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Unverified Commit f17aec0d authored by Reid's avatar Reid Committed by GitHub
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[doc] Fold long code blocks to improve readability (#19926) 


Signed-off-by: default avatarreidliu41 <reid201711@gmail.com>
Co-authored-by: default avatarreidliu41 <reid201711@gmail.com>
parent 493c2753
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docs/ci/update_pytorch_version.md
View file @ f17aec0d
......@@ -91,7 +91,7 @@ source to unblock the update process.
### FlashInfer
Here is how to build and install it from source with torch2.7.0+cu128 in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
```
```bash
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
export FLASHINFER_ENABLE_SM90=1
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
......@@ -105,14 +105,14 @@ team if you want to get the package published there.
### xFormers
Similar to FlashInfer, here is how to build and install xFormers from source:
```
```bash
export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
MAX_JOBS=16 uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
```
### Mamba
```
```bash
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
```
......
docs/cli/README.md
View file @ f17aec0d
......@@ -16,35 +16,33 @@ vllm {chat,complete,serve,bench,collect-env,run-batch}
Start the vLLM OpenAI Compatible API server.
Examples:
??? Examples
```bash
# Start with a model
vllm serve meta-llama/Llama-2-7b-hf
```bash
# Start with a model
vllm serve meta-llama/Llama-2-7b-hf
# Specify the port
vllm serve meta-llama/Llama-2-7b-hf --port 8100
# Specify the port
vllm serve meta-llama/Llama-2-7b-hf --port 8100
# Check with --help for more options
# To list all groups
vllm serve --help=listgroup
# Check with --help for more options
# To list all groups
vllm serve --help=listgroup
# To view a argument group
vllm serve --help=ModelConfig
# To view a argument group
vllm serve --help=ModelConfig
# To view a single argument
vllm serve --help=max-num-seqs
# To view a single argument
vllm serve --help=max-num-seqs
# To search by keyword
vllm serve --help=max
```
# To search by keyword
vllm serve --help=max
```
## chat
Generate chat completions via the running API server.
Examples:
```bash
# Directly connect to localhost API without arguments
vllm chat
......@@ -60,8 +58,6 @@ vllm chat --quick "hi"
Generate text completions based on the given prompt via the running API server.
Examples:
```bash
# Directly connect to localhost API without arguments
vllm complete
......@@ -73,6 +69,8 @@ vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1
vllm complete --quick "The future of AI is"
```
</details>
## bench
Run benchmark tests for latency online serving throughput and offline inference throughput.
......@@ -89,8 +87,6 @@ vllm bench {latency, serve, throughput}
Benchmark the latency of a single batch of requests.
Example:
```bash
vllm bench latency \
--model meta-llama/Llama-3.2-1B-Instruct \
......@@ -104,8 +100,6 @@ vllm bench latency \
Benchmark the online serving throughput.
Example:
```bash
vllm bench serve \
--model meta-llama/Llama-3.2-1B-Instruct \
......@@ -120,8 +114,6 @@ vllm bench serve \
Benchmark offline inference throughput.
Example:
```bash
vllm bench throughput \
--model meta-llama/Llama-3.2-1B-Instruct \
......@@ -143,7 +135,8 @@ vllm collect-env
Run batch prompts and write results to file.
Examples:
<details>
<summary>Examples</summary>
```bash
# Running with a local file
......@@ -159,6 +152,8 @@ vllm run-batch \
--model meta-llama/Meta-Llama-3-8B-Instruct
```
</details>
## More Help
For detailed options of any subcommand, use:
......
docs/configuration/conserving_memory.md
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......@@ -57,19 +57,21 @@ By default, we optimize model inference using CUDA graphs which take up extra me
You can adjust `compilation_config` to achieve a better balance between inference speed and memory usage:
```python
from vllm import LLM
from vllm.config import CompilationConfig, CompilationLevel
llm = LLM(
model="meta-llama/Llama-3.1-8B-Instruct",
compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
# By default, it goes up to max_num_seqs
cudagraph_capture_sizes=[1, 2, 4, 8, 16],
),
)
```
??? Code
```python
from vllm import LLM
from vllm.config import CompilationConfig, CompilationLevel
llm = LLM(
model="meta-llama/Llama-3.1-8B-Instruct",
compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
# By default, it goes up to max_num_seqs
cudagraph_capture_sizes=[1, 2, 4, 8, 16],
),
)
```
You can disable graph capturing completely via the `enforce_eager` flag:
......@@ -127,18 +129,20 @@ reduce the size of the processed multi-modal inputs, which in turn saves memory.
Here are some examples:
```python
from vllm import LLM
??? Code
# Available for Qwen2-VL series models
llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
mm_processor_kwargs={
"max_pixels": 768 * 768, # Default is 1280 * 28 * 28
})
# Available for InternVL series models
llm = LLM(model="OpenGVLab/InternVL2-2B",
mm_processor_kwargs={
"max_dynamic_patch": 4, # Default is 12
})
```
```python
from vllm import LLM
# Available for Qwen2-VL series models
llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
mm_processor_kwargs={
"max_pixels": 768 * 768, # Default is 1280 * 28 * 28
})
# Available for InternVL series models
llm = LLM(model="OpenGVLab/InternVL2-2B",
mm_processor_kwargs={
"max_dynamic_patch": 4, # Default is 12
})
```
docs/configuration/env_vars.md
View file @ f17aec0d
......@@ -7,6 +7,8 @@ vLLM uses the following environment variables to configure the system:
All environment variables used by vLLM are prefixed with `VLLM_`. **Special care should be taken for Kubernetes users**: please do not name the service as `vllm`, otherwise environment variables set by Kubernetes might conflict with vLLM's environment variables, because [Kubernetes sets environment variables for each service with the capitalized service name as the prefix](https://kubernetes.io/docs/concepts/services-networking/service/#environment-variables).
