test

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

.buildkite/check-wheel-size.py

@@ -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]:
@@ -28,18 +28,14 @@ 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
 
 
@@ -49,4 +45,4 @@ if __name__ == "__main__":
         sys.exit(1)
 
     directory = sys.argv[1]
-    sys.exit(check_wheel_size(directory))
+    sys.exit(check_wheel_size(directory))

.buildkite/generate_index.py

@@ -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")))

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

@@ -8,14 +8,11 @@ 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")
@@ -36,8 +33,7 @@ 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)

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

@@ -16,22 +16,19 @@ 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
 
 
@@ -45,10 +42,9 @@ 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

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

@@ -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())
 
@@ -120,8 +120,7 @@ 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
@@ -154,27 +153,26 @@ 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:
@@ -186,39 +184,38 @@ 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))

.buildkite/nightly-benchmarks/scripts/download-tokenizer.py

@@ -14,12 +14,15 @@ 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)

.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py

@@ -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(0.0)
+            means.append(0.)
         else:
             means.append(filtered_df[metric].values[0])
 
@@ -45,6 +45,7 @@ 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()
@@ -59,8 +60,7 @@ 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
 
@@ -80,17 +80,18 @@ 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)

.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py

@@ -34,8 +34,10 @@ 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())
 
@@ -54,16 +56,17 @@ 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")
@@ -73,9 +76,10 @@ 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))

.buildkite/pyproject.toml

@@ -1,46 +0,0 @@
-# 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

.buildkite/release-pipeline.yaml

@@ -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 --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 ."
+      - "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 ."
       - "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 --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 ."
+      - "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 ."
       - "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: vllmbot
+          username: vllm
           password-env: DOCKERHUB_TOKEN
     env:
       DOCKER_BUILDKIT: "1"

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

@@ -3,9 +3,6 @@
 # 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
@@ -77,23 +74,6 @@ 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} \
@@ -181,8 +161,6 @@ 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   
@@ -203,7 +181,6 @@ 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 "${commands_gpu}" \
@@ -234,7 +211,6 @@ else
           -e AWS_SECRET_ACCESS_KEY \
           -v "${HF_CACHE}:${HF_MOUNT}" \
           -e "HF_HOME=${HF_MOUNT}" \
-          -e "PYTHONPATH=${MYPYTHONPATH}" \
           --name "${container_name}" \
           "${image_name}" \
           /bin/bash -c "${commands}"

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

@@ -32,12 +32,9 @@ 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/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]"
+    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"
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

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

@@ -10,17 +10,15 @@ 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_containers and remove_docker_containers_and_exit
+# separate remove_docker_container and remove_docker_container_and_exit
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-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
+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
 
 # 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=$?

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

@@ -11,14 +11,13 @@ 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-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws
+aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-west-2.amazonaws.com
 
 # prune old image and containers to save disk space, and only once a day
 # by using a timestamp file in tmp.
@@ -48,16 +47,8 @@ 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}" \
        ${image_name} \
-       /bin/bash -c "
-            python3 /workspace/vllm/examples/offline_inference/neuron.py;
-            python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
-            for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo 'Running test file: '$f;
-                python3 -m pytest \$f -v --capture=tee-sys;
-            done
-       "
+       /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"

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

@@ -26,27 +26,27 @@ docker run --privileged --net host --shm-size=16G -it \
     && tpu-info \
     && { \
         echo TEST_0: Running test_perf.py; \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
         echo TEST_4_EXIT_CODE: \$?; \
     } & \
     { \
@@ -56,43 +56,43 @@ docker run --privileged --net host --shm-size=16G -it \
     } & \
     { \
         echo TEST_6: Running test_tpu_model_runner.py; \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/worker/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/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; \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/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; \
-    #     python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/; \
+    #     pytest -s -v /workspace/vllm/tests/tpu/lora/; \
     #     echo TEST_13_EXIT_CODE: \$?; \
     # } & \
     wait \

.buildkite/scripts/upload-wheels.sh

@@ -75,4 +75,3 @@ else
 fi
 
 aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
-aws s3 cp index.html "s3://vllm-wheels/$version/vllm/index.html"

.buildkite/test-pipeline.yaml

@@ -32,17 +32,16 @@ steps:
 ##### fast check tests  #####
 
 - label: Documentation Build # 2min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/test_docs"
+  working_dir: "/vllm-workspace/test_docs/docs"
   fast_check: true
   no_gpu: True
   commands:
-  - pip install -r ../requirements/docs.txt
-  # TODO: add `--strict` once warnings in docstrings are fixed
-  - mkdocs build
+  - 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
 
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/mq_llm_engine
@@ -58,13 +57,11 @@ 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
@@ -72,7 +69,7 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Basic Correctness Test # 30min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  #mirror_hardwares: [amd]
   fast_check: true
   torch_nightly: true
   source_file_dependencies:
@@ -89,7 +86,6 @@ 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
@@ -98,7 +94,7 @@ steps:
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
 
 - label: Core Test # 10min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   fast_check: true
   source_file_dependencies:
   - vllm/core
@@ -108,10 +104,10 @@ 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
@@ -125,12 +121,11 @@ 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_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/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/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:
@@ -138,7 +133,6 @@ 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
@@ -149,25 +143,22 @@ 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
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  - python3 rlhf.py
+  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   - popd
 
 - label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -181,7 +172,7 @@ steps:
 #####  1 GPU test  #####
 
 - label: Regression Test # 5min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -191,7 +182,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Engine Test # 10min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/engine
@@ -205,7 +196,7 @@ steps:
   - pytest -v -s tokenization
 
 - label: V1 Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
     - vllm/
     - tests/v1
@@ -218,11 +209,10 @@ 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
@@ -231,8 +221,8 @@ 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/
@@ -247,7 +237,7 @@ 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 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
+    - 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
     - python3 offline_inference/encoder_decoder.py
     - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     - python3 offline_inference/basic/classify.py
@@ -256,7 +246,7 @@ 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: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/prefix_caching
@@ -264,7 +254,6 @@ steps:
     - pytest -v -s prefix_caching
 
 - label: Samplers Test # 36min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/model_executor/layers
   - vllm/sampling_metadata.py
@@ -275,7 +264,7 @@ steps:
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
 
 - label: LogitsProcessor Test # 5min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/model_executor/layers
   - vllm/model_executor/guided_decoding
@@ -286,7 +275,6 @@ steps:
     - 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
@@ -297,7 +285,7 @@ steps:
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/lora
   - tests/lora
@@ -305,7 +293,6 @@ steps:
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests
-  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
     - vllm/
@@ -313,12 +300,9 @@ 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/
@@ -330,7 +314,6 @@ 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/
@@ -339,7 +322,7 @@ steps:
   - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Core Operation Test
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
@@ -347,7 +330,7 @@ steps:
     - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/attention/
   - vllm/attention
@@ -358,7 +341,7 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/quantization/
   - vllm/model_executor/layers/quantization
@@ -368,7 +351,7 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/moe/
   - tests/kernels/moe
@@ -377,7 +360,7 @@ steps:
     - pytest -v -s kernels/moe
 
 - label: Kernels Mamba Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
@@ -385,28 +368,25 @@ steps:
     - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  # mirror_hardwares: [amd]
   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: 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/
@@ -414,7 +394,6 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Quantization Test
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
@@ -423,7 +402,6 @@ 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/
@@ -433,7 +411,6 @@ 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/
@@ -442,7 +419,6 @@ steps:
   - pytest -s entrypoints/openai/correctness/
 
 - label: Encoder Decoder tests # 5min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/encoder_decoder
@@ -450,8 +426,8 @@ 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
@@ -463,7 +439,6 @@ steps:
 #####  models test  #####
 
 - label: Basic Models Test # 24min
-  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -473,55 +448,43 @@ steps:
     - pytest -v -s models/test_registry.py
     - pytest -v -s models/test_utils.py
     - pytest -v -s models/test_vision.py
-    - pytest -v -s models/test_initialization.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'
 
 - label: Language Models Test (Standard)
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
+  #mirror_hardwares: [amd]
   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 Generation) # 1hr20min
-  mirror_hardwares: [amdexperimental]
+- label: Language Models Test (Extended)
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/language/generation
+  - 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'
-    - 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'
+    - pytest -v -s models/language -m 'not core_model'
 
 - label: Multi-Modal Models Test (Standard)
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
+  #mirror_hardwares: [amd]
   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/
@@ -531,7 +494,6 @@ 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/
@@ -541,7 +503,6 @@ 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/
@@ -551,7 +512,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Quantized Models Test
-  mirror_hardwares: [amdexperimental, amdproduction]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization
@@ -560,7 +521,7 @@ 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: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   optional: true
   commands:
     - echo 'Testing custom models...'
@@ -572,7 +533,7 @@ steps:
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -583,7 +544,6 @@ 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
@@ -602,7 +562,7 @@ 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: [amdexperimental]
+  #mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -639,14 +599,13 @@ 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 and general plugin tests, all the code in-between runs on dummy platform
+  # begin platform 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
@@ -657,10 +616,8 @@ 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:
@@ -681,7 +638,6 @@ 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:
@@ -695,7 +651,6 @@ 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
@@ -711,7 +666,6 @@ steps:
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -721,7 +675,6 @@ 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

.github/CODEOWNERS

@@ -13,7 +13,6 @@
 /vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
-/vllm/lora @jeejeelee
 CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1
@@ -41,8 +40,3 @@ 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

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -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 model's output:
+    value: >
+      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the models' 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 reporting 🙏!
+      Thanks for contributing 🎉!
 - type: checkboxes
   id: askllm
   attributes:

.github/ISSUE_TEMPLATE/450-ci-failure.yml

@@ -1,69 +0,0 @@
-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 🙏!

.github/PULL_REQUEST_TEMPLATE.md

@@ -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>** (anything written below this line will be removed by GitHub Actions)
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -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/features/structured_outputs.md
+      - files=docs/source/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
@@ -135,7 +135,9 @@ 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/features/tool_calling.md
+      - 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~=^examples/tool_chat_*
       - files=examples/offline_inference/chat_with_tools.py
       - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
@@ -161,17 +163,6 @@ 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

.github/scripts/cleanup_pr_body.sh

@@ -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 regex as re
+import re
 
 with open("${NEW}", "r") as file:
     content = file.read()

.github/workflows/add_label_automerge.yml

@@ -1,6 +1,4 @@
 name: Add label on auto-merge enabled
-permissions:
-    pull-requests: write
 on:
     pull_request_target:
         types:

.github/workflows/cleanup_pr_body.yml

@@ -20,12 +20,7 @@ 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: ${{ secrets.GITHUB_TOKEN }}
-        run: bash .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
+        run: .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"

.github/workflows/lint-and-deploy.yaml

@@ -2,9 +2,6 @@ name: Lint and Deploy Charts
 
 on: pull_request
 
-permissions:
-  contents: read
-
 jobs:
   lint-and-deploy:
     runs-on: ubuntu-latest

.github/workflows/pre-commit.yml

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

.github/workflows/reminder_comment.yml

@@ -1,6 +1,4 @@
 name: PR Reminder Comment Bot
-permissions:
-  pull-requests: write
 on:
   pull_request_target:
     types: [opened]

.gitignore

@@ -77,6 +77,11 @@ instance/
 # Scrapy stuff:
 .scrapy
 
+# Sphinx documentation
+docs/_build/
+docs/source/getting_started/examples/
+docs/source/api/vllm
+
 # PyBuilder
 .pybuilder/
 target/
@@ -146,7 +151,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-docs/examples
 
 # mypy
 .mypy_cache/

.pre-commit-config.yaml

@@ -16,8 +16,6 @@ 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:
@@ -39,7 +37,6 @@ repos:
   rev: v0.9.29
   hooks:
   - id: pymarkdown
-    exclude: '.*\.inc\.md'
     args: [fix]
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
@@ -128,21 +125,6 @@ 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

.readthedocs.yaml

@@ -8,8 +8,12 @@ build:
   tools:
     python: "3.12"
 
-mkdocs:
-  configuration: mkdocs.yaml
+sphinx:
+  configuration: docs/source/conf.py
+  fail_on_warning: true
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+formats: []
 
 # Optionally declare the Python requirements required to build your docs
 python:

CMakeLists.txt

@@ -29,6 +29,9 @@ set(ignoreMe "${VLLM_PYTHON_PATH}")
 #
 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")
 
@@ -76,15 +79,6 @@ 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.
 #
@@ -232,13 +226,10 @@ 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"
@@ -289,13 +280,14 @@ 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")
@@ -307,8 +299,7 @@ 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.
-  # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_ARCHS)
 
     #
@@ -452,9 +443,8 @@ 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;8.9+PTX" "${CUDA_ARCHS}")
+    "7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   # subtract out the archs that are already built for 3x
   list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
   if (SCALED_MM_2X_ARCHS)
@@ -505,9 +495,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
+      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
@@ -545,7 +533,7 @@ 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;10.0a" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
              "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
@@ -683,8 +671,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     CUDA_ARCHS "${CUDA_ARCHS}")
 
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
-  # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
+  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}")
   if (MARLIN_MOE_ARCHS)
 
     #

CONTRIBUTING.md

@@ -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).
+You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing/overview.html).

