[BugFix][Attention] Fix sliding window attention in V1 giving incorrect results (#17574)

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>

.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(
+                    print(f"Not allowed: Wheel {wheel_path} is larger "
-                        f"Not allowed: Wheel {wheel_path} is larger "
+                          f"({wheel_size_mb:.2f} MB) than the limit "
-                        f"({wheel_size_mb:.2f} MB) than the limit "
+                          f"({VLLM_MAX_SIZE_MB} MB).")
-                        f"({VLLM_MAX_SIZE_MB} MB)."
-                    )
                     print_top_10_largest_files(wheel_path)
                     return 1
                 else:
-                    print(
+                    print(f"Wheel {wheel_path} is within the allowed size "
-                        f"Wheel {wheel_path} is within the allowed size "
+                          f"({wheel_size_mb:.2f} MB).")
-                        f"({wheel_size_mb:.2f} MB)."
-                    )
     return 0
 
 

.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/configs/Meta-Llama-3.2-1B-Instruct-FP8-compressed-tensors.yaml

@@ -1,11 +0,0 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Llama-3.2-1B-Instruct-FP8 -b "auto" -l 1319 -f 5 -t 1
-model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.335
-  - name: "exact_match,flexible-extract"
-    value: 0.323
-limit: 1319
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2.5-1.5B-Instruct.yaml

@@ -1,11 +0,0 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2.5-1.5B-Instruct -b auto -l 1319 -f 5 -t 1
-model_name: "Qwen/Qwen2.5-1.5B-Instruct"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.54
-  - name: "exact_match,flexible-extract"
-    value: 0.59
-limit: 1319
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -1,11 +0,0 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -b auto -l 1319 -f 5 -t 1
-model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.47
-  - name: "exact_match,flexible-extract"
-    value: 0.64
-limit: 1319
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/models-large.txt

@@ -3,4 +3,3 @@ Meta-Llama-3-70B-Instruct.yaml
 Mixtral-8x7B-Instruct-v0.1.yaml
 Qwen2-57B-A14-Instruct.yaml
 DeepSeek-V2-Lite-Chat.yaml
-Meta-Llama-3-8B-QQQ.yaml

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

@@ -1,6 +1,10 @@
-Qwen2.5-1.5B-Instruct.yaml
+Meta-Llama-3-8B-Instruct.yaml
+Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
 Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
-Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
+Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
 Qwen1.5-MoE-W4A16-compressed-tensors.yaml
+Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
+Qwen2-1.5B-Instruct-FP8W8.yaml
+Meta-Llama-3-8B-QQQ.yaml

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

@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from pathlib import Path
-
-import pytest
-
-
-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",
-    )
-
-
-@pytest.fixture(scope="session")
-def config_list_file(pytestconfig, config_dir):
-    rel_path = pytestconfig.getoption("--config-list-file")
-    return config_dir / rel_path
-
-
-@pytest.fixture(scope="session")
-def tp_size(pytestconfig):
-    return pytestconfig.getoption("--tp-size")
-
-
-def pytest_generate_tests(metafunc):
-    if "config_filename" in metafunc.fixturenames:
-        rel_path = metafunc.config.getoption("--config-list-file")
-        config_list_file = Path(rel_path).resolve()
-        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
-                if line.strip() and not line.startswith("#")
-            ]
-        metafunc.parametrize("config_filename", configs)

.buildkite/lm-eval-harness/run-tests.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+
+usage() {
+    echo``
+    echo "Runs lm eval harness on GSM8k using vllm and compares to "
+    echo "precomputed baseline (measured by HF transformers.)"
+    echo
+    echo "usage: ${0} <options>"
+    echo
+    echo "  -c    - path to the test data config (e.g. configs/small-models.txt)"
+    echo "  -t    - tensor parallel size"
+    echo
+}
+
+SUCCESS=0
+
+while getopts "c:t:" OPT; do
+  case ${OPT} in
+    c ) 
+        CONFIG="$OPTARG"
+        ;;
+    t )
+        TP_SIZE="$OPTARG"
+        ;;
+    \? )
+        usage
+        exit 1
+        ;;
+  esac
+done
+
+# Parse list of configs.
+IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < "$CONFIG"
+
+for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
+do
+    LOCAL_SUCCESS=0
+    
+    echo "=== RUNNING MODEL: $MODEL_CONFIG WITH TP SIZE: $TP_SIZE==="
+
+    export LM_EVAL_TEST_DATA_FILE=$PWD/configs/${MODEL_CONFIG}
+    export LM_EVAL_TP_SIZE=$TP_SIZE
+    pytest -s test_lm_eval_correctness.py || LOCAL_SUCCESS=$?
+
+    if [[ $LOCAL_SUCCESS == 0 ]]; then
+        echo "=== PASSED MODEL: ${MODEL_CONFIG} ==="
+    else
+        echo "=== FAILED MODEL: ${MODEL_CONFIG} ==="
+    fi
+
+    SUCCESS=$((SUCCESS + LOCAL_SUCCESS))
+
+done
+
+if [ "${SUCCESS}" -eq "0" ]; then
+    exit 0
+else
+    exit 1
+fi

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

@@ -3,52 +3,67 @@
 LM eval harness on model to compare vs HF baseline computed offline.
 Configs are found in configs/$MODEL.yaml
 
-pytest -s -v test_lm_eval_correctness.py \
+* export LM_EVAL_TEST_DATA_FILE=configs/Meta-Llama-3-70B-Instruct.yaml
-    --config-list-file=configs/models-small.txt \
+* export LM_EVAL_TP_SIZE=4 
-    --tp-size=1
+* pytest -s test_lm_eval_correctness.py
 """
 
+import os
+from pathlib import Path
+
 import lm_eval
-import numpy as np
+import numpy
+import pytest
 import yaml
 
 RTOL = 0.08
+TEST_DATA_FILE = os.environ.get(
+    "LM_EVAL_TEST_DATA_FILE",
+    ".buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml")
+
+TP_SIZE = os.environ.get("LM_EVAL_TP_SIZE", 1)
 
 
-def launch_lm_eval(eval_config, tp_size):
+def launch_lm_eval(eval_config):
-    trust_remote_code = eval_config.get("trust_remote_code", False)
+    trust_remote_code = eval_config.get('trust_remote_code', False)
-    model_args = (
+
-        f"pretrained={eval_config['model_name']},"
+    model_args = f"pretrained={eval_config['model_name']}," \
-        f"tensor_parallel_size={tp_size},"
+                 f"tensor_parallel_size={TP_SIZE}," \
-        f"enforce_eager=true,"
+                 f"add_bos_token=true," \
-        f"add_bos_token=true,"
+                 f"trust_remote_code={trust_remote_code}"
-        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
 
 
-def test_lm_eval_correctness_param(config_filename, tp_size):
+def test_lm_eval_correctness():
-    eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
+    eval_config = yaml.safe_load(
+        Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
 
-    results = launch_lm_eval(eval_config, tp_size)
+    if eval_config[
+            "model_name"] == "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform":  #noqa: E501
+        pytest.skip("FBGEMM is currently failing on main.")
 
+    # Launch eval requests.
+    results = launch_lm_eval(eval_config)
+
+    # Confirm scores match ground truth.
     success = True
     for task in eval_config["tasks"]:
         for metric in task["metrics"]:
             ground_truth = metric["value"]
             measured_value = results["results"][task["name"]][metric["name"]]
-            print(
+            print(f'{task["name"]} | {metric["name"]}: '
-                f"{task['name']} | {metric['name']}: "
+                  f'ground_truth={ground_truth} | measured={measured_value}')
-                f"ground_truth={ground_truth} | measured={measured_value}"
+            success = success and numpy.isclose(
-            )
+                ground_truth, measured_value, rtol=RTOL)
-            success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
 
+    # Assert at the end, print all scores even on failure for debugging.
     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(
+    return json.dumps({
-        {
+        'latency': latency.to_dict(),
-            "latency": latency.to_dict(),
+        'throughput': throughput.to_dict(),
-            "throughput": throughput.to_dict(),
+        'serving': serving.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(
+    raw_results_json = results_to_json(latency_results, throughput_results,
-        latency_results, throughput_results, serving_results
+                                       serving_results)
-    )
 
     # remapping the key, for visualization purpose
     if not latency_results.empty:
-        latency_results = latency_results[list(latency_column_mapping.keys())].rename(
+        latency_results = latency_results[list(
-            columns=latency_column_mapping
+            latency_column_mapping.keys())].rename(
-        )
+                columns=latency_column_mapping)
     if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+        serving_results = serving_results[list(
-            columns=serving_column_mapping
+            serving_column_mapping.keys())].rename(
-        )
+                columns=serving_column_mapping)
     if not throughput_results.empty:
-        throughput_results = throughput_results[
+        throughput_results = throughput_results[list(
-            list(throughput_results_column_mapping.keys())
+            throughput_results_column_mapping.keys())].rename(
-        ].rename(columns=throughput_results_column_mapping)
+                columns=throughput_results_column_mapping)
 
-    processed_results_json = results_to_json(
+    processed_results_json = results_to_json(latency_results,
-        latency_results, throughput_results, serving_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_md_table = tabulate(latency_results,
-        latency_results, headers="keys", tablefmt="pipe", showindex=False
+                                headers='keys',
-    )
+                                tablefmt='pipe',
-    serving_md_table = tabulate(
+                                showindex=False)
-        serving_results, headers="keys", tablefmt="pipe", showindex=False
+    serving_md_table = tabulate(serving_results,
-    )
+                                headers='keys',
-    throughput_md_table = tabulate(
+                                tablefmt='pipe',
-        throughput_results, headers="keys", tablefmt="pipe", showindex=False
+                                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/"
+        results = read_markdown("../.buildkite/nightly-benchmarks/" +
-            + "performance-benchmarks-descriptions.md"
+                                "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")
+        results = latency_results.to_dict(
-            + throughput_results.to_dict(orient="records")
+            orient='records') + throughput_results.to_dict(
-            + serving_results.to_dict(orient="records")
+                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"
+        description="Download and save Hugging Face tokenizer")
-    )
+    parser.add_argument("--model",
-    parser.add_argument("--model", type=str, required=True, help="Name of the model")
+                        type=str,
-    parser.add_argument(
+                        required=True,
-        "--cachedir", type=str, required=True, help="Directory to save the tokenizer"
+                        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."
+        description=
-    )
+        'Parse command line arguments for summary-nightly-results script.')
-    parser.add_argument(
+    parser.add_argument('--results-folder',
-        "--results-folder",
+                        type=str,
-        type=str,
+                        required=True,
-        required=True,
+                        help='The folder where the results are stored.')
-        help="The folder where the results are stored.",
+    parser.add_argument('--description',
-    )
+                        type=str,
-    parser.add_argument(
+                        required=True,
-        "--description", type=str, required=True, help="Description of the results."
+                        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 = df['Test name'].str.contains(model)
-        target = target & df["Engine"].str.contains(method)
+        target = target & df['Engine'].str.contains(method)
-        target = target & df["Test name"].str.contains("qps_" + str(qps))
+        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(
+        serving_results = serving_results[list(
-            columns=serving_column_mapping
+            serving_column_mapping.keys())].rename(
-        )
+                columns=serving_column_mapping)
 
-    serving_md_table_with_headers = tabulate(
+    serving_md_table_with_headers = tabulate(serving_results,
-        serving_results, headers="keys", tablefmt="pipe", showindex=False
+                                             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_lines = serving_md_table_with_headers.split('\n')
-    serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])
+    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

@@ -1,20 +1,20 @@
 steps:
-  - label: "Build wheel - CUDA 12.8"
+  - label: "Build wheel - CUDA 12.4"
     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=12.8.1 --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=12.4.0 --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"
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build wheel - CUDA 12.6"
+  - label: "Build wheel - CUDA 12.1"
     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=12.1.0 --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"
@@ -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 $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
       - "bash .buildkite/scripts/upload-wheels.sh"
@@ -48,7 +48,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --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=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
   - label: "Build and publish TPU release image"
@@ -57,14 +57,12 @@ steps:
     agents:
       queue: tpu_queue_postmerge
     commands:
-      - "yes | docker system prune -a"
-      - "git fetch --all"
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
       - "docker push vllm/vllm-tpu:nightly"
       - "docker push vllm/vllm-tpu:$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
@@ -77,69 +74,38 @@ 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
+if [[ $commands == *" kernels "* ]]; then
   commands="${commands} \
-  --ignore=kernels/core/test_fused_quant_layernorm.py \
+  --ignore=kernels/test_attention_selector.py \
-  --ignore=kernels/core/test_permute_cols.py"
+  --ignore=kernels/test_blocksparse_attention.py \
-fi
+  --ignore=kernels/test_causal_conv1d.py \
-
+  --ignore=kernels/test_cutlass.py \
-if [[ $commands == *" kernels/attention"* ]]; then
+  --ignore=kernels/test_encoder_decoder_attn.py \
-  commands="${commands} \
+  --ignore=kernels/test_flash_attn.py \
-  --ignore=kernels/attention/stest_attention_selector.py \
+  --ignore=kernels/test_flashinfer.py \
-  --ignore=kernels/attention/test_blocksparse_attention.py \
+  --ignore=kernels/test_int8_quant.py \
-  --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/test_machete_gemm.py \
-  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/test_mamba_ssm.py \
-  --ignore=kernels/attention/test_flash_attn.py \
+  --ignore=kernels/test_marlin_gemm.py \
-  --ignore=kernels/attention/test_flashinfer.py \
+  --ignore=kernels/test_moe.py \
-  --ignore=kernels/attention/test_prefix_prefill.py \
+  --ignore=kernels/test_prefix_prefill.py \
-  --ignore=kernels/attention/test_cascade_flash_attn.py \
+  --ignore=kernels/test_rand.py \
-  --ignore=kernels/attention/test_mha_attn.py \
+  --ignore=kernels/test_sampler.py \
-  --ignore=kernels/attention/test_lightning_attn.py \
+  --ignore=kernels/test_cascade_flash_attn.py \
-  --ignore=kernels/attention/test_attention.py"
+  --ignore=kernels/test_mamba_mixer2.py \
-fi
+  --ignore=kernels/test_aqlm.py \
-
+  --ignore=kernels/test_machete_mm.py \
-if [[ $commands == *" kernels/quantization"* ]]; then
+  --ignore=kernels/test_mha_attn.py \
-  commands="${commands} \
+  --ignore=kernels/test_block_fp8.py \
-  --ignore=kernels/quantization/test_int8_quant.py \
+  --ignore=kernels/test_cutlass_moe.py \
-  --ignore=kernels/quantization/test_aqlm.py \
+  --ignore=kernels/test_mamba_ssm_ssd.py \
-  --ignore=kernels/quantization/test_machete_mm.py \
+  --ignore=kernels/test_attention.py \
-  --ignore=kernels/quantization/test_block_fp8.py \
+  --ignore=kernels/test_block_int8.py \
-  --ignore=kernels/quantization/test_block_int8.py \
+  --ignore=kernels/test_fused_quant_layernorm.py \
-  --ignore=kernels/quantization/test_marlin_gemm.py \
+  --ignore=kernels/test_int8_kernel.py \
-  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+  --ignore=kernels/test_triton_moe_ptpc_fp8.py \
-  --ignore=kernels/quantization/test_int8_kernel.py"
+  --ignore=kernels/test_permute_cols.py"
-fi
-
-if [[ $commands == *" kernels/mamba"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/mamba/test_mamba_mixer2.py \
-  --ignore=kernels/mamba/test_causal_conv1d.py \
-  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
-fi
-
-if [[ $commands == *" kernels/moe"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/moe/test_moe.py \
-  --ignore=kernels/moe/test_cutlass_moe.py \
-  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
 fi
 
 #ignore certain Entrypoints/openai tests
@@ -181,8 +147,6 @@ 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 +167,6 @@ 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 "${commands_gpu}" \
@@ -234,7 +197,6 @@ else
           -e AWS_SECRET_ACCESS_KEY \
           -v "${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/embedding/language/test_cls_models.py::test_classification_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
+    pytest -v -s tests/models/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
-    pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    pytest -v -s tests/models/language/pooling/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]"
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.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_container() { docker rm -f hpu-test || true; }
-remove_docker_containers_and_exit() { remove_docker_containers; exit $EXITCODE; }
+remove_docker_container_and_exit() { remove_docker_container; exit $EXITCODE; }
-trap remove_docker_containers_and_exit EXIT
+trap remove_docker_container_and_exit EXIT
-remove_docker_containers
+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}" \
        ${image_name} \
-       /bin/bash -c "
+       /bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys && python3 -m pytest /workspace/vllm/tests/neuron/2_core/ -v --capture=tee-sys"
-            python3 /workspace/vllm/examples/offline_inference/neuron.py;
-            python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
-            for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo 'Running test file: '$f;
-                python3 -m pytest \$f -v --capture=tee-sys;
-            done
-       "

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

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-set -xu
+set -xue
 
 # Build the docker image.
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .
@@ -24,80 +24,31 @@ docker run --privileged --net host --shm-size=16G -it \
     && export VLLM_XLA_CHECK_RECOMPILATION=1 \
     && echo HARDWARE \
     && tpu-info \
-    && { \
+    && echo TEST_0 \
-        echo TEST_0: Running test_perf.py; \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_perf.py \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_perf.py; \
+    && echo TEST_1 \
-        echo TEST_0_EXIT_CODE: \$?; \
+    && pytest -v -s /workspace/vllm/tests/tpu/test_compilation.py \
-    } & \
+    && echo TEST_2 \
-    { \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
-        echo TEST_1: Running test_compilation.py; \
+    && echo TEST_3 \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py; \
+    && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
-        echo TEST_1_EXIT_CODE: \$?; \
+    && echo TEST_4 \
-    } & \
+    && pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
-    { \
+    && echo TEST_5 \
-        echo TEST_2: Running test_basic.py; \
+    && python3 /workspace/vllm/examples/offline_inference/tpu.py \
-        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py; \
+    && echo TEST_6 \
-        echo TEST_2_EXIT_CODE: \$?; \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py \
-    } & \
+    && echo TEST_7 \
-    { \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py \
-        echo TEST_3: Running test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+    && echo TEST_8 \
-        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/v1/tpu/test_topk_topp_sampler.py \
-        echo TEST_3_EXIT_CODE: \$?; \
+    && echo TEST_9 \
-    } & \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py \
-    { \
+    && echo TEST_10 \
-        echo TEST_4: Running test_quantization_accuracy.py; \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py \
-        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
+    && echo TEST_11 \
-        echo TEST_4_EXIT_CODE: \$?; \
+    && pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py" \
-    } & \
+
-    { \
-        echo TEST_5: Running examples/offline_inference/tpu.py; \
-        python3 /workspace/vllm/examples/offline_inference/tpu.py; \
-        echo TEST_5_EXIT_CODE: \$?; \
-    } & \
-    { \
-        echo TEST_6: Running test_tpu_model_runner.py; \
-        python3 -m 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; \
-        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; \
-        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; \
-        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; \
-        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; \
-        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; \
-        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/; \
-    #     echo TEST_13_EXIT_CODE: \$?; \
-    # } & \
-    wait \
-    && echo 'All tests have attempted to run. Check logs for individual test statuses and exit codes.' \
-"
 
 # TODO: This test fails because it uses RANDOM_SEED sampling
 # && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

.buildkite/scripts/upload-wheels.sh

@@ -50,11 +50,11 @@ aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 if [[ $normal_wheel == *"cu118"* ]]; then
     # if $normal_wheel matches cu118, do not upload the index.html
     echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu126"* ]]; then
+elif [[ $normal_wheel == *"cu121"* ]]; then
-    # if $normal_wheel matches cu126, do not upload the index.html
+    # if $normal_wheel matches cu121, do not upload the index.html
-    echo "Skipping index files for cu126 wheels"
+    echo "Skipping index files for cu121 wheels"
 else
-    # only upload index.html for cu128 wheels (default wheels)
+    # only upload index.html for cu124 wheels (default wheels)
     aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
     aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
 fi
@@ -66,13 +66,12 @@ aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
 if [[ $normal_wheel == *"cu118"* ]]; then
     # if $normal_wheel matches cu118, do not upload the index.html
     echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu126"* ]]; then
+elif [[ $normal_wheel == *"cu121"* ]]; then
-    # if $normal_wheel matches cu126, do not upload the index.html
+    # if $normal_wheel matches cu121, do not upload the index.html
-    echo "Skipping index files for cu126 wheels"
+    echo "Skipping index files for cu121 wheels"
 else
-    # only upload index.html for cu128 wheels (default wheels)
+    # only upload index.html for cu124 wheels (default wheels)
     aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
 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/docs"
-  working_dir: "/vllm-workspace/test_docs"
   fast_check: true
   no_gpu: True
   commands:
-  - pip install -r ../requirements/docs.txt
+  - pip install -r ../../requirements/docs.txt
-  # TODO: add `--strict` once warnings in docstrings are fixed
+  - SPHINXOPTS=\"-W\" make html
-  - mkdocs build
+  # Check API reference (if it fails, you may have missing mock imports)
+  - grep \"sig sig-object py\" build/html/api/inference_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
+  - python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.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
@@ -305,21 +293,15 @@ steps:
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests
-  mirror_hardwares: [amdexperimental, amdproduction]
-  torch_nightly: true
   source_file_dependencies:
     - vllm/
     - tests/compile
   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/
   - tests/compile
@@ -330,8 +312,6 @@ 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/
   - tests/compile
@@ -339,7 +319,6 @@ steps:
   - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Core Operation Test
-  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
@@ -347,7 +326,6 @@ steps:
     - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/attention/
   - vllm/attention
@@ -358,7 +336,6 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/quantization/
   - vllm/model_executor/layers/quantization
@@ -368,7 +345,6 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/moe/
   - tests/kernels/moe
@@ -377,7 +353,6 @@ steps:
     - pytest -v -s kernels/moe
 
 - label: Kernels Mamba Test
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
@@ -385,55 +360,48 @@ 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/
   commands:
   - pytest -v -s benchmarks/
 
-- label: Quantization Test
+- label: Quantization Test # 33min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
   - tests/quantization
-  commands:
+  command: VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
-  - 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/
   - vllm/model_executor/layers/quantization
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
+  - bash ./run-tests.sh -c configs/models-small.txt -t 1
 
 - label: OpenAI API correctness
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
@@ -442,7 +410,6 @@ steps:
   - pytest -s entrypoints/openai/correctness/
 
 - label: Encoder Decoder tests # 5min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/encoder_decoder
@@ -450,8 +417,8 @@ 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
@@ -463,104 +430,92 @@ steps:
 #####  models test  #####
 
 - label: Basic Models Test # 24min
-  mirror_hardwares: [amdexperimental, amdproduction]
-  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models
   commands:
     - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
-    - pytest -v -s models/test_utils.py
+    # V1 Test: https://github.com/vllm-project/vllm/issues/14531
-    - pytest -v -s models/test_vision.py
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
-    - pytest -v -s models/test_initialization.py
+    - 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)
+- label: Language Models Test (Standard) # 32min
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
-  torch_nightly: true
   source_file_dependencies:
   - vllm/
-  - tests/models/language
+  - tests/models/decoder_only/language
+  - tests/models/embedding/language
+  - tests/models/encoder_decoder/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 install causal-conv1d
-    - pip freeze | grep -E 'torch'
+    - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
-    - pytest -v -s models/language -m core_model
+    - pytest -v -s models/embedding/language -m core_model
 
-- label: Language Models Test (Extended Generation) # 1hr20min
+- label: Language Models Test (Extended) # 1h10min
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/language/generation
+  - tests/models/decoder_only/language
+  - tests/models/embedding/language
+  - tests/models/encoder_decoder/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 install causal-conv1d
-    - pytest -v -s models/language/generation -m 'not core_model'
+    - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
+    - pytest -v -s models/embedding/language -m 'not core_model'
 
-- label: Language Models Test (Extended Pooling)  # 36min
+- label: Multi-Modal Models Test (Standard) # 40min
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/
+  - tests/models/decoder_only/audio_language
+  - tests/models/decoder_only/vision_language
+  - tests/models/embedding/vision_language
+  - tests/models/encoder_decoder/audio_language
+  - tests/models/encoder_decoder/vision_language
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal
+    - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
+    - pytest -v -s models/decoder_only/vision_language -m 'core_model or quant_model'
+    - pytest -v -s models/embedding/vision_language -m core_model
+    - pytest -v -s models/encoder_decoder/audio_language -m core_model
+    - pytest -v -s models/encoder_decoder/language -m core_model
+    - pytest -v -s models/encoder_decoder/vision_language -m core_model
+    - pytest -v -s models/decoder_only/vision_language/test_interleaved.py
+
+- label: Multi-Modal Models Test (Extended) 1 # 48m
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/language/pooling
+  - tests/models/decoder_only/audio_language
-  commands:
+  - tests/models/decoder_only/vision_language
-    - pytest -v -s models/language/pooling -m 'not core_model'
+  - tests/models/embedding/vision_language
-
+  - tests/models/encoder_decoder/vision_language
-- label: Multi-Modal Models Test (Standard)
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pip freeze | grep -E 'torch'
+    - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
-    - pytest -v -s models/multimodal/processing
+    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
-    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
+    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
-    - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
+    - pytest -v -s models/embedding/vision_language -m 'not core_model'
+    - pytest -v -s models/encoder_decoder/language -m 'not core_model'
+    - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
 
-- label: Multi-Modal Models Test (Extended) 1
+- label: Multi-Modal Models Test (Extended) 2 # 38m
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
+  - tests/models/decoder_only/vision_language
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing models/multimodal -m 'not core_model'
+    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model'
-
-- label: Multi-Modal Models Test (Extended) 2
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
-
-- label: Multi-Modal Models Test (Extended) 3
-  mirror_hardwares: [amdexperimental, amdproduction]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
-
-- label: Quantized Models Test
-  mirror_hardwares: [amdexperimental, amdproduction]
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - tests/models/quantization
-  commands:
-    - pytest -v -s models/quantization
 
 # 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 +527,7 @@ 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 +538,6 @@ 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 +556,7 @@ 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:
@@ -627,8 +581,9 @@ steps:
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
   # Avoid importing model tests that cause CUDA reinitialization error
   - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
   # test sequence parallel
   - pytest -v -s distributed/test_sequence_parallel.py
   # this test fails consistently.
@@ -639,14 +594,13 @@ 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 +611,8 @@ 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 +633,6 @@ 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 +646,6 @@ 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 +661,6 @@ steps:
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -721,7 +670,6 @@ 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
@@ -760,4 +708,4 @@ steps:
   - vllm/model_executor/layers/quantization
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+  - bash ./run-tests.sh -c configs/models-large.txt -t 4

.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

@@ -21,7 +21,7 @@ body:
       It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
       <details>
-      <summary>The output of <code>python collect_env.py</code></summary>
+      <summary>The output of `python collect_env.py`</summary>
 
       ```text
       Your output of `python collect_env.py` here
@@ -75,20 +75,20 @@ body:
       ```
 
       ```
-      The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
+      The error message you got, with the full traceback.
       ```
   validations:
     required: true
 - type: markdown
   attributes:
-    value: |
+    value: >
-      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the model's output:
+      ⚠️ 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 +77,10 @@ instance/
 # Scrapy stuff:
 .scrapy
 
+# Sphinx documentation
+docs/_build/
+docs/source/getting_started/examples/
+
 # PyBuilder
 .pybuilder/
 target/
@@ -146,7 +150,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-docs/examples
 