```python
--8<-- "vllm/envs.py:env-vars-definition"
```
??? Code
```python
--8<-- "vllm/envs.py:env-vars-definition"
```
docs/contributing/README.md
View file @ f17aec0d
......@@ -93,25 +93,27 @@ For additional features and advanced configurations, refer to the official [MkDo
## Testing
```bash
pip install -r requirements/dev.txt
??? note "Commands"
# Linting, formatting and static type checking
pre-commit install --hook-type pre-commit --hook-type commit-msg
```bash
pip install -r requirements/dev.txt
# You can manually run pre-commit with
pre-commit run --all-files
# Linting, formatting and static type checking
pre-commit install --hook-type pre-commit --hook-type commit-msg
# To manually run something from CI that does not run
# locally by default, you can run:
pre-commit run mypy-3.9 --hook-stage manual --all-files
# You can manually run pre-commit with
pre-commit run --all-files
# Unit tests
pytest tests/
# To manually run something from CI that does not run
# locally by default, you can run:
pre-commit run mypy-3.9 --hook-stage manual --all-files
# Run tests for a single test file with detailed output
pytest -s -v tests/test_logger.py
```
# Unit tests
pytest tests/
# Run tests for a single test file with detailed output
pytest -s -v tests/test_logger.py
```
!!! tip
Since the <gh-file:docker/Dockerfile> ships with Python 3.12, all tests in CI (except `mypy`) are run with Python 3.12.
......
docs/contributing/model/basic.md
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......@@ -27,33 +27,35 @@ All vLLM modules within the model must include a `prefix` argument in their cons
The initialization code should look like this:
```python
from torch import nn
from vllm.config import VllmConfig
from vllm.attention import Attention
class MyAttention(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.attn = Attention(prefix=f"{prefix}.attn")
class MyDecoderLayer(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.self_attn = MyAttention(prefix=f"{prefix}.self_attn")
class MyModel(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.layers = nn.ModuleList(
[MyDecoderLayer(vllm_config, prefix=f"{prefix}.layers.{i}") for i in range(vllm_config.model_config.hf_config.num_hidden_layers)]
)
class MyModelForCausalLM(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.model = MyModel(vllm_config, prefix=f"{prefix}.model")
```
??? Code
```python
from torch import nn
from vllm.config import VllmConfig
from vllm.attention import Attention
class MyAttention(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.attn = Attention(prefix=f"{prefix}.attn")
class MyDecoderLayer(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.self_attn = MyAttention(prefix=f"{prefix}.self_attn")
class MyModel(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.layers = nn.ModuleList(
[MyDecoderLayer(vllm_config, prefix=f"{prefix}.layers.{i}") for i in range(vllm_config.model_config.hf_config.num_hidden_layers)]
)
class MyModelForCausalLM(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.model = MyModel(vllm_config, prefix=f"{prefix}.model")
```
### Computation Code
......
docs/contributing/model/multimodal.md
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This diff is collapsed.
docs/contributing/profiling.md
View file @ f17aec0d
......@@ -97,26 +97,26 @@ to manually kill the profiler and generate your `nsys-rep` report.
You can view these profiles either as summaries in the CLI, using `nsys stats [profile-file]`, or in the GUI by installing Nsight [locally following the directions here](https://developer.nvidia.com/nsight-systems/get-started).
CLI example:
```bash
nsys stats report1.nsys-rep
...
** CUDA GPU Kernel Summary (cuda_gpu_kern_sum):
Time (%) Total Time (ns) Instances Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name
-------- --------------- --------- ----------- ----------- -------- --------- ----------- ----------------------------------------------------------------------------------------------------
46.3 10,327,352,338 17,505 589,965.9 144,383.0 27,040 3,126,460 944,263.8 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_of…
14.8 3,305,114,764 5,152 641,520.7 293,408.0 287,296 2,822,716 867,124.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_of…
12.1 2,692,284,876 14,280 188,535.4 83,904.0 19,328 2,862,237 497,999.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off…
9.5 2,116,600,578 33,920 62,399.8 21,504.0 15,326 2,532,285 290,954.1 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_…
5.0 1,119,749,165 18,912 59,208.4 9,056.0 6,784 2,578,366 271,581.7 void vllm::act_and_mul_kernel<c10::BFloat16, &vllm::silu_kernel<c10::BFloat16>, (bool)1>(T1 *, cons…
4.1 916,662,515 21,312 43,011.6 19,776.0 8,928 2,586,205 199,790.1 void cutlass::device_kernel<flash::enable_sm90_or_later<flash::FlashAttnFwdSm90<flash::CollectiveMa…
2.6 587,283,113 37,824 15,526.7 3,008.0 2,719 2,517,756 139,091.1 std::enable_if<T2>(int)0&&vllm::_typeConvert<T1>::exists, void>::type vllm::fused_add_rms_norm_kern…
1.9 418,362,605 18,912 22,121.5 3,871.0 3,328 2,523,870 175,248.2 void vllm::rotary_embedding_kernel<c10::BFloat16, (bool)1>(const long *, T1 *, T1 *, const T1 *, in…
0.7 167,083,069 18,880 8,849.7 2,240.0 1,471 2,499,996 101,436.1 void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0…
...
```
??? CLI example
```bash
nsys stats report1.nsys-rep
...
