# What does this PR do?
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Add TensorRT-LLM weight-only GEMV kernel support. We extract GEMV kernel
from
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv)
to accelerate the decode speed of EETQ when batch_size is smaller or
equal to 4.
- Features
1. There is almost no loss of quantization accuracy.
2. The speed of decoding is 13% - 27% faster than original EETQ which
utilizes GEMM kernel.
- Test
Below is our test on 3090. Environment: torch=2.0.1, cuda=11.8, nvidia
driver: 525.78.01
prompt=1024, max_new_tokens=50
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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?
Superseeds #1459
The fix works as follows.
We updated next_token_chooser to return all logprbs, then
batch_top_n_tokens, now also gets accepted_ids + speculated_length (so
it knows how to interpret the flat logprobs).
We then update the code to return lists ot `Tokens` that it expects.
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# What does this PR do?
fixes launcher doc typos
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Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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---------
Co-authored-by: Choon Meng Tan <choonmeng@aisingapore.org>
Co-authored-by: David Ong Tat-Wee <13075447+ongtw@users.noreply.github.com>
# What does this PR do?
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---------
Co-authored-by: Andres Restrepo <andres@thelinuxkid.com>
This PR adds basic modeling for phi-2
run
```bash
text-generation-server \
serve \
microsoft/phi-2 \
--revision 834565c23f9b28b96ccbeabe614dd906b6db551a
```
test
```bash
curl -s localhost:3000/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json' | jq .
# {
# "generated_text": "\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from data. These"
# }
```
notes
- recently (~1 day ago) the Phi weights and model were updated to
accommodate adding [GQA/MQA attention to the
model.](https://github.com/huggingface/transformers/pull/28163) This
impl expects the original model format so a fixed revision is required
at the moment.
- this PR only includes a basic implementation of the model and can
later be extended for support Flash and Sharded versions as well as make
use of better optimization
# 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.
# What does this PR do?
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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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Close#1418Close#1415
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local directory overloaded still needs the directory to locate the
weights files correctly.
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Works by removing adapter_model.safetensors from being detected as the
core model file (which skips the real peft detection).
# What does this PR do?
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Nicolas Patry