-            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
-            2048, 3072, 4096
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            48,
+            64,
+            96,
+            128,
+            256,
+            512,
+            1024,
+            1536,
+            2048,
+            3072,
+            4096,
        ]
    else:
        batch_sizes = [args.batch_size]
@@ -321,9 +377,21 @@ def main(args: argparse.Namespace):
        return ray.get(outputs)

    outputs = _distribute(
-        "benchmark", [(batch_size, E, hidden_size, topk, dtype, use_fp8_w8a8,
-                       use_int8_w8a16, use_customized_permute)
-                      for batch_size in batch_sizes])
+        "benchmark",
+        [
+            (
+                batch_size,
+                E,
+                hidden_size,
+                topk,
+                dtype,
+                use_fp8_w8a8,
+                use_int8_w8a16,
+                use_customized_permute,
+            )
+            for batch_size in batch_sizes
+        ],
+    )

    for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
        print(f"Batch size: {batch_size}")
@@ -333,13 +401,12 @@ def main(args: argparse.Namespace):

 if __name__ == "__main__":
    parser = FlexibleArgumentParser()
-    parser.add_argument("--model",
-                        type=str,
-                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["auto", "fp8_w8a8", "int8_w8a16"],
-                        default="auto")
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument(
+        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
+    )
    parser.add_argument("--use-customized-permute", action="store_true")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--batch-size", type=int, required=False)
@@ -9,8 +9,11 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
-                        create_kv_caches_with_random)
+from vllm.utils import (
+    STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
+    create_kv_caches_with_random,
+)

 logger = init_logger(__name__)

@@ -38,19 +41,15 @@ def main(
    current_platform.seed_everything(seed)

    scale = float(1.0 / (head_size**0.5))
-    query = torch.empty(num_seqs,
-                        num_query_heads,
-                        head_size,
-                        dtype=dtype,
-                        device=device)
+    query = torch.empty(
+        num_seqs, num_query_heads, head_size, dtype=dtype, device=device
+    )
    query.uniform_(-scale, scale)

    assert num_query_heads % num_kv_heads == 0
    alibi_slopes = None
    if use_alibi:
-        alibi_slopes = torch.randn(num_query_heads,
-                                   dtype=torch.float,
-                                   device=device)
+        alibi_slopes = torch.randn(num_query_heads, dtype=torch.float, device=device)

    seq_lens = [seq_len for _ in range(num_seqs)]
    max_seq_len = max(seq_lens)
@@ -61,24 +60,23 @@ def main(
    block_tables_lst: list[list[int]] = []
    for _ in range(num_seqs):
        block_table = [
-            random.randint(0, NUM_BLOCKS - 1)
-            for _ in range(max_num_blocks_per_seq)
+            random.randint(0, NUM_BLOCKS - 1) for _ in range(max_num_blocks_per_seq)
        ]
        block_tables_lst.append(block_table)

-    block_tables = torch.tensor(block_tables_lst,
-                                dtype=torch.int,
-                                device=device)
+    block_tables = torch.tensor(block_tables_lst, dtype=torch.int, device=device)

    # Create the KV cache.
-    key_caches, value_caches = create_kv_caches_with_random(NUM_BLOCKS,
-                                                            block_size,
-                                                            1,
-                                                            num_kv_heads,
-                                                            head_size,
-                                                            kv_cache_dtype,
-                                                            dtype,
-                                                            device=device)
+    key_caches, value_caches = create_kv_caches_with_random(
+        NUM_BLOCKS,
+        block_size,
+        1,
+        num_kv_heads,
+        head_size,
+        kv_cache_dtype,
+        dtype,
+        device=device,
+    )
    key_cache, value_cache = key_caches[0], value_caches[0]

    # Prepare for the paged attention kernel.
@@ -86,11 +84,11 @@ def main(
    if version == "v2":
        if current_platform.is_rocm():
            global PARTITION_SIZE
-            if not args.custom_paged_attn:
+            if not args.custom_paged_attn and not current_platform.is_navi():
                PARTITION_SIZE = 1024
            else:
                PARTITION_SIZE = PARTITION_SIZE_ROCM
-        num_partitions = ((max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE)
+        num_partitions = (max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE
        tmp_output = torch.empty(
            size=(num_seqs, num_query_heads, num_partitions, head_size),
            dtype=output.dtype,
@@ -110,9 +108,7 @@ def main(
        start_time = time.perf_counter()

        # Using default kv_scale
-        k_scale = v_scale = torch.tensor(1.0,
-                                         dtype=torch.float32,
-                                         device=device)
+        k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32, device=device)

        for _ in range(num_iters):
            if version == "v1":
@@ -166,6 +162,7 @@ def main(
                        scale,
                        block_tables,
                        seq_lens,
+                        None,
                        block_size,
                        max_seq_len,
                        alibi_slopes,
@@ -195,30 +192,29 @@ def main(
    print(f"Kernel running time: {latency * 1000000:.3f} us")


-if __name__ == '__main__':
-    logger.warning("This script benchmarks the paged attention kernel. "
-                   "By default this is no longer used in vLLM inference.")
+if __name__ == "__main__":
+    logger.warning(
+        "This script benchmarks the paged attention kernel. "
+        "By default this is no longer used in vLLM inference."
+    )

-    parser = FlexibleArgumentParser(
-        description="Benchmark the paged attention kernel.")
-    parser.add_argument("--version",
-                        type=str,
-                        choices=["v1", "v2"],
-                        default="v2")
+    parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
+    parser.add_argument("--version", type=str, choices=["v1", "v2"], default="v2")
    parser.add_argument("--batch-size", type=int, default=8)
    parser.add_argument("--seq-len", type=int, default=4096)
    parser.add_argument("--num-query-heads", type=int, default=64)
    parser.add_argument("--num-kv-heads", type=int, default=8)
-    parser.add_argument("--head-size",
-                        type=int,
-                        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-                        default=128)
+    parser.add_argument(
+        "--head-size",
+        type=int,
+        choices=[64, 80, 96, 112, 120, 128, 192, 256],
+        default=128,
+    )
    parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
    parser.add_argument("--use-alibi", action="store_true")
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["half", "bfloat16", "float"],
-                        default="half")
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--profile", action="store_true")
    parser.add_argument(
@@ -228,10 +224,11 @@ if __name__ == '__main__':
        default="auto",
        help="Data type for kv cache storage. If 'auto', will use model "
        "data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. "
-        "ROCm (AMD GPU) supports fp8 (=fp8_e4m3)")
-    parser.add_argument("--custom-paged-attn",
-                        action="store_true",
-                        help="Use custom paged attention")
+        "ROCm (AMD GPU) supports fp8 (=fp8_e4m3)",
+    )
+    parser.add_argument(
+        "--custom-paged-attn", action="store_true", help="Use custom paged attention"
+    )
    args = parser.parse_args()
    print(args)

@@ -10,15 +10,17 @@ from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser


@torch.inference_mode()
-def main(num_tokens: int,
-         hidden_size: int,
-         static_scale: bool,
-         quant_dtype: torch.dtype,
-         dtype: torch.dtype,
-         seed: int = 0,
-         do_profile: bool = False,
-         num_warmup_iters: int = 5,
-         num_iters: int = 100) -> None:
+def main(
+    num_tokens: int,
+    hidden_size: int,
+    static_scale: bool,
+    quant_dtype: torch.dtype,
+    dtype: torch.dtype,
+    seed: int = 0,
+    do_profile: bool = False,
+    num_warmup_iters: int = 5,
+    num_iters: int = 100,
+) -> None:
    current_platform.seed_everything(seed)
    torch.set_default_device("cuda")

@@ -56,7 +58,7 @@ def main(num_tokens: int,
    print(f"Kernel running time: {latency * 1000000:.3f} us")


-if __name__ == '__main__':
+if __name__ == "__main__":

    def to_torch_dtype(dt):
        if dt == "int8":
@@ -66,37 +68,40 @@ if __name__ == '__main__':
        raise ValueError(f"Unsupported dtype: {dt}")

    parser = FlexibleArgumentParser(
-        description="Benchmark the quantization (fp8 or int8) kernel.")
+        description="Benchmark the quantization (fp8 or int8) kernel."
+    )
    parser.add_argument("--num-tokens", type=int, default=4096)
    parser.add_argument("--hidden-size", type=int, default=8192)
    parser.add_argument("--static-scale", action="store_true")
-    parser.add_argument("--quant-dtype",
-                        type=str,
-                        choices=["fp8", "int8"],
-                        default="int8")
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["half", "bfloat16", "float"],
-                        default="half")
+    parser.add_argument(
+        "--quant-dtype", type=str, choices=["fp8", "int8"], default="int8"
+    )
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+    )

    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--profile", action="store_true")
    parser.add_argument("--num-warmup-iters", type=int, default=5)
-    parser.add_argument("--num-iters",
-                        type=int,
-                        default=100,
-                        help="Number of benchmark iterations. "
-                        "If --profile is set, this number is ignored")
+    parser.add_argument(
+        "--num-iters",
+        type=int,
+        default=100,
+        help="Number of benchmark iterations. "
+        "If --profile is set, this number is ignored",
+    )

    args = parser.parse_args()
    print(args)

-    main(num_tokens=args.num_tokens,
-         hidden_size=args.hidden_size,
-         static_scale=args.static_scale,
-         quant_dtype=to_torch_dtype(args.quant_dtype),
-         dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-         seed=args.seed,
-         do_profile=args.profile,
-         num_warmup_iters=args.num_warmup_iters,
-         num_iters=args.num_iters)
+    main(
+        num_tokens=args.num_tokens,
+        hidden_size=args.hidden_size,
+        static_scale=args.static_scale,
+        quant_dtype=to_torch_dtype(args.quant_dtype),
+        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+        seed=args.seed,
+        do_profile=args.profile,
+        num_warmup_iters=args.num_warmup_iters,
+        num_iters=args.num_iters,
+    )
@@ -12,7 +12,6 @@ from vllm.triton_utils import triton


 class HuggingFaceRMSNorm(nn.Module):
-
    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
@@ -114,23 +113,19 @@ def rmsnorm_vllm(

 def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
    dtype = torch.bfloat16
-    x = torch.randn(batch_size,
-                    seq_len,
-                    hidden_size,
-                    dtype=dtype,
-                    device="cuda")
+    x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
    weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
    residual = torch.randn_like(x) if use_residual else None

    output_naive = rmsnorm_naive(
-        x.clone(), weight,
-        residual.clone() if residual is not None else None)
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )
    output_flashinfer = rmsnorm_flashinfer(
-        x.clone(), weight,
-        residual.clone() if residual is not None else None)
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )
    output_vllm = rmsnorm_vllm(
-        x.clone(), weight,
-        residual.clone() if residual is not None else None)
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )

    if use_residual:
        output_naive = output_naive[0]
@@ -141,9 +136,9 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
    print(f"FlashInfer output={output_flashinfer}")
    print(f"vLLM output={output_vllm}")

-    if torch.allclose(output_naive, output_flashinfer, atol=1e-2,
-                      rtol=1e-2) and torch.allclose(
-                          output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+    if torch.allclose(
+        output_naive, output_flashinfer, atol=1e-2, rtol=1e-2
+    ) and torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
        print("✅ All implementations match")
    else:
        print("❌ Implementations differ")
@@ -152,12 +147,10 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
 batch_size_range = [2**i for i in range(0, 7, 2)]
 seq_length_range = [2**i for i in range(6, 11, 1)]
 head_num_range = [32, 48]
-configs = list(
-    itertools.product(head_num_range, batch_size_range, seq_length_range))
+configs = list(itertools.product(head_num_range, batch_size_range, seq_length_range))


 def get_benchmark(use_residual):
-
    @triton.testing.perf_report(
        triton.testing.Benchmark(
            x_names=["head_num", "batch_size", "seq_len"],
@@ -167,19 +160,15 @@ def get_benchmark(use_residual):
            line_names=["HuggingFace", "FlashInfer", "vLLM"],
            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
            ylabel="us",
-            plot_name=
-            f"rmsnorm-perf-{'with' if use_residual else 'without'}-residual",
+            plot_name=f"rmsnorm-perf-{'with' if use_residual else 'without'}-residual",
            args={},
-        ))
+        )
+    )
    def benchmark(head_num, batch_size, seq_len, provider):
        dtype = torch.bfloat16
        hidden_size = head_num * 128  # assuming head_dim = 128

-        x = torch.randn(batch_size,
-                        seq_len,
-                        hidden_size,
-                        dtype=dtype,
-                        device="cuda")
+        x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
        weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
        residual = torch.randn_like(x) if use_residual else None

@@ -240,9 +229,9 @@ if __name__ == "__main__":
        default=4096,
        help="Hidden size (2nd dimension) of the sequence",
    )
-    parser.add_argument("--use-residual",
-                        action="store_true",
-                        help="Whether to use residual connection")
+    parser.add_argument(
+        "--use-residual", action="store_true", help="Whether to use residual connection"
+    )
    parser.add_argument(
        "--save-path",
        type=str,
@@ -253,10 +242,12 @@ if __name__ == "__main__":
    args = parser.parse_args()

    # Run correctness test
-    calculate_diff(batch_size=args.batch_size,
-                   seq_len=args.seq_len,
-                   hidden_size=args.hidden_size,
-                   use_residual=args.use_residual)
+    calculate_diff(
+        batch_size=args.batch_size,
+        seq_len=args.seq_len,
+        hidden_size=args.hidden_size,
+        use_residual=args.use_residual,
+    )