 # mypy
 .mypy_cache/

.pre-commit-config.yaml

@@ -12,44 +12,41 @@ repos:
   - id: yapf
     args: [--in-place, --verbose]
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.11.7
+  rev: v0.9.3
   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
+  rev: v2.4.0
   hooks:
   - id: codespell
     additional_dependencies: ['tomli']
     args: ['--toml', 'pyproject.toml']
 - repo: https://github.com/PyCQA/isort
-  rev: 6.0.1
+  rev: 0a0b7a830386ba6a31c2ec8316849ae4d1b8240d # 6.0.0
   hooks:
   - id: isort
 - repo: https://github.com/pre-commit/mirrors-clang-format
-  rev: v20.1.3
+  rev: v19.1.7
   hooks:
   - id: clang-format
     exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
 - repo: https://github.com/jackdewinter/pymarkdown
-  rev: v0.9.29
+  rev: v0.9.27
   hooks:
   - id: pymarkdown
-    exclude: '.*\.inc\.md'
     args: [fix]
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
   hooks:
   - id: actionlint
 - repo: https://github.com/astral-sh/uv-pre-commit
-  rev: 0.6.17
+  rev: 0.6.2
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128]
+      args: [requirements/test.in, -o, requirements/test.txt]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
@@ -104,8 +101,8 @@ repos:
     args:
       - -c
       - |
-        if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" "$(git rev-parse --git-path COMMIT_EDITMSG)"; then
+        if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" .git/COMMIT_EDITMSG; then
-          printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> "$(git rev-parse --git-path COMMIT_EDITMSG)"
+          printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> .git/COMMIT_EDITMSG
         fi
     language: system
     verbose: true
@@ -128,21 +125,8 @@ repos:
     name: Update Dockerfile dependency graph
     entry: tools/update-dockerfile-graph.sh
     language: script
-  - id: enforce-import-regex-instead-of-re
+    files: ^docker/Dockerfile$
-    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:
+sphinx:
-  configuration: mkdocs.yaml
+  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

@@ -15,6 +15,7 @@ project(vllm_extensions LANGUAGES CXX)
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
 set(VLLM_TARGET_DEVICE "cuda" CACHE STRING "Target device backend for vLLM")
+
 message(STATUS "Build type: ${CMAKE_BUILD_TYPE}")
 message(STATUS "Target device: ${VLLM_TARGET_DEVICE}")
 
@@ -29,6 +30,9 @@ 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")
 
@@ -42,8 +46,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.7.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -76,15 +80,6 @@ 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 +227,10 @@ 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"
@@ -249,7 +241,6 @@ set(VLLM_EXT_SRC
   "csrc/quantization/fp8/common.cu"
   "csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
   "csrc/quantization/gguf/gguf_kernel.cu"
-  "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/prepare_inputs/advance_step.cu"
   "csrc/custom_all_reduce.cu"
@@ -258,8 +249,9 @@ set(VLLM_EXT_SRC
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
-  # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
+  # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
-  set(CUTLASS_REVISION "v3.9.2" CACHE STRING "CUTLASS revision to use")
+  # Please keep this in sync with FetchContent_Declare line below.
+  set(CUTLASS_REVISION "v3.9.0" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -277,7 +269,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
         cutlass
         GIT_REPOSITORY https://github.com/nvidia/cutlass.git
         # Please keep this in sync with CUTLASS_REVISION line above.
-        GIT_TAG ${CUTLASS_REVISION}
+        GIT_TAG v3.9.0
         GIT_PROGRESS TRUE
 
         # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
@@ -289,13 +281,14 @@ 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")
@@ -307,55 +300,10 @@ 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;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
   if (MARLIN_ARCHS)
-
-    #
-    # For the Marlin kernels we automatically generate sources for various
-    # preselected input type pairs and schedules.
-    # Generate sources:
-    set(MARLIN_GEN_SCRIPT
-      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/gptq_marlin/generate_kernels.py)
-    file(MD5 ${MARLIN_GEN_SCRIPT} MARLIN_GEN_SCRIPT_HASH)
-
-    message(STATUS "Marlin generation script hash: ${MARLIN_GEN_SCRIPT_HASH}")
-    message(STATUS "Last run Marlin generate script hash: $CACHE{MARLIN_GEN_SCRIPT_HASH}")
-
-    if (NOT DEFINED CACHE{MARLIN_GEN_SCRIPT_HASH}
-        OR NOT $CACHE{MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MARLIN_GEN_SCRIPT_HASH})
-      execute_process(
-        COMMAND ${CMAKE_COMMAND} -E env
-        PYTHONPATH=$PYTHONPATH
-          ${Python_EXECUTABLE} ${MARLIN_GEN_SCRIPT}
-        RESULT_VARIABLE marlin_generation_result
-        OUTPUT_VARIABLE marlin_generation_result
-        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
-        ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
-      )
-
-      if (NOT marlin_generation_result EQUAL 0)
-        message(FATAL_ERROR "Marlin generation failed."
-                            " Result: \"${marlin_generation_result}\""
-                            "\nCheck the log for details: "
-                            "${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log")
-      else()
-        set(MARLIN_GEN_SCRIPT_HASH ${MARLIN_GEN_SCRIPT_HASH}
-            CACHE STRING "Last run Marlin generate script hash" FORCE)
-        message(STATUS "Marlin generation completed successfully.")
-      endif()
-    else()
-      message(STATUS "Marlin generation script has not changed, skipping generation.")
-    endif()
-
-    file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/kernel_*.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
-      CUDA_ARCHS "${MARLIN_ARCHS}")
-
-    list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
-
     set(MARLIN_SRCS
+       "csrc/quantization/fp8/fp8_marlin.cu"
        "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
        "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
        "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
@@ -427,7 +375,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(SRCS
       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8.cu"
     )
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -452,9 +399,8 @@ 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 ${SCALED_MM_3X_ARCHS})
   if (SCALED_MM_2X_ARCHS)
@@ -505,9 +451,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   if(${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_scaled_mm_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
@@ -545,7 +489,7 @@ 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(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
              "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
@@ -683,8 +627,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     CUDA_ARCHS "${CUDA_ARCHS}")
 
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
-  # 9.0 for latest bf16 atomicAdd PTX
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
 
     #
@@ -702,7 +645,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
         OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
       execute_process(
         COMMAND ${CMAKE_COMMAND} -E env
-        PYTHONPATH=$PYTHONPATH
+        PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH
           ${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
         RESULT_VARIABLE moe_marlin_generation_result
         OUTPUT_VARIABLE moe_marlin_generation_output
@@ -738,17 +681,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 endif()
 
-if(VLLM_GPU_LANG STREQUAL "CUDA")
-  set(MOE_PERMUTE_SRC
-      "csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
-      "csrc/moe/moe_permute_unpermute_op.cu")
-
-  set_gencode_flags_for_srcs(
-    SRCS "${MARLIN_PERMUTE_SRC}"
-    CUDA_ARCHS "${MOE_PERMUTE_ARCHS}")
-
-  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
-endif()
 message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
   _moe_C
@@ -757,8 +689,6 @@ define_gpu_extension_target(
   SOURCES ${VLLM_MOE_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
-  INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   USE_SABI 3
   WITH_SOABI)
 

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">
+    <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/assets/logos/vllm-logo-text-light.png" width=55%>
+    <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>
 
@@ -16,20 +16,18 @@ Easy, fast, and cheap LLM serving for everyone
 ---
 
 *Latest News* 🔥
-- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
-- [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
 - [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
-- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
-
-<details>
-<summary>Previous News</summary>
-
 - [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
 - [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
+- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 - [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
+
+<details>
+<summary>Previous News</summary>
+
 - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
 - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
 - [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
@@ -58,7 +56,7 @@ 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 +72,7 @@ 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 +98,14 @@ 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/auto_tune.sh

@@ -1,212 +0,0 @@
-#!/bin/bash
-
-# This script aims to tune the best server parameter combinations to maximize throughput for given requirement. 
-# The current server parameter combination is  max_num_seqs and max_num_batched_tokens
-# It also supports additional requirement: e2e latency and prefix cache. 
-
-# Pre-requisite:
-# 1. Checkout to your branch, install/ update the correct running env. For TPU, activate conda env and install the corresponding torch, xla version. 
-# 2. If the model is customized, replace the MODEL's config with the customized config.
-# 3. Set variables (ALL REQUIRED)
-#   BASE: your directory for vllm repo
-#   MODEL: the model served by vllm
-#   DOWNLOAD_DIR: directory to download and load model weights.
-#   INPUT_LEN: request input len
-#   OUTPUT_LEN: request output len
-#   MIN_CACHE_HIT_PCT: prefix cache rate
-#   MAX_LATENCY_ALLOWED_MS: (e2e) latency requirement. If there's no latency requirement, set it to a large number like 1000000000
-# 4. Run the script, it might take a long time, you can use tmux to avoid the script stop if disconnection happens.
-# 5. The final result will be saved in RESULT file. 
-
-
-# Example use cases 
-# 1. Given input_len=1800, output_len=20, what's the best max_num_seqs and max_num_batched_tokens to get highest throughput?
-# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=100000000000
-# 2. If we have latency requirement to be lower than 500ms, what's the best server parameter?
-# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=500
-# 3. If we want to reach 60% prefix cache, what's the best server parameter? 
-# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=60, MAX_LATENCY_ALLOWED_MS=500
-
-TAG=$(date +"%Y_%m_%d_%H_%M")
-BASE=""
-MODEL="meta-llama/Llama-3.1-8B-Instruct"
-DOWNLOAD_DIR=""
-INPUT_LEN=4000
-OUTPUT_LEN=16
-MIN_CACHE_HIT_PCT_PCT=0
-MAX_LATENCY_ALLOWED_MS=100000000000
-
-LOG_FOLDER="$BASE/auto-benchmark/$TAG"
-RESULT="$LOG_FOLDER/result.txt"
-
-echo "result file$ $RESULT"
-echo "model: $MODEL"
-echo
-
-rm -rf $LOG_FOLDER
-mkdir -p $LOG_FOLDER
-
-cd "$BASE/vllm"
-# create sonnet-4x.txt so that we can sample 2048 tokens for input
-echo "" > benchmarks/sonnet_4x.txt
-for _ in {1..4}
-do
-cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
-done
-
-pip install datasets
-
-current_hash=$(git rev-parse HEAD)
-echo "hash:$current_hash" >> "$RESULT"
-echo "current_hash: $current_hash"
-
-best_throughput=0
-best_max_num_seqs=0
-best_num_batched_tokens=0
-best_goodput=0
-run_benchmark() {
-    local max_num_seqs=$1
-    local max_num_batched_tokens=$2
-    echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
-    local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
-    echo "vllm_log: $vllm_log"
-    echo
-    rm -f $vllm_log
-
-    # start the server
-    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
-        --disable-log-requests \
-        --port 8004 \
-        --gpu-memory-utilization 0.98 \
-        --max-num-seqs $max_num_seqs \
-        --max-num-batched-tokens $max_num_batched_tokens \
-        --tensor-parallel-size 1 \
-        --enable-prefix-caching \
-        --load-format dummy \
-        --download-dir $DOWNLOAD_DIR \
-        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
-    echo "wait for 10 minutes.."
-    echo
-    # wait for 10 minutes...
-    server_started=0
-    for i in {1..60}; do        
-        if grep -Fq "Application startup complete" "$vllm_log"; then
-            echo "Application started"
-            server_started=1
-            break
-        else
-            # echo "wait for 10 seconds..."
-            sleep 10
-        fi
-    done
- 
-    if (( ! server_started )); then
-        echo "server did not start within 10 minutes, terminate the benchmarking. Please check server log at $vllm_log"
-        echo "pkill -f vllm"
-        echo
-        pkill vllm
-        sleep 10
-        return 1
-    fi
-    
-    echo "run benchmark test..."
-    echo
-    meet_latency_requirement=0
-    # get a basic qps by using request-rate inf
-    bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
-    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    python benchmarks/benchmark_serving.py \
-        --backend vllm \
-        --model $MODEL  \
-        --dataset-name sonnet \
-        --dataset-path benchmarks/sonnet_4x.txt \
-        --sonnet-input-len $INPUT_LEN \
-        --sonnet-output-len $OUTPUT_LEN \
-        --ignore-eos \
-        --disable-tqdm \
-        --request-rate inf \
-        --percentile-metrics ttft,tpot,itl,e2el \
-        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
-        --num-prompts 100 \
-        --sonnet-prefix-len $prefix_len \
-        --port 8004 > "$bm_log"
-    through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
-    e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
-    goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
-
-    if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
-        meet_latency_requirement=1
-    fi
-
-    if (( ! meet_latency_requirement )); then
-    # start from request-rate as int(through_put) + 1
-        request_rate=$((${through_put%.*} + 1))
-        while ((request_rate > 0)); do
-            # clear prefix cache
-            curl -X POST http://0.0.0.0:8004/reset_prefix_cache
-            sleep 5
-            bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
-            python benchmarks/benchmark_serving.py \
-                --backend vllm \
-                --model $MODEL  \
-                --dataset-name sonnet \
-                --dataset-path benchmarks/sonnet_4x.txt \
-                --sonnet-input-len $INPUT_LEN \
-                --sonnet-output-len $OUTPUT_LEN \
-                --ignore_eos \
-                --disable-tqdm \
-                --request-rate $request_rate \
-                --percentile-metrics ttft,tpot,itl,e2el \
-                --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
-                --num-prompts 100 \
-                --sonnet-prefix-len $prefix_len \
-                --port 8004 > "$bm_log"
-            through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
-            e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
-            goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
-            if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
-                meet_latency_requirement=1
-                break
-            fi
-            request_rate=$((request_rate-1))
-        done
-    fi
-    # write the results and update the best result.
-    if ((meet_latency_requirement)); then
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput"
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput" >> "$RESULT"
-        if (( $(echo "$through_put > $best_throughput" | bc -l) )); then
-            best_throughput=$through_put
-            best_max_num_seqs=$max_num_seqs
-            best_num_batched_tokens=$max_num_batched_tokens
-            best_goodput=$goodput
-        fi
-    else
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}" >> "$RESULT"
-    fi
-
-    echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
-
-    echo "pkill -f vllm"
-    echo
-    pkill vllm
-    sleep 10
-    rm -f $vllm_log
-    printf '=%.0s' $(seq 1 20)
-    return 0
-}
-
-
-num_seqs_list="128 256"
-num_batched_tokens_list="512 1024 2048 4096"
-for num_seqs in $num_seqs_list; do
-    for num_batched_tokens in $num_batched_tokens_list; do
-        run_benchmark $num_seqs $num_batched_tokens
-        exit 0
-    done
-done
-echo "finish permutations"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput" >> "$RESULT"
-

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(
+    async with aiohttp.ClientSession(trust_env=True,
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-    ) 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(
+    async with aiohttp.ClientSession(trust_env=True,
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-    ) 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
 
@@ -194,23 +197,15 @@ async def async_request_deepspeed_mii(
     request_func_input: RequestFuncInput,
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
-    api_url = request_func_input.api_url
+    async with aiohttp.ClientSession(trust_env=True,
-    assert api_url.endswith(("completions", "profile")), (
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-        "OpenAI Completions API URL must end with 'completions' or 'profile'."
-    )
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
         payload = {
-            "model": request_func_input.model,
             "prompt": request_func_input.prompt,
             "max_tokens": request_func_input.output_len,
             "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 +216,19 @@ async def async_request_deepspeed_mii(
 
         st = time.perf_counter()
         try:
-            async with session.post(
+            async with session.post(url=request_func_input.api_url,
-                url=api_url, json=payload, headers=headers
+                                    json=payload) as response:
-            ) 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 = (
+                        output.error = ("Unexpected response format: "
-                            "Unexpected response format: "
+                                        "neither 'choices' nor 'text' found")
-                            "neither 'choices' nor 'text' found"
-                        )
                         output.success = False
                     output.success = True
                 else:
@@ -256,20 +249,17 @@ async def async_request_openai_completions(
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
-    assert api_url.endswith(("completions", "profile")), (
+    assert api_url.endswith(
-        "OpenAI Completions API URL must end with 'completions' or 'profile'."
+        ("completions", "profile")
-    )
+    ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
 
-    async with aiohttp.ClientSession(
+    async with aiohttp.ClientSession(trust_env=True,
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-    ) as session:
         payload = {
-            "model": request_func_input.model_name
+            "model": request_func_input.model_name \
-            if request_func_input.model_name
+                if request_func_input.model_name else request_func_input.model,
-            else request_func_input.model,
             "prompt": request_func_input.prompt,
             "temperature": 0.0,
-            "repetition_penalty": 1.0,
             "max_tokens": request_func_input.output_len,
             "logprobs": request_func_input.logprobs,
             "stream": True,
@@ -281,7 +271,9 @@ 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 +282,8 @@ async def async_request_openai_completions(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(
+            async with session.post(url=api_url, json=payload,
-                url=api_url, json=payload, headers=headers
+                                    headers=headers) as response:
-            ) as response:
                 if response.status == 200:
                     first_chunk_received = False
                     async for chunk_bytes in response.content:
@@ -300,7 +291,8 @@ 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 +312,21 @@ 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 +347,23 @@ 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")), (
+    assert api_url.endswith(
-        "OpenAI Chat Completions API URL must end with 'chat/completions'."
+        ("chat/completions", "profile")
-    )
+    ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
 
-    async with aiohttp.ClientSession(
+    async with aiohttp.ClientSession(trust_env=True,
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-    ) 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
+            "model": request_func_input.model_name \
-            if request_func_input.model_name
+                if request_func_input.model_name else request_func_input.model,
-            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 +389,16 @@ async def async_request_openai_chat_completions(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(
+            async with session.post(url=api_url, json=payload,
-                url=api_url, json=payload, headers=headers
+                                    headers=headers) as response:
-            ) 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 +412,13 @@ 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 +444,25 @@ 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")), (
+    assert api_url.endswith(
-        "OpenAI Chat Completions API URL must end with 'transcriptions' "
+        ("transcriptions", "translations"
-    )
+         )), "OpenAI Chat Completions API URL must end with 'transcriptions' "
     "or `translations`."
 
-    async with aiohttp.ClientSession(
+    async with aiohttp.ClientSession(trust_env=True,
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
+                                     timeout=AIOHTTP_TIMEOUT) as session:
-    ) as session:
         content = [{"type": "text", "text": request_func_input.prompt}]
         payload = {
-            "model": request_func_input.model_name
+            "model": request_func_input.model_name \
-            if request_func_input.model_name
+                if request_func_input.model_name else request_func_input.model,
-            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 +477,9 @@ 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 +491,24 @@ async def async_request_openai_audio(
             st = time.perf_counter()
             most_recent_timestamp = st
             try:
-                async with session.post(
+                async with session.post(url=api_url,
-                    url=api_url, data=form, headers=headers
+                                        data=form,
-                ) as response:
+                                        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 +517,12 @@ 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 +543,7 @@ 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 +554,7 @@ 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 +567,23 @@ 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):
-    ):
+        pretrained_model_name_or_path = get_model(
-        pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
+            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(
+            raise ImportError("MistralTokenizer requires vllm package.\n"
-                "MistralTokenizer requires vllm package.\n"
+                              "Please install it with `pip install vllm` "
-                "Please install it with `pip install vllm` "
+                              "to use mistral tokenizer mode.") from e
-                "to use mistral tokenizer mode."
+        return MistralTokenizer.from_pretrained(
-            ) from e
+            str(pretrained_model_name_or_path))
-        return MistralTokenizer.from_pretrained(str(pretrained_model_name_or_path))
     else:
         return AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path,
@@ -614,7 +606,7 @@ ASYNC_REQUEST_FUNCS = {
 }
 
 OPENAI_COMPATIBLE_BACKENDS = [
-    k
+    k for k, v in ASYNC_REQUEST_FUNCS.items()
-    for k, v in ASYNC_REQUEST_FUNCS.items()
+    if v in (async_request_openai_completions,
-    if v in (async_request_openai_completions, async_request_openai_chat_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
+            self,
-    ) -> list[dict]:
+            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(
+    def sample(self, tokenizer: PreTrainedTokenizerBase,
-        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
+               num_requests: int) -> list[SampleRequest]:
-    ) -> 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(
+    def maybe_oversample_requests(self, requests: list[SampleRequest],
-        self, requests: list[SampleRequest], num_requests: int
+                                  num_requests: int) -> None:
-    ) -> 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 (
+    return not (prompt_too_short or output_too_short or prompt_too_long
-        prompt_too_short or output_too_short or prompt_too_long or combined_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:
+    if isinstance(image, dict) and 'bytes' in image:
-        image = Image.open(BytesIO(image["bytes"]))
+        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_url = (image if image.startswith(
-            image if image.startswith(("http://", "file://")) else f"file://{image}"
+            ("http://", "file://")) else f"file://{image}")
-        )
         return {"type": "image_url", "image_url": {"url": image_url}}
 
-    raise ValueError(
+    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
-        f"Invalid image input {image}. Must be a PIL.Image.Image"
+                     " or str or dictionary with raw image bytes.")
-        " or str or dictionary with raw image bytes."
-    )
 
 
 # -----------------------------------------------------------------------------
@@ -313,56 +315,42 @@ class RandomDataset(BenchmarkDataset):
         )
 
         vocab_size = tokenizer.vocab_size
-        num_special_tokens = tokenizer.num_special_tokens_to_add()
-        real_input_len = input_len - num_special_tokens
 
-        prefix_token_ids = (
+        prefix_token_ids = (np.random.randint(
-            np.random.randint(0, vocab_size, size=prefix_len).tolist()
+            0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
-            if prefix_len > 0
-            else []
-        )
 
         # New sampling logic: [X * (1 - b), X * (1 + b)]
-        input_low = int(real_input_len * (1 - range_ratio))
+        input_low = int(input_len * (1 - range_ratio))
-        input_high = int(real_input_len * (1 + range_ratio))
+        input_high = int(input_len * (1 + range_ratio))
         output_low = int(output_len * (1 - range_ratio))
         output_high = int(output_len * (1 + range_ratio))
 
         # 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)
+        input_lens = np.random.randint(input_low,
-        output_lens = np.random.randint(output_low, output_high + 1, size=num_requests)
+                                       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 = (
+            inner_seq = ((offsets[i] + i + np.arange(input_lens[i])) %
-                (offsets[i] + i + np.arange(input_lens[i])) % vocab_size
+                         vocab_size).tolist()
-            ).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.
-            # This is done because in some cases N consecutive tokens
-            # give a string tokenized into != N number of tokens.
-            # For example for GPT2Tokenizer:
-            # [6880, 6881] -> ['Ġcalls', 'here'] ->
-            # [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]
-            ]
-            prompt = tokenizer.decode(re_encoded_sequence)
             total_input_len = prefix_len + int(input_lens[i])
             requests.append(
                 SampleRequest(
                     prompt=prompt,
                     prompt_len=total_input_len,
                     expected_output_len=int(output_lens[i]),
-                )
+                ))
-            )
         return requests
 
 
@@ -389,8 +377,7 @@ class ShareGPTDataset(BenchmarkDataset):
             self.data = json.load(f)
         # Filter entries with at least two conversation turns.
         self.data = [
-            entry
+            entry for entry in self.data
-            for entry in self.data
             if "conversations" in entry and len(entry["conversations"]) >= 2
         ]
         random.seed(self.random_seed)
@@ -416,28 +403,27 @@ 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
+            new_output_len = (len(completion_ids)
-            if not is_valid_sequence(
+                              if output_len is None else output_len)
-                prompt_len,
+            if not is_valid_sequence(prompt_len,
-                new_output_len,
+                                     new_output_len,
-                skip_min_output_len_check=output_len is not None,
+                                     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 +469,20 @@ 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_fmt = tokenizer.apply_chat_template(base_msg,
-            base_msg, add_generation_prompt=True, tokenize=False
+                                                 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 +491,21 @@ class SonnetDataset(BenchmarkDataset):
 
         samples = []
         while len(samples) < num_requests:
-            extra_lines = random.choices(
+            extra_lines = random.choices(self.data,
-                self.data, k=num_input_lines - num_prefix_lines
+                                         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 +525,7 @@ class BurstGPTDataset(BenchmarkDataset):
         super().__init__(**kwargs)
         self.load_data()
 
-    def load_data(
+    def load_data(self, ):
-        self,
-    ):
         if self.dataset_path is None:
             raise ValueError("dataset_path must be provided for loading data.")
 
@@ -557,7 +539,8 @@ 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 +564,7 @@ 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 +576,7 @@ class BurstGPTDataset(BenchmarkDataset):
                     prompt_len=input_len,
                     expected_output_len=output_len,
                     lora_request=lora_req,
-                )
+                ))
-            )
         return samples
 
 
@@ -638,23 +619,20 @@ class HuggingFaceDataset(BenchmarkDataset):
 
 class ConversationDataset(HuggingFaceDataset):
     """Dataset for conversation data with multimodal support."""
-
     SUPPORTED_DATASET_PATHS = {
-        "lmms-lab/LLaVA-OneVision-Data",
+        'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
-        "Aeala/ShareGPT_Vicuna_unfiltered",
     }
     IS_MULTIMODAL = True
 
-    def sample(
+    def sample(self,
-        self,
+               tokenizer: PreTrainedTokenizerBase,
-        tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
-        num_requests: int,
+               output_len: Optional[int] = None,
-        output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
-        enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
-        **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 +648,24 @@ 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 +682,10 @@ class VisionArenaDataset(HuggingFaceDataset):
 
     DEFAULT_OUTPUT_LEN = 128
     SUPPORTED_DATASET_PATHS = {
-        "lmarena-ai/VisionArena-Chat": lambda x: x["conversation"][0][0]["content"],
+        "lmarena-ai/VisionArena-Chat":
-        "lmarena-ai/vision-arena-bench-v0.1": lambda x: x["turns"][0][0]["content"],
+        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 +697,16 @@ 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 +714,15 @@ 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 +747,14 @@ class InstructCoderDataset(HuggingFaceDataset):
         "likaixin/InstructCoder",
     }
 
-    def sample(
+    def sample(self,
-        self,
+               tokenizer: PreTrainedTokenizerBase,
-        tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
-        num_requests: int,
+               output_len: Optional[int] = None,
-        output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
-        enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
-        **kwargs,
+        output_len = (output_len
-    ) -> list:
+                      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:
@@ -783,63 +766,7 @@ 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
-
-
-# -----------------------------------------------------------------------------
-# MT-Bench Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class MTBenchDataset(HuggingFaceDataset):
-    """
-    MT-Bench Dataset.
-    https://huggingface.co/datasets/philschmid/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
-
-    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
-        sampled_requests = []
-
-        for item in self.data:
-            if len(sampled_requests) >= num_requests:
-                break
-            prompt = item["turns"][0]
-
-            # apply template
-            prompt = tokenizer.apply_chat_template(
-                [{"role": "user", "content": prompt}],
-                add_generation_prompt=True,
-                tokenize=False,
-            )
-
-            prompt_len = len(tokenizer(prompt).input_ids)
-            sampled_requests.append(
-                SampleRequest(
-                    prompt=prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                )
-            )
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
@@ -853,27 +780,23 @@ class AIMODataset(HuggingFaceDataset):
     """
     Dataset class for processing a AIMO dataset with reasoning questions.
     """
-
     SUPPORTED_DATASET_PATHS = {
-        "AI-MO/aimo-validation-aime",
+        "AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
-        "AI-MO/NuminaMath-1.5",
+        "AI-MO/NuminaMath-CoT"
-        "AI-MO/NuminaMath-CoT",
     }
 
-    def sample(
+    def sample(self,
-        self,
+               tokenizer: PreTrainedTokenizerBase,
-        tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
-        num_requests: int,
+               output_len: Optional[int] = None,
-        output_len: Optional[int] = None,
+               **kwargs) -> list:
-        **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 +804,10 @@ 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(
+            if dynamic_output and not is_valid_sequence(prompt_len,
-                prompt_len, completion_len, max_prompt_len=2048, max_total_len=32000
+                                                        completion_len,
-            ):
+                                                        max_prompt_len=2048,
+                                                        max_total_len=32000):
                 continue
             sampled_requests.append(
                 SampleRequest(
@@ -891,100 +815,11 @@ 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
 
