test

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

.buildkite/check-wheel-size.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
 import sys
@@ -9,12 +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]:
@@ -29,18 +28,14 @@ def check_wheel_size(directory):
                 wheel_path = os.path.join(root, file_name)
                 wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
                 if wheel_size_mb > VLLM_MAX_SIZE_MB:
-                    print(
-                        f"Not allowed: Wheel {wheel_path} is larger "
-                        f"({wheel_size_mb:.2f} MB) than the limit "
-                        f"({VLLM_MAX_SIZE_MB} MB)."
-                    )
+                    print(f"Not allowed: Wheel {wheel_path} is larger "
+                          f"({wheel_size_mb:.2f} MB) than the limit "
+                          f"({VLLM_MAX_SIZE_MB} MB).")
                     print_top_10_largest_files(wheel_path)
                     return 1
                 else:
-                    print(
-                        f"Wheel {wheel_path} is within the allowed size "
-                        f"({wheel_size_mb:.2f} MB)."
-                    )
+                    print(f"Wheel {wheel_path} is within the allowed size "
+                          f"({wheel_size_mb:.2f} MB).")
     return 0
 
 
@@ -50,4 +45,4 @@ if __name__ == "__main__":
         sys.exit(1)
 
     directory = sys.argv[1]
-    sys.exit(check_wheel_size(directory))
+    sys.exit(check_wheel_size(directory))

.buildkite/generate_index.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import os
@@ -23,5 +22,5 @@ with open("index.html", "w") as f:
     print(f"Generated index.html for {args.wheel}")
     # cloudfront requires escaping the '+' character
     f.write(
-        template.format(wheel=filename, wheel_html_escaped=filename.replace("+", "%2B"))
-    )
+        template.format(wheel=filename,
+                        wheel_html_escaped=filename.replace("+", "%2B")))

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from pathlib import Path
 
 import pytest
@@ -9,14 +8,11 @@ def pytest_addoption(parser):
     parser.addoption(
         "--config-list-file",
         action="store",
-        help="Path to the file listing model config YAMLs (one per line)",
-    )
-    parser.addoption(
-        "--tp-size",
-        action="store",
-        default="1",
-        help="Tensor parallel size to use for evaluation",
-    )
+        help="Path to the file listing model config YAMLs (one per line)")
+    parser.addoption("--tp-size",
+                     action="store",
+                     default="1",
+                     help="Tensor parallel size to use for evaluation")
 
 
 @pytest.fixture(scope="session")
@@ -37,8 +33,7 @@ def pytest_generate_tests(metafunc):
         config_dir = config_list_file.parent
         with open(config_list_file, encoding="utf-8") as f:
             configs = [
-                config_dir / line.strip()
-                for line in f
+                config_dir / line.strip() for line in f
                 if line.strip() and not line.startswith("#")
             ]
         metafunc.parametrize("config_filename", configs)

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
 done
 
 lm_eval --model vllm \
-  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true,trust_remote_code=true,max_model_len=4096" \
+  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
   --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
   --batch_size "$BATCH_SIZE"

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 LM eval harness on model to compare vs HF baseline computed offline.
 Configs are found in configs/$MODEL.yaml
@@ -17,24 +16,19 @@ RTOL = 0.08
 
 
 def launch_lm_eval(eval_config, tp_size):
-    trust_remote_code = eval_config.get("trust_remote_code", False)
-    max_model_len = eval_config.get("max_model_len", 4096)
-    model_args = (
-        f"pretrained={eval_config['model_name']},"
-        f"tensor_parallel_size={tp_size},"
-        f"enforce_eager=true,"
-        f"add_bos_token=true,"
-        f"trust_remote_code={trust_remote_code},"
-        f"max_model_len={max_model_len}"
-    )
+    trust_remote_code = eval_config.get('trust_remote_code', False)
+    model_args = f"pretrained={eval_config['model_name']}," \
+                 f"tensor_parallel_size={tp_size}," \
+                 f"enforce_eager=true," \
+                 f"add_bos_token=true," \
+                 f"trust_remote_code={trust_remote_code}"
     results = lm_eval.simple_evaluate(
         model="vllm",
         model_args=model_args,
         tasks=[task["name"] for task in eval_config["tasks"]],
         num_fewshot=eval_config["num_fewshot"],
         limit=eval_config["limit"],
-        batch_size="auto",
-    )
+        batch_size="auto")
     return results
 
 
@@ -48,10 +42,9 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
         for metric in task["metrics"]:
             ground_truth = metric["value"]
             measured_value = results["results"][task["name"]][metric["name"]]
-            print(
-                f"{task['name']} | {metric['name']}: "
-                f"ground_truth={ground_truth} | measured={measured_value}"
-            )
-            success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
+            print(f'{task["name"]} | {metric["name"]}: '
+                  f'ground_truth={ground_truth} | measured={measured_value}')
+            success = success and np.isclose(
+                ground_truth, measured_value, rtol=RTOL)
 
     assert success

.buildkite/nightly-benchmarks/README.md

@@ -11,7 +11,7 @@ See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performanc
 
 ## Performance benchmark quick overview
 
-**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) and Intel® Xeon® Processors, with different models.
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), with different models.
 
 **Benchmarking Duration**: about 1hr.
 
@@ -28,34 +28,16 @@ See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performanc
 ## Trigger the benchmark
 
 Performance benchmark will be triggered when:
-
 - A PR being merged into vllm.
 - Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
 
-Manually Trigger the benchmark
-
-```bash
-bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
-```
-
-Runtime environment variables:
-
-- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
-- `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
-- `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
-- `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
-- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
-- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
-
 Nightly benchmark will be triggered when:
-
 - Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
 
 ## Performance benchmark details
 
 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
-> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
->
+
 ### Latency test
 
 Here is an example of one test inside `latency-tests.json`:
@@ -78,7 +60,7 @@ Here is an example of one test inside `latency-tests.json`:
 In this example:
 
 - The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
-- The `parameters` attribute control the command line arguments to be used for `vllm bench latency`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `vllm bench latency`. For example, the corresponding command line arguments for `vllm bench latency` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
+- The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
 
 Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
 
@@ -86,13 +68,13 @@ WARNING: The benchmarking script will save json results by itself, so please do
 
 ### Throughput test
 
-The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
+The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
 
 The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
 
 ### Serving test
 
-We test the throughput by using `vllm bench serve` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
+We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
 
 ```json
 [
@@ -104,6 +86,7 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
             "tensor_parallel_size": 1,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -121,8 +104,8 @@ Inside this example:
 
 - The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
 - The `server-parameters` includes the command line arguments for vLLM server.
-- The `client-parameters` includes the command line arguments for `vllm bench serve`.
-- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
+- The `client-parameters` includes the command line arguments for `benchmark_serving.py`.
+- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `benchmark_serving.py`
 
 The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
 
@@ -130,37 +113,12 @@ WARNING: The benchmarking script will save json results by itself, so please do
 
 ### Visualizing the results
 
-The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.
+The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/descriptions.md) with real benchmarking results.
 You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
 If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
 
-The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
-When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
-
-Here is an example using the script to compare result_a and result_b without detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json --ignore_test_name`
-
-|    | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|----------------------------------------|----------------------------------------|----------|
-| 0  | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | 241.620334                             | 294.018783                             | 1.216863 |
-| 2  | 218.298905                             | 262.664916                             | 1.203235 |
-| 3  | 242.743860                             | 299.816190                             | 1.235113 |
-
-Here is an example using the script to compare result_a and result_b with detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
-
-|   | results_a/benchmark_results.json_name | results_a/benchmark_results.json | results_b/benchmark_results.json_name | results_b/benchmark_results.json | perf_ratio        |
-|---|---------------------------------------------|----------------------------------------|---------------------------------------------|----------------------------------------|----------|
-| 0 | serving_llama8B_tp1_sharegpt_qps_1          | 142.633982                             | serving_llama8B_tp1_sharegpt_qps_1          | 156.526018                             | 1.097396 |
-| 1 | serving_llama8B_tp1_sharegpt_qps_16         | 241.620334                             | serving_llama8B_tp1_sharegpt_qps_16         | 294.018783                             | 1.216863 |
-| 2 | serving_llama8B_tp1_sharegpt_qps_4          | 218.298905                             | serving_llama8B_tp1_sharegpt_qps_4          | 262.664916                             | 1.203235 |
-| 3 | serving_llama8B_tp1_sharegpt_qps_inf        | 242.743860                             | serving_llama8B_tp1_sharegpt_qps_inf        | 299.816190                             | 1.235113 |
-| 4 | serving_llama8B_tp2_random_1024_128_qps_1   | 96.613390                              | serving_llama8B_tp4_random_1024_128_qps_1   | 108.404853                             | 1.122048 |
-
 ## Nightly test details
 
 See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
@@ -168,9 +126,9 @@ See [nightly-descriptions.md](nightly-descriptions.md) for the detailed descript
 ### Workflow
 
 - The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
-- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
-- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
-- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
+- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
+- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
 
 ### Nightly tests
 
@@ -180,6 +138,6 @@ In [nightly-tests.json](tests/nightly-tests.json), we include the command line a
 
 The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
 
-WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
 
 WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -1,4 +1,3 @@
-# Nightly benchmark annotation
 
 ## Description
 
@@ -14,15 +13,15 @@ Please download the visualization scripts in the post
 
 - Find the docker we use in `benchmarking pipeline`
 - Deploy the docker, and inside the docker:
-    - Download `nightly-benchmarks.zip`.
-    - In the same folder, run the following code:
+  - Download `nightly-benchmarks.zip`.
+  - In the same folder, run the following code:
 
-    ```bash
-    export HF_TOKEN=<your HF token>
-    apt update
-    apt install -y git
-    unzip nightly-benchmarks.zip
-    VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
-    ```
+  ```console
+  export HF_TOKEN=<your HF token>
+  apt update
+  apt install -y git
+  unzip nightly-benchmarks.zip
+  VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+  ```
 
 And the results will be inside `./benchmarks/results`.

.buildkite/nightly-benchmarks/nightly-descriptions.md

@@ -13,25 +13,25 @@ Latest reproduction guilde: [github issue link](https://github.com/vllm-project/
 ## Setup
 
 - Docker images:
-    - vLLM: `vllm/vllm-openai:v0.6.2`
-    - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
-    - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
-    - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
-        - *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
-    - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
+  - vLLM: `vllm/vllm-openai:v0.6.2`
+  - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
+  - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
+  - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
+    - *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
+  - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
 - Hardware
-    - 8x Nvidia A100 GPUs
+  - 8x Nvidia A100 GPUs
 - Workload:
-    - Dataset
-        - ShareGPT dataset
-        - Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
-        - Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
-        - Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
-    - Models: llama-3 8B, llama-3 70B.
-        - We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
-    - Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
-        - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
-    - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
+  - Dataset
+    - ShareGPT dataset
+    - Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
+    - Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
+    - Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
+  - Models: llama-3 8B, llama-3 70B.
+    - We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
+  - Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
+    - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
+  - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
 
 ## Known issues
 

.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md

@@ -1,12 +1,10 @@
-# Performance benchmarks descriptions
 
 ## Latency tests
 
 - Input length: 32 tokens.
 - Output length: 128 tokens.
 - Batch size: fixed (8).
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: end-to-end latency (mean, median, p99).
 
 {latency_tests_markdown_table}
@@ -16,8 +14,7 @@
 - Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
 - Output length: the corresponding output length of these 200 prompts.
 - Batch size: dynamically determined by vllm to achieve maximum throughput.
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: throughput.
 
 {throughput_tests_markdown_table}
@@ -28,18 +25,12 @@
 - Output length: the corresponding output length of these 200 prompts.
 - Batch size: dynamically determined by vllm and the arrival pattern of the requests.
 - **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- We also added a speculative decoding test for llama-3 70B, under QPS 2
 - Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
-- For CPU, we added random dataset tests to benchmark fixed input/output length with 100 prompts.
 
 {serving_tests_markdown_table}
 
-## Platform Information
-
-{platform_markdown_table}
-
 ## json version of the benchmarking tables
 
 This section contains the data of the markdown tables above in JSON format.

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

@@ -1,66 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-
-import pandas as pd
-
-
-def compare_data_columns(
-    files, name_column, data_column, drop_column, ignore_test_name=False
-):
-    print("\ncompare_data_column: " + data_column)
-    frames = []
-    compare_frames = []
-    for file in files:
-        data_df = pd.read_json(file)
-        serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
-        if ignore_test_name is False:
-            serving_df = serving_df.rename(columns={name_column: file + "_name"})
-            frames.append(serving_df[file + "_name"])
-        serving_df = serving_df.rename(columns={data_column: file})
-        frames.append(serving_df[file])
-        compare_frames.append(serving_df[file])
-        if len(compare_frames) >= 2:
-            # Compare numbers among two files
-            ratio_df = compare_frames[1] / compare_frames[0]
-            frames.append(ratio_df)
-            compare_frames.pop(1)
-
-    concat_df = pd.concat(frames, axis=1)
-    return concat_df
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-f", "--file", action="append", type=str, help="input file name"
-    )
-    parser.add_argument(
-        "--ignore_test_name", action="store_true", help="ignore_test_name or not"
-    )
-    args = parser.parse_args()
-    files = args.file
-    print("comparing : " + ", ".join(files))
-
-    drop_column = "P99"
-    name_column = "Test name"
-    data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
-    html_msgs_for_data_cols = [
-        "Compare Output Tokens /n",
-        "Median TTFT /n",
-        "Median TPOT /n",
-    ]
-    ignore_test_name = args.ignore_test_name
-    with open("perf_comparison.html", "w") as text_file:
-        for i in range(len(data_cols_to_compare)):
-            output_df = compare_data_columns(
-                files,
-                name_column,
-                data_cols_to_compare[i],
-                drop_column,
-                ignore_test_name=ignore_test_name,
-            )
-            print(output_df)
-            html = output_df.to_html()
-            text_file.write(html_msgs_for_data_cols[i])
-            text_file.write(html)

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

@@ -1,13 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import json
 import os
-from importlib import util
 from pathlib import Path
 
 import pandas as pd
-import psutil
 from tabulate import tabulate
 
 results_folder = Path("results/")
@@ -31,11 +28,11 @@ throughput_results = []
 throughput_results_column_mapping = {
     "test_name": "Test name",
     "gpu_type": "GPU",
-    "num_requests": "# of req.",
-    "total_num_tokens": "Total # of tokens",
-    "elapsed_time": "Elapsed time (s)",
+    # "num_requests": "# of req.",
+    # "total_num_tokens": "Total # of tokens",
+    # "elapsed_time": "Elapsed time (s)",
     "requests_per_second": "Tput (req/s)",
-    "tokens_per_second": "Tput (tok/s)",
+    # "tokens_per_second": "Tput (tok/s)",
 }
 
 # serving results and the keys that will be printed into markdown
@@ -43,19 +40,16 @@ serving_results = []
 serving_column_mapping = {
     "test_name": "Test name",
     "gpu_type": "GPU",
-    "completed": "# of req.",
-    "max_concurrency": "# of max concurrency.",
+    # "completed": "# of req.",
     "request_throughput": "Tput (req/s)",
-    "total_token_throughput": "Total Token Tput (tok/s)",
-    "output_throughput": "Output Tput (tok/s)",
-    "total_input_tokens": "Total input tokens",
-    "total_output_tokens": "Total output tokens",
+    # "input_throughput": "Input Tput (tok/s)",
+    # "output_throughput": "Output Tput (tok/s)",
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
-    "mean_tpot_ms": "Mean TPOT (ms)",
-    "median_tpot_ms": "Median",
-    "p99_tpot_ms": "P99",
+    # "mean_tpot_ms": "Mean TPOT (ms)",
+    # "median_tpot_ms": "Median",
+    # "p99_tpot_ms": "P99",
     "mean_itl_ms": "Mean ITL (ms)",
     "median_itl_ms": "Median ITL (ms)",
     "p99_itl_ms": "P99 ITL (ms)",
@@ -71,37 +65,23 @@ def read_markdown(file):
 
 
 def results_to_json(latency, throughput, serving):
-    return json.dumps(
-        {
-            "latency": latency.to_dict(),
-            "throughput": throughput.to_dict(),
-            "serving": serving.to_dict(),
-        }
-    )
-
-
-def get_size_with_unit(bytes, suffix="B"):
-    """
-    Scale bytes to its proper format
-    e.g:
-        1253656 => '1.20MB'
-        1253656678 => '1.17GB'
-    """
-    factor = 1024
-    for unit in ["", "K", "M", "G", "T", "P"]:
-        if bytes < factor:
-            return f"{bytes:.2f}{unit}{suffix}"
-        bytes /= factor
+    return json.dumps({
+        'latency': latency.to_dict(),
+        'throughput': throughput.to_dict(),
+        'serving': serving.to_dict()
+    })
 
 
 if __name__ == "__main__":
+
     # collect results
     for test_file in results_folder.glob("*.json"):
+
         with open(test_file) as f:
             raw_result = json.loads(f.read())
 
         if "serving" in str(test_file):
-            # this result is generated via `vllm bench serve` command
+            # this result is generated via `benchmark_serving.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -121,7 +101,7 @@ if __name__ == "__main__":
             continue
 
         elif "latency" in f.name:
-            # this result is generated via `vllm bench latency` command
+            # this result is generated via `benchmark_latency.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -140,8 +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
@@ -149,7 +128,7 @@ if __name__ == "__main__":
             continue
 
         elif "throughput" in f.name:
-            # this result is generated via `vllm bench throughput` command
+            # this result is generated via `benchmark_throughput.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -174,48 +153,26 @@ if __name__ == "__main__":
     serving_results = pd.DataFrame.from_dict(serving_results)
     throughput_results = pd.DataFrame.from_dict(throughput_results)
 
-    svmem = psutil.virtual_memory()
-    platform_data = {
-        "Physical cores": [psutil.cpu_count(logical=False)],
-        "Total cores": [psutil.cpu_count(logical=True)],
-        "Total Memory": [get_size_with_unit(svmem.total)],
-    }
-
-    if util.find_spec("numa") is not None:
-        from numa import info
-
-        platform_data["Total NUMA nodes"] = [info.get_num_configured_nodes()]
-
-    if util.find_spec("cpuinfo") is not None:
-        from cpuinfo import get_cpu_info
-
-        platform_data["CPU Brand"] = [get_cpu_info()["brand_raw"]]
-
-    platform_results = pd.DataFrame.from_dict(
-        platform_data, orient="index", columns=["Platform Info"]
-    )
-
-    raw_results_json = results_to_json(
-        latency_results, throughput_results, serving_results
-    )
+    raw_results_json = results_to_json(latency_results, throughput_results,
+                                       serving_results)
 
     # remapping the key, for visualization purpose
     if not latency_results.empty:
-        latency_results = latency_results[list(latency_column_mapping.keys())].rename(
-            columns=latency_column_mapping
-        )
+        latency_results = latency_results[list(
+            latency_column_mapping.keys())].rename(
+                columns=latency_column_mapping)
     if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
-            columns=serving_column_mapping
-        )
+        serving_results = serving_results[list(
+            serving_column_mapping.keys())].rename(
+                columns=serving_column_mapping)
     if not throughput_results.empty:
-        throughput_results = throughput_results[
-            list(throughput_results_column_mapping.keys())
-        ].rename(columns=throughput_results_column_mapping)
+        throughput_results = throughput_results[list(
+            throughput_results_column_mapping.keys())].rename(
+                columns=throughput_results_column_mapping)
 
-    processed_results_json = results_to_json(
-        latency_results, throughput_results, serving_results
-    )
+    processed_results_json = results_to_json(latency_results,
+                                             throughput_results,
+                                             serving_results)
 
     for df in [latency_results, serving_results, throughput_results]:
         if df.empty:
@@ -227,43 +184,38 @@ if __name__ == "__main__":
         # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
         # we want to turn it into "8xGPUTYPE"
         df["GPU"] = df["GPU"].apply(
-            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
-        )
+            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")
 
     # get markdown tables
-    latency_md_table = tabulate(
-        latency_results, headers="keys", tablefmt="pipe", showindex=False
-    )
-    serving_md_table = tabulate(
-        serving_results, headers="keys", tablefmt="pipe", showindex=False
-    )
-    throughput_md_table = tabulate(
-        throughput_results, headers="keys", tablefmt="pipe", showindex=False
-    )
-    platform_md_table = tabulate(
-        platform_results, headers="keys", tablefmt="pipe", showindex=True
-    )
+    latency_md_table = tabulate(latency_results,
+                                headers='keys',
+                                tablefmt='pipe',
+                                showindex=False)
+    serving_md_table = tabulate(serving_results,
+                                headers='keys',
+                                tablefmt='pipe',
+                                showindex=False)
+    throughput_md_table = tabulate(throughput_results,
+                                   headers='keys',
+                                   tablefmt='pipe',
+                                   showindex=False)
 
     # document the result
     with open(results_folder / "benchmark_results.md", "w") as f:
-        results = read_markdown(
-            "../.buildkite/nightly-benchmarks/"
-            + "performance-benchmarks-descriptions.md"
-        )
+
+        results = read_markdown("../.buildkite/nightly-benchmarks/" +
+                                "performance-benchmarks-descriptions.md")
         results = results.format(
             latency_tests_markdown_table=latency_md_table,
             throughput_tests_markdown_table=throughput_md_table,
             serving_tests_markdown_table=serving_md_table,
-            platform_markdown_table=platform_md_table,
-            benchmarking_results_in_json_string=processed_results_json,
-        )
+            benchmarking_results_in_json_string=processed_results_json)
         f.write(results)
 
     # document benchmarking results in json
     with open(results_folder / "benchmark_results.json", "w") as f:
-        results = (
-            latency_results.to_dict(orient="records")
-            + throughput_results.to_dict(orient="records")
-            + serving_results.to_dict(orient="records")
-        )
+
+        results = latency_results.to_dict(
+            orient='records') + throughput_results.to_dict(
+                orient='records') + serving_results.to_dict(orient='records')
         f.write(json.dumps(results))

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 
@@ -15,12 +14,15 @@ def main(model, cachedir):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Download and save Hugging Face tokenizer"
-    )
-    parser.add_argument("--model", type=str, required=True, help="Name of the model")
-    parser.add_argument(
-        "--cachedir", type=str, required=True, help="Directory to save the tokenizer"
-    )
+        description="Download and save Hugging Face tokenizer")
+    parser.add_argument("--model",
+                        type=str,
+                        required=True,
+                        help="Name of the model")
+    parser.add_argument("--cachedir",
+                        type=str,
+                        required=True,
+                        help="Directory to save the tokenizer")
 
     args = parser.parse_args()
     main(args.model, args.cachedir)

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import json
@@ -12,33 +11,33 @@ from tabulate import tabulate
 
 def parse_arguments():
     parser = argparse.ArgumentParser(
-        description="Parse command line arguments for summary-nightly-results script."
-    )
-    parser.add_argument(
-        "--results-folder",
-        type=str,
-        required=True,
-        help="The folder where the results are stored.",
-    )
-    parser.add_argument(
-        "--description", type=str, required=True, help="Description of the results."
-    )
+        description=
+        'Parse command line arguments for summary-nightly-results script.')
+    parser.add_argument('--results-folder',
+                        type=str,
+                        required=True,
+                        help='The folder where the results are stored.')
+    parser.add_argument('--description',
+                        type=str,
+                        required=True,
+                        help='Description of the results.')
 
     args = parser.parse_args()
     return args
 
 
 def get_perf(df, method, model, metric):
+
     means = []
 
     for qps in [2, 4, 8, 16, "inf"]:
-        target = df["Test name"].str.contains(model)
-        target = target & df["Engine"].str.contains(method)
-        target = target & df["Test name"].str.contains("qps_" + str(qps))
+        target = df['Test name'].str.contains(model)
+        target = target & df['Engine'].str.contains(method)
+        target = target & df['Test name'].str.contains("qps_" + str(qps))
         filtered_df = df[target]
 
         if filtered_df.empty:
-            means.append(0.0)
+            means.append(0.)
         else:
             means.append(filtered_df[metric].values[0])
 
@@ -46,6 +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()
@@ -60,8 +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
 
@@ -81,17 +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/get-lmdeploy-modelname.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from lmdeploy.serve.openai.api_client import APIClient
 

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

@@ -73,7 +73,7 @@ get_current_llm_serving_engine() {
     echo "Container: vllm"
     # move to a completely irrelevant directory, to avoid import vllm from current folder
     export CURRENT_LLM_SERVING_ENGINE=vllm
-
+    
     return
   fi
 }
@@ -95,14 +95,12 @@ json2args() {
 }
 
 kill_gpu_processes() {
-  pkill -f '[p]ython'
-  pkill -f '[p]ython3'
-  pkill -f '[t]ritonserver'
-  pkill -f '[p]t_main_thread'
-  pkill -f '[t]ext-generation'
-  pkill -f '[l]mdeploy'
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pkill -f '[V]LLM'
+  pkill -f python
+  pkill -f python3
+  pkill -f tritonserver
+  pkill -f pt_main_thread
+  pkill -f text-generation
+  pkill -f lmdeploy
 
   while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
     sleep 1
@@ -127,7 +125,7 @@ ensure_installed() {
 }
 
 run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
+  # run serving tests using `benchmark_serving.py`
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -227,7 +225,7 @@ run_serving_tests() {
 
       if [[ "$dataset_name" = "sharegpt" ]]; then
 
-        client_command="vllm bench serve \
+        client_command="python3 benchmark_serving.py \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -248,7 +246,7 @@ run_serving_tests() {
         sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
         sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
 
-        client_command="vllm bench serve \
+        client_command="python3 benchmark_serving.py \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -267,13 +265,13 @@ run_serving_tests() {
           $client_args"
 
       else
-
+  
         echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
         exit 1
 
       fi
 
-
+        
 
       echo "Running test case $test_name with qps $qps"
       echo "Client command: $client_command"
@@ -304,7 +302,7 @@ run_serving_tests() {
 }
 
 run_genai_perf_tests() {
-  # run genai-perf tests
+  # run genai-perf tests 
 
   # $1: a json file specifying genai-perf test cases
   local genai_perf_test_file
@@ -313,14 +311,14 @@ run_genai_perf_tests() {
   # Iterate over genai-perf tests
   jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
-    test_name=$(echo "$params" | jq -r '.test_name')
-
+    test_name=$(echo "$params" | jq -r '.test_name')    
+    
     # if TEST_SELECTOR is set, only run the test cases that match the selector
     if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
       echo "Skip test case $test_name."
       continue
     fi
-
+    
     # prepend the current serving engine to the test name
     test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
 
@@ -371,10 +369,10 @@ run_genai_perf_tests() {
         qps=$num_prompts
         echo "now qps is $qps"
       fi
-
+    
       new_test_name=$test_name"_qps_"$qps
       backend=$CURRENT_LLM_SERVING_ENGINE
-
+      
       if [[ "$backend" == *"vllm"* ]]; then
         backend="vllm"
       fi
@@ -415,7 +413,7 @@ prepare_dataset() {
   do
     cat sonnet.txt >> sonnet_4x.txt
   done
-
+  
 }
 
 main() {

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

@@ -31,20 +31,6 @@ check_gpus() {
   echo "GPU type is $gpu_type"
 }
 
-check_cpus() {
-  # check the number of CPUs and NUMA Node and GPU type.
-  declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
-  if [[ $numa_count -gt 0 ]]; then
-    echo "NUMA found."
-    echo $numa_count
-  else
-    echo "Need at least 1 NUMA to run benchmarking."
-    exit 1
-  fi
-  declare -g gpu_type="cpu"
-  echo "GPU type is $gpu_type"
-}
-
 check_hf_token() {
   # check if HF_TOKEN is available and valid
   if [[ -z "$HF_TOKEN" ]]; then
@@ -83,22 +69,6 @@ json2args() {
   echo "$args"
 }
 
-json2envs() {
-  # transforms the JSON string to environment variables.
-  # example:
-  # input: { "VLLM_CPU_KVCACHE_SPACE": 5 }
-  # output: VLLM_CPU_KVCACHE_SPACE=5
-  local json_string=$1
-  local args=$(
-    echo "$json_string" | jq -r '
-      to_entries |
-      map((.key ) + "=" + (.value | tostring)) |
-      join(" ")
-    '
-  )
-  echo "$args"
-}
-
 wait_for_server() {
   # wait for vllm server to start
   # return 1 if vllm server crashes
@@ -126,8 +96,7 @@ kill_gpu_processes() {
   ps -aux
   lsof -t -i:8000 | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
+
 
   # wait until GPU memory usage smaller than 1GB
   if command -v nvidia-smi; then
@@ -165,7 +134,7 @@ upload_to_buildkite() {
 }
 
 run_latency_tests() {
-  # run latency tests using `vllm bench latency` command
+  # run latency tests using `benchmark_latency.py`
   # $1: a json file specifying latency test cases
 
   local latency_test_file
@@ -189,24 +158,15 @@ run_latency_tests() {
     # get arguments
     latency_params=$(echo "$params" | jq -r '.parameters')
     latency_args=$(json2args "$latency_params")
-    latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    latency_envs=$(json2envs "$latency_environment_variables")
 
     # check if there is enough GPU to run the test
     tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
-    latency_command=" $latency_envs vllm bench latency \
+    latency_command="python3 benchmark_latency.py \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $latency_args"
 
@@ -232,7 +192,7 @@ run_latency_tests() {
 }
 
 run_throughput_tests() {
-  # run throughput tests using `vllm bench throughput`
+  # run throughput tests using `benchmark_throughput.py`
   # $1: a json file specifying throughput test cases
 
   local throughput_test_file
@@ -256,24 +216,15 @@ run_throughput_tests() {
     # get arguments
     throughput_params=$(echo "$params" | jq -r '.parameters')
     throughput_args=$(json2args "$throughput_params")
-    throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    throughput_envs=$(json2envs "$throughput_environment_variables")
 
     # check if there is enough GPU to run the test
     tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
-    throughput_command=" $throughput_envs vllm bench throughput \
+    throughput_command="python3 benchmark_throughput.py \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $throughput_args"
 
@@ -298,7 +249,7 @@ run_throughput_tests() {
 }
 
 run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
+  # run serving tests using `benchmark_serving.py`
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -321,27 +272,18 @@ run_serving_tests() {
 
     # get client and server arguments
     server_params=$(echo "$params" | jq -r '.server_parameters')
-    server_envs=$(echo "$params" | jq -r '.server_environment_variables')
     client_params=$(echo "$params" | jq -r '.client_parameters')
     server_args=$(json2args "$server_params")
-    server_envs=$(json2envs "$server_envs")
     client_args=$(json2args "$client_params")
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
     echo "Running over qps list $qps_list"
 
-    # check if there is enough resources to run the test
+    # check if there is enough GPU to run the test
     tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
     # check if server model and client model is aligned
@@ -352,33 +294,23 @@ run_serving_tests() {
       continue
     fi
 
-    server_command="$server_envs python3 \
+    server_command="python3 \
       -m vllm.entrypoints.openai.api_server \
       $server_args"
 
     # run the server
     echo "Running test case $test_name"
     echo "Server command: $server_command"
-    # support remote vllm server
-    client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" ]]; then
-      bash -c "$server_command" &
-      server_pid=$!
-      # wait until the server is alive
-      if wait_for_server; then
-        echo ""
-        echo "vLLM server is up and running."
-      else
-        echo ""
-        echo "vLLM failed to start within the timeout period."
-      fi
+    bash -c "$server_command" &
+    server_pid=$!
+
+    # wait until the server is alive
+    if wait_for_server; then
+      echo ""
+      echo "vllm server is up and running."
     else
-      server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
-      if [[ ${REMOTE_PORT} ]]; then
-        client_remote_args=" --host=$REMOTE_HOST --port=$REMOTE_PORT "
-      else
-        client_remote_args=" --host=$REMOTE_HOST "
-      fi
+      echo ""
+      echo "vllm failed to start within the timeout period."
     fi
 
     # iterate over different QPS
@@ -394,13 +326,13 @@ run_serving_tests() {
 
       # pass the tensor parallel size to the client so that it can be displayed
       # on the benchmark dashboard
-      client_command="vllm bench serve \
+      client_command="python3 benchmark_serving.py \
         --save-result \
         --result-dir $RESULTS_FOLDER \
         --result-filename ${new_test_name}.json \
         --request-rate $qps \
         --metadata "tensor_parallel_size=$tp" \
-        $client_args $client_remote_args "
+        $client_args"
 
       echo "Running test case $test_name with qps $qps"
       echo "Client command: $client_command"
@@ -428,14 +360,7 @@ run_serving_tests() {
 }
 
 main() {
-  local ARCH
-  ARCH=''
-  if [ "$ON_CPU" == "1" ];then
-     check_cpus
-     ARCH='-cpu'
-  else
-     check_gpus
-  fi
+  check_gpus
   check_hf_token
 
   # Set to v1 to run v1 benchmark
@@ -448,7 +373,7 @@ main() {
   (which jq) || (apt-get update && apt-get -y install jq)
   (which lsof) || (apt-get update && apt-get install -y lsof)
 
-  # get the current IP address, required by `vllm bench serve` command
+  # get the current IP address, required by benchmark_serving.py
   export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
   # turn of the reporting of the status of each request, to clean up the terminal output
   export VLLM_LOGGING_LEVEL="WARNING"
@@ -461,9 +386,9 @@ main() {
   QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
 
   # benchmarking
-  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
-  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
-  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
+  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
+  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
 
   # postprocess benchmarking results
   pip install tabulate pandas

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import datetime
 import json
@@ -35,8 +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())
 
@@ -55,16 +56,17 @@ if __name__ == "__main__":
     serving_results = pd.DataFrame.from_dict(serving_results)
 
     if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
-            columns=serving_column_mapping
-        )
+        serving_results = serving_results[list(
+            serving_column_mapping.keys())].rename(
+                columns=serving_column_mapping)
 
-    serving_md_table_with_headers = tabulate(
-        serving_results, headers="keys", tablefmt="pipe", showindex=False
-    )
+    serving_md_table_with_headers = tabulate(serving_results,
+                                             headers='keys',
+                                             tablefmt='pipe',
+                                             showindex=False)
     # remove the first line of header
-    serving_md_table_lines = serving_md_table_with_headers.split("\n")
-    serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])
+    serving_md_table_lines = serving_md_table_with_headers.split('\n')
+    serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
 
     prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
     prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
@@ -74,9 +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/nightly-benchmarks/tests/genai-perf-tests.json

@@ -11,7 +11,9 @@
         },
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

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

@@ -1,30 +0,0 @@
-[
-    {
-        "test_name": "latency_llama8B_tp1",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    },
-    {
-        "test_name": "latency_llama8B_tp4",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    }
-]

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

@@ -35,7 +35,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -88,7 +90,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -141,7 +145,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -191,7 +197,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -243,7 +251,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -295,7 +305,9 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
+            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

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

@@ -1,203 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    }
-]

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

@@ -1,205 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_pp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2pp6_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL:": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    }
-]

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

@@ -1,168 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_random_1024_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 1024,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 100,
-            "num_prompts": 100
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp6_random_1024_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 6,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 1024,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 100,
-            "num_prompts": 100
-        }
-    }
-]

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

@@ -7,6 +7,7 @@
             "tensor_parallel_size": 1,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -25,6 +26,7 @@
             "tensor_parallel_size": 4,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -43,6 +45,7 @@
             "tensor_parallel_size": 2,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -57,7 +60,8 @@
         "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
         "qps_list": [2],
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "disable_log_requests": "", 
             "tensor_parallel_size": 4,
             "swap_space": 16,
             "speculative_config": {

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

@@ -1,32 +0,0 @@
-[
-    {
-        "test_name": "throughput_llama8B_tp1",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    },
-    {
-        "test_name": "throughput_llama8B_tp4",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    }
-]

.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

@@ -1,6 +1,5 @@
 steps:
   - label: "Build wheel - CUDA 12.8"
-    id: build-wheel-cuda-12-8
     agents:
       queue: cpu_queue_postmerge
     commands:
@@ -12,11 +11,10 @@ steps:
       DOCKER_BUILDKIT: "1"
 
   - label: "Build wheel - CUDA 12.6"
-    id: build-wheel-cuda-12-6
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
       - "bash .buildkite/scripts/upload-wheels.sh"
@@ -30,11 +28,10 @@ steps:
 
