Commit Graph

99 Commits

Author SHA1 Message Date
Daniël de Kok 90a1d04a2f
Add support for GPTQ-quantized MoE models using MoE Marlin (#2557)
This change add support for MoE models that use GPTQ quantization.
Currently only models with the following properties are supported:

- No `desc_act` with tensor parallelism, unless `group_size=-1`.
- No asymmetric quantization.
- No AWQ.
2024-09-30 11:14:32 +02:00
Daniël de Kok c103760172
Update to moe-kenels 0.3.1 (#2535)
* Update to moe-kenels 0.3.1

* Attempt to fix apt failure
2024-09-19 22:16:32 +02:00
Daniël de Kok ce85efa968
Move to moe-kernels package and switch to common MoE layer (#2511)
* Move to moe-kernels package and switch to common MoE layer

This change introduces the new `moe-kernels` package:

- Add `moe-kernels` as a dependency.
- Introduce a `SparseMoELayer` module that can be used by MoE
  models.
- Port over Mixtral and Deepseek.

* Make `cargo check` pass

* Update runner
2024-09-17 18:08:58 +02:00
Daniël de Kok a3c9c62dc0
hotfix: add syrupy to the right subproject (#2499) 2024-09-06 12:47:06 +02:00
Daniël de Kok 2eb57a15ec
Fix incompatibility with latest `syrupy` and update in Poetry (#2497) 2024-09-06 11:00:52 +02:00
Nicolas Patry 57b3495823
Fixing exl2 and other quanize tests again. (#2419)
* Fixing exl2 and other quanize tests again.

* Mark exl2 as non release (so CI tests them, needs to be removed latet).

* Fixing exl2 (by disabling cuda graphs)

* Fix quantization defaults without cuda graphs on exl2 (linked to new
issues with it).

* Removing serde override.

* Go back to released exl2 and remove log.

* Adding warnings for deprecated bitsandbytes + upgrade info to warn.
2024-08-15 11:12:51 +02:00
Daniël de Kok 922732b255
Install Marlin from standalone package (#2320) 2024-07-29 15:37:10 +02:00
OlivierDehaene e7e3aa6cac
chore: update to torch 2.4 (#2259)
* chore: update to torch 2.4

* remove un-necessary patch

* fix
2024-07-23 20:39:43 +00:00
Daniël de Kok bc9593a5b1
hotfix: pin numpy (#2289) 2024-07-23 17:53:19 +02:00
Daniël de Kok 4ab4173767
Add support for Llama 3 rotary embeddings (#2286)
* Add support for Llama 3 rotary embeddings

* Update transformers to 4.43
2024-07-23 17:18:54 +02:00
Nicolas Patry 6aeb669072
Softcapping for gemma2. (#2273)
* Softcapping for gemma2.

* Less clutter.

* No access to transformers config, only config_dict here.

* 0.0 is the null value in the C++ API.
2024-07-22 18:27:10 +02:00
Nicolas Patry 8390e251d9
Making `make install` work better by default. (#2004)
# What does this PR do?

Making `make install` a much better sane default to start local dev
environments.

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2024-06-04 19:38:46 +02:00
Nicolas Patry d32e33bd48
Fix seeded output. (#1949)
# What does this PR do?

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2024-05-24 15:36:13 +02:00
drbh 40213c957f
Pali gemma modeling (#1895)
This PR adds paligemma modeling code

Blog post: https://huggingface.co/blog/paligemma
Transformers PR: https://github.com/huggingface/transformers/pull/30814

install the latest changes and run with
```bash
# get the weights
# text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf

# run TGI
text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf
```


basic example sending various requests
```python
from huggingface_hub import InferenceClient

client = InferenceClient("http://127.0.0.1:3000")


images = [
    "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png",
]

prompts = [
    "What animal is in this image?",
    "Name three colors in this image.",
    "What are 10 colors in this image?",
    "Where is the cow standing?",
    "answer en Where is the cow standing?",
    "Is there a bird in the image?",
    "Is ther a cow in the image?",
    "Is there a rabbit in the image?",
    "how many birds are in the image?",
    "how many rabbits are in the image?",
]

for img in images:
    print(f"\nImage: {img.split('/')[-1]}")
    for prompt in prompts:
        inputs = f"![]({img}){prompt}\n"
        json_data = {
            "inputs": inputs,
            "parameters": {
                "max_new_tokens": 30,
                "do_sample": False,
            },
        }
        generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False)
        print([f"{prompt}\n{generated_output}"])

```

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-05-16 06:58:47 +02:00
Nicolas Patry dccab72549
(chore): torch 2.3.0 (#1833)
# What does this PR do?