 
-# -----------------------------------------------------------------------------
-# Next Edit Prediction Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-zeta_prompt = """### Instruction:
-You are a code completion assistant and your task is to analyze user edits and then rewrite an excerpt that the user provides, suggesting the appropriate edits within the excerpt, taking into account the cursor location.
-
-### User Edits:
-
-{}
-
-### User Excerpt:
-
-{}
-
-### Response:
-
-"""  # noqa: E501
-
-
-def _format_zeta_prompt(
-    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
-    further extended to support more NEP datasets.
-
-    Args:
-        sample: The dataset sample containing events,
-            inputs, and outputs.
-        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.
-    """
-    events = sample["events"]
-    input = sample["input"]
-    output = sample["output"]
-    prompt = zeta_prompt.format(events, input)
-
-    # following the original implementation, extract the focused region
-    # from the raw output
-    output_start_index = output.find(original_start_marker)
-    output_focused_region = output[output_start_index:]
-    expected_output = output_focused_region
-
-    return {"prompt": prompt, "expected_output": expected_output}
-
-
-class NextEditPredictionDataset(HuggingFaceDataset):
-    """
-    Dataset class for processing a Next Edit Prediction dataset.
-    """
-
-    SUPPORTED_DATASET_PATHS = {
-        "zed-industries/zeta",
-    }
-    MAPPING_PROMPT_FUNCS = {
-        "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)
-        if formatting_prompt_func is None:
-            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
-        samples = []
-        for sample in self.data:
-            sample = formatting_prompt_func(sample)
-            samples.append(
-                SampleRequest(
-                    prompt=sample["prompt"],
-                    prompt_len=len(tokenizer(sample["prompt"]).input_ids),
-                    expected_output_len=len(
-                        tokenizer(sample["expected_output"]).input_ids
-                    ),
-                )
-            )
-            if len(samples) >= num_requests:
-                break
-        self.maybe_oversample_requests(samples, num_requests)
-        return samples
-
-
 # -----------------------------------------------------------------------------
 # ASR Dataset Implementation
 # -----------------------------------------------------------------------------
@@ -1007,22 +842,18 @@ class ASRDataset(HuggingFaceDataset):
     | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
     +----------------+----------------------------------------+--------------------------+-----------------------------+
 
-    """  # noqa: E501
+    """ # noqa: E501
-
     SUPPORTED_DATASET_PATHS = {
-        "openslr/librispeech_asr",
+        "openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
-        "facebook/voxpopuli",
+        "edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
-        "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 +864,8 @@ class ASRDataset(HuggingFaceDataset):
         **kwargs,
     ) -> list:
         import librosa
-
+        output_len = (output_len
-        output_len = output_len if output_len is not None else self.DEFAULT_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 +888,10 @@ class ASRDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
-                )
+                ))
-            )
         if skipped:
-            logger.warning(
+            logger.warning("%d samples discarded from dataset due to" \
-                "%d samples discarded from dataset due to"
+                           " their length being greater than" \
-                " their length being greater than"
+                           " what Whisper supports.", skipped)
-                " 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(
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-    args: argparse.Namespace, results: dict[str, Any]
+                                     results: dict[str, Any]) -> None:
-) -> 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
+        args.input_len +
-    ), (
+        args.output_len), ("Please ensure that max_model_len is greater than"
-        "Please ensure that max_model_len is greater than"
+                           " the sum of input_len and output_len.")
-        " 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(
+    dummy_prompt_token_ids = np.random.randint(10000,
-        10000, size=(args.batch_size, args.input_len)
+                                               size=(args.batch_size,
-    )
+                                                     args.input_len))
-    dummy_prompts: list[PromptType] = [
+    dummy_prompts: list[PromptType] = [{
-        {"prompt_token_ids": batch} for batch in dummy_prompt_token_ids.tolist()
+        "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=[
+                    activities=[
-                    torch.profiler.ProfilerActivity.CPU,
+                        torch.profiler.ProfilerActivity.CPU,
-                    torch.profiler.ProfilerActivity.CUDA,
+                        torch.profiler.ProfilerActivity.CUDA,
-                ],
+                    ],
-                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    str(profile_dir)
+                        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 = (
+            profile_dir = (Path(".") / "vllm_benchmark_result" /
-                Path(".") / "vllm_benchmark_result" / f"latency_result_{time.time()}"
+                           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(
+    parser.add_argument("--num-iters",
-        "--num-iters", type=int, default=30, help="Number of iterations to run."
+                        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=(
+        help=("path to save the pytorch profiler output. Can be visualized "
-            "path to save the pytorch profiler output. Can be visualized "
+              "with ui.perfetto.dev or Tensorboard."),
-            "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=(
+        help=("Do not detokenize responses (i.e. do not include "
-            "Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
-            "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

@@ -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(
+        raise ValueError(f"Invalid mode: {mode}, only support "
-            f"Invalid mode: {mode}, only support 'random', 'tile', 'interleave'"
+                         "'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)
+        "This is warm up request " + str(i) + \
-        for i in range(args.num_documents)
+                ' '.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 "
+        description=
-        "without automatic prefix caching."
+        '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, "
+        help='Range of input lengths for sampling prompts,'
-        'specified as "min:max" (e.g., "128:256").',
+        'specified as "min:max" (e.g., "128:256").')
-    )
 
-    parser.add_argument(
+    parser.add_argument('--num-documents',
-        "--num-documents",
+                        type=int,
-        type=int,
+                        default=8,
-        default=8,
+                        help='Range of input lengths for sampling prompts,'
-        help="Range of input lengths for sampling prompts, "
+                        'specified as "min:max" (e.g., "128:256").')
-        '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(
+    parser.add_argument('--repeat-count',
-        "--repeat-count",
+                        type=int,
-        type=int,
+                        default=2,
-        default=2,
+                        help='Number of times to repeat each prompt')
-        help="Number of times to repeat each prompt",
-    )
 
-    parser.add_argument(
+    parser.add_argument("--repeat-mode",
-        "--repeat-mode",
+                        type=str,
-        type=str,
+                        default='random',
-        default="random",
+                        help='The mode to repeat prompts. The supported '
-        help="The mode to repeat prompts. The supported "
+                        'modes are "random", "tile", and "interleave". '
-        'modes are "random", "tile", and "interleave". '
+                        'See repeat_prompts() in the source code for details.')
-        "See repeat_prompts() in the source code for details.",
-    )
 
-    parser.add_argument(
+    parser.add_argument("--shuffle-seed",
-        "--shuffle-seed",
+                        type=int,
-        type=int,
+                        default=0,
-        default=0,
+                        help='Random seed when the repeat mode is "random"')
-        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 = [
+    dataset = [(data["conversations"][0]["value"],
-        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+                data["conversations"][1]["value"]) for data in dataset]
-        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 = (
+        output_len = (len(completion_token_ids)
-            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
+                      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, (
+        assert (min_len <= prompt_len <= max_len
-            f"prompt_len {prompt_len} out of range {min_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(
+def repeat_and_sort_requests(requests: list[Request],
-    requests: list[Request], repeat_count: int, sort: bool = False
+                             repeat_count: int,
-) -> list[str]:
+                             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(
+            raise ValueError("prefix-len is not supported when "
-                "prefix-len is not supported when dataset-path is provided."
+                             "dataset-path is provided.")
-            )
+        print(f"Start to sample {args.num_prompts} prompts "
-        print(f"Start to sample {args.num_prompts} prompts from {args.dataset_path}")
+              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(
+    sampling_params = SamplingParams(temperature=0,
-        temperature=0,
+                                     max_tokens=args.output_len,
-        max_tokens=args.output_len,
+                                     detokenize=not args.disable_detokenize)
-        detokenize=not args.disable_detokenize,
-    )
 
     print("Testing filtered requests")
-    prompts = repeat_and_sort_requests(
+    prompts = repeat_and_sort_requests(filtered_requests,
-        filtered_requests, repeat_count=args.repeat_count, sort=args.sort
+                                       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 "
+        description=
-        "automatic prefix caching."
+        'Benchmark the performance with or without automatic prefix caching.')
-    )
+    parser.add_argument("--dataset-path",
-    parser.add_argument(
+                        type=str,
-        "--dataset-path", type=str, default=None, help="Path to the dataset."
+                        default=None,
-    )
+                        help="Path to the dataset.")
-    parser.add_argument("--output-len", type=int, default=10)
+    parser.add_argument('--output-len', type=int, default=10)
-    parser.add_argument(
+    parser.add_argument('--num-prompts',
-        "--num-prompts",
+                        type=int,
-        type=int,
+                        required=True,
-        required=True,
+                        help="Number of the prompts sampled from dataset")
-        help="Number of the prompts sampled from dataset",
+    parser.add_argument('--repeat-count',
-    )
+                        type=int,
-    parser.add_argument(
+                        default=1,
-        "--repeat-count",
+                        help='Number of times to repeat each prompt')
-        type=int,
+    parser.add_argument('--sort',
-        default=1,
+                        action='store_true',
-        help="Number of times to repeat each prompt",
+                        help='Sort prompts by input length')
-    )
+    parser.add_argument('--input-length-range',
-    parser.add_argument(
+                        type=str,
-        "--sort", action="store_true", help="Sort prompts by input length"
+                        required=True,
-    )
+                        help='Range of input lengths for sampling prompts,'
-    parser.add_argument(
+                        'specified as "min:max" (e.g., "128:256").')
-        "--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",
+        '--disable-detokenize',
-        action="store_true",
+        action='store_true',
-        help=(
+        help=("Do not detokenize responses (i.e. do not include "
-            "Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
-            "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 = [
+    dataset = [(data["conversations"][0]["value"],
-        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+                data["conversations"][1]["value"]) for data in dataset]
-        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 = (
+        output_len = len(completion_token_ids
-            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
+                         ) 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
+        for request in requests), (
-    ), (
+            "Please ensure that max_model_len is greater than the sum of"
-        "Please ensure that max_model_len is greater than the sum of"
+            " input_len and output_len for all requests.")
-        " 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 = [
+        requests = [(prompt, args.input_len, args.output_len,
-            (prompt, args.input_len, args.output_len, get_random_flag())
+                     get_random_flag()) for _ in range(args.num_prompts)]
-            for _ in range(args.num_prompts)
-        ]
     else:
-        requests = sample_requests(
+        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
-            args.dataset, args.num_prompts, tokenizer, args.output_len
+                                   args.output_len)
-        )
 
     if args.backend == "vllm":
-        elapsed_time = run_vllm(
+        elapsed_time = run_vllm(requests, args.n,
-            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
+                                EngineArgs.from_cli_args(args),
-        )
+                                args.disable_detokenize)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(
+    total_num_tokens = sum(prompt_len + output_len
-        prompt_len + output_len for _, prompt_len, output_len, priority in requests
+                           for _, prompt_len, output_len, priority in requests)
-    )
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-    print(
+          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
-        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"
+        '--output-json',
-    )
-    parser.add_argument(
-        "--dataset", type=str, default=None, help="Path to the dataset."
-    )
-    parser.add_argument(
-        "--input-len",
-        type=int,
-        default=None,
-        help="Input prompt length for each request",
-    )
-    parser.add_argument(
-        "--output-len",
-        type=int,
-        default=None,
-        help="Output length for each request. Overrides the "
-        "output length from the dataset.",
-    )
-    parser.add_argument(
-        "--n", type=int, default=1, help="Number of generated sequences per prompt."
-    )
-    parser.add_argument(
-        "--num-prompts", type=int, default=200, help="Number of prompts to process."
-    )
-    parser.add_argument(
-        "--output-json",
         type=str,
         default=None,
-        help="Path to save the throughput results in JSON format.",
+        help='Path to save the throughput results in JSON format.')
-    )
     parser.add_argument(
-        "--disable-detokenize",
+        '--disable-detokenize',
-        action="store_true",
+        action='store_true',
-        help=(
+        help=("Do not detokenize responses (i.e. do not include "
-            "Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
-            "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 +50,11 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_dataset import (
+from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
-    AIMODataset,
+                               ConversationDataset, HuggingFaceDataset,
-    ASRDataset,
+                               InstructCoderDataset, RandomDataset,
-    BurstGPTDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
-    ConversationDataset,
+                               VisionArenaDataset)
-    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 +117,7 @@ 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 +163,8 @@ def calculate_metrics(
                 # bundled together
                 # Note : this may inflate the output token count slightly
                 output_len = len(
-                    tokenizer(
+                    tokenizer(outputs[i].generated_text,
-                        outputs[i].generated_text, add_special_tokens=False
+                              add_special_tokens=False).input_ids)
-                    ).input_ids
-                )
             actual_output_lens.append(output_len)
             total_input += input_requests[i].prompt_len
             tpot = 0
@@ -205,19 +187,16 @@ def calculate_metrics(
 
         if "ttft" in goodput_config_dict:
             valid_metrics.append(ttfts)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["ttft"] /
-                goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
-            )
         if "tpot" in goodput_config_dict:
             valid_metrics.append(all_tpots)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["tpot"] /
-                goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
-            )
         if "e2el" in goodput_config_dict:
             valid_metrics.append(e2els)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["e2el"] /
-                goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              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 +207,7 @@ 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 +216,27 @@ 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)
+        mean_ttft_ms=np.mean(ttfts or 0) *
-        * 1000,  # ttfts is empty if streaming is not supported by backend
+        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=[
+        percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
-            (p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
+                             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=[
+        percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
-            (p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
+                             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=[
+        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
-            (p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
+                            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=[
+        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
-            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
+                             for p in selected_percentiles],
-        ],
     )
 
     return metrics, actual_output_lens
@@ -295,12 +269,10 @@ 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 = (
+    test_prompt, test_prompt_len, test_output_len, test_mm_content = \
-        input_requests[0].prompt,
+        input_requests[0].prompt, input_requests[0].prompt_len, \
-        input_requests[0].prompt_len,
+        input_requests[0].expected_output_len, \
-        input_requests[0].expected_output_len,
+            input_requests[0].multi_modal_data
-        input_requests[0].multi_modal_data,
-    )
 
     assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
@@ -320,36 +292,36 @@ 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(
+        profile_input = RequestFuncInput(model=model_id,
-            model=model_id,
+                                         model_name=model_name,
-            model_name=model_name,
+                                         prompt=test_prompt,
-            prompt=test_prompt,
+                                         api_url=base_url + "/start_profile",
-            api_url=base_url + "/start_profile",
+                                         prompt_len=test_prompt_len,
-            prompt_len=test_prompt_len,
+                                         output_len=test_output_len,
-            output_len=test_output_len,
+                                         logprobs=logprobs,
-            logprobs=logprobs,
+                                         multi_modal_content=test_mm_content,
-            multi_modal_content=test_mm_content,
+                                         ignore_eos=ignore_eos,
-            ignore_eos=ignore_eos,
+                                         extra_body=extra_body)
-            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 +333,42 @@ 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 = (
+        prompt, prompt_len, output_len, mm_content = request.prompt, \
-            request.prompt,
+            request.prompt_len, request.expected_output_len, \
-            request.prompt_len,
+                request.multi_modal_data
-            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(
+        request_func_input = RequestFuncInput(model=req_model_id,
-            model=req_model_id,
+                                              model_name=req_model_name,
-            model_name=req_model_name,
+                                              prompt=prompt,
-            prompt=prompt,
+                                              api_url=api_url,
-            api_url=api_url,
+                                              prompt_len=prompt_len,
-            prompt_len=prompt_len,
+                                              output_len=output_len,
-            output_len=output_len,
+                                              logprobs=logprobs,
-            logprobs=logprobs,
+                                              multi_modal_content=mm_content,
-            multi_modal_content=mm_content,
+                                              ignore_eos=ignore_eos,
-            ignore_eos=ignore_eos,
+                                              extra_body=extra_body)
-            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 +400,22 @@ 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}".format("Total generated tokens:",
-    print(
+                                 metrics.total_output))
-        "{:<40} {:<10.2f}".format(
+    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
-            "Request throughput (req/s):", metrics.request_throughput
+                                    metrics.request_throughput))
-        )
-    )
     if goodput_config_dict:
-        print(
+        print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
-            "{:<40} {:<10.2f}".format(
+                                        metrics.request_goodput))
-                "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):",
-    print(
+                                    metrics.total_token_throughput))
-        "{:<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 +423,8 @@ 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 +447,29 @@ 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("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
-        print(
+        print("{:<40} {:<10.2f}".format(
-            "{:<40} {:<10.2f}".format(
+            f"Mean {metric_name} (ms):",
-                f"Mean {metric_name} (ms):",
+            getattr(metrics, f"mean_{metric_attribute_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(
-            "{:<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"
+            metrics, f"std_{metric_attribute_name}_ms")
-        )
+        for p, value in getattr(metrics,
-        for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
+                                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 +489,12 @@ 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 +507,31 @@ 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."
+            "number in milliseconds.") from err
-        ) from err
     return goodput_config_dict
 
 
-def save_to_pytorch_benchmark_format(
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-    args: argparse.Namespace, results: dict[str, Any], file_name: str
+                                     results: dict[str, Any],
-) -> None:
+                                     file_name: str) -> None:
     metrics = [
-        "median_ttft_ms",
+        "median_ttft_ms", "mean_ttft_ms", "std_ttft_ms", "p99_ttft_ms",
-        "mean_ttft_ms",
+        "mean_tpot_ms", "median_tpot_ms", "std_tpot_ms", "p99_tpot_ms",
-        "std_ttft_ms",
+        "median_itl_ms", "mean_itl_ms", "std_itl_ms", "p99_itl_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
+            for k in results if k not in metrics and k not in ignored_metrics
-            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 +556,34 @@ 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 = get_tokenizer(tokenizer_id,
-        tokenizer_id,
+                              tokenizer_mode=tokenizer_mode,
-        tokenizer_mode=tokenizer_mode,
+                              trust_remote_code=args.trust_remote_code)
-        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(
+            input_requests = dataset.sample(num_requests=args.num_prompts,
-                num_requests=args.num_prompts,
+                                            input_len=args.sonnet_input_len,
-                input_len=args.sonnet_input_len,
+                                            output_len=args.sonnet_output_len,
-                output_len=args.sonnet_output_len,
+                                            prefix_len=args.sonnet_prefix_len,
-                prefix_len=args.sonnet_prefix_len,
+                                            tokenizer=tokenizer,
-                tokenizer=tokenizer,
+                                            return_prompt_formatted=False)
-                return_prompt_formatted=False,
-            )
         else:
             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.")
-            )
+            input_requests = dataset.sample(num_requests=args.num_prompts,
-            input_requests = dataset.sample(
+                                            input_len=args.sonnet_input_len,
-                num_requests=args.num_prompts,
+                                            output_len=args.sonnet_output_len,
-                input_len=args.sonnet_input_len,
+                                            prefix_len=args.sonnet_prefix_len,
-                output_len=args.sonnet_output_len,
+                                            tokenizer=tokenizer,
-                prefix_len=args.sonnet_prefix_len,
+                                            return_prompt_formatted=True)
-                tokenizer=tokenizer,
-                return_prompt_formatted=True,
-            )
 
     elif args.dataset_name == "hf":
         # all following datasets are implemented from the
@@ -662,45 +595,32 @@ def main(args: argparse.Namespace):
         elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
             dataset_class = InstructCoderDataset
             args.hf_split = "train"
-        elif args.dataset_path in MTBenchDataset.SUPPORTED_DATASET_PATHS:
-            dataset_class = MTBenchDataset
-            args.hf_split = "train"
         elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
             dataset_class = ConversationDataset
         elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
             dataset_class = AIMODataset
             args.hf_split = "train"
-        elif args.dataset_path in NextEditPredictionDataset.SUPPORTED_DATASET_PATHS:  # noqa: E501
-            dataset_class = NextEditPredictionDataset
-            args.hf_split = "train"
         elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
             dataset_class = ASRDataset
             args.hf_split = "train"
         else:
-            supported_datasets = set(
+            supported_datasets = set([
-                [
+                dataset_name for cls in HuggingFaceDataset.__subclasses__()
-                    dataset_name
+                for dataset_name in cls.SUPPORTED_DATASET_PATHS
-                    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 [
+        if (dataset_class.IS_MULTIMODAL and backend not in \
-            "openai-chat",
+            ["openai-chat", "openai-audio"]):
-            "openai-audio",
-        ]:
             # multi-modal benchmark is only available on OpenAI Chat backend.
             raise ValueError(
-                "Multi-modal content is only supported on 'openai-chat' and "
+                "Multi-modal content is only supported on 'openai-chat' and " \
-                "'openai-audio' backend."
+                "'openai-audio' backend.")
-            )
         input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,
@@ -715,24 +635,26 @@ def main(args: argparse.Namespace):
     else:
         # For datasets that follow a similar structure, use a mapping.
         dataset_mapping = {
-            "sharegpt": lambda: ShareGPTDataset(
+            "sharegpt":
-                random_seed=args.seed, dataset_path=args.dataset_path
+            lambda: ShareGPTDataset(random_seed=args.seed,
-            ).sample(
+                                    dataset_path=args.dataset_path).sample(
-                tokenizer=tokenizer,
+                                        tokenizer=tokenizer,
-                num_requests=args.num_prompts,
+                                        num_requests=args.num_prompts,
-                output_len=args.sharegpt_output_len,
+                                        output_len=args.sharegpt_output_len,
-            ),
+                                    ),
-            "burstgpt": lambda: BurstGPTDataset(
+            "burstgpt":
-                random_seed=args.seed, dataset_path=args.dataset_path
+            lambda: BurstGPTDataset(random_seed=args.seed,
-            ).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+                                    dataset_path=args.dataset_path).
-            "random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
+            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 +670,15 @@ def main(args: argparse.Namespace):
             "top_p": args.top_p,
             "top_k": args.top_k,
             "min_p": args.min_p,
-            "temperature": args.temperature,
+            "temperature": args.temperature
-        }.items()
+        }.items() if v is not None
-        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 +702,15 @@ 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 +735,8 @@ def main(args: argparse.Namespace):
                         "Invalid metadata format. Please use KEY=VALUE format."
                     )
         # Traffic
-        result_json["request_rate"] = (
+        result_json["request_rate"] = (args.request_rate if args.request_rate
-            args.request_rate if args.request_rate < float("inf") else "inf"
+                                       < float("inf") else "inf")
-        )
         result_json["burstiness"] = args.burstiness
         result_json["max_concurrency"] = args.max_concurrency
 
@@ -825,31 +746,24 @@ def main(args: argparse.Namespace):
         if not args.save_detailed:
             # Remove fields with too many data points
             for field in [
-                "input_lens",
+                    "input_lens", "output_lens", "ttfts", "itls",
-                "output_lens",
+                    "generated_texts", "errors"
-                "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 = (
+        max_concurrency_str = (f"-concurrency{args.max_concurrency}"
-            f"-concurrency{args.max_concurrency}"
+                               if args.max_concurrency is not None else "")
-            if args.max_concurrency is not None
+        file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  #noqa
-            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(
+        with open(file_name,
-            file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
+                  mode="a+" if args.append_result else "w",
-        ) as outfile:
+                  encoding='utf-8') as outfile:
             # Append a newline.
             if args.append_result and outfile.tell() != 0:
                 outfile.write("\n")
@@ -859,8 +773,7 @@ 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 +802,11 @@ if __name__ == "__main__":
         choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
         help="Name of the dataset to benchmark on.",
     )
-    parser.add_argument(
+    parser.add_argument("--dataset-path",
-        "--dataset-path",
+                        type=str,
-        type=str,
+                        default=None,
-        default=None,
+                        help="Path to the sharegpt/sonnet dataset. "
-        help="Path to the sharegpt/sonnet dataset. "
+                        "Or the huggingface dataset ID if using HF dataset.")
-        "Or the huggingface dataset ID if using HF dataset.",
-    )
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -907,8 +818,7 @@ 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 +829,8 @@ 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 +843,11 @@ if __name__ == "__main__":
         "--logprobs",
         type=int,
         default=None,
-        help=(
+        help=("Number of logprobs-per-token to compute & return as part of "
-            "Number of logprobs-per-token to compute & return as part of "
+              "the request. If unspecified, then either (1) if beam search "
-            "the request. If unspecified, then either (1) if beam search "
+              "is disabled, no logprobs are computed & a single dummy "
-            "is disabled, no logprobs are computed & a single dummy "
+              "logprob is returned for each token; or (2) if beam search "
-            "logprob is returned for each token; or (2) if beam search "
+              "is enabled 1 logprob per token is computed"),
-            "is enabled 1 logprob per token is computed"
-        ),
     )
     parser.add_argument(
         "--request-rate",
@@ -1022,38 +931,35 @@ 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". '
+        "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
-        'Default value is "ttft,tpot,itl".',
+        "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". '
+        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
-        'Default value is "99". '
+        "Default value is \"99\". "
-        'Use "--percentile-metrics" to select metrics.',
+        "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 +967,22 @@ 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 +991,22 @@ 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 +1021,23 @@ if __name__ == "__main__":
         "--random-prefix-len",
         type=int,
         default=0,
-        help=(
+        help=("Number of fixed prefix tokens before the random context "
-            "Number of fixed prefix tokens before the random context "
+              "in a request. "
-            "in a request. "
+              "The total input length is the sum of `random-prefix-len` and "
-            "The total input length is the sum of `random-prefix-len` and "
+              "a random "
-            "a random "
+              "context length sampled from [input_len * (1 - range_ratio), "
-            "context length sampled from [input_len * (1 - range_ratio), "
+              "input_len * (1 + range_ratio)]."),
-            "input_len * (1 + range_ratio)]."
-        ),
     )
 
     hf_group = parser.add_argument_group("hf dataset options")
-    hf_group.add_argument(
+    hf_group.add_argument("--hf-subset",
-        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
+                          type=str,
-    )
+                          default=None,
-    hf_group.add_argument(
+                          help="Subset of the HF dataset.")
-        "--hf-split", type=str, default=None, help="Split 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 +1051,52 @@ 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(
+    parser.add_argument("--served-model-name",
-        "--served-model-name",
+                        type=str,
-        type=str,
+                        default=None,
-        default=None,
+                        help="The model name used in the API. "
-        help="The model name used in the API. "
+                        "If not specified, the model name will be the "
-        "If not specified, the model name will be the "
+                        "same as the ``--model`` argument. ")
-        "same as the ``--model`` argument. ",
-    )
 
-    parser.add_argument(
+    parser.add_argument("--lora-modules",
-        "--lora-modules",
+                        nargs='+',
-        nargs="+",
+                        default=None,
-        default=None,
+                        help="A subset of LoRA module names passed in when "
-        help="A subset of LoRA module names passed in when "
+                        "launching the server. For each request, the "
-        "launching the server. For each request, the "
+                        "script chooses a LoRA module at random.")
-        "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
@@ -113,45 +106,45 @@ class SampleRequest:
     completion: str = None
 
 
-def sample_requests(
+def sample_requests(tokenizer: PreTrainedTokenizerBase,
-    tokenizer: PreTrainedTokenizerBase, args: argparse.Namespace
+                    args: argparse.Namespace) -> list[SampleRequest]:
-) -> list[SampleRequest]:
+    if args.dataset == 'json' or args.dataset == 'json-unique':
-    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(
+            args.json_schema_path = os.path.join(dir_path,
-                dir_path, "structured_schemas", "structured_schema_1.json"
+                                                 "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":
+        if args.dataset == 'json-unique':
-            json_schemas = [copy.deepcopy(schema) for _ in range(args.num_prompts)]
+            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()}"] = {
-                json_schemas[i]["properties"][f"__optional_field_{uuid.uuid4()}"] = {
+                        "type":
-                    "type": "string",
+                        "string",
-                    "description": "An unique optional field to avoid cached schemas",
+                        "description":
-                }
+                        "An unique optional field to avoid cached schemas"
+                    }
         else:
             json_schemas = [schema] * args.num_prompts
 
         def gen_prompt(index: int):
-            return f"Generate an example of a brief user profile given the following schema: {json.dumps(get_schema(index))}"  # noqa: E501
+            return f"Generate an example of a user profile given the following schema: {json.dumps(get_schema(index))}"  # noqa: E501
 
         def get_schema(index: int):
             return json_schemas[index % len(json_schemas)]
 
         requests = [
-            SampleRequest(
+            SampleRequest(prompt=gen_prompt(i),
-                prompt=gen_prompt(i),
+                          prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
-                prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
+                          expected_output_len=args.output_len,
-                expected_output_len=args.output_len,
+                          schema=get_schema(i),
-                schema=get_schema(i),
+                          structure_type=args.structure_type)
-                structure_type=args.structure_type,
-            )
             for i in range(args.num_prompts)
         ]
 