** CUDA GPU Kernel Summary (cuda_gpu_kern_sum):
Time (%) Total Time (ns) Instances Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Name
-------- --------------- --------- ----------- ----------- -------- --------- ----------- ----------------------------------------------------------------------------------------------------
46.3 10,327,352,338 17,505 589,965.9 144,383.0 27,040 3,126,460 944,263.8 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_of…
14.8 3,305,114,764 5,152 641,520.7 293,408.0 287,296 2,822,716 867,124.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_of…
12.1 2,692,284,876 14,280 188,535.4 83,904.0 19,328 2,862,237 497,999.9 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off…
9.5 2,116,600,578 33,920 62,399.8 21,504.0 15,326 2,532,285 290,954.1 sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_…
5.0 1,119,749,165 18,912 59,208.4 9,056.0 6,784 2,578,366 271,581.7 void vllm::act_and_mul_kernel<c10::BFloat16, &vllm::silu_kernel<c10::BFloat16>, (bool)1>(T1 *, cons…
4.1 916,662,515 21,312 43,011.6 19,776.0 8,928 2,586,205 199,790.1 void cutlass::device_kernel<flash::enable_sm90_or_later<flash::FlashAttnFwdSm90<flash::CollectiveMa…
2.6 587,283,113 37,824 15,526.7 3,008.0 2,719 2,517,756 139,091.1 std::enable_if<T2>(int)0&&vllm::_typeConvert<T1>::exists, void>::type vllm::fused_add_rms_norm_kern…
1.9 418,362,605 18,912 22,121.5 3,871.0 3,328 2,523,870 175,248.2 void vllm::rotary_embedding_kernel<c10::BFloat16, (bool)1>(const long *, T1 *, T1 *, const T1 *, in…
0.7 167,083,069 18,880 8,849.7 2,240.0 1,471 2,499,996 101,436.1 void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0…
...
```
GUI example:
......
docs/deployment/docker.md
View file @ f17aec0d
......@@ -97,19 +97,21 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
flags to speed up build process. However, ensure your `max_jobs` is substantially larger than `nvcc_threads` to get the most benefits.
Keep an eye on memory usage with parallel jobs as it can be substantial (see example below).
```console
# Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
python3 use_existing_torch.py
DOCKER_BUILDKIT=1 docker build . \
--file docker/Dockerfile \
--target vllm-openai \
--platform "linux/arm64" \
-t vllm/vllm-gh200-openai:latest \
--build-arg max_jobs=66 \
--build-arg nvcc_threads=2 \
--build-arg torch_cuda_arch_list="9.0 10.0+PTX" \
--build-arg vllm_fa_cmake_gpu_arches="90-real"
```
??? Command
```console
# Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
python3 use_existing_torch.py
DOCKER_BUILDKIT=1 docker build . \
--file docker/Dockerfile \
--target vllm-openai \
--platform "linux/arm64" \
-t vllm/vllm-gh200-openai:latest \
--build-arg max_jobs=66 \
--build-arg nvcc_threads=2 \
--build-arg torch_cuda_arch_list="9.0 10.0+PTX" \
--build-arg vllm_fa_cmake_gpu_arches="90-real"
```
!!! note
If you are building the `linux/arm64` image on a non-ARM host (e.g., an x86_64 machine), you need to ensure your system is set up for cross-compilation using QEMU. This allows your host machine to emulate ARM64 execution.
......
docs/deployment/frameworks/autogen.md
View file @ f17aec0d
......@@ -30,51 +30,53 @@ python -m vllm.entrypoints.openai.api_server \
- Call it with AutoGen:
```python
import asyncio
from autogen_core.models import UserMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
async def main() -> None:
# Create a model client
model_client = OpenAIChatCompletionClient(
model="mistralai/Mistral-7B-Instruct-v0.2",
base_url="http://{your-vllm-host-ip}:{your-vllm-host-port}/v1",
api_key="EMPTY",
model_info={
"vision": False,
"function_calling": False,
"json_output": False,
"family": ModelFamily.MISTRAL,
"structured_output": True,
},
)
messages = [UserMessage(content="Write a very short story about a dragon.", source="user")]
# Create a stream.
stream = model_client.create_stream(messages=messages)
# Iterate over the stream and print the responses.
print("Streamed responses:")
async for response in stream:
if isinstance(response, str):
# A partial response is a string.
print(response, flush=True, end="")
else:
# The last response is a CreateResult object with the complete message.
print("\n\n------------\n")
print("The complete response:", flush=True)
print(response.content, flush=True)
# Close the client when done.
await model_client.close()
asyncio.run(main())
```
??? Code
```python
import asyncio
from autogen_core.models import UserMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
async def main() -> None:
# Create a model client
model_client = OpenAIChatCompletionClient(
model="mistralai/Mistral-7B-Instruct-v0.2",
base_url="http://{your-vllm-host-ip}:{your-vllm-host-port}/v1",
api_key="EMPTY",
model_info={
"vision": False,
"function_calling": False,
"json_output": False,
"family": ModelFamily.MISTRAL,
"structured_output": True,
},
)
messages = [UserMessage(content="Write a very short story about a dragon.", source="user")]
# Create a stream.
stream = model_client.create_stream(messages=messages)
# Iterate over the stream and print the responses.
print("Streamed responses:")
async for response in stream:
if isinstance(response, str):
# A partial response is a string.
print(response, flush=True, end="")
else:
# The last response is a CreateResult object with the complete message.
print("\n\n------------\n")
print("The complete response:", flush=True)
print(response.content, flush=True)
# Close the client when done.
await model_client.close()
asyncio.run(main())
```
For details, see the tutorial:
......