README.md

@@ -1,7 +1,7 @@
 <p align="center">
   <picture>
-    <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%>
+    <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%>
   </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), [AutoRound](https://arxiv.org/abs/2309.05516),INT4, INT8, and FP8.
+- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), 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/latest/contributing/index.html) for how to get involved.
+Please check out [Contributing to vLLM](https://docs.vllm.ai/en/stable/contributing/overview.html) for how to get involved.
 
 ## Sponsors
 
 vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
 
 <!-- Note: Please sort them in alphabetical order. -->
-<!-- Note: Please keep these consistent with docs/community/sponsors.md -->
+<!-- Note: Please keep these consistent with docs/source/community/sponsors.md -->
 Cash Donations:
 - a16z
 - Dropbox

benchmarks/README.md

@@ -146,9 +146,10 @@ 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 \
-    --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5}
+    --num_speculative_tokens 5
 ```
 
 ``` bash
@@ -273,9 +274,10 @@ 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 \
-    --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5}
+    --num_speculative_tokens=5
 ```
 
 ```

benchmarks/backend_request_func.py

@@ -12,7 +12,8 @@ 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.
@@ -42,7 +43,8 @@ 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 = ""
@@ -55,9 +57,8 @@ 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,
@@ -104,7 +105,8 @@ 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
 
@@ -131,9 +133,8 @@ 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,
@@ -158,7 +159,8 @@ 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"]
@@ -170,7 +172,8 @@ 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
 
@@ -194,14 +197,9 @@ async def async_request_deepspeed_mii(
     request_func_input: RequestFuncInput,
     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'."
-    )
+    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,
             "prompt": request_func_input.prompt,
@@ -209,8 +207,6 @@ 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
 
@@ -221,21 +217,19 @@ async def async_request_deepspeed_mii(
 
         st = time.perf_counter()
         try:
-            async with session.post(
-                url=api_url, json=payload, headers=headers
-            ) as response:
+            async with session.post(url=request_func_input.api_url,
+                                    json=payload) 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:
@@ -256,17 +250,15 @@ 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,
@@ -281,7 +273,9 @@ 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
@@ -290,9 +284,8 @@ 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:
@@ -300,7 +293,8 @@ 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)
 
@@ -320,20 +314,21 @@ 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")
+                                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:
@@ -354,22 +349,23 @@ 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,
@@ -395,16 +391,16 @@ 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)
@@ -418,11 +414,13 @@ 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
 
@@ -448,28 +446,25 @@ 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)
@@ -484,9 +479,9 @@ 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))
 
@@ -498,22 +493,24 @@ 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
@@ -522,14 +519,12 @@ 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
 
@@ -550,7 +545,7 @@ 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
@@ -561,8 +556,7 @@ 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
@@ -575,23 +569,23 @@ 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,
@@ -614,7 +608,7 @@ ASYNC_REQUEST_FUNCS = {
 }
 
 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)
 ]

benchmarks/benchmark_dataset.py

@@ -35,7 +35,6 @@ 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__)
@@ -83,12 +82,14 @@ 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
@@ -110,7 +111,8 @@ 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,
@@ -156,9 +158,8 @@ 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.
 
@@ -176,9 +177,8 @@ 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.
@@ -189,9 +189,11 @@ 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)
 
 
 # -----------------------------------------------------------------------------
@@ -216,14 +218,14 @@ 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
@@ -255,28 +257,28 @@ 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 = convert_image_mode(image, "RGB")
+        image = image.convert("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.")
 
 
 # -----------------------------------------------------------------------------
@@ -316,11 +318,8 @@ 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))
@@ -330,17 +329,21 @@ 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.
@@ -351,9 +354,8 @@ 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(
@@ -361,8 +363,7 @@ class RandomDataset(BenchmarkDataset):
                     prompt=prompt,
                     prompt_len=total_input_len,
                     expected_output_len=int(output_lens[i]),
-                )
-            )
+                ))
         return requests
 
 
@@ -389,8 +390,7 @@ 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)
@@ -416,28 +416,27 @@ 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
 
@@ -483,20 +482,20 @@ 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)
@@ -505,23 +504,21 @@ 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
 
 
@@ -541,9 +538,7 @@ 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.")
 
@@ -557,7 +552,8 @@ 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,
@@ -581,8 +577,7 @@ 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.
@@ -594,8 +589,7 @@ class BurstGPTDataset(BenchmarkDataset):
                     prompt_len=input_len,
                     expected_output_len=output_len,
                     lora_request=lora_req,
-                )
-            )
+                ))
         return samples
 
 
@@ -638,23 +632,20 @@ 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
 
@@ -670,22 +661,24 @@ 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
 
@@ -702,8 +695,10 @@ 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
 
@@ -715,14 +710,16 @@ 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)
@@ -730,15 +727,15 @@ 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
 
@@ -763,15 +760,14 @@ 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:
@@ -783,8 +779,7 @@ 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
 
@@ -799,38 +794,38 @@ 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(
@@ -838,8 +833,7 @@ 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
 
@@ -853,27 +847,23 @@ 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
@@ -881,9 +871,10 @@ 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(
@@ -891,8 +882,7 @@ 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
 
@@ -915,25 +905,25 @@ 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.
     """
@@ -963,8 +953,10 @@ 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 = []
@@ -975,10 +967,8 @@ 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)
@@ -1007,22 +997,18 @@ 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(
@@ -1033,8 +1019,8 @@ 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 = []
@@ -1057,14 +1043,10 @@ 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

benchmarks/benchmark_latency.py

@@ -11,9 +11,9 @@ 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
 
-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
@@ -21,14 +21,13 @@ 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)
@@ -43,11 +42,9 @@ 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,
@@ -58,16 +55,18 @@ 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,
@@ -81,13 +80,12 @@ 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)
-                ),
+                    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"))
@@ -105,9 +103,8 @@ def main(args: argparse.Namespace):
     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 = (Path(".") / "vllm_benchmark_result" /
+                           f"latency_result_{time.time()}")
         print(f"Profiling (results will be saved to '{profile_dir}')...")
         run_to_completion(profile_dir=profile_dir)
         return
@@ -138,8 +135,7 @@ 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)
@@ -156,9 +152,10 @@ 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",
@@ -168,10 +165,8 @@ if __name__ == "__main__":
         "--profile-result-dir",
         type=str,
         default=None,
-        help=(
-            "path to save the pytorch profiler output. Can be visualized "
-            "with ui.perfetto.dev or Tensorboard."
-        ),
+        help=("path to save the pytorch profiler output. Can be visualized "
+              "with ui.perfetto.dev or Tensorboard."),
     )
     parser.add_argument(
         "--output-json",
@@ -182,15 +177,10 @@ 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()
     main(args)

benchmarks/benchmark_long_document_qa_throughput.py

@@ -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:
@@ -86,21 +86,20 @@ 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):
@@ -110,16 +109,16 @@ 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)
@@ -143,52 +142,42 @@ 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()

benchmarks/benchmark_prefix_caching.py

@@ -63,7 +63,8 @@ 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)
 
@@ -90,10 +91,8 @@ 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)
@@ -114,9 +113,8 @@ 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))
 
@@ -130,27 +128,27 @@ 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])
@@ -161,14 +159,14 @@ def repeat_and_sort_requests(
 
 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 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 "
+              f"from {args.dataset_path}")
         filtered_requests = sample_requests_from_dataset(
             dataset_path=args.dataset_path,
             num_requests=args.num_prompts,
@@ -198,16 +196,14 @@ 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(
@@ -219,35 +215,29 @@ 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,
@@ -258,12 +248,10 @@ 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)

benchmarks/benchmark_prioritization.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 """Benchmark offline prioritization."""
-
 import argparse
 import dataclasses
 import json
@@ -14,7 +13,7 @@ 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
 
@@ -34,10 +33,8 @@ 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)
@@ -54,9 +51,8 @@ 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
@@ -78,16 +74,13 @@ 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 = []
@@ -104,8 +97,7 @@ 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)
@@ -119,33 +111,26 @@ 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:
@@ -162,44 +147,41 @@ 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(
-        "--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",
+        '--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)

benchmarks/benchmark_serving.py

@@ -20,7 +20,6 @@ On the client side, run:
         --endpoint /generate_stream
     to the end of the command above.
 """
-
 import argparse
 import asyncio
 import gc
@@ -35,16 +34,12 @@ from datetime import datetime
 from typing import Any, Optional
 
 import numpy as np
+from backend_request_func import (ASYNC_REQUEST_FUNCS,
+                                  OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput,
+                                  RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
 
-from backend_request_func import (
-    ASYNC_REQUEST_FUNCS,
-    OPENAI_COMPATIBLE_BACKENDS,
-    RequestFuncInput,
-    RequestFuncOutput,
-)
-
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
@@ -55,21 +50,12 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_dataset import (
-    AIMODataset,
-    ASRDataset,
-    BurstGPTDataset,
-    ConversationDataset,
-    HuggingFaceDataset,
-    InstructCoderDataset,
-    MTBenchDataset,
-    NextEditPredictionDataset,
-    RandomDataset,
-    SampleRequest,
-    ShareGPTDataset,
-    SonnetDataset,
-    VisionArenaDataset,
-)
+from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
+                               ConversationDataset, HuggingFaceDataset,
+                               InstructCoderDataset, MTBenchDataset,
+                               NextEditPredictionDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -132,8 +118,7 @@ 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 request in input_requests:
@@ -179,10 +164,8 @@ def calculate_metrics(
                 # 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
@@ -205,19 +188,16 @@ 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)])
@@ -228,8 +208,7 @@ 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,
@@ -238,31 +217,27 @@ 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
@@ -295,12 +270,10 @@ async def benchmark(
         raise ValueError(f"Unknown backend: {backend}")
 
     print("Starting initial single prompt test run...")
-    test_prompt, test_prompt_len, test_output_len, test_mm_content = (
-        input_requests[0].prompt,
-        input_requests[0].prompt_len,
-        input_requests[0].expected_output_len,
-        input_requests[0].multi_modal_data,
-    )
+    test_prompt, test_prompt_len, test_output_len, test_mm_content = \
+        input_requests[0].prompt, input_requests[0].prompt_len, \
+        input_requests[0].expected_output_len, \
+            input_requests[0].multi_modal_data
 
     assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
@@ -320,36 +293,36 @@ 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...")
 
