# What does this PR do?
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Co-authored-by: zhaosida <zhaosida@corp.netease.com>
# What does this PR do?
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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?
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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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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N/A
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@Narsil
# 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>
# What does this PR do?
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# What does this PR do?
Fixes#531
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# What does this PR do?
See #626
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---------
Co-authored-by: krzim <zimmerk4@live.com>
- 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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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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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
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---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
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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