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2025-02-08 20:25:15 +08:00
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59
examples/offline_inference/openai/openai_batch.md
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@@ -5,50 +5,49 @@ This is a guide to performing batch inference using the OpenAI batch file format
```
## File Format
The OpenAI batch file format consists of a series of json objects on new lines.
[See here for an example file.](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/openai/openai_example_batch.jsonl)
Each line represents a separate request. See the [OpenAI package reference](https://platform.openai.com/docs/api-reference/batch/requestInput) for more details.
```{note}
We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` endpoints (completions coming soon).
```
## Pre-requisites
* The examples in this document use `meta-llama/Meta-Llama-3-8B-Instruct`.
- Create a [user access token](https://huggingface.co/docs/hub/en/security-tokens)
- Install the token on your machine (Run `huggingface-cli login`).
- Get access to the gated model by [visiting the model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and agreeing to the terms and conditions.
## Example 1: Running with a local file
### Step 1: Create your batch file
To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
```
```console
wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai/openai_example_batch.jsonl
```
Once you've created your batch file it should look like this
```
```console
$ cat offline_inference/openai/openai_example_batch.jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
```
### Step 2: Run the batch
The batch running tool is designed to be used from the command line.
You can run the batch with the following command, which will write its results to a file called `results.jsonl`
```
```console
python -m vllm.entrypoints.openai.run_batch -i offline_inference/openai/openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
```
@@ -56,7 +55,7 @@ python -m vllm.entrypoints.openai.run_batch -i offline_inference/openai/openai_e
You should now have your results at `results.jsonl`. You can check your results by running `cat results.jsonl`
```
```console
$ cat results.jsonl
{"id":"vllm-383d1c59835645aeb2e07d004d62a826","custom_id":"request-1","response":{"id":"cmpl-61c020e54b964d5a98fa7527bfcdd378","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! It's great to meet you! I'm here to help with any questions or tasks you may have. What's on your mind today?"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":25,"total_tokens":56,"completion_tokens":31}},"error":null}
{"id":"vllm-42e3d09b14b04568afa3f1797751a267","custom_id":"request-2","response":{"id":"cmpl-f44d049f6b3a42d4b2d7850bb1e31bcc","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"*silence*"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":27,"total_tokens":32,"completion_tokens":5}},"error":null}
@@ -68,7 +67,7 @@ The batch runner supports remote input and output urls that are accessible via h
For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai/openai_example_batch.jsonl`, you can run
```
```console
python -m vllm.entrypoints.openai.run_batch -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai/openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
```
@@ -80,7 +79,7 @@ To integrate with cloud blob storage, we recommend using presigned urls.
### Additional prerequisites
* [Create an S3 bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html).
* [Create an S3 bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html).
* The `awscli` package (Run `pip install awscli`) to configure your credentials and interactively use s3.
- [Configure your credentials](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html).
* The `boto3` python package (Run `pip install boto3`) to generate presigned urls.
@@ -89,13 +88,13 @@ To integrate with cloud blob storage, we recommend using presigned urls.
To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
```
```console
wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai/openai_example_batch.jsonl
```
Once you've created your batch file it should look like this
```
```console
$ cat offline_inference/openai/openai_example_batch.jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
@@ -103,7 +102,7 @@ $ cat offline_inference/openai/openai_example_batch.jsonl
Now upload your batch file to your S3 bucket.
```
```console
aws s3 cp offline_inference/openai/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
```
@@ -111,9 +110,9 @@ aws s3 cp offline_inference/openai/openai_example_batch.jsonl s3://MY_BUCKET/MY_
Presigned urls can only be generated via the SDK. You can run the following python script to generate your presigned urls. Be sure to replace the `MY_BUCKET`, `MY_INPUT_FILE.jsonl`, and `MY_OUTPUT_FILE.jsonl` placeholders with your bucket and file names.
(The script is adapted from https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/python/example_code/s3/s3_basics/presigned_url.py)
(The script is adapted from <https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/python/example_code/s3/s3_basics/presigned_url.py>)
```
```python
import boto3
from botocore.exceptions import ClientError
@@ -149,7 +148,7 @@ print(f"{output_url=}")
This script should output
```
```text
input_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091'
output_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091'
```
@@ -158,7 +157,7 @@ output_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AW
You can now run the batch runner, using the urls generated in the previous section.
```
```console
python -m vllm.entrypoints.openai.run_batch \
-i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
-o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
@@ -169,7 +168,7 @@ python -m vllm.entrypoints.openai.run_batch \
Your results are now on S3. You can view them in your terminal by running
```
```console
aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -
```
@@ -180,10 +179,10 @@ aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -
* Ensure you are using `vllm >= 0.5.5`.
### Step 1: Create your batch file
Add embedding requests to your batch file. The following is an example:
```
```text
{"custom_id": "request-1", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/e5-mistral-7b-instruct", "input": "You are a helpful assistant."}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/e5-mistral-7b-instruct", "input": "You are an unhelpful assistant."}}
```
@@ -198,7 +197,7 @@ You can run the batch using the same command as in earlier examples.