@@ -175,13 +168,11 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
+            SampleRequest(prompt=prompt,
-                prompt=prompt,
+                          prompt_len=input_len,
-                prompt_len=input_len,
+                          expected_output_len=args.output_len,
-                expected_output_len=args.output_len,
+                          schema=schema,
-                schema=schema,
+                          structure_type=args.structure_type)
-                structure_type=args.structure_type,
-            )
             for _ in range(args.num_prompts)
         ]
 
@@ -195,13 +186,11 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
+            SampleRequest(prompt=prompt,
-                prompt=prompt,
+                          prompt_len=input_len,
-                prompt_len=input_len,
+                          expected_output_len=args.output_len,
-                expected_output_len=args.output_len,
+                          schema=regex,
-                schema=regex,
+                          structure_type=args.structure_type)
-                structure_type=args.structure_type,
-            )
             for _ in range(args.num_prompts)
         ]
 
@@ -212,55 +201,47 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
+            SampleRequest(prompt=prompt,
-                prompt=prompt,
+                          prompt_len=input_len,
-                prompt_len=input_len,
+                          expected_output_len=args.output_len,
-                expected_output_len=args.output_len,
+                          schema=choice,
-                schema=choice,
+                          structure_type=args.structure_type)
-                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(
+        print(f"dataset has {len(dataset)} entries after filtering "
-            f"dataset has {len(dataset)} entries after filtering "
+              f"out {num_filtered_out} entries with unsupported features")
-            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(
+            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
-                dataset["prompt"][idx], tokenize=False, add_generation_prompt=True
+                                                   tokenize=False)
-            )
             input_len = len(tokenizer(prompt).input_ids)
             completion = dataset["completion"][idx]
 
             requests.append(
-                SampleRequest(
+                SampleRequest(prompt=prompt,
-                    prompt=prompt,
+                              prompt_len=input_len,
-                    prompt_len=input_len,
+                              expected_output_len=args.output_len,
-                    expected_output_len=args.output_len,
+                              schema=schema,
-                    schema=schema,
+                              structure_type=args.structure_type,
-                    structure_type=args.structure_type,
+                              completion=completion))
-                    completion=completion,
-                )
-            )
 
     return requests
 
@@ -292,8 +273,7 @@ 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 +315,8 @@ 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 +340,16 @@ def calculate_metrics(
 
         if "ttft" in goodput_config_dict:
             valid_metrics.append(ttfts)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["ttft"] /
-                goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
-            )
         if "tpot" in goodput_config_dict:
             valid_metrics.append(all_tpots)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["tpot"] /
-                goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
-            )
         if "e2el" in goodput_config_dict:
             valid_metrics.append(e2els)
-            slo_values.append(
+            slo_values.append(goodput_config_dict["e2el"] /
-                goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
+                              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 +360,7 @@ 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 +369,27 @@ 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)
+        mean_ttft_ms=np.mean(ttfts or 0) *
-        * 1000,  # ttfts is empty if streaming is not supported by backend
+        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=[
+        percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
-            (p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
+                             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=[
+        percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
-            (p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
+                             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=[
+        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
-            (p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
+                            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=[
+        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
-            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
+                             for p in selected_percentiles],
-        ],
     )
 
     return metrics, actual_output_lens
@@ -439,6 +411,7 @@ async def benchmark(
     ignore_eos: bool,
     max_concurrency: Optional[int],
     structured_output_ratio: float,
+    structured_output_backend: str,
     goodput_config_dict: Optional[dict[str, float]] = None,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
@@ -450,17 +423,18 @@ async def benchmark(
         extra_body = {}
         # Add the schema to the extra_body
         extra_body[request.structure_type] = request.schema
+        # Add the specific structured_output_backend
+        extra_body["guided_decoding_backend"] = structured_output_backend
         return extra_body
 
     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 = (
+    test_req_extra_body = (prepare_extra_body(test_request)
-        prepare_extra_body(test_request) if 0 in structured_output_req_idx else None
+                           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):
+    async for i, request in get_request(input_requests, request_rate,
-        extra_body = (
+                                        burstiness):
-            prepare_extra_body(request) if i in structured_output_req_idx else None
+        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}".format("Total generated tokens:",
-    print(
+                                 metrics.total_output))
-        "{:<40} {:<10.2f}".format(
+    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
-            "Request throughput (req/s):", metrics.request_throughput
+                                    metrics.request_throughput))
-        )
-    )
     if goodput_config_dict:
-        print(
+        print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
-            "{:<40} {:<10.2f}".format(
+                                        metrics.request_goodput))
-                "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):",
-    print(
+                                    metrics.total_token_throughput))
-        "{:<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,
+        "duration":
-        "completed": metrics.completed,
+        benchmark_duration,
-        "total_input_tokens": metrics.total_input,
+        "completed":
-        "total_output_tokens": metrics.total_output,
+        metrics.completed,
-        "request_throughput": metrics.request_throughput,
+        "total_input_tokens":
-        "output_throughput": metrics.output_throughput,
+        metrics.total_input,
-        "total_token_throughput": metrics.total_token_throughput,
+        "total_output_tokens":
-        "ttft_description": pd.Series([output.ttft for output in outputs])
+        metrics.total_output,
-        .describe()
+        "request_throughput":
-        .to_dict(),
+        metrics.request_throughput,
-        "tpot_description": pd.Series([output.tpot for output in outputs])
+        "output_throughput":
-        .describe()
+        metrics.output_throughput,
-        .to_dict(),
+        "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 = [
+    ret = [{
-        {"generated": output.generated_text, "expected": gt}
+        'generated': output.generated_text,
-        for output, gt in zip(outputs, expected)
+        '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("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
-        print(
+        print("{:<40} {:<10.2f}".format(
-            "{:<40} {:<10.2f}".format(
+            f"Mean {metric_name} (ms):",
-                f"Mean {metric_name} (ms):",
+            getattr(metrics, f"mean_{metric_attribute_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(
-            "{:<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"
+            metrics, f"std_{metric_attribute_name}_ms")
-        )
+        for p, value in getattr(metrics,
-        for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
+                                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
+        import re
-
+        actual = actual.replace('\n', '').replace(' ', '').strip()
-        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"])
+        score = _eval_correctness(res['expected'], res['generated'])
-        res["correctness"] = score
+        res['correctness'] = score
         scores.append(score)
 
     not_none_scores = [score for score in scores if score is not None]
 
-    return (
+    return (sum(not_none_scores) / len(not_none_scores) *
-        (sum(not_none_scores) / len(not_none_scores) * 100)
+            100) if len(not_none_scores) > 0 else None
-        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."
+            "number in milliseconds.") from err
-        ) 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":
+    if args.dataset == 'grammar':
-        args.structure_type = "guided_grammar"
+        args.structure_type = 'guided_grammar'
-    elif args.dataset == "regex":
+    elif args.dataset == 'regex':
-        args.structure_type = "guided_regex"
+        args.structure_type = 'guided_regex'
-    elif args.dataset == "choice":
+    elif args.dataset == 'choice':
-        args.structure_type = "guided_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 +776,37 @@ 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,
+            structured_output_backend=args.structured_output_backend,
             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,
+            "backend":
-            "model_id": model_id,
+            backend,
-            "tokenizer_id": tokenizer_id,
+            "model_id":
-            "num_prompts": args.num_prompts,
+            model_id,
-            "request_rate": args.request_rate
+            "tokenizer_id":
-            if args.request_rate < float("inf")
+            tokenizer_id,
-            else "inf",
+            "num_prompts":
-            "burstiness": args.burstiness,
+            args.num_prompts,
-            "max_concurrency": args.max_concurrency,
+            "request_rate":
-            "correct_rate(%)": score,
+            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 +815,13 @@ 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 +843,16 @@ if __name__ == "__main__":
         default="/v1/completions",
         help="API endpoint.",
     )
-    parser.add_argument(
+    parser.add_argument("--dataset",
-        "--dataset",
+                        default='json',
-        default="json",
+                        choices=[
-        choices=["json", "json-unique", "grammar", "regex", "choice", "xgrammar_bench"],
+                            '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("--json_schema_path",
-    )
+                        type=str,
+                        default=None,
+                        help="Path to json schema.")
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -894,8 +864,7 @@ 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 +874,15 @@ 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 +959,52 @@ 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". '
+        "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
-        'Default value is "ttft,tpot,itl".',
+        "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". '
+        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
-        'Default value is "99". '
+        "Default value is \"99\". "
-        'Use "--percentile-metrics" to select metrics.',
+        "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(
+    parser.add_argument("--no-structured-output",
-        "--no-structured-output",
+                        action='store_true',
-        action="store_true",
+                        default=False,
-        default=False,
+                        help="Whether to disable JSON decoding or not.")
-        help="Whether to disable JSON decoding or not.",
+    parser.add_argument("--structured-output-ratio",
-    )
+                        type=float,
-    parser.add_argument(
+                        default=1.0,
-        "--structured-output-ratio",
+                        help="Ratio of Structured Outputs requests")
-        type=float,
+    parser.add_argument("--structured-output-backend",
-        default=1.0,
+                        type=str,
-        help="Ratio of Structured Outputs requests",
+                        choices=[
-    )
+                            "outlines", "lm-format-enforcer", "xgrammar",
+                            "guidance", "auto"
+                        ],
+                        default="auto",
+                        help="Backend to use for structured outputs")
 
     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 benchmark_dataset import (AIMODataset, BurstGPTDataset,
-from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
+                               ConversationDataset, InstructCoderDataset,
-
+                               RandomDataset, SampleRequest, ShareGPTDataset,
-from benchmark_dataset import (
+                               SonnetDataset, VisionArenaDataset)
-    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
+        llm.llm_engine.model_config.max_model_len >= (
-        >= (request.prompt_len + request.expected_output_len)
+            request.prompt_len + request.expected_output_len)
-        for request in requests
+        for request in requests), (
-    ), (
+            "Please ensure that max_model_len is greater than the sum of"
-        "Please ensure that max_model_len is greater than the sum of"
+            " prompt_len and expected_output_len for all requests.")
-        " 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(
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
-                prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
-                multi_modal_data=request.multi_modal_data,
+            if "prompt_token_ids" in request.prompt else \
-            )
+            TextPrompt(prompt=request.prompt,
-            if "prompt_token_ids" in request.prompt
+                       multi_modal_data=request.multi_modal_data))
-            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(
+        outputs = llm.generate(prompts,
-            prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
+                               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],
+        requests: list[SampleRequest],
-    n: int,
+        n: int,
-    engine_args: EngineArgs,
+        engine_args: EngineArgs,
-    disable_detokenize: bool = False,
+        disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
-) -> 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
+        llm.llm_engine.model_config.max_model_len >= (
-        >= (request.prompt_len + request.expected_output_len)
+            request.prompt_len + request.expected_output_len)
-        for request in requests
+        for request in requests), (
-    ), (
+            "Please ensure that max_model_len is greater than the sum of "
-        "Please ensure that max_model_len is greater than the sum of "
+            "prompt_len and expected_output_len for all requests.")
-        "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
+            engine_args, disable_frontend_multiprocessing) as llm:
-    ) as llm:
-        model_config = await llm.get_model_config()
         assert all(
-            model_config.max_model_len
+            llm.model_config.max_model_len >= (request.prompt_len +
-            >= (request.prompt_len + request.expected_output_len)
+                                               request.expected_output_len)
-            for request in requests
+            for request in requests), (
-        ), (
+                "Please ensure that max_model_len is greater than the sum of"
-            "Please ensure that max_model_len is greater than the sum of"
+                " prompt_len and expected_output_len for all requests.")
-            " 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(
+                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
-                    prompt_token_ids=request.prompt["prompt_token_ids"],
+                        multi_modal_data=request.multi_modal_data)
-                    multi_modal_data=request.multi_modal_data,
+                if "prompt_token_ids" in request.prompt else \
-                )
+                TextPrompt(prompt=request.prompt,
-                if "prompt_token_ids" in request.prompt
+                           multi_modal_data=request.multi_modal_data))
-                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(
+        for i, (prompt, sp,
-            zip(prompts, sampling_params, lora_requests)
+                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
-        ):
+            generator = llm.generate(prompt,
-            generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
+                                     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 (
+            if (max(max_prompt_len, next_prompt_len) +
-                max(max_prompt_len, next_prompt_len)
+                    max(max_output_len, next_output_len)) <= 2048:
-                + 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(
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-    args: argparse.Namespace, results: dict[str, Any]
+                                     results: dict[str, Any]) -> None:
-) -> 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_subset'] = None
-            common_kwargs["dataset_split"] = "train"
+            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_subset'] = args.hf_subset
-            common_kwargs["dataset_split"] = args.hf_split
+            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_subset'] = None
-            common_kwargs["dataset_split"] = "train"
+            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,
+                requests, args.n, EngineArgs.from_cli_args(args),
-                args.n,
+                args.disable_detokenize)
-                EngineArgs.from_cli_args(args),
-                args.disable_detokenize,
-            )
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
-        elapsed_time = run_hf(
+        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
-            requests,
+                              args.hf_max_batch_size, args.trust_remote_code,
-            args.model,
+                              args.disable_detokenize)
-            tokenizer,
-            args.n,
-            args.hf_max_batch_size,
-            args.trust_remote_code,
-            args.disable_detokenize,
-        )
     elif args.backend == "mii":
-        elapsed_time = run_mii(
+        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
-            requests, args.model, args.tensor_parallel_size, args.output_len
+                               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 += (
+            total_prompt_tokens += len(
-                len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
+                ro.prompt_token_ids) if ro.prompt_token_ids else 0
-            )
+            total_output_tokens += sum(
-            total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
+                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(
+        print("\033[91mWARNING\033[0m: Multi-modal request with "
-            "\033[91mWARNING\033[0m: Multi-modal request with "
+              f"{args.backend} backend detected. The "
-            f"{args.backend} backend detected. The "
+              "following metrics are not accurate because image tokens are not"
-            "following metrics are not accurate because image tokens are not"
+              " counted. See vllm-project/vllm/issues/9778 for details.")
-            " 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(
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
-        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
+          f"{total_output_tokens / elapsed_time:.2f} output 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")
+        print(
-        args.dataset_name = "random"
+            "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
+            getattr(args, "hf_subset", None) is not None
-        or getattr(args, "hf_split", None) is not None
+            or getattr(args, "hf_split", None) is not None):
-    ):
+        warnings.warn("--hf-subset and --hf-split will be ignored \
-        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()
+                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
-            | ConversationDataset.SUPPORTED_DATASET_PATHS
+                | ConversationDataset.SUPPORTED_DATASET_PATHS):
-        ):
+            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
-            assert args.backend == "vllm-chat", (
+        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
-                f"{args.dataset_path} needs to use vllm-chat as the backend."
+                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
-            )  # noqa: E501
+            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm 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:
+    if args.dataset_name != 'random' and args.random_range_ratio is not None:
-        warnings.warn(
+        warnings.warn("--random-range-ratio will be ignored since \
-            "--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 (
+    if args.dataset_name not in {"random", "sonnet", None
-        args.dataset_name not in {"random", "sonnet", None}
+                                 } and args.prefix_len is not None:
-        and args.prefix_len is not None
+        warnings.warn("--prefix-len will be ignored since --dataset-name\
-    ):
-        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 (
+    if args.backend in {"hf", "mii"} and getattr(args, "quantization",
-        args.backend in {"hf", "mii"}
+                                                 None) is not None:
-        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(
+    parser.add_argument("--backend",
-        "--backend",
+                        type=str,
-        type=str,
+                        choices=["vllm", "hf", "mii", "vllm-chat"],
-        choices=["vllm", "hf", "mii", "vllm-chat"],
+                        default="vllm")
-        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"
+        '--output-json',
-    )
-    parser.add_argument(
-        "--input-len",
-        type=int,
-        default=None,
-        help="Input prompt length for each request",
-    )
-    parser.add_argument(
-        "--output-len",
-        type=int,
-        default=None,
-        help="Output length for each request. Overrides the "
-        "output length from the dataset.",
-    )
-    parser.add_argument(
-        "--n", type=int, default=1, help="Number of generated sequences per prompt."
-    )
-    parser.add_argument(
-        "--num-prompts", type=int, default=1000, help="Number of prompts to process."
-    )
-    parser.add_argument(
-        "--hf-max-batch-size",
-        type=int,
-        default=None,
-        help="Maximum batch size for HF backend.",
-    )
-    parser.add_argument(
-        "--output-json",
         type=str,
         default=None,
-        help="Path to save the throughput results in JSON format.",
+        help='Path to save the throughput results in JSON format.')
-    )
+    parser.add_argument("--async-engine",
-    parser.add_argument(
+                        action='store_true',
-        "--async-engine",
+                        default=False,
-        action="store_true",
+                        help="Use vLLM async engine rather than LLM class.")
-        default=False,
+    parser.add_argument("--disable-frontend-multiprocessing",
-        help="Use vLLM async engine rather than LLM class.",
+                        action='store_true',
-    )
+                        default=False,
-    parser.add_argument(
+                        help="Disable decoupled async engine frontend.")
-        "--disable-frontend-multiprocessing",
-        action="store_true",
-        default=False,
-        help="Disable decoupled async engine frontend.",
-    )
     parser.add_argument(
         "--disable-detokenize",
         action="store_true",
-        help=(
+        help=("Do not detokenize the response (i.e. do not include "
-            "Do not detokenize the response (i.e. do not include "
+              "detokenization time in the measurement)"))
-            "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, "
+        help="Path to the lora adapters to use. This can be an absolute path, "
-        "a relative path, or a Hugging Face model identifier.",
+        "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(
+    parser.add_argument("--hf-subset",
-        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
+                        type=str,
-    )
+                        default=None,
-    parser.add_argument(
+                        help="Subset of the HF dataset.")
-        "--hf-split", type=str, default=None, help="Split 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(
+def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
-    args: argparse.Namespace, metrics: dict[str, list], extra_info: dict[str, Any]
+                                        metrics: dict[str, list],
-) -> 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"] = (
+            record["benchmark"]["extra_info"]["args"][
-                extra_info["tensor_parallel_size"]
+                "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(
+def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
-    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
+             **kwargs) -> TMeasurement:
-) -> TMeasurement:
     min_run_time = 1
 
     globals = {
@@ -42,18 +41,16 @@ def bench_fn(
     ).blocked_autorange(min_run_time=min_run_time)
 
 
-def bench_int8(
+def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+               sub_label: str) -> Iterable[TMeasurement]:
-) -> 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(
+    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
-        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
+                                       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(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
-            label,
+                 torch.mm, a.to(dtype=torch.bfloat16),
-            sub_label,
+                 b.to(dtype=torch.bfloat16)))
-            "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(
+        bench_fn(label, sub_label,
-            label,
+                 "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
-            sub_label,
+                 a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
-            "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(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
-            label,
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
-            sub_label,
+                 torch.bfloat16))
-            "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(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
-            label,
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
-            sub_label,
+                 bias))
-            "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(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm",
-            label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            sub_label,
+                 scale_b, torch.bfloat16))
-            "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(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
-            label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            sub_label,
+                 scale_b, torch.bfloat16, bias))
-            "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(
+def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+              sub_label: str) -> Iterable[TMeasurement]:
-) -> 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(
+    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
-        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
+                                       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(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
-            label,
+                 torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
-            sub_label,
+                 b.to(dtype=torch.bfloat16, device="cuda")))
-            "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(
+        bench_fn(label,
-            label,
+                 sub_label,
-            sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm",
-            "pytorch_fp8_fp8_bf16_scaled_mm",
+                 torch._scaled_mm,
-            torch._scaled_mm,
+                 a,
-            a,
+                 b,
-            b,
+                 scale_a=scale_a,
-            scale_a=scale_a,
+                 scale_b=scale_b,
-            scale_b=scale_b,
+                 out_dtype=torch.bfloat16))
-            out_dtype=torch.bfloat16,
-        )
-    )
 
     # pytorch impl: bf16 output, with fp8 fast accum
     timers.append(
-        bench_fn(
+        bench_fn(label,
-            label,
+                 sub_label,
-            sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
-            "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
-            torch._scaled_mm,
+                 a,
-            a,
+                 b,
-            b,
+                 scale_a=scale_a,
-            scale_a=scale_a,
+                 scale_b=scale_b,
-            scale_b=scale_b,
+                 out_dtype=torch.bfloat16,
-            out_dtype=torch.bfloat16,
+                 use_fast_accum=True))
-            use_fast_accum=True,
-        )
-    )
 
     # pytorch impl: fp16 output, without fp8 fast accum
     timers.append(
-        bench_fn(
+        bench_fn(label,
-            label,
+                 sub_label,
-            sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm",
-            "pytorch_fp8_fp8_fp16_scaled_mm",
+                 torch._scaled_mm,
-            torch._scaled_mm,
+                 a,
-            a,
+                 b,
-            b,
+                 scale_a=scale_a,
-            scale_a=scale_a,
+                 scale_b=scale_b,
-            scale_b=scale_b,
+                 out_dtype=torch.float16))
-            out_dtype=torch.float16,
-        )
-    )
 
     # pytorch impl: fp16 output, with fp8 fast accum
     timers.append(
-        bench_fn(
+        bench_fn(label,
-            label,
+                 sub_label,
-            sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
-            "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
-            torch._scaled_mm,
+                 a,
-            a,
+                 b,
-            b,
+                 scale_a=scale_a,
-            scale_a=scale_a,
+                 scale_b=scale_b,
-            scale_b=scale_b,
+                 out_dtype=torch.float16,
-            out_dtype=torch.float16,
+                 use_fast_accum=True))
-            use_fast_accum=True,
-        )
-    )
 
     # cutlass impl: bf16 output
     timers.append(
-        bench_fn(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
-            label,
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
-            sub_label,
+                 torch.bfloat16))
-            "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(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
-            label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            sub_label,
+                 scale_b, torch.bfloat16))
-            "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(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
-            label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            sub_label,
+                 scale_b, torch.float16))
-            "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(
+        bench_fn(label, sub_label,
-            label,
+                 "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
-            sub_label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
+                 scale_b, torch.bfloat16, 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(
+        bench_fn(label, sub_label,
-            label,
+                 "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
-            sub_label,
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
-            "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
+                 scale_b, torch.float16, bias.to(dtype=torch.float16)))
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.float16,
-            bias.to(dtype=torch.float16),
-        )
-    )
 
     return timers
 
 
-def bench(
+def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
+          sub_label: str) -> Iterable[TMeasurement]:
-) -> 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(
+def run(dtype: torch.dtype,
-    dtype: torch.dtype, MKNs: Iterable[tuple[int, int, int]]
+        MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
-) -> 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(
+def make_output(data: Iterable[TMeasurement],
-    data: Iterable[TMeasurement],
+                MKNs: Iterable[tuple[int, int, int]],
-    MKNs: Iterable[tuple[int, int, int]],
+                base_description: str,
-    base_description: str,
+                timestamp=None):
-    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(
+    parser.add_argument("--dtype",
-        "--dtype",
+                        type=to_torch_dtype,
-        type=to_torch_dtype,
+                        required=True,
-        required=True,
+                        help="Available options are ['int8', 'fp8']")
-        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(
+    model_parser.add_argument("--models",
-        "--models",
+                              nargs="+",
-        nargs="+",
+                              type=str,
-        type=str,
+                              default=DEFAULT_MODELS,
-        default=DEFAULT_MODELS,
+                              choices=WEIGHT_SHAPES.keys())
-        choices=WEIGHT_SHAPES.keys(),
+    model_parser.add_argument("--tp-sizes",
-    )
+                              nargs="+",
-    model_parser.add_argument(
+                              type=int,
-        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+                              default=DEFAULT_TP_SIZES)
-    )
+    model_parser.add_argument("--batch-sizes",
-    model_parser.add_argument(
+                              nargs="+",
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+                              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(
+    return torch.round(tensor.clamp(
-        dtype=torch.float8_e4m3fn
+        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(
+def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
-    dtype: torch.dtype, m: int, n: int, k: int
+                      k: int) -> tuple[torch.Tensor, torch.Tensor]:
-) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device='cuda') * 5
-    a = torch.randn((m, k), device="cuda") * 5
+    b = torch.randn((n, k), device='cuda').t() * 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(
+def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
-    dtype: torch.dtype, m: int, n: int, k: int
+                             k: int) -> tuple[torch.Tensor, torch.Tensor]:
-) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device='cuda') * 5
-    a = torch.randn((m, k), device="cuda") * 5
+    b = torch.randn((n, k), device='cuda').t() * 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(
+def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype,
-    num_tensors: int, dtype: torch.dtype, m: int, n: int, k: int
+                        m: int, n: int, k: int) -> \
-) -> tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
+                        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(
+def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
-    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
+             **kwargs) -> TMeasurement:
-) -> TMeasurement:
     min_run_time = 1
 
     globals = {
@@ -46,48 +44,45 @@ def bench_fn(
 
 
 def bench_int8(
-    dtype: torch.dtype,
+        dtype: torch.dtype,
-    m: int,
+        m: int,
-    k: int,
+        k: int,
-    n: int,
+        n: int,
-    label: str,
+        label: str,
-    sub_label: str,
+        sub_label: str,
-    bench_kernels: Optional[list[str]] = None,
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
-) -> 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)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
-    azp = torch.zeros((m,), device="cuda", dtype=torch.int32)
+    azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
-    azp_adj = torch.zeros((n,), 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(
+        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-        ),
+                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
+        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
+        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        ),
+        "cutlass_i8_i8_bf16_scaled_mm":
-        "cutlass_i8_i8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
-            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,
-        "cutlass_i8_i8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
+                                      bias),
-            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.
-        "cutlass_i8_i8_bf16_scaled_mm_azp": lambda: ops.cutlass_scaled_mm_azp(
+                                          bfloat16, azp_adj),
-            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.
-        "cutlass_i8_i8_bf16_scaled_mm_azp_bias": lambda: ops.cutlass_scaled_mm_azp(
+                                          bfloat16, azp_adj, None, bias),
-            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.
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt": lambda: ops.cutlass_scaled_mm_azp(
+                                          bfloat16, azp_adj, 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.
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias": lambda: ops.cutlass_scaled_mm_azp(
+                                          bfloat16, azp_adj, azp, bias),
-            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,
+        dtype: torch.dtype,
-    m: int,
+        m: int,
-    k: int,
+        k: int,
-    n: int,
+        n: int,
-    label: str,
+        label: str,
-    sub_label: str,
+        sub_label: str,
-    bench_kernels: Optional[list[str]] = None,
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
-) -> Iterable[TMeasurement]:
     """Benchmark FP8-based kernels."""
     assert dtype == torch.float8_e4m3fn
     a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
     a_cont = a.contiguous()
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-
+    block_scale_a = torch.rand((m, k // 128),
-    def ceil_div(x: int, y: int) -> int:
+                               device="cuda",
-        return (x + y - 1) // y
+                               dtype=torch.float32)
-
+    block_scale_b = torch.rand((k // 128, n // 128),
-    block_scale_a = torch.rand(
+                               device="cuda",
-        (m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
+                               dtype=torch.float32)
-    )
-    block_scale_b = torch.rand(
-        ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
-    )
     block_scale_a_M_major = block_scale_a.t().contiguous().t()
     block_scale_b_K_major = block_scale_b.t().contiguous().t()
-    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
 
     print(m, k, n)
 
     bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
+        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-        ),
+                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
+        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
+        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        ),
+        "pytorch_fp8_fp8_fp16_scaled_mm":
-        "pytorch_fp8_fp8_fp16_scaled_mm": lambda: torch._scaled_mm(
+        lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.float16
+            a, b, scale_a, scale_b, out_dtype=torch.float16),
-        ),
+        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
-        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
+        lambda: torch._scaled_mm(a,
-            a, b, scale_a, scale_b, out_dtype=torch.float16, use_fast_accum=True
+                                 b,
-        ),
+                                 scale_a,
-        "pytorch_fp8_fp8_bf16_scaled_mm": lambda: torch._scaled_mm(
+                                 scale_b,
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16
+                                 out_dtype=torch.float16,
-        ),
+                                 use_fast_accum=True),
-        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
+        "pytorch_fp8_fp8_bf16_scaled_mm":
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16, use_fast_accum=True
+        lambda: torch._scaled_mm(
-        ),
+            a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
-        "cutlass_fp8_fp8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
-            a, b, scale_a, scale_b, torch.bfloat16
+        lambda: torch._scaled_mm(a,
-        ),
+                                 b,
-        "cutlass_fp8_fp8_fp16_scaled_mm": lambda: ops.cutlass_scaled_mm(
+                                 scale_a,
-            a, b, scale_a, scale_b, torch.float16
+                                 scale_b,
-        ),
+                                 out_dtype=torch.bfloat16,
-        "cutlass_fp8_fp8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
+                                 use_fast_accum=True),
-            a, b, scale_a, scale_b, torch.bfloat16, bias
+        "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_bias": lambda: ops.cutlass_scaled_mm(
+        "cutlass_fp8_fp8_fp16_scaled_mm":
-            a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
-        ),
+        "cutlass_fp8_fp8_bf16_scaled_mm_bias":
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
-            a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
+                                      bias),
-        ),
+        "cutlass_fp8_fp8_fp16_scaled_mm_bias":
-        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
-            a, b, block_scale_a_M_major, block_scale_b_K_major, 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(
+def bench(dtype: torch.dtype,
-    dtype: torch.dtype,
+          m: int,
-    m: int,
+          k: int,
-    k: int,
+          n: int,
-    n: int,
+          label: str,
-    label: str,
+          sub_label: str,
-    sub_label: str,
+          bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
-    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(
+def run(dtype: torch.dtype,
-    dtype: torch.dtype,
+        MKNs: Iterable[tuple[int, int, int]],
-    MKNs: Iterable[tuple[int, int, int]],
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
-    bench_kernels: Optional[list[str]] = None,
-) -> Iterable[TMeasurement]:
     results = []
     for m, k, n in MKNs:
-        timers = bench(
+        timers = bench(dtype,
-            dtype,
+                       m,
-            m,
+                       k,
-            k,
+                       n,
-            n,
+                       f"scaled-{dtype}-gemm",
-            f"scaled-{dtype}-gemm",
+                       f"MKN=({m}x{k}x{n})",
-            f"MKN=({m}x{k}x{n})",
+                       bench_kernels=bench_kernels)
-            bench_kernels=bench_kernels,
-        )
         print_timers(timers)
         results.extend(timers)
     return results
 
