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[Frontend] Pythonic tool parser (#9859)

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Signed-off-by: Mike Depinet <mike@fixie.ai>
This commit is contained in:
Mike Depinet
2024-11-13 20:14:34 -08:00
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parent e0853b6508
commit f67ce05d0b
10 changed files with 806 additions and 26 deletions
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docs/source/serving/openai_compatible_server.md
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@@ -162,7 +162,7 @@ vllm serve <model> --chat-template ./path-to-chat-template.jinja
vLLM community provides a set of chat templates for popular models. You can find them in the examples
directory [here](https://github.com/vllm-project/vllm/tree/main/examples/)
With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies
With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies
both a `type` and a `text` field. An example is provided below:
```python
completion = client.chat.completions.create(
@@ -172,10 +172,10 @@ completion = client.chat.completions.create(
]
)
```
Most chat templates for LLMs expect the `content` to be a `string` but there are some newer models like
Most chat templates for LLMs expect the `content` to be a `string` but there are some newer models like
`meta-llama/Llama-Guard-3-1B` that expect the content to be parsed with the new OpenAI spec. In order to choose which
format the content needs to be parsed in by vLLM, please use the `--chat-template-text-format` argument to specify
between `string` or `openai`. The default value is `string` and vLLM internally converts both spec formats to match
between `string` or `openai`. The default value is `string` and vLLM internally converts both spec formats to match
this, unless explicitly specified.
@@ -191,8 +191,8 @@ this, unless explicitly specified.
### Config file
The `serve` module can also accept arguments from a config file in
`yaml` format. The arguments in the yaml must be specified using the
long form of the argument outlined [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#command-line-arguments-for-the-server):
`yaml` format. The arguments in the yaml must be specified using the
long form of the argument outlined [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#command-line-arguments-for-the-server):
For example:
@@ -208,7 +208,7 @@ uvicorn-log-level: "info"
$ vllm serve SOME_MODEL --config config.yaml
```
---
**NOTE**
**NOTE**
In case an argument is supplied simultaneously using command line and the config file, the value from the commandline will take precedence.
The order of priorities is `command line > config file values > defaults`.
@@ -222,30 +222,30 @@ Please see below for recommended configuration and chat templates to use when fu
### Named Function Calling
vLLM supports named function calling in the chat completion API by default. It does so using Outlines, so this is
enabled by default, and will work with any supported model. You are guaranteed a validly-parsable function call - not a
high-quality one.
vLLM supports named function calling in the chat completion API by default. It does so using Outlines, so this is
enabled by default, and will work with any supported model. You are guaranteed a validly-parsable function call - not a
high-quality one.
vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the `tools` parameter.
To use a named function, you need to define the functions in the `tools` parameter of the chat completion request, and
specify the `name` of one of the tools in the `tool_choice` parameter of the chat completion request.
To use a named function, you need to define the functions in the `tools` parameter of the chat completion request, and
specify the `name` of one of the tools in the `tool_choice` parameter of the chat completion request.
### Automatic Function Calling
To enable this feature, you should set the following flags:
* `--enable-auto-tool-choice` -- **mandatory** Auto tool choice. tells vLLM that you want to enable the model to generate its own tool calls when it
* `--enable-auto-tool-choice` -- **mandatory** Auto tool choice. tells vLLM that you want to enable the model to generate its own tool calls when it
deems appropriate.
* `--tool-call-parser` -- select the tool parser to use (listed below). Additional tool parsers
* `--tool-call-parser` -- select the tool parser to use (listed below). Additional tool parsers
will continue to be added in the future, and also can register your own tool parsers in the `--tool-parser-plugin`.
* `--tool-parser-plugin` -- **optional** tool parser plugin used to register user defined tool parsers into vllm, the registered tool parser name can be specified in `--tool-call-parser`.
