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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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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
wrap text-generation-launcher in docker image
mask ldconfig failures to user (no need in most cases anyway)
---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
# What does this PR do?
- Changed all models to extract `embed_tokens` in order to enable llava
to separately call the embeddings and the core model layers.
- Added VlmCausalLM to inherit from FlashMistral in order to be
maximally supported. The only added logics sits on top and parses images
into pixel values, preallocates input_ids space for the image
embeddings, and passes them for the model.
- Added Clip for the vision tower.
- Didn't add flash for the vision tower since there's no padding anyway.
- Added heuristic (potentially incomplete) to calculate number of
features *before* calculating the clip patches (allows for easier logic
reuse of the LLM under the hood).
Still needs to be done:
- [x] Implement the image parsing in the controller side, to avoid
downloading n times per TP shard and also refusing requests too large
early and avoid issues where the truncation actually truncates the
image.
- [ ] Make sure it works with quantization properly.
- [x] Make sure it works with TP>1
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# What does this PR do?
```
text-generation-launcher --model-id XXX # Uses cuda graphs by default
text-generation-launcher --model-id XXX --cuda-graphs "1,2" #Restrict the number of cuda graphs which saves VRAM
text-generation-launcher --model-id XXX --cuda-graphs "0" # Disabling it entirely
```
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This PR correctly handles batches with a mixture of constrained and non
constrained generations.
Currently if batch contains mixed generations the generation will throw
an error because it will incorrectly attempt to constrain a request with
an empty grammar.
We now handled `None` grammars and only apply the mask if needed
Fixes:
https://github.com/huggingface/text-generation-inference/issues/1643
# What does this PR do?
A few cases where you're using a mistral structure or mixtral structure
but not a llama tokenizer, why not make it to call the AutoTokenizer in
exception handling.
Similar PR #619
@Narsil
# What does this PR do?
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This PR resolves a couple
- [X] adjusts the tool response to align with openai's tools response
type
- [X] bumps pydantic to `2.6.4` in all apps (resolves dependency issue
when running tests)
- [X] bump `outlines` version and fix import for new name
This PR adds `force_downcast_after` to `FastRMSNorm.forward` which is
used in the Gemma model. References
https://github.com/huggingface/transformers/pull/29402 and
https://github.com/huggingface/transformers/pull/29729
Setting `force_downcast_after=True` will perform the `hidden_states *
weight` multiplication in f32 and then downcast to half. This differs
slightly from the current implementation which first casts the
`hidden_states` to a half and then multiples.
Nicolas Patry