Commit Graph

68 Commits

Author SHA1 Message Date
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 ac7076b64d
Upgrading to rust 1.78. (#1851)
# What does this PR do?

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2024-05-06 13:48:11 +02:00
OlivierDehaene b4ef038837
chore: update torch (#1730)
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-04-30 14:04:28 +02:00
oOraph c2fd35d875
Dev/mask ldconfig output v2 (#1716)
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>
2024-04-11 19:31:48 +02:00
OlivierDehaene ad9d6288c8
fix: fix CohereForAI/c4ai-command-r-plus (#1707)
@Narsil @drbh this will update flash attention v2 and vllm.
You will need to re-install them.
2024-04-10 17:20:25 +02:00
OlivierDehaene 4139054b82
v1.4.1 (#1568) 2024-02-16 17:50:57 +01:00
Nicolas Patry 7671a419a0
Upgrade intermediary layer for nvidia too. (#1557)
# What does this PR do?

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2024-02-13 22:46:16 +01:00
OlivierDehaene 0d794af6a5
feat: experimental support for cuda graphs (#1428)
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-02-12 10:09:29 +01:00
drbh bd405e035b
Impl simple mamba model (#1480)
This draft PR is a work in progress implementation of the mamba model.
This PR currently loads weights, and produces correct logits after a
single pass.

This PR still needs to correctly integrate this model so it produces
tokens as expected, and apply optimization to avoid all copies during
runtime/unnecessary operations.

#### Helpful resources
[Mamba: Linear-Time Sequence Modeling with Selective State Spaces
(Albert Gu and Tri Dao)](https://arxiv.org/abs/2312.00752)
https://github.com/johnma2006/mamba-minimal

https://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rs
https://github.com/huggingface/transformers/pull/28094

Notes: this dev work is currently targeting `state-spaces/mamba-130m`,
so if you want to test please use that model. Additionally when starting
the router the prefill needs to be limited: `cargo run --
--max-batch-prefill-tokens 768 --max-input-length 768`


## Update / Current State

Integration tests have been added and basic functionality such as model
loading is supported.

```bash
cd integration-tests
pytest -vv models/test_fused_kernel_mamba.py
```
- [x] add tests
- [x] load model
- [x] make simple request 
- [ ] resolve warmup issue
- [ ] resolve output issues


fetching models tested during dev
```bash
text-generation-server download-weights state-spaces/mamba-130m
text-generation-server download-weights state-spaces/mamba-1.4b
text-generation-server download-weights state-spaces/mamba-2.8b
```

The server can be run 
```bash
cd server
 MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8b
```

router
```bash
cargo run
```

make a request
```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
```

response
```json
{
  "generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data."
}
```

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-02-08 10:19:45 +01:00
OlivierDehaene 3a521c92b3
feat: mixtral (#1328) 2023-12-11 14:43:40 +01:00
fxmarty b2b5df0e94
Add RoCm support (#1243)
This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.

Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?

---------

Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>
2023-11-27 14:08:12 +01:00
Nicolas Patry ed2a3f617e
Exllama v2 (#1211)
# 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>
2023-11-25 22:38:38 +01:00
OlivierDehaene 35509ff5de
chore: update to torch 2.1.0 (#1182)
Close #1142
2023-11-23 13:38:50 +01:00
Nicolas Patry 5ba53d44a1
Fixing eetq dockerfile. (#1081)
# What does this PR do?

Fixes #1079 
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2023-09-29 11:19:06 +02:00
oOraph ae623b8d2d
Install curl to be able to perform more advanced healthchecks (#1033)
# What does this PR do?

Install curl within base image, negligible regarding the image volume
and will allow to easily perform a better health check. Not sure about
the failing github actions though. Should I fix something ?

Signed-off-by: Raphael <oOraph@users.noreply.github.com>
Co-authored-by: Raphael <oOraph@users.noreply.github.com>
2023-09-26 15:23:47 +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

---------



# What does this PR do?

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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 6ec5288ab7
This should prevent the PyTorch overriding. (#767)
# What does this PR do?

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2023-08-03 21:54:39 +02:00
Nicolas Patry 92bb56b0c1
Local gptq support. (#738)
# What does this PR do?

Redoes #719

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2023-07-31 10:32:52 +02:00
OlivierDehaene 2efd46ef95 fix(server): fix missing datasets in quantize 2023-07-27 14:50:45 +02:00
Nicolas Patry d5b5bc750f
feat(server): Add exllama GPTQ CUDA kernel support #553 (#666)
Just trying to get the integration tests to pass.


