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Add vLLM v0.18.1 source tree with KV transfer abort fix

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third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:

  vllm/v1/core/sched/scheduler.py:
    Replace fatal assert with graceful skip when KV transfer callback
    arrives for an already-aborted request during PD disaggregated serving.

Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
2026-05-22 00:30:38 +08:00
parent b6591950bc
commit 445e491123
4285 changed files with 1111303 additions and 1 deletions
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# Benchmark Suites
vLLM provides comprehensive benchmarking tools for performance testing and evaluation:
- **[Benchmark CLI](./cli.md)**: `vllm bench` CLI tools and specialized benchmark scripts for interactive performance testing.
- **[Parameter Sweeps](./sweeps.md)**: Automate `vllm bench` runs for multiple configurations, useful for [optimization and tuning](../configuration/optimization.md).
- **[Performance Dashboard](./dashboard.md)**: Automated CI that publishes benchmarks on each commit.

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# Performance Dashboard
The performance dashboard is used to confirm whether new changes improve/degrade performance under various workloads.
It is updated by triggering benchmark runs on every commit with both the `perf-benchmarks` and `ready` labels, and when a PR is merged into vLLM.
The results are automatically published to the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
## Manually Trigger the benchmark
Use [vllm-ci-test-repo images](https://gallery.ecr.aws/q9t5s3a7/vllm-ci-test-repo) with vLLM benchmark suite.
For x86 CPU environment, please use the image with "-cpu" postfix. For AArch64 CPU environment, please use the image with "-arm64-cpu" postfix.
Here is an example for docker run command for CPU. For GPUs skip setting the `ON_CPU` env var.
```bash
export VLLM_COMMIT=7f42dc20bb2800d09faa72b26f25d54e26f1b694 # use full commit hash from the main branch
export HF_TOKEN=<valid Hugging Face token>
if [[ "$(uname -m)" == aarch64 || "$(uname -m)" == arm64 ]]; then
IMG_SUFFIX="arm64-cpu"
else
IMG_SUFFIX="cpu"
fi
docker run -it --entrypoint /bin/bash -v /data/huggingface:/root/.cache/huggingface -e HF_TOKEN=$HF_TOKEN -e ON_CPU=1 --shm-size=16g --name vllm-cpu-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${VLLM_COMMIT}-${IMG_SUFFIX}
```
Then, run below command inside the docker instance.
```bash
bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
```
When run, benchmark script generates results under **benchmark/results** folder, along with the benchmark_results.md and benchmark_results.json.
### Runtime environment variables
- `ON_CPU`: set the value to '1' on Intel® Xeon® and Arm® Neoverse™ 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.
- `PROMPTS_PER_CONCURRENCY`: Multiplier to compute `num_prompts` for serving tests (`num_prompts = max_concurrency × value`). Overrides JSON `num_prompts`. Default is NULL.
- `ENABLE_ADAPTIVE_CONCURRENCY`: set the value to '1' to enable adaptive SLA-based concurrency search after the static serving max_concurrency sweep. Default value is 0.
- `SLA_TTFT_MS`: default TTFT SLA threshold in milliseconds for adaptive concurrency search. Default value is 3000.
- `SLA_TPOT_MS`: default TPOT SLA threshold in milliseconds for adaptive concurrency search. Default value is 100.
- `ADAPTIVE_MAX_PROBES`: maximum number of extra adaptive search probes. Default value is 8.
- `ADAPTIVE_MAX_CONCURRENCY`: maximum allowed concurrency during adaptive search. Default value is 1024.
### Visualization
The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table 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.
#### Performance Results Comparison
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.
If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
Here is an example using the script to compare result_a and result_b with max concurrency and qps for same Model, Dataset name, input/output length.
`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
***Output Tput (tok/s) — Model : [ meta-llama/Llama-3.1-8B-Instruct ] , Dataset Name : [ random ] , Input Len : [ 2048.0 ] , Output Len : [ 2048.0 ]***
| | # of max concurrency | qps | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio |
| | -------------------- | --- | -------------------------------- | -------------------------------- | ---------- |
| 0 | 12 | inf | 24.98 | 186.03 | 7.45 |
| 1 | 16 | inf | 25.49 | 246.92 | 9.69 |
| 2 | 24 | inf | 27.74 | 293.34 | 10.57 |
| 3 | 32 | inf | 28.61 |306.69 | 10.72 |
***compare-json-results.py – Command-Line Parameters***
compare-json-results.py provides configurable parameters to compare one or more benchmark_results.json files and generate summary tables and plots.