   - label: "Build wheel - CUDA 11.8"
     # depends_on: block-build-cu118-wheel
-    id: build-wheel-cuda-11-8
     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"
@@ -47,26 +44,13 @@ steps:
 
   - label: "Build release image"
     depends_on: block-release-image-build
-    id: build-release-image
     agents:
       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 --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-  - label: "Annotate release workflow"
-    depends_on:
-      - build-release-image
-      - build-wheel-cuda-12-8
-      - build-wheel-cuda-12-6
-      - build-wheel-cuda-11-8
-    id: annotate-release-workflow
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "bash .buildkite/scripts/annotate-release.sh"
-
   - label: "Build and publish TPU release image"
     depends_on: ~
     if: build.env("NIGHTLY") == "1"
@@ -80,16 +64,15 @@ steps:
       - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
     plugins:
       - docker-login#v3.0.0:
-          username: vllmbot
+          username: vllm
           password-env: DOCKERHUB_TOKEN
     env:
       DOCKER_BUILDKIT: "1"
 
   - input: "Provide Release version here"
-    id: input-release-version
     fields:
       - text: "What is the release version?"
-        key: release-version
+        key: "release-version"
 
   - block: "Build CPU release image"
     key: block-cpu-release-image-build
@@ -101,8 +84,7 @@ 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 GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"
@@ -118,7 +100,6 @@ steps:
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"

.buildkite/scripts/annotate-release.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Get release version and strip leading 'v' if present
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version | sed 's/^v//')
-
-if [ -z "$RELEASE_VERSION" ]; then
-  echo "Error: RELEASE_VERSION is empty. 'release-version' metadata might not be set or is invalid."
-  exit 1
-fi
-
-buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
-To download the wheel:
-\`\`\`
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu126/vllm-${RELEASE_VERSION}+cu126-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu118/vllm-${RELEASE_VERSION}+cu118-cp38-abi3-manylinux1_x86_64.whl . 
-\`\`\`
-
-To download and upload the image:
-
-\`\`\`
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT} vllm/vllm-openai
-docker tag vllm/vllm-openai vllm/vllm-openai:latest
-docker tag vllm/vllm-openai vllm/vllm-openai:v${RELEASE_VERSION}
-docker push vllm/vllm-openai:latest
-docker push vllm/vllm-openai:v${RELEASE_VERSION}
-\`\`\`
-EOF 

.buildkite/scripts/ci-clean-log.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-# Usage: ./ci_clean_log.sh ci.log
-# This script strips timestamps and color codes from CI log files.
-
-# Check if argument is given
-if [ $# -lt 1 ]; then
-    echo "Usage: $0 ci.log"
-    exit 1
-fi
-
-INPUT_FILE="$1"
-
-# Strip timestamps
-sed -i 's/^\[[0-9]\{4\}-[0-9]\{2\}-[0-9]\{2\}T[0-9]\{2\}:[0-9]\{2\}:[0-9]\{2\}Z\] //' "$INPUT_FILE"
-
-# Strip colorization
-sed -i -r 's/\x1B\[[0-9;]*[mK]//g' "$INPUT_FILE"

.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
@@ -77,27 +74,6 @@ HF_MOUNT="/root/.cache/huggingface"
 
 commands=$@
 echo "Commands:$commands"
-
-if [[ $commands == *"pytest -v -s basic_correctness/test_basic_correctness.py"* ]]; then
-  commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s basic_correctness/test_basic_correctness.py"}
-fi
-
-if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
-  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
-fi
-
-if [[ $commands == *"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"* ]]; then
-  commands=${commands//"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"/"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2 and not BambaForCausalLM and not Gemma2ForCausalLM and not Grok1ModelForCausalLM and not Zamba2ForCausalLM and not Gemma2Model and not GritLM'"}
-fi
-
-if [[ $commands == *"pytest -v -s compile/test_basic_correctness.py"* ]]; then
-  commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s compile/test_basic_correctness.py"}
-fi
-
-if [[ $commands == *"pytest -v -s lora"* ]]; then
-  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
-fi
-
 #ignore certain kernels tests
 if [[ $commands == *" kernels/core"* ]]; then
   commands="${commands} \
@@ -107,8 +83,10 @@ fi
 
 if [[ $commands == *" kernels/attention"* ]]; then
   commands="${commands} \
-  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/attention/stest_attention_selector.py \
+  --ignore=kernels/attention/test_blocksparse_attention.py \
   --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/attention/test_attention_selector.py \
   --ignore=kernels/attention/test_flash_attn.py \
   --ignore=kernels/attention/test_flashinfer.py \
   --ignore=kernels/attention/test_prefix_prefill.py \
@@ -183,8 +161,6 @@ fi
 
 
 PARALLEL_JOB_COUNT=8
-MYPYTHONPATH=".."
-
 # check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs. 
 if [[ $commands == *"--shard-id="* ]]; then
   # assign job count as the number of shards used   
@@ -205,7 +181,6 @@ if [[ $commands == *"--shard-id="* ]]; then
         -e AWS_SECRET_ACCESS_KEY \
         -v "${HF_CACHE}:${HF_MOUNT}" \
         -e "HF_HOME=${HF_MOUNT}" \
-        -e "PYTHONPATH=${MYPYTHONPATH}" \
         --name "${container_name}_${GPU}" \
         "${image_name}" \
         /bin/bash -c "${commands_gpu}" \
@@ -236,7 +211,6 @@ else
           -e AWS_SECRET_ACCESS_KEY \
           -v "${HF_CACHE}:${HF_MOUNT}" \
           -e "HF_HOME=${HF_MOUNT}" \
-          -e "PYTHONPATH=${MYPYTHONPATH}" \
           --name "${container_name}" \
           "${image_name}" \
           /bin/bash -c "${commands}"

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

@@ -7,7 +7,6 @@ set -ex
 # Setup cleanup
 remove_docker_container() {
   if [[ -n "$container_id" ]]; then
-      podman stop --all -t0
       podman rm -f "$container_id" || true
   fi
   podman system prune -f
@@ -33,12 +32,9 @@ function cpu_tests() {
     set -e
     pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
     pip install sentence-transformers datamodel_code_generator
-    pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
-    pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    pytest -v -s tests/models/language/pooling/test_embedding.py -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/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

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

@@ -6,97 +6,89 @@ set -ex
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
-# used for TP/PP E2E test
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
-export CMAKE_BUILD_PARALLEL_LEVEL=32
-
 # Setup cleanup
-remove_docker_container() {
-    set -e;
-    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
+remove_docker_container() { 
+    set -e; 
+    docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; 
+    docker image rm cpu-test-"$BUILDKITE_BUILD_NUMBER" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 || true; 
 }
 trap remove_docker_container EXIT
 remove_docker_container
 
 # Try building the docker image
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE"  \
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2
 
 function cpu_tests() {
   set -e
   export NUMA_NODE=$2
-
-  # list packages
-  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
-    set -e
-    pip list"
-
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pip list"
+  export BUILDKITE_BUILD_NUMBER=$3
 
   # offline inference
-  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
     set -e
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
 
   # Run basic model test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
-    # Note: disable until supports V1
-    # pytest -v -s tests/kernels/attention/test_cache.py -m cpu_model
-    # pytest -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
-
-    # Note: disable Bart until supports V1
-    pytest -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-    VLLM_CPU_SGL_KERNEL=1 pytest -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-
-    pytest -v -s tests/models/language/pooling -m cpu_model
-    pytest -v -s tests/models/multimodal/generation \
-                --ignore=tests/models/multimodal/generation/test_mllama.py \
-                --ignore=tests/models/multimodal/generation/test_pixtral.py \
-                -m cpu_model"
+    pytest -v -s tests/kernels/test_cache.py -m cpu_model
+    pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model
+    pytest -v -s tests/models/decoder_only/language -m cpu_model
+    pytest -v -s tests/models/embedding/language -m cpu_model
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model
+    pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
+    pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
 
   # Run compressed-tensor test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
 
-  # Note: disable it until supports V1
   # Run AWQ test
-  # docker exec cpu-test-"$NUMA_NODE" bash -c "
-  #   set -e
-  #   VLLM_USE_V1=0 pytest -s -v \
-  #   tests/quantization/test_ipex_quant.py"
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -s -v \
+    tests/quantization/test_ipex_quant.py"
+
+  # Run chunked-prefill and prefix-cache test
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -s -v -k cpu_model \
+    tests/basic_correctness/test_chunked_prefill.py"  
+
+  # online serving
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
+    set -e
+    export VLLM_CPU_KVCACHE_SPACE=10 
+    export VLLM_CPU_OMP_THREADS_BIND=$1
+    python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & 
+    timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
+    python3 benchmarks/benchmark_serving.py \
+      --backend vllm \
+      --dataset-name random \
+      --model facebook/opt-125m \
+      --num-prompts 20 \
+      --endpoint /v1/completions \
+      --tokenizer facebook/opt-125m"
 
   # Run multi-lora tests
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
     tests/lora/test_qwen2vl.py"
-
-  # online serving
-  docker exec cpu-test-"$NUMA_NODE" bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model meta-llama/Llama-3.2-3B-Instruct \
-      --num-prompts 20 \
-      --endpoint /v1/completions'
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
-timeout 1.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE $BUILDKITE_BUILD_NUMBER"

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -16,7 +16,8 @@ DOCKER_BUILDKIT=1 docker build . \
   --build-arg max_jobs=66 \
   --build-arg nvcc_threads=2 \
   --build-arg RUN_WHEEL_CHECK=false \
-  --build-arg torch_cuda_arch_list="9.0+PTX"
+  --build-arg torch_cuda_arch_list="9.0+PTX" \
+  --build-arg vllm_fa_cmake_gpu_arches="90-real"
 
 # Setup cleanup
 remove_docker_container() { docker rm -f gh200-test || true; }

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

@@ -2,55 +2,23 @@
 
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
-set -exuo pipefail
+set -ex
 
 # Try building the docker image
-cat <<EOF | docker build -t hpu-plugin-v1-test-env -f - .
-FROM gaudi-base-image:latest
-
-COPY ./ /workspace/vllm
-
-WORKDIR /workspace/vllm
-
-ENV no_proxy=localhost,127.0.0.1
-ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
-
-RUN VLLM_TARGET_DEVICE=empty pip install .
-RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-WORKDIR /workspace/
-
-RUN git clone https://github.com/vllm-project/vllm-gaudi.git
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-EOF
+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-plugin-v1-test || true; }
-trap 'remove_docker_containers; exit $EXITCODE;' EXIT
-remove_docker_containers
-
-echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
-  -e HABANA_VISIBLE_DEVICES=all \
-  hpu-plugin-v1-test-env \
-  /bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
+remove_docker_container() { docker rm -f hpu-test || true; }
+remove_docker_container_and_exit() { remove_docker_container; exit $EXITCODE; }
+trap remove_docker_container_and_exit EXIT
+remove_docker_container
 
+# Run the image and launch offline inference
+docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
 EXITCODE=$?
-if [ $EXITCODE -eq 0 ]; then
-  echo "Test with basic model passed"
-else
-  echo "Test with basic model FAILED with exit code: $EXITCODE" >&2
-fi
-
-# The trap will handle the container removal and final exit.

.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.
@@ -48,17 +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 "
-            set -e; # Exit on first error
-            python3 /workspace/vllm/examples/offline_inference/neuron.py;
-            python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
-            for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo \"Running test file: \$f\";
-                python3 -m pytest \$f -v --capture=tee-sys;
-            done
-       "
+       /bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys && python3 -m pytest /workspace/vllm/tests/neuron/2_core/ -v --capture=tee-sys"

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

@@ -1,167 +0,0 @@
-#!/bin/bash
-
-set -xu
-
-
-remove_docker_container() { 
-    docker rm -f tpu-test || true;
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
-# Build the docker image.
-docker build -f docker/Dockerfile.tpu -t vllm-tpu .
-
-# Set up cleanup.
-cleanup_docker() {
-  # Get Docker's root directory
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
-cleanup_docker
-
-# For HF_TOKEN.
-source /etc/environment
-
-docker run --privileged --net host --shm-size=16G -it \
-    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c '
-set -e # Exit immediately if a command exits with a non-zero status.
-set -u # Treat unset variables as an error.
-
-echo "--- Starting script inside Docker container ---"
-
-# Create results directory
-RESULTS_DIR=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $RESULTS_DIR"
-
-# Install dependencies
-echo "--- Installing Python dependencies ---"
-python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
-    && python3 -m pip install --progress-bar off hf-transfer
-echo "--- Python dependencies installed ---"
-export VLLM_USE_V1=1
-export VLLM_XLA_CHECK_RECOMPILATION=1
-export VLLM_XLA_CACHE_PATH=
-echo "Using VLLM V1"
-
-echo "--- Hardware Information ---"
-# tpu-info
-echo "--- Starting Tests ---"
-set +e
-overall_script_exit_code=0
-
-# --- Test Definitions ---
-# If a test fails, this function will print logs and will not cause the main script to exit.
-run_test() {
-    local test_num=$1
-    local test_name=$2
-    local test_command=$3
-    local log_file="$RESULTS_DIR/test_${test_num}.log"
-    local actual_exit_code
-
-    echo "--- TEST_$test_num: Running $test_name ---"
-    
-    # Execute the test command.
-    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
-    actual_exit_code=$?
-
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
-
-    if [ "$actual_exit_code" -ne 0 ]; then
-        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
-        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
-        if [ -f "$log_file" ]; then
-            cat "$log_file" >&2
-        else
-            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
-        fi
-        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
-        return "$actual_exit_code" # Return the failure code
-    else
-        echo "TEST_$test_num ($test_name) PASSED."
-        return 0 # Return success
-    fi
-}
-
-# Helper function to call run_test and update the overall script exit code
-run_and_track_test() {
-    local test_num_arg="$1"
-    local test_name_arg="$2"
-    local test_command_arg="$3"
-
-    # Run the test
-    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
-    local test_specific_exit_code=$?
-
-    # If the test failed, set the overall script exit code to 1
-    if [ "$test_specific_exit_code" -ne 0 ]; then
-        # No need for extra echo here, run_test already logged the failure.
-        overall_script_exit_code=1
-    fi
-}
-
-# --- Actual Test Execution ---
-run_and_track_test 1 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
-run_and_track_test 2 "test_moe_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
-run_and_track_test 3 "test_lora.py" \
-    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
-run_and_track_test 4 "test_tpu_qkv_linear.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
-run_and_track_test 5 "test_spmd_model_weight_loading.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
-run_and_track_test 6 "test_kv_cache_update_kernel.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
-run_and_track_test 7 "test_tpu_int8.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_int8.py"
-
-# After all tests have been attempted, exit with the overall status.
-if [ "$overall_script_exit_code" -ne 0 ]; then
-    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
-else
-    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
-fi
-exit "$overall_script_exit_code"
-' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
-
-# Capture the exit code of the docker run command
-DOCKER_RUN_EXIT_CODE=$?
-
-# The trap will run for cleanup.
-# Exit the main script with the Docker run command's exit code.
-if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
-    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
-    exit "$DOCKER_RUN_EXIT_CODE"
-else
-    echo "Docker run command completed successfully."
-    exit 0
-fi
-# TODO: This test fails because it uses RANDOM_SEED sampling
-# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

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

@@ -2,174 +2,102 @@
 
 set -xu
 
-
-remove_docker_container() { 
-    docker rm -f tpu-test || true; 
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
 # Build the docker image.
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .
 
 # Set up cleanup.
-cleanup_docker() {
-  # Get Docker's root directory
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
-cleanup_docker
+remove_docker_container() { docker rm -f tpu-test || true; }
+trap remove_docker_container EXIT
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
 
 # For HF_TOKEN.
 source /etc/environment
-
+# Run a simple end-to-end example.
 docker run --privileged --net host --shm-size=16G -it \
     -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c '
-set -e # Exit immediately if a command exits with a non-zero status.
-set -u # Treat unset variables as an error.
+    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install pytest pytest-asyncio tpu-info \
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && export VLLM_XLA_CACHE_PATH= \
+    && export VLLM_USE_V1=1 \
+    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
+    && echo HARDWARE \
+    && tpu-info \
+    && { \
+        echo TEST_0: Running test_perf.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_perf.py; \
+        echo TEST_0_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_1: Running test_compilation.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py; \
+        echo TEST_1_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_2: Running test_basic.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py; \
+        echo TEST_2_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_3: Running test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        echo TEST_3_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_4: Running test_quantization_accuracy.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
+        echo TEST_4_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_5: Running examples/offline_inference/tpu.py; \
+        python3 /workspace/vllm/examples/offline_inference/tpu.py; \
+        echo TEST_5_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_6: Running test_tpu_model_runner.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/worker/test_tpu_model_runner.py; \
+        echo TEST_6_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_7: Running test_sampler.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py; \
+        echo TEST_7_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_8: Running test_topk_topp_sampler.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py; \
+        echo TEST_8_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_9: Running test_multimodal.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py; \
+        echo TEST_9_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_10: Running test_pallas.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py; \
+        echo TEST_10_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_11: Running test_struct_output_generate.py; \
+        pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py; \
+        echo TEST_11_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_12: Running test_moe_pallas.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py; \
+        echo TEST_12_EXIT_CODE: \$?; \
+    } & \
+    # Disable the TPU LoRA tests until the feature is activated
+    # && { \
+    #     echo TEST_13: Running test_moe_pallas.py; \
+    #     pytest -s -v /workspace/vllm/tests/tpu/lora/; \
+    #     echo TEST_13_EXIT_CODE: \$?; \
+    # } & \
+    wait \
+    && echo 'All tests have attempted to run. Check logs for individual test statuses and exit codes.' \
+"
 
-echo "--- Starting script inside Docker container ---"
-
-# Create results directory
-RESULTS_DIR=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $RESULTS_DIR"
-
-# Install dependencies
-echo "--- Installing Python dependencies ---"
-python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
-    && python3 -m pip install --progress-bar off hf-transfer
-echo "--- Python dependencies installed ---"
-export VLLM_USE_V1=1
-export VLLM_XLA_CHECK_RECOMPILATION=1
-export VLLM_XLA_CACHE_PATH=
-echo "Using VLLM V1"
-
-echo "--- Hardware Information ---"
-# tpu-info
-echo "--- Starting Tests ---"
-set +e
-overall_script_exit_code=0
-
-# --- Test Definitions ---
-# If a test fails, this function will print logs and will not cause the main script to exit.
-run_test() {
-    local test_num=$1
-    local test_name=$2
-    local test_command=$3
-    local log_file="$RESULTS_DIR/test_${test_num}.log"
-    local actual_exit_code
-
-    echo "--- TEST_$test_num: Running $test_name ---"
-    
-    # Execute the test command.
-    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
-    actual_exit_code=$?
-
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
-
-    if [ "$actual_exit_code" -ne 0 ]; then
-        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
-        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
-        if [ -f "$log_file" ]; then
-            cat "$log_file" >&2
-        else
-            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
-        fi
-        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
-        return "$actual_exit_code" # Return the failure code
-    else
-        echo "TEST_$test_num ($test_name) PASSED."
-        return 0 # Return success
-    fi
-}
-
-# Helper function to call run_test and update the overall script exit code
-run_and_track_test() {
-    local test_num_arg="$1"
-    local test_name_arg="$2"
-    local test_command_arg="$3"
-
-    # Run the test
-    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
-    local test_specific_exit_code=$?
-
-    # If the test failed, set the overall script exit code to 1
-    if [ "$test_specific_exit_code" -ne 0 ]; then
-        # No need for extra echo here, run_test already logged the failure.
-        overall_script_exit_code=1
-    fi
-}
-
-# --- Actual Test Execution ---
-run_and_track_test 0 "test_perf.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_perf.py"
-run_and_track_test 1 "test_compilation.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py"
-run_and_track_test 2 "test_basic.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
-run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
-run_and_track_test 4 "test_quantization_accuracy.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
-run_and_track_test 5 "examples/offline_inference/tpu.py" \
-    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
-run_and_track_test 6 "test_tpu_model_runner.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
-run_and_track_test 7 "test_sampler.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py"
-run_and_track_test 8 "test_topk_topp_sampler.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py"
-run_and_track_test 9 "test_multimodal.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
-run_and_track_test 10 "test_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
-
-# After all tests have been attempted, exit with the overall status.
-if [ "$overall_script_exit_code" -ne 0 ]; then
-    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
-else
-    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
-fi
-exit "$overall_script_exit_code"
-' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
-
-# Capture the exit code of the docker run command
-DOCKER_RUN_EXIT_CODE=$?
-
-# The trap will run for cleanup.
-# Exit the main script with the Docker run command's exit code.
-if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
-    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
-    exit "$DOCKER_RUN_EXIT_CODE"
-else
-    echo "Docker run command completed successfully."
-    exit 0
-fi
 # TODO: This test fails because it uses RANDOM_SEED sampling
-# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

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

@@ -11,8 +11,8 @@ container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head
 docker build -t ${image_name} -f docker/Dockerfile.xpu .
 
 # Setup cleanup
-remove_docker_container() {
-  docker rm -f "${container_name}" || true;
+remove_docker_container() { 
+  docker rm -f "${container_name}" || true; 
   docker image rm -f "${image_name}" || true;
   docker system prune -f || true;
 }
@@ -26,18 +26,6 @@ docker run \
     --name "${container_name}" \
     "${image_name}" \
     sh -c '
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    cd tests
-    pytest -v -s v1/core
-    pytest -v -s v1/engine
-    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
-    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
-    pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py
-    pytest -v -s v1/test_serial_utils.py
-    pytest -v -s v1/test_utils.py
-    pytest -v -s v1/test_metrics_reader.py
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/scripts/rerun-test.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-# Usage: ./rerun_test.sh path/to/test.py::test_name
-
-# Check if argument is given
-if [ $# -lt 1 ]; then
-    echo "Usage: $0 path/to/test.py::test_name"
-    echo "Example: $0 tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]"
-    exit 1
-fi
-
-TEST=$1
-COUNT=1
-
-while pytest -sv "$TEST"; do
-    COUNT=$((COUNT + 1))
-    echo "RUN NUMBER ${COUNT}"
-done

.buildkite/scripts/run-benchmarks.sh

@@ -11,10 +11,10 @@ cd "$(dirname "${BASH_SOURCE[0]}")/../.."
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 
 # run python-based benchmarks and upload the result to buildkite
-vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
+python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
 bench_latency_exit_code=$?
 
-vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
+python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
@@ -24,7 +24,7 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
 
 # wait for server to start, timeout after 600 seconds
 timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
-vllm bench serve \
+python3 benchmarks/benchmark_serving.py \
     --backend vllm \
     --dataset-name sharegpt \
     --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \

.buildkite/scripts/tpu/cleanup_docker.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-docker_root=$(docker info -f '{{.DockerRootDir}}')
-if [ -z "$docker_root" ]; then
-  echo "Failed to determine Docker root directory."
-  exit 1
-fi
-echo "Docker root directory: $docker_root"
-# Check disk usage of the filesystem where Docker's root directory is located
-disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-# Define the threshold
-threshold=70
-if [ "$disk_usage" -gt "$threshold" ]; then
-  echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-  # Remove dangling images (those that are not tagged and not used by any container)
-  docker image prune -f
-  # Remove unused volumes / force the system prune for old images as well.
-  docker volume prune -f && docker system prune --force --filter "until=72h" --all
-  echo "Docker images and volumes cleanup completed."
-else
-  echo "Disk usage is below $threshold%. No cleanup needed."
-fi

.buildkite/scripts/tpu/config_v6e_1.env

@@ -1,14 +0,0 @@
-# Environment config
-TEST_NAME=llama8b
-CONTAINER_NAME=tpu-test
-
-# vllm config
-MODEL=meta-llama/Llama-3.1-8B-Instruct
-MAX_NUM_SEQS=256
-MAX_NUM_BATCHED_TOKENS=1024
-TENSOR_PARALLEL_SIZE=1
-MAX_MODEL_LEN=2048
-DOWNLOAD_DIR=/mnt/disks/persist
-EXPECTED_THROUGHPUT=8.0
-INPUT_LEN=1800
-OUTPUT_LEN=128

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -1,90 +0,0 @@
-#!/bin/bash
-
-if [ ! -f "$1" ]; then
-  echo "Error: The env file '$1' does not exist."
-  exit 1  # Exit the script with a non-zero status to indicate an error
-fi
-
-ENV_FILE=$1
-
-# For testing on local vm, use `set -a` to export all variables
-source /etc/environment
-source $ENV_FILE
-
-remove_docker_container() { 
-    docker rm -f $CONTAINER_NAME || true;
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
-LOG_ROOT=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $LOG_ROOT"
-
-if [ -z "$HF_TOKEN" ]; then
-  echo "Error: HF_TOKEN is not set or is empty."  
-  exit 1
-fi
-
-# Make sure mounted disk or dir exists
-if [ ! -d "$DOWNLOAD_DIR" ]; then
-    echo "Error: Folder $DOWNLOAD_DIR does not exist. This is useually a mounted drive. If no mounted drive, just create a folder."
-    exit 1
-fi
-
-echo "Run model $MODEL"
-echo
-
-echo "starting docker...$CONTAINER_NAME"
-echo    
-docker run \
- -v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
- --env-file $ENV_FILE \
- -e HF_TOKEN="$HF_TOKEN" \
- -e TARGET_COMMIT=$BUILDKITE_COMMIT \
- -e MODEL=$MODEL \
- -e WORKSPACE=/workspace \
- --name $CONTAINER_NAME \
- -d \
- --privileged \
- --network host \
- -v /dev/shm:/dev/shm \
- vllm/vllm-tpu-bm tail -f /dev/null
-
-echo "run script..."
-echo
-docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/tpu/run_bm.sh"
-
-echo "copy result back..."
-VLLM_LOG="$LOG_ROOT/$TEST_NAME"_vllm_log.txt
-BM_LOG="$LOG_ROOT/$TEST_NAME"_bm_log.txt
-docker cp "$CONTAINER_NAME:/workspace/vllm_log.txt" "$VLLM_LOG" 
-docker cp "$CONTAINER_NAME:/workspace/bm_log.txt" "$BM_LOG"
-
-throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
-echo "throughput for $TEST_NAME at $BUILDKITE_COMMIT: $throughput"
-
-if [ "$BUILDKITE" = "true" ]; then
-  echo "Running inside Buildkite"
-  buildkite-agent artifact upload "$VLLM_LOG" 
-  buildkite-agent artifact upload "$BM_LOG"
-else
-  echo "Not running inside Buildkite"
-fi
-
-#
-# compare the throughput with EXPECTED_THROUGHPUT 
-# and assert meeting the expectation
-# 
-if [[ -z "$throughput" || ! "$throughput" =~ ^[0-9]+([.][0-9]+)?$ ]]; then
-  echo "Failed to get the throughput"
-  exit 1
-fi
-
-if (( $(echo "$throughput < $EXPECTED_THROUGHPUT" | bc -l) )); then
-  echo "Error: throughput($throughput) is less than expected($EXPECTED_THROUGHPUT)"
-  exit 1
-fi

.buildkite/scripts/tpu/quantized_v6e_1.env

@@ -1,14 +0,0 @@
-# Environment config
-TEST_NAME=llama8bw8a8
-CONTAINER_NAME=tpu-test
-
-# vllm config
-MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
-MAX_NUM_SEQS=128
-MAX_NUM_BATCHED_TOKENS=1024
-TENSOR_PARALLEL_SIZE=1
-MAX_MODEL_LEN=2048
-DOWNLOAD_DIR=/mnt/disks/persist
-EXPECTED_THROUGHPUT=10.0
-INPUT_LEN=1800
-OUTPUT_LEN=128

.buildkite/scripts/tpu/run_bm.sh

@@ -1,93 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-VLLM_LOG="$WORKSPACE/vllm_log.txt"
-BM_LOG="$WORKSPACE/bm_log.txt"
-
-if [ -n "$TARGET_COMMIT" ]; then
-  head_hash=$(git rev-parse HEAD)
-  if [ "$TARGET_COMMIT" != "$head_hash" ]; then
-    echo "Error: target commit $TARGET_COMMIT does not match HEAD: $head_hash"
-    exit 1
-  fi
-fi
-
-echo "model: $MODEL"
-echo
-
-#
-# create a log folder
-#
-mkdir "$WORKSPACE/log"
-
-# TODO: Move to image building.
-pip install pandas
-pip install datasets
-
-#
-# create sonnet_4x
-#
-echo "Create sonnet_4x.txt"
-echo "" > benchmarks/sonnet_4x.txt
-for _ in {1..4}
- do
-  cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
-done
-
-#
-# start vllm service in backend
-#
-echo "lanching vllm..."
-echo "logging to $VLLM_LOG"
-echo
-
-VLLM_USE_V1=1 vllm serve $MODEL \
- --seed 42 \
- --max-num-seqs $MAX_NUM_SEQS \
- --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
- --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
- --no-enable-prefix-caching \
- --download_dir $DOWNLOAD_DIR \
- --max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
-
-
-echo "wait for 20 minutes.."
-echo
-# sleep 1200
-# wait for 10 minutes...
-for i in {1..120}; do
-    # TODO: detect other type of errors.
-    if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
-        echo "Detected RuntimeError, exiting."
-        exit 1
-    elif grep -Fq "Application startup complete" "$VLLM_LOG"; then
-        echo "Application started"
-        break
-    else
-        echo "wait for 10 seconds..."
-        sleep 10
-    fi
-done
-
-#
-# run test
-#
-echo "run benchmark test..."
-echo "logging to $BM_LOG"
-echo
-vllm bench serve \
-    --backend vllm \
-    --model $MODEL  \
-    --dataset-name sonnet \
-    --dataset-path benchmarks/sonnet_4x.txt \
-    --sonnet-input-len $INPUT_LEN \
-    --sonnet-output-len $OUTPUT_LEN \
-    --ignore-eos > "$BM_LOG"
-
-echo "completed..."
-echo
-
-throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
-echo "throughput: $throughput"
-echo

.buildkite/scripts/upload-wheels.sh

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

.buildkite/test-pipeline.yaml

@@ -32,47 +32,36 @@ steps:
 ##### fast check tests  #####
 
 - label: Documentation Build # 2min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/test_docs"
+  working_dir: "/vllm-workspace/test_docs/docs"
   fast_check: true
   no_gpu: True
   commands:
-  - pip install -r ../requirements/docs.txt
-  # TODO: add `--strict` once warnings in docstrings are fixed
-  - mkdocs build
-
-- label: Pytorch Nightly Dependency Override Check # 2min
-  # if this test fails, it means the nightly torch version is not compatible with some
-  # of the dependencies. Please check the error message and add the package to whitelist
-  # in /vllm/tools/generate_nightly_torch_test.py
-  soft_fail: true
-  source_file_dependencies:
-  - requirements/nightly_torch_test.txt
-  commands:
-  - bash standalone_tests/pytorch_nightly_dependency.sh
+  - pip install -r ../../requirements/docs.txt
+  - SPHINXOPTS=\"-W\" make html
+  # Check API reference (if it fails, you may have missing mock imports)
+  - grep \"sig sig-object py\" build/html/api/vllm/vllm.sampling_params.html
 
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/mq_llm_engine
-  - tests/test_inputs.py
-  - tests/test_outputs.py
+  - tests/async_engine
+  - tests/test_inputs
   - tests/multimodal
-  - tests/utils_
+  - tests/test_utils
   - tests/worker
   - tests/standalone_tests/lazy_imports.py
   commands:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
+  - pytest -v -s async_engine # AsyncLLMEngine
+  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
   - pytest -v -s test_inputs.py
-  - pytest -v -s test_outputs.py
   - pytest -v -s multimodal
-  - pytest -v -s utils_ # Utils
+  - 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
@@ -80,7 +69,7 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Basic Correctness Test # 30min
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   fast_check: true
   torch_nightly: true
   source_file_dependencies:
@@ -97,7 +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
@@ -106,7 +94,7 @@ steps:
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
 
 - label: Core Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   fast_check: true
   source_file_dependencies:
   - vllm/core
@@ -115,39 +103,29 @@ steps:
   commands:
   - pytest -v -s core
 
-- label: Entrypoints Test (LLM) # 40min
-  mirror_hardwares: [amdexperimental]
+- label: Entrypoints Test # 40min
   working_dir: "/vllm-workspace/tests"
   fast_check: true
   torch_nightly: true
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/llm
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   - tests/entrypoints/offline_mode
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
+  - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py  --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_openai_schema.py
+  - pytest -v -s entrypoints/test_chat_utils.py
   - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
-- label: Entrypoints Test (API Server) # 40min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  fast_check: true
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/
-  - pytest -v -s entrypoints/test_chat_utils.py
-
 - label: Distributed Tests (4 GPUs) # 10min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -155,59 +133,32 @@ 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
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/test_external_lb_dp.py
-  - tests/v1/test_internal_lb_dp.py
-  - tests/v1/test_hybrid_lb_dp.py
-  - tests/v1/engine/test_engine_core_client.py
   commands:
   # 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 --enforce-eager
+  - python3 ../examples/offline_inference/data_parallel.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_hybrid_lb_dp.py
-  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
-  - pytest -v -s distributed/test_events.py
+  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  - python3 rlhf.py
+  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   - popd
 
-- label: EPLB Algorithm Test
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
-  commands:
-  - pytest -v -s distributed/test_eplb_algo.py
-
-- label: EPLB Execution Test # 5min
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_execute.py
-  commands:
-  - pytest -v -s distributed/test_eplb_execute.py
-
 - label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -215,18 +166,13 @@ steps:
   - tests/tracing
   commands:
   - pytest -v -s metrics
-  - "pip install \
-      'opentelemetry-sdk>=1.26.0' \
-      'opentelemetry-api>=1.26.0' \
-      'opentelemetry-exporter-otlp>=1.26.0' \
-      'opentelemetry-semantic-conventions-ai>=0.4.1'"
   - pytest -v -s tracing
 
 ##### fast check tests  #####
 #####  1 GPU test  #####
 
 - label: Regression Test # 5min
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -236,7 +182,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Engine Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/engine
@@ -244,14 +190,13 @@ steps:
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
-  - tests/test_vllm_port
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py
   # OOM in the CI unless we run this separately
   - pytest -v -s tokenization
 
 - label: V1 Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
     - vllm/
     - tests/v1
@@ -264,22 +209,20 @@ 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/metrics
     - 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
     # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
+    - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
     - 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/
@@ -292,9 +235,9 @@ steps:
     - python3 offline_inference/llm_engine_example.py
     - python3 offline_inference/audio_language.py --seed 0
     - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_pooling.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
@@ -303,24 +246,14 @@ 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]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/prefix_caching
   commands:
     - pytest -v -s prefix_caching
 
-
-- label: Platform Tests (CUDA)
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/
-  - tests/cuda
-  commands:
-    - pytest -v -s cuda/test_cuda_context.py
-
 - label: Samplers Test # 36min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/model_executor/layers
   - vllm/sampling_metadata.py
@@ -330,8 +263,29 @@ steps:
     - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
 
+- label: LogitsProcessor Test # 5min
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/model_executor/layers
+  - vllm/model_executor/guided_decoding
+  - tests/test_logits_processor
+  - tests/model_executor/test_guided_processors
+  commands:
+    - pytest -v -s test_logits_processor.py
+    - pytest -v -s model_executor/test_guided_processors.py
+
+- label: Speculative decoding tests # 40min
+  source_file_dependencies:
+  - vllm/spec_decode
+  - tests/spec_decode
+  - vllm/model_executor/models/eagle.py
+  commands:
+    - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
+    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
+    - 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
@@ -339,7 +293,6 @@ steps:
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
     - vllm/
@@ -347,14 +300,9 @@ steps:
   commands:
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py
-    - pytest -v -s compile/test_fusion_attn.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
-    - pytest -v -s compile/test_fusion_all_reduce.py
 
 - label: PyTorch Fullgraph Smoke Test # 9min
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -364,10 +312,8 @@ steps:
   # these tests need to be separated, cannot combine
   - pytest -v -s compile/piecewise/test_simple.py
   - pytest -v -s compile/piecewise/test_toy_llama.py
-  - pytest -v -s compile/piecewise/test_full_cudagraph.py
 
 - label: PyTorch Fullgraph Test # 18min
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -376,7 +322,7 @@ steps:
   - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Core Operation Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
@@ -384,7 +330,7 @@ steps:
     - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/attention/
   - vllm/attention
@@ -395,27 +341,26 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/quantization/
   - vllm/model_executor/layers/quantization
   - tests/kernels/quantization
   commands:
-    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+    - pytest -v -s kernels/quantization  --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
 