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2024-04-30 18:15:35 +02:00
OlivierDehaene 2d0a7173d4 v2.0.1 2024-04-18 17:20:36 +02:00
Nicolas Patry f9ee2c41b9
Upgrading all versions. (#1759) 2024-04-18 17:17:40 +02:00
OlivierDehaene c38a7d7ddd
v2.0.0 (#1736) 2024-04-12 18:38:34 +02:00
abhishek thakur 10d9083b2d
Update libraries (#1713)
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-04-11 10:37:35 +02:00
OlivierDehaene 4ee0a0c401
v1.4.5 (#1686) 2024-03-29 19:17:24 +01:00
OlivierDehaene 6c4496a1a3
v1.4.4 (#1668) 2024-03-22 18:44:05 +01:00
OlivierDehaene 1e9bcd9dd8
feat: cohere (#1660) 2024-03-22 17:59:25 +01:00
drbh de6cb15fa5
fix: improve tool type, bump pydantic and outlines (#1650)
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
2024-03-21 12:45:56 -04:00
abhishek thakur 0d9917f744
Update peft + transformers + accelerate + bnb + safetensors (#1646) 2024-03-15 13:23:26 +01:00
OlivierDehaene e6bb3ff81f
v1.4.3 (#1609) 2024-02-28 16:12:14 +01:00
OlivierDehaene 9c1cb81cd8
v1.4.2 (#1585) 2024-02-21 14:50:57 +01:00
OlivierDehaene 4139054b82
v1.4.1 (#1568) 2024-02-16 17:50:57 +01:00
drbh cef0553d59
Outlines guided generation (#1539)
This WIP PR starts to add grammar support via outlines, currently this
PR supports very simple regex grammars and does not optimize for
precompiling or caching grammar fsm's.

todo:
- [X] add simple outlines guidance to `NextTokenChooser`
- [X] update protos for grammar
- [X] update generation params API
- [X] constrain simple grammar
- [ ] support parsing more complex grammar into fsm
- [ ] support all outline support grammar types
- [ ] explore optimizations to avoid recompiling grammars

guided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data-raw '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6,
        "grammar": "[\\w-]+@([\\w-]+\\.)+[\\w-]+"
    }
}' | jq
```
response
```json
{
  "generated_text": "david@example.com"
}
```

unguided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6
    }
}' | jq
```
response
```json
{
  "generated_text": "    email = 'david"
}
```
2024-02-15 10:28:10 +01:00
Jason Stillerman 39af000cb9
Update to peft 0.8.2 (#1537)
# What does this PR do?

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      Pull Request section?
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2024-02-08 12:44:04 +01:00
OlivierDehaene c2d4a3b5c7
v1.4.0 (#1494) 2024-01-26 19:04:57 +01:00
drbh 7e2a7433d3
feat: adds phi model (#1442)
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
2024-01-25 15:37:53 +01:00
OlivierDehaene 630800eed3 v1.3.4 2023-12-22 15:46:04 +01:00
OlivierDehaene 9b56d3fbf5
feat: relax mistral requirements (#1351)
Close #1253 
Close #1279
2023-12-15 12:52:24 +01:00
OlivierDehaene f3aea78fb6 v1.3.3 2023-12-15 01:20:42 +01:00
OlivierDehaene 88aae2595d v1.3.2 2023-12-12 18:10:22 +01:00
OlivierDehaene ec6d4592d5 v1.3.1 2023-12-11 16:46:44 +01:00
OlivierDehaene d0841cc8eb v1.3.0 2023-12-11 14:55:03 +01:00
OlivierDehaene ccd5725a0c v1.2.0 2023-11-30 15:18:15 +01:00
OlivierDehaene 35509ff5de
chore: update to torch 2.1.0 (#1182)
Close #1142
2023-11-23 13:38:50 +01:00
Nicolas Patry 00b8f36fba
Prepare for v1.1.1 (#1100)
# What does this PR do?