    # Get the benchmark function with proper use_residual setting
    benchmark = get_benchmark(args.use_residual)
@@ -6,8 +6,7 @@ from typing import Optional
 import nvtx
 import torch

-from vllm.model_executor.layers.rotary_embedding import (RotaryEmbedding,
-                                                         get_rope)
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding, get_rope
 from vllm.platforms import current_platform
 from vllm.utils import FlexibleArgumentParser

@@ -23,7 +22,7 @@ def benchmark_rope_kernels_multi_lora(
    seed: int,
    device: str,
    max_position: int = 8192,
-    base: int = 10000,
+    base: float = 10000,
 ) -> None:
    current_platform.seed_everything(seed)
    torch.set_default_device(device)
@@ -32,40 +31,49 @@ def benchmark_rope_kernels_multi_lora(
    # silulating serving 4 LoRAs
    scaling_factors = [1, 2, 4, 8]
    # batched RoPE can take multiple scaling factors
-    batched_rope = get_rope(head_size, rotary_dim, max_position, base,
-                            is_neox_style, {
-                                "rope_type": "linear",
-                                "factor": tuple(scaling_factors)
-                            })
+    batched_rope = get_rope(
+        head_size,
+        rotary_dim,
+        max_position,
+        base,
+        is_neox_style,
+        {"rope_type": "linear", "factor": tuple(scaling_factors)},
+    )
    # non-batched RoPE takes only one scaling factor, we create multiple
    # instances to simulate the same behavior
    non_batched_ropes: list[RotaryEmbedding] = []
    for scaling_factor in scaling_factors:
        non_batched_ropes.append(
-            get_rope(head_size, rotary_dim, max_position, base, is_neox_style,
-                     {
-                         "rope_type": "linear",
-                         "factor": (scaling_factor, )
-                     }))
+            get_rope(
+                head_size,
+                rotary_dim,
+                max_position,
+                base,
+                is_neox_style,
+                {"rope_type": "linear", "factor": (scaling_factor,)},
+            )
+        )

    positions = torch.randint(0, max_position, (batch_size, seq_len))
-    query = torch.randn(batch_size,
-                        seq_len,
-                        num_heads * head_size,
-                        dtype=dtype)
+    query = torch.randn(batch_size, seq_len, num_heads * head_size, dtype=dtype)
    key = torch.randn_like(query)

    # create query offsets for batched RoPE, we concat multiple kv cache
    # together and each query needs to find the right kv cache of its type
    offset_map = torch.tensor(
        list(
-            accumulate([0] + [
-                max_position * scaling_factor * 2
-                for scaling_factor in scaling_factors[:-1]
-            ])))
-    query_types = torch.randint(0,
-                                len(scaling_factors), (batch_size, seq_len),
-                                device=device)
+            accumulate(
+                [0]
+                + [
+                    max_position * scaling_factor * 2
+                    for scaling_factor in scaling_factors[:-1]
+                ]
+            )
+        )
+    )
+    query_types = torch.randint(
+        0, len(scaling_factors), (batch_size, seq_len), device=device
+    )
    # map query types to offsets
    query_offsets = offset_map[query_types]
    # the kernel takes flattened offsets
@@ -86,27 +94,28 @@ def benchmark_rope_kernels_multi_lora(
    torch.cuda.synchronize()


-if __name__ == '__main__':
+if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description="Benchmark the rotary embedding kernels.")
+        description="Benchmark the rotary embedding kernels."
+    )
    parser.add_argument("--is-neox-style", type=bool, default=True)
    parser.add_argument("--batch-size", type=int, default=16)
    parser.add_argument("--seq-len", type=int, default=512)
    parser.add_argument("--num-heads", type=int, default=8)
-    parser.add_argument("--head-size",
-                        type=int,
-                        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-                        default=128)
+    parser.add_argument(
+        "--head-size",
+        type=int,
+        choices=[64, 80, 96, 112, 120, 128, 192, 256],
+        default=128,
+    )
    parser.add_argument("--rotary-dim", type=int, choices=[16, 32], default=32)
-    parser.add_argument("--dtype",
-                        type=str,
-                        choices=["bfloat16", "float"],
-                        default="float")
+    parser.add_argument(
+        "--dtype", type=str, choices=["bfloat16", "float"], default="float"
+    )
    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--device",
-                        type=str,
-                        choices=["cuda:0", "cuda:1"],
-                        default="cuda:0")
+    parser.add_argument(
+        "--device", type=str, choices=["cuda:0", "cuda:1"], default="cuda:0"
+    )
    args = parser.parse_args()
    print(args)

@@ -14,14 +14,16 @@ import tqdm
 import triton

 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    _w8a8_block_fp8_matmul)
+    _w8a8_block_fp8_matmul,
+)
 from vllm.platforms import current_platform
 from vllm.utils import FlexibleArgumentParser

 mp.set_start_method("spawn", force=True)

-assert current_platform.is_cuda(
-), "Only support tune w8a8 block fp8 kernel on CUDA device."
+assert current_platform.is_cuda(), (
+    "Only support tune w8a8 block fp8 kernel on CUDA device."
+)

 DTYPE_MAP = {
    "float32": torch.float32,
@@ -40,7 +42,7 @@ def w8a8_block_matmul(
    config: dict[str, Any],
    output_dtype: torch.dtype = torch.float16,
 ) -> torch.Tensor:
-    """This function performs matrix multiplication with 
+    """This function performs matrix multiplication with
    block-wise quantization.

    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
@@ -51,7 +53,7 @@ def w8a8_block_matmul(
        B: The input tensor, e.g., weight.
        As: The per-token-group quantization scale for `A`.
        Bs: The per-block quantization scale for `B`.
-        block_size: The block size for per-block quantization. 
+        block_size: The block size for per-block quantization.
                    It should be 2-dim, e.g., [128, 128].
        output_dytpe: The dtype of the returned tensor.

@@ -71,18 +73,18 @@ def w8a8_block_matmul(
    assert triton.cdiv(N, block_n) == Bs.shape[0]
    assert triton.cdiv(K, block_k) == Bs.shape[1]

-    C_shape = A.shape[:-1] + (N, )
+    C_shape = A.shape[:-1] + (N,)
    C = A.new_empty(C_shape, dtype=output_dtype)

    def grid(META):
-        return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
-                triton.cdiv(N, META["BLOCK_SIZE_N"]), )
+        return (
+            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        )

    if A.dtype == torch.float8_e4m3fn:
        kernel = _w8a8_block_fp8_matmul
    else:
-        raise RuntimeError(
-            "Currently, only support tune w8a8 block fp8 kernel.")
+        raise RuntimeError("Currently, only support tune w8a8 block fp8 kernel.")

    kernel[grid](
        A,
@@ -119,14 +121,16 @@ def get_configs_compute_bound():
                for block_n in [32, 64, 128, 256]:
                    for num_warps in [4, 8]:
                        for group_size in [1, 16, 32, 64]:
-                            configs.append({
-                                "BLOCK_SIZE_M": block_m,
-                                "BLOCK_SIZE_N": block_n,
-                                "BLOCK_SIZE_K": block_k,
-                                "GROUP_SIZE_M": group_size,
-                                "num_warps": num_warps,
-                                "num_stages": num_stages,
-                            })
+                            configs.append(
+                                {
+                                    "BLOCK_SIZE_M": block_m,
+                                    "BLOCK_SIZE_N": block_n,
+                                    "BLOCK_SIZE_K": block_k,
+                                    "GROUP_SIZE_M": group_size,
+                                    "num_warps": num_warps,
+                                    "num_stages": num_stages,
+                                }
+                            )
    return configs


@@ -165,15 +169,9 @@ def get_weight_shapes(tp_size):
    return weight_shapes


-def benchmark_config(A,
-                     B,
-                     As,
-                     Bs,
-                     block_size,
-                     config,
-                     out_dtype=torch.float16,
-                     num_iters=10):
-
+def benchmark_config(
+    A, B, As, Bs, block_size, config, out_dtype=torch.float16, num_iters=10
+):
    def run():
        w8a8_block_matmul(A, B, As, Bs, block_size, config, out_dtype)

@@ -206,26 +204,26 @@ def tune(M, N, K, block_size, out_dtype, search_space, input_type):
        fp8_max, fp8_min = fp8_info.max, fp8_info.min

        A_fp32 = (
-            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
-            fp8_max)
+            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
+        )
        A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)

        B_fp32 = (
-            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
-            fp8_max)
+            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
+        )
        B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
    else:
-        raise RuntimeError(
-            "Currently, only support tune w8a8 block fp8 kernel.")
+        raise RuntimeError("Currently, only support tune w8a8 block fp8 kernel.")

    block_n, block_k = block_size[0], block_size[1]
    n_tiles = (N + block_n - 1) // block_n
    k_tiles = (K + block_k - 1) // block_k

-    As = torch.rand(M, k_tiles, dtype=torch.float32,
-                    device="cuda") * factor_for_scale
-    Bs = (torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda") *
-          factor_for_scale)
+    As = torch.rand(M, k_tiles, dtype=torch.float32, device="cuda") * factor_for_scale
+    Bs = (
+        torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda")
+        * factor_for_scale
+    )

    best_config = None
    best_time = float("inf")
@@ -267,7 +265,8 @@ def save_configs(
    device_name = current_platform.get_device_name().replace(" ", "_")
    json_file_name = (
        f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
-        f"block_shape=[{block_n},{block_k}].json")
+        f"block_shape=[{block_n},{block_k}].json"
+    )

    config_file_path = os.path.join(save_path, json_file_name)
    print(f"Writing best config to {config_file_path}...")
@@ -295,8 +294,7 @@ def tune_on_gpu(args_dict):

    search_space = get_configs_compute_bound()
    search_space = [
-        config for config in search_space
-        if block_k % config["BLOCK_SIZE_K"] == 0
+        config for config in search_space if block_k % config["BLOCK_SIZE_K"] == 0
    ]

    start = time.time()
@@ -312,15 +310,11 @@ def tune_on_gpu(args_dict):
                out_dtype,
                search_space,
                input_type,
-            ) for batch_size in tqdm(batch_sizes,
-                                     desc=f"GPU {gpu_id} - Batch sizes")
+            )
+            for batch_size in tqdm(batch_sizes, desc=f"GPU {gpu_id} - Batch sizes")
        ]
-        best_configs = {
-            M: config
-            for M, config in zip(batch_sizes, benchmark_results)
-        }
-        save_configs(N, K, block_n, block_k, best_configs, save_path,
-                     input_type)
+        best_configs = {M: config for M, config in zip(batch_sizes, benchmark_results)}
+        save_configs(N, K, block_n, block_k, best_configs, save_path, input_type)

    end = time.time()
    print(f"Tuning on GPU {gpu_id} took {end - start:.2f} seconds")
@@ -376,13 +370,14 @@ def main(args):

    process_args = []
    for gpu_id in range(num_gpus):
-        process_args.append({
-            "gpu_id": gpu_id,
-            "batch_sizes": batches_per_gpu[gpu_id],
-            "weight_shapes":
-            weight_shapes,  # Each GPU processes all weight shapes
-            "args": args,
-        })
+        process_args.append(
+            {
+                "gpu_id": gpu_id,
+                "batch_sizes": batches_per_gpu[gpu_id],
+                "weight_shapes": weight_shapes,  # Each GPU processes all weight shapes
+                "args": args,
+            }
+        )

    ctx = mp.get_context("spawn")
    with ctx.Pool(num_gpus) as pool:
@@ -398,13 +393,11 @@ Tune triton w8a8 block fp8 for DeepSeek-V3/DeepSeek-R1:
    python3 benchmark_w8a8_block_fp8.py --tp-size 8 --input-type fp8
 Then copy to model_executor/layers/quantization/utils/configs
        """,
-        formatter_class=argparse.RawTextHelpFormatter)
+        formatter_class=argparse.RawTextHelpFormatter,
+    )

    parser.add_argument("--tp-size", "-tp", type=int, default=8)
-    parser.add_argument("--input-type",
-                        type=str,
-                        choices=["fp8"],
-                        default="fp8")
+    parser.add_argument("--input-type", type=str, choices=["fp8"], default="fp8")
    parser.add_argument(
        "--out-dtype",
        type=str,
@@ -11,7 +11,9 @@ from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
 # Import vLLM functions
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+    per_token_group_quant_fp8,
+    w8a8_block_fp8_matmul,
+)
 from vllm.triton_utils import triton


@@ -2,11 +2,11 @@

 import math
 import pickle
-import re
 from collections import defaultdict

 import matplotlib.pyplot as plt
 import pandas as pd
+import regex as re
 import seaborn as sns
 from torch.utils.benchmark import Measurement as TMeasurement

@@ -14,13 +14,14 @@ from vllm.utils import FlexibleArgumentParser

 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description='Benchmark the latency of processing a single batch of '
-        'requests till completion.')
-    parser.add_argument('filename', type=str)
+        description="Benchmark the latency of processing a single batch of "
+        "requests till completion."
+    )
+    parser.add_argument("filename", type=str)

    args = parser.parse_args()

-    with open(args.filename, 'rb') as f:
+    with open(args.filename, "rb") as f:
        data = pickle.load(f)
        raw_results: list[TMeasurement] = data["results"]