-- label: Kernels MoE Test %N
-  mirror_hardwares: [amdexperimental]
+- label: Kernels MoE Test
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/moe/
   - tests/kernels/moe
   - vllm/model_executor/layers/fused_moe/
   commands:
-    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s kernels/moe
 
 - label: Kernels Mamba Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
@@ -423,37 +368,25 @@ steps:
     - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amdexperimental]
+  # 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: Model Executor Test
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/model_executor
-  - tests/model_executor
-  commands:
-    - apt-get update && apt-get install -y curl libsodium23
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
 
 - label: Benchmarks # 9min
-  mirror_hardwares: [amdexperimental]
   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]
   source_file_dependencies:
   - vllm/
   - tests/benchmarks/
@@ -461,19 +394,14 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Quantization Test
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
   - tests/quantization
   commands:
-  # temporary install here since we need nightly, will move to requirements/test.in
-  # after torchao 0.12 release
-  - pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
   - 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/
@@ -483,7 +411,6 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
 
 - label: OpenAI API correctness
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
@@ -492,7 +419,6 @@ steps:
   - pytest -s entrypoints/openai/correctness/
 
 - label: Encoder Decoder tests # 5min
-  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/encoder_decoder
@@ -500,8 +426,8 @@ steps:
     - pytest -v -s encoder_decoder
 
 - label: OpenAI-Compatible Tool Use # 20 min
-  mirror_hardwares: [amdexperimental]
   fast_check: false
+  #mirror_hardwares: [ amd ]
   source_file_dependencies:
     - vllm/
     - tests/tool_use
@@ -513,7 +439,6 @@ steps:
 #####  models test  #####
 
 - label: Basic Models Test # 24min
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -523,67 +448,43 @@ steps:
     - pytest -v -s models/test_registry.py
     - pytest -v -s models/test_utils.py
     - pytest -v -s models/test_vision.py
-    - pytest -v -s models/test_initialization.py
+    # V1 Test: https://github.com/vllm-project/vllm/issues/14531
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'plamo2'
 
 - label: Language Models Test (Standard)
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/models/language
   commands:
-    - pip freeze | grep -E 'torch'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pytest -v -s models/language -m core_model
 
-- label: Language Models Test (Hybrid) # 35 min
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-    - pytest -v -s models/language/generation -m hybrid_model
-
-- label: Language Models Test (Extended Generation) # 1hr20min
-  mirror_hardwares: [amdexperimental]
+- label: Language Models Test (Extended)
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/language/generation
+  - tests/models/language
   commands:
     # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
     - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
-    - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-
-- label: Language Models Test (Extended Pooling)  # 36min
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/pooling
-  commands:
-    - pytest -v -s models/language/pooling -m 'not core_model'
+    - pytest -v -s models/language -m 'not core_model'
 
 - label: Multi-Modal Models Test (Standard)
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pip freeze | grep -E 'torch'
     - pytest -v -s models/multimodal/processing
-    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/test_tensor_schema.py models/multimodal -m core_model
-    - pytest -v -s models/multimodal/test_tensor_schema.py -m core_model  # Needs mp_method="spawn"
+    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
     - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
 - label: Multi-Modal Models Test (Extended) 1
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -593,7 +494,6 @@ steps:
     - pytest -v -s --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing models/multimodal -m 'not core_model'
 
 - label: Multi-Modal Models Test (Extended) 2
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -603,7 +503,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models Test (Extended) 3
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -613,7 +512,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Quantized Models Test
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization
@@ -622,7 +521,7 @@ steps:
 
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   optional: true
   commands:
     - echo 'Testing custom models...'
@@ -630,52 +529,11 @@ steps:
     # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
     # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*
 
-- label: Transformers Nightly Models Test
-  working_dir: "/vllm-workspace/"
-  optional: true
-  commands:
-    - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py
-    - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/audio_language.py --model-type whisper
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
-
-- label: Blackwell Test
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  # optional: true
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - csrc/attention/mla/
-  - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/fusion.py
-  commands:
-    - nvidia-smi
-    - python3 examples/offline_inference/basic/chat.py
-    # Attention
-    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
-    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
-    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
-    - pytest -v -s tests/kernels/test_cutlass_mla_decode.py
-    # Quantization
-    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
-    # Fusion
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-
 #####  1 GPU test  #####
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -686,7 +544,6 @@ steps:
   - pytest -v -s distributed/test_shm_broadcast.py
 
 - label: 2 Node Tests (4 GPUs in total) # 16min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   num_nodes: 2
@@ -696,21 +553,16 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/offline_inference/data_parallel.py
   commands:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - 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'
-    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - 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'
-    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
 
 - label: Distributed Tests (2 GPUs) # 40min
-  mirror_hardwares: [amdexperimental]
+  #mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -725,13 +577,9 @@ steps:
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/test_external_lb_dp.py
-  - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - vllm/v1/engine/
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
@@ -745,19 +593,19 @@ steps:
   - pytest -v -s distributed/test_sequence_parallel.py
   # this test fails consistently.
   # TODO: investigate and fix
+  # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
+  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s models/multimodal/generation/test_maverick.py
 
 - 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
@@ -768,10 +616,28 @@ 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
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 4
+  source_file_dependencies:
+  - vllm/model_executor/layers/sampler.py
+  - vllm/sequence.py
+  - vllm/worker/worker_base.py
+  - vllm/worker/worker.py
+  - vllm/worker/multi_step_worker.py
+  - vllm/worker/model_runner_base.py
+  - vllm/worker/model_runner.py
+  - vllm/worker/multi_step_model_runner.py
+  - vllm/engine
+  - tests/multi_step
+  commands:
+  # this test is quite flaky
+  # TODO: investigate and fix.
+  # - pytest -v -s multi_step/test_correctness_async_llm.py
+  - pytest -v -s multi_step/test_correctness_llm.py
 
 - label: Pipeline Parallelism Test # 45min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -785,7 +651,6 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: LoRA TP Test (Distributed)
-  mirror_hardwares: [amdexperimental]
   num_gpus: 4
   source_file_dependencies:
   - vllm/lora
@@ -798,11 +663,9 @@ steps:
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_multi_loras_with_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -812,7 +675,6 @@ steps:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
 
 - label: Weight Loading Multiple GPU Test - Large Models # optional
-  mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   gpu: a100

.gemini/config.yaml

@@ -1,6 +0,0 @@
-# https://developers.google.com/gemini-code-assist/docs/customize-gemini-behavior-github
-have_fun: false  # Just review the code
-code_review:
-  comment_severity_threshold: HIGH  # Reduce quantity of comments
-  pull_request_opened:
-    summary: false  # Don't summarize the PR in a separate comment

.github/CODEOWNERS

@@ -9,22 +9,15 @@
 /vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
+/vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
-/vllm/lora @jeejeelee
-/vllm/reasoning @aarnphm
-/vllm/entrypoints @aarnphm
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg
-CMakeLists.txt @tlrmchlsmth @LucasWilkinson
-
-# Any change to the VllmConfig changes can have a large user-facing impact,
-# so spam a lot of people
-/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
+CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
-/vllm/v1/structured_output @mgoin @russellb @aarnphm
+/vllm/v1/structured_output @mgoin @russellb
 
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin @simon-mo
@@ -33,42 +26,17 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
-/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
-/tests/kernels @tlrmchlsmth @WoosukKwon @yewentao256
+/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo
+/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb
+/tests/kernels @tlrmchlsmth @WoosukKwon
+/tests/model_executor/test_guided_processors.py @mgoin @russellb
 /tests/models @DarkLight1337 @ywang96
+/tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
+/tests/quantization @mgoin @robertgshaw2-redhat
+/tests/spec_decode @njhill @LiuXiaoxuanPKU
 /tests/test_inputs.py @DarkLight1337 @ywang96
-/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
-/tests/v1/structured_output @mgoin @russellb @aarnphm
-/tests/weight_loading @mgoin @youkaichao @yewentao256
-/tests/lora @jeejeelee
-
-# Docs
-/docs @hmellor
-mkdocs.yaml @hmellor
-
-# CPU
-/vllm/v1/worker/^cpu @bigPYJ1151
-/csrc/cpu @bigPYJ1151
-/vllm/platforms/cpu.py @bigPYJ1151
-/cmake/cpu_extension.cmake @bigPYJ1151
-/docker/Dockerfile.cpu @bigPYJ1151
-
-# Intel GPU
-/vllm/v1/worker/^xpu @jikunshang
-/vllm/platforms/xpu.py @jikunshang
-/docker/Dockerfile.xpu @jikunshang
-
-# Qwen-specific files
-/vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
-/vllm/model_executor/models/qwen* @sighingnow
-
-# Mistral-specific files
-/vllm/model_executor/models/mistral*.py @patrickvonplaten
-/vllm/model_executor/models/mixtral*.py @patrickvonplaten
-/vllm/model_executor/models/voxtral*.py @patrickvonplaten
-/vllm/model_executor/models/pixtral*.py @patrickvonplaten
-/vllm/transformers_utils/configs/mistral.py @patrickvonplaten
-/vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
+/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb
+/tests/v1/structured_output @mgoin @russellb
+/tests/weight_loading @mgoin @youkaichao

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

@@ -8,16 +8,6 @@ body:
   attributes:
     value: >
       #### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
-- type: markdown
-  attributes:
-    value: |
-      ⚠️ **SECURITY WARNING:** Please review any text you paste to ensure it does not contain sensitive information such as:
-      - API tokens or keys (e.g., Hugging Face tokens, OpenAI API keys)
-      - Passwords or authentication credentials
-      - Private URLs or endpoints
-      - Personal or confidential data
-      
-      Consider redacting or replacing sensitive values with placeholders like `<YOUR_TOKEN_HERE>` when sharing configuration or code examples.
 - type: textarea
   attributes:
     label: Your current environment
@@ -91,14 +81,14 @@ body:
     required: true
 - type: markdown
   attributes:
-    value: |
-      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the model's output:
+    value: >
+      ⚠️ Please separate bugs of `transformers` implementation or usage from bugs of `vllm`. If you think anything is wrong with the models' output:
 
       - Try the counterpart of `transformers` first. If the error appears, please go to [their issues](https://github.com/huggingface/transformers/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc).
 
       - If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.
 
-      Thanks for reporting 🙏!
+      Thanks for contributing 🎉!
 - type: checkboxes
   id: askllm
   attributes:

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

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

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -46,7 +46,7 @@ body:
 - type: markdown
   attributes:
     value: >
-      Thanks for contributing 🎉! The vLLM core team hosts a biweekly RFC review session at 9:30AM Pacific Time, while most RFCs can be discussed online, you can optionally sign up for a slot to discuss your RFC online [here](https://docs.google.com/document/d/1CiLVBZeIVfR7_PNAKVSusxpceywkoOOB78qoWqHvSZc/edit).
+      Thanks for contributing 🎉!
 - type: checkboxes
   id: askllm
   attributes:

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,22 +1,6 @@
-<!-- markdownlint-disable -->
-PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.
+FILL IN THE PR DESCRIPTION HERE
 
-## Purpose
+FIX #xxxx (*link existing issues this PR will resolve*)
 
-## Test Plan
-
-## Test Result
-
-## (Optional) Documentation Update
-
----
-<details>
-<summary> Essential Elements of an Effective PR Description Checklist </summary>
-
-- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
-- [ ] The test plan, such as providing test command.
-- [ ] The test results, such as pasting the results comparison before and after, or e2e results
-- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-</details>
-
-**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)
+<!--- pyml disable-next-line no-emphasis-as-heading -->
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -27,22 +27,6 @@ pull_request_rules:
       add:
         - ci/build
 
-- name: label-deepseek
-  description: Automatically apply deepseek label
-  conditions:
-    - or:
-      - files~=^examples/.*deepseek.*\.py
-      - files~=^tests/.*deepseek.*\.py
-      - files~=^vllm/entrypoints/openai/tool_parsers/.*deepseek.*\.py
-      - files~=^vllm/model_executor/models/.*deepseek.*\.py
-      - files~=^vllm/reasoning/.*deepseek.*\.py
-      - files~=^vllm/transformers_utils/.*deepseek.*\.py
-      - title~=(?i)DeepSeek
-  actions:
-    label:
-      add:
-        - deepseek
-
 - name: label-frontend
   description: Automatically apply frontend label
   conditions:
@@ -52,21 +36,6 @@ pull_request_rules:
       add:
         - frontend
 
-- name: label-llama
-  description: Automatically apply llama label
-  conditions:
-    - or:
-      - files~=^examples/.*llama.*\.py
-      - files~=^tests/.*llama.*\.py
-      - files~=^vllm/entrypoints/openai/tool_parsers/llama.*\.py
-      - files~=^vllm/model_executor/models/.*llama.*\.py
-      - files~=^vllm/transformers_utils/configs/.*llama.*\.py
-      - title~=(?i)llama
-  actions:
-    label:
-      add:
-        - llama
-
 - name: label-multi-modality
   description: Automatically apply multi-modality label
   conditions:
@@ -74,84 +43,14 @@ pull_request_rules:
       - files~=^vllm/multimodal/
       - files~=^tests/multimodal/
       - files~=^tests/models/multimodal/
+      - files~=^tests/models/*/audio_language/
+      - files~=^tests/models/*/vision_language/
       - files=tests/models/test_vision.py
   actions:
     label:
       add:
         - multi-modality
 
-- name: label-new-model
-  description: Automatically apply new-model label
-  conditions:
-    - and:
-      - files~=^vllm/model_executor/models/
-      - files=vllm/model_executor/models/registry.py
-  actions:
-    label:
-      add:
-        - new-model
-
-- name: label-performance
-  description: Automatically apply performance label
-  conditions:
-    - or:
-      - files~=^benchmarks/
-      - files~=^vllm/benchmarks/
-      - files~=^tests/benchmarks/
-      - files~=^\.buildkite/nightly-benchmarks/
-  actions:
-    label:
-      add:
-        - performance
-
-- name: label-qwen
-  description: Automatically apply qwen label
-  conditions:
-    - or:
-      - files~=^examples/.*qwen.*\.py
-      - files~=^tests/.*qwen.*\.py
-      - files~=^vllm/model_executor/models/.*qwen.*\.py
-      - files~=^vllm/reasoning/.*qwen.*\.py
-      - title~=(?i)Qwen
-  actions:
-    label:
-      add:
-        - qwen
-
-- name: label-gpt-oss
-  description: Automatically apply gpt-oss label
-  conditions:
-    - or:
-      - files~=^examples/.*gpt[-_]?oss.*\.py
-      - files~=^tests/.*gpt[-_]?oss.*\.py
-      - files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
-      - files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
-      - title~=(?i)gpt[-_]?oss
-  actions:
-    label:
-      add:
-        - gpt-oss
-
-- name: label-rocm
-  description: Automatically apply rocm label
-  conditions:
-    - or:
-      - files~=^csrc/rocm/
-      - files~=^docker/Dockerfile.rocm
-      - files~=^requirements/rocm.*\.txt
-      - files~=^vllm/attention/backends/rocm.*\.py
-      - files~=^vllm/attention/ops/rocm.*\.py
-      - files~=^vllm/model_executor/layers/fused_moe/rocm.*\.py
-      - files~=^vllm/v1/attention/backends/mla/rocm.*\.py
-      - files~=^tests/kernels/.*_rocm.*\.py
-      - files=vllm/platforms/rocm.py
-      - title~=(?i)AMD
-      - title~=(?i)ROCm
-  actions:
-    label:
-      add:
-        - rocm
-
 - name: label-structured-output
   description: Automatically apply structured-output label
   conditions:
@@ -159,10 +58,13 @@ 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
+      - files~=^vllm/model_executor/guided_decoding/
+      - files=tests/model_executor/test_guided_processors.py
+      - files=tests/entrypoints/llm/test_guided_generate.py
       - files~=^tests/v1/structured_output/
       - files=tests/v1/entrypoints/llm/test_guided_generate.py
       - files~=^vllm/v1/structured_output/
@@ -175,12 +77,9 @@ pull_request_rules:
   description: Automatically apply speculative-decoding label
   conditions:
     - or:
-      - files~=^vllm/v1/spec_decode/
-      - files~=^tests/v1/spec_decode/
-      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
-      - files~=^vllm/model_executor/models/.*eagle.*\.py
-      - files=vllm/model_executor/models/mlp_speculator.py
-      - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
+      - files~=^vllm/spec_decode/
+      - files=vllm/model_executor/layers/spec_decode_base_sampler.py
+      - files~=^tests/spec_decode/
   actions:
     label:
       add:
@@ -236,7 +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
@@ -262,17 +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

@@ -15,18 +15,18 @@ NEW=/tmp/new_pr_body.txt
 gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
 cp "${OLD}" "${NEW}"
 
-# Remove markdown comments (like the <!-- markdownlint-disable --> at the start)
-sed -i '/<!--.*-->$/d' "${NEW}"
+# Remove "FIX #xxxx (*link existing issues this PR will resolve*)"
+sed -i '/FIX #xxxx.*$/d' "${NEW}"
 
-# Remove "PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED."
-sed -i '/PLEASE FILL IN THE PR DESCRIPTION HERE.*$/d' "${NEW}"
+# Remove "FILL IN THE PR DESCRIPTION HERE"
+sed -i '/FILL IN THE PR DESCRIPTION HERE/d' "${NEW}"
 
 # Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
 sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"
 
 # 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

@@ -2,13 +2,6 @@ name: Lint and Deploy Charts
 
 on: pull_request
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
 jobs:
   lint-and-deploy:
     runs-on: ubuntu-latest
@@ -72,7 +65,7 @@ jobs:
           export AWS_ACCESS_KEY_ID=minioadmin
           export AWS_SECRET_ACCESS_KEY=minioadmin
           sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
-          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set image.env[2].name=VLLM_CPU_CI_ENV --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string image.env[2].value="1" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
+          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
 
       - name: curl test
         run: |

.github/workflows/matchers/markdownlint.json

@@ -1,17 +0,0 @@
-{
-  "problemMatcher": [
-    {
-      "owner": "markdownlint",
-      "pattern": [
-        {
-          "regexp": "^([^:]*):(\\d+):?(\\d+)?\\s([\\w-\\/]*)\\s(.*)$",
-          "file": 1,
-          "line": 2,
-          "column": 3,
-          "code": 4,
-          "message": 5
-        }
-      ]
-    }
-  ]
-}

.github/workflows/pre-commit.yml

@@ -5,13 +5,6 @@ on:
   push:
     branches: [main]
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
-permissions:
-  contents: read
-
 jobs:
   pre-commit:
     runs-on: ubuntu-latest
@@ -21,7 +14,6 @@ jobs:
       with:
         python-version: "3.12"
     - run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
-    - run: echo "::add-matcher::.github/workflows/matchers/markdownlint.json"
     - run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
     - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
       with:

.github/workflows/reminder_comment.yml

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

.github/workflows/scripts/build.sh

@@ -15,6 +15,7 @@ $python_executable -m pip install -r requirements/build.txt -r requirements/cuda
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
 export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
+export VLLM_FA_CMAKE_GPU_ARCHES="80-real;90-real"
 
 bash tools/check_repo.sh
 

.gitignore

@@ -4,9 +4,6 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*
 
-# triton jit 
-.triton
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -80,6 +77,11 @@ instance/
 # Scrapy stuff:
 .scrapy
 
+# Sphinx documentation
+docs/_build/
+docs/source/getting_started/examples/
+docs/source/api/vllm
+
 # PyBuilder
 .pybuilder/
 target/
@@ -149,9 +151,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-docs/argparse
-docs/examples/*
-!docs/examples/README.md
 
 # mypy
 .mypy_cache/
@@ -205,5 +204,5 @@ benchmarks/**/*.json
 actionlint
 shellcheck*/
 
-# Ignore moe/marlin_moe gen code
+# Ingore moe/marlin_moe gen code
 csrc/moe/marlin_moe_wna16/kernel_*

.markdownlint.yaml

@@ -1,13 +0,0 @@
-MD007:
-  indent: 4
-MD013: false
-MD024:
-  siblings_only: true
-MD033: false
-MD042: false
-MD045: false
-MD046: false
-MD051: false
-MD052: false
-MD053: false
-MD059: false

.pre-commit-config.yaml

@@ -11,19 +11,17 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
-    # Keep the same list from yapfignore here to avoid yapf failing without any inputs
-    exclude: '(.buildkite|benchmarks|build|examples)/.*'
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.11.7
   hooks:
   - id: ruff
     args: [--output-format, github, --fix]
-  - id: ruff-format
-    files: ^(.buildkite|benchmarks|examples)/.*
-- repo: https://github.com/crate-ci/typos
-  rev: v1.34.0
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.4.1
   hooks:
-  - id: typos
+  - id: codespell
+    additional_dependencies: ['tomli']
+    args: ['--toml', 'pyproject.toml']
 - repo: https://github.com/PyCQA/isort
   rev: 6.0.1
   hooks:
@@ -35,12 +33,11 @@ repos:
     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/igorshubovych/markdownlint-cli
-  rev: v0.45.0
+- repo: https://github.com/jackdewinter/pymarkdown
+  rev: v0.9.29
   hooks:
-  - id: markdownlint
-    exclude: '.*\.inc\.md'
-    stages: [manual] # Only run in CI
+  - id: pymarkdown
+    args: [fix]
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
   hooks:
@@ -53,17 +50,12 @@ repos:
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
-  - id: format-torch-nightly-test
-    name: reformat nightly_torch_test.txt to be in sync with test.in
-    language: python
-    entry: python tools/generate_nightly_torch_test.py
-    files: ^requirements/test\.(in|txt)$
   - id: mypy-local
     name: Run mypy for local Python installation
     entry: tools/mypy.sh 0 "local"
     language: python
     types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
+    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
     stages: [pre-commit] # Don't run in CI
   - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.9
@@ -120,11 +112,6 @@ repos:
     entry: python tools/check_spdx_header.py
     language: python
     types: [python]
-  - id: check-root-lazy-imports
-    name: Check root lazy imports
-    entry: python tools/check_init_lazy_imports.py
-    language: python
-    types: [python]
   - id: check-filenames
     name: Check for spaces in all filenames
     entry: bash
@@ -138,39 +125,10 @@ repos:
     name: Update Dockerfile dependency graph
     entry: tools/update-dockerfile-graph.sh
     language: script
-  - id: enforce-import-regex-instead-of-re
-    name: Enforce import regex as re
-    entry: python tools/enforce_regex_import.py
-    language: python
-    types: [python]
-    pass_filenames: false
-    additional_dependencies: [regex]
-  # forbid directly import triton
-  - id: forbid-direct-triton-import
-    name: "Forbid direct 'import triton'"
-    entry: python tools/check_triton_import.py
-    language: python
-    types: [python]
-    pass_filenames: false
-    additional_dependencies: [regex]
-  - id: check-pickle-imports
-    name: Prevent new pickle/cloudpickle imports
-    entry: python tools/check_pickle_imports.py
-    language: python
-    types: [python]
-    pass_filenames: false
-    additional_dependencies: [pathspec, regex]
-  - id: validate-config
-    name: Validate configuration has default values and that each field has a docstring
-    entry: python tools/validate_config.py
-    language: python
-    types: [python]
-    pass_filenames: true
-    files: vllm/config.py|tests/test_config.py|vllm/entrypoints/openai/cli_args.py
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion
-    entry: bash -c 'echo "To bypass all the pre-commit hooks, add --no-verify to git commit. To skip a specific hook, prefix the commit command with SKIP=<hook-id>."'
+    entry: bash -c 'echo "To bypass pre-commit hooks, add --no-verify to git commit."'
     language: system
     verbose: true
     pass_filenames: false

.readthedocs.yaml

@@ -7,12 +7,13 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
-  jobs:
-    post_checkout:
-      - git fetch --unshallow || true
 
-mkdocs:
-  configuration: mkdocs.yaml
+sphinx:
+  configuration: docs/source/conf.py
+  fail_on_warning: true
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+formats: []
 
 # Optionally declare the Python requirements required to build your docs
 python:

CMakeLists.txt

@@ -23,15 +23,15 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")
 
-# Prevent installation of dependencies (cutlass) by default.
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
-
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
 set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 
+# Supported NVIDIA architectures.
+set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
+
 # Supported AMD GPU architectures.
 set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
 
@@ -45,7 +45,7 @@ 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 "2.7.1")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.7.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
 
 #
@@ -79,15 +79,6 @@ endif()
 #
 find_package(Torch REQUIRED)
 
-# Supported NVIDIA architectures.
-# This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
-if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
-   CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
-  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
-else()
-  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
-endif()
-
 #
 # Forward the non-CUDA device extensions to external CMake scripts.
 #
@@ -171,6 +162,7 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
 endif()
 
+
 #
 # Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process.
 # setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache.
@@ -181,6 +173,9 @@ include(FetchContent)
 file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists
 message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}")
 
+#
+# Set rocm version dev int.
+#
 if(VLLM_GPU_LANG STREQUAL "HIP")
   #
   # Overriding the default -O set up by cmake, adding ggdb3 for the most verbose devug info
@@ -188,6 +183,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
   set(CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG "${CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG} -O0 -ggdb3")
   set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -ggdb3")
 
+
   #
   # Certain HIP functions are marked as [[nodiscard]], yet vllm ignores the result which generates
   # a lot of warnings that always mask real issues. Suppressing until this is properly addressed.
@@ -230,17 +226,14 @@ endif()
 #
 
 set(VLLM_EXT_SRC
-  "csrc/mamba/mamba_ssm/selective_scan_fwd.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"
   "csrc/layernorm_quant_kernels.cu"
-  "csrc/sampler.cu"
   "csrc/cuda_view.cu"
   "csrc/quantization/gptq/q_gemm.cu"
   "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
@@ -257,7 +250,7 @@ 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 "v4.0.0" CACHE STRING "CUTLASS revision to use")
+  set(CUTLASS_REVISION "v3.9.2" 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})
@@ -287,17 +280,17 @@ 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"
-    "csrc/quantization/fp8/per_token_group_quant.cu")
+    "csrc/attention/mla/cutlass_mla_entry.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -306,8 +299,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # Only build Marlin kernels if we are building for at least some compatible archs.
   # Keep building Marlin for 9.0 as there are some group sizes and shapes that
   # are not supported by Machete yet.
-  # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_ARCHS)
 
     #
@@ -392,7 +384,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
   # CUDA 12.0 or later
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
     set(SRCS
        "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
@@ -408,7 +400,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
     message(STATUS "Building scaled_mm_c3x_sm90 for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
       message(STATUS "Not building scaled_mm_c3x_sm90 as CUDA Compiler version is "
                      "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
                      "later if you intend on running FP8 quantized models on "
@@ -419,41 +411,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-
-  # The cutlass_scaled_mm kernels for Geforce Blackwell SM120 (c3x, i.e. CUTLASS 3.x) require
+  # The cutlass_scaled_mm kernels for Blackwell (c3x, i.e. CUTLASS 3.x) require
   # CUDA 12.8 or later
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS
-      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
-    )
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_SM120=1")
-    # Let scaled_mm_c2x know it doesn't need to build these arches
-    list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
-    message(STATUS "Building scaled_mm_c3x_sm120 for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building scaled_mm_c3x_sm120 as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or "
-                     "later if you intend on running FP8 quantized models on "
-                     "Blackwell.")
-    else()
-      message(STATUS "Not building scaled_mm_c3x_120 as no compatible archs found "
-                     "in CUDA target architectures")
-    endif()
-  endif()
-
-
-  # The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x)
-  # require CUDA 12.8 or later
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;12.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
     set(SRCS
       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
@@ -468,7 +429,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
     message(STATUS "Building scaled_mm_c3x_sm100 for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
       message(STATUS "Not building scaled_mm_c3x_sm100 as CUDA Compiler version is "
                      "not >= 12.8, we recommend upgrading to CUDA 12.8 or "
                      "later if you intend on running FP8 quantized models on "
@@ -482,9 +443,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   #
   # For the cutlass_scaled_mm kernels we want to build the c2x (CUTLASS 2.x)
   # kernels for the remaining archs that are not already built for 3x.
-  # (Build 8.9 for FP8)
   cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
-    "7.5;8.0;8.7;8.9+PTX" "${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 ${SCALED_MM_3X_ARCHS})
   if (SCALED_MM_2X_ARCHS)
@@ -511,7 +471,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
   # require CUDA 12.2 or later (and only work on Hopper).
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -520,7 +480,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
     message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
       message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
                      "not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
                      "if you intend on running FP8 sparse quantized models on Hopper.")
@@ -530,39 +490,17 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  # The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require
-  # CUDA 12.8 or later
-  cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
-    set(SRCS
-      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${FP4_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
-    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
-  else()
-    message(STATUS "Not building NVFP4 as no compatible archs were found.")
-    # clear FP4_ARCHS
-    set(FP4_ARCHS)
-  endif()
-
   # FP4 Archs and flags
   cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
+      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
     list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4=1")
     message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
   else()
     message(STATUS "Not building NVFP4 as no compatible archs were found.")
@@ -572,10 +510,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # CUTLASS MLA Archs and flags
   cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND MLA_ARCHS)
     set(SRCS
-      "csrc/attention/mla/cutlass_mla_kernels.cu"
-      "csrc/attention/mla/sm100_cutlass_mla_kernel.cu")
+      "csrc/attention/mla/cutlass_mla_kernels.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${MLA_ARCHS}")
@@ -593,12 +530,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # CUTLASS MoE kernels
 
-  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and ONLY works
-  # on Hopper). get_cutlass_(pplx_)moe_mm_data should only be compiled
-  # if it's possible to compile MoE kernels that use its output.
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
+  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
+  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
+  # to compile MoE kernels that use its output.
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
+             "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -612,66 +550,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                      "if you intend on running FP8 quantized MoE models on Hopper.")
     else()
       message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
-    message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
-                     "if you intend on running FP8 quantized MoE models on Blackwell.")
-    else()
-      message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  # moe_data.cu is used by all CUTLASS MoE kernels.
-  cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${CUTLASS_MOE_DATA_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    message(STATUS "Building moe_data for archs: ${CUTLASS_MOE_DATA_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
-      message(STATUS "Not building moe_data as CUDA Compiler version is "
-                     "not >= 12.3, we recommend upgrading to CUDA 12.3 or later "
-                     "if you intend on running FP8 quantized MoE models on Hopper or Blackwell.")
-    else()
-      message(STATUS "Not building moe_data as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
-    message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
-                     "if you intend on running FP8 quantized MoE models on Blackwell.")
-    else()
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
                      "in CUDA target architectures")
     endif()
   endif()
@@ -682,7 +560,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The machete kernels only work on hopper and require CUDA 12.0 or later.
   # Only build Machete kernels if we are building for something compatible with sm90a
   cuda_archs_loose_intersection(MACHETE_ARCHS "9.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND MACHETE_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND MACHETE_ARCHS)
     #
     # For the Machete kernels we automatically generate sources for various
     # preselected input type pairs and schedules.
@@ -734,7 +612,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
     message(STATUS "Building Machete kernels for archs: ${MACHETE_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0
         AND MACHETE_ARCHS)
       message(STATUS "Not building Machete kernels as CUDA Compiler version is "
                      "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
@@ -748,14 +626,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 # if CUDA endif
 endif()
 
-if (VLLM_GPU_LANG STREQUAL "HIP")
-  # Add QuickReduce kernels
-  list(APPEND VLLM_EXT_SRC
-    "csrc/custom_quickreduce.cu"
-  )
-# if ROCM endif
-endif()
-
 message(STATUS "Enabling C extension.")
 define_gpu_extension_target(
   _C
@@ -788,14 +658,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
 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")
-
-  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
-endif()
-
 set_gencode_flags_for_srcs(
   SRCS "${VLLM_MOE_EXT_SRC}"
   CUDA_ARCHS "${CUDA_ARCHS}")
@@ -809,8 +671,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     CUDA_ARCHS "${CUDA_ARCHS}")
 
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
-  # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
 
     #
@@ -864,6 +725,17 @@ 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
@@ -900,7 +772,5 @@ endif()
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
     include(cmake/external_projects/flashmla.cmake)
-
-    # vllm-flash-attn should be last as it overwrites some CMake functions
     include(cmake/external_projects/vllm_flash_attn.cmake)
 endif ()

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

@@ -1,8 +1,7 @@
-<!-- markdownlint-disable MD001 MD041 -->
 <p align="center">
   <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-dark.png">
-    <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-light.png" width=55%>
+    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png">
+    <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
   </picture>
 </p>
 
@@ -17,16 +16,14 @@ Easy, fast, and cheap LLM serving for everyone
 ---
 
 *Latest News* 🔥
-
-- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
 - [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
 - [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 
 <details>
 <summary>Previous News</summary>
 
-- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
@@ -49,7 +46,6 @@ Easy, fast, and cheap LLM serving for everyone
 </details>
 
 ---
-
 ## About
 
 vLLM is a fast and easy-to-use library for LLM inference and serving.
@@ -62,27 +58,28 @@ 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
-- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
+- 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
 
+**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
+
 vLLM is flexible and easy to use with:
 
 - Seamless integration with popular Hugging Face models
 - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
-- Tensor, pipeline, data and expert parallelism support for distributed inference
+- Tensor parallelism and pipeline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
+- 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)
 - Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
-- Embedding Models (e.g., E5-Mistral)
+- Embedding Models (e.g. E5-Mistral)
 - Multi-modal LLMs (e.g., LLaVA)
 
 Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
@@ -96,7 +93,6 @@ pip install vllm
 ```
 
 Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
-
 - [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html)
 - [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)
 - [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
@@ -104,16 +100,15 @@ 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
 - Sequoia Capital
@@ -121,8 +116,6 @@ Cash Donations:
 - ZhenFund
 
 Compute Resources:
-
-- Alibaba Cloud
 - AMD
 - Anyscale
 - AWS
@@ -161,14 +154,12 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-<!-- --8<-- [start:contact-us] -->
-- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues)
+- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
-- For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
+- coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
 - For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
-<!-- --8<-- [end:contact-us] -->
 
 ## Media Kit
 
-- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit)
+- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).

RELEASE.md

@@ -52,39 +52,3 @@ After branch cut, we approach finalizing the release branch with clear criteria
 * Release branch specific changes (e.g. change version identifiers or CI fixes)
 
 Please note: **No feature work allowed for cherry picks**. All PRs that are considered for cherry-picks need to be merged on trunk, the only exception are Release branch specific changes.
-
-## Manual validations
-
-### E2E Performance Validation
-
-Before each release, we perform end-to-end performance validation to ensure no regressions are introduced. This validation uses the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) on PyTorch CI.
-
-**Current Coverage:**
-
-* Models: Llama3, Llama4, and Mixtral
-* Hardware: NVIDIA H100 and AMD MI300x
-* _Note: Coverage may change based on new model releases and hardware availability_
-
-**Performance Validation Process:**
-
-**Step 1: Get Access**
-Request write access to the [pytorch/pytorch-integration-testing](https://github.com/pytorch/pytorch-integration-testing) repository to run the benchmark workflow.
-
-**Step 2: Review Benchmark Setup**
-Familiarize yourself with the benchmark configurations:
-
-* [CUDA setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/cuda)
-* [ROCm setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/rocm)
-
-**Step 3: Run the Benchmark**
-Navigate to the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) and configure:
-
-* **vLLM branch**: Set to the release branch (e.g., `releases/v0.9.2`)
-* **vLLM commit**: Set to the RC commit hash
-
-**Step 4: Review Results**
-Once the workflow completes, benchmark results will be available on the [vLLM benchmark dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) under the corresponding branch and commit.
-
-**Step 5: Performance Comparison**
-Compare the current results against the previous release to verify no performance regressions have occurred. Here is an
-example of [v0.9.1 vs v0.9.2](https://hud.pytorch.org/benchmark/llms?startTime=Thu%2C%2017%20Apr%202025%2021%3A43%3A50%20GMT&stopTime=Wed%2C%2016%20Jul%202025%2021%3A43%3A50%20GMT&granularity=week&lBranch=releases/v0.9.1&lCommit=b6553be1bc75f046b00046a4ad7576364d03c835&rBranch=releases/v0.9.2&rCommit=a5dd03c1ebc5e4f56f3c9d3dc0436e9c582c978f&repoName=vllm-project%2Fvllm&benchmarkName=&modelName=All%20Models&backendName=All%20Backends&modeName=All%20Modes&dtypeName=All%20DType&deviceName=All%20Devices&archName=All%20Platforms).

SECURITY.md

@@ -1,45 +1,11 @@
 # Security Policy
 
-## Reporting security issues
+## Reporting a Vulnerability
 
-Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new).
+If you believe you have found a security vulnerability in vLLM, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
 
-## Issue triage
+Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new). Reports will then be triaged by the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html).
 
-Reports will then be triaged by the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html).
-
-## Threat model
-
-Please see the [Security Guide in the vLLM documentation](https://docs.vllm.ai/en/latest/usage/security.html) for more information on vLLM's security assumptions and recommendations.
+---
 
 Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.
-
-## Issue severity
-
-We will determine the risk of each issue, taking into account our experience dealing with past issues, versions affected, common defaults, and use cases. We use the following severity categories:
-
-### CRITICAL Severity
-
-Vulnerabilities that allow remote attackers to execute arbitrary code, take full control of the system, or significantly compromise confidentiality, integrity, or availability without any interaction or privileges needed, examples include remote code execution via network, deserialization issues that allow exploit chains. Generally those issues which are rated as CVSS  ≥ 9.0.
-
-### HIGH Severity
-
-Serious security flaws that allow elevated impact—like RCE in specific, limited contexts or significant data loss—but require advanced conditions or some trust, examples include RCE in advanced deployment modes (e.g. multi-node), or high impact issues where some sort of privileged network access is required. These issues typically have CVSS scores between 7.0 and 8.9
-
-### MODERATE Severity
-
-Vulnerabilities that cause denial of service or partial disruption, but do not allow arbitrary code execution or data breach and have limited impact. These issues have a CVSS rating between 4.0 and 6.9
-
-### LOW Severity
-
-Minor issues such as informational disclosures, logging errors, non-exploitable flaws, or weaknesses that require local or high-privilege access and offer negligible impact. Examples include side channel attacks or hash collisions. These issues often have CVSS scores less than 4.0
-
-## Prenotification policy
-
-For certain security issues of CRITICAL, HIGH, or MODERATE severity level, we may prenotify certain organizations or vendors that ship vLLM. The purpose of this prenotification is to allow for a coordinated release of fixes for severe issues.
-
-* This prenotification will be in the form of a private email notification. It may also include adding security contacts to the GitHub security advisory, typically a few days before release.
-
-* If you wish to be added to the prenotification group, please send an email copying all the members of the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html). Each vendor contact will be analyzed on a case-by-case basis.
-
-* We may withdraw organizations from receiving future prenotifications if they release fixes or any other information about issues before they are public. Group membership may also change based on policy refinements for who may be included.

benchmarks/README.md

@@ -64,12 +64,6 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
     </tr>
-    <tr>
-      <td><strong>Custom</strong></td>
-      <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">✅</td>
-      <td>Local file: <code>data.jsonl</code></td>
-    </tr>
   </tbody>
 </table>
 
@@ -81,17 +75,13 @@ become available.
 
 **Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
 
-## 🚀 Example - Online Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
+---
+## Example - Online Benchmark
 
 First start serving your model
 
 ```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
 ```
 
 Then run the benchmarking script
@@ -99,7 +89,7 @@ Then run the benchmarking script
 ```bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -110,72 +100,39 @@ vllm bench serve \
 
 If successful, you will see the following output
 
-```text
+```
 ============ Serving Benchmark Result ============
-Successful requests:                     10
-Benchmark duration (s):                  5.78
-Total input tokens:                      1369
-Total generated tokens:                  2212
-Request throughput (req/s):              1.73
-Output token throughput (tok/s):         382.89
-Total Token throughput (tok/s):          619.85
+Successful requests:                     10        
+Benchmark duration (s):                  5.78      
+Total input tokens:                      1369      
+Total generated tokens:                  2212      
+Request throughput (req/s):              1.73      
+Output token throughput (tok/s):         382.89    
+Total Token throughput (tok/s):          619.85    
 ---------------Time to First Token----------------
-Mean TTFT (ms):                          71.54
-Median TTFT (ms):                        73.88
-P99 TTFT (ms):                           79.49
+Mean TTFT (ms):                          71.54     
+Median TTFT (ms):                        73.88     
+P99 TTFT (ms):                           79.49     
 -----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          7.91
-Median TPOT (ms):                        7.96
-P99 TPOT (ms):                           8.03
+Mean TPOT (ms):                          7.91      
+Median TPOT (ms):                        7.96      
+P99 TPOT (ms):                           8.03      
 ---------------Inter-token Latency----------------
-Mean ITL (ms):                           7.74
-Median ITL (ms):                         7.70
-P99 ITL (ms):                            8.39
+Mean ITL (ms):                           7.74      
+Median ITL (ms):                         7.70      
+P99 ITL (ms):                            8.39      
 ==================================================
 ```
 
-### Custom Dataset
-
-If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
-
-```json
-{"prompt": "What is the capital of India?"}
-{"prompt": "What is the capital of Iran?"}
-{"prompt": "What is the capital of China?"}
-```
-
-```bash
-# start server
-VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct
-```
-
-```bash
-# run benchmarking script
-vllm bench serve --port 9001 --save-result --save-detailed \
-  --backend vllm \
-  --model meta-llama/Llama-3.1-8B-Instruct \
-  --endpoint /v1/completions \
-  --dataset-name custom \
-  --dataset-path <path-to-your-data-jsonl> \
-  --custom-skip-chat-template \
-  --num-prompts 80 \
-  --max-concurrency 1 \
-  --temperature=0.3 \
-  --top-p=0.75 \
-  --result-dir "./log/"
-```
-
-You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`.
-
 ### VisionArena Benchmark for Vision Language Models
 
 ```bash
 # need a model with vision capability here
-vllm serve Qwen/Qwen2-VL-7B-Instruct
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -189,13 +146,14 @@ vllm bench serve \
 
 ``` bash
 VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
-    --speculative-config $'{"method": "ngram",
-    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
-    "prompt_lookup_min": 2}'
+    --speculative-model "[ngram]" \
+    --ngram_prompt_lookup_min 2 \
+    --ngram-prompt-lookup-max 5 \
+    --num_speculative_tokens 5
 ```
 
 ``` bash
-vllm bench serve \
+python3 benchmarks/benchmark_serving.py \
     --model meta-llama/Meta-Llama-3-8B-Instruct \
     --dataset-name hf \
     --dataset-path likaixin/InstructCoder \
@@ -205,13 +163,13 @@ vllm bench serve \
 ### Other HuggingFaceDataset Examples
 
 ```bash
-vllm serve Qwen/Qwen2-VL-7B-Instruct
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
-`lmms-lab/LLaVA-OneVision-Data`:
+**`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -222,10 +180,10 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-`Aeala/ShareGPT_Vicuna_unfiltered`:
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -235,10 +193,10 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-`AI-MO/aimo-validation-aime`:
+**`AI-MO/aimo-validation-aime`**
 
 ``` bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
     --model Qwen/QwQ-32B \
     --dataset-name hf \
     --dataset-path AI-MO/aimo-validation-aime \
@@ -246,23 +204,13 @@ vllm bench serve \
     --seed 42
 ```
 
-`philschmid/mt-bench`:
-
-``` bash
-vllm bench serve \
-    --model Qwen/QwQ-32B \
-    --dataset-name hf \
-    --dataset-path philschmid/mt-bench \
-    --num-prompts 80
-```
-
 ### Running With Sampling Parameters
 
 When using OpenAI-compatible backends such as `vllm`, optional sampling
 parameters can be specified. Example client command:
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -274,34 +222,11 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-### Running With Ramp-Up Request Rate
-
-The benchmark tool also supports ramping up the request rate over the
-duration of the benchmark run. This can be useful for stress testing the
-server or finding the maximum throughput that it can handle, given some latency budget.
-
-Two ramp-up strategies are supported:
-
-- `linear`: Increases the request rate linearly from a start value to an end value.
-- `exponential`: Increases the request rate exponentially.
-
-The following arguments can be used to control the ramp-up:
-
-- `--ramp-up-strategy`: The ramp-up strategy to use (`linear` or `exponential`).
-- `--ramp-up-start-rps`: The request rate at the beginning of the benchmark.
-- `--ramp-up-end-rps`: The request rate at the end of the benchmark.
-
-</details>
-
-## 📈 Example - Offline Throughput Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
+---
+## Example - Offline Throughput Benchmark
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --dataset-name sonnet \
   --dataset-path vllm/benchmarks/sonnet.txt \
@@ -310,7 +235,7 @@ vllm bench throughput \
 
 If successful, you will see the following output
 
-```text
+```
 Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s
 Total num prompt tokens:  5014
 Total num output tokens:  1500
@@ -318,8 +243,8 @@ Total num output tokens:  1500
 
 ### VisionArena Benchmark for Vision Language Models
 
-```bash
-vllm bench throughput \
+``` bash
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -330,7 +255,7 @@ vllm bench throughput \
 
 The `num prompt tokens` now includes image token counts
 
-```text
+```
 Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s
 Total num prompt tokens:  14527
 Total num output tokens:  1280
@@ -341,7 +266,7 @@ Total num output tokens:  1280
 ``` bash
 VLLM_WORKER_MULTIPROC_METHOD=spawn \
 VLLM_USE_V1=1 \
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
     --dataset-name=hf \
     --dataset-path=likaixin/InstructCoder \
     --model=meta-llama/Meta-Llama-3-8B-Instruct \
@@ -349,12 +274,13 @@ vllm bench throughput \
     --output-len=100 \
     --num-prompts=2048 \
     --async-engine \
-    --speculative-config $'{"method": "ngram",
-    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
-    "prompt_lookup_min": 2}'
+    --speculative-model="[ngram]" \
+    --ngram_prompt_lookup_min=2 \
+    --ngram-prompt-lookup-max=5 \
+    --num_speculative_tokens=5
 ```
 
-```text
+```
 Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s
 Total num prompt tokens:  261136
 Total num output tokens:  204800
@@ -362,10 +288,10 @@ Total num output tokens:  204800
 
 ### Other HuggingFaceDataset Examples
 
-`lmms-lab/LLaVA-OneVision-Data`:
+**`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -375,10 +301,10 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-`Aeala/ShareGPT_Vicuna_unfiltered`:
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -387,10 +313,10 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-`AI-MO/aimo-validation-aime`:
+**`AI-MO/aimo-validation-aime`**
 
 ```bash
-vllm bench throughput \
+python3 benchmarks/benchmark_throughput.py \
   --model Qwen/QwQ-32B \
   --backend vllm \
   --dataset-name hf \
@@ -399,12 +325,12 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-Benchmark with LoRA adapters:
+### Benchmark with LoRA Adapters
 