You can check your results by running `cat results.jsonl`
```
```console
$ cat results.jsonl
{"id":"vllm-db0f71f7dec244e6bce530e0b4ef908b","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-3580bf4d4ae54d52b67eee266a6eab20","body":{"id":"embd-33ac2efa7996430184461f2e38529746","object":"list","created":444647,"model":"intfloat/e5-mistral-7b-instruct","data":[{"index":0,"object":"embedding","embedding":[0.016204833984375,0.0092010498046875,0.0018358230590820312,-0.0028228759765625,0.001422882080078125,-0.0031147003173828125,...]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0}}},"error":null}
...
@@ -211,10 +210,10 @@ $ cat results.jsonl
* Ensure you are using `vllm >= 0.7.0`.
### Step 1: Create your batch file
Add score requests to your batch file. The following is an example:
```
```text
{"custom_id": "request-1", "method": "POST", "url": "/v1/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "text_1": "What is the capital of France?", "text_2": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "text_1": "What is the capital of France?", "text_2": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
```
@@ -229,7 +228,7 @@ You can run the batch using the same command as in earlier examples.
You can check your results by running `cat results.jsonl`
```
```console
$ cat results.jsonl
{"id":"vllm-f87c5c4539184f618e555744a2965987","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-806ab64512e44071b37d3f7ccd291413","body":{"id":"score-4ee45236897b4d29907d49b01298cdb1","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.0010900497436523438},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
{"id":"vllm-41990c51a26d4fac8419077f12871099","custom_id":"request-2","response":{"status_code":200,"request_id":"vllm-batch-73ce66379026482699f81974e14e1e99","body":{"id":"score-13f2ffe6ba40460fbf9f7f00ad667d75","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.001094818115234375},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}

6
examples/offline_inference/profiling_tpu/README.md
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@@ -29,7 +29,6 @@ python3 profiling.py \
--profile-result-dir profiles
```
### Generate Decode Trace
This example runs Llama 3.1 70B with a batch of 32 requests where each has 1 input token and 128 output tokens. This is set up in attempt to profile just the 32 decodes running in parallel by having an extremely small prefill of 1 token and setting `VLLM_TPU_PROFILE_DELAY_MS=1000` to skip the first second of inference (hopefully prefill).
@@ -51,17 +50,18 @@ python3 profiling.py \
--max-model-len 2048 --tensor-parallel-size 8
```
## Visualizing the profiles
Once you have collected your profiles with this script, you can visualize them using [TensorBoard](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm).
Here are most likely the dependencies you need to install:
```bash
pip install tensorflow-cpu tensorboard-plugin-profile etils importlib_resources
```
Then you just need to point TensorBoard to the directory where you saved the profiles and visit `http://localhost:6006/` in your browser:
```bash
tensorboard --logdir profiles/ --port 6006
```
```

2
examples/online_serving/chart-helm/README.md
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@@ -18,4 +18,4 @@ This directory contains a Helm chart for deploying the vllm application. The cha
- templates/poddisruptionbudget.yaml: Template for Pod Disruption Budget.
- templates/pvc.yaml: Template for Persistent Volume Claims.
- templates/secrets.yaml: Template for Kubernetes Secrets.
- templates/service.yaml: Template for creating Services.
- templates/service.yaml: Template for creating Services.

32
examples/online_serving/opentelemetry/Otel.md
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@@ -1,7 +1,8 @@
# Setup OpenTelemetry POC
1. Install OpenTelemetry packages:
```
```console
pip install \
'opentelemetry-sdk>=1.26.0,<1.27.0' \
'opentelemetry-api>=1.26.0,<1.27.0' \
@@ -10,7 +11,8 @@
```
1. Start Jaeger in a docker container:
```
```console
# From: https://www.jaegertracing.io/docs/1.57/getting-started/
docker run --rm --name jaeger \
-e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
@@ -28,19 +30,23 @@
```
1. In a new shell, export Jaeger IP:
```
```console
export JAEGER_IP=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' jaeger)
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
```
Then set vLLM's service name for OpenTelemetry, enable insecure connections to Jaeger and run vLLM:
```
```console
export OTEL_SERVICE_NAME="vllm-server"
export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
```
1. In a new shell, send requests with trace context from a dummy client
```
```console
export JAEGER_IP=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' jaeger)
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
@@ -48,7 +54,7 @@
python dummy_client.py
```
1. Open Jaeger webui: http://localhost:16686/
1. Open Jaeger webui: <http://localhost:16686/>
In the search pane, select `vllm-server` service and hit `Find Traces`. You should get a list of traces, one for each request.