@@ -38,11 +39,7 @@ if __name__ == "__main__":
            raise Exception("MKN not found")

        kernel = v.task_spec.description
-        results[KN].append({
-            "kernel": kernel,
-            "batch_size": M,
-            "median": v.median
-        })
+        results[KN].append({"kernel": kernel, "batch_size": M, "median": v.median})

    rows = int(math.ceil(len(results) / 2))
    fig, axs = plt.subplots(rows, 2, figsize=(12, 5 * rows))
@@ -50,14 +47,16 @@ if __name__ == "__main__":
    for axs_idx, (shape, data) in enumerate(results.items()):
        plt.sca(axs[axs_idx])
        df = pd.DataFrame(data)
-        sns.lineplot(data=df,
-                     x="batch_size",
-                     y="median",
-                     hue="kernel",
-                     style="kernel",
-                     markers=True,
-                     dashes=False,
-                     palette="Dark2")
+        sns.lineplot(
+            data=df,
+            x="batch_size",
+            y="median",
+            hue="kernel",
+            style="kernel",
+            markers=True,
+            dashes=False,
+            palette="Dark2",
+        )
        plt.title(f"Shape: {shape}")
        plt.ylabel("time (median, s)")
    plt.tight_layout()
@@ -23,6 +23,7 @@ class ArgPool:
    For every invocation during a benchmarking run, it will choose a
    different value from the list.
    """
+
    values: Iterable[Any]

    def __getitem__(self, index):
@@ -30,9 +31,7 @@ class ArgPool:


 class Bench:
-
    class ArgsIterator:
-
        def __init__(self, args_list, kwargs_list):
            assert len(args_list) == len(kwargs_list)
            self.args_list = args_list
@@ -53,10 +52,16 @@ class Bench:
        def n_args(self):
            return self.n

-    def __init__(self, cuda_graph_params: Optional[CudaGraphBenchParams],
-                 label: str, sub_label: str, description: str, fn: Callable,
-                 *args, **kwargs):
-
+    def __init__(
+        self,
+        cuda_graph_params: Optional[CudaGraphBenchParams],
+        label: str,
+        sub_label: str,
+        description: str,
+        fn: Callable,
+        *args,
+        **kwargs,
+    ):
        self.cuda_graph_params = cuda_graph_params
        self.use_cuda_graph = self.cuda_graph_params is not None
        self.label = label
@@ -67,10 +72,8 @@ class Bench:
        # Process args
        self._args = args
        self._kwargs = kwargs
-        self.args_list, self.kwargs_list = self.collapse_argpool(
-            *args, **kwargs)
-        self.args_iterator = self.ArgsIterator(self.args_list,
-                                               self.kwargs_list)
+        self.args_list, self.kwargs_list = self.collapse_argpool(*args, **kwargs)
+        self.args_iterator = self.ArgsIterator(self.args_list, self.kwargs_list)

        # Cudagraph runner
        self.g = None
@@ -100,16 +103,13 @@ class Bench:

        for i in range(argpool_size):
            # collapse args; Just pick the ith value
-            args_list[i] = tuple([
-                arg[i] if isinstance(arg, ArgPool) else arg
-                for arg in args_list[i]
-            ])
+            args_list[i] = tuple(
+                [arg[i] if isinstance(arg, ArgPool) else arg for arg in args_list[i]]
+            )

            # collapse kwargs
            kwargs_i = kwargs_list[i]
-            arg_pool_keys = [
-                k for k, v in kwargs_i.items() if isinstance(v, ArgPool)
-            ]
+            arg_pool_keys = [k for k, v in kwargs_i.items() if isinstance(v, ArgPool)]
            for k in arg_pool_keys:
                # again just pick the ith value
                kwargs_i[k] = kwargs_i[k][i]
@@ -142,7 +142,7 @@ class Bench:

    def run_cudagrah(self) -> TMeasurement:
        assert self.use_cuda_graph
-        globals = {'g': self.g}
+        globals = {"g": self.g}

        return TBenchmark.Timer(
            stmt="g.replay()",
@@ -162,15 +162,15 @@ class Bench:

        has_arg_pool = self.args_iterator.n_args > 1
        if has_arg_pool:
-            setup = '''
+            setup = """
                    args_iterator.reset()
                    args_it = args_iterator.__next__()
-                    '''
-            stmt = '''
+                    """
+            stmt = """
                    args, kwargs = next(args_it)
                    fn(*args, **kwargs)
-                    '''
-            globals = {'fn': self.fn, 'args_iterator': self.args_iterator}
+                    """
+            globals = {"fn": self.fn, "args_iterator": self.args_iterator}
        else:
            # no arg pool. Just use the args and kwargs directly
            self.args_iterator.reset()
@@ -178,10 +178,10 @@ class Bench:
            args, kwargs = next(args_it)

            setup = ""
-            stmt = '''
+            stmt = """
                    fn(*args, **kwargs)
-                   '''
-            globals = {'fn': self.fn, 'args': args, 'kwargs': kwargs}
+                   """
+            globals = {"fn": self.fn, "args": args, "kwargs": kwargs}

        return TBenchmark.Timer(
            stmt=stmt,
@@ -48,4 +48,50 @@ WEIGHT_SHAPES = {
        ([16384, 106496], 1),
        ([53248, 16384], 0),
    ],
+    "meta-llama/Llama-3.1-8B-Instruct": [
+        ([4096, 6144], 1),
+        ([4096, 4096], 0),
+        ([4096, 28672], 1),
+        ([14336, 4096], 0),
+    ],
+    "meta-llama/Llama-3.3-70B-Instruct": [
+        ([8192, 10240], 1),
+        ([8192, 8192], 0),
+        ([8192, 57344], 1),
+        ([28672, 8192], 0),
+    ],
+    "mistralai/Mistral-Large-Instruct-2407": [
+        ([12288, 14336], 1),
+        ([12288, 12288], 0),
+        ([12288, 57344], 1),
+        ([28672, 12288], 0),
+    ],
+    "Qwen/Qwen2.5-7B-Instruct": [
+        ([3584, 4608], 1),
+        ([3584, 3584], 0),
+        ([3584, 37888], 1),
+        ([18944, 3584], 0),
+    ],
+    "Qwen/Qwen2.5-32B-Instruct": [
+        ([5120, 7168], 1),
+        ([5120, 5120], 0),
+        ([5120, 55296], 1),
+        ([27648, 5120], 0),
+    ],
+    "Qwen/Qwen2.5-72B-Instruct": [
+        ([8192, 10240], 1),
+        ([8192, 8192], 0),
+        ([8192, 59136], 1),
+        ([29568, 8192], 0),
+    ],
+    "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": [
+        ([2048, 3072], 1),
+        ([2048, 4096], 1),
+        ([2048, 2048], 0),
+        ([2048, 576], 0),
+        ([2048, 21888], 1),
+        ([10944, 2048], 0),
+        ([2048, 2816], 1),
+        ([1408, 2048], 0),
+    ],
 }
@@ -7,9 +7,8 @@ from vllm import LLM, SamplingParams
 from vllm.utils import FlexibleArgumentParser

 # A very long prompt, total number of tokens is about 15k.
-LONG_PROMPT = ["You are an expert in large language models, aren't you?"
-               ] * 1000
-LONG_PROMPT = ' '.join(LONG_PROMPT)
+LONG_PROMPT = ["You are an expert in large language models, aren't you?"] * 1000
+LONG_PROMPT = " ".join(LONG_PROMPT)


 def main(args):
@@ -30,32 +29,35 @@ def main(args):

    print("------start generating------")
    for i in range(3):
-        profiler.runctx('llm.generate(LONG_PROMPT, sampling_params)',
-                        globals(), locals())
+        profiler.runctx(
+            "llm.generate(LONG_PROMPT, sampling_params)", globals(), locals()
+        )

    # analyze the runtime of hashing function
    stats = pstats.Stats(profiler)
-    stats.sort_stats('cumulative')
+    stats.sort_stats("cumulative")
    total_time = 0
    total_calls = 0
    for func in stats.stats:
-        if 'hash_of_block' in func[2]:
+        if "hash_of_block" in func[2]:
            total_time = stats.stats[func][3]
            total_calls = stats.stats[func][0]
    percentage = (total_time / stats.total_tt) * 100
-    print(f"Hashing took {total_time:.2f} seconds,"
-          f"{percentage:.2f}% of the total runtime.")
+    print(
+        f"Hashing took {total_time:.2f} seconds,{percentage:.2f}% of the total runtime."
+    )


 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
-        description='Benchmark the performance of hashing function in'
-        'automatic prefix caching.')
-    parser.add_argument('--model', type=str, default='lmsys/longchat-7b-16k')
-    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
-    parser.add_argument('--output-len', type=int, default=10)
-    parser.add_argument('--enable-prefix-caching',
-                        action='store_true',
-                        help='enable prefix caching')
+        description="Benchmark the performance of hashing function in"
+        "automatic prefix caching."
+    )
+    parser.add_argument("--model", type=str, default="lmsys/longchat-7b-16k")
+    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
+    parser.add_argument("--output-len", type=int, default=10)
+    parser.add_argument(
+        "--enable-prefix-caching", action="store_true", help="enable prefix caching"
+    )
    args = parser.parse_args()
    main(args)
@@ -0,0 +1,49 @@
+# This local pyproject file is part of the migration from yapf to ruff format.
+# It uses the same core rules as the main pyproject.toml file, but with the
+# following differences:
+# - ruff line length is overridden to 88
+# - deprecated typing ignores (UP006, UP035) have been removed
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint.per-file-ignores]
+"vllm/third_party/**" = ["ALL"]
+"vllm/version.py" = ["F401"]
+"vllm/_version.py" = ["ALL"]
+
+[tool.ruff.lint]
+select = [
+    # pycodestyle
+    "E",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # flake8-logging-format
+    "G",
+]
+ignore = [
+    # star imports
+    "F405", "F403",
+    # lambda expression assignment
+    "E731",
+    # Loop control variable not used within loop body
+    "B007",
+    # f-string format
+    "UP032",
+    # Can remove once 3.10+ is the minimum Python version
+    "UP007",
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["vllm"]
+
+[tool.ruff.format]
+docstring-code-format = true
@@ -1,32 +1,98 @@
 #!/bin/bash

-# Define the model to use
-MODEL=${1:-"Qwen/Qwen2.5-7B-Instruct"}
-
-# Define the backend to use
-BACKEND=${2:-"vllm"}
-
-# Define the dataset to use
-DATASET=${3:-"xgrammar_bench"}
-
+# default values
+MODEL=${MODEL:-"Qwen/Qwen2.5-7B-Instruct"}
+BACKEND=${BACKEND:-"vllm"}
+DATASET=${DATASET:-"xgrammar_bench"}
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-OUTPUT_DIR=${4:-"$SCRIPT_DIR/structured_output_benchmark_results"}
+OUTPUT_DIR=${OUTPUT_DIR:-"$SCRIPT_DIR/structured_output_benchmark_results"}
+PORT=${PORT:-8000}
+STRUCTURED_OUTPUT_RATIO=${STRUCTURED_OUTPUT_RATIO:-1}
+TOTAL_SECONDS=${TOTAL_SECONDS:-90}
+MAX_NEW_TOKENS=${MAX_NEW_TOKENS:-300}
+TOKENIZER_MODE=${TOKENIZER_MODE:-"auto"}

-GUIDED_RATIO=${5:-0.5}
+usage() {
+    echo "Usage: $0 [options]"
+    echo "Options:"
+    echo "  --model MODEL                  Model to benchmark (default: $MODEL)"
+    echo "  --backend BACKEND              Backend to use (default: $BACKEND)" 
+    echo "  --dataset DATASET              Dataset to use (default: $DATASET)"
+    echo "  --max-new-tokens N             Maximum number of tokens to generate (default: $MAX_NEW_TOKENS)"
+    echo "  --output-dir DIR               Output directory for results (default: $OUTPUT_DIR)"
+    echo "  --port PORT                    Port to use (default: $PORT)"
+    echo "  --structured-output-ratio N    Ratio of structured outputs (default: $STRUCTURED_OUTPUT_RATIO)"
+    echo "  --tokenizer-mode MODE          Tokenizer mode to use (default: $TOKENIZER_MODE)"
+    echo "  --total-seconds N              Total seconds to run the benchmark (default: $TOTAL_SECONDS)"
+    echo "  -h, --help                     Show this help message and exit"
+    exit 0
+}
+
+# parse command line arguments
+while [[ $# -gt 0 ]]; do
+  case $1 in
+    --model)
+      MODEL="$2"
+      shift 2
+      ;;
+    --backend)
+      BACKEND="$2"
+      shift 2
+      ;;
+    --dataset)
+      DATASET="$2"
+      shift 2
+      ;;
+    --max-new-tokens)
+      MAX_NEW_TOKENS="$2"
+      shift 2
+      ;;
+    --output-dir)
+      OUTPUT_DIR="$2"
+      shift 2
+      ;;
+    --port)
+      PORT="$2"
+      shift 2
+      ;;
+    --structured-output-ratio)
+      STRUCTURED_OUTPUT_RATIO="$2"
+      shift 2
+      ;;
+    --tokenizer-mode)
+      TOKENIZER_MODE="$2"
+      shift 2
+      ;;
+    --total-seconds)
+      TOTAL_SECONDS="$2"
+      shift 2
+      ;;
+    -h|--help)
+      usage
+      ;;
+    *)
+      echo "Unknown argument: $1\n"
+      usage
+      ;;
+  esac
+done