In most cases, users only need to specify --file to parse the desired benchmark results.
| Parameter | Type | Default Value | Description |
| ---------------------- | ------------------ | ----------------------- | ----------------------------------------------------------------------------------------------------- |
| `--file` | `str` (appendable) | *None* | Input JSON result file(s). Can be specified multiple times to compare multiple benchmark outputs. |
| `--debug` | `bool` | `False` | Enables debug mode. When set, prints all available information to aid troubleshooting and validation. |
| `--plot` / `--no-plot` | `bool` | `True` | Controls whether performance plots are generated. Use `--no-plot` to disable graph generation. |
| `--xaxis` | `str` | `# of max concurrency.` | Column name used as the X-axis in comparison plots (for example, concurrency or batch size). |
| `--latency` | `str` | `p99` | Latency aggregation method used for TTFT/TPOT. Supported values: `median` or `p99`. |
| `--ttft-max-ms` | `float` | `3000.0` | Reference upper bound (milliseconds) for TTFT plots, typically used to visualize SLA thresholds. |
| `--tpot-max-ms` | `float` | `100.0` | Reference upper bound (milliseconds) for TPOT plots, typically used to visualize SLA thresholds. |
***Valid Max Concurrency Summary***
Based on the configured TTFT and TPOT SLA thresholds, compare-json-results.py computes the maximum valid concurrency for each benchmark result.
The “Max # of max concurrency. (Both)” column represents the highest concurrency level that satisfies both TTFT and TPOT constraints simultaneously.
This value is typically used in capacity planning and sizing guides.
| # | Configuration | Max # of max concurrency. (TTFT ≤ 10000 ms) | Max # of max concurrency. (TPOT ≤ 100 ms) | Max # of max concurrency. (Both) | Output Tput @ Both (tok/s) | TTFT @ Both (ms) | TPOT @ Both (ms) |
| - | -------------- | ------------------------------------------- | ----------------------------------------- | -------------------------------- | -------------------------- | ---------------- | ---------------- |
| 0 | results-a | 128.00 | 12.00 | 12.00 | 127.76 | 3000.82 | 93.24 |
| 1 | results-b | 128.00 | 32.00 | 32.00 | 371.42 | 2261.53 | 81.74 |
More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](../../.buildkite/performance-benchmarks/performance-benchmarks-descriptions.md).
## Continuous Benchmarking
The continuous benchmarking provides automated performance monitoring for vLLM across different models and GPU devices. This helps track vLLM's performance characteristics over time and identify any performance regressions or improvements.
### How It Works
The continuous benchmarking is triggered via a [GitHub workflow CI](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) in the PyTorch infrastructure repository, which runs automatically every 4 hours. The workflow executes three types of performance tests:
- **Serving tests**: Measure request handling and API performance
- **Throughput tests**: Evaluate token generation rates
- **Latency tests**: Assess response time characteristics
### Benchmark Configuration
The benchmarking currently runs on a predefined set of models configured in the [vllm-benchmarks directory](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks). To add new models for benchmarking:
1. Navigate to the appropriate GPU directory in the benchmarks configuration
2. Add your model specifications to the corresponding configuration files
3. The new models will be included in the next scheduled benchmark run

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# Parameter Sweeps
`vllm bench sweep` is a suite of commands designed to run benchmarks across multiple configurations and compare them by visualizing the results.
## Online Benchmark
### Basic
`vllm bench sweep serve` starts `vllm serve` and iteratively runs `vllm bench serve` for each server configuration.
!!! tip
If you only need to run benchmarks for a single server configuration, consider using [GuideLLM](https://github.com/vllm-project/guidellm), an established performance benchmarking framework with live progress updates and automatic report generation. It is also more flexible than `vllm bench serve` in terms of dataset loading, request formatting, and workload patterns.
Follow these steps to run the script:
1. Construct the base command to `vllm serve`, and pass it to the `--serve-cmd` option.
2. Construct the base command to `vllm bench serve`, and pass it to the `--bench-cmd` option.
3. (Optional) If you would like to vary the settings of `vllm serve`, create a new JSON file and populate it with the parameter combinations you want to test. Pass the file path to `--serve-params`.