 ``` bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model meta-llama/Llama-2-7b-hf \
   --backend vllm \
   --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
@@ -415,204 +341,3 @@ vllm bench throughput \
   --enable-lora \
   --lora-path yard1/llama-2-7b-sql-lora-test
   ```
-
-</details>
-
-## 🛠️ Example - Structured Output Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of structured output generation (JSON, grammar, regex).
-
-### Server Setup
-
-```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B
-```
-
-### JSON Schema Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset json \
-  --structured-output-ratio 1.0 \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Grammar-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset grammar \
-  --structure-type grammar \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Regex-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset regex \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Choice-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset choice \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### XGrammar Benchmark Dataset
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset xgrammar_bench \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-</details>
-
-## 📚 Example - Long Document QA Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of long document question-answering with prefix caching.
-
-### Basic Long Document QA Test
-
-```bash
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 16 \
-  --document-length 2000 \
-  --output-len 50 \
-  --repeat-count 5
-```
-
-### Different Repeat Modes
-
-```bash
-# Random mode (default) - shuffle prompts randomly
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode random
-
-# Tile mode - repeat entire prompt list in sequence
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode tile
-
-# Interleave mode - repeat each prompt consecutively
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode interleave
-```
-
-</details>
-
-## 🗂️ Example - Prefix Caching Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the efficiency of automatic prefix caching.
-
-### Fixed Prompt with Prefix Caching
-
-```bash
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-prompts 1 \
-  --repeat-count 100 \
-  --input-length-range 128:256
-```
-
-### ShareGPT Dataset with Prefix Caching
-
-```bash
-# download dataset
-# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --enable-prefix-caching \
-  --num-prompts 20 \
-  --repeat-count 5 \
-  --input-length-range 128:256
-```
-
-</details>
-
-## ⚡ Example - Request Prioritization Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of request prioritization in vLLM.
-
-### Basic Prioritization Test
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority
-```
-
-### Multiple Sequences per Prompt
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority \
-  --n 2
-```
-
-</details>

benchmarks/auto_tune.sh

@@ -0,0 +1,212 @@
+#!/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/auto_tune/README.md

@@ -1,145 +0,0 @@
-# Automated vLLM Server Parameter Tuning
-
-This script automates the process of finding the optimal server parameter combination (`max-num-seqs` and `max-num-batched-tokens`) to maximize throughput for a vLLM server. It also supports additional constraints such as E2E latency and prefix cache hit rate.
-
-## Table of Contents
-
-- [Prerequisites](#prerequisites)
-- [Configuration](#configuration)
-- [How to Run](#how-to-run)
-- [Example Use Cases](#example-use-cases)
-- [Output](#output)
-- [How It Works](#how-it-works)
-
-## Prerequisites
-
-Before running the script, please ensure the following steps are completed:
-
-1. **Clone vLLM & Set Up Branch**: Clone the vLLM repository and check out to your desired branch.
-
-```bash
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-# git checkout <your-branch>
-```
-
-1. **Install Environment**: Install or update the correct running environment. For TPU usage, activate your `conda` environment and install the corresponding `torch` and `torch_xla` versions.
-
-2. **Model Configuration**: If you are using a customized model, ensure its configuration files are correctly placed and accessible.
-
-## Configuration
-
-You must set the following variables at the top of the script before execution.
-
-| Variable | Description | Example Value |
-| --- | --- | --- |
-| `BASE` | **Required.** The absolute path to the parent directory of your vLLM repository directory. | `"$HOME"` |
-| `MODEL` | **Required.** The Hugging Face model identifier to be served by vllm. | `"meta-llama/Llama-3.1-8B-Instruct"` |
-| `SYSTEM`| **Required.** The hardware you are running on. Choices: `TPU` or `GPU`. (For other systems, it might not support saving profiles) | `"TPU"` |
-| `TP` | **Required.** The tensor-parallelism size. | `1` |
-| `DOWNLOAD_DIR` | **Required.** Directory to download and load model weights from. | `""` (default download path) |
-| `INPUT_LEN` | **Required.** Request input length. | `4000` |
-| `OUTPUT_LEN` | **Required.** Request output length. | `16` |
-| `MAX_MODEL_LEN` | **Required.** Max model length. | `4096` |
-| `MIN_CACHE_HIT_PCT` | Prefix cache hit rate in percentage (0-100). Set to `0` to disable. | `60` |
-| `MAX_LATENCY_ALLOWED_MS` | The maximum allowed P99 end-to-end latency in milliseconds. Set to a very large number (e.g., `100000000000`) to effectively ignore the latency constraint. | `500` |
-| `NUM_SEQS_LIST` | A space-separated string of `max-num-seqs` values to test. | `"128 256"` |
-| `NUM_BATCHED_TOKENS_LIST` | A space-separated string of `max-num-batched-tokens` values to test. | `"1024 2048 4096"` |
-
-**Note**: The default `NUM_SEQS_LIST` and `NUM_BATCHED_TOKENS_LIST` are set for medium-sized inputs/outputs. For very short contexts (e.g., 20 input, 20 output tokens), you may need to test larger values for `max-num-seqs`.
-
-## How to Run
-
-1. **Configure**: Edit the script and set the variables in the [Configuration](#configuration) section.
-2. **Execute**: Run the script. Since the process can take a long time, it is highly recommended to use a terminal multiplexer like `tmux` or `screen` to prevent the script from stopping if your connection is lost.
-
-```bash
-cd <FOLDER_OF_THIS_SCRIPT>
-bash auto_tune.sh
-```
-
-    Please note that the `bash auto_tune.sh` command cannot contain full or partial path with keyword `vllm`, otherwise `pkill -f vllm` command will also kill this script itself.
-
-## Example Use Cases
-
-Here are a few examples of how to configure the script for different goals:
-
-### 1. Maximize Throughput (No Latency Constraint)
-
-- **Goal**: Find the best `max-num-seqs` and `max-num-batched-tokens` to get the highest possible throughput for 1800 input tokens and 20 output tokens.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=100000000000 # A very large number
-```
-
-#### 2. Maximize Throughput with a Latency Requirement
-
-- **Goal**: Find the best server parameters when P99 end-to-end latency must be below 500ms.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=500
-```
-
-#### 3. Maximize Throughput with Prefix Caching and Latency Requirements
-
-- **Goal**: Find the best server parameters assuming a 60% prefix cache hit rate and a latency requirement of 500ms.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=60
-MAX_LATENCY_ALLOWED_MS=500
-```
-
-## Output
-
-After the script finishes, you will find the results in a new, timestamped directory created inside `$BASE/auto-benchmark/`.
-
-- **Log Files**: The directory (`$BASE/auto-benchmark/YYYY_MM_DD_HH_MM/`) contains detailed logs for each run:
-    - `vllm_log_...txt`: The log output from the vLLM server for each parameter combination.
-    - `bm_log_...txt`: The log output from the `vllm bench serve` command for each benchmark run.
-
-- **Final Result Summary**: A file named `result.txt` is created in the log directory. It contains a summary of each tested combination and concludes with the overall best parameters found.
-
-```text
-# Example result.txt content
-hash:a1b2c3d4...
-max_num_seqs: 128, max_num_batched_tokens: 2048, request_rate: 10.0, e2el: 450.5, throughput: 9.8, goodput: 9.8
-max_num_seqs: 128, max_num_batched_tokens: 4096 does not meet latency requirement 500
-...
-best_max_num_seqs: 256, best_num_batched_tokens: 2048, best_throughput: 12.5, profile saved in: /home/user/vllm/auto-benchmark/2024_08_01_10_30/profile
-```
-
-  If it cannot find the best parameters, the final row will be `best_max_num_seqs: 0, best_num_batched_tokens: 0, best_throughput: 0`. This can be due to either the server not starting properly, or the latency requirement being too strict.
-
-- **Profiler Trace**: A directory named `profile` is created inside the log directory. It contains the profiler trace file (e.g., `.xplane.pb` for TPU or a `.json` trace for GPU) from the single best-performing run.
-
-## How It Works
-
-The script follows a systematic process to find the optimal parameters:
-
-1. **Find Max GPU Memory Utilization**: The script first determines the highest safe `gpu-memory-utilization` (starting from 0.98 and decreasing) that does not cause an Out-Of-Memory (OOM) error when launching the server. This ensures the benchmark runs use the maximum available memory without crashing.
-
-2. **Iterate and Benchmark**: It then enters a nested loop, iterating through every combination of `max-num-seqs` and `max-num-batched-tokens` provided in the configuration lists.
-
-3. **Latency-Aware Throughput Search**: For each parameter combination:
-    - The vLLM server is started.
-    - A benchmark is first run with an infinite request rate (`--request-rate inf`).
-    - If the resulting P99 E2E latency is within the `MAX_LATENCY_ALLOWED_MS` limit, this throughput is considered the maximum for this configuration.
-    - If the latency is too high, the script performs a search by iteratively decreasing the request rate until the latency constraint is met. This finds the highest sustainable throughput for the given parameters and latency requirement.
-
-4. **Track Best Result**: Throughout the process, the script tracks the parameter combination that has yielded the highest valid throughput so far.
-
-5. **Profile Collection**: For the best-performing run, the script saves the vLLM profiler output, which can be used for deep-dive performance analysis with tools like TensorBoard.

benchmarks/auto_tune/auto_tune.sh

@@ -1,292 +0,0 @@
-#!/bin/bash
-
-# This script aims to tune the best server parameter combinations to maximize throughput for given requirement.
-# See details in README (benchmarks/auto_tune/README.md).
-
-TAG=$(date +"%Y_%m_%d_%H_%M")
-SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-BASE="$SCRIPT_DIR/../../.."
-MODEL="meta-llama/Llama-3.1-8B-Instruct"
-SYSTEM="TPU"
-TP=1
-DOWNLOAD_DIR=""
-INPUT_LEN=4000
-OUTPUT_LEN=16
-MAX_MODEL_LEN=4096
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=100000000000
-NUM_SEQS_LIST="128 256"
-NUM_BATCHED_TOKENS_LIST="512 1024 2048 4096"
-
-LOG_FOLDER="$BASE/auto-benchmark/$TAG"
-RESULT="$LOG_FOLDER/result.txt"
-PROFILE_PATH="$LOG_FOLDER/profile"
-
-echo "result file: $RESULT"
-echo "model: $MODEL"
-
-rm -rf $LOG_FOLDER
-rm -rf $PROFILE_PATH
-mkdir -p $LOG_FOLDER
-mkdir -p $PROFILE_PATH
-
-cd "$BASE/vllm"
-
-pip install -q datasets
-
-current_hash=$(git rev-parse HEAD)
-echo "hash:$current_hash" >> "$RESULT"
-echo "current_hash: $current_hash"
-
-TOTAL_LEN=$((INPUT_LEN + OUTPUT_LEN))
-RED='\033[0;31m'
-if (( TOTAL_LEN > MAX_MODEL_LEN )); then
-    echo -e "${RED}FAILED: INPUT_LEN($INPUT_LEN) + OUTPUT_LEN($OUTPUT_LEN) = $TOTAL_LEN, which is > MAX_MODEL_LEN = $MAX_MODEL_LEN.\033[0m" >&2
-    exit 1
-fi
-
-best_throughput=0
-best_max_num_seqs=0
-best_num_batched_tokens=0
-best_goodput=0
-best_request_rate=0
-
-start_server() {
-    local gpu_memory_utilization=$1
-    local max_num_seqs=$2
-    local max_num_batched_tokens=$3
-    local vllm_log=$4
-    local profile_dir=$5
-
-    pkill -if vllm
-
-    # Define the common arguments as a bash array.
-    # Each argument and its value are separate elements.
-    local common_args_array=(
-        "$MODEL"
-        "--disable-log-requests"
-        "--port" "8004"
-        "--gpu-memory-utilization" "$gpu_memory_utilization"
-        "--max-num-seqs" "$max_num_seqs"
-        "--max-num-batched-tokens" "$max_num_batched_tokens"
-        "--tensor-parallel-size" "$TP"
-        "--enable-prefix-caching"
-        "--load-format" "dummy"
-        "--download-dir" "$DOWNLOAD_DIR"
-        "--max-model-len" "$MAX_MODEL_LEN"
-    )
-
-    # Use the array expansion "${common_args_array[@]}"
-    # This correctly passes each element as a separate argument.
-    if [[ -n "$profile_dir" ]]; then
-        # Start server with profiling enabled
-        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
-            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
-    else
-        # Start server without profiling
-        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 \
-            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
-    fi
-
-    # wait for 10 minutes...
-    server_started=0
-    for i in {1..60}; do
-        RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
-        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1)
-        if [[ "$STATUS_CODE" -eq 200 ]]; then
-            server_started=1
-            break
-        else
-            sleep 10
-        fi
-    done
-
-    if (( ! server_started )); then
-        echo "server did not start within 10 minutes. Please check server log at $vllm_log".
-        return 1
-    else
-        return 0
-    fi
-}
-
-run_benchmark() {
-    local max_num_seqs=$1
-    local max_num_batched_tokens=$2
-    local gpu_memory_utilization=$3
-    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
-    pkill -if vllm
-
-    echo "starting server..."
-    # Call start_server without a profile_dir to avoid profiling overhead
-    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log ""
-    result=$?
-    if [[ "$result" -eq 1 ]]; then
-        echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
-    else
-        echo "server started."
-    fi
-    echo
-
-    echo "run benchmark test..."
-    meet_latency_requirement=0
-    # get a basic qps by using request-rate inf
-    bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
-    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
-    # --profile flag is removed from this call
-    vllm bench serve \
-        --backend vllm \
-        --model $MODEL  \
-        --dataset-name random \
-        --random-input-len $adjusted_input_len \
-        --random-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 1000 \
-        --random-prefix-len $prefix_len \
-        --port 8004 &> "$bm_log"
-    throughput=$(grep "Request throughput (req/s):" "$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 <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
-        meet_latency_requirement=1
-        request_rate=inf
-    fi
-
-    if (( ! meet_latency_requirement )); then
-    # start from request-rate as int(throughput) + 1
-        request_rate=$((${throughput%.*} + 1))
-        while ((request_rate > 0)); do
-            # clear prefix cache
-            curl -X POST http://0.0.0.0:8004/reset_prefix_cache
-            sleep 5
-            bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
-            vllm bench serve \
-                --backend vllm \
-                --model $MODEL  \
-                --dataset-name random \
-                --random-input-len $adjusted_input_len \
-                --random-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 \
-                --random-prefix-len $prefix_len \
-                --port 8004 &> "$bm_log"
-            throughput=$(grep "Request throughput (req/s):" "$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, throughput: $throughput, goodput: $goodput"
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, throughput: $throughput, goodput: $goodput" >> "$RESULT"
-        if (( $(echo "$throughput > $best_throughput" | bc -l) )); then
-            best_throughput=$throughput
-            best_max_num_seqs=$max_num_seqs
-            best_num_batched_tokens=$max_num_batched_tokens
-            best_goodput=$goodput
-            best_request_rate=$request_rate
-        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"
-
-    pkill -if vllm
-    sleep 10
-    printf '=%.0s' $(seq 1 20)
-    return 0
-}
-
-read -r -a num_seqs_list <<< "$NUM_SEQS_LIST"
-read -r -a num_batched_tokens_list <<< "$NUM_BATCHED_TOKENS_LIST"
-
-# first find out the max gpu-memory-utilization without HBM OOM.
-gpu_memory_utilization=0.98
-find_gpu_memory_utilization=0
-while (( $(echo "$gpu_memory_utilization >= 0.9" | bc -l) )); do
-    # Pass empty string for profile_dir argument
-    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log" ""
-    result=$?
-    if [[ "$result" -eq 0 ]]; then
-        find_gpu_memory_utilization=1
-        break
-    else
-        gpu_memory_utilization=$(echo "$gpu_memory_utilization - 0.01" | bc)
-    fi
-done
-
-if [[ "$find_gpu_memory_utilization" -eq 1 ]]; then
-    echo "Using gpu_memory_utilization=$gpu_memory_utilization to serve model."
-else
-    echo "Cannot find a proper gpu_memory_utilization over 0.9 to serve the model, please check logs in $LOG_FOLDER."
-    exit 1
-fi
-
-for num_seqs in "${num_seqs_list[@]}"; do
-    for num_batched_tokens in "${num_batched_tokens_list[@]}"; do
-        run_benchmark $num_seqs $num_batched_tokens $gpu_memory_utilization
-    done
-done
-echo "finish permutations"
-
-# =================================================================================
-# FINAL PROFILING RUN FOR THE BEST CONFIGURATION
-# =================================================================================
-if (( $(echo "$best_throughput > 0" | bc -l) )); then
-    echo
-    echo "Benchmark tuning finished. Now running profiling on the best configuration found..."
-    echo "Best config: max_num_seqs: $best_max_num_seqs, max_num_batched_tokens: $best_num_batched_tokens, throughput: $best_throughput"
-    echo
-
-    vllm_log="$LOG_FOLDER/vllm_log_BEST_PROFILE.txt"
-    bm_log="$LOG_FOLDER/bm_log_BEST_PROFILE.txt"
-
-    # Start server with the best params and profiling ENABLED
-    echo "Starting server for profiling..."
-    start_server $gpu_memory_utilization $best_max_num_seqs $best_num_batched_tokens "$vllm_log" "$PROFILE_PATH"
-
-    # Run benchmark with the best params and the --profile flag
-    echo "Running benchmark with profiling..."
-    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
-    vllm bench serve \
-        --backend vllm \
-        --model $MODEL \
-        --dataset-name random \
-        --random-input-len $adjusted_input_len \
-        --random-output-len $OUTPUT_LEN \
-        --ignore-eos \
-        --disable-tqdm \
-        --request-rate $best_request_rate \
-        --percentile-metrics ttft,tpot,itl,e2el \
-        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
-        --num-prompts 100 \
-        --random-prefix-len $prefix_len \
-        --port 8004 \
-        --profile &> "$bm_log"
-else
-    echo "No configuration met the latency requirements. Skipping final profiling run."
-fi
-pkill -if vllm
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"

benchmarks/backend_request_func.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import io
 import json
@@ -13,7 +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.
@@ -31,7 +31,7 @@ class RequestFuncInput:
     model_name: Optional[str] = None
     logprobs: Optional[int] = None
     extra_body: Optional[dict] = None
-    multi_modal_content: Optional[dict | list[dict]] = None
+    multi_modal_content: Optional[dict] = None
     ignore_eos: bool = False
     language: Optional[str] = None
 
@@ -43,7 +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 = ""
@@ -56,9 +57,8 @@ async def async_request_tgi(
     api_url = request_func_input.api_url
     assert api_url.endswith("generate_stream")
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
         params = {
             "max_new_tokens": request_func_input.output_len,
             "do_sample": True,
@@ -105,7 +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
 
@@ -132,9 +133,8 @@ async def async_request_trt_llm(
     api_url = request_func_input.api_url
     assert api_url.endswith("generate_stream")
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
         payload = {
             "accumulate_tokens": True,
             "text_input": request_func_input.prompt,
@@ -159,7 +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"]
@@ -171,7 +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
 
@@ -195,14 +197,9 @@ async def async_request_deepspeed_mii(
     request_func_input: RequestFuncInput,
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
-    api_url = request_func_input.api_url
-    assert api_url.endswith(("completions", "profile")), (
-        "OpenAI Completions API URL must end with 'completions' or 'profile'."
-    )
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
         payload = {
             "model": request_func_input.model,
             "prompt": request_func_input.prompt,
@@ -210,8 +207,6 @@ async def async_request_deepspeed_mii(
             "temperature": 0.01,  # deepspeed-mii does not accept 0.0 temp.
             "top_p": 1.0,
         }
-        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
-
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
@@ -222,21 +217,19 @@ async def async_request_deepspeed_mii(
 
         st = time.perf_counter()
         try:
-            async with session.post(
-                url=api_url, json=payload, headers=headers
-            ) as response:
+            async with session.post(url=request_func_input.api_url,
+                                    json=payload) as response:
                 if response.status == 200:
                     parsed_resp = await response.json()
                     output.latency = time.perf_counter() - st
                     if "choices" in parsed_resp:
-                        output.generated_text = parsed_resp["choices"][0]["text"]
+                        output.generated_text = parsed_resp["choices"][0][
+                            "text"]
                     elif "text" in parsed_resp:
                         output.generated_text = parsed_resp["text"][0]
                     else:
-                        output.error = (
-                            "Unexpected response format: "
-                            "neither 'choices' nor 'text' found"
-                        )
+                        output.error = ("Unexpected response format: "
+                                        "neither 'choices' nor 'text' found")
                         output.success = False
                     output.success = True
                 else:
@@ -257,17 +250,15 @@ async def async_request_openai_completions(
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
-    assert api_url.endswith(("completions", "profile")), (
-        "OpenAI Completions API URL must end with 'completions' or 'profile'."
-    )
+    assert api_url.endswith(
+        ("completions", "profile")
+    ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
         payload = {
-            "model": request_func_input.model_name
-            if request_func_input.model_name
-            else request_func_input.model,
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
             "prompt": request_func_input.prompt,
             "temperature": 0.0,
             "repetition_penalty": 1.0,
@@ -282,7 +273,9 @@ async def async_request_openai_completions(
             payload["ignore_eos"] = request_func_input.ignore_eos
         if request_func_input.extra_body:
             payload.update(request_func_input.extra_body)
-        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
+        headers = {
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
+        }
 
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
@@ -291,9 +284,8 @@ async def async_request_openai_completions(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(
-                url=api_url, json=payload, headers=headers
-            ) as response:
+            async with session.post(url=api_url, json=payload,
+                                    headers=headers) as response:
                 if response.status == 200:
                     first_chunk_received = False
                     async for chunk_bytes in response.content:
@@ -301,7 +293,8 @@ async def async_request_openai_completions(
                         if not chunk_bytes:
                             continue
 
-                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
+                        chunk = chunk_bytes.decode("utf-8").removeprefix(
+                            "data: ")
                         if chunk != "[DONE]":
                             data = json.loads(chunk)
 
@@ -321,20 +314,21 @@ async def async_request_openai_completions(
 
                                 # Decoding phase
                                 else:
-                                    output.itl.append(timestamp - most_recent_timestamp)
+                                    output.itl.append(timestamp -
+                                                      most_recent_timestamp)
 
                                 most_recent_timestamp = timestamp
                                 generated_text += text or ""
-                            if usage := data.get("usage"):
-                                output.output_tokens = usage.get("completion_tokens")
+                            elif usage := data.get("usage"):
+                                output.output_tokens = usage.get(
+                                    "completion_tokens")
                     if first_chunk_received:
                         output.success = True
                     else:
                         output.success = False
                         output.error = (
                             "Never received a valid chunk to calculate TTFT."
-                            "This response will be marked as failed!"
-                        )
+                            "This response will be marked as failed!")
                     output.generated_text = generated_text
                     output.latency = most_recent_timestamp - st
                 else:
@@ -355,30 +349,23 @@ async def async_request_openai_chat_completions(
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
-    assert api_url.endswith(("chat/completions", "profile")), (
-        "OpenAI Chat Completions API URL must end with 'chat/completions'."
-    )
+    assert api_url.endswith(
+        ("chat/completions", "profile")
+    ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
         content = [{"type": "text", "text": request_func_input.prompt}]
         if request_func_input.multi_modal_content:
-            mm_content = request_func_input.multi_modal_content
-            if isinstance(mm_content, list):
-                content.extend(mm_content)
-            elif isinstance(mm_content, dict):
-                content.append(mm_content)
-            else:
-                raise TypeError(
-                    "multi_modal_content must be a dict or list[dict] for openai-chat"
-                )
+            content.append(request_func_input.multi_modal_content)
         payload = {
-            "model": request_func_input.model_name
-            if request_func_input.model_name
-            else request_func_input.model,
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
             "messages": [
-                {"role": "user", "content": content},
+                {
+                    "role": "user",
+                    "content": content
+                },
             ],
             "temperature": 0.0,
             "max_completion_tokens": request_func_input.output_len,
@@ -404,22 +391,16 @@ async def async_request_openai_chat_completions(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(
-                url=api_url, json=payload, headers=headers
-            ) as response:
+            async with session.post(url=api_url, json=payload,
+                                    headers=headers) as response:
                 if response.status == 200:
                     async for chunk_bytes in response.content:
                         chunk_bytes = chunk_bytes.strip()
                         if not chunk_bytes:
                             continue
-                        chunk_bytes = chunk_bytes.decode("utf-8")
-                        # NOTE: SSE comments (often used as pings) start with a colon.
-                        # These are not JSON data payload and should be skipped.
-                        if chunk_bytes.startswith(":"):
-                            continue
-
-                        chunk = chunk_bytes.removeprefix("data: ")
 
+                        chunk = chunk_bytes.decode("utf-8").removeprefix(
+                            "data: ")
                         if chunk != "[DONE]":
                             timestamp = time.perf_counter()
                             data = json.loads(chunk)
@@ -433,11 +414,13 @@ async def async_request_openai_chat_completions(
 
                                 # Decoding phase
                                 else:
-                                    output.itl.append(timestamp - most_recent_timestamp)
+                                    output.itl.append(timestamp -
+                                                      most_recent_timestamp)
 
                                 generated_text += content or ""
                             elif usage := data.get("usage"):
-                                output.output_tokens = usage.get("completion_tokens")
+                                output.output_tokens = usage.get(
+                                    "completion_tokens")
 
                             most_recent_timestamp = timestamp
 
@@ -463,28 +446,25 @@ async def async_request_openai_audio(
 ) -> RequestFuncOutput:
     # Lazy import without PlaceholderModule to avoid vllm dep.
     import soundfile
-
     api_url = request_func_input.api_url
-    assert api_url.endswith(("transcriptions", "translations")), (
-        "OpenAI Chat Completions API URL must end with 'transcriptions' "
-    )
+    assert api_url.endswith(
+        ("transcriptions", "translations"
+         )), "OpenAI Chat Completions API URL must end with 'transcriptions' "
     "or `translations`."
 
-    async with aiohttp.ClientSession(
-        trust_env=True, timeout=AIOHTTP_TIMEOUT
-    ) as session:
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
         content = [{"type": "text", "text": request_func_input.prompt}]
         payload = {
-            "model": request_func_input.model_name
-            if request_func_input.model_name
-            else request_func_input.model,
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
             "temperature": 0.0,
             "max_completion_tokens": request_func_input.output_len,
             "stream": True,
             "language": "en",
             # Flattened due to multipart/form-data
             "stream_include_usage": True,
-            "stream_continuous_usage_stats": True,
+            "stream_continuous_usage_stats": True
         }
         if request_func_input.extra_body:
             payload.update(request_func_input.extra_body)
@@ -499,12 +479,9 @@ async def async_request_openai_audio(
             buffer.seek(0)
             return buffer
 
-        mm_audio = request_func_input.multi_modal_content
-        if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
-            raise TypeError("multi_modal_content must be a dict containing 'audio'")
-        with to_bytes(*mm_audio["audio"]) as f:
+        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))
 
@@ -516,22 +493,24 @@ async def async_request_openai_audio(
             st = time.perf_counter()
             most_recent_timestamp = st
             try:
-                async with session.post(
-                    url=api_url, data=form, headers=headers
-                ) as response:
+                async with session.post(url=api_url,
+                                        data=form,
+                                        headers=headers) as response:
                     if response.status == 200:
                         async for chunk_bytes in response.content:
                             chunk_bytes = chunk_bytes.strip()
                             if not chunk_bytes:
                                 continue
 
-                            chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
+                            chunk = chunk_bytes.decode("utf-8").removeprefix(
+                                "data: ")
                             if chunk != "[DONE]":
                                 timestamp = time.perf_counter()
                                 data = json.loads(chunk)
 
                                 if choices := data.get("choices"):
-                                    content = choices[0]["delta"].get("content")
+                                    content = choices[0]["delta"].get(
+                                        "content")
                                     # First token
                                     if ttft == 0.0:
                                         ttft = timestamp - st
@@ -540,14 +519,12 @@ async def async_request_openai_audio(
                                     # Decoding phase
                                     else:
                                         output.itl.append(
-                                            timestamp - most_recent_timestamp
-                                        )
+                                            timestamp - most_recent_timestamp)
 
                                     generated_text += content or ""
                                 elif usage := data.get("usage"):
                                     output.output_tokens = usage.get(
-                                        "completion_tokens"
-                                    )
+                                        "completion_tokens")
 
                                 most_recent_timestamp = timestamp
 
@@ -568,7 +545,7 @@ async def async_request_openai_audio(
 
 
 def get_model(pretrained_model_name_or_path: str) -> str:
-    if os.getenv("VLLM_USE_MODELSCOPE", "False").lower() == "true":
+    if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
 
         from vllm.model_executor.model_loader.weight_utils import get_lock
@@ -579,8 +556,7 @@ def get_model(pretrained_model_name_or_path: str) -> str:
             model_path = snapshot_download(
                 model_id=pretrained_model_name_or_path,
                 local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"],
-            )
+                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
 
             return model_path
     return pretrained_model_name_or_path
@@ -593,23 +569,23 @@ def get_tokenizer(
     **kwargs,
 ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
     if pretrained_model_name_or_path is not None and not os.path.exists(
-        pretrained_model_name_or_path
-    ):
-        pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
+            pretrained_model_name_or_path):
+        pretrained_model_name_or_path = get_model(
+            pretrained_model_name_or_path)
     if tokenizer_mode == "slow":
         if kwargs.get("use_fast", False):
-            raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
+            raise ValueError(
+                "Cannot use the fast tokenizer in slow tokenizer mode.")
         kwargs["use_fast"] = False
     if tokenizer_mode == "mistral":
         try:
             from vllm.transformers_utils.tokenizer import MistralTokenizer
         except ImportError as e:
-            raise ImportError(
-                "MistralTokenizer requires vllm package.\n"
-                "Please install it with `pip install vllm` "
-                "to use mistral tokenizer mode."
-            ) from e
-        return MistralTokenizer.from_pretrained(str(pretrained_model_name_or_path))
+            raise ImportError("MistralTokenizer requires vllm package.\n"
+                              "Please install it with `pip install vllm` "
+                              "to use mistral tokenizer mode.") from e
+        return MistralTokenizer.from_pretrained(
+            str(pretrained_model_name_or_path))
     else:
         return AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path,
@@ -629,11 +605,10 @@ ASYNC_REQUEST_FUNCS = {
     "tensorrt-llm": async_request_trt_llm,
     "scalellm": async_request_openai_completions,
     "sglang": async_request_openai_completions,
-    "llama.cpp": async_request_openai_completions,
 }
 
 OPENAI_COMPATIBLE_BACKENDS = [
-    k
-    for k, v in ASYNC_REQUEST_FUNCS.items()
-    if v in (async_request_openai_completions, async_request_openai_chat_completions)
+    k for k, v in ASYNC_REQUEST_FUNCS.items()
+    if v in (async_request_openai_completions,
+             async_request_openai_chat_completions)
 ]

benchmarks/benchmark_block_pool.py

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

benchmarks/benchmark_dataset.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 This module defines a framework for sampling benchmark requests from various
 datasets. Each dataset subclass of BenchmarkDataset must implement sample
@@ -10,6 +9,9 @@ generation. Supported dataset types include:
   - BurstGPT
   - HuggingFace
   - VisionArena
+
+TODO: Implement CustomDataset to parse a JSON file and convert its contents into
+SampleRequest instances, similar to the approach used in ShareGPT.
 """
 
 import base64
@@ -33,7 +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__)
@@ -52,7 +53,7 @@ class SampleRequest:
     prompt: Union[str, Any]
     prompt_len: int
     expected_output_len: int
-    multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
+    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
     lora_request: Optional[LoRARequest] = None
 
 
@@ -81,12 +82,14 @@ class BenchmarkDataset(ABC):
         self.dataset_path = dataset_path
         # Set the random seed, ensuring that a None value is replaced with the
         # default seed.
-        self.random_seed = random_seed if random_seed is not None else self.DEFAULT_SEED
+        self.random_seed = (random_seed
+                            if random_seed is not None else self.DEFAULT_SEED)
         self.data = None
 
     def apply_multimodal_chat_transformation(
-        self, prompt: str, mm_content: Optional[MultiModalDataDict] = None
-    ) -> list[dict]:
+            self,
+            prompt: str,
+            mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
         """
         Transform a prompt and optional multimodal content into a chat format.
         This method is used for chat models that expect a specific conversation
@@ -108,7 +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,
@@ -154,9 +158,8 @@ class BenchmarkDataset(ABC):
         return lora_request, lora_tokenizer_cache[lora_id] or tokenizer
 
     @abstractmethod
-    def sample(
-        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
-    ) -> list[SampleRequest]:
+    def sample(self, tokenizer: PreTrainedTokenizerBase,
+               num_requests: int) -> list[SampleRequest]:
         """
         Abstract method to generate sample requests from the dataset.
 
@@ -174,9 +177,8 @@ class BenchmarkDataset(ABC):
         """
         raise NotImplementedError("sample must be implemented in subclasses.")
 
-    def maybe_oversample_requests(
-        self, requests: list[SampleRequest], num_requests: int
-    ) -> None:
+    def maybe_oversample_requests(self, requests: list[SampleRequest],
+                                  num_requests: int) -> None:
         """
         Oversamples the list of requests if its size is less than the desired
         number.
@@ -187,9 +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)
 
 
 # -----------------------------------------------------------------------------
@@ -214,14 +218,14 @@ def is_valid_sequence(
     """
     # Check for invalid conditions
     prompt_too_short = prompt_len < min_len
-    output_too_short = (not skip_min_output_len_check) and (output_len < min_len)
+    output_too_short = (not skip_min_output_len_check) and (output_len
+                                                            < min_len)
     prompt_too_long = prompt_len > max_prompt_len
     combined_too_long = (prompt_len + output_len) > max_total_len
 
     # Return True if none of the invalid conditions are met
-    return not (
-        prompt_too_short or output_too_short or prompt_too_long or combined_too_long
-    )
+    return not (prompt_too_short or output_too_short or prompt_too_long
+                or combined_too_long)
 
 
 @cache
@@ -253,28 +257,28 @@ def process_image(image: Any) -> Mapping[str, Any]:
     Raises:
         ValueError: If the input is not a supported type.
     """
-    if isinstance(image, dict) and "bytes" in image:
-        image = Image.open(BytesIO(image["bytes"]))
+    if isinstance(image, dict) and 'bytes' in image:
+        image = Image.open(BytesIO(image['bytes']))
     if isinstance(image, Image.Image):
-        image = convert_image_mode(image, "RGB")
+        image = image.convert("RGB")
         with io.BytesIO() as image_data:
             image.save(image_data, format="JPEG")
-            image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
+            image_base64 = base64.b64encode(
+                image_data.getvalue()).decode("utf-8")
         return {
             "type": "image_url",
-            "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
+            "image_url": {
+                "url": f"data:image/jpeg;base64,{image_base64}"
+            },
         }
 
     if isinstance(image, str):
-        image_url = (
-            image if image.startswith(("http://", "file://")) else f"file://{image}"
-        )
+        image_url = (image if image.startswith(
+            ("http://", "file://")) else f"file://{image}")
         return {"type": "image_url", "image_url": {"url": image_url}}
 
-    raise ValueError(
-        f"Invalid image input {image}. Must be a PIL.Image.Image"
-        " or str or dictionary with raw image bytes."
-    )
+    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
+                     " or str or dictionary with raw image bytes.")
 
 
 # -----------------------------------------------------------------------------
@@ -314,34 +318,32 @@ class RandomDataset(BenchmarkDataset):
         num_special_tokens = tokenizer.num_special_tokens_to_add()
         real_input_len = input_len - num_special_tokens
 
-        prefix_token_ids = (
-            np.random.randint(0, vocab_size, size=prefix_len).tolist()
-            if prefix_len > 0
-            else []
-        )
+        prefix_token_ids = (np.random.randint(
+            0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
 
         # New sampling logic: [X * (1 - b), X * (1 + b)]
         input_low = int(real_input_len * (1 - range_ratio))
         input_high = int(real_input_len * (1 + range_ratio))
         output_low = int(output_len * (1 - range_ratio))
-        # Ensure the lower bound for output length is at least 1 to prevent
-        # sampling 0 tokens, which can cause request failures.
-        output_low = max(output_low, 1)
         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)
-        output_lens = np.random.randint(output_low, output_high + 1, size=num_requests)
+        input_lens = np.random.randint(input_low,
+                                       input_high + 1,
+                                       size=num_requests)
+        output_lens = np.random.randint(output_low,
+                                        output_high + 1,
+                                        size=num_requests)
         offsets = np.random.randint(0, vocab_size, size=num_requests)
 
         requests = []
         for i in range(num_requests):
-            inner_seq = (
-                (offsets[i] + i + np.arange(input_lens[i])) % vocab_size
-            ).tolist()
+            inner_seq = ((offsets[i] + i + np.arange(input_lens[i])) %
+                         vocab_size).tolist()
             token_sequence = prefix_token_ids + inner_seq
             prompt = tokenizer.decode(token_sequence)
             # After decoding the prompt we have to encode and decode it again.
@@ -352,19 +354,16 @@ class RandomDataset(BenchmarkDataset):
             # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
             # To avoid uncontrolled change of the prompt length,
             # the encoded sequence is truncated before being decode again.
-            total_input_len = prefix_len + int(input_lens[i])
-            re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
-                :total_input_len
-            ]
+            re_encoded_sequence = tokenizer.encode(
+                prompt, add_special_tokens=False)[:input_lens[i]]
             prompt = tokenizer.decode(re_encoded_sequence)
-            total_input_len = len(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
 
 
@@ -391,8 +390,7 @@ class ShareGPTDataset(BenchmarkDataset):
             self.data = json.load(f)
         # Filter entries with at least two conversation turns.
         self.data = [
-            entry
-            for entry in self.data
+            entry for entry in self.data
             if "conversations" in entry and len(entry["conversations"]) >= 2
         ]
         random.seed(self.random_seed)
@@ -418,123 +416,31 @@ class ShareGPTDataset(BenchmarkDataset):
             )
 
             lora_request, tokenizer = self.get_random_lora_request(
-                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
-            )
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
             prompt_ids = tokenizer(prompt).input_ids
             completion_ids = tokenizer(completion).input_ids
             prompt_len = len(prompt_ids)
-            new_output_len = len(completion_ids) if output_len is None else output_len
-            if not is_valid_sequence(
-                prompt_len,
-                new_output_len,
-                skip_min_output_len_check=output_len is not None,
-            ):
+            new_output_len = (len(completion_ids)
+                              if output_len is None else output_len)
+            if not is_valid_sequence(prompt_len,
+                                     new_output_len,
+                                     skip_min_output_len_check=output_len
+                                     is not None):
                 continue
             if enable_multimodal_chat:
-                prompt = self.apply_multimodal_chat_transformation(prompt, None)
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, None)
             samples.append(
                 SampleRequest(
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=new_output_len,
                     lora_request=lora_request,
-                )
-            )
+                ))
         self.maybe_oversample_requests(samples, num_requests)
         return samples
 
 
-# -----------------------------------------------------------------------------
-# Custom Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class CustomDataset(BenchmarkDataset):
-    """
-    Implements the Custom dataset.  Loads data from a JSONL file and generates
-    sample requests based on conversation turns. E.g.,
-    ```
-    {"prompt": "What is the capital of India?"}
-    {"prompt": "What is the capital of Iran?"}
-    {"prompt": "What is the capital of China?"}
-    ```
-    """
-
-    def __init__(self, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.load_data()
-
-    def load_data(self) -> None:
-        if self.dataset_path is None:
-            raise ValueError("dataset_path must be provided for loading data.")
-
-        # self.data will be a list of dictionaries
-        # e.g., [{"prompt": "What is the capital of India?"}, ...]
-        # This will be the standardized format which load_data()
-        # has to convert into depending on the filetype of dataset_path.
-        # sample() will assume this standardized format of self.data
-        self.data = []
-
-        # Load the JSONL file
-        if self.dataset_path.endswith(".jsonl"):
-            jsonl_data = pd.read_json(path_or_buf=self.dataset_path, lines=True)
-
-            # check if the JSONL file has a 'prompt' column
-            if "prompt" not in jsonl_data.columns:
-                raise ValueError("JSONL file must contain a 'prompt' column.")
-
-            # Convert each row to a dictionary and append to self.data
-            # This will convert the DataFrame to a list of dictionaries
-            # where each dictionary corresponds to a row in the DataFrame.
-            # This is the standardized format we want for self.data
-            for _, row in jsonl_data.iterrows():
-                self.data.append(row.to_dict())
-        else:
-            raise NotImplementedError(
-                "Only JSONL format is supported for CustomDataset."
-            )
-
-        random.seed(self.random_seed)
-        random.shuffle(self.data)
-
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        lora_path: Optional[str] = None,
-        max_loras: Optional[int] = None,
-        output_len: Optional[int] = None,
-        enable_multimodal_chat: bool = False,
-        skip_chat_template: bool = False,
-        **kwargs,
-    ) -> list:
-        sampled_requests = []
-        for item in self.data:
-            if len(sampled_requests) >= num_requests:
-                break
-            prompt = item["prompt"]
-
-            # apply template
-            if not skip_chat_template:
-                prompt = tokenizer.apply_chat_template(
-                    [{"role": "user", "content": prompt}],
-                    add_generation_prompt=True,
-                    tokenize=False,
-                )
-
-            prompt_len = len(tokenizer(prompt).input_ids)
-            sampled_requests.append(
-                SampleRequest(
-                    prompt=prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                )
-            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
-
-        return sampled_requests
-
-
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------
@@ -576,20 +482,20 @@ class SonnetDataset(BenchmarkDataset):
     ) -> list:
         # Calculate average token length for a poem line.
         tokenized_lines = [tokenizer(line).input_ids for line in self.data]
-        avg_len = sum(len(tokens) for tokens in tokenized_lines) / len(tokenized_lines)
+        avg_len = sum(len(tokens)
+                      for tokens in tokenized_lines) / len(tokenized_lines)
 
         # Build the base prompt.
         base_prompt = "Pick as many lines as you can from these poem lines:\n"
         base_msg = [{"role": "user", "content": base_prompt}]
-        base_fmt = tokenizer.apply_chat_template(
-            base_msg, add_generation_prompt=True, tokenize=False
-        )
+        base_fmt = tokenizer.apply_chat_template(base_msg,
+                                                 add_generation_prompt=True,
+                                                 tokenize=False)