     if lora_modules:
         # For each input request, choose a LoRA module at random.
         lora_modules = iter(
-            [random.choice(lora_modules) for _ in range(len(input_requests))]
-        )
+            [random.choice(lora_modules) \
+                for _ in range(len(input_requests))])
 
     if profile:
         print("Starting profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            model_name=model_name,
-            prompt=test_prompt,
-            api_url=base_url + "/start_profile",
-            prompt_len=test_prompt_len,
-            output_len=test_output_len,
-            logprobs=logprobs,
-            multi_modal_content=test_mm_content,
-            ignore_eos=ignore_eos,
-            extra_body=extra_body,
-        )
+        profile_input = RequestFuncInput(model=model_id,
+                                         model_name=model_name,
+                                         prompt=test_prompt,
+                                         api_url=base_url + "/start_profile",
+                                         prompt_len=test_prompt_len,
+                                         output_len=test_output_len,
+                                         logprobs=logprobs,
+                                         multi_modal_content=test_mm_content,
+                                         ignore_eos=ignore_eos,
+                                         extra_body=extra_body)
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
             print("Profiler started")
 
-    distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
+    if burstiness == 1.0:
+        distribution = "Poisson process"
+    else:
+        distribution = "Gamma distribution"
 
     print(f"Traffic request rate: {request_rate}")
     print(f"Burstiness factor: {burstiness} ({distribution})")
@@ -361,45 +334,42 @@ 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] = []
     async for request in get_request(input_requests, request_rate, burstiness):
-        prompt, prompt_len, output_len, mm_content = (
-            request.prompt,
-            request.prompt_len,
-            request.expected_output_len,
-            request.multi_modal_data,
-        )
+        prompt, prompt_len, output_len, mm_content = request.prompt, \
+            request.prompt_len, request.expected_output_len, \
+                request.multi_modal_data
         req_model_id, req_model_name = model_id, model_name
         if lora_modules:
             req_lora_module = next(lora_modules)
             req_model_id, req_model_name = req_lora_module, req_lora_module
 
-        request_func_input = RequestFuncInput(
-            model=req_model_id,
-            model_name=req_model_name,
-            prompt=prompt,
-            api_url=api_url,
-            prompt_len=prompt_len,
-            output_len=output_len,
-            logprobs=logprobs,
-            multi_modal_content=mm_content,
-            ignore_eos=ignore_eos,
-            extra_body=extra_body,
-        )
+        request_func_input = RequestFuncInput(model=req_model_id,
+                                              model_name=req_model_name,
+                                              prompt=prompt,
+                                              api_url=api_url,
+                                              prompt_len=prompt_len,
+                                              output_len=output_len,
+                                              logprobs=logprobs,
+                                              multi_modal_content=mm_content,
+                                              ignore_eos=ignore_eos,
+                                              extra_body=extra_body)
         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:
@@ -431,32 +401,22 @@ 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,
@@ -464,7 +424,8 @@ async def benchmark(
         "total_input_tokens": metrics.total_input,
         "total_output_tokens": metrics.total_output,
         "request_throughput": metrics.request_throughput,
-        "request_goodput:": metrics.request_goodput if goodput_config_dict else None,
+        "request_goodput:":
+        metrics.request_goodput if goodput_config_dict else None,
         "output_throughput": metrics.output_throughput,
         "total_token_throughput": metrics.total_token_throughput,
         "input_lens": [output.prompt_len for output in outputs],
@@ -487,35 +448,29 @@ 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")
 
@@ -535,14 +490,12 @@ 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
 
 
@@ -555,42 +508,31 @@ 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
 
 
-def save_to_pytorch_benchmark_format(
-    args: argparse.Namespace, results: dict[str, Any], file_name: str
-) -> None:
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                     results: dict[str, Any],
+                                     file_name: str) -> None:
     metrics = [
-        "median_ttft_ms",
-        "mean_ttft_ms",
-        "std_ttft_ms",
-        "p99_ttft_ms",
-        "mean_tpot_ms",
-        "median_tpot_ms",
-        "std_tpot_ms",
-        "p99_tpot_ms",
-        "median_itl_ms",
-        "mean_itl_ms",
-        "std_itl_ms",
-        "p99_itl_ms",
+        "median_ttft_ms", "mean_ttft_ms", "std_ttft_ms", "p99_ttft_ms",
+        "mean_tpot_ms", "median_tpot_ms", "std_tpot_ms", "p99_tpot_ms",
+        "median_itl_ms", "mean_itl_ms", "std_itl_ms", "p99_itl_ms"
     ]
     # These raw data might be useful, but they are rather big. They can be added
     # later if needed
     ignored_metrics = ["ttfts", "itls", "generated_texts", "errors"]
     pt_records = convert_to_pytorch_benchmark_format(
         args=args,
-        metrics={k: [results[k]] for k in metrics},
+        metrics={k: [results[k]]
+                 for k in metrics},
         extra_info={
             k: results[k]
-            for k in results
-            if k not in metrics and k not in ignored_metrics
-        },
-    )
+            for k in results if k not in metrics and k not in ignored_metrics
+        })
     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(file_name)[0]}.pytorch.json"
@@ -615,42 +557,34 @@ def main(args: argparse.Namespace):
         api_url = f"http://{args.host}:{args.port}{args.endpoint}"
         base_url = f"http://{args.host}:{args.port}"
 
-    tokenizer = get_tokenizer(
-        tokenizer_id,
-        tokenizer_mode=tokenizer_mode,
-        trust_remote_code=args.trust_remote_code,
-    )
+    tokenizer = get_tokenizer(tokenizer_id,
+                              tokenizer_mode=tokenizer_mode,
+                              trust_remote_code=args.trust_remote_code)
 
     if args.dataset_name is None:
         raise ValueError(
             "Please specify '--dataset-name' and the corresponding "
-            "'--dataset-path' if required."
-        )
+            "'--dataset-path' if required.")
 
     if args.dataset_name == "sonnet":
         dataset = SonnetDataset(dataset_path=args.dataset_path)
         # For the "sonnet" dataset, formatting depends on the backend.
         if args.backend == "openai-chat":
-            input_requests = dataset.sample(
-                num_requests=args.num_prompts,
-                input_len=args.sonnet_input_len,
-                output_len=args.sonnet_output_len,
-                prefix_len=args.sonnet_prefix_len,
-                tokenizer=tokenizer,
-                return_prompt_formatted=False,
-            )
+            input_requests = dataset.sample(num_requests=args.num_prompts,
+                                            input_len=args.sonnet_input_len,
+                                            output_len=args.sonnet_output_len,
+                                            prefix_len=args.sonnet_prefix_len,
+                                            tokenizer=tokenizer,
+                                            return_prompt_formatted=False)
         else:
             assert tokenizer.chat_template or tokenizer.default_chat_template, (
-                "Tokenizer/model must have chat template for sonnet dataset."
-            )
-            input_requests = dataset.sample(
-                num_requests=args.num_prompts,
-                input_len=args.sonnet_input_len,
-                output_len=args.sonnet_output_len,
-                prefix_len=args.sonnet_prefix_len,
-                tokenizer=tokenizer,
-                return_prompt_formatted=True,
-            )
+                "Tokenizer/model must have chat template for sonnet dataset.")
+            input_requests = dataset.sample(num_requests=args.num_prompts,
+                                            input_len=args.sonnet_input_len,
+                                            output_len=args.sonnet_output_len,
+                                            prefix_len=args.sonnet_prefix_len,
+                                            tokenizer=tokenizer,
+                                            return_prompt_formatted=True)
 
     elif args.dataset_name == "hf":
         # all following datasets are implemented from the
@@ -677,30 +611,23 @@ def main(args: argparse.Namespace):
             dataset_class = ASRDataset
             args.hf_split = "train"
         else:
-            supported_datasets = set(
-                [
-                    dataset_name
-                    for cls in HuggingFaceDataset.__subclasses__()
-                    for dataset_name in cls.SUPPORTED_DATASET_PATHS
-                ]
-            )
+            supported_datasets = set([
+                dataset_name for cls in HuggingFaceDataset.__subclasses__()
+                for dataset_name in cls.SUPPORTED_DATASET_PATHS
+            ])
             raise ValueError(
                 f"Unsupported dataset path: {args.dataset_path}. "
                 "Huggingface dataset only supports dataset_path"
                 f" from one of following: {supported_datasets}. "
                 "Please consider contributing if you would "
-                "like to add support for additional dataset formats."
-            )
+                "like to add support for additional dataset formats.")
 
-        if dataset_class.IS_MULTIMODAL and backend not in [
-            "openai-chat",
-            "openai-audio",
-        ]:
+        if (dataset_class.IS_MULTIMODAL and backend not in \
+            ["openai-chat", "openai-audio"]):
             # multi-modal benchmark is only available on OpenAI Chat backend.
             raise ValueError(
-                "Multi-modal content is only supported on 'openai-chat' and "
-                "'openai-audio' backend."
-            )
+                "Multi-modal content is only supported on 'openai-chat' and " \
+                "'openai-audio' backend.")
         input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,
@@ -715,24 +642,26 @@ def main(args: argparse.Namespace):
     else:
         # For datasets that follow a similar structure, use a mapping.
         dataset_mapping = {
-            "sharegpt": lambda: ShareGPTDataset(
-                random_seed=args.seed, dataset_path=args.dataset_path
-            ).sample(
-                tokenizer=tokenizer,
-                num_requests=args.num_prompts,
-                output_len=args.sharegpt_output_len,
-            ),
-            "burstgpt": lambda: BurstGPTDataset(
-                random_seed=args.seed, dataset_path=args.dataset_path
-            ).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
-            "random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
+            "sharegpt":
+            lambda: ShareGPTDataset(random_seed=args.seed,
+                                    dataset_path=args.dataset_path).sample(
+                                        tokenizer=tokenizer,
+                                        num_requests=args.num_prompts,
+                                        output_len=args.sharegpt_output_len,
+                                    ),
+            "burstgpt":
+            lambda: BurstGPTDataset(random_seed=args.seed,
+                                    dataset_path=args.dataset_path).
+            sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+            "random":
+            lambda: RandomDataset(dataset_path=args.dataset_path).sample(
                 tokenizer=tokenizer,
                 num_requests=args.num_prompts,
                 prefix_len=args.random_prefix_len,
                 input_len=args.random_input_len,
                 output_len=args.random_output_len,
                 range_ratio=args.random_range_ratio,
-            ),
+            )
         }
 
         try:
@@ -748,16 +677,15 @@ def main(args: argparse.Namespace):
             "top_p": args.top_p,
             "top_k": args.top_k,
             "min_p": args.min_p,
-            "temperature": args.temperature,
-        }.items()
-        if v is not None
+            "temperature": args.temperature
+        }.items() if v is not None
     }
 
     # Sampling parameters are only supported by openai-compatible backend.
     if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
         raise ValueError(
-            "Sampling parameters are only supported by openai-compatible backends."
-        )
+            "Sampling parameters are only supported by openai-compatible "
+            "backends.")
 
     if "temperature" not in sampling_params:
         sampling_params["temperature"] = 0.0  # Default to greedy decoding.
@@ -781,14 +709,15 @@ 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,
             goodput_config_dict=goodput_config_dict,
             max_concurrency=args.max_concurrency,
             lora_modules=args.lora_modules,
             extra_body=sampling_params,
-        )
-    )
+        ))
 