docs/deployment/frameworks/cerebrium.md
View file @ f17aec0d
......@@ -34,25 +34,27 @@ vllm = "latest"
Next, let us add our code to handle inference for the LLM of your choice (`mistralai/Mistral-7B-Instruct-v0.1` for this example), add the following code to your `main.py`:
```python
from vllm import LLM, SamplingParams
??? Code
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.1")
```python
from vllm import LLM, SamplingParams
def run(prompts: list[str], temperature: float = 0.8, top_p: float = 0.95):
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.1")
sampling_params = SamplingParams(temperature=temperature, top_p=top_p)
outputs = llm.generate(prompts, sampling_params)
def run(prompts: list[str], temperature: float = 0.8, top_p: float = 0.95):
# Print the outputs.
results = []
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
results.append({"prompt": prompt, "generated_text": generated_text})
sampling_params = SamplingParams(temperature=temperature, top_p=top_p)
outputs = llm.generate(prompts, sampling_params)
return {"results": results}
```
# Print the outputs.
results = []
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
results.append({"prompt": prompt, "generated_text": generated_text})
return {"results": results}
```
Then, run the following code to deploy it to the cloud:
......@@ -62,47 +64,51 @@ cerebrium deploy
If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
```python
curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \
-H 'Content-Type: application/json' \
-H 'Authorization: <JWT TOKEN>' \
--data '{
"prompts": [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is"
]
}'
```
??? Command
```python
curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \
-H 'Content-Type: application/json' \
-H 'Authorization: <JWT TOKEN>' \
--data '{
"prompts": [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is"
]
}'
```
You should get a response like:
```python
{
"run_id": "52911756-3066-9ae8-bcc9-d9129d1bd262",
"result": {
"result": [
{
"prompt": "Hello, my name is",
"generated_text": " Sarah, and I'm a teacher. I teach elementary school students. One of"
},
{
"prompt": "The president of the United States is",
"generated_text": " elected every four years. This is a democratic system.\n\n5. What"
},
{
"prompt": "The capital of France is",
"generated_text": " Paris.\n"
},
{
"prompt": "The future of AI is",
"generated_text": " bright, but it's important to approach it with a balanced and nuanced perspective."
}
]
},
"run_time_ms": 152.53663063049316
}
```
??? Response
```python
{
"run_id": "52911756-3066-9ae8-bcc9-d9129d1bd262",
"result": {
"result": [
{
"prompt": "Hello, my name is",
"generated_text": " Sarah, and I'm a teacher. I teach elementary school students. One of"
},
{
"prompt": "The president of the United States is",
"generated_text": " elected every four years. This is a democratic system.\n\n5. What"
},
{
"prompt": "The capital of France is",
"generated_text": " Paris.\n"
},
{
"prompt": "The future of AI is",
"generated_text": " bright, but it's important to approach it with a balanced and nuanced perspective."
}
]
},
"run_time_ms": 152.53663063049316
}
```
You now have an autoscaling endpoint where you only pay for the compute you use!
docs/deployment/frameworks/dstack.md
View file @ f17aec0d
......@@ -26,75 +26,81 @@ dstack init
Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
```yaml
type: service
python: "3.11"
env:
- MODEL=NousResearch/Llama-2-7b-chat-hf
port: 8000
resources:
gpu: 24GB
commands:
- pip install vllm
- vllm serve $MODEL --port 8000
model:
format: openai
type: chat
name: NousResearch/Llama-2-7b-chat-hf
```
??? Config
```yaml
type: service
python: "3.11"
env:
- MODEL=NousResearch/Llama-2-7b-chat-hf
port: 8000
resources:
gpu: 24GB
commands:
- pip install vllm
- vllm serve $MODEL --port 8000
model:
format: openai
type: chat
name: NousResearch/Llama-2-7b-chat-hf
```
Then, run the following CLI for provisioning:
```console
$ dstack run . -f serve.dstack.yml
⠸ Getting run plan...
Configuration serve.dstack.yml
Project deep-diver-main
User deep-diver
Min resources 2..xCPU, 8GB.., 1xGPU (24GB)
Max price -
Max duration -
Spot policy auto
Retry policy no
# BACKEND REGION INSTANCE RESOURCES SPOT PRICE
1 gcp us-central1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
2 gcp us-east1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
3 gcp us-west1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
...
Shown 3 of 193 offers, $5.876 max
Continue? [y/n]: y
⠙ Submitting run...
⠏ Launching spicy-treefrog-1 (pulling)
spicy-treefrog-1 provisioning completed (running)
Service is published at ...
```
??? Command
```console
$ dstack run . -f serve.dstack.yml
⠸ Getting run plan...
Configuration serve.dstack.yml
Project deep-diver-main
User deep-diver
Min resources 2..xCPU, 8GB.., 1xGPU (24GB)
Max price -
Max duration -
Spot policy auto
Retry policy no
# BACKEND REGION INSTANCE RESOURCES SPOT PRICE
1 gcp us-central1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
2 gcp us-east1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
3 gcp us-west1 g2-standard-4 4xCPU, 16GB, 1xL4 (24GB), 100GB (disk) yes $0.223804
...
Shown 3 of 193 offers, $5.876 max
Continue? [y/n]: y
⠙ Submitting run...
⠏ Launching spicy-treefrog-1 (pulling)
spicy-treefrog-1 provisioning completed (running)
Service is published at ...
```
After the provisioning, you can interact with the model by using the OpenAI SDK:
```python
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.<gateway domain>",
api_key="<YOUR-DSTACK-SERVER-ACCESS-TOKEN>"
)
completion = client.chat.completions.create(
model="NousResearch/Llama-2-7b-chat-hf",
messages=[
{
"role": "user",
"content": "Compose a poem that explains the concept of recursion in programming.",
}
]
)
print(completion.choices[0].message.content)
```
??? Code
```python
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.<gateway domain>",
api_key="<YOUR-DSTACK-SERVER-ACCESS-TOKEN>"
)
completion = client.chat.completions.create(
model="NousResearch/Llama-2-7b-chat-hf",
messages=[
{
"role": "user",
"content": "Compose a poem that explains the concept of recursion in programming.",
}
]
)
print(completion.choices[0].message.content)
```
!!! note
dstack automatically handles authentication on the gateway using dstack's tokens. Meanwhile, if you don't want to configure a gateway, you can provision dstack `Task` instead of `Service`. The `Task` is for development purpose only. If you want to know more about hands-on materials how to serve vLLM using dstack, check out [this repository](https://github.com/dstackai/dstack-examples/tree/main/deployment/vllm)
docs/deployment/frameworks/haystack.md
View file @ f17aec0d
......@@ -27,29 +27,29 @@ vllm serve mistralai/Mistral-7B-Instruct-v0.1
- Use the `OpenAIGenerator` and `OpenAIChatGenerator` components in Haystack to query the vLLM server.