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2023-10-05 16:09:49 +02:00
Nicolas Patry a049864270
Preping 1.1.0 (#1066)
# What does this PR do?

Upgrade all relevant versions and dependencies.

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2023-09-27 10:40:18 +02:00
Nicolas Patry c5de7cd886
Add AWQ quantization inference support (#1019) (#1054)
# Add AWQ quantization inference support

Fixes
https://github.com/huggingface/text-generation-inference/issues/781

This PR (partially) adds support for AWQ quantization for inference.
More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
general, AWQ is faster and more accurate than GPTQ, which is currently
supported by TGI.

This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
(in `requirements.txt`, just one line change).

Quick way to test this PR would be bring up TGI as follows:

```
text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq

text-generation-launcher \
--huggingface-hub-cache ~/.cache/huggingface/hub/ \
--model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
--trust-remote-code --port 8080 \
--max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize awq
```

Please note:
* This PR was tested with FlashAttention v2 and vLLM.
* This PR adds support for AWQ inference, not quantizing the models.
That needs to be done outside of TGI, instructions

[here](f084f40bd9).
* This PR only adds support for `FlashLlama` models for now.
* Multi-GPU setup has not been tested. 
* No integration tests have been added so far, will add later if
maintainers are interested in this change.
* This PR can be tested on any of the models released

[here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).

Please refer to the linked issue for benchmarks for

[abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
vs

[TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).

Please note, AWQ has released faster (and in case of Llama, fused)
kernels for 4-bit GEMM, currently at the top of the `main` branch at
https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
that has been tested to work. We can switch to latest commit later on.

## Who can review?

@OlivierDehaene OR @Narsil

---------



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---------

Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>
2023-09-25 15:31:27 +02:00
Nicolas Patry 5485c142e8
New release. (#941)
# What does this PR do?

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2023-08-29 14:28:22 +02:00
Nicolas Patry bce5e22444
Adding Idefics multi modal model. (#842)
Co-Authored-By: Victor Sanh <victorsanh@gmail.com>


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Co-authored-by: Victor Sanh <victorsanh@gmail.com>
2023-08-17 14:38:49 +02:00
Nicolas Patry 09eca64227
Version 1.0.1 (#836)
# What does this PR do?

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2023-08-14 11:23:11 +02:00
Nicolas Patry cc7bb5084d
Upgrade transformers (fix protobuf==3.20 issue) (#795)
# What does this PR do?

Fixes #531

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2023-08-11 16:46:08 +02:00
Nicolas Patry 16fadcec57
Merge BNB 4bit. (#770)
# What does this PR do?


See #626 
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Co-authored-by: krzim <zimmerk4@live.com>
2023-08-03 23:00:59 +02:00
Nicolas Patry 6ec5288ab7
This should prevent the PyTorch overriding. (#767)
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2023-08-03 21:54:39 +02:00
Nicolas Patry ac736fd89c
feat(server): Add native support for PEFT Lora models (#762)
- Will detect `peft` model by finding `adapter_config.json`.
- This triggers a totally dedicated `download-weights` path
- This path, loads the adapter config, finds the base model_id
- It loads the base_model
- Then peft_model
- Then `merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.
- The chosen location is a **local folder with the name of the user
  chosen model id**

PROs:

- Easier than to expect user to merge manually
- Barely any change outside of `download-weights` command.
- This means everything will work in a single load.
- Should enable out of the box SM + HFE

CONs:

- Creates a local merged model in unusual location, potentially
  not saved across docker reloads, or ovewriting some files if the PEFT
  itself was local and containing other files in addition to the lora

Alternatives considered:
- Add `local_files_only=True` every where (discard because of massive
  code change for not a good enough reason)
- Return something to `launcher` about the new model-id (a cleaner
  location for this new model), but it would
  introduce new communication somewhere where we didn't need it before.
- Using the HF cache folder and *stopping* the flow after
  `download-weights` and asking user to restart with the actual local
  model location


Fix #482 


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2023-08-03 17:22:45 +02:00
OlivierDehaene 3ef5ffbc64
v1.0.0 (#727) 2023-07-28 17:43:46 +02:00