     # Save config and results to json
     if args.save_result or args.append_result:
@@ -813,9 +742,8 @@ def main(args: argparse.Namespace):
                         "Invalid metadata format. Please use KEY=VALUE format."
                     )
         # Traffic
-        result_json["request_rate"] = (
-            args.request_rate if args.request_rate < float("inf") else "inf"
-        )
+        result_json["request_rate"] = (args.request_rate if args.request_rate
+                                       < float("inf") else "inf")
         result_json["burstiness"] = args.burstiness
         result_json["max_concurrency"] = args.max_concurrency
 
@@ -825,31 +753,24 @@ def main(args: argparse.Namespace):
         if not args.save_detailed:
             # Remove fields with too many data points
             for field in [
-                "input_lens",
-                "output_lens",
-                "ttfts",
-                "itls",
-                "generated_texts",
-                "errors",
+                    "input_lens", "output_lens", "ttfts", "itls",
+                    "generated_texts", "errors"
             ]:
                 if field in result_json:
                     del result_json[field]
 
         # Save to file
         base_model_id = model_id.split("/")[-1]
-        max_concurrency_str = (
-            f"-concurrency{args.max_concurrency}"
-            if args.max_concurrency is not None
-            else ""
-        )
-        file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
+        max_concurrency_str = (f"-concurrency{args.max_concurrency}"
+                               if args.max_concurrency is not None else "")
+        file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  #noqa
         if args.result_filename:
             file_name = args.result_filename
         if args.result_dir:
             file_name = os.path.join(args.result_dir, file_name)
-        with open(
-            file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
-        ) as outfile:
+        with open(file_name,
+                  mode="a+" if args.append_result else "w",
+                  encoding='utf-8') as outfile:
             # Append a newline.
             if args.append_result and outfile.tell() != 0:
                 outfile.write("\n")
@@ -859,8 +780,7 @@ def main(args: argparse.Namespace):
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(
-        description="Benchmark the online serving throughput."
-    )
+        description="Benchmark the online serving throughput.")
     parser.add_argument(
         "--backend",
         type=str,
@@ -889,13 +809,11 @@ if __name__ == "__main__":
         choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
         help="Name of the dataset to benchmark on.",
     )
-    parser.add_argument(
-        "--dataset-path",
-        type=str,
-        default=None,
-        help="Path to the sharegpt/sonnet dataset. "
-        "Or the huggingface dataset ID if using HF dataset.",
-    )
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the sharegpt/sonnet dataset. "
+                        "Or the huggingface dataset ID if using HF dataset.")
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -907,8 +825,7 @@ 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",
@@ -919,7 +836,8 @@ 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("--use-beam-search", action="store_true")
     parser.add_argument(
@@ -932,13 +850,11 @@ if __name__ == "__main__":
         "--logprobs",
         type=int,
         default=None,
-        help=(
-            "Number of logprobs-per-token to compute & return as part of "
-            "the request. If unspecified, then either (1) if beam search "
-            "is disabled, no logprobs are computed & a single dummy "
-            "logprob is returned for each token; or (2) if beam search "
-            "is enabled 1 logprob per token is computed"
-        ),
+        help=("Number of logprobs-per-token to compute & return as part of "
+              "the request. If unspecified, then either (1) if beam search "
+              "is disabled, no logprobs are computed & a single dummy "
+              "logprob is returned for each token; or (2) if beam search "
+              "is enabled 1 logprob per token is computed"),
     )
     parser.add_argument(
         "--request-rate",
@@ -1022,38 +938,35 @@ 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")
 
     # group for dataset specific arguments
     sonnet_group = parser.add_argument_group("sonnet dataset options")
@@ -1061,19 +974,22 @@ if __name__ == "__main__":
         "--sonnet-input-len",
         type=int,
         default=550,
-        help="Number of input tokens per request, used only for sonnet dataset.",
+        help=
+        "Number of input tokens per request, used only for sonnet dataset.",
     )
     sonnet_group.add_argument(
         "--sonnet-output-len",
         type=int,
         default=150,
-        help="Number of output tokens per request, used only for sonnet dataset.",
+        help=
+        "Number of output tokens per request, used only for sonnet dataset.",
     )
     sonnet_group.add_argument(
         "--sonnet-prefix-len",
         type=int,
         default=200,
-        help="Number of prefix tokens per request, used only for sonnet dataset.",
+        help=
+        "Number of prefix tokens per request, used only for sonnet dataset.",
     )
 
     sharegpt_group = parser.add_argument_group("sharegpt dataset options")
@@ -1082,21 +998,22 @@ if __name__ == "__main__":
         type=int,
         default=None,
         help="Output length for each request. Overrides the output length "
-        "from the ShareGPT dataset.",
-    )
+        "from the ShareGPT dataset.")
 
     random_group = parser.add_argument_group("random dataset options")
     random_group.add_argument(
         "--random-input-len",
         type=int,
         default=1024,
-        help="Number of input tokens per request, used only for random sampling.",
+        help=
+        "Number of input tokens per request, used only for random sampling.",
     )
     random_group.add_argument(
         "--random-output-len",
         type=int,
         default=128,
-        help="Number of output tokens per request, used only for random sampling.",
+        help=
+        "Number of output tokens per request, used only for random sampling.",
     )
     random_group.add_argument(
         "--random-range-ratio",
@@ -1111,23 +1028,23 @@ if __name__ == "__main__":
         "--random-prefix-len",
         type=int,
         default=0,
-        help=(
-            "Number of fixed prefix tokens before the random context "
-            "in a request. "
-            "The total input length is the sum of `random-prefix-len` and "
-            "a random "
-            "context length sampled from [input_len * (1 - range_ratio), "
-            "input_len * (1 + range_ratio)]."
-        ),
+        help=("Number of fixed prefix tokens before the random context "
+              "in a request. "
+              "The total input length is the sum of `random-prefix-len` and "
+              "a random "
+              "context length sampled from [input_len * (1 - range_ratio), "
+              "input_len * (1 + range_ratio)]."),
     )
 
     hf_group = parser.add_argument_group("hf dataset options")
-    hf_group.add_argument(
-        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
-    )
-    hf_group.add_argument(
-        "--hf-split", type=str, default=None, help="Split of the HF dataset."
-    )
+    hf_group.add_argument("--hf-subset",
+                          type=str,
+                          default=None,
+                          help="Subset of the HF dataset.")
+    hf_group.add_argument("--hf-split",
+                          type=str,
+                          default=None,
+                          help="Split of the HF dataset.")
     hf_group.add_argument(
         "--hf-output-len",
         type=int,
@@ -1141,58 +1058,52 @@ if __name__ == "__main__":
         "--top-p",
         type=float,
         default=None,
-        help="Top-p sampling parameter. Only has effect on openai-compatible backends.",
-    )
+        help="Top-p sampling parameter. Only has effect on openai-compatible "
+        "backends.")
     sampling_group.add_argument(
         "--top-k",
         type=int,
         default=None,
-        help="Top-k sampling parameter. Only has effect on openai-compatible backends.",
-    )
+        help="Top-k sampling parameter. Only has effect on openai-compatible "
+        "backends.")
     sampling_group.add_argument(
         "--min-p",
         type=float,
         default=None,
-        help="Min-p sampling parameter. Only has effect on openai-compatible backends.",
-    )
+        help="Min-p sampling parameter. Only has effect on openai-compatible "
+        "backends.")
     sampling_group.add_argument(
         "--temperature",
         type=float,
         default=None,
         help="Temperature sampling parameter. Only has effect on "
         "openai-compatible backends. If not specified, default to greedy "
-        "decoding (i.e. temperature==0.0).",
-    )
+        "decoding (i.e. temperature==0.0).")
 
     parser.add_argument(
-        "--tokenizer-mode",
+        '--tokenizer-mode',
         type=str,
         default="auto",
-        choices=["auto", "slow", "mistral", "custom"],
+        choices=['auto', 'slow', 'mistral', 'custom'],
         help='The tokenizer mode.\n\n* "auto" will use the '
         'fast tokenizer if available.\n* "slow" will '
-        "always use the slow tokenizer. \n* "
+        'always use the slow tokenizer. \n* '
         '"mistral" will always use the `mistral_common` tokenizer. \n*'
-        '"custom" will use --tokenizer to select the preregistered tokenizer.',
-    )
+        '"custom" will use --tokenizer to select the preregistered tokenizer.')
 
-    parser.add_argument(
-        "--served-model-name",
-        type=str,
-        default=None,
-        help="The model name used in the API. "
-        "If not specified, the model name will be the "
-        "same as the ``--model`` argument. ",
-    )
+    parser.add_argument("--served-model-name",
+                        type=str,
+                        default=None,
+                        help="The model name used in the API. "
+                        "If not specified, the model name will be the "
+                        "same as the ``--model`` argument. ")
 
-    parser.add_argument(
-        "--lora-modules",
-        nargs="+",
-        default=None,
-        help="A subset of LoRA module names passed in when "
-        "launching the server. For each request, the "
-        "script chooses a LoRA module at random.",
-    )
+    parser.add_argument("--lora-modules",
+                        nargs='+',
+                        default=None,
+                        help="A subset of LoRA module names passed in when "
+                        "launching the server. For each request, the "
+                        "script chooses a LoRA module at random.")
 
     args = parser.parse_args()
 

benchmarks/benchmark_serving_structured_output.py

@@ -19,7 +19,6 @@ On the client side, run:
         --endpoint /generate_stream
     to the end of the command above.
 """
-
 import argparse
 import asyncio
 import copy
@@ -37,15 +36,11 @@ 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:
@@ -57,8 +52,7 @@ 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
 
@@ -104,7 +98,6 @@ 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
@@ -113,28 +106,32 @@ 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
 
@@ -145,13 +142,11 @@ def sample_requests(
             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)
         ]
 
@@ -175,13 +170,11 @@ def sample_requests(
         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)
         ]
 
@@ -195,13 +188,11 @@ def sample_requests(
         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)
         ]
 
@@ -212,55 +203,48 @@ def sample_requests(
         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
 
@@ -292,8 +276,7 @@ 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):
@@ -335,8 +318,8 @@ 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
@@ -360,19 +343,16 @@ 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)])
@@ -383,8 +363,7 @@ 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,
@@ -393,31 +372,27 @@ 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
@@ -454,13 +429,12 @@ 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,
@@ -474,8 +448,7 @@ 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...")
 