 
-def make_output(
+def make_output(data: Iterable[TMeasurement],
-    data: Iterable[TMeasurement],
+                MKNs: Iterable[tuple[int, int, int]],
-    MKNs: Iterable[tuple[int, int, int]],
+                base_description: str,
-    base_description: str,
+                timestamp=None):
-    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(
+    parser.add_argument("--dtype",
-        "--dtype",
+                        type=to_torch_dtype,
-        type=to_torch_dtype,
+                        required=True,
-        required=True,
+                        help="Available options are ['int8', 'fp8']")
-        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(
+    model_parser.add_argument("--models",
-        "--models",
+                              nargs="+",
-        nargs="+",
+                              type=str,
-        type=str,
+                              default=DEFAULT_MODELS,
-        default=DEFAULT_MODELS,
+                              choices=WEIGHT_SHAPES.keys())
-        choices=WEIGHT_SHAPES.keys(),
+    model_parser.add_argument("--tp-sizes",
-    )
+                              nargs="+",
-    model_parser.add_argument(
+                              type=int,
-        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+                              default=DEFAULT_TP_SIZES)
-    )
+    model_parser.add_argument("--batch-sizes",
-    model_parser.add_argument(
+                              nargs="+",
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+                              type=int,
-    )
+                              default=DEFAULT_BATCH_SIZES)
     model_parser.set_defaults(func=run_model_bench)
 
     args = parser.parse_args()

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')}"}
+        headers = {
-        async with session.post(url=url, json=data, headers=headers) as response:
+            "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(
+        async for _ in forward_request('http://localhost:8100/v1/completions',
-            "http://localhost:8100/v1/completions", prefill_request
+                                       prefill_request):
-        ):
             continue
 
         # return decode
-        generator = forward_request(
+        generator = forward_request('http://localhost:8200/v1/completions',
-            "http://localhost:8200/v1/completions", original_request_data
+                                    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,
+                        method=request.method,
-                    url=target_url,
+                        url=target_url,
-                    headers=request.headers,
+                        headers=request.headers,
-                    data=request.content,
+                        data=request.content,
                 ) as response:
                     # Start sending the response
-                    resp = web.StreamResponse(
+                    resp = web.StreamResponse(status=response.status,
-                        status=response.status, headers=response.headers
+                                              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['name'] = name
-                x["qps"] = qps
+                x['qps'] = qps
                 data.append(x)
 
     df = pd.DataFrame.from_dict(data)
-    dis_df = df[df["name"] == "disagg_prefill"]
+    dis_df = df[df['name'] == 'disagg_prefill']
-    chu_df = df[df["name"] == "chunked_prefill"]
+    chu_df = df[df['name'] == 'chunked_prefill']
 
-    plt.style.use("bmh")
+    plt.style.use('bmh')
-    plt.rcParams["font.size"] = 20
+    plt.rcParams['font.size'] = 20
 
     for key in [
-        "mean_ttft_ms",
+            'mean_ttft_ms', 'median_ttft_ms', 'p99_ttft_ms', 'mean_itl_ms',
-        "median_ttft_ms",
+            'median_itl_ms', 'p99_itl_ms'
-        "p99_ttft_ms",
-        "mean_itl_ms",
-        "median_itl_ms",
-        "p99_itl_ms",
     ]:
+
         fig, ax = plt.subplots(figsize=(11, 7))
-        plt.plot(
+        plt.plot(dis_df['qps'],
-            dis_df["qps"], dis_df[key], label="disagg_prefill", marker="o", linewidth=4
+                 dis_df[key],
-        )
+                 label='disagg_prefill',
-        plt.plot(
+                 marker='o',
-            chu_df["qps"], chu_df[key], label="chunked_prefill", marker="o", linewidth=4
+                 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 (
+        return (f'N {self.num_tokens} '
-            f"N {self.num_tokens} "
+                f'x D {self.hidden_size} '
-            f"x D {self.hidden_size} "
+                f'x R {self.add_residual} '
-            f"x R {self.add_residual} "
+                f'x DT {self.dtype}')
-            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(
+    bench_params = list(map(lambda x: \
-        map(lambda x: bench_params_t(x[0], x[1], x[2], x[3]), combinations)
+        bench_params_t(x[0], x[1], x[2], x[3]), combinations))
-    )
     return bench_params
 
 
 # Reference impls
-def unfused_int8_impl(
+def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
-    rms_norm_layer: RMSNorm,
+                      residual: Optional[torch.Tensor],
-    x: torch.Tensor,
+                      quant_dtype: torch.dtype):
-    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(
+def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
-    rms_norm_layer: RMSNorm,
+                     residual: Optional[torch.Tensor],
-    x: torch.Tensor,
+                     quant_dtype: torch.dtype):
-    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
+        rms_norm_layer: RMSNorm,  # this stores the weights
-    x: torch.Tensor,
+        x: torch.Tensor,
-    residual: Optional[torch.Tensor],
+        residual: Optional[torch.Tensor],
-    quant_dtype: torch.dtype,
+        quant_dtype: torch.dtype):
-):
+    out, _ = ops.rms_norm_dynamic_per_token_quant(x,
-    out, _ = ops.rms_norm_dynamic_per_token_quant(
+                                                  rms_norm_layer.weight,
-        x, rms_norm_layer.weight, 1e-6, quant_dtype, residual=residual
+                                                  1e-6,
-    )
+                                                  quant_dtype,
+                                                  residual=residual)
 
 
 # Bench functions
-def bench_fn(
+def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
-    rms_norm_layer: RMSNorm,
+             quant_dtype: torch.dtype, label: str, sub_label: str,
-    x: torch.Tensor,
+             fn: Callable, description: str) -> TMeasurement:
-    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 = (
+    x = torch.randn(params.num_tokens,
-        torch.randn(
+                    params.hidden_size,
-            params.num_tokens, params.hidden_size, dtype=params.dtype, device="cuda"
+                    dtype=params.dtype,
-        )
+                    device='cuda') * scale
-        * scale
+    residual = (torch.randn_like(x) * scale).to(device='cuda') \
-    )
+            if params.add_residual else None
-    residual = (
-        (torch.randn_like(x) * scale).to(device="cuda") if params.add_residual else None
-    )
 
     timers = []
 
     # unfused int8 impl.
     timers.append(
-        bench_fn(
+        bench_fn(layer, x, residual, torch.int8, label, sub_label,
-            layer,
+                 unfused_int8_impl, "unfused_int8_impl"))
-            x,
-            residual,
-            torch.int8,
-            label,
-            sub_label,
-            unfused_int8_impl,
-            "unfused_int8_impl",
-        )
-    )
 
     # unfused fp8 impl.
     timers.append(
-        bench_fn(
+        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
-            layer,
+                 unfused_fp8_impl, "unfused_fp8_impl"))
-            x,
-            residual,
-            torch.float8_e4m3fn,
-            label,
-            sub_label,
-            unfused_fp8_impl,
-            "unfused_fp8_impl",
-        )
-    )
 
     # fused int8 impl.
     timers.append(
-        bench_fn(
+        bench_fn(layer, x, residual, torch.int8, label, sub_label, fused_impl,
-            layer,
+                 "fused_int8_impl"))
-            x,
-            residual,
-            torch.int8,
-            label,
-            sub_label,
-            fused_impl,
-            "fused_int8_impl",
-        )
-    )
 
     # fused fp8 impl.
     timers.append(
-        bench_fn(
+        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
-            layer,
+                 fused_impl, "fused_fp8_impl"))
-            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,
+    dequantize_weight, generic_dequantize_gemm, get_int_dtype,
-    generic_dequantize_gemm,
+    optimized_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,  #  [..., in_features]
-    input: torch.Tensor,
+        weights: torch.Tensor,
-    weights: torch.Tensor,
+        scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
 ) -> torch.Tensor:
     output = F.linear(input, weights)
     return output
 
 
 def dequant_out_scale(
-    # [..., in_features]
+    input: torch.Tensor,  #  [..., in_features]
-    input: torch.Tensor,
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    # [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
-    codes: torch.IntTensor,
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
     output_partition_sizes: torch.IntTensor,
     bias: Optional[torch.Tensor],
 ) -> torch.Tensor:
+
     weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
 
     if bias is None:
@@ -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,  #  [..., in_features]
-    input: torch.Tensor,
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    # [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
-    codes: torch.IntTensor,
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
     output_partition_sizes: torch.IntTensor,
     bias: Optional[torch.Tensor],
 ) -> torch.Tensor:
+
     weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
 
-    b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
+    b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
+        -1, weights.shape[1])
     weights *= b_scales
     return F.linear(input, weights, bias)
 
 
 def dequant_no_scale(
-    # [..., in_features]
+    input: torch.Tensor,  #  [..., in_features]
-    input: torch.Tensor,
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    # [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
-    codes: torch.IntTensor,
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
     output_partition_sizes: torch.IntTensor,
     bias: Optional[torch.Tensor],
 ) -> torch.Tensor:
+
     weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
 
     return F.linear(input, weights, bias)
@@ -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(
+    codes = torch.randint(-code_range,
-        -code_range,
+                          code_range,
-        code_range,
+                          size=(n, k // ingroups, nbooks),
-        size=(n, k // ingroups, nbooks),
+                          dtype=get_int_dtype(bits),
-        dtype=get_int_dtype(bits),
+                          device=device)
-        device=device,
-    )
 
-    codebooks = torch.randn(
+    codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
+                            dtype=torch.float16,
-        dtype=torch.float16,
+                            device=device)
-        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(
+    parser.add_argument("--nbooks",
-        "--nbooks", type=int, default=1, help="Number of codebooks (default: 1)"
+                        type=int,
-    )
+                        default=1,
-    parser.add_argument(
+                        help="Number of codebooks (default: 1)")
-        "--bits",
+    parser.add_argument("--bits",
-        type=int,
+                        type=int,
-        default=16,
+                        default=16,
-        help="Number of bits per code element (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(f" | {method.__name__.replace('_', ' ')} (µs)", end='')
-        print("")
+        print('')
 
         # These are reasonable prefill sizes.
-        ksandpartions = (
+        ksandpartions = ((4096, (4096, 4096, 4096)), (4096, (4096, )),
-            (4096, (4096, 4096, 4096)),
+                         (4096, (11008, 11008)), (11008, (4096, )))
-            (4096, (4096,)),
-            (4096, (11008, 11008)),
-            (11008, (4096,)),
-        )
 
         # reasonable ranges for m.
         for m in [
-            1,
+                1, 2, 4, 8, 10, 12, 14, 16, 24, 32, 48, 52, 56, 64, 96, 112,
-            2,
+                128, 256, 512, 1024, 1536, 2048, 3072, 4096
-            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(
+def run_timing(num_calls: int, m: int, k: int, parts: torch.Tensor,
-    num_calls: int, m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
+               nbooks: int, bits: int, method) -> float:
-) -> 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(
+    codes = torch.randint(-code_range,
-        -code_range,
+                          code_range,
-        code_range,
+                          size=(n, k // ingroups, nbooks),
-        size=(n, k // ingroups, nbooks),
+                          dtype=get_int_dtype(bits),
-        dtype=get_int_dtype(bits),
+                          device=device)
-        device=device,
-    )
 
-    codebooks = torch.randn(
+    codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
+                            dtype=torch.float16,
-        dtype=torch.float16,
+                            device=device)
-        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(
+        raise ImportError("bitblas version is wrong. Please "
-            "bitblas version is wrong. Please "
+                          f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
-            f"install bitblas>={MINIMUM_BITBLAS_VERSION}"
-        )
 except ImportError as e:
     bitblas_import_exception = e
-    raise ValueError(
+    raise ValueError("Trying to use the bitblas backend, but could not import"
-        "Trying to use the bitblas backend, but could not import"
+                     f"with the following error: {bitblas_import_exception}. "
-        f"with the following error: {bitblas_import_exception}. "
+                     "Please install bitblas through the following command: "
-        "Please install bitblas through the following command: "
+                     f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
-        f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+                     ) from bitblas_import_exception
-    ) 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(
+parser.add_argument("--group_size",
-    "--group_size", type=int, default=None, help="Group size for grouped quantization."
+                    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(
+parser.add_argument("--with_bias",
-    "--with_bias", action="store_true", help="Include bias in the benchmark."
+                    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(
+parser.add_argument("--with_zeros",
-    "--with_zeros", action="store_true", help="Include zeros in the quantization."
+                    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[1] = max(col_widths[1],
-    col_widths[2] = max(
+                        len(input_args_str) + 2,
-        col_widths[2],
+                        len(headers[1]) + 2)
-        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
+    col_widths[2] = max(col_widths[2],
-        len(headers[2]) + 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 (
+from vllm.model_executor.layers.fused_moe.fused_moe import (cutlass_moe_fp8,
-    cutlass_moe_fp8,
+                                                            fused_experts,
-    fused_experts,
+                                                            fused_topk)
-    fused_topk,
-)
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = [
-    "nm-testing/Mixtral-8x7B-Instruct-v0.1",
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1", "nm-testing/deepseekv2-lite",
-    "nm-testing/deepseekv2-lite",
+    "ibm-granite/granite-3.0-1b-a400m", "ibm-granite/granite-3.0-3b-a800m"
-    "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(
+    return torch.round(tensor.clamp(
-        dtype=torch.float8_e4m3fn
+        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
-    )
 
 
-def bench_run(
+def bench_run(results: list[benchmark.Measurement], model: str,
-    results: list[benchmark.Measurement],
+              num_experts: int, topk: int, per_act_token: bool,
-    model: str,
+              per_out_ch: bool, mkn: tuple[int, int, int]):
-    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(
+        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
-            model, num_experts, topk, per_act_token, per_out_ch, mkn
+        "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(
+    w1_q = torch.empty((num_experts, 2 * n, k),
-        (num_experts, 2 * n, k), device="cuda", dtype=torch.float8_e4m3fn
+                       device="cuda",
-    )
+                       dtype=torch.float8_e4m3fn)
-    w2_q = torch.empty((num_experts, k, n), device="cuda", dtype=torch.float8_e4m3fn)
+    w2_q = torch.empty((num_experts, k, n),
-    w1_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
+                       device="cuda",
-    w2_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
+                       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)
+    ab_strides1 = torch.full((num_experts, ),
-    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
+                             k,
-    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
+                             device="cuda",
-    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+                             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])
@@ -84,121 +90,82 @@ def bench_run(
 
     score = torch.randn((m, num_experts), device="cuda", dtype=dtype)
 
-    topk_weights, topk_ids, token_expert_indices = fused_topk(
+    topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
-        a, score, topk, renormalize=False
-    )
 
-    def run_triton_moe(
+    def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
-        a: torch.Tensor,
+                       topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-        w1: torch.Tensor,
+                       w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-        w2: torch.Tensor,
+                       a_scale: torch.Tensor, num_repeats: int):
-        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(
+            fused_experts(a,
-                a,
+                          w1,
-                w1,
+                          w2,
-                w2,
+                          topk_weights,
-                topk_weights,
+                          topk_ids,
-                topk_ids,
+                          use_fp8_w8a8=True,
-                use_fp8_w8a8=True,
+                          w1_scale=w1_scale,
-                w1_scale=w1_scale,
+                          w2_scale=w2_scale,
-                w2_scale=w2_scale,
+                          a1_scale=a_scale)
-                a1_scale=a_scale,
-            )
 
-    def run_cutlass_moe(
+    def run_cutlass_moe(a: torch.Tensor, a_scale: torch.Tensor,
-        a: torch.Tensor,
+                        w1: torch.Tensor, w2: torch.Tensor,
-        a_scale: torch.Tensor,
+                        w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-        w1: torch.Tensor,
+                        topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-        w2: torch.Tensor,
+                        ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
-        w1_scale: torch.Tensor,
+                        ab_strides2: torch.Tensor, c_strides2: torch.Tensor,
-        w2_scale: torch.Tensor,
+                        num_repeats: int):
-        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(
+            cutlass_moe_fp8(a,
-                a,
+                            w1,
-                w1,
+                            w2,
-                w2,
+                            w1_scale,
-                w1_scale,
+                            w2_scale,
-                w2_scale,
+                            topk_weights,
-                topk_weights,
+                            topk_ids,
-                topk_ids,
+                            ab_strides1,
-                ab_strides1,
+                            c_strides1,
-                c_strides1,
+                            ab_strides2,
-                ab_strides2,
+                            c_strides2,
-                c_strides2,
+                            a1_scale=a_scale)
-                a1_scale=a_scale,
-            )
 
     def run_cutlass_from_graph(
-        a: torch.Tensor,
+            a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
-        a_scale: torch.Tensor,
+            w2_q: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-        w1_q: torch.Tensor,
+            topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-        w2_q: torch.Tensor,
+            ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
-        w1_scale: torch.Tensor,
+            ab_strides2: torch.Tensor, c_strides2: 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))
+                VllmConfig(parallel_config=ParallelConfig(
-        ):
+                    pipeline_parallel_size=1))):
-            return cutlass_moe_fp8(
+            return cutlass_moe_fp8(a,
-                a,
+                                   w1_q,
-                w1_q,
+                                   w2_q,
-                w2_q,
+                                   w1_scale,
-                w1_scale,
+                                   w2_scale,
-                w2_scale,
+                                   topk_weights,
-                topk_weights,
+                                   topk_ids,
-                topk_ids,
+                                   ab_strides1,
-                ab_strides1,
+                                   c_strides1,
-                c_strides1,
+                                   ab_strides2,
-                ab_strides2,
+                                   c_strides2,
-                c_strides2,
+                                   a1_scale=a_scale)
-                a1_scale=a_scale,
-            )
 
-    def run_triton_from_graph(
+    def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
-        a: torch.Tensor,
+                              w2: torch.Tensor, topk_weights: torch.Tensor,
-        w1: torch.Tensor,
+                              topk_ids: torch.Tensor, w1_scale: torch.Tensor,
-        w2: torch.Tensor,
+                              w2_scale: torch.Tensor, a_scale: 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))
+                VllmConfig(parallel_config=ParallelConfig(
-        ):
+                    pipeline_parallel_size=1))):
-            return fused_experts(
+            return fused_experts(a,
-                a,
+                                 w1,
-                w1,
+                                 w2,
-                w2,
+                                 topk_weights,
-                topk_weights,
+                                 topk_ids,
-                topk_ids,
+                                 use_fp8_w8a8=True,
-                use_fp8_w8a8=True,
+                                 w1_scale=w1_scale,
-                w1_scale=w1_scale,
+                                 w2_scale=w2_scale,
-                w2_scale=w2_scale,
+                                 a1_scale=a_scale)
-                a1_scale=a_scale,
-            )
 
     def replay_graph(graph, num_repeats):
         for _ in range(num_repeats):
@@ -208,35 +175,16 @@ 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(
+        run_cutlass_from_graph(a, a_scale, w1_q, w2_q, w1_scale, w2_scale,
-            a,
+                               topk_weights, topk_ids, ab_strides1, c_strides1,
-            a_scale,
+                               ab_strides2, c_strides2)
-            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(
+        run_triton_from_graph(a, w1_q_notransp, w2_q_notransp, topk_weights,
-            a,
+                              topk_ids, w1_scale, w2_scale, a_scale)
-            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 +224,18 @@ def bench_run(
     }
 
     # Warmup
-    run_triton_moe(
+    run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids,
-        a,
+                   w1_scale, w2_scale, a_scale, num_warmup)
-        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 +247,22 @@ 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(
+    run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights,
-        a,
+                    topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2,
-        a_scale,
+                    num_warmup)
-        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 +274,7 @@ 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 +302,8 @@ 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(
+                            bench_run(results, model, num_experts, topk,
-                                results,
+                                      per_act_token, per_out_ch, mkn)
-                                model,
-                                num_experts,
-                                topk,
-                                per_act_token,
-                                per_out_ch,
-                                mkn,
-                            )
 
     compare = benchmark.Compare(results)
     compare.print()
@@ -393,8 +311,7 @@ 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 +319,21 @@ 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("--tp-sizes",
-    parser.add_argument(
+                        nargs="+",
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+                        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(
+def main(num_tokens: int,
-    num_tokens: int,
+         hidden_size: int,
-    hidden_size: int,
+         add_residual: bool,
-    add_residual: bool,
+         dtype: torch.dtype,
-    dtype: torch.dtype,
+         seed: int = 0,
-    seed: int = 0,
+         do_profile: bool = False,
-    do_profile: bool = False,
+         num_warmup_iters: int = 5,
-    num_warmup_iters: int = 5,
+         num_iters: int = 100) -> None:
-    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__":
+if __name__ == '__main__':
-    parser = FlexibleArgumentParser(description="Benchmark the layernorm kernel.")
+    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(
+    parser.add_argument("--dtype",
-        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+                        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(
+    parser.add_argument("--num-iters",
-        "--num-iters",
+                        type=int,
-        type=int,
+                        default=100,
-        default=100,
+                        help="Number of benchmark iterations. "
-        help="Number of benchmark iterations. "
+                        "If --profile is set, this number is ignored")
-        "If --profile is set, this number is ignored",
-    )
 
     args = parser.parse_args()
     print(args)
 
-    main(
+    main(num_tokens=args.num_tokens,
-        num_tokens=args.num_tokens,
+         hidden_size=args.hidden_size,
-        hidden_size=args.hidden_size,
+         add_residual=args.add_residual,
-        add_residual=args.add_residual,
+         dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+         seed=args.seed,
-        seed=args.seed,
+         do_profile=args.profile,
-        do_profile=args.profile,
+         num_warmup_iters=args.num_warmup_iters,
-        num_warmup_iters=args.num_warmup_iters,
+         num_iters=args.num_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_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, marlin_permute_scales,
-    GPTQ_MARLIN_MIN_THREAD_N,
+    marlin_zero_points)
-    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,
+    pack_rows, quantize_weights)
-    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(
+def quantize_and_pack(atype: torch.dtype,
-    atype: torch.dtype,
+                      w: torch.Tensor,
-    w: torch.Tensor,
+                      wtype: ScalarType,
-    wtype: ScalarType,
+                      stype: Optional[torch.dtype],
-    stype: Optional[torch.dtype],
+                      group_size: Optional[int],
-    group_size: Optional[int],
+                      zero_points: bool = False):
-    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(
+def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
-    shape: tuple[int, int, int], types: TypeConfig, group_size: Optional[int]
+                         group_size: Optional[int]) -> list[BenchmarkTensors]:
-) -> 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(
+    num_weights = math.ceil(2 * 50 * 1024**2 * 8 /
-        2 * 50 * 1024**2 * 8 / (k * n * types.weight_type.size_bits)
+                            (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,
+            a.dtype, w, types.weight_type, types.group_scale_type, group_size,
-            w,
+            types.group_zero_type is not None)
-            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 = (
+        w_ch_s = None if types.channel_scale_type is None else\
-            None
+            rand_data((n,), types.channel_scale_type)
-            if types.channel_scale_type is None
+        w_tok_s = None if types.token_scale_type is None else\
-            else rand_data((n,), types.channel_scale_type)
+            rand_data((m,), types.token_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(
+            BenchmarkTensors(w_ref=w_ref,
-                w_ref=w_ref,
+                             a=a,
-                a=a,
+                             w_q=w_q_packed,
-                w_q=w_q_packed,
+                             wtype=types.weight_type,
-                wtype=types.weight_type,
+                             w_g_s=w_s,
-                w_g_s=w_s,
+                             w_g_zp=w_zp,
-                w_g_zp=w_zp,
+                             group_size=group_size,
-                group_size=group_size,
+                             w_ch_s=w_ch_s,
-                w_ch_s=w_ch_s,
+                             w_tok_s=w_tok_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(
+    workspace = MarlinWorkspace(bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N,
-        bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+                                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(
+        w_zp = marlin_zero_points(bt.w_g_zp, bt.w_ref.shape[0],
-            bt.w_g_zp, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
+                                  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(
+        w_s = marlin_permute_scales(bt.w_g_s, bt.w_ref.shape[0],
-            bt.w_g_s, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.group_size
+                                    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(
+    w_q = ops.gptq_marlin_repack(bt.w_q, sort_indices, bt.w_ref.shape[0],
-        bt.w_q, sort_indices, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
+                                 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(
+        fn = lambda: ops.gptq_marlin_gemm(a=bt.a,
-            a=bt.a,
+                                          b_q_weight=w_q,
-            b_q_weight=w_q,
+                                          b_scales=w_s,
-            b_scales=w_s,
+                                          b_zeros=w_zp,
-            b_zeros=w_zp,
+                                          g_idx=g_idx,
-            g_idx=g_idx,
+                                          perm=sort_indices,
-            perm=sort_indices,
+                                          workspace=workspace.scratch,
-            workspace=workspace.scratch,
+                                          b_q_type=bt.wtype,
-            b_q_type=bt.wtype,
+                                          size_m=bt.a.shape[0],
-            size_m=bt.a.shape[0],
+                                          size_n=bt.w_ref.shape[1],
-            size_n=bt.w_ref.shape[1],
+                                          size_k=bt.w_ref.shape[0],
-            size_k=bt.w_ref.shape[0],
+                                          is_k_full=True,
-            is_k_full=True,
+                                          is_zp_float=False)
-            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(
+        fn = lambda: ops.marlin_qqq_gemm(a=bt.a,
-            a=bt.a,
+                                         b_q_weight=w_q,
-            b_q_weight=w_q,
+                                         s_group=w_s,
-            s_group=w_s,
+                                         s_tok=s_tok,
-            s_tok=s_tok,
+                                         s_ch=s_ch,
-            s_ch=s_ch,
+                                         workspace=workspace.scratch,
-            workspace=workspace.scratch,
+                                         size_m=bt.a.shape[0],
-            size_m=bt.a.shape[0],
+                                         size_n=bt.w_ref.shape[1],
-            size_n=bt.w_ref.shape[1],
+                                         size_k=bt.w_ref.shape[0])
-            size_k=bt.w_ref.shape[0],
-        )
 
     return fn
 
 
-def machete_create_bench_fn(
+def machete_create_bench_fn(bt: BenchmarkTensors,
-    bt: BenchmarkTensors, out_type=torch.dtype, schedule=None
+                            out_type=torch.dtype,
-) -> Callable:
+                            schedule=None) -> Callable:
     w_q = bt.w_q.t().contiguous().t()  # make col major
-    w_q = ops.machete_prepack_B(
+    w_q = ops.machete_prepack_B(w_q, bt.a.dtype, bt.wtype,
-        w_q, bt.a.dtype, bt.wtype, None if bt.w_g_s is None else bt.w_g_s.dtype
+                                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 (
+        with nvtx.annotate("mm-bench"), nvtx.annotate(
-            nvtx.annotate("mm-bench"),
+                f"{label}|{sub_label}|{description}"):
-            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(
+def bench(types: TypeConfig,
-    types: TypeConfig,
+          group_size: int,
-    group_size: int,
+          m: int,
-    m: int,
+          k: int,
-    k: int,
+          n: int,
-    n: int,
+          label: str,
-    label: str,
+          sub_label: str,
-    sub_label: str,
+          sweep_schedules: bool = True) -> list[TMeasurement]:
-    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,
+            label, sub_label, "torch.matmul (fp16)",
-            sub_label,
+            [torch_matmul_f16_create_bench_fn(bt)
-            "torch.matmul (fp16)",
+             for bt in benchmark_tensors]))
-            [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,
+                label, sub_label,
-                sub_label,
+                f"cutlass_scaled_mm ({terse_type_name(types.act_type)})", [
-                f"cutlass_scaled_mm ({terse_type_name(types.act_type)})",
+                    cutlass_scaled_mm_create_bench_fn(bt)
-                [cutlass_scaled_mm_create_bench_fn(bt) for bt in benchmark_tensors],
+                    for bt in benchmark_tensors
-            )
+                ]))
-        )
 
     if types.act_type != torch.float8_e4m3fn:
         timers.append(
-            bench_fns(
+            bench_fns(label, sub_label, f"marlin ({name_type_string})",
-                label,
+                      [marlin_create_bench_fn(bt)
-                sub_label,
+                       for bt in benchmark_tensors]))
-                f"marlin ({name_type_string})",
-                [marlin_create_bench_fn(bt) for bt in benchmark_tensors],
-            )
-        )
 