         base_offset = len(tokenizer(base_fmt).input_ids)
         if input_len <= base_offset:
             raise ValueError(
                 f"'input_len' must be higher than the base prompt length "
-                f"({base_offset})."
-            )
+                f"({base_offset}).")
 
         # Determine how many poem lines to use.
         num_input_lines = round((input_len - base_offset) / avg_len)
@@ -598,23 +504,21 @@ class SonnetDataset(BenchmarkDataset):
 
         samples = []
         while len(samples) < num_requests:
-            extra_lines = random.choices(
-                self.data, k=num_input_lines - num_prefix_lines
-            )
+            extra_lines = random.choices(self.data,
+                                         k=num_input_lines - num_prefix_lines)
             prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
             msg = [{"role": "user", "content": prompt}]
             prompt_formatted = tokenizer.apply_chat_template(
-                msg, add_generation_prompt=True, tokenize=False
-            )
+                msg, add_generation_prompt=True, tokenize=False)
             prompt_len = len(tokenizer(prompt_formatted).input_ids)
             if prompt_len <= input_len:
                 samples.append(
                     SampleRequest(
-                        prompt=prompt_formatted if return_prompt_formatted else prompt,
+                        prompt=prompt_formatted
+                        if return_prompt_formatted else prompt,
                         prompt_len=prompt_len,
                         expected_output_len=output_len,
-                    )
-                )
+                    ))
         return samples
 
 
@@ -634,9 +538,7 @@ class BurstGPTDataset(BenchmarkDataset):
         super().__init__(**kwargs)
         self.load_data()
 
-    def load_data(
-        self,
-    ):
+    def load_data(self, ):
         if self.dataset_path is None:
             raise ValueError("dataset_path must be provided for loading data.")
 
@@ -650,7 +552,8 @@ class BurstGPTDataset(BenchmarkDataset):
 
     def _sample_loaded_data(self, num_requests: int) -> list:
         if num_requests <= len(self.data):
-            data = self.data.sample(n=num_requests, random_state=self.random_seed)
+            data = self.data.sample(n=num_requests,
+                                    random_state=self.random_seed)
         else:
             data = self.data.sample(
                 n=num_requests,
@@ -674,8 +577,7 @@ class BurstGPTDataset(BenchmarkDataset):
             input_len = int(data[i][2])
             output_len = int(data[i][3])
             lora_req, tokenizer = self.get_random_lora_request(
-                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
-            )
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
             vocab_size = tokenizer.vocab_size
             # Generate a synthetic prompt: a list of token IDs computed as (i +
             # j) modulo vocab_size.
@@ -687,8 +589,7 @@ class BurstGPTDataset(BenchmarkDataset):
                     prompt_len=input_len,
                     expected_output_len=output_len,
                     lora_request=lora_req,
-                )
-            )
+                ))
         return samples
 
 
@@ -704,7 +605,6 @@ class HuggingFaceDataset(BenchmarkDataset):
         self,
         dataset_path: str,
         dataset_split: str,
-        no_stream: bool = False,
         dataset_subset: Optional[str] = None,
         **kwargs,
     ) -> None:
@@ -712,7 +612,6 @@ class HuggingFaceDataset(BenchmarkDataset):
 
         self.dataset_split = dataset_split
         self.dataset_subset = dataset_subset
-        self.load_stream = not no_stream
         self.load_data()
 
     def load_data(self) -> None:
@@ -721,7 +620,7 @@ class HuggingFaceDataset(BenchmarkDataset):
             self.dataset_path,
             name=self.dataset_subset,
             split=self.dataset_split,
-            streaming=self.load_stream,
+            streaming=True,
         )
         self.data = self.data.shuffle(seed=self.random_seed)
 
@@ -733,23 +632,20 @@ class HuggingFaceDataset(BenchmarkDataset):
 
 class ConversationDataset(HuggingFaceDataset):
     """Dataset for conversation data with multimodal support."""
-
     SUPPORTED_DATASET_PATHS = {
-        "lmms-lab/LLaVA-OneVision-Data",
-        "Aeala/ShareGPT_Vicuna_unfiltered",
+        'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
     }
     IS_MULTIMODAL = True
 
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        output_len: Optional[int] = None,
-        enable_multimodal_chat: bool = False,
-        **kwargs,
-    ) -> list:
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
         # Filter examples with at least 2 conversations
-        filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
+        filtered_data = self.data.filter(
+            lambda x: len(x["conversations"]) >= 2)
         sampled_requests = []
         dynamic_output = output_len is None
 
@@ -765,22 +661,24 @@ class ConversationDataset(HuggingFaceDataset):
             completion_len = len(completion_ids)
             output_len = completion_len if dynamic_output else output_len
             assert isinstance(output_len, int) and output_len > 0
-            if dynamic_output and not is_valid_sequence(prompt_len, completion_len):
+            if dynamic_output and not is_valid_sequence(
+                    prompt_len, completion_len):
                 continue
-            mm_content = process_image(item["image"]) if "image" in item else None
+            mm_content = process_image(
+                item["image"]) if "image" in item else None
             if enable_multimodal_chat:
                 # Note: when chat is enabled the request prompt_len is no longer
                 # accurate and we will be using request output to count the
                 # actual prompt len and output len
-                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, mm_content)
             sampled_requests.append(
                 SampleRequest(
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
-                )
-            )
+                ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
@@ -797,8 +695,10 @@ class VisionArenaDataset(HuggingFaceDataset):
 
     DEFAULT_OUTPUT_LEN = 128
     SUPPORTED_DATASET_PATHS = {
-        "lmarena-ai/VisionArena-Chat": lambda x: x["conversation"][0][0]["content"],
-        "lmarena-ai/vision-arena-bench-v0.1": lambda x: x["turns"][0][0]["content"],
+        "lmarena-ai/VisionArena-Chat":
+        lambda x: x["conversation"][0][0]["content"],
+        "lmarena-ai/vision-arena-bench-v0.1":
+        lambda x: x["turns"][0][0]["content"]
     }
     IS_MULTIMODAL = True
 
@@ -810,14 +710,16 @@ class VisionArenaDataset(HuggingFaceDataset):
         enable_multimodal_chat: bool = False,
         **kwargs,
     ) -> list:
-        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
             parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
             if parser_fn is None:
-                raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
+                raise ValueError(
+                    f"Unsupported dataset path: {self.dataset_path}")
             prompt = parser_fn(item)
             mm_content = process_image(item["images"][0])
             prompt_len = len(tokenizer(prompt).input_ids)
@@ -825,15 +727,15 @@ class VisionArenaDataset(HuggingFaceDataset):
                 # Note: when chat is enabled the request prompt_len is no longer
                 # accurate and we will be using request output to count the
                 # actual prompt len
-                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, mm_content)
             sampled_requests.append(
                 SampleRequest(
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
-                )
-            )
+                ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
@@ -858,36 +760,26 @@ class InstructCoderDataset(HuggingFaceDataset):
         "likaixin/InstructCoder",
     }
 
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        output_len: Optional[int] = None,
-        enable_multimodal_chat: bool = False,
-        **kwargs,
-    ) -> list:
-        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt = f"{item['input']}\n\n{item['instruction']} Just output \
-            the code, do not include any explanation."
-
-            # apply template
-            prompt = tokenizer.apply_chat_template(
-                [{"role": "user", "content": prompt}],
-                add_generation_prompt=True,
-                tokenize=False,
-            )
+            prompt = f"{item['instruction']}:\n{item['input']}"
             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
 
@@ -902,38 +794,38 @@ class MTBenchDataset(HuggingFaceDataset):
     MT-Bench Dataset.
     https://huggingface.co/datasets/philschmid/mt-bench
 
-    We create a single turn dataset for MT-Bench.
+    We create a single turn dataset for MT-Bench. 
     This is similar to Spec decoding benchmark setup in vLLM
     https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
-    """  # noqa: E501
+    """ # noqa: E501
 
     DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
     SUPPORTED_DATASET_PATHS = {
         "philschmid/mt-bench",
     }
 
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        output_len: Optional[int] = None,
-        enable_multimodal_chat: bool = False,
-        **kwargs,
-    ) -> list:
-        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
 
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt = item["turns"][0]
+            prompt = item['turns'][0]
 
             # apply template
-            prompt = tokenizer.apply_chat_template(
-                [{"role": "user", "content": prompt}],
-                add_generation_prompt=True,
-                tokenize=False,
-            )
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
 
             prompt_len = len(tokenizer(prompt).input_ids)
             sampled_requests.append(
@@ -941,8 +833,7 @@ class MTBenchDataset(HuggingFaceDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
-                )
-            )
+                ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
@@ -956,27 +847,23 @@ class AIMODataset(HuggingFaceDataset):
     """
     Dataset class for processing a AIMO dataset with reasoning questions.
     """
-
     SUPPORTED_DATASET_PATHS = {
-        "AI-MO/aimo-validation-aime",
-        "AI-MO/NuminaMath-1.5",
-        "AI-MO/NuminaMath-CoT",
+        "AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
+        "AI-MO/NuminaMath-CoT"
     }
 
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        output_len: Optional[int] = None,
-        **kwargs,
-    ) -> list:
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               **kwargs) -> list:
         sampled_requests = []
         dynamic_output = output_len is None
 
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt, completion = item["problem"], item["solution"]
+            prompt, completion = item['problem'], item["solution"]
 
             prompt_ids = tokenizer(prompt).input_ids
             completion_ids = tokenizer(completion).input_ids
@@ -984,9 +871,10 @@ class AIMODataset(HuggingFaceDataset):
             completion_len = len(completion_ids)
             output_len = completion_len if dynamic_output else output_len
             assert isinstance(output_len, int) and output_len > 0
-            if dynamic_output and not is_valid_sequence(
-                prompt_len, completion_len, max_prompt_len=2048, max_total_len=32000
-            ):
+            if dynamic_output and not is_valid_sequence(prompt_len,
+                                                        completion_len,
+                                                        max_prompt_len=2048,
+                                                        max_total_len=32000):
                 continue
             sampled_requests.append(
                 SampleRequest(
@@ -994,8 +882,7 @@ class AIMODataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=None,
-                )
-            )
+                ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
@@ -1018,25 +905,25 @@ You are a code completion assistant and your task is to analyze user edits and t
 
 ### Response:
 
-"""  # noqa: E501
+""" # noqa: E501
 
 
 def _format_zeta_prompt(
-    sample: dict, original_start_marker: str = "<|editable_region_start|>"
-) -> dict:
+        sample: dict,
+        original_start_marker: str = "<|editable_region_start|>") -> dict:
     """Format the zeta prompt for the Next Edit Prediction (NEP) dataset.
-
-    This function formats examples from the NEP dataset
-    into prompts and expected outputs. It could be
+    
+    This function formats examples from the NEP dataset 
+    into prompts and expected outputs. It could be 
     further extended to support more NEP datasets.
-
+    
     Args:
-        sample: The dataset sample containing events,
+        sample: The dataset sample containing events, 
             inputs, and outputs.
-        original_start_marker: The marker indicating the
-            start of the editable region. Defaults to
+        original_start_marker: The marker indicating the 
+            start of the editable region. Defaults to 
             "<|editable_region_start|>".
-
+            
     Returns:
         A dictionary with the formatted prompts and expected outputs.
     """
@@ -1066,8 +953,10 @@ class NextEditPredictionDataset(HuggingFaceDataset):
         "zed-industries/zeta": _format_zeta_prompt,
     }
 
-    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
-        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
+    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
+               **kwargs):
+        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
+            self.dataset_path)
         if formatting_prompt_func is None:
             raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
         samples = []
@@ -1078,10 +967,8 @@ class NextEditPredictionDataset(HuggingFaceDataset):
                     prompt=sample["prompt"],
                     prompt_len=len(tokenizer(sample["prompt"]).input_ids),
                     expected_output_len=len(
-                        tokenizer(sample["expected_output"]).input_ids
-                    ),
-                )
-            )
+                        tokenizer(sample["expected_output"]).input_ids),
+                ))
             if len(samples) >= num_requests:
                 break
         self.maybe_oversample_requests(samples, num_requests)
@@ -1110,22 +997,18 @@ class ASRDataset(HuggingFaceDataset):
     | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
     +----------------+----------------------------------------+--------------------------+-----------------------------+
 
-    """  # noqa: E501
-
+    """ # noqa: E501
     SUPPORTED_DATASET_PATHS = {
-        "openslr/librispeech_asr",
-        "facebook/voxpopuli",
-        "LIUM/tedlium",
-        "edinburghcstr/ami",
-        "speechcolab/gigaspeech",
-        "kensho/spgispeech",
+        "openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
+        "edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
     }
 
     DEFAULT_OUTPUT_LEN = 128
     IS_MULTIMODAL = True
 
     # TODO Whisper-specific. Abstract interface when more models are supported.
-    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
+    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|>"\
+                              "<|notimestamps|>"
     skip_long_audios: bool = True
 
     def sample(
@@ -1136,8 +1019,8 @@ class ASRDataset(HuggingFaceDataset):
         **kwargs,
     ) -> list:
         import librosa
-
-        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
         prompt_len = len(tokenizer(prompt).input_ids)
         sampled_requests = []
@@ -1160,14 +1043,10 @@ class ASRDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
-                )
-            )
+                ))
         if skipped:
-            logger.warning(
-                "%d samples discarded from dataset due to"
-                " their length being greater than"
-                " what Whisper supports.",
-                skipped,
-            )
+            logger.warning("%d samples discarded from dataset due to" \
+                           " their length being greater than" \
+                           " what Whisper supports.", skipped)
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests

benchmarks/benchmark_latency.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark the latency of processing a single batch of requests."""
 
 import argparse
@@ -7,14 +6,14 @@ import dataclasses
 import json
 import os
 import time
+from pathlib import Path
 from typing import Any, Optional
 
 import numpy as np
-from tqdm import tqdm
-from typing_extensions import deprecated
-
-import vllm.envs as envs
+import torch
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
+from tqdm import tqdm
+
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.inputs import PromptType
@@ -22,23 +21,18 @@ from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser
 
 
-def save_to_pytorch_benchmark_format(
-    args: argparse.Namespace, results: dict[str, Any]
-) -> None:
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                     results: dict[str, Any]) -> None:
     pt_records = convert_to_pytorch_benchmark_format(
         args=args,
         metrics={"latency": results["latencies"]},
-        extra_info={k: results[k] for k in ["avg_latency", "percentiles"]},
-    )
+        extra_info={k: results[k]
+                    for k in ["avg_latency", "percentiles"]})
     if pt_records:
         pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
         write_to_json(pt_file, pt_records)
 
 
-@deprecated(
-    "benchmark_latency.py is deprecated and will be removed in a "
-    "future version. Please use 'vllm bench latency' instead.",
-)
 def main(args: argparse.Namespace):
     print(args)
 
@@ -48,11 +42,9 @@ def main(args: argparse.Namespace):
     # the engine will automatically process the request in multiple batches.
     llm = LLM(**dataclasses.asdict(engine_args))
     assert llm.llm_engine.model_config.max_model_len >= (
-        args.input_len + args.output_len
-    ), (
-        "Please ensure that max_model_len is greater than"
-        " the sum of input_len and output_len."
-    )
+        args.input_len +
+        args.output_len), ("Please ensure that max_model_len is greater than"
+                           " the sum of input_len and output_len.")
 
     sampling_params = SamplingParams(
         n=args.n,
@@ -63,16 +55,18 @@ def main(args: argparse.Namespace):
         detokenize=not args.disable_detokenize,
     )
     print(sampling_params)
-    dummy_prompt_token_ids = np.random.randint(
-        10000, size=(args.batch_size, args.input_len)
-    )
-    dummy_prompts: list[PromptType] = [
-        {"prompt_token_ids": batch} for batch in dummy_prompt_token_ids.tolist()
-    ]
+    dummy_prompt_token_ids = np.random.randint(10000,
+                                               size=(args.batch_size,
+                                                     args.input_len))
+    dummy_prompts: list[PromptType] = [{
+        "prompt_token_ids": batch
+    } for batch in dummy_prompt_token_ids.tolist()]
 
     def llm_generate():
         if not args.use_beam_search:
-            llm.generate(dummy_prompts, sampling_params=sampling_params, use_tqdm=False)
+            llm.generate(dummy_prompts,
+                         sampling_params=sampling_params,
+                         use_tqdm=False)
         else:
             llm.beam_search(
                 dummy_prompts,
@@ -85,9 +79,16 @@ def main(args: argparse.Namespace):
 
     def run_to_completion(profile_dir: Optional[str] = None):
         if profile_dir:
-            llm.start_profile()
-            llm_generate()
-            llm.stop_profile()
+            with torch.profiler.profile(
+                    activities=[
+                        torch.profiler.ProfilerActivity.CPU,
+                        torch.profiler.ProfilerActivity.CUDA,
+                    ],
+                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                        str(profile_dir)),
+            ) as p:
+                llm_generate()
+            print(p.key_averages().table(sort_by="self_cuda_time_total"))
         else:
             start_time = time.perf_counter()
             llm_generate()
@@ -100,7 +101,10 @@ def main(args: argparse.Namespace):
         run_to_completion(profile_dir=None)
 
     if args.profile:
-        profile_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profile_dir = args.profile_result_dir
+        if not profile_dir:
+            profile_dir = (Path(".") / "vllm_benchmark_result" /
+                           f"latency_result_{time.time()}")
         print(f"Profiling (results will be saved to '{profile_dir}')...")
         run_to_completion(profile_dir=profile_dir)
         return
@@ -128,11 +132,10 @@ def main(args: argparse.Namespace):
         save_to_pytorch_benchmark_format(args, results)
 
 
-def create_argument_parser():
+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)
@@ -149,14 +152,22 @@ def create_argument_parser():
         default=10,
         help="Number of iterations to run for warmup.",
     )
-    parser.add_argument(
-        "--num-iters", type=int, default=30, help="Number of iterations to run."
-    )
+    parser.add_argument("--num-iters",
+                        type=int,
+                        default=30,
+                        help="Number of iterations to run.")
     parser.add_argument(
         "--profile",
         action="store_true",
         help="profile the generation process of a single batch",
     )
+    parser.add_argument(
+        "--profile-result-dir",
+        type=str,
+        default=None,
+        help=("path to save the pytorch profiler output. Can be visualized "
+              "with ui.perfetto.dev or Tensorboard."),
+    )
     parser.add_argument(
         "--output-json",
         type=str,
@@ -166,26 +177,10 @@ def create_argument_parser():
     parser.add_argument(
         "--disable-detokenize",
         action="store_true",
-        help=(
-            "Do not detokenize responses (i.e. do not include "
-            "detokenization time in the latency measurement)"
-        ),
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
     )
 
     parser = EngineArgs.add_cli_args(parser)
-    # V1 enables prefix caching by default which skews the latency
-    # numbers. We need to disable prefix caching by default.
-    parser.set_defaults(enable_prefix_caching=False)
-
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
-    if args.profile and not envs.VLLM_TORCH_PROFILER_DIR:
-        raise OSError(
-            "The environment variable 'VLLM_TORCH_PROFILER_DIR' is not set. "
-            "Please set it to a valid path to use torch profiler."
-        )
     main(args)

benchmarks/benchmark_long_document_qa_throughput.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Offline benchmark to test the long document QA throughput.
 
@@ -77,7 +76,7 @@ def repeat_prompts(prompts, repeat_count, mode: str):
             - 'random': Shuffle the prompts randomly after repetition.
             - 'tile': Repeat the entire prompt list in sequence.
               Example: [1, 2, 3] -> [1, 2, 3, 1, 2, 3].
-            - 'interleave': Repeat each prompt consecutively before moving to
+            - 'interleave': Repeat each prompt consecutively before moving to 
               the next. Example: [1, 2, 3] -> [1, 1, 2, 2, 3, 3].
 
     Returns:
@@ -87,21 +86,20 @@ def repeat_prompts(prompts, repeat_count, mode: str):
         ValueError: If an invalid mode is provided.
     """
     print("Repeat mode: ", mode)
-    if mode == "random":
+    if mode == 'random':
         repeated_prompts = prompts * repeat_count
         random.shuffle(repeated_prompts)
         return repeated_prompts
-    elif mode == "tile":
+    elif mode == 'tile':
         return prompts * repeat_count
-    elif mode == "interleave":
+    elif mode == 'interleave':
         repeated_prompts = []
         for prompt in prompts:
             repeated_prompts.extend([prompt] * repeat_count)
         return repeated_prompts
     else:
-        raise ValueError(
-            f"Invalid mode: {mode}, only support 'random', 'tile', 'interleave'"
-        )
+        raise ValueError(f"Invalid mode: {mode}, only support "
+                         "'random', 'tile', 'interleave'")
 
 
 def main(args):
@@ -111,16 +109,16 @@ def main(args):
     # we append the document id at the beginning to avoid any of the document
     # being the prefix of other documents
     prompts = [
-        str(i) + " ".join(["hi"] * args.document_length)
+        str(i) + ' '.join(['hi'] * args.document_length)
         for i in range(args.num_documents)
     ]
 
     prompts = repeat_prompts(prompts, args.repeat_count, mode=args.repeat_mode)
 
     warmup_prompts = [
-        "This is warm up request " + str(i) + " ".join(["hi"] * args.document_length)
-        for i in range(args.num_documents)
-    ]
+        "This is warm up request " + str(i) + \
+                ' '.join(['hi'] * args.document_length)
+        for i in range(args.num_documents)]
 
     # Create the LLM engine
     engine_args = EngineArgs.from_cli_args(args)
@@ -142,61 +140,45 @@ def main(args):
     )
 
 
-def create_argument_parser():
+if __name__ == "__main__":
     parser = FlexibleArgumentParser(
-        description="Benchmark the performance with or "
-        "without automatic prefix caching."
-    )
+        description=
+        'Benchmark the performance with or without automatic prefix caching.')
 
     parser.add_argument(
-        "--document-length",
+        '--document-length',
         type=int,
         # Roughly the number of tokens for a system paper,
         # excluding images
         default=20000,
-        help="Range of input lengths for sampling prompts, "
-        'specified as "min:max" (e.g., "128:256").',
-    )
+        help='Range of input lengths for sampling prompts,'
+        'specified as "min:max" (e.g., "128:256").')
 
-    parser.add_argument(
-        "--num-documents",
-        type=int,
-        default=8,
-        help="Range of input lengths for sampling prompts, "
-        'specified as "min:max" (e.g., "128:256").',
-    )
+    parser.add_argument('--num-documents',
+                        type=int,
+                        default=8,
+                        help='Range of input lengths for sampling prompts,'
+                        'specified as "min:max" (e.g., "128:256").')
 
-    parser.add_argument("--output-len", type=int, default=10)
+    parser.add_argument('--output-len', type=int, default=10)
 
-    parser.add_argument(
-        "--repeat-count",
-        type=int,
-        default=2,
-        help="Number of times to repeat each prompt",
-    )
+    parser.add_argument('--repeat-count',
+                        type=int,
+                        default=2,
+                        help='Number of times to repeat each prompt')
 
-    parser.add_argument(
-        "--repeat-mode",
-        type=str,
-        default="random",
-        help="The mode to repeat prompts. The supported "
-        'modes are "random", "tile", and "interleave". '
-        "See repeat_prompts() in the source code for details.",
-    )
+    parser.add_argument("--repeat-mode",
+                        type=str,
+                        default='random',
+                        help='The mode to repeat prompts. The supported '
+                        'modes are "random", "tile", and "interleave". '
+                        'See repeat_prompts() in the source code for details.')
 
-    parser.add_argument(
-        "--shuffle-seed",
-        type=int,
-        default=0,
-        help='Random seed when the repeat mode is "random"',
-    )
+    parser.add_argument("--shuffle-seed",
+                        type=int,
+                        default=0,
+                        help='Random seed when the repeat mode is "random"')
 
     parser = EngineArgs.add_cli_args(parser)
-
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_ngram_proposer.py

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

benchmarks/benchmark_prefix_caching.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Benchmark the efficiency of prefix caching.
 
@@ -64,7 +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)
 
@@ -91,10 +91,8 @@ def sample_requests_from_dataset(
     # Filter out the conversations with less than 2 turns.
     dataset = [data for data in dataset if len(data["conversations"]) >= 2]
     # Only keep the first two turns of each conversation.
-    dataset = [
-        (data["conversations"][0]["value"], data["conversations"][1]["value"])
-        for data in dataset
-    ]
+    dataset = [(data["conversations"][0]["value"],
+                data["conversations"][1]["value"]) for data in dataset]
 
     # Shuffle the dataset.
     random.shuffle(dataset)
@@ -115,9 +113,8 @@ def sample_requests_from_dataset(
         completion = dataset[i][1]
         completion_token_ids = tokenizer(completion).input_ids
         prompt_len = len(prompt_token_ids)
-        output_len = (
-            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
-        )
+        output_len = (len(completion_token_ids)
+                      if fixed_output_len is None else fixed_output_len)
         if min_len <= prompt_len <= max_len:
             filtered_requests.append(Request(prompt, prompt_len, output_len))
 
@@ -131,27 +128,27 @@ def sample_requests_from_random(
     fixed_output_len: Optional[int],
     prefix_len: int,
 ) -> list[Request]:
+
     requests = []
     prefix_token_ids = sample_tokens(tokenizer, prefix_len)
     min_len, max_len = input_length_range
 
     for i in range(num_requests):
         unique_part_token_ids = sample_tokens(
-            tokenizer, random.randint(min_len - prefix_len, max_len - prefix_len)
-        )
+            tokenizer,
+            random.randint(min_len - prefix_len, max_len - prefix_len))
         prompt_token_ids = prefix_token_ids + unique_part_token_ids
         prompt = tokenizer.decode(prompt_token_ids)
         prompt_len = len(prompt_token_ids)
-        assert min_len <= prompt_len <= max_len, (
-            f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
-        )
+        assert (min_len <= prompt_len <= max_len
+                ), f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
         requests.append(Request(prompt, prompt_len, fixed_output_len))
     return requests
 
 
-def repeat_and_sort_requests(
-    requests: list[Request], repeat_count: int, sort: bool = False
-) -> list[str]:
+def repeat_and_sort_requests(requests: list[Request],
+                             repeat_count: int,
+                             sort: bool = False) -> list[str]:
     repeated_requests = requests * repeat_count
     if sort:
         repeated_requests.sort(key=lambda x: x[1])
@@ -162,14 +159,14 @@ def repeat_and_sort_requests(
 
 def main(args):
     tokenizer = get_tokenizer(args.model, trust_remote_code=True)
-    input_length_range = tuple(map(int, args.input_length_range.split(":")))
+    input_length_range = tuple(map(int, args.input_length_range.split(':')))
     random.seed(args.seed)
     if args.dataset_path is not None:
         if args.prefix_len > 0:
-            raise ValueError(
-                "prefix-len is not supported when dataset-path is provided."
-            )
-        print(f"Start to sample {args.num_prompts} prompts from {args.dataset_path}")
+            raise ValueError("prefix-len is not supported when "
+                             "dataset-path is provided.")
+        print(f"Start to sample {args.num_prompts} prompts "
+              f"from {args.dataset_path}")
         filtered_requests = sample_requests_from_dataset(
             dataset_path=args.dataset_path,
             num_requests=args.num_prompts,
@@ -199,16 +196,14 @@ def main(args):
 
     llm = LLM(**dataclasses.asdict(engine_args))
 
-    sampling_params = SamplingParams(
-        temperature=0,
-        max_tokens=args.output_len,
-        detokenize=not args.disable_detokenize,
-    )
+    sampling_params = SamplingParams(temperature=0,
+                                     max_tokens=args.output_len,
+                                     detokenize=not args.disable_detokenize)
 
     print("Testing filtered requests")
-    prompts = repeat_and_sort_requests(
-        filtered_requests, repeat_count=args.repeat_count, sort=args.sort
-    )
+    prompts = repeat_and_sort_requests(filtered_requests,
+                                       repeat_count=args.repeat_count,
+                                       sort=args.sort)
 
     print("------start generating------")
     test_prefix(
@@ -218,37 +213,31 @@ def main(args):
     )
 
 
-def create_argument_parser():
+if __name__ == "__main__":
     parser = FlexibleArgumentParser(
-        description="Benchmark the performance with or without "
-        "automatic prefix caching."
-    )
-    parser.add_argument(
-        "--dataset-path", type=str, default=None, help="Path to the dataset."
-    )
-    parser.add_argument("--output-len", type=int, default=10)
-    parser.add_argument(
-        "--num-prompts",
-        type=int,
-        required=True,
-        help="Number of the prompts sampled from dataset",
-    )
-    parser.add_argument(
-        "--repeat-count",
-        type=int,
-        default=1,
-        help="Number of times to repeat each prompt",
-    )
-    parser.add_argument(
-        "--sort", action="store_true", help="Sort prompts by input length"
-    )
-    parser.add_argument(
-        "--input-length-range",
-        type=str,
-        required=True,
-        help="Range of input lengths for sampling prompts,"
-        'specified as "min:max" (e.g., "128:256").',
-    )
+        description=
+        'Benchmark the performance with or without automatic prefix caching.')
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset.")
+    parser.add_argument('--output-len', type=int, default=10)
+    parser.add_argument('--num-prompts',
+                        type=int,
+                        required=True,
+                        help="Number of the prompts sampled from dataset")
+    parser.add_argument('--repeat-count',
+                        type=int,
+                        default=1,
+                        help='Number of times to repeat each prompt')
+    parser.add_argument('--sort',
+                        action='store_true',
+                        help='Sort prompts by input length')
+    parser.add_argument('--input-length-range',
+                        type=str,
+                        required=True,
+                        help='Range of input lengths for sampling prompts,'
+                        'specified as "min:max" (e.g., "128:256").')
     parser.add_argument(
         "--prefix-len",
         type=int,
@@ -259,20 +248,12 @@ def create_argument_parser():
         "when dataset-path is not provided.",
     )
     parser.add_argument(
-        "--disable-detokenize",
-        action="store_true",
-        help=(
-            "Do not detokenize responses (i.e. do not include "
-            "detokenization time in the latency measurement)"
-        ),
+        '--disable-detokenize',
+        action='store_true',
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
     )
 
     parser = EngineArgs.add_cli_args(parser)
-
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_prioritization.py

@@ -1,7 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark offline prioritization."""
-
 import argparse
 import dataclasses
 import json
@@ -15,7 +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
 
@@ -35,10 +33,8 @@ def sample_requests(
     # Filter out the conversations with less than 2 turns.
     dataset = [data for data in dataset if len(data["conversations"]) >= 2]
     # Only keep the first two turns of each conversation.
-    dataset = [
-        (data["conversations"][0]["value"], data["conversations"][1]["value"])
-        for data in dataset
-    ]
+    dataset = [(data["conversations"][0]["value"],
+                data["conversations"][1]["value"]) for data in dataset]
 
     # Shuffle the dataset.
     random.shuffle(dataset)
@@ -55,9 +51,8 @@ def sample_requests(
         completion = dataset[i][1]
         completion_token_ids = tokenizer(completion).input_ids
         prompt_len = len(prompt_token_ids)
-        output_len = (
-            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
-        )
+        output_len = len(completion_token_ids
+                         ) if fixed_output_len is None else fixed_output_len
         if prompt_len < 4 or output_len < 4:
             # Prune too short sequences.
             continue
@@ -79,16 +74,13 @@ def run_vllm(
     disable_detokenize: bool = False,
 ) -> float:
     from vllm import LLM, SamplingParams
-
     llm = LLM(**dataclasses.asdict(engine_args))
 
     assert all(
         llm.llm_engine.model_config.max_model_len >= (request[1] + request[2])
-        for request in requests
-    ), (
-        "Please ensure that max_model_len is greater than the sum of"
-        " input_len and output_len for all requests."
-    )
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of"
+            " input_len and output_len for all requests.")
 
     # Add the requests to the engine.
     prompts = []
@@ -105,8 +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)
@@ -120,33 +111,26 @@ def main(args: argparse.Namespace):
 
     # Sample the requests.
     tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer, trust_remote_code=args.trust_remote_code
-    )
+        args.tokenizer, trust_remote_code=args.trust_remote_code)
     if args.dataset is None:
         # Synthesize a prompt with the given input length.
         prompt = "hi" * (args.input_len - 1)
-        requests = [
-            (prompt, args.input_len, args.output_len, get_random_flag())
-            for _ in range(args.num_prompts)
-        ]
+        requests = [(prompt, args.input_len, args.output_len,
+                     get_random_flag()) for _ in range(args.num_prompts)]
     else:
-        requests = sample_requests(
-            args.dataset, args.num_prompts, tokenizer, args.output_len
-        )
+        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
+                                   args.output_len)
 
     if args.backend == "vllm":
-        elapsed_time = run_vllm(
-            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
-        )
+        elapsed_time = run_vllm(requests, args.n,
+                                EngineArgs.from_cli_args(args),
+                                args.disable_detokenize)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(
-        prompt_len + output_len for _, prompt_len, output_len, priority in requests
-    )
-    print(
-        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-        f"{total_num_tokens / elapsed_time:.2f} tokens/s"
-    )
+    total_num_tokens = sum(prompt_len + output_len
+                           for _, prompt_len, output_len, priority in requests)
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
 
     # Output JSON results if specified
     if args.output_json:
@@ -161,55 +145,46 @@ def main(args: argparse.Namespace):
             json.dump(results, f, indent=4)
 
 
-def create_argument_parser():
+if __name__ == "__main__":
     parser = FlexibleArgumentParser(description="Benchmark the throughput.")
+    parser.add_argument("--backend",
+                        type=str,
+                        choices=["vllm", "hf", "mii"],
+                        default="vllm")
+    parser.add_argument("--dataset",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset.")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Input prompt length for each request")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=None,
+                        help="Output length for each request. Overrides the "
+                        "output length from the dataset.")
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=200,
+                        help="Number of prompts to process.")
     parser.add_argument(
-        "--backend", type=str, choices=["vllm", "hf", "mii"], default="vllm"
-    )
-    parser.add_argument(
-        "--dataset", type=str, default=None, help="Path to the dataset."
-    )
-    parser.add_argument(
-        "--input-len",
-        type=int,
-        default=None,
-        help="Input prompt length for each request",
-    )
-    parser.add_argument(
-        "--output-len",
-        type=int,
-        default=None,
-        help="Output length for each request. Overrides the "
-        "output length from the dataset.",
-    )
-    parser.add_argument(
-        "--n", type=int, default=1, help="Number of generated sequences per prompt."
-    )
-    parser.add_argument(
-        "--num-prompts", type=int, default=200, help="Number of prompts to process."
-    )
-    parser.add_argument(
-        "--output-json",
+        '--output-json',
         type=str,
         default=None,
-        help="Path to save the throughput results in JSON format.",
-    )
+        help='Path to save the throughput results in JSON format.')
     parser.add_argument(
-        "--disable-detokenize",
-        action="store_true",
-        help=(
-            "Do not detokenize responses (i.e. do not include "
-            "detokenization time in the latency measurement)"
-        ),
+        '--disable-detokenize',
+        action='store_true',
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
     )
 
     parser = EngineArgs.add_cli_args(parser)
-
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

benchmarks/benchmark_serving_structured_output.py

@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 r"""Benchmark online serving throughput with structured outputs.
 
 On the server side, run one of the following commands:
     (vLLM OpenAI API server)
-    vllm serve <your_model>
+    vllm serve <your_model> --disable-log-requests
 
 On the client side, run:
     python benchmarks/benchmark_serving_structured_output.py \
@@ -12,6 +11,7 @@ On the client side, run:
         --model <your_model> \
         --dataset json \
         --structured-output-ratio 1.0 \
+        --structured-output-backend auto \
         --request-rate 10 \
         --num-prompts 1000
 
@@ -19,7 +19,6 @@ On the client side, run:
         --endpoint /generate_stream
     to the end of the command above.
 """
-
 import argparse
 import asyncio
 import copy
@@ -37,15 +36,11 @@ from typing import Optional
 import datasets
 import numpy as np
 import pandas as pd
+from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
+                                  RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
 
-from backend_request_func import (
-    ASYNC_REQUEST_FUNCS,
-    RequestFuncInput,
-    RequestFuncOutput,
-)
-
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
@@ -57,8 +52,7 @@ except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
 from vllm.v1.structured_output.backend_xgrammar import (
-    has_xgrammar_unsupported_json_features,
-)
+    has_xgrammar_unsupported_json_features)
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
@@ -104,7 +98,6 @@ class SampleRequest:
         prompt_len: The length of the prompt in tokens.
         expected_output_len: The expected length of the output in tokens.
     """
-
     prompt: str
     prompt_len: int
     expected_output_len: int
@@ -113,28 +106,32 @@ class SampleRequest:
     completion: str = None
 
 
-def sample_requests(
-    tokenizer: PreTrainedTokenizerBase, args: argparse.Namespace
-) -> list[SampleRequest]:
-    if args.dataset == "json" or args.dataset == "json-unique":
+def sample_requests(tokenizer: PreTrainedTokenizerBase,
+                    args: argparse.Namespace) -> list[SampleRequest]:
+    if args.dataset == 'json' or args.dataset == 'json-unique':
         if args.json_schema_path is None:
             dir_path = os.path.dirname(os.path.realpath(__file__))
-            args.json_schema_path = os.path.join(
-                dir_path, "structured_schemas", "structured_schema_1.json"
-            )
+            args.json_schema_path = os.path.join(dir_path,
+                                                 "structured_schemas",
+                                                 "structured_schema_1.json")
         json_schemas = []
         with open(args.json_schema_path) as f:
             schema = json.load(f)
 
-        if args.dataset == "json-unique":
-            json_schemas = [copy.deepcopy(schema) for _ in range(args.num_prompts)]
+        if args.dataset == 'json-unique':
+            json_schemas = [
+                copy.deepcopy(schema) for _ in range(args.num_prompts)
+            ]
             for i in range(len(json_schemas)):
                 if "properties" not in json_schemas[i]:
                     json_schemas[i]["properties"] = {}
-                json_schemas[i]["properties"][f"__optional_field_{uuid.uuid4()}"] = {
-                    "type": "string",
-                    "description": "An unique optional field to avoid cached schemas",
-                }
+                json_schemas[i]["properties"][
+                    f"__optional_field_{uuid.uuid4()}"] = {
+                        "type":
+                        "string",
+                        "description":
+                        "An unique optional field to avoid cached schemas"
+                    }
         else:
             json_schemas = [schema] * args.num_prompts
 
@@ -145,13 +142,11 @@ def sample_requests(
             return json_schemas[index % len(json_schemas)]
 
         requests = [
-            SampleRequest(
-                prompt=gen_prompt(i),
-                prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
-                expected_output_len=args.output_len,
-                schema=get_schema(i),
-                structure_type=args.structure_type,
-            )
+            SampleRequest(prompt=gen_prompt(i),
+                          prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
+                          expected_output_len=args.output_len,
+                          schema=get_schema(i),
+                          structure_type=args.structure_type)
             for i in range(args.num_prompts)
         ]
 
@@ -175,13 +170,11 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
-                prompt=prompt,
-                prompt_len=input_len,
-                expected_output_len=args.output_len,
-                schema=schema,
-                structure_type=args.structure_type,
-            )
+            SampleRequest(prompt=prompt,
+                          prompt_len=input_len,
+                          expected_output_len=args.output_len,
+                          schema=schema,
+                          structure_type=args.structure_type)
             for _ in range(args.num_prompts)
         ]
 
@@ -195,13 +188,11 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
-                prompt=prompt,
-                prompt_len=input_len,
-                expected_output_len=args.output_len,
-                schema=regex,
-                structure_type=args.structure_type,
-            )
+            SampleRequest(prompt=prompt,
+                          prompt_len=input_len,
+                          expected_output_len=args.output_len,
+                          schema=regex,
+                          structure_type=args.structure_type)
             for _ in range(args.num_prompts)
         ]
 
@@ -212,55 +203,48 @@ def sample_requests(
         input_len = len(tokenizer(prompt).input_ids)
         print(f"Input length of the prompt: {input_len} tokens")
         requests = [
-            SampleRequest(
-                prompt=prompt,
-                prompt_len=input_len,
-                expected_output_len=args.output_len,
-                schema=choice,
-                structure_type=args.structure_type,
-            )
+            SampleRequest(prompt=prompt,
+                          prompt_len=input_len,
+                          expected_output_len=args.output_len,
+                          schema=choice,
+                          structure_type=args.structure_type)
             for _ in range(args.num_prompts)
         ]
 
     elif args.dataset == "xgrammar_bench":
         requests: list[SampleRequest] = []
-        dataset = datasets.load_dataset("NousResearch/json-mode-eval", split="train")
+        dataset = datasets.load_dataset("NousResearch/json-mode-eval",
+                                        split="train")
         full_dataset_len = len(dataset)
 
         def _filter_func(item):
             import json
-
             schema = json.loads(item["schema"])
             return not has_xgrammar_unsupported_json_features(schema)
 
         dataset = dataset.filter(_filter_func)
         num_filtered_out = full_dataset_len - len(dataset)
-        print(
-            f"dataset has {len(dataset)} entries after filtering "
-            f"out {num_filtered_out} entries with unsupported features"
-        )
+        print(f"dataset has {len(dataset)} entries after filtering "
+              f"out {num_filtered_out} entries with unsupported features")
         len_dataset = len(dataset)
         for data_point_idx in range(args.num_prompts):
             idx = data_point_idx
             while idx >= len_dataset:
                 idx -= len_dataset
             schema = dataset["schema"][idx]
-            prompt = tokenizer.apply_chat_template(
-                dataset["prompt"][idx], tokenize=False, add_generation_prompt=True
-            )
+            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
+                                                   tokenize=False,
+                                                   add_generation_prompt=True)
             input_len = len(tokenizer(prompt).input_ids)
             completion = dataset["completion"][idx]
 
             requests.append(
-                SampleRequest(
-                    prompt=prompt,
-                    prompt_len=input_len,
-                    expected_output_len=args.output_len,
-                    schema=schema,
-                    structure_type=args.structure_type,
-                    completion=completion,
-                )
-            )
+                SampleRequest(prompt=prompt,
+                              prompt_len=input_len,
+                              expected_output_len=args.output_len,
+                              schema=schema,
+                              structure_type=args.structure_type,
+                              completion=completion))
 
     return requests
 
@@ -292,8 +276,7 @@ async def get_request(
 
     # Calculate scale parameter theta to maintain the desired request_rate.
     assert burstiness > 0, (
-        f"A positive burstiness factor is expected, but given {burstiness}."
-    )
+        f"A positive burstiness factor is expected, but given {burstiness}.")
     theta = 1.0 / (request_rate * burstiness)
 
     for i, request in enumerate(input_requests):