- Example: Tuning `--max-num-seqs` and `--max-num-batched-tokens`:
```json
[
{
"max_num_seqs": 32,
"max_num_batched_tokens": 1024
},
{
"max_num_seqs": 64,
"max_num_batched_tokens": 1024
},
{
"max_num_seqs": 64,
"max_num_batched_tokens": 2048
},
{
"max_num_seqs": 128,
"max_num_batched_tokens": 2048
},
{
"max_num_seqs": 128,
"max_num_batched_tokens": 4096
},
{
"max_num_seqs": 256,
"max_num_batched_tokens": 4096
}
]
```
4. (Optional) If you would like to vary the settings of `vllm bench serve`, create a new JSON file and populate it with the parameter combinations you want to test. Pass the file path to `--bench-params`.
- Example: Using different input/output lengths for random dataset:
```json
[
{
"_benchmark_name": "scenario_A",
"random_input_len": 128,
"random_output_len": 32
},
{
"_benchmark_name": "scenario_B",
"random_input_len": 256,
"random_output_len": 64
},
{
"_benchmark_name": "scenario_C",
"random_input_len": 512,
"random_output_len": 128
}
]
```
5. Set `--output-dir` and optionally `--experiment-name` to control where to save the results.
Example command:
```bash
vllm bench sweep serve \
--serve-cmd 'vllm serve meta-llama/Llama-2-7b-chat-hf' \
--bench-cmd 'vllm bench serve --model meta-llama/Llama-2-7b-chat-hf --backend vllm --endpoint /v1/completions --dataset-name sharegpt --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json' \
--serve-params benchmarks/serve_hparams.json \
--bench-params benchmarks/bench_hparams.json \
--output-dir benchmarks/results \
--experiment-name demo
```
By default, each parameter combination is benchmarked 3 times to make the results more reliable. You can adjust the number of runs by setting `--num-runs`.
!!! important
If both `--serve-params` and `--bench-params` are passed, the script will iterate over the Cartesian product between them.
You can use `--dry-run` to preview the commands to be run.
We only start the server once for each `--serve-params`, and keep it running for multiple `--bench-params`.
Between each benchmark run, we call all `/reset_*_cache` endpoints to get a clean slate for the next run.
In case you are using a custom `--serve-cmd`, you can override the commands used for resetting the state by setting `--after-bench-cmd`.
!!! note
You should set `_benchmark_name` to provide a human-readable name for parameter combinations involving many variables.
This becomes mandatory if the file name would otherwise exceed the maximum path length allowed by the filesystem.
!!! tip
You can use the `--resume` option to continue the parameter sweep if an unexpected error occurs, e.g., timeout when connecting to HF Hub.
### Workload Explorer
`vllm bench sweep serve_workload` is a variant of `vllm bench sweep serve` that explores different workload levels in order to find the tradeoff between latency and throughput. The results can also be [visualized](#visualization) to determine the feasible SLAs.
The workload can be expressed in terms of request rate or concurrency (choose using `--workload-var`).
Example command:
```bash
vllm bench sweep serve_workload \
--serve-cmd 'vllm serve meta-llama/Llama-2-7b-chat-hf' \
--bench-cmd 'vllm bench serve --model meta-llama/Llama-2-7b-chat-hf --backend vllm --endpoint /v1/completions --dataset-name sharegpt --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 100' \
--workload-var max_concurrency \
--serve-params benchmarks/serve_hparams.json \
--bench-params benchmarks/bench_hparams.json \
--num-runs 1 \
--output-dir benchmarks/results \
--experiment-name demo
```
The algorithm for exploring different workload levels can be summarized as follows:
1. Run the benchmark by sending requests one at a time (serial inference, lowest workload). This results in the lowest possible latency and throughput.
2. Run the benchmark by sending all requests at once (batch inference, highest workload). This results in the highest possible latency and throughput.
3. Estimate the value of `workload_var` corresponding to Step 2.
4. Run the benchmark over intermediate values of `workload_var` uniformly using the remaining iterations.
You can override the number of iterations in the algorithm by setting `--workload-iters`.
!!! tip
This is our equivalent of [GuideLLM's `--profile sweep`](https://github.com/vllm-project/guidellm/blob/v0.5.3/src/guidellm/benchmark/profiles.py#L575).
In general, `--workload-var max_concurrency` produces more reliable results because it directly controls the workload imposed on the vLLM engine.