![Traces](https://i.imgur.com/GYHhFjo.png)
@@ -57,26 +63,32 @@
![Spans details](https://i.imgur.com/OPf6CBL.png)
## Exporter Protocol
OpenTelemetry supports either `grpc` or `http/protobuf` as the transport protocol for trace data in the exporter.
By default, `grpc` is used. To set `http/protobuf` as the protocol, configure the `OTEL_EXPORTER_OTLP_TRACES_PROTOCOL` environment variable as follows:
```
```console
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
```
## Instrumentation of FastAPI
OpenTelemetry allows automatic instrumentation of FastAPI.
1. Install the instrumentation library
```
```console
pip install opentelemetry-instrumentation-fastapi
```
1. Run vLLM with `opentelemetry-instrument`
```
```console
opentelemetry-instrument vllm serve facebook/opt-125m
```
1. Send a request to vLLM and find its trace in Jaeger. It should contain spans from FastAPI.
![FastAPI Spans](https://i.imgur.com/hywvoOJ.png)
![FastAPI Spans](https://i.imgur.com/hywvoOJ.png)

14
examples/online_serving/prometheus_grafana/README.md
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@@ -1,14 +1,16 @@
# Prometheus and Grafana
# Prometheus and Grafana
This is a simple example that shows you how to connect vLLM metric logging to the Prometheus/Grafana stack. For this example, we launch Prometheus and Grafana via Docker. You can checkout other methods through [Prometheus](https://prometheus.io/) and [Grafana](https://grafana.com/) websites.
This is a simple example that shows you how to connect vLLM metric logging to the Prometheus/Grafana stack. For this example, we launch Prometheus and Grafana via Docker. You can checkout other methods through [Prometheus](https://prometheus.io/) and [Grafana](https://grafana.com/) websites.
Install:
Install:
- [`docker`](https://docs.docker.com/engine/install/)
- [`docker compose`](https://docs.docker.com/compose/install/linux/#install-using-the-repository)
## Launch
Prometheus metric logging is enabled by default in the OpenAI-compatible server. Launch via the entrypoint:
```bash
vllm serve mistralai/Mistral-7B-v0.1 \
--max-model-len 2048 \
@@ -16,11 +18,13 @@ vllm serve mistralai/Mistral-7B-v0.1 \
```
Launch Prometheus and Grafana servers with `docker compose`:
```bash
docker compose up
```
Submit some sample requests to the server:
```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
@@ -41,13 +45,13 @@ Navigate to [`http://localhost:3000`](http://localhost:3000). Log in with the de
### Add Prometheus Data Source
Navigate to [`http://localhost:3000/connections/datasources/new`](http://localhost:3000/connections/datasources/new) and select Prometheus.
Navigate to [`http://localhost:3000/connections/datasources/new`](http://localhost:3000/connections/datasources/new) and select Prometheus.
On Prometheus configuration page, we need to add the `Prometheus Server URL` in `Connection`. For this setup, Grafana and Prometheus are running in separate containers, but Docker creates DNS name for each containers. You can just use `http://prometheus:9090`.
Click `Save & Test`. You should get a green check saying "Successfully queried the Prometheus API.".
### Import Dashboard
### Import Dashboard
Navigate to [`http://localhost:3000/dashboard/import`](http://localhost:3000/dashboard/import), upload `grafana.json`, and select the `prometheus` datasource. You should see a screen that looks like the following:

5
examples/other/logging_configuration.md
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@@ -15,7 +15,6 @@ more-complex-and-more-flexible.
- Leave `VLLM_CONFIGURE_LOGGING` unset or set `VLLM_CONFIGURE_LOGGING=1` and
set `VLLM_LOGGING_CONFIG_PATH=<path-to-logging-config.json>`
## Logging Configuration Environment Variables
### `VLLM_CONFIGURE_LOGGING`
@@ -45,7 +44,6 @@ schema](https://docs.python.org/3/library/logging.config.html#dictionary-schema-
If `VLLM_LOGGING_CONFIG_PATH` is specified, but `VLLM_CONFIGURE_LOGGING` is
disabled, an error will occur while starting vLLM.
## Examples
### Example 1: Customize vLLM root logger
@@ -98,7 +96,6 @@ VLLM_LOGGING_CONFIG_PATH=/path/to/logging_config.json \
vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
```
### Example 2: Silence a particular vLLM logger
To silence a particular vLLM logger, it is necessary to provide custom logging
@@ -153,7 +150,6 @@ VLLM_LOGGING_CONFIG_PATH=/path/to/logging_config.json \
vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
```
### Example 3: Disable vLLM default logging configuration
To disable vLLM's default logging configuration and silence all vLLM loggers,
@@ -166,7 +162,6 @@ VLLM_CONFIGURE_LOGGING=0 \
vllm serve mistralai/Mistral-7B-v0.1 --max-model-len 2048
```
## Additional resources
- [`logging.config` Dictionary Schema Details](https://docs.python.org/3/library/logging.config.html#dictionary-schema-details)