Commit Graph

166 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 4c2848b24b
Small cleanup. (#1560)
Using a single `os.getenv` statement instead of multiple.
Should make truthful values easier to catch

In the end didn't move towards full CLI because modifying globals in
Python is error prone (depends on code import order).

Added an error when mamba is launched with TP.


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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).
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2024-02-14 15:30:07 +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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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
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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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
Dean Wyatte 13c62be467
GPTNeoX: Use static rotary embedding (#1498)
# What does this PR do?

`transformers` 4.35 removed rotary embeddings from GPTNeoX's weights
([link to line
diff](253f9a3f97 (diff-0e2a05d86c82e96f516db8c14070ceb36f53ca44c6bc21a9cd92ad2e777b9cf1R298))).
This applies the same fix as
https://github.com/huggingface/text-generation-inference/pull/793 which
generates them on-the-fly using the appropriate value from the config
file

Fixes
https://github.com/huggingface/text-generation-inference/issues/1460

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


## Who can review?

@OlivierDehaene OR @Narsil
2024-02-01 09:34:11 +01:00
Nicolas Patry 069895b985
Fixing top_n_tokens. (#1497)
# What does this PR do?

Superseeds #1459

The fix works as follows.
We updated next_token_chooser to return all logprbs, then
batch_top_n_tokens, now also gets accepted_ids + speculated_length (so
it knows how to interpret the flat logprobs).

We then update the code to return lists ot `Tokens` that it expects.
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2024-01-26 20:13:47 +01:00
OlivierDehaene c2d4a3b5c7
v1.4.0 (#1494) 2024-01-26 19:04:57 +01:00
Nicolas Patry ac49972752
Add sealion mpt support (#1477)
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      Pull Request section?
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---------

Co-authored-by: Choon Meng Tan <choonmeng@aisingapore.org>
Co-authored-by: David Ong Tat-Wee <13075447+ongtw@users.noreply.github.com>
2024-01-26 14:05:02 +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
PYNing da27fbdfdb
Fix local load for Medusa (#1420)
# What does this PR do?

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Close #1418 
Close #1415

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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Here are the
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- [ ] Did you write any new necessary tests?


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2024-01-10 18:36:20 +01:00
OlivierDehaene 564199bab3
feat: update exllamav2 kernels (#1370)
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-12-21 17:25:22 +01:00
OlivierDehaene 1b1bfa49b0
fix: fix logic if sliding window key is not present in config (#1352) 2023-12-15 14:56:17 +01:00
OlivierDehaene 9b56d3fbf5
feat: relax mistral requirements (#1351)
Close #1253 
Close #1279
2023-12-15 12:52:24 +01:00
OlivierDehaene 37555cf4e8
fix: max_past default value must be -1, not 0 (#1348) 2023-12-15 01:18:39 +01:00
OlivierDehaene 50b495f3d8
feat: add more latency metrics in forward (#1346) 2023-12-14 15:59:38 +01:00
OlivierDehaene 44b267ab22 fix: fix gpt-q params loading 2023-12-14 11:02:16 +01:00
OlivierDehaene 82670d9786
feat: add quant to mixtral (#1337) 2023-12-12 17:55:03 +01:00
OlivierDehaene ec6d4592d5 v1.3.1 2023-12-11 16:46:44 +01:00
OlivierDehaene 72ee382ded chore: formatting 2023-12-11 14:49:52 +01:00
OlivierDehaene 3a521c92b3
feat: mixtral (#1328) 2023-12-11 14:43:40 +01:00
Nicolas Patry 9ecfa16b12
Speculative (#1308) 2023-12-11 12:46:30 +01:00
Nicolas Patry ba552e1a82
Let each model resolve their own default dtype. (#1287)
# What does this PR do?

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2023-11-28 17:54:26 +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
Vince Jankovics c6bb76703f
Fix IDEFICS dtype (#1214)
# What does this PR do?

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This forces the use of `bfloat16` for IDEFICS. The issue is that with
`float16` the 80b model gives garbage output. Let me know if this
solution is not appropriate and I'll adjust accordingly. For the details
see below.

The current behaviour:
```sh
$ curl 127.0.0.1:8080/generate -X POST -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'
{"generated_text":""}
```

On closer inspection with:
```python
import requests

headers = { "Content-Type": "application/json"}

query = "What is Deep Learning?"
data = {
    "inputs": query,
    "parameters": {
        "max_new_tokens": 10,
        "return_full_text": True,
        "decoder_input_details": True,
        "do_sample": False,
    },
}

api_url = "http://127.0.0.1:8080"
response = requests.post(api_url + "/generate", headers=headers, json=data).json()

for i in ['prefill', 'tokens']:
    print(f'### {i}')
    print(repr(''.join([t['text'] for t in response['details'][i]])))
```

Prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>'
########
```

With the change in this PR it prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'\n\nDeep Learning is a subset of machine'
```

Note, using the Transformers implementation (with
`IdeficsForVisionText2Text.from_pretrained`) produces the latter
(correct) output as well.
This only happens with the 80b model, the 9b model is not as sensitive
to the dtype (as also mentioned in the code).