     # machete
     timers.append(
-        bench_fns(
+        bench_fns(label, sub_label, f"machete ({name_type_string})", [
-            label,
+            machete_create_bench_fn(bt, out_type=types.output_type)
-            sub_label,
+            for bt in benchmark_tensors
-            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(
+            res = bench_fns(label, sub_label, "machete_best", [
-                label,
+                machete_create_bench_fn(
-                sub_label,
+                    bt, out_type=types.output_type, schedule=schedule)
-                "machete_best",
+                for bt in benchmark_tensors
-                [
+            ])
-                    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 = pd.DataFrame(
-            _SWEEP_SCHEDULES_RESULTS.loc[len(_SWEEP_SCHEDULES_RESULTS)] = results_row
+                    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
+        weight_type=scalar_types.uint4b8 if args.group_zero_type is None \
-        if args.group_zero_type is None
+            else scalar_types.uint4,
-        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(
+        timers = bench(types,
-            types,
+                       args.group_size,
-            args.group_size,
+                       m,
-            m,
+                       k,
-            k,
+                       n,
-            n,
+                       f"{args.act_type}-gemm",
-            f"{args.act_type}-gemm",
+                       f"MKN=({m}x{k}x{n})",
-            f"MKN=({m}x{k}x{n})",
+                       sweep_schedules=args.sweep_schedules)
-            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 = (
+    m_increment, k_increment, n_increment = \
-        int(x) for x in args.dim_increment.split(",")
+        (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(
+        pkl.dump({
-            {
+            "args": args_dict,
-                "args": args_dict,
+            "results": all_results,
-                "results": all_results,
+        }, f)
-            },
-            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(
+    parser.add_argument("--sweep-csv-out",
-        "--sweep-csv-out",
+                        help="CSV to store sweep results",
-        help="CSV to store sweep results",
+                        default="sch_sweep_results.csv")
-        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(
+    model_parser.add_argument("--tp-sizes",
-        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
+                              nargs="+",
-    )
+                              type=int,
-    model_parser.add_argument(
+                              default=DEFAULT_TP_SIZES)
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_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_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
-    GPTQ_MARLIN_24_MIN_THREAD_N,
+    GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES)
-    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_AMPERE_M_CUBLAS_THRESHOLD, ALLSPARK_SUPPORTED_QUANT_TYPES)
-    ALLSPARK_SUPPORTED_QUANT_TYPES,
-)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    GPTQ_MARLIN_MAX_PARALLEL,
+    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
-    GPTQ_MARLIN_MIN_THREAD_N,
+    MARLIN_SUPPORTED_GROUP_SIZES, query_marlin_supported_quant_types)
-    MARLIN_SUPPORTED_GROUP_SIZES,
-    query_marlin_supported_quant_types,
-)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
-    MarlinWorkspace,
+    MarlinWorkspace, marlin_quantize)
-    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_pack, gptq_quantize_weights, quantize_weights, sort_weights)
-    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(
+def bench_run(results: list[benchmark.Measurement], model: str,
-    results: list[benchmark.Measurement],
+              act_order: bool, is_k_full: bool, quant_type: ScalarType,
-    model: str,
+              group_size: int, size_m: int, size_k: int, size_n: int):
-    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(
+    sub_label = ("{}, act={} k_full={}, q={}, g={}, "
-        model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
+                 "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_w_ref, marlin_24_q_w_comp, marlin_24_meta,
-        marlin_24_quantize(b, quant_type, group_size)
+     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(
+    (w_ref, q_w, s, g_idx,
-        b, quant_type, group_size, act_order
+     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(
+    marlin_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
-        size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+                                       GPTQ_MARLIN_MAX_PARALLEL)
-    )
 
-    marlin_24_workspace = MarlinWorkspace(
+    marlin_24_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_24_MIN_THREAD_N,
-        size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
+                                          GPTQ_MARLIN_24_MAX_PARALLEL)
-    )
     marlin_zp = torch.zeros_like(marlin_s, dtype=torch.int)
 
     # AllSpark W8A16 quant
-    as_supported_case = (
+    as_supported_case = (quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
-        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
+                         and group_size == -1 and not act_order and is_k_full)
-        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_reorder, s_reorder, zp_reorder = \
-                qw, s, zp, has_zp
+                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 (
+    if (quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
+            and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES):
-        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 (
+                if len(args.limit_act_order
-                    len(args.limit_act_order) > 0
+                       ) > 0 and act_order not in args.limit_act_order:
-                    and act_order not in args.limit_act_order
-                ):
                     continue
 
                 for is_k_full in K_FULL_OPTS:
-                    if (
+                    if len(args.limit_k_full
-                        len(args.limit_k_full) > 0
+                           ) > 0 and is_k_full not in args.limit_k_full:
-                        and is_k_full not in args.limit_k_full
-                    ):
                         continue
 
-                    for quant_type in query_marlin_supported_quant_types(False):
+                    for quant_type in query_marlin_supported_quant_types(
-                        if (
+                            False):
-                            len(args.limit_num_bits) > 0
+                        if len(args.limit_num_bits) > 0 and \
-                            and quant_type.size_bits not in args.limit_num_bits
+                            quant_type.size_bits not in args.limit_num_bits:
-                        ):
                             continue
 
                         for group_size in MARLIN_SUPPORTED_GROUP_SIZES:
-                            if (
+                            if len(
-                                len(args.limit_group_size) > 0
+                                    args.limit_group_size
-                                and group_size not in 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(
+                                bench_run(results, model, act_order, is_k_full,
-                                    results,
+                                          quant_type, group_size, size_m,
-                                    model,
+                                          size_k, size_n)
-                                    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(
+    parser.add_argument("--batch-sizes",
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_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

@@ -6,17 +6,16 @@ import time
 from contextlib import nullcontext
 from datetime import datetime
 from itertools import product
-from types import SimpleNamespace
 from typing import Any, TypedDict
 
 import ray
 import torch
+import triton
 from ray.experimental.tqdm_ray import tqdm
+from transformers import AutoConfig
 
 from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
-from vllm.transformers_utils.config import get_config
-from vllm.triton_utils import triton
 from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -31,60 +30,56 @@ class BenchmarkConfig(TypedDict):
     num_stages: int
 
 
-def benchmark_config(
+def benchmark_config(config: BenchmarkConfig,
-    config: BenchmarkConfig,
+                     num_tokens: int,
-    num_tokens: int,
+                     num_experts: int,
-    num_experts: int,
+                     shard_intermediate_size: int,
-    shard_intermediate_size: int,
+                     hidden_size: int,
-    hidden_size: int,
+                     topk: int,
-    topk: int,
+                     dtype: torch.dtype,
-    dtype: torch.dtype,
+                     use_fp8_w8a8: bool,
-    use_fp8_w8a8: bool,
+                     use_int8_w8a16: bool,
-    use_int8_w8a16: bool,
+                     num_iters: int = 100,
-    num_iters: int = 100,
+                     block_quant_shape: List[int] = None,
-    block_quant_shape: List[int] = None,
+                     use_deep_gemm: bool = False) -> float:
-    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(
+        w1 = torch.randint(-127,
-            -127,
+                           127, (
-            127,
+                               num_experts,
-            (
+                               shard_intermediate_size,
-                num_experts,
+                               hidden_size,
-                shard_intermediate_size,
+                           ),
-                hidden_size,
+                           dtype=torch.int8)
-            ),
+        w2 = torch.randint(-127,
-            dtype=torch.int8,
+                           127, (
-        )
+                               num_experts,
-        w2 = torch.randint(
+                               hidden_size,
-            -127,
+                               shard_intermediate_size // 2,
-            127,
+                           ),
-            (
+                           dtype=torch.int8)
-                num_experts,
-                hidden_size,
-                shard_intermediate_size // 2,
-            ),
-            dtype=torch.int8,
-        )
     else:
-        w1 = torch.randn(
+        w1 = torch.randn(num_experts,
-            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
+                         shard_intermediate_size,
-        )
+                         hidden_size,
-        w2 = torch.randn(
+                         dtype=init_dtype)
-            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
+        w2 = torch.randn(num_experts,
-        )
+                         hidden_size,
-    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)
+                         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(
+        w1_scale = torch.randn((num_experts, 2 * shard_intermediate_size),
-            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
+                               dtype=torch.float32)
-        )
         w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
     if use_fp8_w8a8:
         if block_quant_shape:
@@ -97,14 +92,10 @@ 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 = (
+            w1_scale = torch.rand((E, n_tiles_w1, k_tiles_w1),
-                torch.rand((E, n_tiles_w1, k_tiles_w1), dtype=torch.float32)
+                                  dtype=torch.float32) * factor_for_scale
-                * factor_for_scale
+            w2_scale = torch.rand((E, n_tiles_w2, k_tiles_w2),
-            )
+                                  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 +113,10 @@ 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(
+                topk_weights, topk_ids = fused_topk(x, input_gating, topk,
-                    x, input_gating, topk, False
+                                                    False)
-                )
                 return fused_experts(
                     x,
                     w1,
@@ -223,7 +212,8 @@ 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 +249,20 @@ 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 (
+            if config["BLOCK_SIZE_K"] % block_k != 0 or config[
-                config["BLOCK_SIZE_K"] % block_k != 0
+                    "BLOCK_SIZE_N"] % block_n != 0:
-                or config["BLOCK_SIZE_N"] % block_n != 0
-            ):
                 configs.remove(config)
     return configs
 
 
-def prune_rocm_search_space(
+def prune_rocm_search_space(num_tokens, shard_intermediate_size, hidden_size,
-    num_tokens, shard_intermediate_size, hidden_size, search_space, is_fp16, topk
+                            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(
+    pruned_space_1 = prune_rocm_configs(num_tokens * topk, N1, K1,
-        num_tokens * topk, N1, K1, search_space, is_fp16
+                                        search_space, is_fp16)
-    )
+    pruned_space_2 = prune_rocm_configs(num_tokens * topk, N2, K2,
-    pruned_space_2 = prune_rocm_configs(
+                                        search_space, is_fp16)
-        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 +300,14 @@ 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 (
+            if (matrix_instr_nonkdim > BLOCK_SIZE_M
-                matrix_instr_nonkdim > BLOCK_SIZE_M
+                    or matrix_instr_nonkdim > BLOCK_SIZE_N):
-                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 +328,8 @@ 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 = (
+        LDS = (BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a +
-            BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a
+               BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b)
-            + BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b
-        )
         if LDS > 65536:
             continue
         # Skip small block sizes and num_warps for large gemm
@@ -380,6 +363,7 @@ 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 +387,36 @@ 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_str = get_config_dtype_str(dtype,
-            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+                                         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(
+        op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
-            num_experts, shard_intermediate_size // 2, dtype_str
+                                    dtype_str)
-        )
         if op_config is None:
-            config = get_default_config(
+            config = get_default_config(num_tokens,
-                num_tokens,
+                                        num_experts,
-                num_experts,
+                                        shard_intermediate_size,
-                shard_intermediate_size,
+                                        hidden_size,
-                hidden_size,
+                                        topk,
-                topk,
+                                        dtype_str,
-                dtype_str,
+                                        is_marlin=False)
-                is_marlin=False,
-            )
         else:
-            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
+            config = op_config[min(op_config.keys(),
-        kernel_time = benchmark_config(
+                                   key=lambda x: abs(x - num_tokens))]
-            config,
+        kernel_time = benchmark_config(config,
-            num_tokens,
+                                       num_tokens,
-            num_experts,
+                                       num_experts,
-            shard_intermediate_size,
+                                       shard_intermediate_size,
-            hidden_size,
+                                       hidden_size,
-            topk,
+                                       topk,
-            dtype,
+                                       dtype,
-            use_fp8_w8a8,
+                                       use_fp8_w8a8,
-            use_int8_w8a16,
+                                       use_int8_w8a16,
-            num_iters=100,
+                                       num_iters=100,
-            block_quant_shape=block_quant_shape,
+                                       block_quant_shape=block_quant_shape,
-            use_deep_gemm=use_deep_gemm,
+                                       use_deep_gemm=use_deep_gemm)
-        )
         return config, kernel_time
 
     def tune(
@@ -457,22 +437,13 @@ 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(
+            search_space = prune_rocm_search_space(num_tokens,
-                num_tokens,
+                                                   shard_intermediate_size,
-                shard_intermediate_size,
+                                                   hidden_size, search_space,
-                hidden_size,
+                                                   is_fp16, topk)
-                search_space,
-                is_fp16,
-                topk,
-            )
 
-        need_device_guard = False
+        with torch.cuda.device(self.device_id) if current_platform.is_rocm(
-        if current_platform.is_rocm():
+        ) else nullcontext():
-            visible_device = os.environ.get("ROCR_VISIBLE_DEVICES", None)
-            if visible_device != f"{self.device_id}":
-                need_device_guard = True
-
-        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 +458,7 @@ 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 +474,42 @@ class BenchmarkWorker:
 
 def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
     return {
-        "BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
+        "BLOCK_SIZE_M":
-        "BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
+        config["BLOCK_SIZE_M"],
-        "BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
+        "BLOCK_SIZE_N":
-        "GROUP_SIZE_M": config["GROUP_SIZE_M"],
+        config["BLOCK_SIZE_N"],
-        "num_warps": config["num_warps"],
+        "BLOCK_SIZE_K":
-        "num_stages": config["num_stages"],
+        config["BLOCK_SIZE_K"],
-        **(
+        "GROUP_SIZE_M":
-            {"waves_per_eu": config["waves_per_eu"]} if "waves_per_eu" in config else {}
+        config["GROUP_SIZE_M"],
-        ),
+        "num_warps":
-        **(
+        config["num_warps"],
-            {"matrix_instr_nonkdim": config["matrix_instr_nonkdim"]}
+        "num_stages":
-            if "matrix_instr_nonkdim" in config
+        config["num_stages"],
-            else {}
+        **({
-        ),
+            "waves_per_eu": config["waves_per_eu"]
-        **({"kpack": config["kpack"]} if "kpack" in config else {}),
+        } 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(
+def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
-    configs: dict[int, BenchmarkConfig],
+                 shard_intermediate_size: int, hidden_size: int, topk: int,
-    num_experts: int,
+                 dtype: torch.dtype, use_fp8_w8a8: bool, use_int8_w8a16: bool,
-    shard_intermediate_size: int,
+                 block_quant_shape: List[int]) -> None:
-    hidden_size: int,
+    dtype_str = get_config_dtype_str(dtype,
-    topk: int,
+                                     use_int8_w8a16=use_int8_w8a16,
-    dtype: torch.dtype,
+                                     use_fp8_w8a8=use_fp8_w8a8)
-    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(
+    filename = get_config_file_name(num_experts, shard_intermediate_size // 2,
-        num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape
+                                    dtype_str, block_quant_shape)
-    )
 
     print(f"Writing best config to {filename}...")
     with open(filename, "w") as f:
@@ -550,20 +518,18 @@ 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 = AutoConfig.from_pretrained(
-    if args.model_prefix:
+        args.model, trust_remote_code=args.trust_remote_code)
-        config = getattr(config, args.model_prefix)
-    config = SimpleNamespace(**config)
-
     if config.architectures[0] == "DbrxForCausalLM":
         E = config.ffn_config.moe_num_experts
         topk = config.ffn_config.moe_top_k
@@ -574,12 +540,15 @@ 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] == "DeepseekV3ForCausalLM"
+          or config.architectures[0] == "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
@@ -594,51 +563,21 @@ def main(args: argparse.Namespace):
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
 
     hidden_size = config.hidden_size
-    dtype = (
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_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,
+            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
-            2,
+            2048, 3072, 4096
-            4,
-            8,
-            16,
-            24,
-            32,
-            48,
-            64,
-            96,
-            128,
-            256,
-            512,
-            1024,
-            1536,
-            2048,
-            3072,
-            4096,
         ]
     else:
         batch_sizes = [args.batch_size]
 
     use_deep_gemm = bool(args.use_deep_gemm)
 
-    if current_platform.is_rocm() and "HIP_VISIBLE_DEVICES" in os.environ:
-        # 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."
-        )
-        val = os.environ["HIP_VISIBLE_DEVICES"]
-        os.environ["ROCR_VISIBLE_DEVICES"] = val
-        del os.environ["HIP_VISIBLE_DEVICES"]
-
     ray.init()
     num_gpus = int(ray.available_resources()["GPU"])
     workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
@@ -661,59 +600,25 @@ def main(args: argparse.Namespace):
 
         start = time.time()
         configs = _distribute(
-            "tune",
+            "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)
-                    batch_size,
+                     for batch_size in batch_sizes])
-                    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(
+        save_configs(best_configs, E, shard_intermediate_size, hidden_size,
-            best_configs,
+                     topk, dtype, use_fp8_w8a8, use_int8_w8a16,
-            E,
+                     block_quant_shape)
-            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)
-                    batch_size,
+             for batch_size in batch_sizes])
-                    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}")
@@ -722,21 +627,23 @@ def main(args: argparse.Namespace):
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser()
-    parser.add_argument(
+    parser.add_argument("--model",
-        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+                        type=str,
-    )
+                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    parser.add_argument(
+    parser.add_argument("--tp-size",
-        "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
+                        "-tp",
-    )
+                        "--tensor-parallel-size",
-    parser.add_argument(
+                        type=int,
-        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
+                        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)
     parser.add_argument("--tune", action="store_true")
     parser.add_argument("--trust-remote-code", action="store_true")
-    parser.add_argument("--model-prefix", type=str, required=False)
     args = parser.parse_args()
 
     main(args)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -1,416 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import argparse
-from typing import Any, TypedDict
-
-import ray
-import torch
-from transformers import AutoConfig
-
-from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
-    _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
-from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-
-FP8_DTYPE = current_platform.fp8_dtype()
-
-
-class BenchmarkConfig(TypedDict):
-    BLOCK_SIZE_M: int
-    BLOCK_SIZE_N: int
-    BLOCK_SIZE_K: int
-    GROUP_SIZE_M: int
-    num_warps: int
-    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:
-    # 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)
-    if use_fp8_w8a8:
-        align_block_size = 128  # deepgemm needs 128 m aligned block
-        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
-    else:
-        align_block_size = None
-        qhidden_states = hidden_states
-
-    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
-    )
-
-    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,
-                )
-            )
-        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
-            )
-
-    # JIT compilation & warmup
-    run()
-    torch.cuda.synchronize()
-
-    # Capture 10 invocations with CUDA graph
-    graph = torch.cuda.CUDAGraph()
-    with torch.cuda.graph(graph):
-        for _ in range(10):
-            run()
-    torch.cuda.synchronize()
-
-    # Warmup
-    for _ in range(5):
-        graph.replay()
-    torch.cuda.synchronize()
-
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-
-    latencies: list[float] = []
-    for i in range(num_iters):
-        prepare(i)
-        torch.cuda.synchronize()
-
-        start_event.record()
-        graph.replay()
-        end_event.record()
-        end_event.synchronize()
-        latencies.append(start_event.elapsed_time(end_event))
-    avg = sum(latencies) / (num_iters * 10) * 1000  # us
-    graph.reset()
-    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:
-    # 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)
-    if use_fp8_w8a8:
-        align_block_size = 128  # deepgemm needs 128 m aligned block
-        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
-    else:
-        align_block_size = None
-        qhidden_states = hidden_states
-
-    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
-    )
-
-    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,
-                )
-            )
-            # convert to fp16/bf16 as gemm output
-            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
-            )
-            # convert to fp16/bf16 as gemm output
-            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,
-            )
-        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
-            )
-
-    # JIT compilation & warmup
-    input = prepare()
-    run(input)
-    torch.cuda.synchronize()
-
-    # Capture 10 invocations with CUDA graph
-    graph = torch.cuda.CUDAGraph()
-    with torch.cuda.graph(graph):
-        for _ in range(10):
-            run(input)
-    torch.cuda.synchronize()
-
-    # Warmup
-    for _ in range(5):
-        graph.replay()
-    torch.cuda.synchronize()
-
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-
-    latencies: list[float] = []
-    for i in range(num_iters):
-        torch.cuda.synchronize()
-        start_event.record()
-        graph.replay()
-        end_event.record()
-        end_event.synchronize()
-        latencies.append(start_event.elapsed_time(end_event))
-    avg = sum(latencies) / (num_iters * 10) * 1000  # us
-    graph.reset()
-    return avg
-
-
-@ray.remote(num_gpus=1)
-class BenchmarkWorker:
-    def __init__(self, seed: int) -> None:
-        torch.set_default_device("cuda")
-        current_platform.seed_everything(seed)
-        self.seed = seed
-        # Get the device ID to allocate tensors and kernels
-        # on the respective GPU. This is required for Ray to work
-        # correctly with multi-GPU tuning on the ROCm platform.
-        self.device_id = int(ray.get_gpu_ids()[0])
-
-    def benchmark(
-        self,
-        num_tokens: int,
-        num_experts: int,
-        hidden_size: int,
-        topk: int,
-        dtype: torch.dtype,
-        use_fp8_w8a8: bool,
-        use_int8_w8a16: bool,
-        use_customized_permute: bool = False,
-    ) -> tuple[dict[str, int], float]:
-        current_platform.seed_everything(self.seed)
-
-        permute_time = benchmark_permute(
-            num_tokens,
-            num_experts,
-            hidden_size,
-            topk,
-            dtype,
-            use_fp8_w8a8,
-            use_int8_w8a16,
-            num_iters=100,
-            use_customized_permute=use_customized_permute,
-        )
-        unpermute_time = benchmark_unpermute(
-            num_tokens,
-            num_experts,
-            hidden_size,
-            topk,
-            dtype,
-            use_fp8_w8a8,
-            use_int8_w8a16,
-            num_iters=100,
-            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", {})
-    if isinstance(quantization_config, dict):
-        return quantization_config.get("weight_block_size", default_value)
-    return default_value
-
-
-def main(args: argparse.Namespace):
-    print(args)
-
-    config = AutoConfig.from_pretrained(
-        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"
-    ):
-        E = config.n_routed_experts
-        topk = config.num_experts_per_tok
-    elif config.architectures[0] in ["Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"]:
-        E = config.num_experts
-        topk = config.num_experts_per_tok
-
-    else:
-        # Support for llama4
-        config = config.get_text_config()
-        # Default: Mixtral.
-        E = config.num_local_experts
-        topk = config.num_experts_per_tok
-
-    hidden_size = config.hidden_size
-    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
-    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
-    use_int8_w8a16 = args.dtype == "int8_w8a16"
-    use_customized_permute = args.use_customized_permute
-
-    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,
-        ]
-    else:
-        batch_sizes = [args.batch_size]
-
-    ray.init()
-    num_gpus = int(ray.available_resources()["GPU"])
-    workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
-
-    def _distribute(method: str, inputs: list[Any]) -> list[Any]:
-        outputs = []
-        worker_idx = 0
-        for input_args in inputs:
-            worker = workers[worker_idx]
-            worker_method = getattr(worker, method)
-            output = worker_method.remote(*input_args)
-            outputs.append(output)
-            worker_idx = (worker_idx + 1) % num_gpus
-        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
-        ],
-    )
-
-    for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
-        print(f"Batch size: {batch_size}")
-        print(f"Permute time: {permute:.2f} us")
-        print(f"Unpermute time: {unpermute:.2f} us")
-
-
-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("--use-customized-permute", action="store_true")
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--batch-size", type=int, required=False)
-    parser.add_argument("--trust-remote-code", action="store_true")
-    args = parser.parse_args()
-
-    main(args)

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 (
+from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
-    STR_DTYPE_TO_TORCH_DTYPE,
+                        create_kv_caches_with_random)
-    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(
+    query = torch.empty(num_seqs,
-        num_seqs, num_query_heads, head_size, dtype=dtype, device=device
+                        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(
+    key_caches, value_caches = create_kv_caches_with_random(NUM_BLOCKS,
-        NUM_BLOCKS,
+                                                            block_size,
-        block_size,
+                                                            1,
-        1,
+                                                            num_kv_heads,
-        num_kv_heads,
+                                                            head_size,
-        head_size,
+                                                            kv_cache_dtype,
-        kv_cache_dtype,
+                                                            dtype,
-        dtype,
+                                                            device=device)
-        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__":
+if __name__ == '__main__':
-    logger.warning(
+    logger.warning("This script benchmarks the paged attention kernel. "
-        "This script benchmarks the paged attention kernel. "
+                   "By default this is no longer used in vLLM inference.")
-        "By default this is no longer used in vLLM inference."
-    )
 
-    parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
+    parser = FlexibleArgumentParser(
-    parser.add_argument("--version", type=str, choices=["v1", "v2"], default="v2")
+        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(
+    parser.add_argument("--head-size",
-        "--head-size",
+                        type=int,
-        type=int,
+                        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-        choices=[64, 80, 96, 112, 120, 128, 192, 256],
+                        default=128)
-        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(
+    parser.add_argument("--dtype",
-        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+                        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)",
+        "ROCm (AMD GPU) supports fp8 (=fp8_e4m3)")
-    )
+    parser.add_argument("--custom-paged-attn",
-    parser.add_argument(
+                        action="store_true",
-        "--custom-paged-attn", action="store_true", help="Use custom paged attention"
+                        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(
+def main(num_tokens: int,
-    num_tokens: int,
+         hidden_size: int,
-    hidden_size: int,
+         static_scale: bool,
-    static_scale: bool,
+         quant_dtype: torch.dtype,
-    quant_dtype: torch.dtype,
+         dtype: torch.dtype,
-    dtype: torch.dtype,
+         seed: int = 0,
-    seed: int = 0,
+         do_profile: bool = False,
-    do_profile: bool = False,
+         num_warmup_iters: int = 5,
-    num_warmup_iters: int = 5,
+         num_iters: int = 100) -> None:
-    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(
+    parser.add_argument("--quant-dtype",
-        "--quant-dtype", type=str, choices=["fp8", "int8"], default="int8"
+                        type=str,
-    )
+                        choices=["fp8", "int8"],
-    parser.add_argument(
+                        default="int8")
-        "--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(
+    parser.add_argument("--num-iters",
-        "--num-iters",
+                        type=int,
-        type=int,
+                        default=100,
-        default=100,
+                        help="Number of benchmark iterations. "
-        help="Number of benchmark iterations. "
+                        "If --profile is set, this number is ignored")
-        "If --profile is set, this number is ignored",
-    )
 
     args = parser.parse_args()
     print(args)
 