@@ -494,7 +467,10 @@ async def benchmark(
         if profile_output.success:
             print("Profiler started")
 
-    distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
+    if burstiness == 1.0:
+        distribution = "Poisson process"
+    else:
+        distribution = "Gamma distribution"
 
     print(f"Traffic request rate: {request_rate}")
     print(f"Burstiness factor: {burstiness} ({distribution})")
@@ -506,21 +482,24 @@ 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,
@@ -533,9 +512,8 @@ 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:
@@ -567,58 +545,54 @@ 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"
@@ -632,35 +606,29 @@ 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")
 
@@ -670,13 +638,13 @@ async def benchmark(
 
 
 def evaluate(ret, args):
+
     def _eval_correctness_json(expected, actual):
         # extract json string from string using regex
-        import regex as re
-
-        actual = actual.replace("\n", "").replace(" ", "").strip()
+        import 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
@@ -687,33 +655,29 @@ def evaluate(ret, args):
         return actual in args.choice
 
     def _eval_correctness_regex(expected, actual):
-        import regex as re
-
+        import 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):
@@ -725,10 +689,9 @@ 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
 
 
@@ -742,14 +705,12 @@ 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
 
 
@@ -775,19 +736,19 @@ 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]}"
@@ -815,29 +776,36 @@ 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}
 
@@ -846,14 +814,13 @@ 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,
@@ -875,14 +842,16 @@ 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,
@@ -894,8 +863,7 @@ 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,
@@ -905,13 +873,15 @@ 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",
@@ -988,51 +958,44 @@ 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)

benchmarks/benchmark_throughput.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 """Benchmark offline inference throughput."""
-
 import argparse
 import dataclasses
 import json
@@ -12,25 +11,18 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from tqdm import tqdm
-from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
-
-from benchmark_dataset import (
-    AIMODataset,
-    BurstGPTDataset,
-    ConversationDataset,
-    InstructCoderDataset,
-    RandomDataset,
-    SampleRequest,
-    ShareGPTDataset,
-    SonnetDataset,
-    VisionArenaDataset,
-)
+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 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
@@ -45,30 +37,23 @@ 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,
@@ -77,8 +62,7 @@ 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]
@@ -88,9 +72,10 @@ 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"
@@ -106,35 +91,30 @@ 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] = []
@@ -148,8 +128,7 @@ 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()
@@ -166,17 +145,13 @@ 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:
-        model_config = await llm.get_model_config()
+            engine_args, disable_frontend_multiprocessing) as llm:
         assert all(
-            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.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]] = []
@@ -184,15 +159,11 @@ 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,
@@ -201,16 +172,17 @@ 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:
@@ -229,8 +201,7 @@ 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
@@ -253,15 +224,14 @@ 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,
@@ -291,7 +261,6 @@ 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]
 
@@ -303,9 +272,8 @@ 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={
@@ -313,9 +281,9 @@ def save_to_pytorch_benchmark_format(
             "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"
@@ -347,8 +315,7 @@ 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
@@ -357,21 +324,21 @@ 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
@@ -386,10 +353,10 @@ 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:
@@ -400,34 +367,23 @@ 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}")
 
@@ -439,31 +395,28 @@ 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}")
 
@@ -491,8 +444,7 @@ 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):
@@ -505,8 +457,9 @@ 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")
 
@@ -514,55 +467,41 @@ 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")
 
@@ -572,10 +511,8 @@ 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":
@@ -583,32 +520,29 @@ 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,
@@ -616,70 +550,57 @@ 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>]]]]",
-    )
+        "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.")
     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.",
-    )
-    parser.add_argument(
-        "--output-json",
+        '--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,
@@ -693,8 +614,7 @@ 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",
@@ -708,12 +628,14 @@ 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()

benchmarks/benchmark_utils.py

@@ -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(
             },
         }
 
-        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)
 
@@ -50,6 +50,7 @@ def convert_to_pytorch_benchmark_format(
 
 
 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()}

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -23,9 +23,8 @@ 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 = {
@@ -42,18 +41,16 @@ def bench_fn(
     ).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):
@@ -66,107 +63,54 @@ def bench_int8(
     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):
@@ -180,165 +124,97 @@ def bench_fp8(
 
     # 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:
@@ -352,12 +228,12 @@ 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)
 
@@ -365,12 +241,10 @@ def run(
 
 
 # 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)
 
@@ -384,7 +258,8 @@ 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, MKNs)
 
@@ -444,7 +319,7 @@ def run_model_bench(args):
         pkl.dump(all_data, f)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
 
     def to_torch_dtype(dt):
         if dt == "int8":
@@ -469,15 +344,12 @@ 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")
@@ -496,19 +368,19 @@ 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()

benchmarks/cutlass_benchmarks/utils.py

@@ -10,9 +10,8 @@ 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:
@@ -27,11 +26,10 @@ 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)
@@ -51,7 +49,9 @@ 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
@@ -62,11 +62,10 @@ 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()
 
@@ -87,9 +86,9 @@ def make_rand_sparse_tensors(
     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)

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -16,8 +16,7 @@ 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())
@@ -26,9 +25,8 @@ 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 = {
@@ -46,48 +44,45 @@ def bench_fn(
 
 
 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 = []
@@ -101,73 +96,73 @@ 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)
-
-    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 = torch.rand((m, k // 128),
+                               device="cuda",
+                               dtype=torch.float32)
+    block_scale_b = torch.rand((k // 128, 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 = []
@@ -180,15 +175,13 @@ 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:
@@ -202,33 +195,27 @@ 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)
 
@@ -239,7 +226,8 @@ 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, MKNs, bench_kernels=args.kernels)
     make_output(data, MKNs, f"square_bench-{args.dtype}")
@@ -297,7 +285,7 @@ def run_model_bench(args):
         pkl.dump(all_data, f)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
 
     def to_torch_dtype(dt):
         if dt == "int8":
@@ -322,21 +310,19 @@ 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")
@@ -357,19 +343,19 @@ 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()

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -42,4 +42,4 @@ WEIGHT_SHAPES = {
         ([8192, 57344], 1),
         ([28672, 8192], 0),
     ],
-}
+}

benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py

@@ -12,37 +12,39 @@ 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
 
@@ -51,12 +53,11 @@ 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)

benchmarks/disagg_benchmarks/round_robin_proxy.py

@@ -8,6 +8,7 @@ from aiohttp import web
 
 
 class RoundRobinProxy:
+
     def __init__(self, target_ports):
         self.target_ports = target_ports
         self.port_cycle = itertools.cycle(self.target_ports)
@@ -20,15 +21,14 @@ 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())

benchmarks/disagg_benchmarks/visualize_benchmark_results.py

@@ -6,41 +6,43 @@ 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)

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -24,12 +24,10 @@ 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]:
@@ -40,19 +38,15 @@ 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:
@@ -64,12 +58,9 @@ def unfused_int8_impl(
     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:
@@ -82,27 +73,22 @@ def unfused_fp8_impl(
 
 
 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 = {
@@ -120,81 +106,43 @@ def bench_fn(
         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)
 
@@ -209,12 +157,13 @@ 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
@@ -223,5 +172,5 @@ def main():
         pkl.dump(timers, f)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

benchmarks/kernels/benchmark_aqlm.py

@@ -9,39 +9,32 @@ 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(
-    # [..., in_features]
-    input: torch.Tensor,
-    weights: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
+        input: torch.Tensor,  #  [..., in_features]
+        weights: torch.Tensor,
+        scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
 ) -> torch.Tensor:
     output = F.linear(input, weights)
     return output
 
 
 def dequant_out_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
+    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]
     output_partition_sizes: torch.IntTensor,
     bias: Optional[torch.Tensor],
 ) -> torch.Tensor:
+
     weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
 
     if bias is None:
@@ -53,42 +46,40 @@ 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(
-    # [..., 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,
+    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]
     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(
-    # [..., 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,
+    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]
     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)
@@ -98,26 +89,23 @@ 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):
@@ -150,25 +138,24 @@ 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()
@@ -178,7 +165,7 @@ 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.
@@ -197,54 +184,31 @@ 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
@@ -265,7 +229,7 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
             )
 
     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
@@ -285,36 +249,32 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
             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)
 

benchmarks/kernels/benchmark_bitblas.py

@@ -3,33 +3,27 @@
 # 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(
@@ -38,9 +32,10 @@ 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,
@@ -87,17 +82,17 @@ 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,
@@ -175,7 +170,8 @@ 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)
@@ -210,12 +206,12 @@ 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.
 
@@ -236,6 +232,5 @@ 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)

benchmarks/kernels/benchmark_cutlass_fp4_moe.py

@@ -1,489 +0,0 @@
-# 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)

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -6,18 +6,14 @@ 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]
@@ -28,27 +24,19 @@ 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}")
 
@@ -62,17 +50,35 @@ def bench_run(
 
     _, 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])
@@ -85,120 +91,82 @@ def bench_run(
     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):
@@ -208,35 +176,16 @@ def bench_run(
     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
@@ -276,27 +225,18 @@ def bench_run(
     }
 
     # 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)
@@ -308,35 +248,22 @@ def bench_run(
             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)
@@ -348,8 +275,7 @@ def bench_run(
             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):
@@ -377,15 +303,8 @@ 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()
@@ -393,8 +312,7 @@ 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="+",
@@ -402,14 +320,21 @@ 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()

benchmarks/kernels/benchmark_layernorm.py

@@ -10,16 +10,14 @@ 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")
 
@@ -58,35 +56,33 @@ def main(
     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)

benchmarks/kernels/benchmark_machete.py

@@ -20,18 +20,12 @@ 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
 
@@ -88,14 +82,12 @@ 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(
@@ -104,24 +96,21 @@ def quantize_and_pack(
         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)
 
@@ -135,13 +124,8 @@ def create_bench_tensors(
             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)
@@ -149,30 +133,21 @@ def create_bench_tensors(
 
         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
 
@@ -195,57 +170,50 @@ 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
@@ -253,35 +221,36 @@ 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:
@@ -306,24 +275,26 @@ def machete_create_bench_fn(
 # 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
@@ -333,20 +304,19 @@ _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:
@@ -362,45 +332,31 @@ def bench(
     # 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
@@ -415,8 +371,7 @@ def bench(
             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")
@@ -428,17 +383,11 @@ def bench(
             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,
@@ -449,8 +398,10 @@ def bench(
                 "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:
@@ -471,9 +422,8 @@ 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,
@@ -483,16 +433,14 @@ 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)
 
@@ -506,6 +454,7 @@ def make_output(
     base_description: str,
     timestamp=None,
 ):
+
     print(f"== All Results {base_description} ====")
     print_timers(data)
 
@@ -519,7 +468,8 @@ 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)
 
@@ -529,9 +479,8 @@ 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))
@@ -543,6 +492,7 @@ def run_range_bench(args):
 
 
 def run_model_bench(args):
+
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")
@@ -585,13 +535,10 @@ 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__":
@@ -607,6 +554,7 @@ 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))
 
@@ -632,32 +580,32 @@ 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",
@@ -670,11 +618,9 @@ 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")
@@ -688,20 +634,17 @@ 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")
@@ -712,12 +655,14 @@ 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()

benchmarks/kernels/benchmark_marlin.py

@@ -6,34 +6,19 @@ 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
 
@@ -44,29 +29,22 @@ 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
     (
@@ -79,16 +57,14 @@ def bench_run(
     ) = 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"
@@ -98,37 +74,32 @@ def bench_run(
         (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 = {
@@ -165,7 +136,8 @@ def bench_run(
         "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,
@@ -186,63 +158,60 @@ def bench_run(
             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):
@@ -264,50 +233,37 @@ 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()
@@ -318,8 +274,7 @@ 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="+",
@@ -327,9 +282,10 @@ 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=[])

benchmarks/kernels/benchmark_moe.py

@@ -31,60 +31,56 @@ 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:
@@ -97,14 +93,10 @@ def benchmark_config(
             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)
@@ -122,12 +114,10 @@ def benchmark_config(
 
     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,
@@ -223,7 +213,8 @@ 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():
@@ -259,25 +250,20 @@ def get_configs_compute_bound(use_fp16, block_quant_shape) -> list[dict[str, int
     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
 
@@ -315,14 +301,14 @@ 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
@@ -343,10 +329,8 @@ 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
@@ -380,6 +364,7 @@ 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)
@@ -403,40 +388,36 @@ 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(
@@ -457,14 +438,10 @@ 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():
@@ -472,7 +449,8 @@ 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(
@@ -487,8 +465,7 @@ 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
@@ -504,44 +481,42 @@ 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:
@@ -550,16 +525,18 @@ def save_configs(
 
 
 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)
@@ -574,12 +551,14 @@ 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
@@ -594,35 +573,16 @@ 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]
@@ -633,8 +593,7 @@ 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"]
@@ -661,59 +620,25 @@ 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}")
@@ -722,15 +647,18 @@ 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)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -8,9 +8,7 @@ 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
@@ -29,17 +27,15 @@ 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)
@@ -50,41 +46,36 @@ def benchmark_permute(
         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()
@@ -120,17 +111,15 @@ def benchmark_permute(
     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)
@@ -144,74 +133,46 @@ def benchmark_unpermute(
     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()
@@ -248,6 +209,7 @@ def benchmark_unpermute(
 