 # Create output directory if it doesn't exist
 mkdir -p "$OUTPUT_DIR"

 # Define QPS values to test
-QPS_VALUES=(70 60 50 25 20 15 10)
+QPS_VALUES=(25 20 15 10 5 1)

 # Common parameters
 COMMON_PARAMS="--backend $BACKEND \
               --model $MODEL \
               --dataset $DATASET \
-               --structured-output-ratio $GUIDED_RATIO \
+               --structured-output-ratio $STRUCTURED_OUTPUT_RATIO \
               --save-results \
-               --result-dir $OUTPUT_DIR"
+               --result-dir $OUTPUT_DIR \
+               --output-len $MAX_NEW_TOKENS \
+               --port $PORT \
+               --tokenizer-mode $TOKENIZER_MODE"

 echo "Starting structured output benchmark with model: $MODEL"
 echo "Backend: $BACKEND"
@@ -45,12 +111,15 @@ for qps in "${QPS_VALUES[@]}"; do
  # Construct filename for this run
  FILENAME="${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"

+  NUM_PROMPTS=$(echo "$TOTAL_SECONDS * $qps" | bc)
+  NUM_PROMPTS=${NUM_PROMPTS%.*}  # Remove fractional part
+  echo "Running benchmark with $NUM_PROMPTS prompts"
+
  # Run the benchmark
  python "$SCRIPT_DIR/benchmark_serving_structured_output.py" $COMMON_PARAMS \
    --request-rate $qps \
    --result-filename "$FILENAME" \
-    --tokenizer-mode ${TOKENIZER_MODE:-"auto"} \
-    --port ${PORT:-8000}
+    --num-prompts $NUM_PROMPTS

  echo "Completed benchmark with QPS: $qps"
  echo "----------------------------------------"
@@ -46,22 +46,38 @@ else()
 endif()


+# Ensure the vllm/vllm_flash_attn directory exists before installation
+install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")" ALL_COMPONENTS)
+
+# Make sure vllm-flash-attn install rules are nested under vllm/
+# This is here to support installing all components under the same prefix with cmake --install.
+# setup.py installs every component separately but uses the same prefix for all.
+# ALL_COMPONENTS is used to avoid duplication for FA2 and FA3,
+# and these statements don't hurt when installing neither component.
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" ALL_COMPONENTS)
+install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
+install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" ALL_COMPONENTS)
+
 # Fetch the vllm-flash-attn library
 FetchContent_MakeAvailable(vllm-flash-attn)
 message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")

+# Restore the install prefix
+install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
+
 # Copy over the vllm-flash-attn python files (duplicated for fa2 and fa3, in
 # case only one is built, in the case both are built redundant work is done)
 install(
  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-  DESTINATION vllm_flash_attn
+  DESTINATION vllm/vllm_flash_attn
  COMPONENT _vllm_fa2_C
  FILES_MATCHING PATTERN "*.py"
 )

 install(
  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-  DESTINATION vllm_flash_attn
+  DESTINATION vllm/vllm_flash_attn
  COMPONENT _vllm_fa3_C
  FILES_MATCHING PATTERN "*.py"
 )
@@ -76,7 +76,7 @@ function (hipify_sources_target OUT_SRCS NAME ORIG_SRCS)
  set(CSRC_BUILD_DIR ${CMAKE_CURRENT_BINARY_DIR}/csrc)
  add_custom_target(
    hipify${NAME}
-    COMMAND ${CMAKE_SOURCE_DIR}/cmake/hipify.py -p ${CMAKE_SOURCE_DIR}/csrc -o ${CSRC_BUILD_DIR} ${SRCS}
+    COMMAND ${Python_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/hipify.py -p ${CMAKE_SOURCE_DIR}/csrc -o ${CSRC_BUILD_DIR} ${SRCS}
    DEPENDS ${CMAKE_SOURCE_DIR}/cmake/hipify.py ${SRCS}
    BYPRODUCTS ${HIP_SRCS}
    COMMENT "Running hipify on ${NAME} extension source files.")
@@ -228,11 +228,26 @@ macro(set_gencode_flags_for_srcs)
                        "${multiValueArgs}" ${ARGN} )

  foreach(_ARCH ${arg_CUDA_ARCHS})
-    string(REPLACE "." "" _ARCH "${_ARCH}")
-    set_gencode_flag_for_srcs(
-      SRCS ${arg_SRCS}
-      ARCH "compute_${_ARCH}"
-      CODE "sm_${_ARCH}")
+    # handle +PTX suffix: generate both sm and ptx codes if requested
+    string(FIND "${_ARCH}" "+PTX" _HAS_PTX)
+    if(NOT _HAS_PTX EQUAL -1)
+      string(REPLACE "+PTX" "" _BASE_ARCH "${_ARCH}")
+      string(REPLACE "." "" _STRIPPED_ARCH "${_BASE_ARCH}")
+      set_gencode_flag_for_srcs(
+        SRCS ${arg_SRCS}
+        ARCH "compute_${_STRIPPED_ARCH}"
+        CODE "sm_${_STRIPPED_ARCH}")
+      set_gencode_flag_for_srcs(
+        SRCS ${arg_SRCS}
+        ARCH "compute_${_STRIPPED_ARCH}"
+        CODE "compute_${_STRIPPED_ARCH}")
+    else()
+      string(REPLACE "." "" _STRIPPED_ARCH "${_ARCH}")
+      set_gencode_flag_for_srcs(
+        SRCS ${arg_SRCS}
+        ARCH "compute_${_STRIPPED_ARCH}"
+        CODE "sm_${_STRIPPED_ARCH}")
+    endif()
  endforeach()

  if (${arg_BUILD_PTX_FOR_ARCH})
@@ -251,7 +266,10 @@ endmacro()
 #
 # For the given `SRC_CUDA_ARCHS` list of gencode versions in the form 
 #  `<major>.<minor>[letter]` compute the "loose intersection" with the 
-#  `TGT_CUDA_ARCHS` list of gencodes. 
+#  `TGT_CUDA_ARCHS` list of gencodes. We also support the `+PTX` suffix in
+#  `SRC_CUDA_ARCHS` which indicates that the PTX code should be built when there
+#  is a CUDA_ARCH in `TGT_CUDA_ARCHS` that is equal to or larger than the
+#  architecture in `SRC_CUDA_ARCHS`.
 # The loose intersection is defined as:
 #   { max{ x \in tgt | x <= y } | y \in src, { x \in tgt | x <= y } != {} }
 #  where `<=` is the version comparison operator.
@@ -268,44 +286,63 @@ endmacro()
 #   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
 #   OUT_CUDA_ARCHS="8.0;8.6;9.0;9.0a"
 #
+# Example With PTX:
+#   SRC_CUDA_ARCHS="8.0+PTX"
+#   TGT_CUDA_ARCHS="9.0"
+#   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
+#   OUT_CUDA_ARCHS="8.0+PTX"
+#
 function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
-  list(REMOVE_DUPLICATES SRC_CUDA_ARCHS)
-  set(TGT_CUDA_ARCHS_ ${TGT_CUDA_ARCHS})
+  set(_SRC_CUDA_ARCHS "${SRC_CUDA_ARCHS}")
+  set(_TGT_CUDA_ARCHS ${TGT_CUDA_ARCHS})
+
+  # handle +PTX suffix: separate base arch for matching, record PTX requests
+  set(_PTX_ARCHS)
+  foreach(_arch ${_SRC_CUDA_ARCHS})
+    if(_arch MATCHES "\\+PTX$")
+      string(REPLACE "+PTX" "" _base "${_arch}")
+      list(APPEND _PTX_ARCHS "${_base}")
+      list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
+      list(APPEND _SRC_CUDA_ARCHS "${_base}")
+    endif()
+  endforeach()
+  list(REMOVE_DUPLICATES _PTX_ARCHS)
+  list(REMOVE_DUPLICATES _SRC_CUDA_ARCHS)

  # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
  # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS
  set(_CUDA_ARCHS)
-  if ("9.0a" IN_LIST SRC_CUDA_ARCHS)
-    list(REMOVE_ITEM SRC_CUDA_ARCHS "9.0a")
-    if ("9.0" IN_LIST TGT_CUDA_ARCHS_)
-      list(REMOVE_ITEM TGT_CUDA_ARCHS_ "9.0")
+  if ("9.0a" IN_LIST _SRC_CUDA_ARCHS)
+    list(REMOVE_ITEM _SRC_CUDA_ARCHS "9.0a")
+    if ("9.0" IN_LIST TGT_CUDA_ARCHS)
+      list(REMOVE_ITEM _TGT_CUDA_ARCHS "9.0")
      set(_CUDA_ARCHS "9.0a")
    endif()
  endif()

-  if ("10.0a" IN_LIST SRC_CUDA_ARCHS)
-    list(REMOVE_ITEM SRC_CUDA_ARCHS "10.0a")
+  if ("10.0a" IN_LIST _SRC_CUDA_ARCHS)
+    list(REMOVE_ITEM _SRC_CUDA_ARCHS "10.0a")
    if ("10.0" IN_LIST TGT_CUDA_ARCHS)
-      list(REMOVE_ITEM TGT_CUDA_ARCHS_ "10.0")
+      list(REMOVE_ITEM _TGT_CUDA_ARCHS "10.0")
      set(_CUDA_ARCHS "10.0a")
    endif()
  endif()

-  list(SORT SRC_CUDA_ARCHS COMPARE NATURAL ORDER ASCENDING)
+  list(SORT _SRC_CUDA_ARCHS COMPARE NATURAL ORDER ASCENDING)

  # for each ARCH in TGT_CUDA_ARCHS find the highest arch in SRC_CUDA_ARCHS that
  # is less or equal to ARCH (but has the same major version since SASS binary
  # compatibility is only forward compatible within the same major version).
-  foreach(_ARCH ${TGT_CUDA_ARCHS_})
+  foreach(_ARCH ${_TGT_CUDA_ARCHS})
    set(_TMP_ARCH)
    # Extract the major version of the target arch
    string(REGEX REPLACE "^([0-9]+)\\..*$" "\\1" TGT_ARCH_MAJOR "${_ARCH}")
-    foreach(_SRC_ARCH ${SRC_CUDA_ARCHS})
+    foreach(_SRC_ARCH ${_SRC_CUDA_ARCHS})
      # Extract the major version of the source arch
      string(REGEX REPLACE "^([0-9]+)\\..*$" "\\1" SRC_ARCH_MAJOR "${_SRC_ARCH}")
-      # Check major-version match AND version-less-or-equal
+      # Check version-less-or-equal, and allow PTX arches to match across majors
      if (_SRC_ARCH VERSION_LESS_EQUAL _ARCH)
-        if (SRC_ARCH_MAJOR STREQUAL TGT_ARCH_MAJOR)
+        if (_SRC_ARCH IN_LIST _PTX_ARCHS OR SRC_ARCH_MAJOR STREQUAL TGT_ARCH_MAJOR)
          set(_TMP_ARCH "${_SRC_ARCH}")
        endif()
      else()
@@ -321,6 +358,18 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
  endforeach()

  list(REMOVE_DUPLICATES _CUDA_ARCHS)
+  
+  # reapply +PTX suffix to architectures that requested PTX
+  set(_FINAL_ARCHS)
+  foreach(_arch ${_CUDA_ARCHS})
+    if(_arch IN_LIST _PTX_ARCHS)
+      list(APPEND _FINAL_ARCHS "${_arch}+PTX")
+    else()
+      list(APPEND _FINAL_ARCHS "${_arch}")
+    endif()
+  endforeach()
+  set(_CUDA_ARCHS ${_FINAL_ARCHS})
+  
  set(${OUT_CUDA_ARCHS} ${_CUDA_ARCHS} PARENT_SCOPE)
 endfunction()

@@ -70,6 +70,9 @@ __device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
  int64_t num_tokens = input.numel() / input.size(-1);                   \
  dim3 grid(num_tokens);                                                 \
  dim3 block(std::min(d, 1024));                                         \
+  if (num_tokens == 0) {                                                 \
+    return;                                                              \
+  }                                                                      \
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));      \
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
  VLLM_DISPATCH_FLOATING_TYPES(                                          \
@@ -172,7 +172,7 @@ __device__ void paged_attention_kernel(

  // Load the query to registers.
  // Each thread in a thread group has a different part of the query.
-  // For example, if the the thread group size is 4, then the first thread in
+  // For example, if the thread group size is 4, then the first thread in
  // the group has 0, 4, 8, ... th vectors of the query, and the second thread
  // has 1, 5, 9, ... th vectors of the query, and so on. NOTE(woosuk): Because
  // q is split from a qkv tensor, it may not be contiguous.
@@ -259,7 +259,7 @@ __device__ void paged_attention_kernel(

