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This PR allows for messages to be formatted as simple strings, or as an
array of objects including image urls. This is done by formatting
content arrays into a simple string.
Example using `llava-hf/llava-v1.6-mistral-7b-hf`
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Whats in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"
}
}
]
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
is equivlant to this more simple request
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": "Whats in this image?\n"
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
output
```
# {"id":"","object":"text_completion","created":1714406985,"model":"llava-hf/llava-v1.6-mistral-7b-hf","system_fingerprint":"2.0.1-native","choices":[{"index":0,"message":{"role":"assistant","content":" This is an illustration of an anthropomorphic rabbit in a spacesuit, standing on what"},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":2945,"completion_tokens":20,"total_tokens":2965}}%
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Thank you so much for the work you are doing, this is my little
contribution to this great thing you have built. I hope it is useful and
helpful, please don't hesitate to discuss any matters that are not
clear!
I am basing my implementation of frequency penalty on OpenAI's
implementation:
https://platform.openai.com/docs/guides/text-generation/parameter-details
The problem I see with TGI's current implementation is that is not
taking into account the frequency of tokens which have already been
sampled in the current generation stream. Also, the scaling is of the
adjusted token logits is done differently for positive and negative
logits. While in OpenAI's implementation token frequency is taking into
account and the scaling is always done with a subtraction (if penalty is
positive) or add operation (if penalty is negative).
This leads to corrupt generations as I mentioned in issue #1810 .
Moreover, after my tests, other issues are also gone like the one about
some request's with ``penalty_frequency = 1.0`` overruling other
requests (with ``frequency_penalty = 0.0``) in the same batch and
therefore corrupting all generations in the batch. Basically, padding
does not affect this implementation so I believe this ``score *=
input_ids.ne(0)`` is not needed anymore.
Frequency penalty | -1.0 | 0.0 | 1.0
-- | -- | -- | --
Before my change | https://paste.mozilla.org/JxqGJkWY |
https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw
After my change | https://paste.mozilla.org/7gXCi7zo |
https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC
---------
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
This PR adds a short "how it works" section to guidance and includes a
mention to the outlines library that enables grammars/tools
*and a small formatting change
---------
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
# What does this PR do?
Just unifying some branches and making intentions clearer (no cuda graph
when 0 all the way in the launcher)
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# What does this PR do?
This PR adds the missing `tool_prompt` parameter in Python client
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# What does this PR do?
This should enable more aggressive by default stacking, meaning better
throughput (in throughput constrained environements).
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
On TPU (and probably inferentia). The model needs to know right off the
bat about BATCH_SIZE and MAX_TOTAL_TOKENS (since the entire cache will
be determined by both).
This PR sends that information to the shards to they can allocate
accordingly. Should be no-op for other backends.
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This PR resolves an issue with the penalty processors during batched
generation where extra padding tokens incorrectly impact the penalty
scores.
generation is impacted in the case where at least one item in the batch
includes a `frequency_penalty`
reproduction script below
```python
import requests
from concurrent import futures
import time
headers = {
"Content-Type": "application/json",
}
json_data = {
"inputs": "[INST] Whats the capitol of France? [/INST]",
"parameters": {
"max_new_tokens": 100,
"seed": 20,
"do_sample": False,
},
}
json_data2 = {
"inputs": "<s>[INST]Write a mind bending story: I saw a puppy a cat a rat and a raccoon during my bike ride in the park[/INST]",
"parameters": {
"max_new_tokens": 100,
"seed": 2,
"do_sample": False,
# OFFENDING LINE
"frequency_penalty": 1.05,
},
}
base_url = "http://localhost:3000/generate"
def req():
response = requests.post(base_url, headers=headers, json=json_data)
print("[req ]", response.json())
def req2():
response = requests.post(base_url, headers=headers, json=json_data2)
print("[req2]", response.json())
n = 1
for i in range(0, 3):
print(f"- {n} threads -")
with futures.ThreadPoolExecutor(max_workers=n) as executor:
executor.submit(req)
for i in range(3):
executor.submit(req2)
n += 1
# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': ' As""%\n================'}
# [req2] {'generated_text': ' As""%%$\n================'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# output with this PR's changes:
# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
```
**divergence from expected generation is easier to reproduce with
batched grammar requests as they are more sensitive to unexpected
outputs.
this PR resolves the issue by setting the penalty score to 0 where input
ids are padding tokens (0).
---------
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
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This PR bumps the client tests from `google/flan-t5-xxl` to
`meta-llama/Llama-2-7b-chat-hf` to resolve issues when calling the
endpoint and `google/flan-t5-xxl` is not available
run with
```bash
make python-client-tests
clients/python/tests/test_client.py .............. [ 43%]
clients/python/tests/test_errors.py .......... [ 75%]
clients/python/tests/test_inference_api.py ...... [ 93%]
clients/python/tests/test_types.py .. [100%]
```
**note `google/flan-t5-xxl` function is currently unused but still
included in the `conftest.py`
`/v1/chat/completions` and `/v1/completions` have different output types
depending on the `stream` parameter. This PR aims at fixing the
inconsistency in the auto-generated
[openapi.json](https://huggingface.github.io/text-generation-inference/openapi.json)
specs.
cc @OlivierDehaene @drbh I reused what had been done for the `/`
endpoint but haven't tested anything myself. Could you confirm this is
the correct way of handling things?
Also, should I update the openapi.json file manually? If yes, how can I
do it?
This PR makes tool calling aware of the name of the function selected.
Fixes:
https://github.com/huggingface/text-generation-inference/issues/1657
Thank you @puppetm4st3r for the helpful snippets, large parts of this PR
are simply refactors of the code shared 🙏
**opening draft PR because small tweaks are needed before merging
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# What does this PR do?
compliation -> compilation
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# What does this PR do?
- Renamed `max_input_length` into `max_input_tokens` for consistency
(backward compatible change, will yell if both are set.)
- Will now use the config for `max_input_tokens` `max_total_token` and
`max_batch_total_tokens`.
- Capping the values to 16k in order to save VRAM on behalf of users
(overriddable by simply setting the values).
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# What does this PR do?
I have suggested similar changes over at
https://github.com/huggingface/text-embeddings-inference/pull/201.
Here being my additional question, why `debug` is enabled during release
building? (hence I didn't add the flag to script things)
Applying the following optimizations:
- `lto` (link time optimizations) over all code (including dependencies)
- Using a single `codegen-unit` to apply optimizations within 1 code
unit at build time
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# What does this PR do?
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Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have
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members/contributors who may be interested in your PR.
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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Nicolas Patry