 @ray.remote(num_gpus=1)
 class BenchmarkWorker:
+
     def __init__(self, seed: int) -> None:
         torch.set_default_device("cuda")
         current_platform.seed_everything(seed)
@@ -279,8 +241,7 @@ 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,
@@ -290,15 +251,15 @@ 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
 
 
@@ -306,21 +267,20 @@ 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
 
@@ -339,24 +299,8 @@ 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]
@@ -377,21 +321,9 @@ 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}")
@@ -401,12 +333,13 @@ 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)

benchmarks/kernels/benchmark_paged_attention.py

@@ -9,11 +9,8 @@ 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__)
 
@@ -41,15 +38,19 @@ 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)
@@ -60,23 +61,24 @@ 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.
@@ -84,11 +86,11 @@ def main(
     if version == "v2":
         if current_platform.is_rocm():
             global PARTITION_SIZE
-            if not args.custom_paged_attn and not current_platform.is_navi():
+            if not args.custom_paged_attn:
                 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,
@@ -108,7 +110,9 @@ 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":
@@ -162,7 +166,6 @@ def main(
                         scale,
                         block_tables,
                         seq_lens,
-                        None,
                         block_size,
                         max_seq_len,
                         alibi_slopes,
@@ -192,29 +195,30 @@ 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(
@@ -224,11 +228,10 @@ 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)
 

benchmarks/kernels/benchmark_quant.py

@@ -10,17 +10,15 @@ 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")
 
@@ -58,7 +56,7 @@ def main(
     print(f"Kernel running time: {latency * 1000000:.3f} us")
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
 
     def to_torch_dtype(dt):
         if dt == "int8":
@@ -68,40 +66,37 @@ 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)

benchmarks/kernels/benchmark_rmsnorm.py

@@ -12,6 +12,7 @@ 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))
@@ -113,19 +114,23 @@ 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]
@@ -136,9 +141,9 @@ 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")
@@ -147,10 +152,12 @@ 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"],
@@ -160,15 +167,19 @@ 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
 
@@ -229,9 +240,9 @@ 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,
@@ -242,12 +253,10 @@ 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)

benchmarks/kernels/benchmark_rope.py

@@ -6,7 +6,8 @@ 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
 
@@ -31,49 +32,40 @@ 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
@@ -94,28 +86,27 @@ 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)
 

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -14,16 +14,14 @@ 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,
@@ -42,7 +40,7 @@ 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`.
@@ -53,7 +51,7 @@ 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.
 
@@ -73,18 +71,18 @@ 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,
@@ -121,16 +119,14 @@ 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
 
 
@@ -169,9 +165,15 @@ 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)
 
@@ -204,26 +206,26 @@ 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")
@@ -265,8 +267,7 @@ 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}...")
@@ -294,7 +295,8 @@ 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()
@@ -310,11 +312,15 @@ 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")
@@ -370,14 +376,13 @@ 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:
@@ -393,11 +398,13 @@ 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,

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -11,9 +11,7 @@ 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
 
 

benchmarks/kernels/graph_machete_bench.py

@@ -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
 
@@ -14,14 +14,13 @@ 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"]
 
@@ -39,7 +38,11 @@ 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))
@@ -47,16 +50,14 @@ 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()

benchmarks/kernels/utils.py

@@ -23,7 +23,6 @@ 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):
@@ -31,7 +30,9 @@ 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
@@ -52,16 +53,10 @@ 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
@@ -72,8 +67,10 @@ 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
@@ -103,13 +100,16 @@ 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,

benchmarks/overheads/benchmark_hashing.py

@@ -7,8 +7,9 @@ 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):
@@ -29,35 +30,32 @@ 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,{percentage:.2f}% of the total runtime."
-    )
+    print(f"Hashing took {total_time:.2f} seconds,"
+          f"{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)

benchmarks/pyproject.toml

@@ -1,49 +0,0 @@
-# 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

benchmarks/run_structured_output_benchmark.sh

@@ -1,98 +1,32 @@
 #!/bin/bash
 
-# default values
-MODEL=${MODEL:-"Qwen/Qwen2.5-7B-Instruct"}
-BACKEND=${BACKEND:-"vllm"}
-DATASET=${DATASET:-"xgrammar_bench"}
+# Define the model to use
+MODEL=${1:-"Qwen/Qwen2.5-7B-Instruct"}
+
+# Define the backend to use
+BACKEND=${2:-"vllm"}
+
+# Define the dataset to use
+DATASET=${3:-"xgrammar_bench"}
+
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-OUTPUT_DIR=${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"}
+OUTPUT_DIR=${4:-"$SCRIPT_DIR/structured_output_benchmark_results"}
 
-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: $1\n"
-      usage
-      ;;
-  esac
-done
+GUIDED_RATIO=${5:-0.5}
 
 # Create output directory if it doesn't exist
 mkdir -p "$OUTPUT_DIR"
 
 # Define QPS values to test
-QPS_VALUES=(25 20 15 10 5 1)
+QPS_VALUES=(70 60 50 25 20 15 10)
 
 # Common parameters
 COMMON_PARAMS="--backend $BACKEND \
                --model $MODEL \
                --dataset $DATASET \
-               --structured-output-ratio $STRUCTURED_OUTPUT_RATIO \
+               --structured-output-ratio $GUIDED_RATIO \
                --save-results \
-               --result-dir $OUTPUT_DIR \
-               --output-len $MAX_NEW_TOKENS \
-               --port $PORT \
-               --tokenizer-mode $TOKENIZER_MODE"
+               --result-dir $OUTPUT_DIR"
 
 echo "Starting structured output benchmark with model: $MODEL"
 echo "Backend: $BACKEND"
@@ -111,15 +45,12 @@ for qps in "${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" \
-    --num-prompts $NUM_PROMPTS
+    --tokenizer-mode ${TOKENIZER_MODE:-"auto"} \
+    --port ${PORT:-8000}
 
   echo "Completed benchmark with QPS: $qps"
   echo "----------------------------------------"

cmake/utils.cmake

@@ -228,26 +228,11 @@ macro(set_gencode_flags_for_srcs)
                         "${multiValueArgs}" ${ARGN} )
 
   foreach(_ARCH ${arg_CUDA_ARCHS})
-    # handle +PTX suffix: generate both sm and ptx codes if requested
-    string(FIND "${_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_${_STRIPPED_ARCH}")
-    endif()
+    string(REPLACE "." "" _ARCH "${_ARCH}")
+    set_gencode_flag_for_srcs(
+      SRCS ${arg_SRCS}
+      ARCH "compute_${_ARCH}"
+      CODE "sm_${_ARCH}")
   endforeach()
 
   if (${arg_BUILD_PTX_FOR_ARCH})
@@ -266,10 +251,7 @@ endmacro()
 #
 # For the given `SRC_CUDA_ARCHS` list of gencode versions in the form 
 #  `<major>.<minor>[letter]` compute the "loose intersection" with the 
-#  `TGT_CUDA_ARCHS` list of gencodes. We also support the `+PTX` suffix in
-#  `SRC_CUDA_ARCHS` which indicates that the PTX code should be built when there
-#  is a CUDA_ARCH in `TGT_CUDA_ARCHS` that is equal to or larger than the
-#  architecture in `SRC_CUDA_ARCHS`.
+#  `TGT_CUDA_ARCHS` list of gencodes. 
 # The loose intersection is defined as:
 #   { max{ x \in tgt | x <= y } | y \in src, { x \in tgt | x <= y } != {} }
 #  where `<=` is the version comparison operator.
@@ -286,63 +268,44 @@ endmacro()
 #   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
 #   OUT_CUDA_ARCHS="8.0;8.6;9.0;9.0a"
 #
-# Example With PTX:
-#   SRC_CUDA_ARCHS="8.0+PTX"
-#   TGT_CUDA_ARCHS="9.0"
-#   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
-#   OUT_CUDA_ARCHS="8.0+PTX"
-#
 function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
-  set(_SRC_CUDA_ARCHS "${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)
+  list(REMOVE_DUPLICATES SRC_CUDA_ARCHS)
+  set(TGT_CUDA_ARCHS_ ${TGT_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 "${_SRC_ARCH}")
-      # Check version-less-or-equal, and allow PTX arches to match across majors
+      # Check major-version match AND version-less-or-equal
       if (_SRC_ARCH VERSION_LESS_EQUAL _ARCH)
-        if (_SRC_ARCH IN_LIST _PTX_ARCHS OR SRC_ARCH_MAJOR STREQUAL TGT_ARCH_MAJOR)
+        if (SRC_ARCH_MAJOR STREQUAL TGT_ARCH_MAJOR)
           set(_TMP_ARCH "${_SRC_ARCH}")
         endif()
       else()
@@ -358,18 +321,6 @@ 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()
 

csrc/activation_kernels.cu

@@ -70,9 +70,6 @@ __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(                                          \

csrc/attention/attention_kernels.cuh

@@ -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 thread group size is 4, then the first thread in
+  // For example, if the 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 thread group size is 4, then the first thread in
+    // For example, if the 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++) {

csrc/attention/merge_attn_states.cu

@@ -143,14 +143,6 @@ 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>();

csrc/attention/vertical_slash_index.cu

@@ -1,401 +0,0 @@
-// 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);
-}

csrc/core/scalar_type.hpp

@@ -315,8 +315,6 @@ 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 =
@@ -334,7 +332,6 @@ 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;

csrc/cpu/cpu_types_x86.hpp

@@ -19,7 +19,6 @@ 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, ...) \

csrc/cutlass_extensions/common.hpp

@@ -15,6 +15,15 @@
                 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,

csrc/dispatch_utils.h

@@ -65,19 +65,5 @@
   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__))

csrc/mamba/causal_conv1d/causal_conv1d.cu

@@ -13,10 +13,6 @@
 #include <cub/block/block_load.cuh>
 #include <cub/block/block_store.cuh>
 
-#ifdef USE_ROCM
-    namespace cub = hipcub;
-#endif
-
 #include "static_switch.h"
 
 
@@ -505,9 +501,15 @@ 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);
 

csrc/mamba/mamba_ssm/selective_scan_fwd.cu

@@ -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(
-                    (void *) kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
+                    kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
             }
             kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
             C10_CUDA_KERNEL_LAUNCH_CHECK();

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -31,10 +31,7 @@ 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",
-    "vllm::kFE2M1f"
-]
+SCALAR_TYPES = ["vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn"]
 THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128)]
 
 THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
@@ -42,7 +39,7 @@ 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, 1, 2, 4, 8]
+GROUP_BLOCKS = [0, -1, 2, 4, 8]
 DTYPES = ["fp16", "bf16"]
 
 
@@ -75,12 +72,6 @@ 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

csrc/moe/marlin_moe_wna16/kernel.h

@@ -7,18 +7,17 @@
 #include "quantization/gptq_marlin/marlin_dtypes.cuh"
 #include "core/scalar_type.hpp"
 