-    main(
+    main(num_tokens=args.num_tokens,
-        num_tokens=args.num_tokens,
+         hidden_size=args.hidden_size,
-        hidden_size=args.hidden_size,
+         static_scale=args.static_scale,
-        static_scale=args.static_scale,
+         quant_dtype=to_torch_dtype(args.quant_dtype),
-        quant_dtype=to_torch_dtype(args.quant_dtype),
+         dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-        dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
+         seed=args.seed,
-        seed=args.seed,
+         do_profile=args.profile,
-        do_profile=args.profile,
+         num_warmup_iters=args.num_warmup_iters,
-        num_warmup_iters=args.num_warmup_iters,
+         num_iters=args.num_iters)
-        num_iters=args.num_iters,
-    )

benchmarks/kernels/benchmark_rmsnorm.py

@@ -4,14 +4,15 @@ import itertools
 from typing import Optional, Union
 
 import torch
+import triton
 from flashinfer.norm import fused_add_rmsnorm, rmsnorm
 from torch import nn
 
 from vllm import _custom_ops as vllm_ops
-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(
+    if torch.allclose(output_naive, output_flashinfer, atol=1e-2,
-        output_naive, output_flashinfer, atol=1e-2, rtol=1e-2
+                      rtol=1e-2) and torch.allclose(
-    ) and torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+                          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(
+    parser.add_argument("--use-residual",
-        "--use-residual", action="store_true", help="Whether to use residual connection"
+                        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(
+    calculate_diff(batch_size=args.batch_size,
-        batch_size=args.batch_size,
+                   seq_len=args.seq_len,
-        seq_len=args.seq_len,
+                   hidden_size=args.hidden_size,
-        hidden_size=args.hidden_size,
+                   use_residual=args.use_residual)
-        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(
+    batched_rope = get_rope(head_size, rotary_dim, max_position, base,
-        head_size,
+                            is_neox_style, {
-        rotary_dim,
+                                "rope_type": "linear",
-        max_position,
+                                "factor": tuple(scaling_factors)
-        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(
+            get_rope(head_size, rotary_dim, max_position, base, is_neox_style,
-                head_size,
+                     {
-                rotary_dim,
+                         "rope_type": "linear",
-                max_position,
+                         "factor": (scaling_factor, )
-                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(
+            accumulate([0] + [
-                [0]
+                max_position * scaling_factor * 2
-                + [
+                for scaling_factor in scaling_factors[:-1]
-                    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)
-        )
-    )
-    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(
+    parser.add_argument("--head-size",
-        "--head-size",
+                        type=int,
-        type=int,
+                        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-        choices=[64, 80, 96, 112, 120, 128, 192, 256],
+                        default=128)
-        default=128,
-    )
     parser.add_argument("--rotary-dim", type=int, choices=[16, 32], default=32)
-    parser.add_argument(
+    parser.add_argument("--dtype",
-        "--dtype", type=str, choices=["bfloat16", "float"], default="float"
+                        type=str,
-    )
+                        choices=["bfloat16", "float"],
+                        default="float")
     parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument(
+    parser.add_argument("--device",
-        "--device", type=str, choices=["cuda:0", "cuda:1"], default="cuda:0"
+                        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(), (
+assert current_platform.is_cuda(
-    "Only support tune w8a8 block fp8 kernel on CUDA device."
+), "Only support tune w8a8 block fp8 kernel on CUDA device."
-)
 
 DTYPE_MAP = {
     "float32": torch.float32,
@@ -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 (
+        return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
-            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+                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(
+                            configs.append({
-                                {
+                                "BLOCK_SIZE_M": block_m,
-                                    "BLOCK_SIZE_M": block_m,
+                                "BLOCK_SIZE_N": block_n,
-                                    "BLOCK_SIZE_N": block_n,
+                                "BLOCK_SIZE_K": block_k,
-                                    "BLOCK_SIZE_K": block_k,
+                                "GROUP_SIZE_M": group_size,
-                                    "GROUP_SIZE_M": group_size,
+                                "num_warps": num_warps,
-                                    "num_warps": num_warps,
+                                "num_stages": num_stages,
-                                    "num_stages": num_stages,
+                            })
-                                }
-                            )
     return configs
 
 
@@ -169,9 +165,15 @@ def get_weight_shapes(tp_size):
     return weight_shapes
 
 
-def benchmark_config(
+def benchmark_config(A,
-    A, B, As, Bs, block_size, config, out_dtype=torch.float16, num_iters=10
+                     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
+    As = torch.rand(M, k_tiles, dtype=torch.float32,
-    Bs = (
+                    device="cuda") * factor_for_scale
-        torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda")
+    Bs = (torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda") *
-        * factor_for_scale
+          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,
-            for batch_size in tqdm(batch_sizes, desc=f"GPU {gpu_id} - Batch sizes")
+                                     desc=f"GPU {gpu_id} - Batch sizes")
         ]
-        best_configs = {M: config for M, config in zip(batch_sizes, benchmark_results)}
+        best_configs = {
-        save_configs(N, K, block_n, block_k, best_configs, save_path, input_type)
+            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(
+        process_args.append({
-            {
+            "gpu_id": gpu_id,
-                "gpu_id": gpu_id,
+            "batch_sizes": batches_per_gpu[gpu_id],
-                "batch_sizes": batches_per_gpu[gpu_id],
+            "weight_shapes":
-                "weight_shapes": weight_shapes,  # Each GPU processes all weight shapes
+            weight_shapes,  # Each GPU processes all weight shapes
-                "args": args,
+            "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

@@ -6,15 +6,13 @@ import time
 # Import DeepGEMM functions
 import deep_gemm
 import torch
+import triton
 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,
+    per_token_group_quant_fp8, w8a8_block_fp8_matmul)
-    w8a8_block_fp8_matmul,
-)
-from vllm.triton_utils import triton
 
 
 # Copied from

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 "
+        description='Benchmark the latency of processing a single batch of '
-        "requests till completion."
+        'requests till completion.')
-    )
+    parser.add_argument('filename', type=str)
-    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(
+        sns.lineplot(data=df,
-            data=df,
+                     x="batch_size",
-            x="batch_size",
+                     y="median",
-            y="median",
+                     hue="kernel",
-            hue="kernel",
+                     style="kernel",
-            style="kernel",
+                     markers=True,
-            markers=True,
+                     dashes=False,
-            dashes=False,
+                     palette="Dark2")
-            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__(
+    def __init__(self, cuda_graph_params: Optional[CudaGraphBenchParams],
-        self,
+                 label: str, sub_label: str, description: str, fn: Callable,
-        cuda_graph_params: Optional[CudaGraphBenchParams],
+                 *args, **kwargs):
-        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_list, self.kwargs_list = self.collapse_argpool(
-        self.args_iterator = self.ArgsIterator(self.args_list, self.kwargs_list)
+            *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(
+            args_list[i] = tuple([
-                [arg[i] if isinstance(arg, ArgPool) else arg for arg in args_list[i]]
+                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 = ["You are an expert in large language models, aren't you?"
-LONG_PROMPT = " ".join(LONG_PROMPT)
+               ] * 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(
+        profiler.runctx('llm.generate(LONG_PROMPT, sampling_params)',
-            "llm.generate(LONG_PROMPT, sampling_params)", globals(), locals()
+                        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(
+    print(f"Hashing took {total_time:.2f} seconds,"
-        f"Hashing took {total_time:.2f} seconds,{percentage:.2f}% of the total runtime."
+          f"{percentage:.2f}% of the total runtime.")
-    )
 
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(
-        description="Benchmark the performance of hashing function in"
+        description='Benchmark the performance of hashing function in'
-        "automatic prefix caching."
+        'automatic prefix caching.')
-    )
+    parser.add_argument('--model', type=str, default='lmsys/longchat-7b-16k')
-    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("--tensor-parallel-size", "-tp", type=int, default=1)
+    parser.add_argument('--output-len', type=int, default=10)
-    parser.add_argument("--output-len", type=int, default=10)
+    parser.add_argument('--enable-prefix-caching',
-    parser.add_argument(
+                        action='store_true',
-        "--enable-prefix-caching", action="store_true", help="enable prefix caching"
+                        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

@@ -1,102 +1,41 @@
 #!/bin/bash
 
-# default values
+# Define the model to use
-MODEL=${MODEL:-"Qwen/Qwen2.5-7B-Instruct"}
+MODEL=${1:-"Qwen/Qwen2.5-7B-Instruct"}
-BACKEND=${BACKEND:-"vllm"}
+
-DATASET=${DATASET:-"xgrammar_bench"}
+# Define the backend to use
+BACKEND=${2:-"vllm"}
+
+# Define the dataset to use
+DATASET=${3:-"xgrammar_bench"}
+
+# Define the guided decoding backend
+GUIDED_BACKEND=${4:-"xgrammar"}
+
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-OUTPUT_DIR=${OUTPUT_DIR:-"$SCRIPT_DIR/structured_output_benchmark_results"}
+OUTPUT_DIR=${5:-"$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"}
 
-usage() {
+GUIDED_RATIO=${6:-0.5}
-    echo "Usage: $0 [options]"
-    echo "Options:"
-    echo "  --model MODEL                  Model to benchmark (default: $MODEL)"
-    echo "  --backend BACKEND              Backend to use (default: $BACKEND)" 
-    echo "  --dataset DATASET              Dataset to use (default: $DATASET)"
-    echo "  --max-new-tokens N             Maximum number of tokens to generate (default: $MAX_NEW_TOKENS)"
-    echo "  --output-dir DIR               Output directory for results (default: $OUTPUT_DIR)"
-    echo "  --port PORT                    Port to use (default: $PORT)"
-    echo "  --structured-output-ratio N    Ratio of structured outputs (default: $STRUCTURED_OUTPUT_RATIO)"
-    echo "  --tokenizer-mode MODE          Tokenizer mode to use (default: $TOKENIZER_MODE)"
-    echo "  --total-seconds N              Total seconds to run the benchmark (default: $TOTAL_SECONDS)"
-    echo "  -h, --help                     Show this help message and exit"
-    exit 0
-}
-
-# parse command line arguments
-while [[ $# -gt 0 ]]; do
-  case $1 in
-    --model)
-      MODEL="$2"
-      shift 2
-      ;;
-    --backend)
-      BACKEND="$2"
-      shift 2
-      ;;
-    --dataset)
-      DATASET="$2"
-      shift 2
-      ;;
-    --max-new-tokens)
-      MAX_NEW_TOKENS="$2"
-      shift 2
-      ;;
-    --output-dir)
-      OUTPUT_DIR="$2"
-      shift 2
-      ;;
-    --port)
-      PORT="$2"
-      shift 2
-      ;;
-    --structured-output-ratio)
-      STRUCTURED_OUTPUT_RATIO="$2"
-      shift 2
-      ;;
-    --tokenizer-mode)
-      TOKENIZER_MODE="$2"
-      shift 2
-      ;;
-    --total-seconds)
-      TOTAL_SECONDS="$2"
-      shift 2
-      ;;
-    -h|--help)
-      usage
-      ;;
-    *)
-      echo "Unknown argument: $1\n"
-      usage
-      ;;
-  esac
-done
 
 # Create output directory if it doesn't exist
 mkdir -p "$OUTPUT_DIR"
 
 # Define QPS values to test
-QPS_VALUES=(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-backend $GUIDED_BACKEND \
+               --structured-output-ratio $GUIDED_RATIO \
                --save-results \
-               --result-dir $OUTPUT_DIR \
+               --result-dir $OUTPUT_DIR"
-               --output-len $MAX_NEW_TOKENS \
-               --port $PORT \
-               --tokenizer-mode $TOKENIZER_MODE"
 
 echo "Starting structured output benchmark with model: $MODEL"
 echo "Backend: $BACKEND"
 echo "Dataset: $DATASET"
+echo "Structured output backend: $GUIDED_BACKEND"
 echo "Results will be saved to: $OUTPUT_DIR"
 echo "----------------------------------------"
 
@@ -109,17 +48,14 @@ for qps in "${QPS_VALUES[@]}"; do
   GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "unknown")
 
   # Construct filename for this run
-  FILENAME="${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"
+  FILENAME="${GUIDED_BACKEND}_${BACKEND}_${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/cpu_extension.cmake

@@ -167,33 +167,6 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
 
     FetchContent_MakeAvailable(oneDNN)
     
-    list(APPEND LIBS dnnl)
-elseif(POWER10_FOUND)
-    FetchContent_Declare(
-        oneDNN
-        GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-        GIT_TAG v3.7.2
-        GIT_PROGRESS TRUE
-        GIT_SHALLOW TRUE
-    )
-
-    set(ONEDNN_LIBRARY_TYPE "STATIC")
-    set(ONEDNN_BUILD_DOC "OFF")
-    set(ONEDNN_BUILD_EXAMPLES "OFF")
-    set(ONEDNN_BUILD_TESTS "OFF")
-    set(ONEDNN_ENABLE_WORKLOAD "INFERENCE")
-    set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
-    set(ONEDNN_BUILD_GRAPH "OFF")
-    set(ONEDNN_ENABLE_JIT_PROFILING "OFF")
-    set(ONEDNN_ENABLE_ITT_TASKS "OFF")
-    set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
-    set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
-    set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
-
-    set(DNNL_CPU_RUNTIME "OMP")
-
-    FetchContent_MakeAvailable(oneDNN)
-
     list(APPEND LIBS dnnl)
 endif()
 
@@ -224,10 +197,6 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
         "csrc/cpu/quant.cpp"
         "csrc/cpu/shm.cpp"
         ${VLLM_EXT_SRC})
-elseif(POWER10_FOUND)
-    set(VLLM_EXT_SRC
-        "csrc/cpu/quant.cpp"
-        ${VLLM_EXT_SRC})
 endif()
 
 #

cmake/utils.cmake

@@ -228,26 +228,11 @@ macro(set_gencode_flags_for_srcs)
                         "${multiValueArgs}" ${ARGN} )
 
   foreach(_ARCH ${arg_CUDA_ARCHS})
-    # handle +PTX suffix: generate both sm and ptx codes if requested
+    string(REPLACE "." "" _ARCH "${_ARCH}")
-    string(FIND "${_ARCH}" "+PTX" _HAS_PTX)
+    set_gencode_flag_for_srcs(
-    if(NOT _HAS_PTX EQUAL -1)
+      SRCS ${arg_SRCS}
-      string(REPLACE "+PTX" "" _BASE_ARCH "${_ARCH}")
+      ARCH "compute_${_ARCH}"
-      string(REPLACE "." "" _STRIPPED_ARCH "${_BASE_ARCH}")
+      CODE "sm_${_ARCH}")
-      set_gencode_flag_for_srcs(
-        SRCS ${arg_SRCS}
-        ARCH "compute_${_STRIPPED_ARCH}"
-        CODE "sm_${_STRIPPED_ARCH}")
-      set_gencode_flag_for_srcs(
-        SRCS ${arg_SRCS}
-        ARCH "compute_${_STRIPPED_ARCH}"
-        CODE "compute_${_STRIPPED_ARCH}")
-    else()
-      string(REPLACE "." "" _STRIPPED_ARCH "${_ARCH}")
-      set_gencode_flag_for_srcs(
-        SRCS ${arg_SRCS}
-        ARCH "compute_${_STRIPPED_ARCH}"
-        CODE "sm_${_STRIPPED_ARCH}")
-    endif()
   endforeach()
 
   if (${arg_BUILD_PTX_FOR_ARCH})
@@ -266,10 +251,7 @@ endmacro()
 #
 # For the given `SRC_CUDA_ARCHS` list of gencode versions in the form 
 #  `<major>.<minor>[letter]` compute the "loose intersection" with the 
-#  `TGT_CUDA_ARCHS` list of gencodes. We also support the `+PTX` suffix in
+#  `TGT_CUDA_ARCHS` list of gencodes. 
-#  `SRC_CUDA_ARCHS` which indicates that the PTX code should be built when there
-#  is a CUDA_ARCH in `TGT_CUDA_ARCHS` that is equal to or larger than the
-#  architecture in `SRC_CUDA_ARCHS`.
 # The loose intersection is defined as:
 #   { max{ x \in tgt | x <= y } | y \in src, { x \in tgt | x <= y } != {} }
 #  where `<=` is the version comparison operator.
@@ -286,63 +268,44 @@ endmacro()
 #   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
 #   OUT_CUDA_ARCHS="8.0;8.6;9.0;9.0a"
 #
-# Example With PTX:
-#   SRC_CUDA_ARCHS="8.0+PTX"
-#   TGT_CUDA_ARCHS="9.0"
-#   cuda_archs_loose_intersection(OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
-#   OUT_CUDA_ARCHS="8.0+PTX"
-#
 function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_ARCHS)
-  set(_SRC_CUDA_ARCHS "${SRC_CUDA_ARCHS}")
+  list(REMOVE_DUPLICATES SRC_CUDA_ARCHS)
-  set(_TGT_CUDA_ARCHS ${TGT_CUDA_ARCHS})
+  set(TGT_CUDA_ARCHS_ ${TGT_CUDA_ARCHS})
-
-  # handle +PTX suffix: separate base arch for matching, record PTX requests
-  set(_PTX_ARCHS)
-  foreach(_arch ${_SRC_CUDA_ARCHS})
-    if(_arch MATCHES "\\+PTX$")
-      string(REPLACE "+PTX" "" _base "${_arch}")
-      list(APPEND _PTX_ARCHS "${_base}")
-      list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
-      list(APPEND _SRC_CUDA_ARCHS "${_base}")
-    endif()
-  endforeach()
-  list(REMOVE_DUPLICATES _PTX_ARCHS)
-  list(REMOVE_DUPLICATES _SRC_CUDA_ARCHS)
 
   # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
   # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS
   set(_CUDA_ARCHS)
-  if ("9.0a" IN_LIST _SRC_CUDA_ARCHS)
+  if ("9.0a" IN_LIST SRC_CUDA_ARCHS)
-    list(REMOVE_ITEM _SRC_CUDA_ARCHS "9.0a")
+    list(REMOVE_ITEM SRC_CUDA_ARCHS "9.0a")
-    if ("9.0" IN_LIST TGT_CUDA_ARCHS)
+    if ("9.0" IN_LIST TGT_CUDA_ARCHS_)
-      list(REMOVE_ITEM _TGT_CUDA_ARCHS "9.0")
+      list(REMOVE_ITEM TGT_CUDA_ARCHS_ "9.0")
       set(_CUDA_ARCHS "9.0a")
     endif()
   endif()
 
-  if ("10.0a" IN_LIST _SRC_CUDA_ARCHS)
+  if ("10.0a" IN_LIST SRC_CUDA_ARCHS)
-    list(REMOVE_ITEM _SRC_CUDA_ARCHS "10.0a")
+    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/math.hpp

@@ -7,22 +7,3 @@ inline constexpr uint32_t next_pow_2(uint32_t const num) {
   if (num <= 1) return num;
   return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1));
 }
-
-template <typename A, typename B>
-static inline constexpr auto div_ceil(A a, B b) {
-  return (a + b - 1) / b;
-}
-
-// Round a down to the next multiple of b. The caller is responsible for making
-// sure that b is non-zero
-template <typename T>
-inline constexpr T round_to_previous_multiple_of(T a, T b) {
-  return a % b == 0 ? a : (a / b) * b;
-}
-
-// Round a up to the next multiple of b. The caller is responsible for making
-// sure that b is non-zero
-template <typename T>
-inline constexpr T round_to_next_multiple_of(T a, T b) {
-  return a % b == 0 ? a : ((a / b) + 1) * b;
-}

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_vsx.hpp

@@ -4,7 +4,6 @@
 
 #include <altivec.h>
 #include <cmath>
-#include <algorithm>
 #include <torch/all.h>
 
 namespace vec_op {
@@ -63,10 +62,6 @@ typedef struct f32x4x4_t {
   __vector float val[4];
 } f32x4x4_t;
 
-typedef struct i32x4x4_t {
-  __vector int32_t val[4];
-} i32x4x4_t;
-
 struct FP32Vec8;
 struct FP32Vec16;
 
@@ -103,28 +98,6 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
     vec_xst(reg.val[0], 0, (signed short*)ptr);
     vec_xst(reg.val[1], 16, (signed short*)ptr);
   }
-
-  void save(void* ptr, const int elem_num) const {
-    const int clamped_elem = std::max(0, std::min(elem_num, 16));
-
-    // Calculate elements to store in each 128-bit part (8 elements each)
-    const int elements_val0 = std::min(clamped_elem, 8);
-    const int elements_val1 = std::max(clamped_elem - 8, 0);
-
-    // Convert elements to bytes (2 bytes per element)
-    const size_t bytes_val0 = elements_val0 * sizeof(signed short);
-    const size_t bytes_val1 = elements_val1 * sizeof(signed short);
-
-    signed short* dest = static_cast<signed short*>(ptr);
-    // Store the first part using vec_xst_len
-    if (bytes_val0 > 0) {
-      vec_xst_len(reg.val[0], dest, bytes_val0);
-    }
-    // Store the second part if needed
-    if (bytes_val1 > 0) {
-      vec_xst_len(reg.val[1], dest + elements_val0, bytes_val1);
-    }
-  }
 };
 
 const static __vector signed short zero = vec_splats((signed short)0);
@@ -284,64 +257,6 @@ struct FP32Vec8 : public Vec<FP32Vec8> {
   }
 };
 
-struct INT32Vec16 : public Vec<INT32Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  union AliasReg {
-    i32x4x4_t reg;
-    int32_t values[VEC_ELEM_NUM];
-  };
-
-  i32x4x4_t reg;
-
-  explicit INT32Vec16(const void* data_ptr) {
-    reg.val[0] = vec_xl(0, reinterpret_cast<const __vector int32_t*>(data_ptr));
-    reg.val[1] =
-        vec_xl(16, reinterpret_cast<const __vector int32_t*>(data_ptr));
-    reg.val[2] =
-        vec_xl(32, reinterpret_cast<const __vector int32_t*>(data_ptr));
-    reg.val[3] =
-        vec_xl(48, reinterpret_cast<const __vector int32_t*>(data_ptr));
-  }
-
-  void save(int32_t* ptr) const {
-    vec_xst(reg.val[0], 0, reinterpret_cast<__vector int32_t*>(ptr));
-    vec_xst(reg.val[1], 16, reinterpret_cast<__vector int32_t*>(ptr));
-    vec_xst(reg.val[2], 32, reinterpret_cast<__vector int32_t*>(ptr));
-    vec_xst(reg.val[3], 48, reinterpret_cast<__vector int32_t*>(ptr));
-  }
-
-  void save(int32_t* ptr, const int elem_num) const {
-    const int elements_in_chunk1 =
-        (elem_num >= 0) ? ((elem_num >= 4) ? 4 : elem_num) : 0;
-    const int elements_in_chunk2 =
-        (elem_num > 4) ? ((elem_num >= 8) ? 4 : elem_num - 4) : 0;
-    const int elements_in_chunk3 =
-        (elem_num > 8) ? ((elem_num >= 12) ? 4 : elem_num - 8) : 0;
-    const int elements_in_chunk4 =
-        (elem_num > 12) ? ((elem_num >= 16) ? 4 : elem_num - 12) : 0;
-
-    const size_t bytes_chunk1 =
-        static_cast<size_t>(elements_in_chunk1 * sizeof(int32_t));
-    const size_t bytes_chunk2 =
-        static_cast<size_t>(elements_in_chunk2 * sizeof(int32_t));
-    const size_t bytes_chunk3 =
-        static_cast<size_t>(elements_in_chunk3 * sizeof(int32_t));
-    const size_t bytes_chunk4 =
-        static_cast<size_t>(elements_in_chunk4 * sizeof(int32_t));
-
-    vec_xst_len(reg.val[0], reinterpret_cast<int32_t*>(ptr), bytes_chunk1);
-    vec_xst_len(reg.val[1],
-                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 16),
-                bytes_chunk2);
-    vec_xst_len(reg.val[2],
-                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 32),
-                bytes_chunk3);
-    vec_xst_len(reg.val[3],
-                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 48),
-                bytes_chunk4);
-  }
-};
-
 struct FP32Vec16 : public Vec<FP32Vec16> {
   constexpr static int VEC_ELEM_NUM = 16;
   union AliasReg {
@@ -404,13 +319,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
-  explicit FP32Vec16(const INT32Vec16& v) {
-    reg.val[0] = vec_ctf(v.reg.val[0], 0);
-    reg.val[1] = vec_ctf(v.reg.val[1], 0);
-    reg.val[2] = vec_ctf(v.reg.val[2], 0);
-    reg.val[3] = vec_ctf(v.reg.val[3], 0);
-  }
-
   FP32Vec16 operator*(const FP32Vec16& b) const {
     return FP32Vec16(f32x4x4_t({vec_mul(reg.val[0], b.reg.val[0]),
                                 vec_mul(reg.val[1], b.reg.val[1]),
@@ -439,117 +347,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
                                 vec_div(reg.val[3], b.reg.val[3])}));
   }
 