    // Load a key to registers.
    // Each thread in a thread group has a different part of the key.
-    // For example, if the the thread group size is 4, then the first thread in
+    // For example, if the thread group size is 4, then the first thread in
    // the group has 0, 4, 8, ... th vectors of the key, and the second thread
    // has 1, 5, 9, ... th vectors of the key, and so on.
    for (int i = 0; i < NUM_TOKENS_PER_THREAD_GROUP; i++) {
@@ -143,6 +143,14 @@ void merge_attn_states_launcher(torch::Tensor& output,
  const uint pack_size = 16 / sizeof(scalar_t);
  TORCH_CHECK(head_size % pack_size == 0,
              "headsize must be multiple of pack_size:", pack_size);
+  TORCH_CHECK(output.stride(-2) == head_size && output.stride(-1) == 1,
+              "output heads must be contiguous in memory");
+  TORCH_CHECK(
+      prefix_output.stride(-2) == head_size && prefix_output.stride(-1) == 1,
+      "prefix_output heads must be contiguous in memory");
+  TORCH_CHECK(
+      suffix_output.stride(-2) == head_size && suffix_output.stride(-1) == 1,
+      "suffix_output heads must be contiguous in memory");
  float* output_lse_ptr = nullptr;
  if (output_lse.has_value()) {
    output_lse_ptr = output_lse.value().data_ptr<float>();
@@ -0,0 +1,401 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+#include <assert.h>
+
+#include <cuda.h>
+
+#include <torch/all.h>
+
+__device__ int64_t save_blocks(int* block_offset, int64_t range_start,
+                               int64_t range_end, int64_t block_size,
+                               int64_t input_block_count, int64_t kv_seqlen) {
+  if (range_start >= kv_seqlen) {
+    return input_block_count;
+  }
+  if (range_end > kv_seqlen) {
+    range_end = kv_seqlen;
+  }
+  int64_t current_block_count = input_block_count;
+  for (int idx = range_start; idx < range_end; idx += block_size) {
+    block_offset[current_block_count++] = idx;
+  }
+  return current_block_count;
+}
+
+__global__ void convert_vertical_slash_indexes_kernel(
+    const int* q_seqlens,         // [BATCH, ]
+    const int* kv_seqlens,        // [BATCH, ]
+    const int* vertical_indexes,  // [BATCH, N_HEADS, NNZ_V]
+    const int* slash_indexes,     // [BATCH, N_HEADS, NNZ_S]
+    int* block_count,             // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* block_offset,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_S]
+    int* column_count,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* column_index,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_V]
+    int64_t N_HEADS, int64_t N_ROWS, int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
+    int64_t NNZ_V, int64_t NNZ_S,
+    bool causal  // True for intra, False for succ
+) {
+  const int batch_idx = blockIdx.y;
+  const int head_idx = blockIdx.x;
+  const int group_idx = blockIdx.z;
+
+  int64_t q_seqlen = q_seqlens[batch_idx];
+  int64_t kv_seqlen = kv_seqlens[batch_idx];
+  int64_t block_idx_m = group_idx * blockDim.x + threadIdx.x;
+  int64_t start_m = block_idx_m * BLOCK_SIZE_M;
+  if (start_m >= q_seqlen) {
+    return;
+  }
+  int64_t end_m = start_m + BLOCK_SIZE_M;
+  vertical_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_V;
+  slash_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_S;
+  int64_t row_offset = (batch_idx * N_HEADS + head_idx) * N_ROWS + block_idx_m;
+  block_count += row_offset;
+  block_offset += row_offset * NNZ_S;
+  column_count += row_offset;
+  column_index += row_offset * NNZ_V;
+
+  bool has_slash = true;
+  int64_t tmp_col_cnt = 0, tmp_blk_cnt = 0;
+  int64_t s = 0, v = 0;
+  int64_t v_idx = vertical_indexes[v++];
+  int64_t s_idx = slash_indexes[s++];
+  if (causal) {
+    while (s_idx >= end_m + (kv_seqlen - q_seqlen) && s < NNZ_S) {
+      s_idx = slash_indexes[s++];
+    }
+    if (s_idx > end_m + (kv_seqlen - q_seqlen)) has_slash = false;
+    s_idx = max((kv_seqlen - q_seqlen) + end_m - s_idx, BLOCK_SIZE_M);
+  } else {
+    while (s_idx >= end_m + kv_seqlen && s < NNZ_S) {
+      s_idx = slash_indexes[s++];
+    }
+    if (s_idx > end_m + kv_seqlen) has_slash = false;
+    s_idx = max(kv_seqlen + end_m - s_idx, BLOCK_SIZE_M);
+  }
+
+  int64_t range_start = s_idx - BLOCK_SIZE_M, range_end = s_idx;
+  if (!has_slash) {
+    if (causal) {
+      range_start = (kv_seqlen - q_seqlen) + end_m;
+      range_end = (kv_seqlen - q_seqlen) + end_m + BLOCK_SIZE_N;
+    } else {
+      range_start = kv_seqlen;
+      range_end = kv_seqlen + BLOCK_SIZE_N;
+    }
+  }
+
+  bool slash_finished = false;
+  while (1) {
+    if (v_idx < range_end) {
+      if (v_idx < range_start) {
+        column_index[tmp_col_cnt++] = v_idx;
+      }
+      if (v < NNZ_V) {
+        v_idx = vertical_indexes[v++];
+      } else {
+        if (causal)
+          v_idx = end_m + BLOCK_SIZE_N + (kv_seqlen - q_seqlen);
+        else
+          v_idx = end_m + BLOCK_SIZE_N + kv_seqlen;
+      }
+    } else {
+      if ((s < NNZ_S && causal) ||
+          (s < NNZ_S && !causal && slash_indexes[s] >= start_m)) {
+        if (causal)
+          s_idx = max((kv_seqlen - q_seqlen) + end_m - slash_indexes[s++],
+                      BLOCK_SIZE_M);
+        else
+          s_idx = max(kv_seqlen + end_m - slash_indexes[s++], BLOCK_SIZE_M);
+      } else {
+        if (v == NNZ_V || (v_idx > range_start && causal)) {
+          // add the last vertical if no more slash
+          if (v == NNZ_V && !causal && v_idx < kv_seqlen) {
+            column_index[tmp_col_cnt++] = v_idx;
+          }
+          tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                    BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+          break;
+        } else {
+          if (causal) {
+            range_start = (kv_seqlen - q_seqlen) + end_m;
+            range_end = (kv_seqlen - q_seqlen) + end_m + BLOCK_SIZE_N;
+          } else {
+            // if slash_finished but there are vertical left, save current
+            // blocks
+            tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                      BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+            range_start = kv_seqlen;
+            range_end = kv_seqlen + BLOCK_SIZE_N;
+          }
+          slash_finished = true;
+        }
+      }
+      if (!slash_finished) {
+        if (s_idx > range_end + BLOCK_SIZE_M) {
+          tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                    BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+          range_start = s_idx - BLOCK_SIZE_M;
+          range_end = s_idx;
+        } else if (s_idx > range_end) {
+          range_end += BLOCK_SIZE_M;
+        }
+      }
+    }
+  }
+
+  block_count[0] = tmp_blk_cnt;
+  column_count[0] = tmp_col_cnt;
+}
+
+void convert_vertical_slash_indexes_64x64(
+    const int* q_seqlens,         // [BATCH, ]
+    const int* kv_seqlens,        // [BATCH, ]
+    const int* vertical_indexes,  // [BATCH, N_HEADS, NNZ_V]
+    const int* slash_indexes,     // [BATCH, N_HEADS, NNZ_S]
+    int* block_count,             // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* block_offset,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_S]
+    int* column_count,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* column_index,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_V]
+    int64_t BATCH_SIZE, int64_t N_HEADS, int64_t N_ROWS, int64_t BLOCK_SIZE_M,
+    int64_t BLOCK_SIZE_N, int64_t NNZ_V, int64_t NNZ_S, bool causal) {
+  const int N_THREADS = 64;
+  const dim3 dimBlock(N_THREADS);
+  const dim3 dimGrid(N_HEADS, BATCH_SIZE, (N_ROWS + N_THREADS - 1) / N_THREADS);
+  convert_vertical_slash_indexes_kernel<<<dimGrid, dimBlock>>>(
+      q_seqlens, kv_seqlens, vertical_indexes, slash_indexes, block_count,
+      block_offset, column_count, column_index, N_HEADS, N_ROWS, BLOCK_SIZE_M,
+      BLOCK_SIZE_N, NNZ_V, NNZ_S, causal);
+}
+
+/**
+ * Implements the Algorithm 4 in paper https://arxiv.org/abs/2407.02490.
+ *
+ * This function builds the index of each row of blocks from vertical indices
+ * and slash indices. The vertical indices are treated as points, while the
+ * slash indices are converted as ranges. The output consists of the merged
+ * ranges and separate column indices, where the ranges are represented by
+ * block indices.
+ *
+ * The implementation is referenced from the original MInference repo:
+ * https://github.com/microsoft/MInference/blob/main/csrc/vertical_slash_index.cu.
+ */
+void convert_vertical_slash_indexes(
+    torch::Tensor& block_count,      // [BATCH, N_HEADS, NUM_ROWS]
+    torch::Tensor& block_offset,     // [BATCH, N_HEADS, NUM_ROWS, NNZ_S]
+    torch::Tensor& column_count,     // [BATCH, N_HEADS, NUM_ROWS]
+    torch::Tensor& column_index,     // [BATCH, N_HEADS, NUM_ROWS, NNZ_V]
+    torch::Tensor q_seqlens,         // [BATCH, ]
+    torch::Tensor kv_seqlens,        // [BATCH, ]
+    torch::Tensor vertical_indexes,  // [BATCH, N_HEADS, NNZ_V]
+    torch::Tensor slash_indexes,     // [BATCH, N_HEADS, NNZ_S]
+    int64_t context_size, int64_t block_size_M, int64_t block_size_N,
+    bool causal) {
+  cudaSetDevice(q_seqlens.get_device());
+
+  int batch_size = slash_indexes.size(0);
+  int num_heads = slash_indexes.size(1);
+  int nnz_slash = slash_indexes.size(2);
+  int nnz_vertical = vertical_indexes.size(2);
+  int num_rows = (context_size + block_size_M - 1) / block_size_M;
+
+  convert_vertical_slash_indexes_64x64(
+      q_seqlens.data_ptr<int>(), kv_seqlens.data_ptr<int>(),
+      vertical_indexes.data_ptr<int>(), slash_indexes.data_ptr<int>(),
+      block_count.data_ptr<int>(), block_offset.data_ptr<int>(),
+      column_count.data_ptr<int>(), column_index.data_ptr<int>(), batch_size,
+      num_heads, num_rows, block_size_M, block_size_N, nnz_vertical, nnz_slash,
+      causal);
+}
+
+__global__ void convert_vertical_slash_indexes_kernel_mergehead(
+    const int* q_seqlens,         // [BATCH, ]
+    const int* kv_seqlens,        // [BATCH, ]
+    const int* vertical_indexes,  // [BATCH, N_HEADS, NNZ_V]
+    const int* slash_indexes,     // [BATCH, N_HEADS, NNZ_S]
+    const int* per_head_vertical_topkv, const int* per_head_slash_topkv,
+    int* block_count,   // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* block_offset,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_S]
+    int* column_count,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* column_index,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_V]
+    int64_t N_HEADS, int64_t N_ROWS, int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
+    int64_t NNZ_V, int64_t NNZ_S,
+    bool causal  // True for intra, False for succ
+) {
+  const int batch_idx = blockIdx.y;
+  const int head_idx = blockIdx.x;
+  const int group_idx = blockIdx.z;
+
+  int64_t q_seqlen = q_seqlens[batch_idx];
+  int64_t kv_seqlen = kv_seqlens[batch_idx];
+  int64_t block_idx_m = group_idx * blockDim.x + threadIdx.x;
+  int64_t start_m = block_idx_m * BLOCK_SIZE_M;
+  if (start_m >= q_seqlen) {
+    return;
+  }
+  int64_t end_m = start_m + BLOCK_SIZE_M;
+  vertical_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_V;
+  slash_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_S;
+  int64_t row_offset = (batch_idx * N_HEADS + head_idx) * N_ROWS + block_idx_m;
+  block_count += row_offset;
+  block_offset += row_offset * NNZ_S;
+  column_count += row_offset;
+  column_index += row_offset * NNZ_V;
+
+  // MergeHead: each head has it's unique max topk NNZ_V，NNZ_S. (NNZ_V，NNZ_S
+  // above is buffer size, use to compute offset)
+  NNZ_S = per_head_slash_topkv[head_idx];
+  NNZ_V = per_head_vertical_topkv[head_idx];
+
+  bool has_slash = true;
+  int64_t tmp_col_cnt = 0, tmp_blk_cnt = 0;
+  int64_t s = 0, v = 0;
+  int64_t v_idx = vertical_indexes[v++];
+  int64_t s_idx = slash_indexes[s++];
+  if (causal) {
+    while (s_idx >= end_m + (kv_seqlen - q_seqlen) && s < NNZ_S) {
+      s_idx = slash_indexes[s++];
+    }
+    if (s_idx > end_m + (kv_seqlen - q_seqlen)) has_slash = false;
+    s_idx = max((kv_seqlen - q_seqlen) + end_m - s_idx, BLOCK_SIZE_M);
+  } else {
+    while (s_idx >= end_m + kv_seqlen && s < NNZ_S) {
+      s_idx = slash_indexes[s++];
+    }
+    if (s_idx > end_m + kv_seqlen) has_slash = false;
+    s_idx = max(kv_seqlen + end_m - s_idx, BLOCK_SIZE_M);
+  }
+
+  int64_t range_start = s_idx - BLOCK_SIZE_M, range_end = s_idx;
+  if (!has_slash) {
+    if (causal) {
+      range_start = (kv_seqlen - q_seqlen) + end_m;
+      range_end = (kv_seqlen - q_seqlen) + end_m + BLOCK_SIZE_N;
+    } else {
+      range_start = kv_seqlen;
+      range_end = kv_seqlen + BLOCK_SIZE_N;
+    }
+  }
+
+  bool slash_finished = false;
+  while (1) {
+    if (v_idx < range_end) {
+      if (v_idx < range_start) {
+        column_index[tmp_col_cnt++] = v_idx;
+      }
+      if (v < NNZ_V) {
+        v_idx = vertical_indexes[v++];
+      } else {
+        if (causal)
+          v_idx = end_m + BLOCK_SIZE_N + (kv_seqlen - q_seqlen);
+        else
+          v_idx = end_m + BLOCK_SIZE_N + kv_seqlen;
+      }
+    } else {
+      if ((s < NNZ_S && causal) ||
+          (s < NNZ_S && !causal && slash_indexes[s] >= start_m)) {
+        if (causal)
+          s_idx = max((kv_seqlen - q_seqlen) + end_m - slash_indexes[s++],
+                      BLOCK_SIZE_M);
+        else
+          s_idx = max(kv_seqlen + end_m - slash_indexes[s++], BLOCK_SIZE_M);
+      } else {
+        if (v == NNZ_V || (v_idx > range_start && causal)) {
+          // add the last vertical if no more slash
+          if (v == NNZ_V && !causal && v_idx < kv_seqlen) {
+            column_index[tmp_col_cnt++] = v_idx;
+          }
+          tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                    BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+          break;
+        } else {
+          if (causal) {
+            range_start = (kv_seqlen - q_seqlen) + end_m;
+            range_end = (kv_seqlen - q_seqlen) + end_m + BLOCK_SIZE_N;
+          } else {
+            // if slash_finished but there are vertical left, save current
+            // blocks
+            tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                      BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+            range_start = kv_seqlen;
+            range_end = kv_seqlen + BLOCK_SIZE_N;
+          }
+          slash_finished = true;
+        }
+      }
+      if (!slash_finished) {
+        if (s_idx > range_end + BLOCK_SIZE_M) {
+          tmp_blk_cnt = save_blocks(block_offset, range_start, range_end,
+                                    BLOCK_SIZE_N, tmp_blk_cnt, kv_seqlen);
+          range_start = s_idx - BLOCK_SIZE_M;
+          range_end = s_idx;
+        } else if (s_idx > range_end) {
+          range_end += BLOCK_SIZE_M;
+        }
+      }
+    }
+  }
+
+  block_count[0] = tmp_blk_cnt;
+  column_count[0] = tmp_col_cnt;
+}
+
+void convert_vertical_slash_indexes_64x64_mergehead(
+    const int* q_seqlens,         // [BATCH, ]
+    const int* kv_seqlens,        // [BATCH, ]
+    const int* vertical_indexes,  // [BATCH, N_HEADS, NNZ_V]
+    const int* slash_indexes,     // [BATCH, N_HEADS, NNZ_S]
+    int* per_head_vertical_topkv, int* per_head_slash_topkv,
+    int* block_count,   // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* block_offset,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_S]
+    int* column_count,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
+    int* column_index,  // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), NNZ_V]
+    int64_t BATCH_SIZE, int64_t N_HEADS, int64_t N_ROWS, int64_t BLOCK_SIZE_M,
+    int64_t BLOCK_SIZE_N, int64_t NNZ_V, int64_t NNZ_S, bool causal) {
+  const int N_THREADS = 64;
+  const dim3 dimBlock(N_THREADS);
+  const dim3 dimGrid(N_HEADS, BATCH_SIZE, (N_ROWS + N_THREADS - 1) / N_THREADS);
+  convert_vertical_slash_indexes_kernel_mergehead<<<dimGrid, dimBlock>>>(
+      q_seqlens, kv_seqlens, vertical_indexes, slash_indexes,
+      per_head_vertical_topkv, per_head_slash_topkv, block_count, block_offset,
+      column_count, column_index, N_HEADS, N_ROWS, BLOCK_SIZE_M, BLOCK_SIZE_N,
+      NNZ_V, NNZ_S, causal);
+}
+
+/**
+ * Implements the Algorithm 4 in paper https://arxiv.org/abs/2407.02490.
+ *
+ * Like the above convert_vertical_slash_indexes, but with
+ * pre-computed vertical and slash counts.
+ */
+void convert_vertical_slash_indexes_mergehead(
+    torch::Tensor& block_count,            // [BATCH, N_HEADS, NUM_ROWS]
+    torch::Tensor& block_offset,           // [BATCH, N_HEADS, NUM_ROWS, NNZ_S]
+    torch::Tensor& column_count,           // [BATCH, N_HEADS, NUM_ROWS]
+    torch::Tensor& column_index,           // [BATCH, N_HEADS, NUM_ROWS, NNZ_V]
+    torch::Tensor q_seqlens,               // [BATCH, ]
+    torch::Tensor kv_seqlens,              // [BATCH, ]
+    torch::Tensor vertical_indexes,        // [BATCH, N_HEADS, NNZ_V]
+    torch::Tensor slash_indexes,           // [BATCH, N_HEADS, NNZ_S]
+    torch::Tensor vertical_indices_count,  // [N_HEADS, ]
+    torch::Tensor slash_indices_count,     // [N_HEADS, ]
+    int64_t context_size, int64_t block_size_M, int64_t block_size_N,
+    bool causal) {
+  cudaSetDevice(q_seqlens.get_device());
+
+  int batch_size = slash_indexes.size(0);
+  int num_heads = slash_indexes.size(1);
+  int nnz_slash = slash_indexes.size(2);
+  int nnz_vertical = vertical_indexes.size(2);
+  int num_rows = (context_size + block_size_M - 1) / block_size_M;
+
+  convert_vertical_slash_indexes_64x64_mergehead(
+      q_seqlens.data_ptr<int>(), kv_seqlens.data_ptr<int>(),
+      vertical_indexes.data_ptr<int>(), slash_indexes.data_ptr<int>(),
+      vertical_indices_count.data_ptr<int>(),
+      slash_indices_count.data_ptr<int>(), block_count.data_ptr<int>(),
+      block_offset.data_ptr<int>(), column_count.data_ptr<int>(),
+      column_index.data_ptr<int>(), batch_size, num_heads, num_rows,
+      block_size_M, block_size_N, nnz_vertical, nnz_slash, causal);
+}
@@ -315,6 +315,8 @@ static inline constexpr auto kS8 = ScalarType::int_(8);
 static inline constexpr auto kU8 = ScalarType::uint(8);
 static inline constexpr auto kU8B128 = ScalarType::uint(8, 128);