-#define MARLIN_KERNEL_PARAMS                                          \
-  const int4 *__restrict__ A, const int4 *__restrict__ B,             \
-      int4 *__restrict__ C, int4 *__restrict__ C_tmp,                 \
-      const int4 *__restrict__ scales_ptr,                            \
-      const uint16_t *__restrict__ scale2_ptr,                        \
-      const int4 *__restrict__ zp_ptr, const int *__restrict__ g_idx, \
-      const int32_t *__restrict__ sorted_token_ids_ptr,               \
-      const int32_t *__restrict__ expert_ids_ptr,                     \
-      const int32_t *__restrict__ num_tokens_past_padded_ptr,         \
-      const float *__restrict__ topk_weights_ptr, int top_k,          \
-      bool mul_topk_weights, bool is_ep, int num_groups, int prob_m,  \
-      int prob_n, int prob_k, int *locks, bool use_atomic_add,        \
+#define MARLIN_KERNEL_PARAMS                                                \
+  const int4 *__restrict__ A, const int4 *__restrict__ B,                   \
+      int4 *__restrict__ C, int4 *__restrict__ C_tmp,                       \
+      const int4 *__restrict__ 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,              \
       bool use_fp32_reduce, int max_shared_mem
 
 namespace MARLIN_NAMESPACE_NAME {

csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -301,11 +301,9 @@ __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 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 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
@@ -343,16 +341,6 @@ __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();
@@ -360,8 +348,7 @@ __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 / (w_type == vllm::kFE2M1f ? 16 : 8);
+  const int scales_expert_stride = prob_n * prob_k / group_size / 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);
@@ -473,16 +460,9 @@ __global__ void Marlin(
       if (mul_topk_weights) {
   #pragma unroll
         for (int i = 0; i < 4; i++) {
-          int idx = tid4 * 4 + i;
-          idx = idx < block_num_valid_tokens ? idx : 0;
-          if constexpr (w_type == vllm::kFE2M1f) {
-            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]]));
-          }
+          sh_block_topk_weights[tid4 * 4 + i] =
+              Dtype::num2num2(Dtype::float2num(
+                  topk_weights_ptr[sh_block_sorted_ids[tid4 * 4 + i]]));
         }
       }
     }
@@ -513,11 +493,6 @@ __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) {
@@ -631,7 +606,7 @@ __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 / (w_type == vllm::kFE2M1f ? 2 : 1)
+          ? thread_k_blocks / group_blocks
           : 1;
   constexpr int s_sh_stage = s_tb_groups * s_sh_stride;
   int s_gl_rd_delta = s_gl_stride;
@@ -689,8 +664,7 @@ __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) /
-                    (w_type == vllm::kFE2M1f ? 2 : 1) +
+      s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) +
                 s_sh_stride * slice_col + threadIdx.x;
     }
   }
@@ -714,20 +688,10 @@ __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 && w_type == vllm::kFE2M1f) {
-    auto warp_id = threadIdx.x / 32;
-    int n_warps = thread_n_blocks / 4;
-    int warp_row = warp_id / n_warps;
-
+  if constexpr (group_blocks != -1)
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) / 4;
-    s_sh_rd = s_sh_rd * 2 + warp_row % 2;
-
-  } 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)))
+  else if constexpr (group_blocks == -1 && (m_block_size_8 || has_zp))
     s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
               (threadIdx.x % 32) / 8;
   else
@@ -837,7 +801,7 @@ __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;
     }
 
@@ -1057,19 +1021,12 @@ __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 * (w_type == vllm::kFE2M1f ? 2 : 1));
+          int cur_group_id = k_blocks / group_blocks;
 
           int4* sh_s_stage = sh_s + s_sh_stage * pipe;
 
-          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)];
-          }
+          reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
+              sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
         }
       }
 
@@ -1242,7 +1199,22 @@ __global__ void Marlin(
   };
 
   auto dequant_data = [&](int q, scalar_t2* frag_b_ptr) {
-    dequant<scalar_t2, w_type_id, dequant_skip_flop>(q, 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);
+      }
+    }
   };
 
   // Execute the actual tensor core matmul of a sub-tile.
@@ -1272,23 +1244,13 @@ __global__ void Marlin(
         dequant_data(zp_quant_1, reinterpret_cast<scalar_t2*>(&frag_zp) + 2);
       }
     }
-    if constexpr (!dequant_skip_flop && has_zp && is_zp_float) {
+    if constexpr (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
@@ -1297,10 +1259,7 @@ __global__ void Marlin(
       FragB frag_b1;
       int b_quant_0, b_quant_1;
 
-      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) {
+      if constexpr (w_type.size_bits() == 4) {
         b_quant_0 = frag_b_quant[k2][0][j];
         b_quant_1 = b_quant_0 >> 8;
       } else {
@@ -1313,11 +1272,6 @@ __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);
@@ -1325,8 +1279,7 @@ __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 (!dequant_skip_flop && has_zp && !is_zp_float &&
-                           group_blocks == -1) {
+      } else if constexpr (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],
@@ -1334,7 +1287,7 @@ __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 (!dequant_skip_flop && has_zp && group_blocks != -1) {
+      } else if constexpr (has_zp && group_blocks != -1) {
         if (is_new_zp)
           frag_zp[j] = __hmul2(frag_zp[j],
                                *reinterpret_cast<scalar_t2*>(&frag_s[k2][j]));
@@ -1601,17 +1554,10 @@ __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 && dequant_skip_flop || !has_zp)) {
+                    w_type.size_bits() == 4 && !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;
@@ -1702,9 +1648,7 @@ __global__ void Marlin(
       if constexpr (has_zp && !is_zp_float && group_blocks == -1) {
         if (i == 0) {
           fetch_col_zp_to_shared();
-          if constexpr (!dequant_skip_flop) {
-            fetch_col_scale_to_shared();
-          }
+          fetch_col_scale_to_shared();
         }
       }
       fetch_to_shared(i, i, i < slice_iters, i);
@@ -1767,20 +1711,17 @@ __global__ void Marlin(
 
       if constexpr (has_act_order) {
         slice_k_start += tb_k * stages;
-
-        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();
-          }
+        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) {
@@ -1796,8 +1737,7 @@ __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 && dequant_skip_flop || !has_zp)) {
+      if constexpr (!has_act_order && group_blocks == -1 && !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]);
@@ -1807,8 +1747,7 @@ __global__ void Marlin(
       }
 
       thread_block_reduce();
-      if constexpr (!has_act_order && group_blocks == -1 &&
-                    (has_zp && dequant_skip_flop || !has_zp)) {
+      if constexpr (!has_act_order && group_blocks == -1 && !has_zp) {
         if (w_type.size_bits() == 8 || (last || use_atomic_add)) {
           cp_async_wait<0>();
           __syncthreads();
@@ -1832,8 +1771,7 @@ __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 && dequant_skip_flop || !has_zp)) {
+                    w_type.size_bits() == 8 && !has_zp) {
         if (threadIdx.x / 32 < thread_n_blocks / 4) {
   #pragma unroll
           for (int i = 0; i < thread_m_blocks; i++) {

csrc/moe/marlin_moe_wna16/ops.cu

@@ -291,7 +291,6 @@ 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)   \
@@ -339,21 +338,6 @@ 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)    \
@@ -410,8 +394,6 @@ 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)
 
@@ -483,7 +465,7 @@ 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* s2, void* zp, void* g_idx, void* perm, void* a_tmp,
+               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,
@@ -497,16 +479,14 @@ 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 == vllm::kU8,
-        "q_type must be u4 or u8 when has_zp = True. Got = ", q_type.str());
+    TORCH_CHECK(q_type == vllm::kU4,
+                "q_type must be u4 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 == vllm::kFE2M1f,
-        "q_type must be uint4b8, uint8b128, float8_e4m3fn or float4_e2m1f when "
-        "has_zp = False. Got = ",
-        q_type.str());
+    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(prob_m > 0 && prob_n > 0 && prob_k > 0, "Invalid MNK = [", prob_m,
@@ -539,7 +519,6 @@ 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;
@@ -648,7 +627,7 @@ 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, s2_ptr, zp_ptr, g_idx_ptr,
+      A_ptr, B_ptr, C_ptr, C_tmp_ptr, s_ptr, zp_ptr, g_idx_ptr,
       sorted_token_ids_ptr, expert_ids_ptr, num_tokens_past_padded_ptr,
       topk_weights_ptr, top_k, mul_topk_weights, is_ep, num_groups, prob_m,
       prob_n, prob_k, locks, use_atomic_add, use_fp32_reduce, max_shared_mem);
@@ -660,7 +639,6 @@ 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,
@@ -812,17 +790,6 @@ 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();
@@ -835,14 +802,13 @@ torch::Tensor moe_wna16_marlin_gemm(
 
   if (has_zp) {
     TORCH_CHECK(
-        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());
+        b_q_type == vllm::kU4,
+        "b_q_type must be u4 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 == vllm::kFE2M1f,
-                "b_q_type must be uint4b8, uint8b128, float8_e4m3fn or "
-                "float4_e2m1f when "
-                "has_zp = False. Got = ",
+                    b_q_type == vllm::kFE4M3fn,
+                "b_q_type must be uint4b8, uint8b128 or fp8e4m3 when has_zp = "
+                "False. Got = ",
                 b_q_type.str());
   }
 
@@ -888,16 +854,9 @@ 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>(), scales_ptr, global_scale.data_ptr<at::Half>(),
+        c_tmp.data_ptr<float>(), b_scales.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(),
@@ -907,18 +866,11 @@ 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>(), 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>(),
+        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>(),
         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,

csrc/moe/moe_align_sum_kernels.cu

@@ -326,7 +326,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
   }
 
   if (use_global_memory) {
-    VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
+    VLLM_DISPATCH_INTEGRAL_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_AND_UNSIGNED_TYPES(
+    VLLM_DISPATCH_INTEGRAL_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_AND_UNSIGNED_TYPES(
+    VLLM_DISPATCH_INTEGRAL_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_AND_UNSIGNED_TYPES(
+  VLLM_DISPATCH_INTEGRAL_TYPES(
       topk_ids.scalar_type(), "sgl_moe_align_block_size_kernel", [&] {
         // calc needed amount of shared mem for `cumsum` tensors
         auto options_int =

csrc/moe/moe_ops.h

@@ -28,6 +28,4 @@ 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
-
-bool moe_permute_unpermute_supported();
+#endif

csrc/moe/moe_permute_unpermute_op.cu

@@ -5,9 +5,6 @@
 #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]
@@ -130,45 +127,7 @@ 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);
-}
+}

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -1,9 +1,6 @@
 
 #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) {}
@@ -134,6 +131,9 @@ __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[];
@@ -226,6 +226,4 @@ 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
+}

csrc/moe/topk_softmax_kernels.cu

@@ -108,17 +108,9 @@ __launch_bounds__(TPB) __global__
     }
 }
 
-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)
+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)
 {
 
     using cub_kvp = cub::KeyValuePair<int, float>;
@@ -190,9 +182,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   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, typename IndType>
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG>
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
-    void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, IndType* indices,
+    void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, int* 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.
@@ -405,8 +397,8 @@ struct TopkConstants
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, typename IndType>
-void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
+template <int EXPERTS, int WARPS_PER_TB>
+void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, int* 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;
@@ -429,11 +421,10 @@ 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,
-    IndType* topk_indicies,
+    int* topk_indicies,
     int* token_expert_indices,
     float* softmax_workspace,
     const int num_tokens,
@@ -502,32 +493,14 @@ 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());
-
-    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);
-    }
+    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);
 }

csrc/moe/torch_bindings.cpp

@@ -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
@@ -44,8 +44,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
 
   m.def(
       "moe_wna16_marlin_gemm(Tensor! a, Tensor? c_or_none,"
-      "Tensor! b_q_weight, Tensor! b_scales, Tensor? global_scale, Tensor? "
-      "b_zeros_or_none,"
+      "Tensor! b_q_weight, Tensor! b_scales, 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,"
@@ -77,9 +76,7 @@ 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)->()");
-
-  m.def("moe_permute_unpermute_supported() -> bool");
-  m.impl("moe_permute_unpermute_supported", &moe_permute_unpermute_supported);
+  // conditionally compiled so impl registration is in source file
 
 #endif
 }

csrc/ops.h

@@ -59,31 +59,6 @@ void merge_attn_states(torch::Tensor& output,
                        const torch::Tensor& prefix_lse,
                        const torch::Tensor& suffix_output,
                        const torch::Tensor& suffix_lse);
-
-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);
-
-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, int64_t context_size,
-    int64_t block_size_M, int64_t block_size_N, bool causal);
 #endif
 
 void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
@@ -233,12 +208,6 @@ void cutlass_moe_mm(
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
     torch::Tensor const& b_strides, torch::Tensor const& c_strides);
 