```python
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret
generator = OpenAIChatGenerator(
# for compatibility with the OpenAI API, a placeholder api_key is needed
api_key=Secret.from_token("VLLM-PLACEHOLDER-API-KEY"),
model="mistralai/Mistral-7B-Instruct-v0.1",
api_base_url="http://{your-vLLM-host-ip}:{your-vLLM-host-port}/v1",
generation_kwargs = {"max_tokens": 512}
)
response = generator.run(
messages=[ChatMessage.from_user("Hi. Can you help me plan my next trip to Italy?")]
)
print("-"*30)
print(response)
print("-"*30)
```
Output e.g.:
??? Code
```python
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret
generator = OpenAIChatGenerator(
# for compatibility with the OpenAI API, a placeholder api_key is needed
api_key=Secret.from_token("VLLM-PLACEHOLDER-API-KEY"),
model="mistralai/Mistral-7B-Instruct-v0.1",
api_base_url="http://{your-vLLM-host-ip}:{your-vLLM-host-port}/v1",
generation_kwargs = {"max_tokens": 512}
)
response = generator.run(
messages=[ChatMessage.from_user("Hi. Can you help me plan my next trip to Italy?")]
)
print("-"*30)
print(response)
print("-"*30)
```
```console
------------------------------
......
docs/deployment/frameworks/litellm.md
View file @ f17aec0d
......@@ -34,21 +34,23 @@ vllm serve qwen/Qwen1.5-0.5B-Chat
- Call it with litellm:
```python
import litellm
??? Code
messages = [{ "content": "Hello, how are you?","role": "user"}]
```python
import litellm
# hosted_vllm is prefix key word and necessary
response = litellm.completion(
model="hosted_vllm/qwen/Qwen1.5-0.5B-Chat", # pass the vllm model name
messages=messages,
api_base="http://{your-vllm-server-host}:{your-vllm-server-port}/v1",
temperature=0.2,
max_tokens=80)
print(response)
```
messages = [{ "content": "Hello, how are you?","role": "user"}]
# hosted_vllm is prefix key word and necessary
response = litellm.completion(
model="hosted_vllm/qwen/Qwen1.5-0.5B-Chat", # pass the vllm model name
messages=messages,
api_base="http://{your-vllm-server-host}:{your-vllm-server-port}/v1",
temperature=0.2,
max_tokens=80)
print(response)
```
### Embeddings
......
docs/deployment/frameworks/lws.md
View file @ f17aec0d
......@@ -17,99 +17,101 @@ vLLM can be deployed with [LWS](https://github.com/kubernetes-sigs/lws) on Kuber
Deploy the following yaml file `lws.yaml`
```yaml
apiVersion: leaderworkerset.x-k8s.io/v1
kind: LeaderWorkerSet
metadata:
name: vllm
spec:
replicas: 2
leaderWorkerTemplate:
size: 2
restartPolicy: RecreateGroupOnPodRestart
leaderTemplate:
metadata:
labels:
role: leader
spec:
containers:
- name: vllm-leader
image: docker.io/vllm/vllm-openai:latest
env:
- name: HUGGING_FACE_HUB_TOKEN
value: <your-hf-token>
command:
- sh
- -c
- "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=$(LWS_GROUP_SIZE);
python3 -m vllm.entrypoints.openai.api_server --port 8080 --model meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline_parallel_size 2"
resources:
limits:
nvidia.com/gpu: "8"
memory: 1124Gi
ephemeral-storage: 800Gi
requests:
ephemeral-storage: 800Gi
cpu: 125
ports:
- containerPort: 8080
readinessProbe:
tcpSocket:
port: 8080
initialDelaySeconds: 15
periodSeconds: 10
volumeMounts:
- mountPath: /dev/shm
name: dshm
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: 15Gi
workerTemplate:
spec:
containers:
- name: vllm-worker
image: docker.io/vllm/vllm-openai:latest
command:
- sh
- -c
- "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(LWS_LEADER_ADDRESS)"
resources:
limits:
nvidia.com/gpu: "8"
memory: 1124Gi
ephemeral-storage: 800Gi
requests:
ephemeral-storage: 800Gi
cpu: 125
env:
- name: HUGGING_FACE_HUB_TOKEN
value: <your-hf-token>
volumeMounts:
- mountPath: /dev/shm
name: dshm
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: 15Gi
---
apiVersion: v1
kind: Service
metadata:
name: vllm-leader
spec:
ports:
- name: http
port: 8080
protocol: TCP
targetPort: 8080
selector:
leaderworkerset.sigs.k8s.io/name: vllm
role: leader
type: ClusterIP
```
??? Yaml
```yaml
apiVersion: leaderworkerset.x-k8s.io/v1
kind: LeaderWorkerSet
metadata:
name: vllm
spec:
replicas: 2
leaderWorkerTemplate:
size: 2
restartPolicy: RecreateGroupOnPodRestart
leaderTemplate:
metadata:
labels:
role: leader
spec:
containers:
- name: vllm-leader
image: docker.io/vllm/vllm-openai:latest
env:
- name: HUGGING_FACE_HUB_TOKEN
value: <your-hf-token>
command:
- sh
- -c
- "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=$(LWS_GROUP_SIZE);
python3 -m vllm.entrypoints.openai.api_server --port 8080 --model meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline_parallel_size 2"
resources:
limits:
nvidia.com/gpu: "8"
memory: 1124Gi
ephemeral-storage: 800Gi
requests:
ephemeral-storage: 800Gi
cpu: 125
ports:
- containerPort: 8080
readinessProbe:
tcpSocket:
port: 8080
initialDelaySeconds: 15
periodSeconds: 10
volumeMounts:
- mountPath: /dev/shm
name: dshm
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: 15Gi
workerTemplate:
spec:
containers:
- name: vllm-worker
image: docker.io/vllm/vllm-openai:latest
command:
- sh
- -c
- "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(LWS_LEADER_ADDRESS)"
resources:
limits:
nvidia.com/gpu: "8"
memory: 1124Gi
ephemeral-storage: 800Gi
requests:
ephemeral-storage: 800Gi
cpu: 125
env:
- name: HUGGING_FACE_HUB_TOKEN
value: <your-hf-token>
volumeMounts:
- mountPath: /dev/shm
name: dshm