+static inline constexpr auto kFE2M1f =
+    ScalarType::float_(2, 1, true, ScalarType::NAN_NONE);
 static inline constexpr auto kFE3M2f =
    ScalarType::float_(3, 2, true, ScalarType::NAN_NONE);
 static inline constexpr auto kFE4M3fn =
@@ -332,6 +334,7 @@ static inline constexpr auto kInt8 = kS8;
 static inline constexpr auto kUint8 = kU8;
 static inline constexpr auto kUint8b128 = kU8B128;

+static inline constexpr auto kFloat4_e2m1f = kFE2M1f;
 static inline constexpr auto kFloat6_e3m2f = kFE3M2f;
 static inline constexpr auto kFloat8_e4m3fn = kFE4M3fn;
 static inline constexpr auto kFloat8_e5m2 = kFE5M2;
@@ -19,6 +19,7 @@ namespace vec_op {
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES_FP8(...)        \
  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)     \
  AT_DISPATCH_CASE(at::ScalarType::Float8_e5m2, __VA_ARGS__)

 #define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
@@ -15,15 +15,6 @@
                cutlassGetStatusString(error));     \
  }

-/**
- * Panic wrapper for unwinding CUDA runtime errors
- */
-#define CUDA_CHECK(status)                                        \
-  {                                                               \
-    cudaError_t error = status;                                   \
-    TORCH_CHECK(error == cudaSuccess, cudaGetErrorString(error)); \
-  }
-
 inline int get_cuda_max_shared_memory_per_block_opt_in(int const device) {
  int max_shared_mem_per_block_opt_in = 0;
  cudaDeviceGetAttribute(&max_shared_mem_per_block_opt_in,
@@ -65,5 +65,19 @@
  AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__)   \
  AT_DISPATCH_CASE(at::ScalarType::Long, __VA_ARGS__)

+#define VLLM_DISPATCH_CASE_INTEGRAL_AND_UNSIGNED_TYPES(...) \
+  AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::Short, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__)        \
+  AT_DISPATCH_CASE(at::ScalarType::Long, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::UInt16, __VA_ARGS__)     \
+  AT_DISPATCH_CASE(at::ScalarType::UInt32, __VA_ARGS__)     \
+  AT_DISPATCH_CASE(at::ScalarType::UInt64, __VA_ARGS__)
+
 #define VLLM_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \
  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__))
+
+#define VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(                                              \
+      TYPE, NAME, VLLM_DISPATCH_CASE_INTEGRAL_AND_UNSIGNED_TYPES(__VA_ARGS__))
@@ -13,6 +13,10 @@
 #include <cub/block/block_load.cuh>
 #include <cub/block/block_store.cuh>

+#ifdef USE_ROCM
+    namespace cub = hipcub;
+#endif
+
 #include "static_switch.h"


@@ -501,15 +505,9 @@ void causal_conv1d_fwd_launch(ConvParamsBase &params, cudaStream_t stream) {
        auto kernel = &causal_conv1d_fwd_kernel<Ktraits>;

        if (kSmemSize >= 48 * 1024) {
-            #ifndef USE_ROCM
-            C10_CUDA_CHECK(cudaFuncSetAttribute(
-                kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
-            #else
-            // There is a slight signature discrepancy in HIP and CUDA "FuncSetAttribute" function.
            C10_CUDA_CHECK(cudaFuncSetAttribute(
                (void *) kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            std::cerr << "Warning (causal_conv1d fwd launch): attempting to set maxDynamicSharedMemorySize on an AMD GPU which is currently a non-op (in ROCm versions <= 6.1). This might lead to undefined behavior. \n" << std::endl;
-            #endif
        }
        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);

@@ -321,7 +321,7 @@ void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
            auto kernel = &selective_scan_fwd_kernel<Ktraits>;
            if (kSmemSize >= 48 * 1024) {
                C10_CUDA_CHECK(cudaFuncSetAttribute(
-                    kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
+                    (void *) kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            }
            kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
@@ -31,7 +31,10 @@ TEMPLATE = ("template __global__ void Marlin<"

 # int8 with zero point case (vllm::kU8) is also supported,
 # we don't add it to reduce wheel size.
-SCALAR_TYPES = ["vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn"]
+SCALAR_TYPES = [
+    "vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn",
+    "vllm::kFE2M1f"
+]
 THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128)]

 THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
@@ -39,7 +42,7 @@ THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
 #   = 0 : act order case
 #   = -1 : channelwise quantization
 #   > 0 : group_size=16*group_blocks
-GROUP_BLOCKS = [0, -1, 2, 4, 8]
+GROUP_BLOCKS = [0, -1, 1, 2, 4, 8]
 DTYPES = ["fp16", "bf16"]


@@ -72,6 +75,12 @@ def generate_new_kernels():
            # for fp8
            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 not in [1, 2]:
+                continue
+            # other quantization methods don't support group_size = 16
+            if scalar_type != "vllm::kFE2M1f" and group_blocks == 1:
+                continue

            k_blocks = thread_configs[0] // 16
            n_blocks = thread_configs[1] // 16
@@ -7,17 +7,18 @@
 #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 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__ 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,        \
      bool use_fp32_reduce, int max_shared_mem

 namespace MARLIN_NAMESPACE_NAME {
@@ -301,9 +301,11 @@ __global__ void Marlin(
    int4* __restrict__ C_tmp,    // fp32 tmp output buffer (for reduce)
    const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                          // (k/groupsize)xn
-    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
+    const uint16_t* __restrict__ scale2_ptr,  // fp16 global scale (for nvfp4
+                                              // only)
+    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
    const int32_t* __restrict__ sorted_token_ids_ptr,        // moe sorted_ids
    const int32_t* __restrict__ expert_ids_ptr,              // moe expert ids
    const int32_t* __restrict__ num_tokens_past_padded_ptr,  // moe num tokens
@@ -341,6 +343,16 @@ __global__ void Marlin(
  extern __shared__ int4 sh[];
  static constexpr auto w_type = vllm::ScalarType::from_id(w_type_id);
  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 ||
+      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;
+
  constexpr bool has_act_order = group_blocks == 0;

  constexpr int pack_factor = 32 / w_type.size_bits();
@@ -348,7 +360,8 @@ __global__ void Marlin(
  constexpr int moe_block_size = m_block_size_8 ? 8 : (16 * thread_m_blocks);
  const int group_size =
      (!has_act_order && group_blocks == -1) ? prob_k : prob_k / num_groups;
-  const int scales_expert_stride = prob_n * prob_k / group_size / 8;
+  const int scales_expert_stride =
+      prob_n * prob_k / group_size / (w_type == vllm::kFE2M1f ? 16 : 8);
  const int zp_expert_stride =
      is_zp_float ? prob_n * prob_k / group_size / 8
                  : prob_n * prob_k / group_size / (pack_factor * 4);
@@ -460,9 +473,16 @@ __global__ void Marlin(
      if (mul_topk_weights) {
  #pragma unroll
        for (int i = 0; i < 4; i++) {
-          sh_block_topk_weights[tid4 * 4 + i] =
-              Dtype::num2num2(Dtype::float2num(
-                  topk_weights_ptr[sh_block_sorted_ids[tid4 * 4 + i]]));
+          int idx = tid4 * 4 + i;
+          idx = idx < block_num_valid_tokens ? idx : 0;
+          if constexpr (w_type == vllm::kFE2M1f) {
+            sh_block_topk_weights[idx] = __hmul2(
+                global_scale, Dtype::num2num2(Dtype::float2num(
+                                  topk_weights_ptr[sh_block_sorted_ids[idx]])));
+          } else {
+            sh_block_topk_weights[idx] = Dtype::num2num2(
+                Dtype::float2num(topk_weights_ptr[sh_block_sorted_ids[idx]]));
+          }
        }
      }
    }
@@ -493,6 +513,11 @@ __global__ void Marlin(
      expert_id = expert_ids_ptr[block_id];
    }