@@ -335,8 +318,8 @@ def calculate_metrics(
             # multiple output tokens may be bundled together
             # Note : this may inflate the output token count slightly
             output_len = len(
-                tokenizer(outputs[i].generated_text, add_special_tokens=False).input_ids
-            )
+                tokenizer(outputs[i].generated_text,
+                          add_special_tokens=False).input_ids)
             actual_output_lens.append(output_len)
             total_input += input_requests[i].prompt_len
             tpot = 0
@@ -360,19 +343,16 @@ def calculate_metrics(
 
         if "ttft" in goodput_config_dict:
             valid_metrics.append(ttfts)
-            slo_values.append(
-                goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
-            )
+            slo_values.append(goodput_config_dict["ttft"] /
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
         if "tpot" in goodput_config_dict:
             valid_metrics.append(all_tpots)
-            slo_values.append(
-                goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
-            )
+            slo_values.append(goodput_config_dict["tpot"] /
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
         if "e2el" in goodput_config_dict:
             valid_metrics.append(e2els)
-            slo_values.append(
-                goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
-            )
+            slo_values.append(goodput_config_dict["e2el"] /
+                              MILLISECONDS_TO_SECONDS_CONVERSION)
 
         for req_metric in zip(*valid_metrics):
             is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
@@ -383,8 +363,7 @@ def calculate_metrics(
         warnings.warn(
             "All requests failed. This is likely due to a misconfiguration "
             "on the benchmark arguments.",
-            stacklevel=2,
-        )
+            stacklevel=2)
     metrics = BenchmarkMetrics(
         completed=completed,
         total_input=total_input,
@@ -393,31 +372,27 @@ def calculate_metrics(
         request_goodput=good_completed / dur_s,
         output_throughput=sum(actual_output_lens) / dur_s,
         total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
-        mean_ttft_ms=np.mean(ttfts or 0)
-        * 1000,  # ttfts is empty if streaming is not supported by backend
+        mean_ttft_ms=np.mean(ttfts or 0) *
+        1000,  # ttfts is empty if streaming is not supported by backend
         std_ttft_ms=np.std(ttfts or 0) * 1000,
         median_ttft_ms=np.median(ttfts or 0) * 1000,
-        percentiles_ttft_ms=[
-            (p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
-        ],
+        percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
+                             for p in selected_percentiles],
         mean_tpot_ms=np.mean(tpots or 0) * 1000,
         std_tpot_ms=np.std(tpots or 0) * 1000,
         median_tpot_ms=np.median(tpots or 0) * 1000,
-        percentiles_tpot_ms=[
-            (p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
-        ],
+        percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
+                             for p in selected_percentiles],
         mean_itl_ms=np.mean(itls or 0) * 1000,
         std_itl_ms=np.std(itls or 0) * 1000,
         median_itl_ms=np.median(itls or 0) * 1000,
-        percentiles_itl_ms=[
-            (p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
-        ],
+        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
+                            for p in selected_percentiles],
         mean_e2el_ms=np.mean(e2els or 0) * 1000,
         std_e2el_ms=np.std(e2els or 0) * 1000,
         median_e2el_ms=np.median(e2els or 0) * 1000,
-        percentiles_e2el_ms=[
-            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
-        ],
+        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
+                             for p in selected_percentiles],
     )
 
     return metrics, actual_output_lens
@@ -454,13 +429,12 @@ async def benchmark(
 
     print("Starting initial single prompt test run...")
     structured_output_req_idx = random.sample(
-        range(len(input_requests)), int(len(input_requests) * structured_output_ratio)
-    )
+        range(len(input_requests)),
+        int(len(input_requests) * structured_output_ratio))
 
     test_request = input_requests[0]
-    test_req_extra_body = (
-        prepare_extra_body(test_request) if 0 in structured_output_req_idx else None
-    )
+    test_req_extra_body = (prepare_extra_body(test_request)
+                           if 0 in structured_output_req_idx else None)
     test_input = RequestFuncInput(
         model=model_id,
         prompt=test_request.prompt,
@@ -474,8 +448,7 @@ async def benchmark(
     if not test_output.success:
         raise ValueError(
             "Initial test run failed - Please make sure benchmark arguments "
-            f"are correctly specified. Error: {test_output.error}"
-        )
+            f"are correctly specified. Error: {test_output.error}")
     else:
         print("Initial test run completed. Starting main benchmark run...")
 
@@ -494,7 +467,10 @@ async def benchmark(
         if profile_output.success:
             print("Profiler started")
 
-    distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
+    if burstiness == 1.0:
+        distribution = "Poisson process"
+    else:
+        distribution = "Gamma distribution"
 
     print(f"Traffic request rate: {request_rate}")
     print(f"Burstiness factor: {burstiness} ({distribution})")
@@ -506,21 +482,24 @@ async def benchmark(
     # and it will simplify the code in limited_request_func.
     #    semaphore = (asyncio.Semaphore(max_concurrency)
     #                 if max_concurrency else contextlib.nullcontext())
-    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None
+    semaphore = (asyncio.Semaphore(max_concurrency)
+                 if max_concurrency else None)
 
     async def limited_request_func(request_func_input, pbar):
         if semaphore is None:
-            return await request_func(request_func_input=request_func_input, pbar=pbar)
+            return await request_func(request_func_input=request_func_input,
+                                      pbar=pbar)
         async with semaphore:
-            return await request_func(request_func_input=request_func_input, pbar=pbar)
+            return await request_func(request_func_input=request_func_input,
+                                      pbar=pbar)
 
     benchmark_start_time = time.perf_counter()
     tasks: list[asyncio.Task] = []
     expected: list[str] = []
-    async for i, request in get_request(input_requests, request_rate, burstiness):
-        extra_body = (
-            prepare_extra_body(request) if i in structured_output_req_idx else None
-        )
+    async for i, request in get_request(input_requests, request_rate,
+                                        burstiness):
+        extra_body = prepare_extra_body(
+            request) if i in structured_output_req_idx else None
         request_func_input = RequestFuncInput(
             model=model_id,
             prompt=request.prompt,
@@ -533,11 +512,24 @@ 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:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_request.prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_request.prompt_len,
+            output_len=test_request.expected_output_len,
+            extra_body={test_request.structure_type: test_request.schema},
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler stopped")
+
     if pbar is not None:
         pbar.close()
 
@@ -553,62 +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))
-    if max_concurrency is not None:
-        print("{:<40} {:<10}".format("Maximum request concurrency:", max_concurrency))
-    if request_rate != float("inf"):
-        print("{:<40} {:<10.2f}".format("Request rate configured (RPS):", request_rate))
-    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
+    print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
+                                    benchmark_duration))
     print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
-    print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
-    print(
-        "{:<40} {:<10.2f}".format(
-            "Request throughput (req/s):", metrics.request_throughput
-        )
-    )
+    print("{:<40} {:<10}".format("Total generated tokens:",
+                                 metrics.total_output))
+    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
+                                    metrics.request_throughput))
     if goodput_config_dict:
-        print(
-            "{:<40} {:<10.2f}".format(
-                "Request goodput (req/s):", metrics.request_goodput
-            )
-        )
-    print(
-        "{:<40} {:<10.2f}".format(
-            "Output token throughput (tok/s):", metrics.output_throughput
-        )
-    )
-    print(
-        "{:<40} {:<10.2f}".format(
-            "Total Token throughput (tok/s):", metrics.total_token_throughput
-        )
-    )
+        print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
+                                        metrics.request_goodput))
+    print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
+                                    metrics.output_throughput))
+    print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
+                                    metrics.total_token_throughput))
 
     result = {
-        "duration": benchmark_duration,
-        "completed": metrics.completed,
-        "total_input_tokens": metrics.total_input,
-        "total_output_tokens": metrics.total_output,
-        "request_throughput": metrics.request_throughput,
-        "output_throughput": metrics.output_throughput,
-        "total_token_throughput": metrics.total_token_throughput,
-        "ttft_description": pd.Series([output.ttft for output in outputs])
-        .describe()
-        .to_dict(),
-        "tpot_description": pd.Series([output.tpot for output in outputs])
-        .describe()
-        .to_dict(),
+        "duration":
+        benchmark_duration,
+        "completed":
+        metrics.completed,
+        "total_input_tokens":
+        metrics.total_input,
+        "total_output_tokens":
+        metrics.total_output,
+        "request_throughput":
+        metrics.request_throughput,
+        "output_throughput":
+        metrics.output_throughput,
+        "total_token_throughput":
+        metrics.total_token_throughput,
+        "ttft_description":
+        pd.Series([output.ttft for output in outputs]).describe().to_dict(),
+        "tpot_description":
+        pd.Series([output.tpot for output in outputs]).describe().to_dict(),
         "input_lens": [output.prompt_len for output in outputs],
-        "output_lens": actual_output_lens,
+        "output_lens":
+        actual_output_lens,
         "ttfts": [output.ttft for output in outputs],
         "itls": [output.itl for output in outputs],
         "errors": [output.error for output in outputs],
     }
 
-    ret = [
-        {"generated": output.generated_text, "expected": gt}
-        for output, gt in zip(outputs, expected)
-    ]
+    ret = [{
+        'generated': output.generated_text,
+        'expected': gt
+    } for output, gt in zip(outputs, expected)]
 
     def process_one_metric(
         # E.g., "ttft"
@@ -622,65 +606,45 @@ async def benchmark(
         # metric.
         if metric_attribute_name not in selected_percentile_metrics:
             return
-        print("{s:{c}^{n}}".format(s=metric_header, n=50, c="-"))
-        print(
-            "{:<40} {:<10.2f}".format(
-                f"Mean {metric_name} (ms):",
-                getattr(metrics, f"mean_{metric_attribute_name}_ms"),
-            )
-        )
-        print(
-            "{:<40} {:<10.2f}".format(
-                f"Median {metric_name} (ms):",
-                getattr(metrics, f"median_{metric_attribute_name}_ms"),
-            )
-        )
+        print("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
+        print("{:<40} {:<10.2f}".format(
+            f"Mean {metric_name} (ms):",
+            getattr(metrics, f"mean_{metric_attribute_name}_ms")))
+        print("{:<40} {:<10.2f}".format(
+            f"Median {metric_name} (ms):",
+            getattr(metrics, f"median_{metric_attribute_name}_ms")))
         result[f"mean_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"mean_{metric_attribute_name}_ms"
-        )
+            metrics, f"mean_{metric_attribute_name}_ms")
         result[f"median_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"median_{metric_attribute_name}_ms"
-        )
+            metrics, f"median_{metric_attribute_name}_ms")
         result[f"std_{metric_attribute_name}_ms"] = getattr(
-            metrics, f"std_{metric_attribute_name}_ms"
-        )
-        for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
+            metrics, f"std_{metric_attribute_name}_ms")
+        for p, value in getattr(metrics,
+                                f"percentiles_{metric_attribute_name}_ms"):
             p_word = str(int(p)) if int(p) == p else str(p)
-            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
+            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
+                                            value))
             result[f"p{p_word}_{metric_attribute_name}_ms"] = value
 
     process_one_metric("ttft", "TTFT", "Time to First Token")
-    process_one_metric("tpot", "TPOT", "Time per Output Token (excl. 1st token)")
+    process_one_metric("tpot", "TPOT",
+                       "Time per Output Token (excl. 1st token)")
     process_one_metric("itl", "ITL", "Inter-token Latency")
     process_one_metric("e2el", "E2EL", "End-to-end Latency")
 
     print("=" * 50)
 
-    if profile:
-        print("Stopping profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_request.prompt,
-            api_url=base_url + "/stop_profile",
-            prompt_len=test_request.prompt_len,
-            output_len=test_request.expected_output_len,
-            extra_body={test_request.structure_type: test_request.schema},
-        )
-        profile_output = await request_func(request_func_input=profile_input)
-        if profile_output.success:
-            print("Profiler stopped")
-
     return result, ret
 
 
 def evaluate(ret, args):
+
     def _eval_correctness_json(expected, actual):
         # extract json string from string using regex
-        import regex as re
-
-        actual = actual.replace("\n", "").replace(" ", "").strip()
+        import re
+        actual = actual.replace('\n', '').replace(' ', '').strip()
         try:
-            actual = re.search(r"\{.*\}", actual).group()
+            actual = re.search(r'\{.*\}', actual).group()
             actual = json.loads(actual)
         except Exception:
             return False
@@ -691,33 +655,29 @@ def evaluate(ret, args):
         return actual in args.choice
 
     def _eval_correctness_regex(expected, actual):
-        import regex as re
-
+        import re
         return re.match(args.regex, actual) is not None
 
     def _eval_correctness(expected, actual):
-        if args.structure_type == "guided_json":
+        if args.structure_type == 'guided_json':
             return _eval_correctness_json(expected, actual)
-        elif args.structure_type == "guided_regex":
+        elif args.structure_type == 'guided_regex':
             return _eval_correctness_regex(expected, actual)
-        elif args.structure_type == "guided_choice":
+        elif args.structure_type == 'guided_choice':
             return _eval_correctness_choice(expected, actual)
         else:
             return None
 
     scores = []
     for res in ret:
-        score = _eval_correctness(res["expected"], res["generated"])
-        res["correctness"] = score
+        score = _eval_correctness(res['expected'], res['generated'])
+        res['correctness'] = score
         scores.append(score)
 
     not_none_scores = [score for score in scores if score is not None]
 
-    return (
-        (sum(not_none_scores) / len(not_none_scores) * 100)
-        if len(not_none_scores) > 0
-        else None
-    )
+    return (sum(not_none_scores) / len(not_none_scores) *
+            100) if len(not_none_scores) > 0 else None
 
 
 def parse_goodput(slo_pairs):
@@ -729,10 +689,9 @@ def parse_goodput(slo_pairs):
     except ValueError as err:
         raise argparse.ArgumentTypeError(
             "Invalid format found for service level objectives. "
-            'Specify service level objectives for goodput as "KEY:VALUE" '
+            "Specify service level objectives for goodput as \"KEY:VALUE\" "
             "pairs, where the key is a metric name, and the value is a "
-            "number in milliseconds."
-        ) from err
+            "number in milliseconds.") from err
     return goodput_config_dict
 
 
@@ -746,14 +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
 
 
@@ -779,19 +736,19 @@ def main(args: argparse.Namespace):
         tokenizer_mode=args.tokenizer_mode,
     )
 
-    if args.dataset == "grammar":
-        args.structure_type = "guided_grammar"
-    elif args.dataset == "regex":
-        args.structure_type = "guided_regex"
-    elif args.dataset == "choice":
-        args.structure_type = "guided_choice"
+    if args.dataset == 'grammar':
+        args.structure_type = 'guided_grammar'
+    elif args.dataset == 'regex':
+        args.structure_type = 'guided_regex'
+    elif args.dataset == 'choice':
+        args.structure_type = 'guided_choice'
     else:
-        args.structure_type = "guided_json"
+        args.structure_type = 'guided_json'
 
     if args.no_structured_output:
         args.structured_output_ratio = 0
     if args.save_results:
-        result_file_name = f"{args.structured_output_ratio}guided"
+        result_file_name = f'{args.structured_output_ratio}guided'
         result_file_name += f"_{backend}"
         result_file_name += f"_{args.request_rate}qps"
         result_file_name += f"_{args.model.split('/')[-1]}"
@@ -819,29 +776,36 @@ def main(args: argparse.Namespace):
             disable_tqdm=args.disable_tqdm,
             profile=args.profile,
             selected_percentile_metrics=args.percentile_metrics.split(","),
-            selected_percentiles=[float(p) for p in args.metric_percentiles.split(",")],
+            selected_percentiles=[
+                float(p) for p in args.metric_percentiles.split(",")
+            ],
             ignore_eos=args.ignore_eos,
             max_concurrency=args.max_concurrency,
             structured_output_ratio=args.structured_output_ratio,
             goodput_config_dict=goodput_config_dict,
-        )
-    )
+        ))
 
     # Save config and results to json
     score = evaluate(ret, args)
-    print("correct_rate(%)", score, "\n")
+    print("correct_rate(%)", score, '\n')
     if args.save_results:
         results = {
-            "backend": backend,
-            "model_id": model_id,
-            "tokenizer_id": tokenizer_id,
-            "num_prompts": args.num_prompts,
-            "request_rate": args.request_rate
-            if args.request_rate < float("inf")
-            else "inf",
-            "burstiness": args.burstiness,
-            "max_concurrency": args.max_concurrency,
-            "correct_rate(%)": score,
+            "backend":
+            backend,
+            "model_id":
+            model_id,
+            "tokenizer_id":
+            tokenizer_id,
+            "num_prompts":
+            args.num_prompts,
+            "request_rate":
+            args.request_rate if args.request_rate < float("inf") else "inf",
+            "burstiness":
+            args.burstiness,
+            "max_concurrency":
+            args.max_concurrency,
+            "correct_rate(%)":
+            score
         }
         results = {"outputs": ret, **results, **benchmark_result}
 
@@ -850,14 +814,13 @@ def main(args: argparse.Namespace):
             result_file_name = args.result_filename
         if args.result_dir:
             result_file_name = os.path.join(args.result_dir, result_file_name)
-        with open(result_file_name, "w", encoding="utf-8") as outfile:
+        with open(result_file_name, "w", encoding='utf-8') as outfile:
             json.dump(results, outfile, indent=4)
 
 
-def create_argument_parser():
+if __name__ == "__main__":
     parser = FlexibleArgumentParser(
-        description="Benchmark the online serving throughput."
-    )
+        description="Benchmark the online serving throughput.")
     parser.add_argument(
         "--backend",
         type=str,
@@ -879,14 +842,16 @@ def create_argument_parser():
         default="/v1/completions",
         help="API endpoint.",
     )
-    parser.add_argument(
-        "--dataset",
-        default="json",
-        choices=["json", "json-unique", "grammar", "regex", "choice", "xgrammar_bench"],
-    )
-    parser.add_argument(
-        "--json-schema-path", type=str, default=None, help="Path to json schema."
-    )
+    parser.add_argument("--dataset",
+                        default='json',
+                        choices=[
+                            'json', 'json-unique', 'grammar', 'regex',
+                            'choice', 'xgrammar_bench'
+                        ])
+    parser.add_argument("--json-schema-path",
+                        type=str,
+                        default=None,
+                        help="Path to json schema.")
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -898,8 +863,7 @@ def create_argument_parser():
         "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,
@@ -909,13 +873,15 @@ def create_argument_parser():
     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",
@@ -992,56 +958,44 @@ def create_argument_parser():
         "--ignore-eos",
         action="store_true",
         help="Set ignore_eos flag when sending the benchmark request."
-        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.",
-    )
+        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
     parser.add_argument(
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
         help="Comma-separated list of selected metrics to report percentils. "
         "This argument specifies the metrics to report percentiles. "
-        'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
-        'Default value is "ttft,tpot,itl".',
-    )
+        "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
+        "Default value is \"ttft,tpot,itl\".")
     parser.add_argument(
         "--metric-percentiles",
         type=str,
         default="99",
         help="Comma-separated list of percentiles for selected metrics. "
-        'To report 25-th, 50-th, and 75-th percentiles, use "25,50,75". '
-        'Default value is "99". '
-        'Use "--percentile-metrics" to select metrics.',
+        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
+        "Default value is \"99\". "
+        "Use \"--percentile-metrics\" to select metrics.",
     )
     parser.add_argument(
         "--goodput",
         nargs="+",
         required=False,
-        help='Specify service level objectives for goodput as "KEY:VALUE" '
+        help="Specify service level objectives for goodput as \"KEY:VALUE\" "
         "pairs, where the key is a metric name, and the value is in "
-        'milliseconds. Multiple "KEY:VALUE" pairs can be provided, '
+        "milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, "
         "separated by spaces. Allowed request level metric names are "
-        '"ttft", "tpot", "e2el". For more context on the definition of '
+        "\"ttft\", \"tpot\", \"e2el\". For more context on the definition of "
         "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
-        "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
-    )
+        "and the blog: https://hao-ai-lab.github.io/blogs/distserve")
 
-    parser.add_argument(
-        "--no-structured-output",
-        action="store_true",
-        default=False,
-        help="Whether to disable JSON decoding or not.",
-    )
-    parser.add_argument(
-        "--structured-output-ratio",
-        type=float,
-        default=1.0,
-        help="Ratio of Structured Outputs requests",
-    )
+    parser.add_argument("--no-structured-output",
+                        action='store_true',
+                        default=False,
+                        help="Whether to disable JSON decoding or not.")
+    parser.add_argument("--structured-output-ratio",
+                        type=float,
+                        default=1.0,
+                        help="Ratio of Structured Outputs requests")
 
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -1,7 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark offline inference throughput."""
-
 import argparse
 import dataclasses
 import json
@@ -13,26 +11,18 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from tqdm import tqdm
-from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
-from typing_extensions import deprecated
-
-from benchmark_dataset import (
-    AIMODataset,
-    BurstGPTDataset,
-    ConversationDataset,
-    InstructCoderDataset,
-    RandomDataset,
-    SampleRequest,
-    ShareGPTDataset,
-    SonnetDataset,
-    VisionArenaDataset,
-)
+from benchmark_dataset import (AIMODataset, BurstGPTDataset,
+                               ConversationDataset, InstructCoderDataset,
+                               RandomDataset, SampleRequest, ShareGPTDataset,
+                               SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
+from tqdm import tqdm
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedTokenizerBase)
+
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
-    build_async_engine_client_from_engine_args,
-)
+    build_async_engine_client_from_engine_args)
 from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
@@ -47,30 +37,23 @@ def run_vllm(
     disable_detokenize: bool = False,
 ) -> tuple[float, Optional[list[RequestOutput]]]:
     from vllm import LLM, SamplingParams
-
     llm = LLM(**dataclasses.asdict(engine_args))
     assert all(
-        llm.llm_engine.model_config.max_model_len
-        >= (request.prompt_len + request.expected_output_len)
-        for request in requests
-    ), (
-        "Please ensure that max_model_len is greater than the sum of"
-        " prompt_len and expected_output_len for all requests."
-    )
+        llm.llm_engine.model_config.max_model_len >= (
+            request.prompt_len + request.expected_output_len)
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of"
+            " prompt_len and expected_output_len for all requests.")
     # Add the requests to the engine.
     prompts: list[Union[TextPrompt, TokensPrompt]] = []
     sampling_params: list[SamplingParams] = []
     for request in requests:
         prompts.append(
-            TokensPrompt(
-                prompt_token_ids=request.prompt["prompt_token_ids"],
-                multi_modal_data=request.multi_modal_data,
-            )
-            if "prompt_token_ids" in request.prompt
-            else TextPrompt(
-                prompt=request.prompt, multi_modal_data=request.multi_modal_data
-            )
-        )
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
+            if "prompt_token_ids" in request.prompt else \
+            TextPrompt(prompt=request.prompt,
+                       multi_modal_data=request.multi_modal_data))
         sampling_params.append(
             SamplingParams(
                 n=n,
@@ -79,8 +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]
@@ -90,15 +72,16 @@ def run_vllm(
     outputs = None
     if not use_beam_search:
         start = time.perf_counter()
-        outputs = llm.generate(
-            prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
-        )
+        outputs = llm.generate(prompts,
+                               sampling_params,
+                               lora_request=lora_requests,
+                               use_tqdm=True)
         end = time.perf_counter()
     else:
         assert lora_requests is None, "BeamSearch API does not support LoRA"
         prompts = [request.prompt for request in requests]
         # output_len should be the same for all requests.
-        output_len = requests[0].expected_output_len
+        output_len = requests[0][2]
         for request in requests:
             assert request.expected_output_len == output_len
         start = time.perf_counter()
@@ -108,35 +91,30 @@ def run_vllm(
                 beam_width=n,
                 max_tokens=output_len,
                 ignore_eos=True,
-            ),
-        )
+            ))
         end = time.perf_counter()
     return end - start, outputs
 
 
 def run_vllm_chat(
-    requests: list[SampleRequest],
-    n: int,
-    engine_args: EngineArgs,
-    disable_detokenize: bool = False,
-) -> tuple[float, list[RequestOutput]]:
+        requests: list[SampleRequest],
+        n: int,
+        engine_args: EngineArgs,
+        disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
     """
     Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
     multimodal models as it properly handles multimodal inputs and chat
     formatting. For non-multimodal models, use run_vllm() instead.
     """
     from vllm import LLM, SamplingParams
-
     llm = LLM(**dataclasses.asdict(engine_args))
 
     assert all(
-        llm.llm_engine.model_config.max_model_len
-        >= (request.prompt_len + request.expected_output_len)
-        for request in requests
-    ), (
-        "Please ensure that max_model_len is greater than the sum of "
-        "prompt_len and expected_output_len for all requests."
-    )
+        llm.llm_engine.model_config.max_model_len >= (
+            request.prompt_len + request.expected_output_len)
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of "
+            "prompt_len and expected_output_len for all requests.")
 
     prompts = []
     sampling_params: list[SamplingParams] = []
@@ -150,8 +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()
@@ -168,18 +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=disable_frontend_multiprocessing,
-    ) as llm:
-        model_config = await llm.get_model_config()
+            engine_args, disable_frontend_multiprocessing) as llm:
         assert all(
-            model_config.max_model_len
-            >= (request.prompt_len + request.expected_output_len)
-            for request in requests
-        ), (
-            "Please ensure that max_model_len is greater than the sum of"
-            " prompt_len and expected_output_len for all requests."
-        )
+            llm.model_config.max_model_len >= (request.prompt_len +
+                                               request.expected_output_len)
+            for request in requests), (
+                "Please ensure that max_model_len is greater than the sum of"
+                " prompt_len and expected_output_len for all requests.")
 
         # Add the requests to the engine.
         prompts: list[Union[TextPrompt, TokensPrompt]] = []
@@ -187,15 +159,11 @@ async def run_vllm_async(
         lora_requests: list[Optional[LoRARequest]] = []
         for request in requests:
             prompts.append(
-                TokensPrompt(
-                    prompt_token_ids=request.prompt["prompt_token_ids"],
-                    multi_modal_data=request.multi_modal_data,
-                )
-                if "prompt_token_ids" in request.prompt
-                else TextPrompt(
-                    prompt=request.prompt, multi_modal_data=request.multi_modal_data
-                )
-            )
+                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                        multi_modal_data=request.multi_modal_data)
+                if "prompt_token_ids" in request.prompt else \
+                TextPrompt(prompt=request.prompt,
+                           multi_modal_data=request.multi_modal_data))
             sampling_params.append(
                 SamplingParams(
                     n=n,
@@ -204,16 +172,17 @@ async def run_vllm_async(
                     ignore_eos=True,
                     max_tokens=request.expected_output_len,
                     detokenize=not disable_detokenize,
-                )
-            )
+                ))
             lora_requests.append(request.lora_request)
 
         generators = []
         start = time.perf_counter()
-        for i, (prompt, sp, lr) in enumerate(
-            zip(prompts, sampling_params, lora_requests)
-        ):
-            generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
+        for i, (prompt, sp,
+                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
+            generator = llm.generate(prompt,
+                                     sp,
+                                     lora_request=lr,
+                                     request_id=f"test{i}")
             generators.append(generator)
         all_gens = merge_async_iterators(*generators)
         async for i, res in all_gens:
@@ -232,8 +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
@@ -256,15 +224,14 @@ def run_hf(
             # Check if we can add more requests to the batch.
             next_prompt_len = requests[i + 1].prompt_len
             next_output_len = requests[i + 1].expected_output_len
-            if (
-                max(max_prompt_len, next_prompt_len)
-                + max(max_output_len, next_output_len)
-            ) <= 2048:
+            if (max(max_prompt_len, next_prompt_len) +
+                    max(max_output_len, next_output_len)) <= 2048:
                 # We can add more requests to the batch.
                 continue
 
         # Generate the sequences.
-        input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
+        input_ids = tokenizer(batch, return_tensors="pt",
+                              padding=True).input_ids
         llm_outputs = llm.generate(
             input_ids=input_ids.cuda(),
             do_sample=True,
@@ -294,7 +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]
 
@@ -306,9 +272,8 @@ def run_mii(
     return end - start
 
 
-def save_to_pytorch_benchmark_format(
-    args: argparse.Namespace, results: dict[str, Any]
-) -> None:
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                     results: dict[str, Any]) -> None:
     pt_records = convert_to_pytorch_benchmark_format(
         args=args,
         metrics={
@@ -316,9 +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"
@@ -350,32 +315,30 @@ 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
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        common_kwargs["no_stream"] = args.no_stream
         if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
             dataset_cls = VisionArenaDataset
-            common_kwargs["dataset_subset"] = None
-            common_kwargs["dataset_split"] = "train"
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
             sample_kwargs["enable_multimodal_chat"] = True
         elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
             dataset_cls = InstructCoderDataset
-            common_kwargs["dataset_split"] = "train"
+            common_kwargs['dataset_split'] = "train"
         elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
             dataset_cls = ConversationDataset
-            common_kwargs["dataset_subset"] = args.hf_subset
-            common_kwargs["dataset_split"] = args.hf_split
+            common_kwargs['dataset_subset'] = args.hf_subset
+            common_kwargs['dataset_split'] = args.hf_split
             sample_kwargs["enable_multimodal_chat"] = True
         elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
             dataset_cls = AIMODataset
-            common_kwargs["dataset_subset"] = None
-            common_kwargs["dataset_split"] = "train"
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
     # Remove None values
@@ -383,10 +346,6 @@ def get_requests(args, tokenizer):
     return dataset_cls(**common_kwargs).sample(**sample_kwargs)
 
 
-@deprecated(
-    "benchmark_throughput.py is deprecated and will be removed in a "
-    "future version. Please use 'vllm bench throughput' instead.",
-)
 def main(args: argparse.Namespace):
     if args.seed is None:
         args.seed = 0
@@ -394,10 +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:
@@ -408,34 +367,23 @@ def main(args: argparse.Namespace):
                     AsyncEngineArgs.from_cli_args(args),
                     args.disable_frontend_multiprocessing,
                     args.disable_detokenize,
-                )
-            )
+                ))
         else:
             elapsed_time, request_outputs = run_vllm(
-                requests,
-                args.n,
-                EngineArgs.from_cli_args(args),
-                args.disable_detokenize,
-            )
+                requests, args.n, EngineArgs.from_cli_args(args),
+                args.disable_detokenize)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
-        elapsed_time = run_hf(
-            requests,
-            args.model,
-            tokenizer,
-            args.n,
-            args.hf_max_batch_size,
-            args.trust_remote_code,
-            args.disable_detokenize,
-        )
+        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
+                              args.hf_max_batch_size, args.trust_remote_code,
+                              args.disable_detokenize)
     elif args.backend == "mii":
-        elapsed_time = run_mii(
-            requests, args.model, args.tensor_parallel_size, args.output_len
-        )
+        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
+                               args.output_len)
     elif args.backend == "vllm-chat":
         elapsed_time, request_outputs = run_vllm_chat(
-            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
-        )
+            requests, args.n, EngineArgs.from_cli_args(args),
+            args.disable_detokenize)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
 
@@ -447,31 +395,28 @@ def main(args: argparse.Namespace):
         for ro in request_outputs:
             if not isinstance(ro, RequestOutput):
                 continue
-            total_prompt_tokens += (
-                len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
-            )
-            total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
+            total_prompt_tokens += len(
+                ro.prompt_token_ids) if ro.prompt_token_ids else 0
+            total_output_tokens += sum(
+                len(o.token_ids) for o in ro.outputs if o)
         total_num_tokens = total_prompt_tokens + total_output_tokens
     else:
-        total_num_tokens = sum(r.prompt_len + r.expected_output_len for r in requests)
+        total_num_tokens = sum(r.prompt_len + r.expected_output_len
+                               for r in requests)
         total_output_tokens = sum(r.expected_output_len for r in requests)
         total_prompt_tokens = total_num_tokens - total_output_tokens
 
     if is_multi_modal and args.backend != "vllm-chat":
-        print(
-            "\033[91mWARNING\033[0m: Multi-modal request with "
-            f"{args.backend} backend detected. The "
-            "following metrics are not accurate because image tokens are not"
-            " counted. See vllm-project/vllm/issues/9778 for details."
-        )
+        print("\033[91mWARNING\033[0m: Multi-modal request with "
+              f"{args.backend} backend detected. The "
+              "following metrics are not accurate because image tokens are not"
+              " counted. See vllm-project/vllm/issues/9778 for details.")
         # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
         # vllm-chat backend counts the image tokens now
 
-    print(
-        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
-        f"{total_output_tokens / elapsed_time:.2f} output tokens/s"
-    )
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
+          f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
     print(f"Total num prompt tokens:  {total_prompt_tokens}")
     print(f"Total num output tokens:  {total_output_tokens}")
 
@@ -499,8 +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):
@@ -513,8 +457,9 @@ def validate_args(args):
 
     # === Dataset Configuration ===
     if not args.dataset and not args.dataset_path:
-        print("When dataset path is not set, it will default to random dataset")
-        args.dataset_name = "random"
+        print(
+            "When dataset path is not set, it will default to random dataset")
+        args.dataset_name = 'random'
         if args.input_len is None:
             raise ValueError("input_len must be provided for a random dataset")
 
@@ -522,55 +467,41 @@ def validate_args(args):
     # --hf-subset and --hf-split: only used
     # when dataset_name is 'hf'
     if args.dataset_name != "hf" and (
-        getattr(args, "hf_subset", None) is not None
-        or getattr(args, "hf_split", None) is not None
-    ):
-        warnings.warn(
-            "--hf-subset and --hf-split will be ignored \
+            getattr(args, "hf_subset", None) is not None
+            or getattr(args, "hf_split", None) is not None):
+        warnings.warn("--hf-subset and --hf-split will be ignored \
                 since --dataset-name is not 'hf'.",
-            stacklevel=2,
-        )
+                      stacklevel=2)
     elif args.dataset_name == "hf":
         if args.dataset_path in (
-            VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
-            | ConversationDataset.SUPPORTED_DATASET_PATHS
-        ):
-            assert args.backend == "vllm-chat", (
-                f"{args.dataset_path} needs to use vllm-chat as the backend."
-            )  # noqa: E501
-        elif args.dataset_path in (
-            InstructCoderDataset.SUPPORTED_DATASET_PATHS
-            | AIMODataset.SUPPORTED_DATASET_PATHS
-        ):
-            assert args.backend == "vllm", (
-                f"{args.dataset_path} needs to use vllm as the backend."
-            )  # noqa: E501
+                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
+                | ConversationDataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
+        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
+                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
         else:
-            raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")
+            raise ValueError(
+                f"{args.dataset_path} is not supported by hf dataset.")
 
     # --random-range-ratio: only used when dataset_name is 'random'
-    if args.dataset_name != "random" and args.random_range_ratio is not None:
-        warnings.warn(
-            "--random-range-ratio will be ignored since \
+    if args.dataset_name != 'random' and args.random_range_ratio is not None:
+        warnings.warn("--random-range-ratio will be ignored since \
                 --dataset-name is not 'random'.",
-            stacklevel=2,
-        )
+                      stacklevel=2)
 
     # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
     # set.
-    if (
-        args.dataset_name not in {"random", "sonnet", None}
-        and args.prefix_len is not None
-    ):
-        warnings.warn(
-            "--prefix-len will be ignored since --dataset-name\
+    if args.dataset_name not in {"random", "sonnet", None
+                                 } and args.prefix_len is not None:
+        warnings.warn("--prefix-len will be ignored since --dataset-name\
                  is not 'random', 'sonnet', or not set.",
-            stacklevel=2,
-        )
+                      stacklevel=2)
 
     # === LoRA Settings ===
     if getattr(args, "enable_lora", False) and args.backend != "vllm":
-        raise ValueError("LoRA benchmarking is only supported for vLLM backend")
+        raise ValueError(
+            "LoRA benchmarking is only supported for vLLM backend")
     if getattr(args, "enable_lora", False) and args.lora_path is None:
         raise ValueError("LoRA path must be provided when enable_lora is True")
 
@@ -580,10 +511,8 @@ def validate_args(args):
     if args.backend != "hf" and args.hf_max_batch_size is not None:
         raise ValueError("HF max batch size is only for HF backend.")
 
-    if (
-        args.backend in {"hf", "mii"}
-        and getattr(args, "quantization", None) is not None
-    ):
+    if args.backend in {"hf", "mii"} and getattr(args, "quantization",
+                                                 None) is not None:
         raise ValueError("Quantization is only for vLLM backend.")
 
     if args.backend == "mii" and args.dtype != "auto":
@@ -591,37 +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")
 
 
-def create_argument_parser():
+if __name__ == "__main__":
     parser = FlexibleArgumentParser(description="Benchmark the throughput.")
-    parser.add_argument(
-        "--backend",
-        type=str,
-        choices=["vllm", "hf", "mii", "vllm-chat"],
-        default="vllm",
-    )
+    parser.add_argument("--backend",
+                        type=str,
+                        choices=["vllm", "hf", "mii", "vllm-chat"],
+                        default="vllm")
     parser.add_argument(
         "--dataset-name",
         type=str,
         choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
         help="Name of the dataset to benchmark on.",
-        default="sharegpt",
-    )
-    parser.add_argument(
-        "--no-stream",
-        action="store_true",
-        help="Do not load the dataset in streaming mode.",
-    )
+        default="sharegpt")
     parser.add_argument(
         "--dataset",
         type=str,
@@ -629,70 +550,57 @@ def create_argument_parser():
         help="Path to the ShareGPT dataset, will be deprecated in\
             the next release. The dataset is expected to "
         "be a json in form of list[dict[..., conversations: "
-        "list[dict[..., value: <prompt_or_response>]]]]",
-    )
+        "list[dict[..., value: <prompt_or_response>]]]]")
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Input prompt length for each request")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=None,
+                        help="Output length for each request. Overrides the "
+                        "output length from the dataset.")
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--hf-max-batch-size",
+                        type=int,
+                        default=None,
+                        help="Maximum batch size for HF backend.")
     parser.add_argument(
-        "--dataset-path", type=str, default=None, help="Path to the dataset"
-    )
-    parser.add_argument(
-        "--input-len",
-        type=int,
-        default=None,
-        help="Input prompt length for each request",
-    )
-    parser.add_argument(
-        "--output-len",
-        type=int,
-        default=None,
-        help="Output length for each request. Overrides the "
-        "output length from the dataset.",
-    )
-    parser.add_argument(
-        "--n", type=int, default=1, help="Number of generated sequences per prompt."
-    )
-    parser.add_argument(
-        "--num-prompts", type=int, default=1000, help="Number of prompts to process."
-    )
-    parser.add_argument(
-        "--hf-max-batch-size",
-        type=int,
-        default=None,
-        help="Maximum batch size for HF backend.",
-    )
-    parser.add_argument(
-        "--output-json",
+        '--output-json',
         type=str,
         default=None,
-        help="Path to save the throughput results in JSON format.",
-    )
-    parser.add_argument(
-        "--async-engine",
-        action="store_true",
-        default=False,
-        help="Use vLLM async engine rather than LLM class.",
-    )
-    parser.add_argument(
-        "--disable-frontend-multiprocessing",
-        action="store_true",
-        default=False,
-        help="Disable decoupled async engine frontend.",
-    )
+        help='Path to save the throughput results in JSON format.')
+    parser.add_argument("--async-engine",
+                        action='store_true',
+                        default=False,
+                        help="Use vLLM async engine rather than LLM class.")
+    parser.add_argument("--disable-frontend-multiprocessing",
+                        action='store_true',
+                        default=False,
+                        help="Disable decoupled async engine frontend.")
     parser.add_argument(
         "--disable-detokenize",
         action="store_true",
-        help=(
-            "Do not detokenize the response (i.e. do not include "
-            "detokenization time in the measurement)"
-        ),
-    )
+        help=("Do not detokenize the response (i.e. do not include "
+              "detokenization time in the measurement)"))
     # LoRA
     parser.add_argument(
         "--lora-path",
         type=str,
         default=None,
-        help="Path to the LoRA adapters to use. This can be an absolute path, "
-        "a relative path, or a Hugging Face model identifier.",
-    )
+        help="Path to the lora adapters to use. This can be an absolute path, "
+        "a relative path, or a Hugging Face model identifier.")
     parser.add_argument(
         "--prefix-len",
         type=int,
@@ -706,8 +614,7 @@ def create_argument_parser():
         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",
@@ -721,20 +628,16 @@ def create_argument_parser():
     )
 
     # hf dtaset
-    parser.add_argument(
-        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
-    )
-    parser.add_argument(
-        "--hf-split", type=str, default=None, help="Split of the HF dataset."
-    )
+    parser.add_argument("--hf-subset",
+                        type=str,
+                        default=None,
+                        help="Subset of the HF dataset.")
+    parser.add_argument("--hf-split",
+                        type=str,
+                        default=None,
+                        help="Split of the HF dataset.")
 
     parser = AsyncEngineArgs.add_cli_args(parser)
-
-    return parser
-
-
-if __name__ == "__main__":
-    parser = create_argument_parser()
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

benchmarks/benchmark_utils.py

@@ -1,17 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 import argparse
 import json
 import math
 import os
-import time
-from types import TracebackType
-from typing import Any, Optional, Union
+from typing import Any
 
 
-def convert_to_pytorch_benchmark_format(
-    args: argparse.Namespace, metrics: dict[str, list], extra_info: dict[str, Any]
-) -> list:
+def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                        metrics: dict[str, list],
+                                        extra_info: dict[str, Any]) -> list:
     """
     Save the benchmark results in the format used by PyTorch OSS benchmark with
     on metric per record
@@ -39,12 +37,12 @@ def convert_to_pytorch_benchmark_format(
             },
         }
 
-        tp = record["benchmark"]["extra_info"]["args"].get("tensor_parallel_size")
+        tp = record["benchmark"]["extra_info"]["args"].get(
+            "tensor_parallel_size")
         # Save tensor_parallel_size parameter if it's part of the metadata
