This PR adds support for reading the `OAI_ENABLED` env var which will
changes the function called when the `/invocations` is called.
If `OAI_ENABLED=true` the `chat_completions` method is used otherwise it
defaults to `compat_generate`.
example running the router
```bash
OAI_ENABLED=true \
cargo run -- \
--tokenizer-name mistralai/Mistral-7B-Instruct-v0.2
```
example request
```bash
curl localhost:3000/invocations \
-X POST \
-d '{ "model": "tgi", "messages": [ { "role": "user", "content": "What is the IP address of the Google DNS servers?" } ], "stream": false, "max_tokens": 20, "logprobs": true, "seed": 0 }' \
-H 'Content-Type: application/json' | jq
```
**please let me know if any naming changes are needed or if any other
routes need similar functionality.
This PR just bumps the latest rust version and makes clippy happy
```bash
cargo clippy --all -- -D warnings
# Finished dev [unoptimized + debuginfo] target(s) in 0.10s
```
This PR adds support to handle the custom jinja function
`raise_exception` and passes the `bos` and `eos` tokens into the
template
Additionally this PR adds 3 tests to validate and show examples of what
can and cannot be parsed currently.
```bash
cargo test --package text-generation-router --lib -- infer::tests --nocapture
# Finished test [unoptimized + debuginfo] target(s) in 7.82s
# Running unittests src/lib.rs (target/debug/deps/text_generation_router-18a0bbf99c2ca1b4)
# running 3 tests
# test infer::tests::test_chat_template_valid_with_raise ... ok
# test infer::tests::test_chat_template ... ok
# test infer::tests::test_chat_template_invalid_with_raise ... ok
# test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 15 filtered out; finished in 0.00s
```
This PR adds support to make TGI a drop in replacement for OpenAI
clients by exposing the same HTTP interface.
Notes
- TGI inits a single model at startup so the `model` field is unused in
HTTP requests.
- `max_tokens` and `stream` should work as expected but other params may
be (unimplemented or not supported)
General approach
- fetch the `tokenizer_config` at startup from the hub
- pass `tokenizer_config` into `Infer` so we have it at request time
- use the `chat_template` on the config to format chat request
- parse jinja template and render chat string
- pass inputs into existing generate function
- wrap generation output in expected structure before returning
# How to test
### Streaming curl
```bash
curl localhost:3000/v1/chat/completions \
-X POST \
-d '{
"model": "tgi",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What is deep learning?"
}
],
"stream": true,
"max_tokens": 20
}' \
-H 'Content-Type: application/json'
```
It is also possible to use the `openai` python library and change the
base url
### 🌊 STREAMING REQUEST
```python
from openai import OpenAI
# init the client but point it to TGI
client = OpenAI(
base_url="http://localhost:3000/v1",
api_key="not needed for a local LLM"
)
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "system", "content": "You are a helpful assistant." },
{"role": "user", "content": "What is deep learning?"}
],
stream=True
)
# iterate and print stream
for message in chat_completion:
print(message)
# ChatCompletionChunk(id='', choices=[Choice(delta=ChoiceDelta(content=' that', function_call=None, role='assistant', tool_calls=None), finish_reason=None, index=2, logprobs=None)], created=1704486761, model='', object='text_completion', system_fingerprint='')
```
### 🚗 SYNCHRONOUS REQUEST
```python
from openai import OpenAI
# init the client but point it to TGI
client = OpenAI(
base_url="http://localhost:3000/v1",
api_key="not needed for a local LLM"
)
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "system", "content": "You are a helpful assistant." },
{"role": "user", "content": "What is deep learning?"}
],
stream=False
)
print(chat_completion)
# ChatCompletion(id='', choices=[Choice(finish_reason=None, index=0, logprobs=None, message=ChatCompletionMessage(content='\nDeep learning is a new field of research that has been gaining traction in the last ...', role='assistant', function_call=None, tool_calls=None))], created=1704486762, model='', object='text_completion', system_fingerprint='', usage=CompletionUsage(completion_tokens=100, prompt_tokens=76, total_tokens=176))
```
## How to run dev
```bash
cd text-generation-inference/server
MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 text-generation-server serve --trust-remote-code gpt2
```
***note many of the existing `chat_templates` use non standard `jinja`
(ie. adding a `raise` to the template) which will throw an error when
parsing; hence using `upstage/SOLAR-10.7B-Instruct-v1.0` since it has a
valid template
```bash
cd text-generation-inference/router
cargo run -- --tokenizer-name upstage/SOLAR-10.7B-Instruct-v1.0
```
trigger
```bash
curl localhost:3000/v1/chat/completions \
-X POST \
-d '{ "model": "gpt-3.5-turbo", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the IP address of the Google DNS servers?" } ], "stream": true, "max_tokens": 20, "logprobs": true }' \
-H 'Content-Type: application/json'
```
^ supports `stream: true` and `stream: false` requests
# What does this PR do?
Fixes#637
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# What does this PR do?
See #1165
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---------
Co-authored-by: Florian Zimmermeister <flozi00.fz@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-24-153.ec2.internal>
# What does this PR do?
Now clients which do not specify a max_length will be implying
`max_new_tokens = max_total_tokens - input_length`.
This is a serious change, but which seems more in line with what users
expect from standing server.
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# What does this PR do?
Upgrade all relevant versions and dependencies.
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# What does this PR do?
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# What does this PR do?
In title. Adds argument `--hostname` in router to support something like
`--hostname ::`. Tested with
```commandline
cargo run -- --port 8080 --hostname ::
curl -I -X GET 'http://[::1]:8080/health' # failed before this commit
```
Trigger CI
---------
Co-authored-by: Phil Chen <philchen2000@gmail.com>
# This PR adds an http header option to disable buffering for the
generate_stream endpoint response stream.
Problem: If a model is run behind a proxy server such as nginx that has
buffering enabled then the response stream from generate_stream gets
aggregated into a single response which basically disables streaming.
Instead of getting a chunked response where each token is presented over
time the response presents everything all at once.
Solution: This change adds the `X-Accel-Buffering` http header which
disables buffering for the generate_stream response, allowing the
response to stream properly.
This PR starts the minimal possible amount of explanation I could think
of. It tries to explain how dynamic batching occurs, the interactions
with past key values and ignores the padding problem.
Maybe some drawings could help too but I kept it to text for now.
drbh