+    if constexpr (w_type == vllm::kFE2M1f) {
+      uint16_t val = scale2_ptr[expert_id];
+      global_scale = Dtype::num2num2(*reinterpret_cast<scalar_t*>(&val));
+    }
+
    B_expert_off = expert_id * prob_n * prob_k / (pack_factor * 4);
    scales_ptr += (expert_id - old_expert_id) * scales_expert_stride;
    if constexpr (has_zp) {
@@ -606,7 +631,7 @@ __global__ void Marlin(
  constexpr int s_sh_stride = 16 * thread_n_blocks / 8;
  constexpr int s_tb_groups =
      !has_act_order && group_blocks != -1 && group_blocks < thread_k_blocks
-          ? thread_k_blocks / group_blocks
+          ? thread_k_blocks / group_blocks / (w_type == vllm::kFE2M1f ? 2 : 1)
          : 1;
  constexpr int s_sh_stage = s_tb_groups * s_sh_stride;
  int s_gl_rd_delta = s_gl_stride;
@@ -664,7 +689,8 @@ __global__ void Marlin(
    if constexpr (group_blocks == -1) {
      s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
    } else {
-      s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) +
+      s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) /
+                    (w_type == vllm::kFE2M1f ? 2 : 1) +
                s_sh_stride * slice_col + threadIdx.x;
    }
  }
@@ -688,10 +714,20 @@ __global__ void Marlin(
  // we scale a `half2` tile in column-major layout in the former and in
  // row-major in the latter case.
  int s_sh_rd;
-  if constexpr (group_blocks != -1)
+  if constexpr (group_blocks != -1 && w_type == vllm::kFE2M1f) {
+    auto warp_id = threadIdx.x / 32;
+    int n_warps = thread_n_blocks / 4;
+    int warp_row = warp_id / n_warps;
+
    s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
              (threadIdx.x % 32) / 4;
-  else if constexpr (group_blocks == -1 && (m_block_size_8 || has_zp))
+    s_sh_rd = s_sh_rd * 2 + warp_row % 2;
+
+  } else if constexpr (group_blocks != -1)
+    s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+              (threadIdx.x % 32) / 4;
+  else if constexpr (group_blocks == -1 &&
+                     (m_block_size_8 || (has_zp && !dequant_skip_flop)))
    s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
              (threadIdx.x % 32) / 8;
  else
@@ -801,7 +837,7 @@ __global__ void Marlin(
    sh_first_group_id = first_group_id;
    sh_num_groups = last_group_id - first_group_id + 1;

-    if (sh_num_groups < act_s_max_num_groups) {
+    if (sh_num_groups > act_s_max_num_groups) {
      sh_num_groups = act_s_max_num_groups;
    }

@@ -1021,12 +1057,19 @@ __global__ void Marlin(
          cur_k += k_iter_size * (k % b_sh_wr_iters);

          int k_blocks = cur_k / 16;
-          int cur_group_id = k_blocks / group_blocks;
+          int cur_group_id =
+              k_blocks / (group_blocks * (w_type == vllm::kFE2M1f ? 2 : 1));

          int4* sh_s_stage = sh_s + s_sh_stage * pipe;

-          reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
-              sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
+          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 {
+            reinterpret_cast<int2*>(&frag_s[k % 2])[0] =
+                reinterpret_cast<int2*>(
+                    sh_s_stage)[s_sh_rd + cur_group_id * (2 * s_sh_stride)];
+          }
        }
      }

@@ -1199,22 +1242,7 @@ __global__ void Marlin(
  };

  auto dequant_data = [&](int q, scalar_t2* frag_b_ptr) {
-    if constexpr (has_zp && is_zp_float || !has_zp) {
-      dequant<scalar_t2, w_type_id>(q, frag_b_ptr);
-    } else {
-      static_assert(has_zp && !is_zp_float);
-      static_assert(w_type_id == vllm::kU4.id() || w_type_id == vllm::kU8.id());
-      // If (has_zp && !is_zp_float),
-      // we use not-zp version `dequant` function
-      // to improve numerical accuracy.
-      // Since both weight and zero point are dequanted using this logic,
-      // the final dequanted weight would be correct.
-      if constexpr (w_type_id == vllm::kU4.id()) {
-        dequant<scalar_t2, vllm::kU4B8.id()>(q, frag_b_ptr);
-      } else if constexpr (w_type_id == vllm::kU8.id()) {
-        dequant<scalar_t2, vllm::kU8B128.id()>(q, frag_b_ptr);
-      }
-    }
+    dequant<scalar_t2, w_type_id, dequant_skip_flop>(q, frag_b_ptr);
  };

  // Execute the actual tensor core matmul of a sub-tile.
@@ -1244,13 +1272,23 @@ __global__ void Marlin(
        dequant_data(zp_quant_1, reinterpret_cast<scalar_t2*>(&frag_zp) + 2);
      }
    }
-    if constexpr (has_zp && is_zp_float) {
+    if constexpr (!dequant_skip_flop && has_zp && is_zp_float) {
      if (is_new_zp) {
        reinterpret_cast<int4*>(&frag_zp)[0] =
            reinterpret_cast<int4*>(&frag_zpf[k2])[0];
      }
    }

+    if constexpr (w_type == vllm::kFE2M1f) {
+      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>(
+          s_quant_1, reinterpret_cast<scalar_t2*>(&frag_s[k2]) + 2);
+    }
+
  // We have the m dimension as the inner loop in order to encourage overlapping
  // dequantization and matmul operations.
  #pragma unroll
@@ -1259,7 +1297,10 @@ __global__ void Marlin(
      FragB frag_b1;
      int b_quant_0, b_quant_1;

-      if constexpr (w_type.size_bits() == 4) {
+      if constexpr (w_type_id == vllm::kFE2M1f.id()) {
+        b_quant_1 = frag_b_quant[k2][0][j];
+        b_quant_0 = b_quant_1 << 8;
+      } else if constexpr (w_type.size_bits() == 4) {
        b_quant_0 = frag_b_quant[k2][0][j];
        b_quant_1 = b_quant_0 >> 8;
      } else {
@@ -1272,6 +1313,11 @@ __global__ void Marlin(
      dequant_data(b_quant_0, reinterpret_cast<scalar_t2*>(&frag_b0));
      dequant_data(b_quant_1, reinterpret_cast<scalar_t2*>(&frag_b1));

+      if constexpr (dequant_skip_flop && has_zp && !is_zp_float) {
+        sub_zp<scalar_t>(frag_b0, frag_zp[j], 0);
+        sub_zp<scalar_t>(frag_b1, frag_zp[j], 1);
+      }
+
      // Apply scale to frag_b0
      if constexpr (has_act_order) {
        static_assert(group_blocks != -1);
@@ -1279,7 +1325,8 @@ __global__ void Marlin(
                         act_frag_s[k2][2][j], act_frag_s[k2][3][j], 0);
        scale4<scalar_t>(frag_b1, act_frag_s[k2][0][j], act_frag_s[k2][1][j],
                         act_frag_s[k2][2][j], act_frag_s[k2][3][j], 1);
-      } else if constexpr (has_zp && !is_zp_float && group_blocks == -1) {
+      } else if constexpr (!dequant_skip_flop && has_zp && !is_zp_float &&
+                           group_blocks == -1) {
        int idx = (threadIdx.x / 4) % 2;
        scalar_t2 s2 = Dtype::nums2num2(
            reinterpret_cast<scalar_t*>(&frag_s[j / 2][j % 2 * 2 + 0])[idx],
@@ -1287,7 +1334,7 @@ __global__ void Marlin(
        if (is_new_zp) frag_zp[j] = __hmul2(frag_zp[j], s2);
        scale_and_sub<scalar_t>(frag_b0, s2.x, frag_zp[j].x);
        scale_and_sub<scalar_t>(frag_b1, s2.y, frag_zp[j].y);
-      } else if constexpr (has_zp && group_blocks != -1) {
+      } else if constexpr (!dequant_skip_flop && has_zp && group_blocks != -1) {
        if (is_new_zp)
          frag_zp[j] = __hmul2(frag_zp[j],
                               *reinterpret_cast<scalar_t2*>(&frag_s[k2][j]));
@@ -1554,10 +1601,17 @@ __global__ void Marlin(
      // For per-column quantization we finally apply the scale here (only for
      // 4-bit)
      if constexpr (!has_act_order && group_blocks == -1 &&
-                    w_type.size_bits() == 4 && !has_zp) {
+                    w_type.size_bits() == 4 &&
+                    (has_zp && dequant_skip_flop || !has_zp)) {
        res = __hmul2(res, s[0]);
      }

+      if constexpr (w_type == vllm::kFE2M1f) {
+        if (!mul_topk_weights) {
+          res = __hmul2(res, global_scale);
+        }
+      }
+
      if constexpr (m_block_size_8) {
        ((scalar_t*)sh_red)[idx] = res.x;
        ((scalar_t*)sh_red)[idx + 8 * c_sh_stride] = res.y;
@@ -1648,7 +1702,9 @@ __global__ void Marlin(
      if constexpr (has_zp && !is_zp_float && group_blocks == -1) {
        if (i == 0) {
          fetch_col_zp_to_shared();
-          fetch_col_scale_to_shared();
+          if constexpr (!dequant_skip_flop) {
+            fetch_col_scale_to_shared();
+          }
        }
      }
      fetch_to_shared(i, i, i < slice_iters, i);
@@ -1711,17 +1767,20 @@ __global__ void Marlin(

      if constexpr (has_act_order) {
        slice_k_start += tb_k * stages;
-        slice_k_start_shared_fetch += tb_k * stages;
-        int first_group_id = g_idx[slice_k_start];
-        int last_g_idx = slice_k_start + stages * tb_k * 2;
-        if (last_g_idx >= prob_k) {
-          last_g_idx = prob_k - 1;
-        }
-        int last_group_id = g_idx[last_g_idx];
-        if (last_group_id >= sh_first_group_id + sh_num_groups) {
-          fetch_act_order_scales_to_shared(false, first_group_id,
-                                           last_group_id);
-          __syncthreads();
+
+        if (slice_k_start < prob_k) {
+          slice_k_start_shared_fetch += tb_k * stages;
+          int first_group_id = g_idx[slice_k_start];
+          int last_g_idx = slice_k_start + stages * tb_k * 2;
+          if (last_g_idx >= prob_k) {
+            last_g_idx = prob_k - 1;
+          }
+          int last_group_id = g_idx[last_g_idx];
+          if (last_group_id >= sh_first_group_id + sh_num_groups) {
+            fetch_act_order_scales_to_shared(false, first_group_id,
+                                             last_group_id);
+            __syncthreads();
+          }
        }
      }
      if (slice_iters == 0) {
@@ -1737,7 +1796,8 @@ __global__ void Marlin(
      bool last = slice_idx == slice_count - 1;
      // For per-column scales, we only fetch them here in the final step before
      // write-out
-      if constexpr (!has_act_order && group_blocks == -1 && !has_zp) {
+      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)) {
          if (s_sh_wr_pred) {
            cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
@@ -1747,7 +1807,8 @@ __global__ void Marlin(
      }

      thread_block_reduce();
-      if constexpr (!has_act_order && group_blocks == -1 && !has_zp) {
+      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)) {
          cp_async_wait<0>();
          __syncthreads();
@@ -1771,7 +1832,8 @@ __global__ void Marlin(
      // that converts the fp32 results to fp16 (so that we avoid possible
      // overflow in fp16)
      if constexpr (!has_act_order && group_blocks == -1 &&
-                    w_type.size_bits() == 8 && !has_zp) {
+                    w_type.size_bits() == 8 &&
+                    (has_zp && dequant_skip_flop || !has_zp)) {
        if (threadIdx.x / 32 < thread_n_blocks / 4) {
  #pragma unroll
          for (int i = 0; i < thread_m_blocks; i++) {
@@ -291,6 +291,7 @@ bool is_valid_config(thread_config_t const& th_config, bool m_block_size_8,
  // BIGGROUP: cases for big group size (group_blocks in [-1, 8])
  // FZP: cases for float-zero-point (is_zp_float = true)
  // ACT: cases for act order case (group_blocks == 0)
+  // FP4: cases for nvfp4(e2m1) (group_blocks == 1)
  #define COMMON_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, true, 2, NUM_THREADS, false)   \
@@ -338,6 +339,21 @@ bool is_valid_config(thread_config_t const& th_config, bool m_block_size_8,
    _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)    \
@@ -394,6 +410,8 @@ MarlinFuncPtr get_marlin_kernel(const vllm::ScalarType q_type,