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: 15Gi
---
apiVersion: v1
kind: Service
metadata:
name: vllm-leader
spec:
ports:
- name: http
port: 8080
protocol: TCP
targetPort: 8080
selector:
leaderworkerset.sigs.k8s.io/name: vllm
role: leader
type: ClusterIP
```
```bash
kubectl apply -f lws.yaml
......@@ -175,25 +177,27 @@ curl http://localhost:8080/v1/completions \
The output should be similar to the following
```text
{
"id": "cmpl-1bb34faba88b43f9862cfbfb2200949d",
"object": "text_completion",
"created": 1715138766,
"model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
"choices": [
??? Output
```text
{
"index": 0,
"text": " top destination for foodies, with",
"logprobs": null,
"finish_reason": "length",
"stop_reason": null
"id": "cmpl-1bb34faba88b43f9862cfbfb2200949d",
"object": "text_completion",
"created": 1715138766,
"model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
"choices": [
{
"index": 0,
"text": " top destination for foodies, with",
"logprobs": null,
"finish_reason": "length",
"stop_reason": null
}
],
"usage": {
"prompt_tokens": 5,
"total_tokens": 12,
"completion_tokens": 7
}
}
],
"usage": {
"prompt_tokens": 5,
"total_tokens": 12,
"completion_tokens": 7
}
}
```
```
docs/deployment/frameworks/skypilot.md
View file @ f17aec0d
......@@ -24,48 +24,50 @@ sky check
See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm/serve.yaml).
```yaml
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log &
echo 'Waiting for vllm api server to start...'
while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true
python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
-m $MODEL_NAME \
--port 8811 \
--model-url http://localhost:8081/v1 \
--stop-token-ids 128009,128001
```
??? Yaml
```yaml
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log &
echo 'Waiting for vllm api server to start...'
while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true
python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
-m $MODEL_NAME \
--port 8811 \
--model-url http://localhost:8081/v1 \
--stop-token-ids 128009,128001
```
Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):
......@@ -93,68 +95,67 @@ HF_TOKEN="your-huggingface-token" \
SkyPilot can scale up the service to multiple service replicas with built-in autoscaling, load-balancing and fault-tolerance. You can do it by adding a services section to the YAML file.
```yaml
service:
replicas: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_completion_tokens: 1
```
<details>
<summary>Click to see the full recipe YAML</summary>
```yaml
service:
replicas: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
??? Yaml
```yaml
service:
replicas: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_completion_tokens: 1
```
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
</details>
??? Yaml
```yaml
service:
replicas: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_completion_tokens: 1
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
Start the serving the Llama-3 8B model on multiple replicas:
......@@ -170,8 +171,7 @@ Wait until the service is ready:
watch -n10 sky serve status vllm
```
<details>
<summary>Example outputs:</summary>
Example outputs:
```console
Services
......@@ -184,29 +184,29 @@ vllm 1 1 xx.yy.zz.121 18 mins ago 1x GCP([Spot]{'L4': 1}) R
vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP([Spot]{'L4': 1}) READY us-east4
```
</details>
After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
```console
ENDPOINT=$(sky serve status --endpoint 8081 vllm)
curl -L http://$ENDPOINT/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Who are you?"
}
],
"stop_token_ids": [128009, 128001]
}'
```
??? Commands
```bash
ENDPOINT=$(sky serve status --endpoint 8081 vllm)
curl -L http://$ENDPOINT/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Who are you?"
}
],
"stop_token_ids": [128009, 128001]
}'
```
To enable autoscaling, you could replace the `replicas` with the following configs in `service`:
......@@ -220,57 +220,54 @@ service:
This will scale the service up to when the QPS exceeds 2 for each replica.
<details>
<summary>Click to see the full recipe YAML</summary>
```yaml
service:
replica_policy:
min_replicas: 2
max_replicas: 4
target_qps_per_replica: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_completion_tokens: 1
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
</details>
??? Yaml
```yaml
service:
replica_policy:
min_replicas: 2
max_replicas: 4
target_qps_per_replica: 2
# An actual request for readiness probe.
readiness_probe:
path: /v1/chat/completions
post_data:
model: $MODEL_NAME
messages:
- role: user
content: Hello! What is your name?
max_completion_tokens: 1
resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True
disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best
ports: 8081 # Expose to internet traffic.
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
run: |
conda activate vllm
echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \
--model $MODEL_NAME \
--trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
To update the service with the new config:
......@@ -288,38 +285,35 @@ sky serve down vllm
It is also possible to access the Llama-3 service with a separate GUI frontend, so the user requests send to the GUI will be load-balanced across replicas.
