Commit Graph

45 Commits

Author SHA1 Message Date
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
Nicolas Patry d6b0fb9e25
Improving mamba runtime by using updates (#1552)
- Move float16 to bfloat16, which has less imprecisions (load test are
  failing with the update kernels + f16, all working under bf16).

  Another note, is that we are not respecting the layer norm in f32
  defined in the configuration (this is OK in my book, but that could
  impact the f16 precision)

- Moved to update kernels. Triton overhead is super high, removed by
  switching to cuda graphs works great (update cuda graph is available
  in TRT-LLM if needed, seems *exactly* like the regular ssm kernel.

- Moved inference_params struct in order to make only 2 tensors, to
  reduce the overhead of copying back and forth to the cuda graphs.

- Left over overhead seems entirely in the tokenization bit. (Still 4
  copies are paid before launching the graph)


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Fixes # (issue)


## Before submitting
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other checks if that's the case).
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2024-02-14 09:54:10 +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
Ilyas Moutawwakil a4e5801684
ROCm AWQ support (#1514)
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This PR adds the possibility to run AWQ models with Exllama/GPTQ
kernels, specifically for ROCm devices that support Exllama kernels but
not AWQ's GEMM.

This is done by :
- un-packing, reordering and re-packing AWQ weights when `--quantize
gptq` but the model's `quant_method=awq`.
- avoiding overflows when adding 1 to zeros in exllama and triton.

Ref: https://github.com/casper-hansen/AutoAWQ/pull/313

## Before submitting
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other checks if that's the case).
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---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2024-02-09 10:45:16 +01:00
OlivierDehaene 09b7c26bbd
feat(server): add frequency penalty (#1541) 2024-02-08 18:41:25 +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 c2d4a3b5c7
v1.4.0 (#1494) 2024-01-26 19:04:57 +01:00
Nicolas Patry b95732180d
Reinstate exl2 with tp (#1490)
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2024-01-26 14:00:29 +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
Nicolas Patry 7e542d4d05
Fixing non divisible embeddings. (#1476)
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2024-01-24 13:08:41 +01:00
OlivierDehaene 630800eed3 v1.3.4 2023-12-22 15:46:04 +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 72ee382ded chore: formatting 2023-12-11 14:49:52 +01:00
Nicolas Patry 9ecfa16b12
Speculative (#1308) 2023-12-11 12:46:30 +01:00
OlivierDehaene ccd5725a0c v1.2.0 2023-11-30 15:18:15 +01:00
Nicolas Patry 624800c4de
Make GPTQ test less flaky (#1295)
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2023-11-28 21:22:35 +01:00
Nicolas Patry ba552e1a82
Let each model resolve their own default dtype. (#1287)
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2023-11-28 17:54:26 +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)
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2023-10-05 16:09:49 +02:00
OlivierDehaene 3b56d7669b
feat: add mistral model (#1071) 2023-09-28 09:55:47 +02:00
OlivierDehaene 47954b81e9
feat: format code (#1070) 2023-09-27 12:22:09 +02:00
Nicolas Patry b32e9ce9d5
Remove the stripping of the prefix space (and any other mangling that tokenizers might do). (#1065)
Superseed #1024


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---------

Co-authored-by: bangoz <ch_xie@pku.edu.cn>
2023-09-27 12:13:45 +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

---------



# 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 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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      Pull Request section?
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      to it if that's the case.
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---------

Co-authored-by: Victor Sanh <victorsanh@gmail.com>
2023-08-17 14:38:49 +02:00
OlivierDehaene 73a4d65d26
feat: add cuda memory fraction (#659)
Close #673
2023-07-24 11:43:58 +02:00
Nicolas Patry d5b5bc750f
feat(server): Add exllama GPTQ CUDA kernel support #553 (#666)
Just trying to get the integration tests to pass.


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      Pull Request section?
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      to it if that's the case.
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---------

Co-authored-by: Felix Marty <9808326+fxmarty@users.noreply.github.com>
2023-07-21 10:59:00 +02:00
Nicolas Patry 1da07e85aa
feat(server): Add Non flash MPT. (#514)
# What does this PR do?


This adds a non flash version of MPT.
Flash is harder because we need to create a bias ready cuda kernel of
flash attention.

Fixes
https://github.com/huggingface/text-generation-inference/issues/361
Fixes
https://github.com/huggingface/text-generation-inference/issues/491
Fixes
https://github.com/huggingface/text-generation-inference/issues/290
2023-07-03 13:01:46 +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 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 895c5f1562
feat(server): only compute prefill logprobs when asked (#406)
Close #288
2023-06-02 17:12:30 +02:00
OlivierDehaene 87dc034b59
feat(server): add retry on download (#384) 2023-05-31 10:57:53 +02:00
OlivierDehaene 444400b457 increase health checks 2023-05-31 10:55:59 +02:00
OlivierDehaene b8b950b37c
feat(server): support RefinedWeb models (#379) 2023-05-30 18:25:19 +02:00
OlivierDehaene 62f91f78ac
feat(server): support vectorized warpers in flash causal lm (#317)
Co-authored-by: Joel Lamy-Poirier <joel.lamy-poirier@servicenow.com>
2023-05-26 12:30:27 +02:00
OlivierDehaene 942005386a
feat(router): log input/ouput at debug level (#364)
@njhill FYI
2023-05-23 20:47:37 +02:00
OlivierDehaene cfaa858070
feat(server): support fp16 for t5 (#360)
Fixes #349
2023-05-23 18:16:48 +02:00
OlivierDehaene 91d9beec90
fix(server): fix init for flash causal lm (#352)
Fixes #347
2023-05-22 15:05:32 +02:00
OlivierDehaene 5a58226130
fix(server): fix decode token (#334)
Fixes #333

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-05-16 23:23:27 +02:00
OlivierDehaene dbdc587ddd
feat(integration-tests): improve comparison and health checks (#336) 2023-05-16 20:22:11 +02:00
OlivierDehaene e71471bec9
feat: add snapshot testing (#282) 2023-05-15 23:36:30 +02:00