-void cutlass_fp4_group_mm(
-    torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
-    const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
-    const torch::Tensor& alphas, const torch::Tensor& problem_sizes,
-    const torch::Tensor& expert_offsets, const torch::Tensor& sf_offsets);
-
 void get_cutlass_moe_mm_data(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
@@ -266,12 +235,6 @@ std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a);
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
                       torch::Tensor& output_scale,
                       torch::Tensor const& input_scale);
-
-void scaled_fp4_experts_quant(
-    torch::Tensor& output, torch::Tensor& output_scale,
-    torch::Tensor const& input, torch::Tensor const& input_global_scale,
-    torch::Tensor const& input_offset_by_experts,
-    torch::Tensor const& output_scale_offset_by_experts);
 #endif
 
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,

csrc/pos_encoding_kernels.cu

@@ -44,8 +44,7 @@ inline __device__ void apply_rotary_embedding(
                                    // head_size]
     const scalar_t* cache_ptr, const int head_size, const int num_heads,
     const int num_kv_heads, const int rot_dim, const int token_idx,
-    const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride) {
+    const int64_t query_stride, const int64_t key_stride) {
   const int embed_dim = rot_dim / 2;
   const scalar_t* cos_ptr = cache_ptr;
   const scalar_t* sin_ptr = cache_ptr + embed_dim;
@@ -53,8 +52,7 @@ inline __device__ void apply_rotary_embedding(
   const int nq = num_heads * embed_dim;
   for (int i = threadIdx.x; i < nq; i += blockDim.x) {
     const int head_idx = i / embed_dim;
-    const int64_t token_head =
-        token_idx * query_stride + head_idx * head_stride;
+    const int64_t token_head = token_idx * query_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
     apply_token_rotary_embedding<scalar_t, IS_NEOX>(
         query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
@@ -64,8 +62,7 @@ inline __device__ void apply_rotary_embedding(
     const int nk = num_kv_heads * embed_dim;
     for (int i = threadIdx.x; i < nk; i += blockDim.x) {
       const int head_idx = i / embed_dim;
-      const int64_t token_head =
-          token_idx * key_stride + head_idx * head_stride;
+      const int64_t token_head = token_idx * key_stride + head_idx * head_size;
       const int rot_offset = i % embed_dim;
       apply_token_rotary_embedding<scalar_t, IS_NEOX>(
           key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
@@ -87,8 +84,7 @@ __global__ void rotary_embedding_kernel(
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
                                                  // 2]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride, const int num_heads, const int num_kv_heads,
-    const int head_size) {
+    const int num_heads, const int num_kv_heads, const int head_size) {
   // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
@@ -96,7 +92,7 @@ __global__ void rotary_embedding_kernel(
 
   apply_rotary_embedding<scalar_t, IS_NEOX>(
       query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride, head_stride);
+      token_idx, query_stride, key_stride);
 }
 
 template <typename scalar_t, bool IS_NEOX>
@@ -113,9 +109,9 @@ __global__ void batched_rotary_embedding_kernel(
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
                                                  // 2]
     const int64_t* __restrict__ cos_sin_cache_offsets,  // [batch_size, seq_len]
+                                                        // or [num_tokens]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride, const int num_heads, const int num_kv_heads,
-    const int head_size) {
+    const int num_heads, const int num_kv_heads, const int head_size) {
   // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
@@ -125,7 +121,7 @@ __global__ void batched_rotary_embedding_kernel(
 
   apply_rotary_embedding<scalar_t, IS_NEOX>(
       query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride, head_stride);
+      token_idx, query_stride, key_stride);
 }
 
 }  // namespace vllm
@@ -183,12 +179,6 @@ void rotary_embedding(
   int seq_dim_idx = positions_ndim - 1;
   int64_t query_stride = query.stride(seq_dim_idx);
   int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
-  // Determine head stride: for [*, heads, head_size] use stride of last dim;
-  // for flat [*, heads*head_size], heads blocks are contiguous of size
-  // head_size
-  int query_ndim = query.dim();
-  int64_t head_stride =
-      (query_ndim == positions_ndim + 2) ? query.stride(-2) : head_size;
 
   dim3 grid(num_tokens);
   dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
@@ -200,14 +190,14 @@ void rotary_embedding(
           positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
           key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
           cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride, key_stride,
-          head_stride, num_heads, num_kv_heads, head_size);
+          num_heads, num_kv_heads, head_size);
     } else {
       vllm::rotary_embedding_kernel<scalar_t, false>
           <<<grid, block, 0, stream>>>(
               positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
               key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
               cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
+              key_stride, num_heads, num_kv_heads, head_size);
     }
   });
 }
@@ -273,12 +263,6 @@ void batched_rotary_embedding(
   int seq_dim_idx = positions_ndim - 1;
   int64_t query_stride = query.stride(seq_dim_idx);
   int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
-  // Determine head stride: for [*, heads, head_size] use stride of last dim;
-  // for flat [*, heads*head_size], heads blocks are contiguous of size
-  // head_size
-  int query_ndim = query.dim();
-  int64_t head_stride =
-      (query_ndim == positions_ndim + 2) ? query.stride(-2) : head_size;
 
   dim3 grid(num_tokens);
   dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
@@ -292,7 +276,7 @@ void batched_rotary_embedding(
               key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
               cos_sin_cache.data_ptr<scalar_t>(),
               cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
+              key_stride, num_heads, num_kv_heads, head_size);
     } else {
       vllm::batched_rotary_embedding_kernel<scalar_t, false>
           <<<grid, block, 0, stream>>>(
@@ -300,7 +284,7 @@ void batched_rotary_embedding(
               key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
               cos_sin_cache.data_ptr<scalar_t>(),
               cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, head_stride, num_heads, num_kv_heads, head_size);
+              key_stride, num_heads, num_kv_heads, head_size);
     }
   });
 }

csrc/quantization/activation_kernels.cu

@@ -112,8 +112,7 @@ __global__ void act_and_mul_quant_kernel(
 void silu_and_mul_quant(torch::Tensor& out,    // [..., d]
                         torch::Tensor& input,  // [..., 2 * d]
                         torch::Tensor& scale) {
-  TORCH_CHECK(out.dtype() == torch::kFloat8_e4m3fn ||
-              out.dtype() == torch::kFloat8_e4m3fnuz);
+  TORCH_CHECK(out.dtype() == torch::kFloat8_e4m3fn);
   TORCH_CHECK(input.dtype() == torch::kFloat16 ||
               input.dtype() == torch::kBFloat16);
   TORCH_CHECK(input.size(-1) % 2 == 0);

csrc/quantization/compressed_tensors/int8_quant_kernels.cu

@@ -26,13 +26,7 @@ static inline __device__ int8_t float_to_int8_rn(float x) {
   float dst = std::nearbyint(x);
 
   // saturate
-
-  // See https://github.com/pytorch/pytorch/issues/127666
-  // See https://github.com/llvm/llvm-project/issues/95183
-  // hip-clang std::clamp __glibcxx_assert_fail host function when building on
-  // Arch/gcc14. The following replaces std::clamp usage with similar logic
-  // dst = std::clamp(dst, i8_min, i8_max);
-  dst = (dst < i8_min) ? i8_min : (dst > i8_max) ? i8_max : dst;
+  dst = std::clamp(dst, i8_min, i8_max);
   return static_cast<int8_t>(dst);
 #else
   // CUDA path
@@ -85,13 +79,7 @@ static inline __device__ int8_t int32_to_int8(int32_t x) {
       static_cast<int32_t>(std::numeric_limits<int8_t>::max());
 
   // saturate
-
-  // See https://github.com/pytorch/pytorch/issues/127666
-  // See https://github.com/llvm/llvm-project/issues/95183
-  // hip-clang std::clamp __glibcxx_assert_fail host function when building on
-  // Arch/gcc14. The following replaces std::clamp usage with similar logic
-  // int32_t dst = std::clamp(x, i8_min, i8_max);
-  int32_t dst = (x < i8_min) ? i8_min : (x > i8_max) ? i8_max : x;
+  int32_t dst = std::clamp(x, i8_min, i8_max);
   return static_cast<int8_t>(dst);
 #else
   // CUDA path

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp

@@ -1,6 +1,5 @@
 #include <torch/all.h>
 #include "cuda_utils.h"
-#include "cutlass_extensions/common.hpp"
 
 template <typename Fp8Func, typename Int8Func, typename BlockwiseFunc>
 void dispatch_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
@@ -29,46 +28,29 @@ void dispatch_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
       }
     }
   } else {
-    TORCH_CHECK(a_scales.dim() == 2, "a scale must be 2d tensor.");
-    TORCH_CHECK(b_scales.dim() == 2, "b scale must be 2d tensor.");
-    int32_t version_num = get_sm_version_num();
-    if (version_num >= 100) {
-      TORCH_CHECK(
-          a.size(0) == a_scales.size(0) &&
-              cuda_utils::ceil_div(a.size(1), int64_t(128)) == a_scales.size(1),
-          "a_scale_group_shape must be [1, 128].");
-      TORCH_CHECK(
-          cuda_utils::ceil_div(b.size(0), int64_t(128)) == b_scales.size(0) &&
-              cuda_utils::ceil_div(b.size(1), int64_t(128)) == b_scales.size(1),
-          "b_scale_group_shape must be [128, 128].");
-    } else {
-      // TODO: Remove this after using cutlass sm90 blockwise scaling gemm
-      // kernel, or introducing ceil_div to the load_init() of mainloop.
-      using GroupShape = std::array<int64_t, 2>;
-      auto make_group_shape = [](torch::Tensor const& x,
-                                 torch::Tensor const& s) -> GroupShape {
-        TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
-        return {cuda_utils::ceil_div(x.size(0), s.size(0)),
-                cuda_utils::ceil_div(x.size(1), s.size(1))};
-      };
+    using GroupShape = std::array<int64_t, 2>;
+    auto make_group_shape = [](torch::Tensor const& x,
+                               torch::Tensor const& s) -> GroupShape {
+      TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
+      return {cuda_utils::ceil_div(x.size(0), s.size(0)),
+              cuda_utils::ceil_div(x.size(1), s.size(1))};
+    };
 
-      GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
-      GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
-
-      // 1x128 per-token group scales for activations
-      // 128x128 blockwise scales for weights
-      TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
-                   b_scale_group_shape == GroupShape{128, 128} &&
-                   a.dtype() == torch::kFloat8_e4m3fn &&
-                   b.dtype() == torch::kFloat8_e4m3fn),
-                  "cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
-                  "a_scale_group_shape must be [1, 128]. Got: [",
-                  a_scale_group_shape[0], ", ", a_scale_group_shape[1],
-                  "]\n"
-                  "b_scale_group_shape must be [128, 128]. Got: [",
-                  b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
-    }
+    GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
+    GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
 
+    // 1x128 per-token group scales for activations
+    // 128x128 blockwise scales for weights
+    TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
+                 b_scale_group_shape == GroupShape{128, 128} &&
+                 a.dtype() == torch::kFloat8_e4m3fn &&
+                 b.dtype() == torch::kFloat8_e4m3fn),
+                "cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
+                "a_scale_group_shape must be [1, 128]. Got: [",
+                a_scale_group_shape[0], ", ", a_scale_group_shape[1],
+                "]\n"
+                "b_scale_group_shape must be [128, 128]. Got: [",
+                b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
     TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");
     blockwise_func(c, a, b, a_scales, b_scales);
   }