<details>
<summary>Click to see the full GUI YAML</summary>
??? Yaml
```yaml
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
resources:
cpus: 2
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
# Install Gradio for web UI.
pip install gradio openai
run: |
conda activate vllm
export PATH=$PATH:/sbin
echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true
python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
-m $MODEL_NAME \
--port 8811 \
--model-url http://$ENDPOINT/v1 \
--stop-token-ids 128009,128001 | tee ~/gradio.log
```
```yaml
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
resources:
cpus: 2
</details>
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
# Install Gradio for web UI.
pip install gradio openai
run: |
conda activate vllm
export PATH=$PATH:/sbin
echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true
python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
-m $MODEL_NAME \
--port 8811 \
--model-url http://$ENDPOINT/v1 \
--stop-token-ids 128009,128001 | tee ~/gradio.log
```
1. Start the chat web UI:
......
docs/deployment/integrations/production-stack.md
View file @ f17aec0d
......@@ -60,22 +60,22 @@ And then you can send out a query to the OpenAI-compatible API to check the avai
curl -o- http://localhost:30080/models
```
Expected output:
??? Output
```json
{
"object": "list",
"data": [
```json
{
"id": "facebook/opt-125m",
"object": "model",
"created": 1737428424,
"owned_by": "vllm",
"root": null
"object": "list",
"data": [
{
"id": "facebook/opt-125m",
"object": "model",
"created": 1737428424,
"owned_by": "vllm",
"root": null
}
]
}
]
}
```
```
To send an actual chatting request, you can issue a curl request to the OpenAI `/completion` endpoint:
......@@ -89,23 +89,23 @@ curl -X POST http://localhost:30080/completions \
}'
```
Expected output:
??? Output
```json
{
"id": "completion-id",
"object": "text_completion",
"created": 1737428424,
"model": "facebook/opt-125m",
"choices": [
```json
{
"text": " there was a brave knight who...",
"index": 0,
"finish_reason": "length"
"id": "completion-id",
"object": "text_completion",
"created": 1737428424,
"model": "facebook/opt-125m",
"choices": [
{
"text": " there was a brave knight who...",
"index": 0,
"finish_reason": "length"
}
]
}
]
}
```
```
### Uninstall
......@@ -121,23 +121,25 @@ sudo helm uninstall vllm
The core vLLM production stack configuration is managed with YAML. Here is the example configuration used in the installation above:
```yaml
servingEngineSpec:
runtimeClassName: ""
modelSpec:
- name: "opt125m"
repository: "vllm/vllm-openai"
tag: "latest"
modelURL: "facebook/opt-125m"
??? Yaml
replicaCount: 1
```yaml
servingEngineSpec:
runtimeClassName: ""
modelSpec:
- name: "opt125m"
repository: "vllm/vllm-openai"
tag: "latest"
modelURL: "facebook/opt-125m"
requestCPU: 6
requestMemory: "16Gi"
requestGPU: 1
replicaCount: 1
pvcStorage: "10Gi"
```
requestCPU: 6
requestMemory: "16Gi"
requestGPU: 1
pvcStorage: "10Gi"
```
In this YAML configuration:
* **`modelSpec`** includes:
......
docs/deployment/k8s.md
View file @ f17aec0d
......@@ -29,85 +29,89 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
```bash
cat <<EOF |kubectl apply -f -
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: vllm-models
spec:
accessModes:
- ReadWriteOnce
volumeMode: Filesystem
resources:
requests:
storage: 50Gi
---
apiVersion: v1
kind: Secret
metadata:
name: hf-token-secret
type: Opaque
data:
token: $(HF_TOKEN)
EOF
```
??? Config
```bash
cat <<EOF |kubectl apply -f -
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: vllm-models
spec:
accessModes:
- ReadWriteOnce
volumeMode: Filesystem
resources:
requests:
storage: 50Gi
---
apiVersion: v1
kind: Secret
metadata:
name: hf-token-secret
type: Opaque
data:
token: $(HF_TOKEN)
EOF
```
Next, start the vLLM server as a Kubernetes Deployment and Service:
```bash
cat <<EOF |kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-server
spec:
replicas: 1
selector:
matchLabels:
app.kubernetes.io/name: vllm
template:
??? Config
```bash
cat <<EOF |kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app.kubernetes.io/name: vllm
name: vllm-server
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
command: ["/bin/sh", "-c"]
args: [
"vllm serve meta-llama/Llama-3.2-1B-Instruct"
]
env:
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
ports:
- containerPort: 8000
volumeMounts:
replicas: 1
selector:
matchLabels:
app.kubernetes.io/name: vllm
template:
metadata:
labels:
app.kubernetes.io/name: vllm
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
command: ["/bin/sh", "-c"]
args: [
"vllm serve meta-llama/Llama-3.2-1B-Instruct"
]
env:
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
ports:
- containerPort: 8000
volumeMounts:
- name: llama-storage
mountPath: /root/.cache/huggingface
volumes:
- name: llama-storage
mountPath: /root/.cache/huggingface
volumes:
- name: llama-storage
persistentVolumeClaim:
claimName: vllm-models
---
apiVersion: v1
kind: Service
metadata:
name: vllm-server
spec:
selector:
app.kubernetes.io/name: vllm
ports:
- protocol: TCP
port: 8000
targetPort: 8000
type: ClusterIP
EOF
```
persistentVolumeClaim:
claimName: vllm-models
---
apiVersion: v1
kind: Service
metadata:
name: vllm-server
spec:
selector:
app.kubernetes.io/name: vllm
ports:
- protocol: TCP
port: 8000
targetPort: 8000
type: ClusterIP
EOF
```
We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
......@@ -128,6 +132,9 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
PVC is used to store the model cache and it is optional, you can use hostPath or other storage options
<details>
<summary>Yaml</summary>
```yaml
apiVersion: v1
kind: PersistentVolumeClaim
......@@ -144,6 +151,8 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
volumeMode: Filesystem
```
</details>
Secret is optional and only required for accessing gated models, you can skip this step if you are not using gated models
```yaml
......@@ -156,13 +165,16 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
stringData:
token: "REPLACE_WITH_TOKEN"
```
Next to create the deployment file for vLLM to run the model server. The following example deploys the `Mistral-7B-Instruct-v0.3` model.