         if not tp and "tensor_parallel_size" in extra_info:
-            record["benchmark"]["extra_info"]["args"]["tensor_parallel_size"] = (
-                extra_info["tensor_parallel_size"]
-            )
+            record["benchmark"]["extra_info"]["args"][
+                "tensor_parallel_size"] = extra_info["tensor_parallel_size"]
 
         records.append(record)
 
@@ -52,6 +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()}
@@ -67,59 +66,4 @@ class InfEncoder(json.JSONEncoder):
 
 def write_to_json(filename: str, records: list) -> None:
     with open(filename, "w") as f:
-        json.dump(
-            records,
-            f,
-            cls=InfEncoder,
-            default=lambda o: f"<{type(o).__name__} object is not JSON serializable>",
-        )
-
-
-# Collect time and generate time metrics
-#
-# Example Usage:
-#   collector = TimeCollector(TimeCollector.US)
-#   for _ in range(total_iteration):
-#      with collector:
-#          ...
-#   collector.dump_avg_max()
-class TimeCollector:
-    NS: int = 1
-    US: int = NS * 1000
-    MS: int = US * 1000
-    S: int = MS * 1000
-
-    def __init__(self, scale: int) -> None:
-        self.cnt: int = 0
-        self._sum: int = 0
-        self._max: Optional[int] = None
-        self.scale = scale
-        self.start_time: int = time.monotonic_ns()
-
-    def collect(self, v: int) -> None:
-        self.cnt += 1
-        self._sum += v
-        if self._max is None:
-            self._max = v
-        else:
-            self._max = max(self._max, v)
-
-    def avg(self) -> Union[float, str]:
-        return self._sum * 1.0 / self.cnt / self.scale if self.cnt > 0 else "N/A"
-
-    def max(self) -> Union[float, str]:
-        return self._max / self.scale if self._max else "N/A"
-
-    def dump_avg_max(self) -> list[Union[float, str]]:
-        return [self.avg(), self.max()]
-
-    def __enter__(self) -> None:
-        self.start_time = time.monotonic_ns()
-
-    def __exit__(
-        self,
-        exc_type: Optional[type[BaseException]],
-        exc_value: Optional[BaseException],
-        exc_traceback: Optional[TracebackType],
-    ) -> None:
-        self.collect(time.monotonic_ns() - self.start_time)
+        json.dump(records, f, cls=InfEncoder)

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy
@@ -24,9 +23,8 @@ DEFAULT_TP_SIZES = [1]
 
 
 # bench
-def bench_fn(
-    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
-) -> TMeasurement:
+def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
+             **kwargs) -> TMeasurement:
     min_run_time = 1
 
     globals = {
@@ -43,18 +41,16 @@ def bench_fn(
     ).blocked_autorange(min_run_time=min_run_time)
 
 
-def bench_int8(
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
-) -> Iterable[TMeasurement]:
+def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+               sub_label: str) -> Iterable[TMeasurement]:
     assert dtype == torch.int8
     b_compressed, e, a, b = make_rand_sparse_tensors(torch.int8, m, n, k)
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
 
-    out = ops.cutlass_scaled_sparse_mm(
-        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
-    )
+    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
+                                       torch.bfloat16)
     out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)
 
     if not torch.allclose(out, out_ref):
@@ -67,107 +63,54 @@ def bench_int8(
     timers = []
     # pytorch impl - bfloat16
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_bf16_bf16_bf16_matmul-no-scales",
-            torch.mm,
-            a.to(dtype=torch.bfloat16),
-            b.to(dtype=torch.bfloat16),
-        )
-    )
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16),
+                 b.to(dtype=torch.bfloat16)))
 
     # pytorch impl - float16
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_fp16_fp16_fp16_matmul-no-scales",
-            torch.mm,
-            a.to(dtype=torch.float16),
-            b.to(dtype=torch.float16),
-        )
-    )
+        bench_fn(label, sub_label,
+                 "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
+                 a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
 
     # cutlass impl
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_i8_i8_bf16_scaled_mm",
-            ops.cutlass_scaled_mm,
-            a,
-            b,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
 
     # cutlass with bias
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_i8_i8_bf16_scaled_mm_bias",
-            ops.cutlass_scaled_mm,
-            a,
-            b,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-            bias,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
+                 bias))
 
     # cutlass sparse impl
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_i8_i8_bf16_scaled_sparse_mm",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.bfloat16))
 
     # cutlass sparse with bias
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-            bias,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.bfloat16, bias))
 
     return timers
 
 
-def bench_fp8(
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
-) -> Iterable[TMeasurement]:
+def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+              sub_label: str) -> Iterable[TMeasurement]:
     assert dtype == torch.float8_e4m3fn
-    b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n, k)
+    b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n,
+                                                     k)
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
 
-    out = ops.cutlass_scaled_sparse_mm(
-        a, b_compressed, e, scale_a, scale_b, torch.bfloat16
-    )
+    out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
+                                       torch.bfloat16)
     out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)
 
     if not torch.allclose(out, out_ref):
@@ -181,165 +124,97 @@ def bench_fp8(
 
     # pytorch impl w. bf16
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_bf16_bf16_bf16_matmul-no-scales",
-            torch.mm,
-            a.to(dtype=torch.bfloat16, device="cuda"),
-            b.to(dtype=torch.bfloat16, device="cuda"),
-        )
-    )
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
+                 b.to(dtype=torch.bfloat16, device="cuda")))
 
     # pytorch impl: bf16 output, without fp8 fast accum
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_fp8_fp8_bf16_scaled_mm",
-            torch._scaled_mm,
-            a,
-            b,
-            scale_a=scale_a,
-            scale_b=scale_b,
-            out_dtype=torch.bfloat16,
-        )
-    )
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16))
 
     # pytorch impl: bf16 output, with fp8 fast accum
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
-            torch._scaled_mm,
-            a,
-            b,
-            scale_a=scale_a,
-            scale_b=scale_b,
-            out_dtype=torch.bfloat16,
-            use_fast_accum=True,
-        )
-    )
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16,
+                 use_fast_accum=True))
 
     # pytorch impl: fp16 output, without fp8 fast accum
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_fp8_fp8_fp16_scaled_mm",
-            torch._scaled_mm,
-            a,
-            b,
-            scale_a=scale_a,
-            scale_b=scale_b,
-            out_dtype=torch.float16,
-        )
-    )
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16))
 
     # pytorch impl: fp16 output, with fp8 fast accum
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
-            torch._scaled_mm,
-            a,
-            b,
-            scale_a=scale_a,
-            scale_b=scale_b,
-            out_dtype=torch.float16,
-            use_fast_accum=True,
-        )
-    )
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16,
+                 use_fast_accum=True))
 
     # cutlass impl: bf16 output
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_fp8_fp8_bf16_scaled_mm",
-            ops.cutlass_scaled_mm,
-            a,
-            b,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
 
     # cutlass impl: bf16 output
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.bfloat16))
 
     # cutlass impl: fp16 output
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.float16,
-        )
-    )
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.float16))
 
     # cutlass impl: bf16 output, with bias
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.bfloat16,
-            bias,
-        )
-    )
+        bench_fn(label, sub_label,
+                 "cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.bfloat16, bias))
 
     # cutlass impl: fp16 output, with bias
     timers.append(
-        bench_fn(
-            label,
-            sub_label,
-            "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
-            ops.cutlass_scaled_sparse_mm,
-            a,
-            b_compressed,
-            e,
-            scale_a,
-            scale_b,
-            torch.float16,
-            bias.to(dtype=torch.float16),
-        )
-    )
+        bench_fn(label, sub_label,
+                 "cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
+                 ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
+                 scale_b, torch.float16, bias.to(dtype=torch.float16)))
 
     return timers
 
 
-def bench(
-    dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
-) -> Iterable[TMeasurement]:
+def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+          sub_label: str) -> Iterable[TMeasurement]:
     if dtype == torch.int8:
         return bench_int8(dtype, m, k, n, label, sub_label)
     if dtype == torch.float8_e4m3fn:
@@ -353,12 +228,12 @@ def print_timers(timers: Iterable[TMeasurement]):
     compare.print()
 
 
-def run(
-    dtype: torch.dtype, MKNs: Iterable[tuple[int, int, int]]
-) -> Iterable[TMeasurement]:
+def run(dtype: torch.dtype,
+        MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
     results = []
     for m, k, n in MKNs:
-        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm", f"MKN=({m}x{k}x{n})")
+        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
+                       f"MKN=({m}x{k}x{n})")
         print_timers(timers)
         results.extend(timers)
 
@@ -366,12 +241,10 @@ def run(
 
 
 # output makers
-def make_output(
-    data: Iterable[TMeasurement],
-    MKNs: Iterable[tuple[int, int, int]],
-    base_description: str,
-    timestamp=None,
-):
+def make_output(data: Iterable[TMeasurement],
+                MKNs: Iterable[tuple[int, int, int]],
+                base_description: str,
+                timestamp=None):
     print(f"== All Results {base_description} ====")
     print_timers(data)
 
@@ -385,7 +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)
 
@@ -445,7 +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":
@@ -470,15 +344,12 @@ Benchmark Cutlass GEMM.
     Output:
         - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
             """,  # noqa: E501
-        formatter_class=argparse.RawTextHelpFormatter,
-    )
+        formatter_class=argparse.RawTextHelpFormatter)
 
-    parser.add_argument(
-        "--dtype",
-        type=to_torch_dtype,
-        required=True,
-        help="Available options are ['int8', 'fp8']",
-    )
+    parser.add_argument("--dtype",
+                        type=to_torch_dtype,
+                        required=True,
+                        help="Available options are ['int8', 'fp8']")
     subparsers = parser.add_subparsers(dest="cmd")
 
     square_parser = subparsers.add_parser("square_bench")
@@ -497,19 +368,19 @@ Benchmark Cutlass GEMM.
     range_parser.set_defaults(func=run_range_bench)
 
     model_parser = subparsers.add_parser("model_bench")
-    model_parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=DEFAULT_MODELS,
-        choices=WEIGHT_SHAPES.keys(),
-    )
-    model_parser.add_argument(
-        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
-    )
-    model_parser.add_argument(
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
-    )
+    model_parser.add_argument("--models",
+                              nargs="+",
+                              type=str,
+                              default=DEFAULT_MODELS,
+                              choices=WEIGHT_SHAPES.keys())
+    model_parser.add_argument("--tp-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_TP_SIZES)
+    model_parser.add_argument("--batch-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_BATCH_SIZES)
     model_parser.set_defaults(func=run_model_bench)
 
     args = parser.parse_args()

benchmarks/cutlass_benchmarks/utils.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Cutlass bench utils
 from collections.abc import Iterable
@@ -11,9 +10,8 @@ import vllm._custom_ops as ops
 
 def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
     finfo = torch.finfo(torch.float8_e4m3fn)
-    return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
-        dtype=torch.float8_e4m3fn
-    )
+    return torch.round(tensor.clamp(
+        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
 
 
 def to_int8(tensor: torch.Tensor) -> torch.Tensor:
@@ -28,11 +26,10 @@ def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
     return tensor.to(dtype=torch.float16)
 
 
-def make_rand_tensors(
-    dtype: torch.dtype, m: int, n: int, k: int
-) -> tuple[torch.Tensor, torch.Tensor]:
-    a = torch.randn((m, k), device="cuda") * 5
-    b = torch.randn((n, k), device="cuda").t() * 5
+def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
+                      k: int) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device='cuda') * 5
+    b = torch.randn((n, k), device='cuda').t() * 5
 
     if dtype == torch.int8:
         return to_int8(a), to_int8(b)
@@ -52,7 +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
@@ -63,11 +62,10 @@ def prune_to_2_4(tensor):
     return pruned.reshape(original_shape)
 
 
-def make_rand_sparse_tensors(
-    dtype: torch.dtype, m: int, n: int, k: int
-) -> tuple[torch.Tensor, torch.Tensor]:
-    a = torch.randn((m, k), device="cuda") * 5
-    b = torch.randn((n, k), device="cuda").t() * 5
+def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
+                             k: int) -> tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device='cuda') * 5
+    b = torch.randn((n, k), device='cuda').t() * 5
 
     b = prune_to_2_4(b.t()).t()
 
@@ -88,9 +86,9 @@ def make_rand_sparse_tensors(
     return b_compressed, e, a, b
 
 
-def make_n_rand_sparse_tensors(
-    num_tensors: int, dtype: torch.dtype, m: int, n: int, k: int
-) -> tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
+def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype,
+                        m: int, n: int, k: int) -> \
+                        tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
     ABs = []
     for _ in range(num_tensors):
         b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy
@@ -17,9 +16,8 @@ from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    w8a8_block_fp8_matmul,
-)
-from vllm.utils import FlexibleArgumentParser, cdiv
+    w8a8_block_fp8_matmul)
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
@@ -27,9 +25,8 @@ DEFAULT_TP_SIZES = [1]
 
 
 # bench
-def bench_fn(
-    label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
-) -> TMeasurement:
+def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
+             **kwargs) -> TMeasurement:
     min_run_time = 1
 
     globals = {
@@ -47,48 +44,45 @@ def bench_fn(
 
 
 def bench_int8(
-    dtype: torch.dtype,
-    m: int,
-    k: int,
-    n: int,
-    label: str,
-    sub_label: str,
-    bench_kernels: Optional[list[str]] = None,
-) -> Iterable[TMeasurement]:
+        dtype: torch.dtype,
+        m: int,
+        k: int,
+        n: int,
+        label: str,
+        sub_label: str,
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
     """Benchmark INT8-based kernels."""
     assert dtype == torch.int8
     a, b = make_rand_tensors(torch.int8, m, n, k)
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
-    azp = torch.zeros((m,), device="cuda", dtype=torch.int32)
-    azp_adj = torch.zeros((n,), device="cuda", dtype=torch.int32)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+    azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
+    azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
 
     bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
-            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-        ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
-            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.bfloat16
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.bfloat16, bias
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm_azp": lambda: ops.cutlass_scaled_mm_azp(
-            a, b, scale_a, scale_b, torch.bfloat16, azp_adj
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_bias": lambda: ops.cutlass_scaled_mm_azp(
-            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, None, bias
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt": lambda: ops.cutlass_scaled_mm_azp(
-            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp
-        ),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias": lambda: ops.cutlass_scaled_mm_azp(
-            a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp, bias
-        ),
+        "pytorch_bf16_bf16_bf16_matmul-no-scales":
+        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+                         ),
+        "pytorch_fp16_fp16_fp16_matmul-no-scales":
+        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
+        "cutlass_i8_i8_bf16_scaled_mm":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
+        "cutlass_i8_i8_bf16_scaled_mm_bias":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
+                                      bias),
+        "cutlass_i8_i8_bf16_scaled_mm_azp":
+        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
+                                          bfloat16, azp_adj),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_bias":
+        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
+                                          bfloat16, azp_adj, None, bias),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_pt":
+        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
+                                          bfloat16, azp_adj, azp),
+        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias":
+        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
+                                          bfloat16, azp_adj, azp, bias),
     }
 
     timers = []
@@ -102,68 +96,73 @@ def bench_int8(
 
 
 def bench_fp8(
-    dtype: torch.dtype,
-    m: int,
-    k: int,
-    n: int,
-    label: str,
-    sub_label: str,
-    bench_kernels: Optional[list[str]] = None,
-) -> Iterable[TMeasurement]:
+        dtype: torch.dtype,
+        m: int,
+        k: int,
+        n: int,
+        label: str,
+        sub_label: str,
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
     """Benchmark FP8-based kernels."""
     assert dtype == torch.float8_e4m3fn
     a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
     a_cont = a.contiguous()
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-
-    block_scale_a = torch.rand((m, cdiv(k, 128)), device="cuda", dtype=torch.float32)
-    block_scale_b = torch.rand(
-        cdiv(k, 128), cdiv(n, 128), device="cuda", dtype=torch.float32
-    )
+    block_scale_a = torch.rand((m, k // 128),
+                               device="cuda",
+                               dtype=torch.float32)
+    block_scale_b = torch.rand((k // 128, n // 128),
+                               device="cuda",
+                               dtype=torch.float32)
     block_scale_a_M_major = block_scale_a.t().contiguous().t()
     block_scale_b_K_major = block_scale_b.t().contiguous().t()
-    bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
 
     print(m, k, n)
 
     bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
-            a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-        ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
-            a.to(dtype=torch.float16), b.to(dtype=torch.float16)
-        ),
-        "pytorch_fp8_fp8_fp16_scaled_mm": lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.float16
-        ),
-        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.float16, use_fast_accum=True
-        ),
-        "pytorch_fp8_fp8_bf16_scaled_mm": lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16
-        ),
-        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16, use_fast_accum=True
-        ),
-        "cutlass_fp8_fp8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.bfloat16
-        ),
-        "cutlass_fp8_fp8_fp16_scaled_mm": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.float16
-        ),
-        "cutlass_fp8_fp8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.bfloat16, bias
-        ),
-        "cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
-            a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
-        ),
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
-            a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
-        ),
-        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(
-            a, b, block_scale_a_M_major, block_scale_b_K_major, torch.float16
-        ),
+        "pytorch_bf16_bf16_bf16_matmul-no-scales":
+        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
+                         ),
+        "pytorch_fp16_fp16_fp16_matmul-no-scales":
+        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
+        "pytorch_fp8_fp8_fp16_scaled_mm":
+        lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.float16),
+        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
+        lambda: torch._scaled_mm(a,
+                                 b,
+                                 scale_a,
+                                 scale_b,
+                                 out_dtype=torch.float16,
+                                 use_fast_accum=True),
+        "pytorch_fp8_fp8_bf16_scaled_mm":
+        lambda: torch._scaled_mm(
+            a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
+        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
+        lambda: torch._scaled_mm(a,
+                                 b,
+                                 scale_a,
+                                 scale_b,
+                                 out_dtype=torch.bfloat16,
+                                 use_fast_accum=True),
+        "cutlass_fp8_fp8_bf16_scaled_mm":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
+        "cutlass_fp8_fp8_fp16_scaled_mm":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
+        "cutlass_fp8_fp8_bf16_scaled_mm_bias":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
+                                      bias),
+        "cutlass_fp8_fp8_fp16_scaled_mm_bias":
+        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
+                                      bias.to(dtype=torch.float16)),
+        "triton_fp8_fp8_fp16_scaled_mm_blockwise":
+        lambda: w8a8_block_fp8_matmul(a_cont, b.t(), block_scale_a,
+                                      block_scale_b.t(), (128, 128)),
+        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise":
+        lambda: ops.cutlass_scaled_mm(a, b, block_scale_a_M_major,
+                                      block_scale_b_K_major, torch.float16),
     }
 
     timers = []
@@ -176,15 +175,13 @@ def bench_fp8(
     return timers
 
 
-def bench(
-    dtype: torch.dtype,
-    m: int,
-    k: int,
-    n: int,
-    label: str,
-    sub_label: str,
-    bench_kernels: Optional[list[str]] = None,
-) -> Iterable[TMeasurement]:
+def bench(dtype: torch.dtype,
+          m: int,
+          k: int,
+          n: int,
+          label: str,
+          sub_label: str,
+          bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
     if dtype == torch.int8:
         return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
     if dtype == torch.float8_e4m3fn:
@@ -198,33 +195,27 @@ def print_timers(timers: Iterable[TMeasurement]):
     compare.print()
 
 
-def run(
-    dtype: torch.dtype,
-    MKNs: Iterable[tuple[int, int, int]],
-    bench_kernels: Optional[list[str]] = None,
-) -> Iterable[TMeasurement]:
+def run(dtype: torch.dtype,
+        MKNs: Iterable[tuple[int, int, int]],
+        bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
     results = []
     for m, k, n in MKNs:
-        timers = bench(
-            dtype,
-            m,
-            k,
-            n,
-            f"scaled-{dtype}-gemm",
-            f"MKN=({m}x{k}x{n})",
-            bench_kernels=bench_kernels,
-        )
+        timers = bench(dtype,
+                       m,
+                       k,
+                       n,
+                       f"scaled-{dtype}-gemm",
+                       f"MKN=({m}x{k}x{n})",
+                       bench_kernels=bench_kernels)
         print_timers(timers)
         results.extend(timers)
     return results
 
 
-def make_output(
-    data: Iterable[TMeasurement],
-    MKNs: Iterable[tuple[int, int, int]],
-    base_description: str,
-    timestamp=None,
-):
+def make_output(data: Iterable[TMeasurement],
+                MKNs: Iterable[tuple[int, int, int]],
+                base_description: str,
+                timestamp=None):
     print(f"== All Results {base_description} ====")
     print_timers(data)
 
@@ -235,7 +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}")
@@ -293,7 +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":
@@ -318,21 +310,19 @@ Benchmark Cutlass GEMM.
     Output:
         - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
             """,  # noqa: E501
-        formatter_class=argparse.RawTextHelpFormatter,
-    )
+        formatter_class=argparse.RawTextHelpFormatter)
 
-    parser.add_argument(
-        "--dtype",
-        type=to_torch_dtype,
-        required=True,
-        help="Available options are ['int8', 'fp8']",
-    )
+    parser.add_argument("--dtype",
+                        type=to_torch_dtype,
+                        required=True,
+                        help="Available options are ['int8', 'fp8']")
     parser.add_argument(
         "--kernels",
         nargs="+",
         type=str,
         default=None,
-        help="Exact names of the kernels to benchmark. If not set, runs all kernels.",
+        help=
+        "Exact names of the kernels to benchmark. If not set, runs all kernels."
     )
 
     subparsers = parser.add_subparsers(dest="cmd")
@@ -353,19 +343,19 @@ Benchmark Cutlass GEMM.
     range_parser.set_defaults(func=run_range_bench)
 
     model_parser = subparsers.add_parser("model_bench")
-    model_parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=DEFAULT_MODELS,
-        choices=WEIGHT_SHAPES.keys(),
-    )
-    model_parser.add_argument(
-        "--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
-    )
-    model_parser.add_argument(
-        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
-    )
+    model_parser.add_argument("--models",
+                              nargs="+",
+                              type=str,
+                              default=DEFAULT_MODELS,
+                              choices=WEIGHT_SHAPES.keys())
+    model_parser.add_argument("--tp-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_TP_SIZES)
+    model_parser.add_argument("--batch-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_BATCH_SIZES)
     model_parser.set_defaults(func=run_model_bench)
 
     args = parser.parse_args()

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Weight Shapes are in the format
 # ([K, N], TP_SPLIT_DIM)
@@ -43,4 +42,4 @@ WEIGHT_SHAPES = {
         ([8192, 57344], 1),
         ([28672, 8192], 0),
     ],
-}
+}

benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh

@@ -3,7 +3,7 @@
 # benchmark the overhead of disaggregated prefill.
 # methodology:
 # - send all request to prefill vLLM instance. It will buffer KV cache.
-# - then send all request to decode instance.
+# - then send all request to decode instance. 
 # - The TTFT of decode instance is the overhead.
 
 set -ex
@@ -12,8 +12,6 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
   sleep 10
 
   # remove vllm config file
@@ -63,7 +61,7 @@ benchmark() {
     --gpu-memory-utilization 0.6 \
     --kv-transfer-config \
     '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
-
+    
 
   CUDA_VISIBLE_DEVICES=1 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -78,38 +76,38 @@ benchmark() {
   wait_for_server 8200
 
   # let the prefill instance finish prefill
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8100 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename disagg_prefill_tp1.json \
-    --request-rate "inf"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8100 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename disagg_prefill_tp1.json \
+          --request-rate "inf"
 
 
   # send the request to decode.
   # The TTFT of this command will be the overhead of disagg prefill impl.
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8200 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename disagg_prefill_tp1_overhead.json \
-    --request-rate "$qps"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8200 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename disagg_prefill_tp1_overhead.json \
+          --request-rate "$qps"
   kill_gpu_processes
 
 }

benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh

@@ -18,8 +18,6 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
   for port in 8000 8100 8200; do lsof -t -i:$port | xargs -r kill -9; done
   sleep 1
 }
@@ -60,7 +58,7 @@ launch_chunked_prefill() {
 
 
 launch_disagg_prefill() {
-  model="meta-llama/Meta-Llama-3.1-8B-Instruct"
+  model="meta-llama/Meta-Llama-3.1-8B-Instruct" 
   # disagg prefill
   CUDA_VISIBLE_DEVICES=0 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -99,20 +97,20 @@ benchmark() {
   output_len=$2
   tag=$3
 
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8000 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename "$tag"-qps-"$qps".json \
-    --request-rate "$qps"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8000 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename "$tag"-qps-"$qps".json \
+          --request-rate "$qps"
 
   sleep 2
 }

benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
 
@@ -13,37 +12,39 @@ app = Quart(__name__)
 
 async def forward_request(url, data):
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
-        async with session.post(url=url, json=data, headers=headers) as response:
+        headers = {
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
+        }
+        async with session.post(url=url, json=data,
+                                headers=headers) as response:
             if response.status == 200:
                 # if response.headers.get('Transfer-Encoding') == 'chunked':
                 if True:
-                    async for chunk_bytes in response.content.iter_chunked(1024):
+                    async for chunk_bytes in response.content.iter_chunked(
+                            1024):
                         yield chunk_bytes
                 else:
                     content = await response.read()
                     yield content
 
 
-@app.route("/v1/completions", methods=["POST"])
+@app.route('/v1/completions', methods=['POST'])
 async def handle_request():
     try:
         original_request_data = await request.get_json()
 
         prefill_request = original_request_data.copy()
         # change max_tokens = 1 to let it only do prefill
-        prefill_request["max_tokens"] = 1
+        prefill_request['max_tokens'] = 1
 
         # finish prefill
-        async for _ in forward_request(
-            "http://localhost:8100/v1/completions", prefill_request
-        ):
+        async for _ in forward_request('http://localhost:8100/v1/completions',
+                                       prefill_request):
             continue
 
         # return decode
-        generator = forward_request(
-            "http://localhost:8200/v1/completions", original_request_data
-        )
+        generator = forward_request('http://localhost:8200/v1/completions',
+                                    original_request_data)
         response = await make_response(generator)
         response.timeout = None
 
@@ -52,12 +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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import asyncio
 import itertools
@@ -9,6 +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)
@@ -21,15 +21,14 @@ class RoundRobinProxy:
             try:
                 # Forward the request
                 async with session.request(
-                    method=request.method,
-                    url=target_url,
-                    headers=request.headers,
-                    data=request.content,
+                        method=request.method,
+                        url=target_url,
+                        headers=request.headers,
+                        data=request.content,
                 ) as response:
                     # Start sending the response
-                    resp = web.StreamResponse(
-                        status=response.status, headers=response.headers
-                    )
+                    resp = web.StreamResponse(status=response.status,
+                                              headers=response.headers)
                     await resp.prepare(request)
 
                     # Stream the response content
@@ -46,11 +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")
@@ -59,5 +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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import json
 
@@ -7,41 +6,43 @@ import matplotlib.pyplot as plt
 import pandas as pd
 
 if __name__ == "__main__":
+
     data = []
-    for name in ["disagg_prefill", "chunked_prefill"]:
+    for name in ['disagg_prefill', 'chunked_prefill']:
         for qps in [2, 4, 6, 8]:
             with open(f"results/{name}-qps-{qps}.json") as f:
                 x = json.load(f)
-                x["name"] = name
-                x["qps"] = qps
+                x['name'] = name
+                x['qps'] = qps
                 data.append(x)
 
     df = pd.DataFrame.from_dict(data)
-    dis_df = df[df["name"] == "disagg_prefill"]
-    chu_df = df[df["name"] == "chunked_prefill"]
+    dis_df = df[df['name'] == 'disagg_prefill']
+    chu_df = df[df['name'] == 'chunked_prefill']
 
-    plt.style.use("bmh")
-    plt.rcParams["font.size"] = 20
+    plt.style.use('bmh')
+    plt.rcParams['font.size'] = 20
 
     for key in [
-        "mean_ttft_ms",
-        "median_ttft_ms",
-        "p99_ttft_ms",
-        "mean_itl_ms",
-        "median_itl_ms",
-        "p99_itl_ms",
+            'mean_ttft_ms', 'median_ttft_ms', 'p99_ttft_ms', 'mean_itl_ms',
+            'median_itl_ms', 'p99_itl_ms'
     ]:
+
         fig, ax = plt.subplots(figsize=(11, 7))
-        plt.plot(
-            dis_df["qps"], dis_df[key], label="disagg_prefill", marker="o", linewidth=4
-        )
-        plt.plot(
-            chu_df["qps"], chu_df[key], label="chunked_prefill", marker="o", linewidth=4
-        )
+        plt.plot(dis_df['qps'],
+                 dis_df[key],
+                 label='disagg_prefill',
+                 marker='o',
+                 linewidth=4)
+        plt.plot(chu_df['qps'],
+                 chu_df[key],
+                 label='chunked_prefill',
+                 marker='o',
+                 linewidth=4)
         ax.legend()
 
-        ax.set_xlabel("QPS")
+        ax.set_xlabel('QPS')
         ax.set_ylabel(key)
         ax.set_ylim(bottom=0)
-        fig.savefig(f"results/{key}.png")
+        fig.savefig(f'results/{key}.png')
         plt.close(fig)

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pickle as pkl
 import time
@@ -25,12 +24,10 @@ class bench_params_t:
     dtype: torch.dtype
 
     def description(self):
-        return (
-            f"N {self.num_tokens} "
-            f"x D {self.hidden_size} "
-            f"x R {self.add_residual} "
-            f"x DT {self.dtype}"
-        )
+        return (f'N {self.num_tokens} '
+                f'x D {self.hidden_size} '
+                f'x R {self.add_residual} '
+                f'x DT {self.dtype}')
 
 
 def get_bench_params() -> list[bench_params_t]:
@@ -41,19 +38,15 @@ def get_bench_params() -> list[bench_params_t]:
     DTYPES = [torch.bfloat16, torch.float]
 
     combinations = product(NUM_TOKENS, HIDDEN_SIZES, ADD_RESIDUAL, DTYPES)
-    bench_params = list(
-        map(lambda x: bench_params_t(x[0], x[1], x[2], x[3]), combinations)
-    )
+    bench_params = list(map(lambda x: \
+        bench_params_t(x[0], x[1], x[2], x[3]), combinations))
     return bench_params
 
 
 # Reference impls
-def unfused_int8_impl(
-    rms_norm_layer: RMSNorm,
-    x: torch.Tensor,
-    residual: Optional[torch.Tensor],
-    quant_dtype: torch.dtype,
-):
+def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
+                      residual: Optional[torch.Tensor],
+                      quant_dtype: torch.dtype):
     # Norm
     torch_out = None
     if residual is None:
@@ -65,12 +58,9 @@ def unfused_int8_impl(
     torch_out, _, _ = ops.scaled_int8_quant(torch_out)
 
 
-def unfused_fp8_impl(
-    rms_norm_layer: RMSNorm,
-    x: torch.Tensor,
-    residual: Optional[torch.Tensor],
-    quant_dtype: torch.dtype,
-):
+def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
+                     residual: Optional[torch.Tensor],
+                     quant_dtype: torch.dtype):
     # Norm
     torch_out = None
     if residual is None:
@@ -83,27 +73,22 @@ def unfused_fp8_impl(
 
 
 def fused_impl(
-    rms_norm_layer: RMSNorm,  # this stores the weights
-    x: torch.Tensor,
-    residual: Optional[torch.Tensor],
-    quant_dtype: torch.dtype,
-):
-    out, _ = ops.rms_norm_dynamic_per_token_quant(
-        x, rms_norm_layer.weight, 1e-6, quant_dtype, residual=residual
-    )
+        rms_norm_layer: RMSNorm,  # this stores the weights
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        quant_dtype: torch.dtype):
+    out, _ = ops.rms_norm_dynamic_per_token_quant(x,
+                                                  rms_norm_layer.weight,
+                                                  1e-6,
+                                                  quant_dtype,
+                                                  residual=residual)
 
 
 # Bench functions
-def bench_fn(
-    rms_norm_layer: RMSNorm,
-    x: torch.Tensor,
-    residual: torch.Tensor,
-    quant_dtype: torch.dtype,
-    label: str,
-    sub_label: str,
-    fn: Callable,
-    description: str,
-) -> TMeasurement:
+def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
+             quant_dtype: torch.dtype, label: str, sub_label: str,
+             fn: Callable, description: str) -> TMeasurement:
+
     min_run_time = 1
 
     globals = {
@@ -121,81 +106,43 @@ def bench_fn(
         description=description,
     ).blocked_autorange(min_run_time=min_run_time)
 
+def bench(params: bench_params_t, label: str, sub_label: str) \
+        -> Iterable[TMeasurement]:
 
-def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasurement]:
     # Make inputs
     layer = RMSNorm(params.hidden_size, 1e-6).to(dtype=params.dtype)
     # Make weights
     layer.weight.data.normal_(mean=1.0, std=0.1)
     # Make inputs
     scale = 1 / params.hidden_size
-    x = (
-        torch.randn(
-            params.num_tokens, params.hidden_size, dtype=params.dtype, device="cuda"
-        )
-        * scale
-    )
-    residual = (
-        (torch.randn_like(x) * scale).to(device="cuda") if params.add_residual else None
-    )
+    x = torch.randn(params.num_tokens,
+                    params.hidden_size,
+                    dtype=params.dtype,
+                    device='cuda') * scale
+    residual = (torch.randn_like(x) * scale).to(device='cuda') \
+            if params.add_residual else None
 
     timers = []
 
     # unfused int8 impl.
     timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.int8,
-            label,
-            sub_label,
-            unfused_int8_impl,
-            "unfused_int8_impl",
-        )
-    )
+        bench_fn(layer, x, residual, torch.int8, label, sub_label,
+                 unfused_int8_impl, "unfused_int8_impl"))
 
     # unfused fp8 impl.
     timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.float8_e4m3fn,
-            label,
-            sub_label,
-            unfused_fp8_impl,
-            "unfused_fp8_impl",
-        )
-    )
+        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
+                 unfused_fp8_impl, "unfused_fp8_impl"))
 
     # fused int8 impl.
     timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.int8,
-            label,
-            sub_label,
-            fused_impl,
-            "fused_int8_impl",
-        )
-    )
+        bench_fn(layer, x, residual, torch.int8, label, sub_label, fused_impl,
+                 "fused_int8_impl"))
 
     # fused fp8 impl.
     timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.float8_e4m3fn,
-            label,
-            sub_label,
-            fused_impl,
-            "fused_fp8_impl",
-        )
-    )
+        bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
+                 fused_impl, "fused_fp8_impl"))
 
     print_timers(timers)
 
@@ -210,12 +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
@@ -224,5 +172,5 @@ def main():
         pkl.dump(timers, f)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

benchmarks/kernels/bench_fp8_gemm.py

@@ -1,159 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm._custom_ops import cutlass_scaled_mm as vllm_scaled_mm
-from vllm._custom_ops import scaled_fp8_quant as vllm_scaled_fp8_quant
-from vllm.triton_utils import triton
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "fp8-tensor-w-token-a": dict(
-        w="tensor", a="token", no_a_quant=False, enabled=False
-    ),
-    "fp8-tensor-w-tensor-a": dict(
-        w="tensor", a="tensor", no_a_quant=False, enabled=True
-    ),
-    "fp8-channel-w-token-a": dict(
-        w="channel", a="token", no_a_quant=False, enabled=True
-    ),
-    "fp8-channel-w-tensor-a": dict(
-        w="channel", a="tensor", no_a_quant=False, enabled=False
-    ),
-    "fp8-tensor-w-token-a-noquant": dict(
-        w="tensor", a="token", no_a_quant=True, enabled=False
-    ),
-    "fp8-tensor-w-tensor-a-noquant": dict(
-        w="tensor", a="tensor", no_a_quant=True, enabled=True
-    ),
-    "fp8-channel-w-token-a-noquant": dict(
-        w="channel", a="token", no_a_quant=True, enabled=True
-    ),
-    "fp8-channel-w-tensor-a-noquant": dict(
-        w="channel", a="tensor", no_a_quant=True, enabled=False
-    ),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def _quant_weight_fp8(b: torch.Tensor, w_type: str, device: str):
-    if w_type == "tensor":
-        scale_b = torch.ones(1, device=device, dtype=torch.float32)
-        b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-    else:
-        b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, use_per_token_if_dynamic=True)
-    return b_fp8.t(), scale_b_fp8
-
-
-def build_fp8_runner(cfg, a, b, dtype, device):
-    b_fp8, scale_b_fp8 = _quant_weight_fp8(b, cfg["w"], device)
-
-    scale_a_const = (
-        torch.ones(1, device=device, dtype=torch.float32)
-        if cfg["a"] == "tensor"
-        else None
-    )
-
-    if cfg["no_a_quant"]:
-        if cfg["a"] == "tensor":
-            a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a_const)
-        else:
-            a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, use_per_token_if_dynamic=True)
-
-        def run():
-            return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-        return run
-
-    if cfg["a"] == "tensor":
-
-        def run():
-            a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a_const)
-            return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-    else:
-
-        def run():
-            a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, use_per_token_if_dynamic=True)
-            return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-    return run
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs FP8 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_fp8_runner(cfg, a, b, dtype, device)
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        print(f"{model}, N={N} K={K}, BF16 vs FP8 GEMMs TFLOP/s:")
-        benchmark.run(
-            print_data=True,
-            show_plots=True,
-            save_path=f"bench_fp8_res_n{N}_k{K}",
-            N=N,
-            K=K,
-        )
-
-    print("Benchmark finished!")

benchmarks/kernels/bench_int8_gemm.py

@@ -1,169 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm._custom_ops import cutlass_scaled_mm as vllm_scaled_mm
-from vllm._custom_ops import scaled_int8_quant as vllm_scaled_int8_quant
-from vllm.triton_utils import triton
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "int8-tensor-w-token-a": dict(
-        w="tensor", a="token", no_a_quant=False, enabled=False
-    ),
-    "int8-tensor-w-tensor-a": dict(
-        w="tensor", a="tensor", no_a_quant=False, enabled=True
-    ),
-    "int8-channel-w-token-a": dict(
-        w="channel", a="token", no_a_quant=False, enabled=True
-    ),
-    "int8-channel-w-tensor-a": dict(
-        w="channel", a="tensor", no_a_quant=False, enabled=False
-    ),
-    "int8-tensor-w-token-a-noquant": dict(
-        w="tensor", a="token", no_a_quant=True, enabled=False
-    ),
-    "int8-tensor-w-tensor-a-noquant": dict(
-        w="tensor", a="tensor", no_a_quant=True, enabled=True
-    ),
-    "int8-channel-w-token-a-noquant": dict(
-        w="channel", a="token", no_a_quant=True, enabled=True
-    ),
-    "int8-channel-w-tensor-a-noquant": dict(
-        w="channel", a="tensor", no_a_quant=True, enabled=False
-    ),
-}
-
-
-def _quant_weight(b, w_type, device):
-    if w_type == "tensor":
-        scale_b = torch.ones(1, device=device, dtype=torch.float32)
-        b_int8, scale_b_int8, _ = vllm_scaled_int8_quant(b, scale_b)
-        assert scale_b_int8.numel() == 1
-    else:  # channel
-        b_int8, scale_b_int8, _ = vllm_scaled_int8_quant(b)
-        assert scale_b_int8.numel() == b.shape[0]
-    return b_int8.t(), scale_b_int8
-
-
-def build_int8_runner(cfg, a, b, dtype, device):
-    # quant before running the kernel
-    b_int8, scale_b_int8 = _quant_weight(b, cfg["w"], device)
-
-    scale_a_const = None
-    if cfg["a"] == "tensor":
-        scale_a_const = torch.ones(1, device=device, dtype=torch.float32)
-
-    # no quant, create activation ahead
-    if cfg["no_a_quant"]:
-        if cfg["a"] == "tensor":
-            a_int8, scale_a_int8, _ = vllm_scaled_int8_quant(a, scale_a_const)
-        else:  # token
-            a_int8, scale_a_int8, _ = vllm_scaled_int8_quant(a)
-
-        def run_quant():
-            return vllm_scaled_mm(a_int8, b_int8, scale_a_int8, scale_b_int8, dtype)
-
-        return run_quant
-
-    # dynamic quant, create activation inside
-    if cfg["a"] == "tensor":
-
-        def run_quant():
-            a_int8, scale_a_int8, _ = vllm_scaled_int8_quant(a, scale_a_const)
-            return vllm_scaled_mm(a_int8, b_int8, scale_a_int8, scale_b_int8, dtype)
-
-    else:  # token
-
-        def run_quant():
-            a_int8, scale_a_int8, _ = vllm_scaled_int8_quant(a)
-            return vllm_scaled_mm(a_int8, b_int8, scale_a_int8, scale_b_int8, dtype)
-
-    return run_quant
-
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v.get("enabled")]
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=[k for k in _enabled],
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs INT8 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_int8_runner(cfg, a, b, dtype, device)
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    KN_model_names = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            KN_model_names.append(KN)
-    return KN_model_names
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-        help="List of models to benchmark",
-    )
-    parser.add_argument(
-        "--tp-sizes",
-        nargs="+",
-        type=int,
-        default=[1],
-        help="List of tensor parallel sizes",
-    )
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        print(f"{model}, N={N} K={K}, BF16 vs INT8 GEMMs TFLOP/s:")
-        benchmark.run(
-            print_data=True,
-            show_plots=True,
-            save_path=f"bench_int8_res_n{N}_k{K}",
-            N=N,
-            K=K,
-        )
-
-    print("Benchmark finished!")

benchmarks/kernels/bench_nvfp4_gemm.py

@@ -1,141 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm import _custom_ops as ops
-from vllm.platforms import current_platform
-from vllm.scalar_type import scalar_types
-from vllm.triton_utils import triton
-
-if not current_platform.has_device_capability(100):
-    raise RuntimeError("NVFP4 requires compute capability of 10.0 (Blackwell)")
-
-
-FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
-FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "nvfp4": dict(no_a_quant=False, enabled=True),
-    "nvfp4-noquant": dict(no_a_quant=True, enabled=True),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def _quant_weight_nvfp4(b: torch.Tensor, device: str):
-    # Compute global scale for weight
-    b_amax = torch.abs(b).max().to(torch.float32)
-    b_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
-    b_fp4, scale_b_fp4 = ops.scaled_fp4_quant(b, b_global_scale)
-    return b_fp4, scale_b_fp4, b_global_scale
-
-
-def build_nvfp4_runner(cfg, a, b, dtype, device):
-    b_fp4, scale_b_fp4, b_global_scale = _quant_weight_nvfp4(b, device)
-
-    # Compute global scale for activation
-    # NOTE: This is generally provided ahead-of-time by the model checkpoint.
-    a_amax = torch.abs(a).max().to(torch.float32)
-    a_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
-
-    # Alpha for the GEMM operation
-    alpha = 1.0 / (a_global_scale * b_global_scale)
-
-    if cfg["no_a_quant"]:
-        # Pre-quantize activation
-        a_fp4, scale_a_fp4 = ops.scaled_fp4_quant(a, a_global_scale)
-
-        def run():
-            return ops.cutlass_scaled_fp4_mm(
-                a_fp4, b_fp4, scale_a_fp4, scale_b_fp4, alpha, dtype
-            )
-
-        return run
-
-    # Quantize activation on-the-fly
-    def run():
-        a_fp4, scale_a_fp4 = ops.scaled_fp4_quant(a, a_global_scale)
-        return ops.cutlass_scaled_fp4_mm(
-            a_fp4, b_fp4, scale_a_fp4, scale_b_fp4, alpha, dtype
-        )
-
-    return run
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs NVFP4 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_nvfp4_runner(cfg, a, b, dtype, device)
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        print(f"{model}, N={N} K={K}, BF16 vs NVFP4 GEMMs TFLOP/s:")
-        benchmark.run(
-            print_data=True,
-            show_plots=True,
-            save_path=f"bench_nvfp4_res_n{N}_k{K}",
-            N=N,
-            K=K,
-        )
-
-    print("Benchmark finished!")