-  FP32Vec16 clamp(const FP32Vec16& min, const FP32Vec16& max) const {
-    return FP32Vec16(f32x4x4_t(
-        {vec_min(max.reg.val[0], vec_max(min.reg.val[0], reg.val[0])),
-         vec_min(max.reg.val[1], vec_max(min.reg.val[1], reg.val[1])),
-         vec_min(max.reg.val[2], vec_max(min.reg.val[2], reg.val[2])),
-         vec_min(max.reg.val[3], vec_max(min.reg.val[3], reg.val[3]))}));
-  }
-
-  FP32Vec16 max(const FP32Vec16& b) const {
-    return FP32Vec16(f32x4x4_t({vec_max(reg.val[0], b.reg.val[0]),
-                                vec_max(reg.val[1], b.reg.val[1]),
-                                vec_max(reg.val[2], b.reg.val[2]),
-                                vec_max(reg.val[3], b.reg.val[3])}));
-  }
-
-  FP32Vec16 max(const FP32Vec16& b, int elem_num) const {
-    FP32Vec16 result;
-
-    // Create a vector of element indices for each chunk
-    __vector unsigned int indices = {0, 1, 2, 3};
-    __vector unsigned int elem_num_vec =
-        vec_splats(static_cast<unsigned int>(elem_num));
-
-    // Compute masks for each chunk
-    __vector unsigned int chunk_offset0 = {0, 0, 0,
-                                           0};  // Chunk 0: Elements 0-3
-    __vector unsigned int chunk_offset1 = {4, 4, 4,
-                                           4};  // Chunk 1: Elements 4-7
-    __vector unsigned int chunk_offset2 = {8, 8, 8,
-                                           8};  // Chunk 2: Elements 8-11
-    __vector unsigned int chunk_offset3 = {12, 12, 12,
-                                           12};  // Chunk 3: Elements 12-15
-
-    // Compute masks for each chunk
-    __vector bool int mask0 = vec_cmplt(indices + chunk_offset0, elem_num_vec);
-    __vector bool int mask1 = vec_cmplt(indices + chunk_offset1, elem_num_vec);
-    __vector bool int mask2 = vec_cmplt(indices + chunk_offset2, elem_num_vec);
-    __vector bool int mask3 = vec_cmplt(indices + chunk_offset3, elem_num_vec);
-
-    // Apply masks to compute the result for each chunk
-    result.reg.val[0] = vec_sel(this->reg.val[0],
-                                vec_max(this->reg.val[0], b.reg.val[0]), mask0);
-    result.reg.val[1] = vec_sel(this->reg.val[1],
-                                vec_max(this->reg.val[1], b.reg.val[1]), mask1);
-    result.reg.val[2] = vec_sel(this->reg.val[2],
-                                vec_max(this->reg.val[2], b.reg.val[2]), mask2);
-    result.reg.val[3] = vec_sel(this->reg.val[3],
-                                vec_max(this->reg.val[3], b.reg.val[3]), mask3);
-
-    return FP32Vec16(result.reg);
-  }
-
-  FP32Vec16 min(const FP32Vec16& b) const {
-    return FP32Vec16(f32x4x4_t({vec_min(reg.val[0], b.reg.val[0]),
-                                vec_min(reg.val[1], b.reg.val[1]),
-                                vec_min(reg.val[2], b.reg.val[2]),
-                                vec_min(reg.val[3], b.reg.val[3])}));
-  }
-
-  FP32Vec16 min(const FP32Vec16& b, int elem_num) const {
-    FP32Vec16 result;
-
-    vector unsigned int indices = {0, 1, 2, 3};
-    vector unsigned int elem_num_vec =
-        vec_splats(static_cast<unsigned int>(elem_num));
-
-    vector unsigned int chunk_offset0 = {0, 0, 0, 0};
-    vector unsigned int chunk_offset1 = {4, 4, 4, 4};
-    vector unsigned int chunk_offset2 = {8, 8, 8, 8};
-    vector unsigned int chunk_offset3 = {12, 12, 12, 12};
-
-    vector bool int mask0 = vec_cmplt(indices + chunk_offset0, elem_num_vec);
-    vector bool int mask1 = vec_cmplt(indices + chunk_offset1, elem_num_vec);
-    vector bool int mask2 = vec_cmplt(indices + chunk_offset2, elem_num_vec);
-    vector bool int mask3 = vec_cmplt(indices + chunk_offset3, elem_num_vec);
-
-    result.reg.val[0] = vec_sel(this->reg.val[0],
-                                vec_min(this->reg.val[0], b.reg.val[0]), mask0);
-    result.reg.val[1] = vec_sel(this->reg.val[1],
-                                vec_min(this->reg.val[1], b.reg.val[1]), mask1);
-    result.reg.val[2] = vec_sel(this->reg.val[2],
-                                vec_min(this->reg.val[2], b.reg.val[2]), mask2);
-    result.reg.val[3] = vec_sel(this->reg.val[3],
-                                vec_min(this->reg.val[3], b.reg.val[3]), mask3);
-
-    return FP32Vec16(result.reg);
-  }
-
-  FP32Vec16 abs() const {
-    return FP32Vec16(f32x4x4_t({vec_abs(reg.val[0]), vec_abs(reg.val[1]),
-                                vec_abs(reg.val[2]), vec_abs(reg.val[3])}));
-  }
-
-  float reduce_max() {
-    __vector float max01 = vec_max(reg.val[0], reg.val[1]);
-    __vector float max23 = vec_max(reg.val[2], reg.val[3]);
-    __vector float max_all = vec_max(max01, max23);
-    __vector float temp = vec_max(max_all, vec_sld(max_all, max_all, 8));
-    temp = vec_max(temp, vec_sld(temp, temp, 4));
-    return vec_extract(temp, 0);
-  }
-
-  float reduce_min() {
-    __vector float min01 = vec_min(reg.val[0], reg.val[1]);
-    __vector float min23 = vec_min(reg.val[2], reg.val[3]);
-    __vector float min_all = vec_min(min01, min23);
-    __vector float temp = vec_min(min_all, vec_sld(min_all, min_all, 8));
-    temp = vec_min(temp, vec_sld(temp, temp, 4));
-    return vec_extract(temp, 0);
-  }
-
   float reduce_sum() const {
     AliasReg ar;
     ar.reg = reg;
@@ -580,68 +377,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
     vec_xst(reg.val[2], 32, ptr);
     vec_xst(reg.val[3], 48, ptr);
   }
-
-  void save(float* ptr, const int elem_num) const {
-    const int elements_in_chunk1 =
-        (elem_num >= 0) ? ((elem_num >= 4) ? 4 : elem_num) : 0;
-    const int elements_in_chunk2 =
-        (elem_num > 4) ? ((elem_num >= 8) ? 4 : elem_num - 4) : 0;
-    const int elements_in_chunk3 =
-        (elem_num > 8) ? ((elem_num >= 12) ? 4 : elem_num - 8) : 0;
-    const int elements_in_chunk4 =
-        (elem_num > 12) ? ((elem_num >= 16) ? 4 : elem_num - 12) : 0;
-
-    const size_t bytes_chunk1 =
-        static_cast<size_t>(elements_in_chunk1 * sizeof(float));
-    const size_t bytes_chunk2 =
-        static_cast<size_t>(elements_in_chunk2 * sizeof(float));
-    const size_t bytes_chunk3 =
-        static_cast<size_t>(elements_in_chunk3 * sizeof(float));
-    const size_t bytes_chunk4 =
-        static_cast<size_t>(elements_in_chunk4 * sizeof(float));
-
-    vec_xst_len(reg.val[0], ptr, bytes_chunk1);
-    vec_xst_len(reg.val[1],
-                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 16),
-                bytes_chunk2);
-    vec_xst_len(reg.val[2],
-                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 32),
-                bytes_chunk3);
-    vec_xst_len(reg.val[3],
-                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 48),
-                bytes_chunk4);
-  }
-};
-
-struct INT8Vec16 : public Vec<INT8Vec16> {
-  constexpr static int VEC_NUM_ELEM = 16;  // 128 bits / 8 bits = 16
-
-  union AliasReg {
-    __vector signed char reg;
-    int8_t values[VEC_NUM_ELEM];
-  };
-
-  __vector signed char reg;
-
-  explicit INT8Vec16(const FP32Vec16& vec) {
-    __vector signed int ret[4];
-    ret[0] = vec_cts(vec.reg.val[0], 0);
-    ret[1] = vec_cts(vec.reg.val[1], 0);
-    ret[2] = vec_cts(vec.reg.val[2], 0);
-    ret[3] = vec_cts(vec.reg.val[3], 0);
-
-    __vector signed short packed1 = vec_packs(ret[0], ret[1]);
-    __vector signed short packed2 = vec_packs(ret[2], ret[3]);
-
-    reg = vec_packs(packed1, packed2);
-  }
-
-  void save(void* ptr) const {
-    *reinterpret_cast<__vector signed char*>(ptr) = reg;
-  }
-  void save(signed char* ptr, const int elem_num) {
-    vec_xst_len(reg, ptr, static_cast<size_t>(elem_num));
-  }
 };
 
 template <typename T>

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/cpu/pos_encoding.cpp

@@ -9,8 +9,7 @@ void rotary_embedding_impl(
     scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
                                    /// head_size] or [num_tokens, num_heads,
                                    /// head_size]
-    scalar_t* __restrict__ key,  // nullptr (optional) or
+    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
@@ -86,13 +85,10 @@ void rotary_embedding_impl(
       compute_loop(token_head, cache_ptr, query);
     }
 
-    if (key != nullptr) {
+    for (int i = 0; i < num_kv_heads; ++i) {
-      for (int i = 0; i < num_kv_heads; ++i) {
+      const int head_idx = i;
-        const int head_idx = i;
+      const int64_t token_head = token_idx * key_stride + head_idx * head_size;
-        const int64_t token_head =
+      compute_loop(token_head, cache_ptr, key);
-            token_idx * key_stride + head_idx * head_size;
-        compute_loop(token_head, cache_ptr, key);
-      }
     }
   }
 }
@@ -104,8 +100,7 @@ void rotary_embedding_gptj_impl(
     scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
                                    /// head_size] or [num_tokens, num_heads,
                                    /// head_size]
-    scalar_t* __restrict__ key,  // nullptr (optional) or
+    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
@@ -143,10 +138,6 @@ void rotary_embedding_gptj_impl(
     }
   }
 
-  if (key == nullptr) {
-    return;
-  }
-
 #pragma omp parallel for collapse(2)
   for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
     for (int i = 0; i < num_kv_heads; ++i) {
@@ -177,13 +168,13 @@ void rotary_embedding_gptj_impl(
 };  // namespace
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                      std::optional<torch::Tensor> key, int64_t head_size,
+                      torch::Tensor& key, int64_t head_size,
                       torch::Tensor& cos_sin_cache, bool is_neox) {
   int num_tokens = positions.numel();
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;
-  int num_kv_heads = key.has_value() ? key->size(-1) / head_size : num_heads;
+  int num_kv_heads = key.size(-1) / head_size;
-  int64_t key_stride = key.has_value() ? key->stride(-2) : 0;
+  int64_t key_stride = key.stride(-2);
   int64_t query_stride = query.stride(-2);
 
   VLLM_DISPATCH_FLOATING_TYPES(
@@ -192,15 +183,15 @@ void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
         if (is_neox) {
           rotary_embedding_impl(
               positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
+              rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
-              key_stride, num_heads, num_kv_heads, head_size, num_tokens);
+              head_size, num_tokens);
         } else {
           rotary_embedding_gptj_impl(
               positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
+              rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
-              key_stride, num_heads, num_kv_heads, head_size, num_tokens);
+              head_size, num_tokens);
         }
 
         CPU_KERNEL_GUARD_OUT(rotary_embedding_impl)

csrc/cpu/quant.cpp

@@ -239,280 +239,6 @@ void static_quant_epilogue(const float* input, scalar_t* output,
   }
 }
 
-template <bool AZP, bool PerChannel, bool Bias, typename scalar_t>
-void dynamic_quant_epilogue(const float* input, scalar_t* output,
-                            const float* a_scale, const float* b_scale,
-                            const int32_t* azp, const int32_t* azp_adj,
-                            const scalar_t* bias, const int num_tokens,
-                            const int hidden_size) {
-  CPU_KERNEL_GUARD_IN(dynamic_quant_epilogue)
-  using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
-  using azp_adj_load_vec_t =
-      typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
-  using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
-  constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
-
-  #pragma omp parallel for
-  for (int i = 0; i < num_tokens; ++i) {
-    int j = 0;
-    cvt_vec_t token_scale_vec(a_scale[i]);
-    cvt_vec_t token_zp_scale_vec;
-    if constexpr (AZP) {
-      float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
-      if constexpr (!PerChannel) {
-        zp_scale_val *= *b_scale;
-      }
-      token_zp_scale_vec = cvt_vec_t(zp_scale_val);
-    }
-
-    for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
-      cvt_vec_t elems_fp32(input + i * hidden_size + j);
-      elems_fp32 = elems_fp32 * token_scale_vec;
-
-      if constexpr (AZP) {
-        azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
-        cvt_vec_t azp_adj_fp32(azp_adj_vec);
-        azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
-
-        if constexpr (PerChannel) {
-          cvt_vec_t b_scale_vec(b_scale + j);
-          azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
-        }
-
-        elems_fp32 = elems_fp32 - azp_adj_fp32;
-      }
-
-      if constexpr (Bias) {
-        load_vec_t bias_vec(bias + j);
-        cvt_vec_t bias_vec_fp32(bias_vec);
-        elems_fp32 = elems_fp32 + bias_vec_fp32;
-      }
-
-      load_vec_t elems_out(elems_fp32);
-      elems_out.save(output + i * hidden_size + j);
-    }
-
-    cvt_vec_t elems_fp32(input + i * hidden_size + j);
-    elems_fp32 = elems_fp32 * token_scale_vec;
-
-    if constexpr (AZP) {
-      azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
-      cvt_vec_t azp_adj_fp32(azp_adj_vec);
-      azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
-
-      if constexpr (PerChannel) {
-        cvt_vec_t b_scale_vec(b_scale + j);
-        azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
-      }
-
-      elems_fp32 = elems_fp32 - azp_adj_fp32;
-    }
-
-    if constexpr (Bias) {
-      load_vec_t bias_vec(bias + j);
-      cvt_vec_t bias_vec_fp32(bias_vec);
-      elems_fp32 = elems_fp32 + bias_vec_fp32;
-    }
-
-    load_vec_t elems_out(elems_fp32);
-    elems_out.save(output + i * hidden_size + j, hidden_size - j);
-  }
-}
-#elif defined(__powerpc64__)
-template <bool AZP, typename scalar_t>
-void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
-                                   const float* scale, const int32_t* azp,
-                                   const int num_tokens,
-                                   const int hidden_size) {
-  using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
-  using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
-  constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
-
-  constexpr float i8_min =
-      static_cast<float>(std::numeric_limits<int8_t>::min());
-  constexpr float i8_max =
-      static_cast<float>(std::numeric_limits<int8_t>::max());
-
-  const cvt_vec_t inv_scale(1.0 / *scale);
-  const cvt_vec_t i8_min_vec(i8_min);
-  const cvt_vec_t i8_max_vec(i8_max);
-
-  cvt_vec_t zp_vec;
-  if constexpr (AZP) {
-    zp_vec = cvt_vec_t(static_cast<float>(*azp));
-  }
-  #pragma omp parallel for
-  for (int i = 0; i < num_tokens; ++i) {
-    int j = 0;
-    for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
-      load_vec_t elems(input + i * hidden_size + j);
-      cvt_vec_t elems_fp32(elems);
-      elems_fp32 = elems_fp32 * inv_scale;
-      if constexpr (AZP) {
-        elems_fp32 = elems_fp32 + zp_vec;
-      }
-      elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
-      vec_op::INT8Vec16 elems_int8(elems_fp32);
-      elems_int8.save(output + i * hidden_size + j);
-    }
-    load_vec_t elems(input + i * hidden_size + j);
-    cvt_vec_t elems_fp32(elems);
-    elems_fp32 = elems_fp32 * inv_scale;
-
-    if constexpr (AZP) {
-      elems_fp32 = elems_fp32 + zp_vec;
-    }
-
-    elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
-    vec_op::INT8Vec16 elems_int8(elems_fp32);
-    elems_int8.save(output + i * hidden_size + j, hidden_size - j);
-  }
-}
-template <bool AZP, typename scalar_t>
-void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
-                                    float* scale, int32_t* azp,
-                                    const int num_tokens,
-                                    const int hidden_size) {
-  using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
-  using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
-  constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
-
-  constexpr float i8_min =
-      static_cast<float>(std::numeric_limits<int8_t>::min());
-  constexpr float i8_max =
-      static_cast<float>(std::numeric_limits<int8_t>::max());
-  const cvt_vec_t i8_min_vec(i8_min);
-  const cvt_vec_t i8_max_vec(i8_max);
-
-  #pragma omp parallel for
-  for (int i = 0; i < num_tokens; ++i) {
-    cvt_vec_t max_value(std::numeric_limits<float>::lowest());
-    cvt_vec_t min_value(std::numeric_limits<float>::max());
-    {
-      int j = 0;
-      for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
-        load_vec_t elems(input + i * hidden_size + j);
-        cvt_vec_t elems_fp32(elems);
-        if constexpr (AZP) {
-          max_value = max_value.max(elems_fp32);
-          min_value = min_value.min(elems_fp32);
-        } else {
-          max_value = max_value.max(elems_fp32.abs());
-        }
-      }
-
-      load_vec_t elems(input + i * hidden_size + j);
-      cvt_vec_t elems_fp32(elems);
-
-      if (j + vec_elem_num == hidden_size) {
-        if constexpr (AZP) {
-          max_value = max_value.max(elems_fp32);
-          min_value = min_value.min(elems_fp32);
-        } else {
-          max_value = max_value.max(elems_fp32.abs());
-        }
-      } else {
-        if constexpr (AZP) {
-          max_value = max_value.max(elems_fp32, hidden_size - j);
-          min_value = min_value.min(elems_fp32, hidden_size - j);
-        } else {
-          max_value = max_value.max(elems_fp32.abs(), hidden_size - j);
-        }
-      }
-    }
-
-    float scale_val, azp_val;
-    if constexpr (AZP) {
-      float max_scalar = max_value.reduce_max();
-      float min_scalar = min_value.reduce_min();
-      scale_val = (max_scalar - min_scalar) / 255.0f;
-      azp_val = std::nearbyint(-128.0f - min_scalar / scale_val);
-      azp[i] = static_cast<int32_t>(azp_val);
-      scale[i] = scale_val;
-    } else {
-      scale_val = max_value.reduce_max() / 127.0f;
-      scale[i] = scale_val;
-    }
-
-    const cvt_vec_t inv_scale(1.0 / scale_val);
-    const cvt_vec_t azp_vec(azp_val);
-
-    {
-      int j = 0;
-      for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
-        load_vec_t elems(input + i * hidden_size + j);
-        cvt_vec_t elems_fp32(elems);
-        elems_fp32 = (elems_fp32 * inv_scale);
-
-        if constexpr (AZP) {
-          elems_fp32 = elems_fp32 + azp_vec;
-        }
-        elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
-        vec_op::INT8Vec16 elems_int8(elems_fp32);
-        elems_int8.save(output + i * hidden_size + j);
-      }
-
-      load_vec_t elems(input + i * hidden_size + j);
-      cvt_vec_t elems_fp32(elems);
-      elems_fp32 = (elems_fp32 * inv_scale);
-
-      if constexpr (AZP) {
-        elems_fp32 = elems_fp32 + azp_vec;
-      }
-      elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
-      vec_op::INT8Vec16 elems_int8(elems_fp32);
-      elems_int8.save(output + i * hidden_size + j, hidden_size - j);
-    }
-  }
-}
-template <bool PerChannel, typename scalar_t>
-void static_quant_epilogue(const float* input, scalar_t* output,
-                           const float a_scale, const float* b_scale,
-                           const int32_t* azp_with_adj, const int num_tokens,
-                           const int hidden_size) {
-  CPU_KERNEL_GUARD_IN(dynamic_output_scale_impl)
-  using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
-  using azp_adj_load_vec_t =
-      typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
-  using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
-  constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
-
-  #pragma omp parallel for
-  for (int i = 0; i < num_tokens; ++i) {
-    cvt_vec_t a_scale_vec(a_scale);
-    cvt_vec_t b_scale_vec(*b_scale);
-    cvt_vec_t scale_vec = a_scale_vec * b_scale_vec;
-
-    int j = 0;
-    for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
-      cvt_vec_t elems_fp32(input + i * hidden_size + j);
-      azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
-      cvt_vec_t azp_adj_fp32(azp_adj_vec);
-
-      if constexpr (PerChannel) {
-        b_scale_vec = cvt_vec_t(b_scale + j);
-        scale_vec = b_scale_vec * a_scale_vec;
-      }
-      elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
-      load_vec_t elems_out(elems_fp32);
-      elems_out.save(output + i * hidden_size + j);
-    }
-
-    cvt_vec_t elems_fp32(input + i * hidden_size + j);
-    azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
-    cvt_vec_t azp_adj_fp32(azp_adj_vec);
-
-    if constexpr (PerChannel) {
-      b_scale_vec = cvt_vec_t(b_scale + j);
-      scale_vec = b_scale_vec * a_scale_vec;
-    }
-
-    elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
-
-    load_vec_t elems_out(elems_fp32);
-    elems_out.save(output + i * hidden_size + j, hidden_size - j);
-  }
-}
 template <bool AZP, bool PerChannel, bool Bias, typename scalar_t>
 void dynamic_quant_epilogue(const float* input, scalar_t* output,
                             const float* a_scale, const float* b_scale,
@@ -598,8 +324,7 @@ void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
                                    const float* scale, const int32_t* azp,
                                    const int num_tokens,
                                    const int hidden_size) {
-  TORCH_CHECK(
+  TORCH_CHECK(false, "static_scaled_int8_quant_impl requires AVX512 support.")
-      false, "static_scaled_int8_quant_impl requires AVX512/powerpc64 support.")
 }
 
 template <typename scalar_t>
@@ -607,9 +332,7 @@ void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
                                     float* scale, int32_t* azp,
                                     const int num_tokens,
                                     const int hidden_size) {
-  TORCH_CHECK(
+  TORCH_CHECK(false, "dynamic_scaled_int8_quant_impl requires AVX512 support.")
-      false,
-      "dynamic_scaled_int8_quant_impl requires AVX512/powerpc64 support.")
 }
 
 template <bool PerChannel, typename scalar_t>
@@ -617,7 +340,7 @@ void static_quant_epilogue(const float* input, scalar_t* output,
                            const float a_scale, const float* b_scale,
                            const int32_t* azp_with_adj, const int num_tokens,
                            const int hidden_size) {
-  TORCH_CHECK(false, "static_quant_epilogue requires AVX512/powerpc64 support.")
+  TORCH_CHECK(false, "static_quant_epilogue requires AVX512 support.")
 }
 
 template <typename scalar_t>
@@ -626,8 +349,7 @@ void dynamic_quant_epilogue(const float* input, scalar_t* output,
                             const int32_t* azp, const int32_t* azp_with_adj,
                             const scalar_t* bias, const int num_tokens,
                             const int hidden_size) {
-  TORCH_CHECK(false,
+  TORCH_CHECK(false, "dynamic_quant_epilogue requires AVX512 support.")
-              "dynamic_quant_epilogue requires AVX512/powerpc64 support.")
 }
 #endif
 }  // namespace
@@ -889,58 +611,3 @@ void dynamic_scaled_int8_quant(
         }
       });
 }
-
-#if defined(__powerpc64__)
-void int8_scaled_mm_ppc64le(torch::Tensor& c,        // [M, OC], row-major
-                            const torch::Tensor& a,  // [M, IC], row-major
-                            const torch::Tensor& b,  // [IC, OC], column-major
-                            const torch::Tensor& a_scales,
-                            const torch::Tensor& b_scales,
-                            const std::optional<torch::Tensor>& bias  // [OC]
-) {
-  CPU_KERNEL_GUARD_IN(cutlass_scaled_mm)
-  // Checks for conformality
-  TORCH_CHECK(a.dtype() == torch::kInt8 && b.dtype() == torch::kInt8,
-              "int8_scaled_mm_ppc64le only supports INT8 inputs.");
-  TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2);
-  TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) &&
-              b.size(1) == c.size(1));
-  // We dont need this
-  TORCH_CHECK(a_scales.numel() == 1 || a_scales.numel() == a.size(0));
-  TORCH_CHECK(b_scales.numel() == 1 || b_scales.numel() == b.size(1));
-
-  // Check for strides and alignment
-  TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1);  // Row-major
-  TORCH_CHECK(b.stride(0) == 1);                      // Column-major
-  TORCH_CHECK(c.stride(0) % 16 == 0 &&
-              b.stride(1) % 16 == 0);  // 16 Byte Alignment
-  TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
-
-  if (bias) {
-    TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous() &&
-                bias->dim() == 1);
-  }
-  VLLM_DISPATCH_FLOATING_TYPES(c.scalar_type(), "int8_scaled_mm_ppc64le", [&] {
-    torch::Tensor tmp_fp32_out = torch::empty_like(c, ::at::ScalarType::Float);
-    // Compute C_inter=s_b * (A@B)
-    DNNLPrimitiveHelper<true>::gemm_s8s8_jit<float, void>(
-        a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
-        tmp_fp32_out.data_ptr<float>(), nullptr, a.size(0), b.size(1),
-        a.size(1), nullptr, b_scales.data_ptr<float>(), 0, b_scales.numel());
-    if (bias.has_value()) {
-      // Compute C=s_a * C_inter + bias
-      dynamic_quant_epilogue<false, true, true>(
-          tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
-          a_scales.data_ptr<float>(), nullptr, nullptr, nullptr,
-          bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
-    } else {
-      // Compute C=s_a * C_inter
-      dynamic_quant_epilogue<false, true, false, scalar_t>(
-          tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
-          a_scales.data_ptr<float>(), nullptr, nullptr, nullptr, nullptr,
-          c.size(0), c.size(1));
-    }
-  });
-}
-
-#endif

csrc/cpu/torch_bindings.cpp

@@ -18,14 +18,6 @@ void int8_scaled_mm_azp(torch::Tensor& c, const torch::Tensor& a,
                         const std::optional<torch::Tensor>& azp,
                         const std::optional<torch::Tensor>& bias);
 
-#if defined(__powerpc64__)
-void int8_scaled_mm_ppc64le(torch::Tensor& c, const torch::Tensor& a,
-                            const torch::Tensor& b,
-                            const torch::Tensor& a_scales,
-                            const torch::Tensor& b_scales,
-                            const std::optional<torch::Tensor>& bias);
-#endif
-
 void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
                         torch::Tensor& kv_cache, double scale,
                         torch::Tensor& block_tables, torch::Tensor& seq_lens);
@@ -125,7 +117,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // Apply GPT-NeoX or GPT-J style rotary embedding to query and key.
   ops.def(
       "rotary_embedding(Tensor positions, Tensor! query,"
-      "                 Tensor!? key, int head_size,"
+      "                 Tensor! key, int head_size,"
       "                 Tensor cos_sin_cache, bool is_neox) -> ()");
   ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
 
@@ -158,33 +150,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                  Tensor b_scales, Tensor azp_adj,"
       "                  Tensor? azp, Tensor? bias) -> ()");
   ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
-#elif defined(__powerpc64__)
-  // Compute int8 quantized tensor for given scaling factor.
-  ops.def(
-      "static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale,"
-      "Tensor? azp) -> ()");
-  ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant);
-
-  // Compute int8 quantized tensor and scaling factor
-  ops.def(
-      "dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, "
-      "Tensor!? azp) -> ()");
-  ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
-           &dynamic_scaled_int8_quant);
-  // W8A8 GEMM, supporting symmetric quantization.
-  ops.def(
-      "cutlass_scaled_mm(Tensor! out, Tensor a,"
-      "                  Tensor b, Tensor a_scales,"
-      "                  Tensor b_scales, Tensor? bias) -> ()");
-  ops.impl("cutlass_scaled_mm", torch::kCPU, &int8_scaled_mm_ppc64le);
-  // w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
-  // quantization.
-  ops.def(
-      "cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
-      "                  Tensor b, Tensor a_scales,"
-      "                  Tensor b_scales, Tensor azp_adj,"
-      "                  Tensor? azp, Tensor? bias) -> ()");
-  ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
 #endif
 
 // SHM CCL

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,
@@ -50,13 +59,3 @@ struct enable_sm90_only : Kernel {
 #endif
   }
 };
-
-template <typename Kernel>
-struct enable_sm100_only : Kernel {
-  template <typename... Args>
-  CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__ && __CUDA_ARCH__ == 1000
-    Kernel::operator()(std::forward<Args>(args)...);
-#endif
-  }
-};

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/layernorm_kernels.cu

@@ -140,10 +140,6 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
               torch::Tensor& input,   // [..., hidden_size]
               torch::Tensor& weight,  // [hidden_size]
               double epsilon) {
-  TORCH_CHECK(out.is_contiguous());
-  TORCH_CHECK(input.is_contiguous());
-  TORCH_CHECK(weight.is_contiguous());
-
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 

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_kernels/marlin_moe_kernel_ku4.cu

@@ -0,0 +1,31 @@
+#include "marlin_moe_kernel_ku4.h"
+
+namespace marlin_moe {
+
+// We return bool so we can create these different kernel calls as a sequence
+// of if-elseif's.
+bool call_marlin_moe_kernel_ku4(
+    vllm::ScalarType const& q_type, int thread_n_blocks, int thread_k_blocks,
+    bool has_act_order, int group_blocks, int num_threads, int blocks,
+    int max_shared_mem, cudaStream_t stream, const int4* A_ptr,
+    const int4* B_ptr, int4* C_ptr, const int* sorted_ids_ptr,
+    const float* topk_weights_ptr, const int4* s_ptr, const int4* zp_ptr,
+    const int* g_idx_ptr, int* expert_offsets_ptr, int num_groups,
+    int expert_idx, int num_experts, int topk, int prob_m, int prob_n,
+    int prob_k, int tot_m, int* locks, bool replicate_input, bool apply_weights,
+    int m_block, int max_par, int cfg_max_m_blocks) {
+  bool has_zp = true;
+
+  if (false) {
+  }
+  AWQ_CALL_IF_MOE(vllm::kU4, 16, 4, 256)
+  AWQ_CALL_IF_MOE(vllm::kU4, 8, 8, 256)
+  AWQ_CALL_IF_MOE(vllm::kU4, 8, 4, 128)
+  AWQ_CALL_IF_MOE(vllm::kU4, 4, 8, 128)
+  else {
+    return false;
+  }
+  return true;
+}
+
+}  // namespace marlin_moe