Here are two examples for using NVIDIA GPU and AMD GPU.
NVIDIA GPU:
<details>
<summary>Yaml</summary>
```yaml
apiVersion: apps/v1
kind: Deployment
......@@ -233,10 +245,15 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
periodSeconds: 5
```
</details>
AMD GPU:
You can refer to the `deployment.yaml` below if using AMD ROCm GPU like MI300X.
<details>
<summary>Yaml</summary>
```yaml
apiVersion: apps/v1
kind: Deployment
......@@ -305,12 +322,17 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
mountPath: /dev/shm
```
</details>
You can get the full example with steps and sample yaml files from <https://github.com/ROCm/k8s-device-plugin/tree/master/example/vllm-serve>.
2. Create a Kubernetes Service for vLLM
Next, create a Kubernetes Service file to expose the `mistral-7b` deployment:
<details>
<summary>Yaml</summary>
```yaml
apiVersion: v1
kind: Service
......@@ -330,6 +352,8 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
type: ClusterIP
```
</details>
3. Deploy and Test
Apply the deployment and service configurations using `kubectl apply -f <filename>`:
......
docs/deployment/nginx.md
View file @ f17aec0d
......@@ -36,23 +36,25 @@ docker build . -f Dockerfile.nginx --tag nginx-lb
Create a file named `nginx_conf/nginx.conf`. Note that you can add as many servers as you'd like. In the below example we'll start with two. To add more, add another `server vllmN:8000 max_fails=3 fail_timeout=10000s;` entry to `upstream backend`.
```console
upstream backend {
least_conn;
server vllm0:8000 max_fails=3 fail_timeout=10000s;
server vllm1:8000 max_fails=3 fail_timeout=10000s;
}
server {
listen 80;
location / {
proxy_pass http://backend;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
??? Config
```console
upstream backend {
least_conn;
server vllm0:8000 max_fails=3 fail_timeout=10000s;
server vllm1:8000 max_fails=3 fail_timeout=10000s;
}
}
```
server {
listen 80;
location / {
proxy_pass http://backend;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
}
```
[](){ #nginxloadbalancer-nginx-vllm-container }
......@@ -93,30 +95,32 @@ Notes:
- The below example assumes GPU backend used. If you are using CPU backend, remove `--gpus device=ID`, add `VLLM_CPU_KVCACHE_SPACE` and `VLLM_CPU_OMP_THREADS_BIND` environment variables to the docker run command.
- Adjust the model name that you want to use in your vLLM servers if you don't want to use `Llama-2-7b-chat-hf`.
```console
mkdir -p ~/.cache/huggingface/hub/
hf_cache_dir=~/.cache/huggingface/
docker run \
-itd \
--ipc host \
--network vllm_nginx \
--gpus device=0 \
--shm-size=10.24gb \
-v $hf_cache_dir:/root/.cache/huggingface/ \
-p 8081:8000 \
--name vllm0 vllm \
--model meta-llama/Llama-2-7b-chat-hf
docker run \
-itd \
--ipc host \
--network vllm_nginx \
--gpus device=1 \
--shm-size=10.24gb \
-v $hf_cache_dir:/root/.cache/huggingface/ \
-p 8082:8000 \
--name vllm1 vllm \
--model meta-llama/Llama-2-7b-chat-hf
```
??? Commands
```console
mkdir -p ~/.cache/huggingface/hub/
hf_cache_dir=~/.cache/huggingface/
docker run \
-itd \
--ipc host \
--network vllm_nginx \
--gpus device=0 \
--shm-size=10.24gb \
-v $hf_cache_dir:/root/.cache/huggingface/ \
-p 8081:8000 \
--name vllm0 vllm \
--model meta-llama/Llama-2-7b-chat-hf
docker run \
-itd \
--ipc host \
--network vllm_nginx \
--gpus device=1 \
--shm-size=10.24gb \
-v $hf_cache_dir:/root/.cache/huggingface/ \
-p 8082:8000 \
--name vllm1 vllm \
--model meta-llama/Llama-2-7b-chat-hf
```
!!! note
If you are behind proxy, you can pass the proxy settings to the docker run command via `-e http_proxy=$http_proxy -e https_proxy=$https_proxy`.
......
docs/design/arch_overview.md
View file @ f17aec0d
......@@ -22,31 +22,33 @@ server.
Here is a sample of `LLM` class usage:
```python
from vllm import LLM, SamplingParams
# Define a list of input prompts
prompts = [
"Hello, my name is",
"The capital of France is",
"The largest ocean is",
]
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Print the generated outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
??? Code
```python
from vllm import LLM, SamplingParams
# Define a list of input prompts
prompts = [
"Hello, my name is",
"The capital of France is",
"The largest ocean is",
]
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Print the generated outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
More API details can be found in the [Offline Inference](#offline-inference-api) section of the API docs.
......@@ -178,32 +180,34 @@ vision-language model.
To avoid accidentally passing incorrect arguments, the constructor is now keyword-only. This ensures that the constructor will raise an error if old configurations are passed. vLLM developers have already made this change for all models within vLLM. For out-of-tree registered models, developers need to update their models, for example by adding shim code to adapt the old constructor signature to the new one:
```python
class MyOldModel(nn.Module):
def __init__(
self,
config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
...
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
super().__init__(config, cache_config, quant_config, lora_config, prefix)
if __version__ >= "0.6.4":
MyModel = MyNewModel
else:
MyModel = MyOldModel
```
??? Code
```python
class MyOldModel(nn.Module):
def __init__(
self,
config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
...
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
super().__init__(config, cache_config, quant_config, lora_config, prefix)
if __version__ >= "0.6.4":
MyModel = MyNewModel
else:
MyModel = MyOldModel
```
This way, the model can work with both old and new versions of vLLM.
......
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