* `--chat-template` -- **optional** for auto tool choice. the path to the chat template which handles `tool`-role messages and `assistant`-role messages
that contain previously generated tool calls. Hermes, Mistral and Llama models have tool-compatible chat templates in their
`tokenizer_config.json` files, but you can specify a custom template. This argument can be set to `tool_use` if your model has a tool use-specific chat
* `--chat-template` -- **optional** for auto tool choice. the path to the chat template which handles `tool`-role messages and `assistant`-role messages
that contain previously generated tool calls. Hermes, Mistral and Llama models have tool-compatible chat templates in their
`tokenizer_config.json` files, but you can specify a custom template. This argument can be set to `tool_use` if your model has a tool use-specific chat
template configured in the `tokenizer_config.json`. In this case, it will be used per the `transformers` specification. More on this [here](https://huggingface.co/docs/transformers/en/chat_templating#why-do-some-models-have-multiple-templates)
from HuggingFace; and you can find an example of this in a `tokenizer_config.json` [here](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B/blob/main/tokenizer_config.json)
If your favorite tool-calling model is not supported, please feel free to contribute a parser & tool use chat template!
If your favorite tool-calling model is not supported, please feel free to contribute a parser & tool use chat template!
#### Hermes Models (`hermes`)
@@ -256,8 +256,8 @@ All Nous Research Hermes-series models newer than Hermes 2 Pro should be support
* `NousResearch/Hermes-3-*`
_Note that the Hermes 2 **Theta** models are known to have degraded tool call quality & capabilities due to the merge
step in their creation_.
_Note that the Hermes 2 **Theta** models are known to have degraded tool call quality & capabilities due to the merge
step in their creation_.
Flags: `--tool-call-parser hermes`
@@ -269,9 +269,9 @@ Supported models:
* Additional mistral function-calling models are compatible as well.
Known issues:
1. Mistral 7B struggles to generate parallel tool calls correctly.
2. Mistral's `tokenizer_config.json` chat template requires tool call IDs that are exactly 9 digits, which is
much shorter than what vLLM generates. Since an exception is thrown when this condition
1. Mistral 7B struggles to generate parallel tool calls correctly.
2. Mistral's `tokenizer_config.json` chat template requires tool call IDs that are exactly 9 digits, which is
much shorter than what vLLM generates. Since an exception is thrown when this condition
is not met, the following additional chat templates are provided:
* `examples/tool_chat_template_mistral.jinja` - this is the "official" Mistral chat template, but tweaked so that
@@ -291,11 +291,11 @@ Supported models:
* `meta-llama/Meta-Llama-3.1-405B-Instruct`
* `meta-llama/Meta-Llama-3.1-405B-Instruct-FP8`
The tool calling that is supported is the [JSON based tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling).
The tool calling that is supported is the [JSON based tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling). For [pythonic tool calling](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/text_prompt_format.md#zero-shot-function-calling) in Llama-3.2 models, see the `pythonic` tool parser below.
Other tool calling formats like the built in python tool calling or custom tool calling are not supported.
Known issues:
1. Parallel tool calls are not supported.
1. Parallel tool calls are not supported.
2. The model can generate parameters with a wrong format, such as generating
an array serialized as string instead of an array.
@@ -341,6 +341,34 @@ AI21's Jamba-1.5 models are supported.
Flags: `--tool-call-parser jamba`
#### Models with Pythonic Tool Calls (`pythonic`)
A growing number of models output a python list to represent tool calls instead of using JSON. This has the advantage of inherently supporting parallel tool calls and removing ambiguity around the JSON schema required for tool calls. The `pythonic` tool parser can support such models.
As a concrete example, these models may look up the weather in San Francisco and Seattle by generating:
```python
[get_weather(city='San Francisco', metric='celsius'), get_weather(city='Seattle', metric='celsius')]
```
Limitations:
* The model must not generate both text and tool calls in the same generation. This may not be hard to change for a specific model, but the community currently lacks consensus on which tokens to emit when starting and ending tool calls. (In particular, the Llama 3.2 models emit no such tokens.)
* Llama's smaller models struggle to use tools effectively.
Example supported models:
* `meta-llama/Llama-3.2-1B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`)
* `meta-llama/Llama-3.2-3B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`)
* `Team-ACE/ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`)
* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`)
Flags: `--tool-call-parser pythonic --chat-template {see_above}`
---
**WARNING**
Llama's smaller models frequently fail to emit tool calls in the correct format. Your mileage may vary.
---
### How to write a tool parser plugin
A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py.