  BIGGROUP_GET_IF(vllm::kFE4M3fn)

+  FP4_GET_IF(vllm::kFE2M1f)
+
  ACT_GET_IF(vllm::kU4B8)
  ACT_GET_IF(vllm::kU8B128)

@@ -465,7 +483,7 @@ 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* zp, void* g_idx, void* perm, void* a_tmp,
+               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,
@@ -479,14 +497,16 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
  bool m_block_size_8 = moe_block_size == 8;

  if (has_zp) {
-    TORCH_CHECK(q_type == vllm::kU4,
-                "q_type must be u4 when has_zp = True. Got = ", q_type.str());
+    TORCH_CHECK(
+        q_type == vllm::kU4 || q_type == vllm::kU8,
+        "q_type must be u4 or u8 when has_zp = True. Got = ", q_type.str());
  } else {
-    TORCH_CHECK(q_type == vllm::kU4B8 || q_type == vllm::kU8B128 ||
-                    q_type == vllm::kFE4M3fn,
-                "q_type must be uint4b8, uint8b128 or fp8e4m3 when has_zp = "
-                "False. Got = ",
-                q_type.str());
+    TORCH_CHECK(
+        q_type == vllm::kU4B8 || q_type == vllm::kU8B128 ||
+            q_type == vllm::kFE4M3fn || q_type == vllm::kFE2M1f,
+        "q_type must be uint4b8, uint8b128, float8_e4m3fn or float4_e2m1f when "
+        "has_zp = False. Got = ",
+        q_type.str());
  }

  TORCH_CHECK(prob_m > 0 && prob_n > 0 && prob_k > 0, "Invalid MNK = [", prob_m,
@@ -519,6 +539,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
  int4* C_ptr = (int4*)C;
  int4* C_tmp_ptr = (int4*)C_tmp;
  const int4* s_ptr = (const int4*)s;
+  const uint16_t* s2_ptr = (const uint16_t*)s2;
  const int4* zp_ptr = (const int4*)zp;
  const int* g_idx_ptr = (const int*)g_idx;
  const int* perm_ptr = (const int*)perm;
@@ -627,7 +648,7 @@ 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, zp_ptr, g_idx_ptr,
+      A_ptr, B_ptr, C_ptr, C_tmp_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);
@@ -639,6 +660,7 @@ 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,
+    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,
    std::optional<torch::Tensor> const& perm_or_none, torch::Tensor& workspace,
@@ -790,6 +812,17 @@ 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.");
+  } 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::Tensor b_zeros;
  if (b_zeros_or_none.has_value()) {
    b_zeros = b_zeros_or_none.value();
@@ -802,13 +835,14 @@ torch::Tensor moe_wna16_marlin_gemm(

  if (has_zp) {
    TORCH_CHECK(
-        b_q_type == vllm::kU4,
-        "b_q_type must be u4 when has_zp = True. Got = ", b_q_type.str());
+        b_q_type == vllm::kU4 || b_q_type == vllm::kU8,
+        "b_q_type must be u4 or u8 when has_zp = True. Got = ", b_q_type.str());
  } else {
    TORCH_CHECK(b_q_type == vllm::kU4B8 || b_q_type == vllm::kU8B128 ||
-                    b_q_type == vllm::kFE4M3fn,
-                "b_q_type must be uint4b8, uint8b128 or fp8e4m3 when has_zp = "
-                "False. Got = ",
+                    b_q_type == vllm::kFE4M3fn || b_q_type == vllm::kFE2M1f,
+                "b_q_type must be uint4b8, uint8b128, float8_e4m3fn or "
+                "float4_e2m1f when "
+                "has_zp = False. Got = ",
                b_q_type.str());
  }

@@ -854,9 +888,16 @@ torch::Tensor moe_wna16_marlin_gemm(

  int dev = a.get_device();
  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>();
+    } 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>(), b_scales.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(),
@@ -866,11 +907,18 @@ torch::Tensor moe_wna16_marlin_gemm(
        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>();
+    } 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>(),
-        b_scales.data_ptr<at::BFloat16>(), b_zeros.data_ptr(), g_idx.data_ptr(),
-        perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(),
+        c.data_ptr<at::BFloat16>(), c_tmp.data_ptr<float>(), 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,
@@ -326,7 +326,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
  }

  if (use_global_memory) {
-    VLLM_DISPATCH_INTEGRAL_TYPES(
+    VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
        topk_ids.scalar_type(), "moe_align_block_size_global_mem_kernel", [&] {
          // calc needed amount of shared mem for `tokens_cnts` and `cumsum`
          // tensors
@@ -351,7 +351,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
              cumsum_buffer.data_ptr<int32_t>());
        });
  } else if (use_i16) {
-    VLLM_DISPATCH_INTEGRAL_TYPES(
+    VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
        topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
          // set dynamic shared mem
          auto kernel =
@@ -366,7 +366,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
              topk_ids.numel());
        });
  } else {
-    VLLM_DISPATCH_INTEGRAL_TYPES(
+    VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
        topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
          auto kernel =
              vllm::moe::moe_align_block_size_kernel<scalar_t, int32_t>;
@@ -391,7 +391,7 @@ void sgl_moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
  TORCH_CHECK(num_experts == 256,
              "sgl_moe_align_block_size kernel only supports deepseek v3.");

-  VLLM_DISPATCH_INTEGRAL_TYPES(
+  VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
      topk_ids.scalar_type(), "sgl_moe_align_block_size_kernel", [&] {
        // calc needed amount of shared mem for `cumsum` tensors
        auto options_int =
@@ -28,4 +28,6 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                             torch::Tensor num_tokens_post_pad, int64_t top_k,
                             int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
                             int64_t BLOCK_SIZE_K, int64_t bit);
-#endif
+#endif
+
+bool moe_permute_unpermute_supported();
@@ -5,6 +5,9 @@
 #include "permute_unpermute_kernels/dispatch.h"
 #include "core/registration.h"

+// moe_permute kernels require at least CUDA 12.0
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+
 void moe_permute(
    const torch::Tensor& input,                      // [n_token, hidden]
    const torch::Tensor& topk_weights,               //[n_token, topk]
@@ -127,7 +130,45 @@ void moe_unpermute(
  });
 }

+#else
+
+void moe_permute(const torch::Tensor& input, const torch::Tensor& topk_weights,
+                 torch::Tensor& topk_ids,
+                 const torch::Tensor& token_expert_indicies,
+                 const std::optional<torch::Tensor>& expert_map,
+                 int64_t n_expert, int64_t n_local_expert, int64_t topk,
+                 const std::optional<int64_t>& align_block_size,
+                 torch::Tensor& permuted_input,
+                 torch::Tensor& expert_first_token_offset,
+                 torch::Tensor& src_row_id2dst_row_id_map,
+                 torch::Tensor& m_indices) {
+  TORCH_CHECK(false, "moe_unpermute is not supported on CUDA < 12.0");
+}
+
+void moe_unpermute(const torch::Tensor& input,
+                   const torch::Tensor& topk_weights, torch::Tensor& topk_ids,
+                   const torch::Tensor& token_expert_indicies,
+                   const std::optional<torch::Tensor>& expert_map,
+                   int64_t n_expert, int64_t n_local_expert, int64_t topk,
+                   const std::optional<int64_t>& align_block_size,
+                   torch::Tensor& permuted_input,
+                   torch::Tensor& expert_first_token_offset,
+                   torch::Tensor& src_row_id2dst_row_id_map,
+                   torch::Tensor& m_indices) {
+  TORCH_CHECK(false, "moe_unpermute is not supported on CUDA < 12.0");
+}
+
+#endif
+
+bool moe_permute_unpermute_supported() {
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+  return true;
+#else
+  return false;
+#endif
+}
+
 TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
  m.impl("moe_permute", &moe_permute);
  m.impl("moe_unpermute", &moe_unpermute);
-}
+}
@@ -1,6 +1,9 @@

 #include "moe_permute_unpermute_kernel.h"

+// moe_permute kernels require at least CUDA 12.0
+#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12000)
+
 // CubKeyValueSorter definition begin
 CubKeyValueSorter::CubKeyValueSorter()
    : num_experts_(0), num_bits_(sizeof(int) * 8) {}
@@ -131,9 +134,6 @@ __global__ void preprocessTopkIdKernel(int* topk_id_ptr, int size,
                                       int num_experts) {
  auto tidx = threadIdx.x;
  auto bidx = blockIdx.x;
-  auto lidx = tidx & 31;
-  auto widx = tidx >> 5;
-  auto warp_count = (blockDim.x + 31) >> 5;
  auto offset = bidx * blockDim.x;
  auto bound = min(offset + blockDim.x, size);
  extern __shared__ int smem_expert_map[];
@@ -226,4 +226,6 @@ void getMIndices(int64_t* expert_first_token_offset,
        expert_first_token_offset, align_expert_first_token_offset, m_indices,
        num_local_expert, align_block_size);
  }
-}
+}
+
+#endif
@@ -108,9 +108,17 @@ __launch_bounds__(TPB) __global__
    }
 }

-template <int TPB>
-__launch_bounds__(TPB) __global__ void moeTopK(const float* inputs_after_softmax, const bool* finished, float* output,
-    int* indices, int* source_rows, const int num_experts, const int k, const int start_expert, const int end_expert)
+template <int TPB, typename IndType>
+__launch_bounds__(TPB) __global__ void moeTopK(
+    const float* inputs_after_softmax,
+    const bool* finished,
+    float* output,
+    IndType* indices,
+    int* source_rows,
+    const int num_experts,
+    const int k,
+    const int start_expert,
+    const int end_expert)
 {

    using cub_kvp = cub::KeyValuePair<int, float>;
@@ -182,9 +190,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(const float* inputs_after_softmax
  2) This implementation assumes k is small, but will work for any k.
 */

-template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG>
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, typename IndType>
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
-    void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, int* indices,
+    void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, IndType* indices,
        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.
@@ -397,8 +405,8 @@ struct TopkConstants
 };
 } // namespace detail

-template <int EXPERTS, int WARPS_PER_TB>
-void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, int* indices,
+template <int EXPERTS, int WARPS_PER_TB, 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;
@@ -421,10 +429,11 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
        token_expert_indices, num_tokens, topk, 0, num_experts,         \
        stream);

+template <typename IndType>
 void topkGatingSoftmaxKernelLauncher(
    const float* gating_output,
    float* topk_weights,
-    int* topk_indicies,
+    IndType* topk_indicies,
    int* token_expert_indices,
    float* softmax_workspace,
    const int num_tokens,
@@ -493,14 +502,32 @@ void topk_softmax(
    const at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    torch::Tensor softmax_workspace = torch::empty({workspace_size}, gating_output.options());
-    vllm::moe::topkGatingSoftmaxKernelLauncher(
-        gating_output.data_ptr<float>(),
-        topk_weights.data_ptr<float>(),
-        topk_indices.data_ptr<int>(),
-        token_expert_indices.data_ptr<int>(),
-        softmax_workspace.data_ptr<float>(),
-        num_tokens,
-        num_experts,
-        topk,
-        stream);
+
+    if(topk_indices.scalar_type() == at::ScalarType::Int)
+    {
+        vllm::moe::topkGatingSoftmaxKernelLauncher(
+            gating_output.data_ptr<float>(),
+            topk_weights.data_ptr<float>(),
+            topk_indices.data_ptr<int>(),
+            token_expert_indices.data_ptr<int>(),
+            softmax_workspace.data_ptr<float>(),
+            num_tokens,
+            num_experts,
+            topk,
+            stream);
+    }
+    else
+    {
+        assert(topk_indices.scalar_type() == at::ScalarType::UInt32);
+        vllm::moe::topkGatingSoftmaxKernelLauncher(
+            gating_output.data_ptr<float>(),
+            topk_weights.data_ptr<float>(),
+            topk_indices.data_ptr<uint32_t>(),
+            token_expert_indices.data_ptr<int>(),
+            softmax_workspace.data_ptr<float>(),
+            num_tokens,
+            num_experts,
+            topk,
+            stream);
+    }
 }
@@ -10,7 +10,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {

  // Calculate the result of moe by summing up the partial results
  // from all selected experts.
-  m.def("moe_sum(Tensor! input, Tensor output) -> ()");
+  m.def("moe_sum(Tensor input, Tensor! output) -> ()");
  m.impl("moe_sum", torch::kCUDA, &moe_sum);

  // Aligning the number of tokens to be processed by each expert such
@@ -44,7 +44,8 @@ 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? b_zeros_or_none,"
+      "Tensor! b_q_weight, 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,"
      "Tensor! expert_ids, Tensor! num_tokens_past_padded,"
@@ -76,7 +77,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
      "Tensor topk_ids,Tensor src_row_id2dst_row_id_map, Tensor "
      "expert_first_token_offset, int n_expert, int n_local_expert,int "
      "topk, Tensor! hidden_states)->()");
-  // conditionally compiled so impl registration is in source file
+
+  m.def("moe_permute_unpermute_supported() -> bool");
+  m.impl("moe_permute_unpermute_supported", &moe_permute_unpermute_supported);

 #endif
 }
--- a/Show More
+++ b/Show More