# What does this PR do?

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---------

Co-authored-by: Felix Marty <9808326+fxmarty@users.noreply.github.com>
2023-07-21 10:59:00 +02:00
OlivierDehaene 3b71c38558
feat(server): flash attention v2 (#624) 2023-07-18 16:21:18 +02:00
OlivierDehaene e28a809004
v0.9.0 (#525) 2023-07-01 19:25:41 +02:00
OlivierDehaene e74bd41e0f
feat(server): add paged attention to flash models (#516)
Closes #478
2023-06-30 19:09:59 +02:00
Nicolas Patry aefde28b45
feat(server): Add inference support for GPTQ (llama + falcon tested) + Quantization script (#438)
Let's start discussing implementation.

- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).

Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.

My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.

# 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
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- [ ] Did you write any new necessary tests?


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---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
2023-06-26 12:27:01 +02:00
Nicolas Patry abd58ff82c
feat(server): Rework model loading (#344)
# What does this PR do?

Reworked the loading logic. Idea is to use cleaner loading code:

- Remove need for `no_init_weights`
- Remove all weird `bnb_linear` and `load_weights` and
`post_load_weights`.

New code layout:

- New class `Weights` in charge of handling loading the weights from
multiple files into appropiate tensors (potentially sharded)
- TP layers now are "shells", they contain the code to know what kind of
sharding we need + eventual `all_reduce`. They do not inherit from
linear, but they contain some kind of Linear instead
- the contained linear can be either FastLinear, BnbLinear or GPTq
Linear next.
- All modeling code is explictly made for sharding, process group is
just no-ops for non sharded code (removes a lot of test cases)

![Screenshot from 2023-05-19
23-19-59](https://github.com/huggingface/text-generation-inference/assets/204321/9a802654-74a3-488c-87a8-073743a6143f)

---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
2023-06-08 14:51:52 +02:00
OlivierDehaene 22c4fd07ab fix(docker): use ubuntu20.04 2023-05-12 18:38:59 +02:00
OlivierDehaene 119f7e0687 fix(docker): remove quantize default 2023-05-12 17:56:32 +02:00
OlivierDehaene 8a8f43410d
chore(docker): use nvidia base image (#318) 2023-05-12 17:32:40 +02:00
OlivierDehaene 35ab6cfcf1 fix(docker): remove CUDA_VERSION 2023-05-10 16:16:06 +02:00
OlivierDehaene 1585404464
fix(docker): remove nvidia require cuda env (#310) 2023-05-10 15:29:21 +02:00
OlivierDehaene 49cffad1bc
fix(docker): fix nvidia env vars (#305) 2023-05-09 19:02:52 +02:00
OlivierDehaene bc5c07231e
fix(docker): fix docker build (#299) 2023-05-09 14:39:59 +02:00
OlivierDehaene e250282213
feat(docker): add benchmarking tool to docker image (#298) 2023-05-09 13:19:31 +02:00
OlivierDehaene e9b01b3433
fix(dockerfile): fix nvidia env vars (#297)
Fixes #291
2023-05-09 12:36:02 +02:00
Nicolas Patry 411b0d4e1f
chore(github): add templates (#264) 2023-05-02 15:43:19 +02:00
OlivierDehaene 593a563414
feat(docker): add nvidia env vars (#255) 2023-04-27 19:18:33 +02:00
OlivierDehaene 98a3e0d135
chore(server): update huggingface-hub (#227) 2023-04-24 15:57:13 +02:00
OlivierDehaene 97df0c7bc0
misc: update to rust 1.69 (#221) 2023-04-21 21:00:30 +02:00
OlivierDehaene b6ee0ec7b0
feat(router): add git sha to info route (#208) 2023-04-19 21:36:59 +02:00
OlivierDehaene 6837b2eb77
fix(docker): remove unused dependencies (#205) 2023-04-19 19:39:31 +02:00
OlivierDehaene 5d27f5259b
fix(server): fix hf_transfer issue with private repos (#203) 2023-04-19 17:36:16 +02:00
OlivierDehaene 7a1ba58557
fix(docker): fix docker image dependencies (#187) 2023-04-17 00:26:47 +02:00
OlivierDehaene 379c5c4da2
fix(docker): revert dockerfile changes (#186) 2023-04-14 19:30:30 +02:00
OlivierDehaene f9047562d0
fix(docker): fix image (#185) 2023-04-14 18:58:38 +02:00
OlivierDehaene 1bb394631d
fix(docker): fix docker image (#184) 2023-04-14 17:31:13 +02:00
OlivierDehaene 53ee09c0b0
fea(dockerfile): better layer caching (#159) 2023-04-14 10:12:21 +02:00
OlivierDehaene 1883d8ecde
feat(docker): improve flash_attention caching (#160) 2023-04-09 19:59:16 +02:00
OlivierDehaene d503e8f09d
feat: aws sagemaker compatible image (#147)
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>
2023-03-29 21:38:30 +02:00
OlivierDehaene 05e9a796cc
feat(server): flash neoX (#133) 2023-03-24 14:02:14 +01:00
OlivierDehaene e3ded361b2
feat(ci): improve CI speed (#94) 2023-03-03 15:07:27 +01:00