# What does this PR do?
Literally just adds the name field to the Message class.
I verified this change by building a new docker container (using the
`Dockerfile` in the repo) and trialing with a `chat_template` that uses
the `name` field.
Here's the previous behavior:
Input messages:
```
{
"messages": [
{"role": "system", "content": "You are a succinct but helpful AI Assistant listening to a chat server. Address everyone by @<username>"},
{"role": "user", "name": "Aaron", "content": "Hello There!"},
{"role": "assistant", "content": " Hello @Aaron! How can I assist you today?"},
{"role": "user", "name": "Sally", "content": "Hiya everyone. Is @Aaron is this room?"}
],
"model": "meta-llama/Llama-2-7b-chat-hf"
}
```
Response before the modification:
```
Hello @Aaron! Yes, you are in the chat room. How can I assist you today? 😊
Hiya everyone! *waves* It's great to see you all here. Is there something on your mind that you'd like to talk about or ask? I'm here to listen and help in any way I can. 🤖
```
Response after my modification:
```
Hello @Sally! Yes, @Aaron is currently in the chat room. How may I assist you today?
```
Fixes#1558
## Before submitting
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## Who can review?
@Narsil
---------
Co-authored-by: Aaron Mihalik <aaron.mihalik@parsons.us>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
This PR adds a simple custom `deserialize_with` function that parses a
string or an object with a content property. This should help support
more token configuration files stored on the hub
This PR adds support to read the `add_generation_prompt` from the config
and use it in the chat template. If `add_generation_prompt` does not
exist we default to false
# What does this PR do?
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This PR fixes the issue with loading a local tokenizer config.
Previously the default functionality would look in the current working
directory. Now if a local model path is specified we will check that
directory for the tokenizer_config.
## Examples of valid commands
uses tokenizer_config from hub
```
text-generation-launcher --model-id HuggingFaceH4/zephyr-7b-beta
```
use tokenizer_config from local model path
```
text-generation-launcher \
--model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/
```
use specific tokenizer_config file
```
text-generation-launcher \
--model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/ \
--tokenizer-config-path ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/tokenizer_config.json
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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# What does this PR do?
Sending compute type from the environment instead of hardcoded string
Using env is slow, therefore getting it from global state instead.
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# What does this PR do?
Ideally this is done client side, but this is a recurring request,
therefore we implemented it.
- Runs only if rust tokenizer is present (not encumbering the main
inference pipeline is important).
- Returns simple results, ID, text (gotten with offsets from the
original string) and offsets (so users can do things like highlighting
text).
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This PR adds a new page to the docs that describes the Messages API and
how to use it.
Additionally this page will contain cloud provider specific information
for enabling and using this feature. This PR includes a SageMaker
example/information.
This PR makes some minor tweaks to the new OpenAI-compatible chat
endpoint #1427 in `GenerateParameters`:
- Disables `decoder_input_details` when streaming is enabled. This was
causing all streaming chat requests to fail before, since
[`decoder_input_details`==true is not enabled when streaming
tokens](98e5faff9d/router/src/validation.rs (L406)).
- Passes through `temperature` and `top_p` hyperparameters from the API
request to `GenerateParameters`
## Testing
```bash
curl localhost:8080/v1/chat/completions \
-X POST \
-d '{
"model": "",
"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'
```
Should work correctly. Currently, most recent release from `main`
returns error:
```
data:{"error":"Input validation error: `decoder_input_details` == true is not supported when streaming tokens","error_type":"validation"}
```
It's my first time contributing to this project, so I could be missing
something. Would especially appreciate @drbh's eyes on this one
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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Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
# 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.
The only difference is that now it pushes to
registry.internal.huggingface.tech/api-inference/community/text-generation-inference/sagemaker:...
instead of
registry.internal.huggingface.tech/api-inference/community/text-generation-inference:sagemaker-...
---------
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
@njhill, @yk FYI
generated_text was concatenated to the user prompt for legacy reason. We
want to remove this behaviour as we don't think it is useful and even
detrimonial to usability.
We also remove the unused Vec.
There's currently a discrepancy in the tokenization between the router
and python server code. The latter includes special tokens but former
does not.
This results in a token count mismatch for seq2seq models such as mt0
where the tokenizer emits an EOS token at the end.
This in turn results in some unexpected/incorrect output, in particular
when batch concatenation is involved, because the python code uses the
input length passed from the router for each row.
As far as I can tell, it is better to include this token in the encoder
`input_ids`, so I guess it's best to just adjust on the router side.
- Avoid theoretical hang in batcher loop
- Avoid a couple of clones in the router generate method
- Keep attention mask tensors as integers
- Remove num_heads attribute
Co-authored-by: OlivierDehaene <Olivier.dehaene@gmail.com>
OlivierDehaene