Nevertheless, we default to `--workload-var request_rate` to maintain similar behavior as GuideLLM.
## Startup Benchmark
`vllm bench sweep startup` runs `vllm bench startup` across parameter combinations to compare cold/warm startup time for different engine settings.
Follow these steps to run the script:
1. (Optional) Construct the base command to `vllm bench startup`, and pass it to `--startup-cmd` (default: `vllm bench startup`).
2. (Optional) Reuse a `--serve-params` JSON from `vllm bench sweep serve` to vary engine settings. Only parameters supported by `vllm bench startup` are applied.
3. (Optional) Create a `--startup-params` JSON to vary startup-specific options like iteration counts.
4. Determine where you want to save the results, and pass that to `--output-dir`.
Example `--serve-params`:
```json
[
{
"_benchmark_name": "tp1",
"model": "Qwen/Qwen3-0.6B",
"tensor_parallel_size": 1,
"gpu_memory_utilization": 0.9
},
{
"_benchmark_name": "tp2",
"model": "Qwen/Qwen3-0.6B",
"tensor_parallel_size": 2,
"gpu_memory_utilization": 0.9
}
]
```
Example `--startup-params`:
```json
[
{
"_benchmark_name": "qwen3-0.6",
"num_iters_cold": 2,
"num_iters_warmup": 1,
"num_iters_warm": 2
}
]
```
Example command:
```bash
vllm bench sweep startup \
--startup-cmd 'vllm bench startup --model Qwen/Qwen3-0.6B' \
--serve-params benchmarks/serve_hparams.json \
--startup-params benchmarks/startup_hparams.json \
--output-dir benchmarks/results \
--experiment-name demo
```
!!! important
By default, unsupported parameters in `--serve-params` or `--startup-params` are ignored with a warning.
Use `--strict-params` to fail fast on unknown keys.
## Visualization
### Basic
`vllm bench sweep plot` can be used to plot performance curves from parameter sweep results.
Control the variables to plot via `--var-x` and `--var-y`, optionally applying `--filter-by` and `--bin-by` to the values. The plot is organized according to `--fig-by`, `--row-by`, `--col-by`, and `--curve-by`.
Example commands for visualizing [Workload Explorer](#workload-explorer) results:
```bash
EXPERIMENT_DIR=${1:-"benchmarks/results/demo"}
# Latency increases as the workload increases
vllm bench sweep plot $EXPERIMENT_DIR \
--var-x max_concurrency \
--var-y median_ttft_ms \
--col-by _benchmark_name \
--curve-by max_num_seqs,max_num_batched_tokens \
--fig-name latency_curve
# Throughput saturates as workload increases
vllm bench sweep plot $EXPERIMENT_DIR \
--var-x max_concurrency \
--var-y total_token_throughput \
--col-by _benchmark_name \
--curve-by max_num_seqs,max_num_batched_tokens \
--fig-name throughput_curve
# Tradeoff between latency and throughput
vllm bench sweep plot $EXPERIMENT_DIR \
--var-x total_token_throughput \
--var-y median_ttft_ms \
--col-by _benchmark_name \
--curve-by max_num_seqs,max_num_batched_tokens \
--fig-name latency_throughput
```
!!! tip
You can use `--dry-run` to preview the figures to be plotted.
### Pareto chart
`vllm bench sweep plot_pareto` helps pick configurations that balance per-user and per-GPU throughput.
Higher concurrency or batch size can raise GPU efficiency (per-GPU), but can add per user latency; lower concurrency improves per-user rate but underutilizes GPUs; The Pareto frontier shows the best achievable pairs across your runs.
- x-axis: tokens/s/user = `output_throughput` ÷ concurrency (`--user-count-var`, default `max_concurrency`, fallback `max_concurrent_requests`).
- y-axis: tokens/s/GPU = `output_throughput` ÷ GPU count (`--gpu-count-var` if set; else gpu_count is TP×PP*DP).
- Output: a single figure at `OUTPUT_DIR/pareto/PARETO.png`.
- Show the configuration used in each data point `--label-by` (default: `max_concurrency,gpu_count`).
Example:
```bash
EXPERIMENT_DIR=${1:-"benchmarks/results/demo"}
vllm bench sweep plot_pareto $EXPERIMENT_DIR \
--label-by max_concurrency,tensor_parallel_size,pipeline_parallel_size
```
!!! tip
You can use `--dry-run` to preview the figures to be plotted.