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28 Commits

Author SHA1 Message Date
Nicolas Patry c7e570e59d
Pickle conversion now requires `--trust-remote-code`. (#1704)
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Fixes # (issue)


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2024-04-05 13:32:53 +02:00
Nicolas Patry bf700e7eef
Revamp medusa implementation so that every model can benefit. (#1588)
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2024-02-26 19:49:28 +01:00
OlivierDehaene c2d4a3b5c7
v1.4.0 (#1494) 2024-01-26 19:04:57 +01:00
PYNing da27fbdfdb
Fix local load for Medusa (#1420)
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Close #1418 
Close #1415

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2024-01-10 18:36:20 +01:00
OlivierDehaene 564f2a3b75
fix: fix local loading for .bin models (#1419) 2024-01-09 15:21:00 +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
Nicolas Patry ba552e1a82
Let each model resolve their own default dtype. (#1287)
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2023-11-28 17:54:26 +01:00
Traun Leyden e12c34bd25
Load PEFT weights from local directory (#1260)
# What does this PR do?

Enables PEFT weights to be loaded from a local directory, as opposed to
a hf hub repository. It is a continuation of the work in PR
https://github.com/huggingface/text-generation-inference/pull/762

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Fixes #1259 


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Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-11-23 12:56:17 +01:00
OlivierDehaene 47954b81e9
feat: format code (#1070) 2023-09-27 12:22:09 +02:00
Nicolas Patry 95a4bb696a
Support eetq weight only quantization (#1068)
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Co-authored-by: zhaosida <zhaosida@corp.netease.com>
2023-09-27 11:42:57 +02:00
zhangsibo1129 eba6ab1c5d
fix discard_names bug in safetensors convertion (#1052)
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Model Class attributes `_tied_weights_keys`, `
_keys_to_ignore_on_load_missing` can only be `None` or a List.
`getattr(class_, "_keys_to_ignore_on_load_missing", [])` will return
`None` if `_keys_to_ignore_on_load_missing` is None, and
`discard_names.extend(None)` will trigger an exception, even though
`_tied_weights_keys` exists.

## Who can review?

@OlivierDehaene  @Narsil

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
2023-09-26 15:05:40 +02:00
zhangsibo1129 edc95a0e7d
support local model config file (#1058)
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Support local config file to avoid unexpected `discard_names`, which
causes #1057.

In the case of launching local mode without `model.safetensors` file,
the original code will result `discard_names = []` when
`hf_hub_download` throws an connection error.
```python
# server/text_generation_server/cli.py
    try:
        import transformers
        import json
    
    
        config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
        with open(config_filename, "r") as f:
            config = json.load(f)
        architecture = config["architectures"][0]
    
        class_ = getattr(transformers, architecture)
    
        # Name for this varible depends on transformers version.
        discard_names = getattr(class_, "_tied_weights_keys", [])
        discard_names.extend(getattr(class_, "_keys_to_ignore_on_load_missing", []))
    
    except Exception as e:
        discard_names = []
```

The expected `_tied_weights_keys` of OPT-1.3b is `["lm_head.weight"]`,
and its tied weight `"model.decoder.embed_tokens.weight"` will be kept
in the safetensors conversion. But the above empty `discard_names` will
lead to `"lm_head.weight"` being kept and
`"model.decoder.embed_tokens.weight"` being discard in the subsequent
method `_remove_duplicate_names`, which causes error #1057.

So add a local mode branch to get the expected `discard_names` like
follows. This modification also applies to other models

```python
# server/text_generation_server/cli.py
        if is_local_model:
            config_filename = os.path.join(model_id, "config.json")
        else:
            config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
```


In addition, when `_tied_weights_keys` or
`_keys_to_ignore_on_load_missing` is `None`, the above code will also
throw an error unexpectedly. This is fixed in PR #1052


## Before submitting
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@Narsil
2023-09-26 14:57:53 +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 4486f78cf9
Fixing the lora adaptation on docker. (#935)
# What does this PR do?

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Fixes # (issue)


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2023-08-28 11:13:24 +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 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 


# What does this PR do?

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2023-08-03 17:22:45 +02:00
Nicolas Patry 4d38a1c4ad
feat(server): Reworking the quantization script so it's still universal (not llama specific) (#587)
but should work on more configurations (no need for 2 GPUs, less RAM
usage).


# What does this PR do?

Reworking the quantization script so it's still universal (not llama
specific)

but should work on more configurations (no need for 2 GPUs, less RAM
usage).

Still need to investigate the potential differences in quantization
results.


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2023-07-18 12:19:05 +02:00
Nicolas Patry e943a294bc
fix(server): harden the weights choice to save on disk. (#561)
- Look at `transformers` base class to check for
  `_key_to_ignore_on_load_missing` or `_tied_weights` which are the
  standard attributes to select the keys to NOT save on disk (since they
  are ignored)

- Modified safetensors code (to be reflected in safetensors even if it's
  an internal function).
  
- Will not work for trust_remote_code=True repos (like santacoder).

Should help with :
https://github.com/huggingface/text-generation-inference/issues/555
and : https://github.com/huggingface/text-generation-inference/pull/501
and https://github.com/huggingface/text-generation-inference/issues/556
and
https://github.com/huggingface/text-generation-inference/issues/482#issuecomment-1623713593
2023-07-07 14:50:12 +02:00
Nicolas Patry ecf6dc3a5a
feat: Add the option to force another dtype than `f16`. (#513) 2023-06-30 20:30:09 +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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---------

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
OlivierDehaene e3e487dc71
feat(server): support trust_remote_code (#363) 2023-05-23 20:40:39 +02:00
Nicolas Patry 76a48cd365
feat(server): GPTQ quantization (step1) (#277)
Changes only the type from `bool` to `Option<Enum>` pretty much
everywhere.
- Use `Optional[str]` in Python (easier to manage than importing type
everywhere). Except for the cli to get proper validation
- Updated all models to handle gracefully new values. (Error out if
unknown value, or gptq since not implemented).

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2023-05-12 14:46:41 +02:00
OlivierDehaene 85aa7e2e7b
feat(server): support hf endpoint weight layout (#266) 2023-05-03 11:36:24 +02:00
OlivierDehaene 7a1ba58557
fix(docker): fix docker image dependencies (#187) 2023-04-17 00:26:47 +02:00
OlivierDehaene 610bb1f978
feat(benchmark): tui based benchmarking tool (#149) 2023-03-30 15:26:27 +02:00
OlivierDehaene 3fef90d50f
feat(clients): Python client (#103) 2023-03-07 18:52:22 +01:00