The reason for "forcing" this in the IDEFICS init method, is because if
quantization is used, then the dtype cannot be set explicitly. And since
it's left as `None`, it's set to `float16` by default
[here](96a982ad8f/server/text_generation_server/models/__init__.py (L90)).
I.e. there's no other way to manually change the dtype if someone is
using quantization:
```sh
$ docker run .... ghcr.io/huggingface/text-generation-inference:latest --model-id HuggingFaceM4/idefics-80b-instruct --dtype bfloat16 --quantize bitsandbytes-nf4
.....
2023-10-31T12:42:26.710401Z  INFO shard-manager: text_generation_launcher: Starting shard rank=0
2023-10-31T12:42:30.315734Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:

Traceback (most recent call last):

  File "/opt/conda/bin/text-generation-server", line 8, in <module>
    sys.exit(app())

  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 80, in serve
    raise RuntimeError(

RuntimeError: Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model.
 rank=0
Error: ShardCannotStart
2023-10-31T12:42:30.414010Z ERROR text_generation_launcher: Shard 0 failed to start
2023-10-31T12:42:30.414044Z  INFO text_generation_launcher: Shutting down shards
```

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-11-23 15:00:09 +01:00
OlivierDehaene 96a982ad8f fix: better warmup error 2023-10-25 10:18:58 +02:00
OlivierDehaene 12590fdcce
feat: paged attention v2 (#1183) 2023-10-23 12:29:25 +02:00
Mario928 9179605e1e
Fix: Replace view() with reshape() in neox_modeling.py to resolve RuntimeError (#1155) 2023-10-19 11:54:26 +02:00
momonga 7402a355dc
Fix calling cuda() on load_in_8bit (#1153)
This PR addresses an issue where calling `model = model.cuda()` would
throw a ValueError when `quantize` is set to "bitsandbytes".

```
> File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 147, in serve_inner
    model = get_model(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/__init__.py", line 295, in get_model
    return CausalLM(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/causal_lm.py", line 515, in __init__
    model = model.cuda()
  File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 1998, in cuda
    raise ValueError(
ValueError: Calling `cuda()` is not supported for `4-bit` or `8-bit` quantized models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.
```

Co-authored-by: mmnga <mmnga1mmnga@gmail.com>
2023-10-19 10:42:03 +02:00
Nicolas Patry e9cdf6225f
Hotfixing idefics base64 parsing. (#1103)
# What does this PR do?

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2023-10-05 13:35:26 +02:00
Nicolas Patry 66ce2fa7c1
Receive base64 encoded images for idefics. (#1096)
# What does this PR do?

Fix #1095

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2023-10-04 17:35:29 +02:00
Nicolas Patry 1bebb9e76b
Update idefics_image_processing.py (#1091)
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2023-10-03 12:25:06 +02:00
Nicolas Patry 85acb11ba0
Handling bloom prefix. (#1090)
# What does this PR do?

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2023-10-03 11:55:10 +02:00
Leo Tronchon b8fefa6b55
raise exception on invalid images (#999)
# What does this PR do?
This PR is meant to handle cases in which the images provided are
invalid.

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---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-10-03 10:26:10 +02:00
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
OlivierDehaene 3b56d7669b
feat: add mistral model (#1071) 2023-09-28 09:55:47 +02:00
zhangsibo1129 1e3ec3c91f
Complete FastLinear.load parameters in OPTDecoder initialization (#1060)
# What does this PR do?

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`FastLinear.load` requires 4 parameters, but in the following only 3 are
given. This PR fix this.

```python
# server/text_generation_server/models/custom_modeling/opt_modeling.py
        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = FastLinear.load(
                config, prefix="model.decoder.project_out", bias=False
            )
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = FastLinear.load(
                config, prefix="model.decoder.project_in", bias=False
```

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2023-09-27 12:25:59 +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
Vincent Brouwers 8672cad2cb
Fix top_n_tokens returning non-log probs for some models (#1023)
# What does this PR do?

I made an embarrassing mistake where I accidentally passed normal
softmax probabilities into `batch_top_tokens` for `CausalLM` and
`Seq2SeqLM`.

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2023-09-26 16:16:43 +02:00
Victor SANH 1fff6746ab
Fix position ids logic instantiation of idefics vision part (#1064)
Problem and fix is described here:
https://huggingface.co/HuggingFaceM4/idefics-9b/discussions/9

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-09-26 15:41:15 +02:00
Nicolas Patry 2f51645ad7
Fix GQA llama + AWQ (#1061)
# What does this PR do?

Fixes #1056

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2023-09-26 08:27:50 +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?

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---------



# 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 fef36cea42
Fixing t5 loading. (#1042)
# What does this PR do?

Fixes #1038

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2023-09-25 12:22:28 +02:00
Vincent Brouwers 123749a3c9
Fix missing arguments in Galactica's from_pb (#1022)
# What does this PR do?

Fixes #1004

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2023-09-21 08:15:59 +02:00
Wang, Yi eeaa22ab04
enable bfloat16 for cpu (#1034)
if there's no cuda. disable custom kernels

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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
2023-09-19 17:19:28 +02:00
xiaobin 4cce84301b
fit for baichuan models (#981)
As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.

By the way, the changes of this time mainly for supporting Baichuan-7B.

---------

Co-authored-by: xiaoyuze <xiaoyuze@baichuan.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-09-08 16:51:34 +02:00
Victor SANH 2bc287bfcd
small fix on idefics (#954)
transposing the fixes from
https://github.com/huggingface/transformers/pull/25787
2023-09-01 18:44:34 +02:00