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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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Fixes # (issue)


## 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?
- [ ] 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?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have
passed. Feel free to tag
members/contributors who may be interested in your PR.

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---------

Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>
This commit is contained in:
Nicolas Patry 2023-09-25 15:31:27 +02:00 committed by GitHub
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16
Dockerfile
View File

@ -111,22 +111,22 @@ RUN make build-flash-attention-v2
# Build Transformers exllama kernels
FROM kernel-builder as exllama-kernels-builder
WORKDIR /usr/src
COPY server/exllama_kernels/ .
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
# Build Transformers awq kernels
FROM kernel-builder as awq-kernels-builder
WORKDIR /usr/src
COPY server/Makefile-awq Makefile
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" make build-awq
# Build Transformers CUDA kernels
FROM kernel-builder as custom-kernels-builder
WORKDIR /usr/src
COPY server/custom_kernels/ .
# Build specific version of transformers
RUN python setup.py build
@ -175,6 +175,8 @@ COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86
COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
# Copy build artifacts from exllama kernels builder
COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
# Copy build artifacts from awq kernels builder
COPY --from=awq-kernels-builder /usr/src/llm-awq/awq/kernels/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
# Copy builds artifacts from vllm builder
COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages

2
docs/source/basic_tutorials/preparing_model.md
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@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
## Quantization
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes` or `gptq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq). To get more information about quantization, please refer to (./conceptual/quantization.md)
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to (./conceptual/quantization.md)
## RoPE Scaling

104
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"logprob": -1.5859375,
"special": false,
"text": " difference"
},
{
"id": 1546,
"logprob": -0.02633667,
"special": false,
"text": " between"
},
{
"id": 21784,
"logprob": -1.4335938,
"special": false,
"text": " Deep"
},
{
"id": 29257,
"logprob": -0.15991211,
"special": false,
"text": " Learning"
},
{
"id": 322,
"logprob": -0.17456055,
"special": false,
"text": " and"
},
{
"id": 6189,
"logprob": -0.62060547,
"special": false,
"text": " Machine"
}
],
"top_tokens": null
},
"generated_text": "\nWhat is the difference between Deep Learning and Machine"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.6914062,
"text": "What"
},
{
"id": 338,
"logprob": -1.4746094,
"text": "is"
},
{
"id": 21784,
"logprob": -9.390625,
"text": "Deep"
},
{
"id": 29257,
"logprob": -1.8623047,
"text": "Learning"
},
{
"id": 29973,
"logprob": -0.7558594,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -1.9228516,
"special": false,
"text": "\n"
},
{
"id": 5618,
"logprob": -2.4609375,
"special": false,
"text": "What"
},
{
"id": 338,
"logprob": -0.57177734,
"special": false,
"text": " is"
},
{
"id": 278,
"logprob": -1.5722656,
"special": false,
"text": " the"
},
{
"id": 4328,
"logprob": -1.5859375,
"special": false,
"text": " difference"
},
{
"id": 1546,
"logprob": -0.02633667,
"special": false,
"text": " between"
},
{
"id": 21784,
"logprob": -1.4335938,
"special": false,
"text": " Deep"
},
{
"id": 29257,
"logprob": -0.15991211,
"special": false,
"text": " Learning"
},
{
"id": 322,
"logprob": -0.17456055,
"special": false,
"text": " and"
},
{
"id": 6189,
"logprob": -0.62060547,
"special": false,
"text": " Machine"
}
],
"top_tokens": null
},
"generated_text": "\nWhat is the difference between Deep Learning and Machine"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.6914062,
"text": "What"
},
{
"id": 338,
"logprob": -1.4746094,
"text": "is"
},
{
"id": 21784,
"logprob": -9.390625,
"text": "Deep"
},
{
"id": 29257,
"logprob": -1.8623047,
"text": "Learning"
},
{
"id": 29973,
"logprob": -0.7558594,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -1.9228516,
"special": false,
"text": "\n"
},
{
"id": 5618,
"logprob": -2.4609375,
"special": false,
"text": "What"
},
{
"id": 338,
"logprob": -0.57177734,
"special": false,
"text": " is"
},
{
"id": 278,
"logprob": -1.5722656,
"special": false,
"text": " the"
},
{
"id": 4328,
"logprob": -1.5859375,
"special": false,
"text": " difference"
},
{
"id": 1546,
"logprob": -0.02633667,
"special": false,
"text": " between"
},
{
"id": 21784,
"logprob": -1.4335938,
"special": false,
"text": " Deep"
},
{
"id": 29257,
"logprob": -0.15991211,
"special": false,
"text": " Learning"
},
{
"id": 322,
"logprob": -0.17456055,
"special": false,
"text": " and"
},
{
"id": 6189,
"logprob": -0.62060547,
"special": false,
"text": " Machine"
}
],
"top_tokens": null
},
"generated_text": "\nWhat is the difference between Deep Learning and Machine"
}
]

104
integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_sharded.json Normal file
View File

@ -0,0 +1,104 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.6914062,
"text": "What"
},
{
"id": 338,
"logprob": -1.4746094,
"text": "is"
},
{
"id": 21784,
"logprob": -9.390625,
"text": "Deep"
},
{
"id": 29257,
"logprob": -1.8623047,
"text": "Learning"
},
{
"id": 29973,
"logprob": -0.7558594,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -1.9228516,
"special": false,
"text": "\n"
},
{
"id": 5618,
"logprob": -2.4609375,
"special": false,
"text": "What"
},
{
"id": 338,
"logprob": -0.57177734,
"special": false,
"text": " is"
},
{
"id": 278,
"logprob": -1.5722656,
"special": false,
"text": " the"
},
{
"id": 4328,
"logprob": -1.5927734,
"special": false,
"text": " difference"
},
{
"id": 1546,
"logprob": -0.026428223,
"special": false,
"text": " between"
},
{
"id": 21784,
"logprob": -1.4267578,
"special": false,
"text": " Deep"
},
{
"id": 29257,
"logprob": -0.16015625,
"special": false,
"text": " Learning"
},
{
"id": 322,
"logprob": -0.17382812,
"special": false,
"text": " and"
},
{
"id": 6189,
"logprob": -0.62060547,
"special": false,
"text": " Machine"
}
],
"top_tokens": null
},
"generated_text": "\nWhat is the difference between Deep Learning and Machine"
}

64
integration-tests/models/test_flash_awq.py Normal file
View File

@ -0,0 +1,64 @@
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle(launcher):
with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=1, quantize="awq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq(flash_llama_awq_handle):
await flash_llama_awq_handle.health(300)
return flash_llama_awq_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq(flash_llama_awq, response_snapshot):
response = await flash_llama_awq.generate(
"What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_all_params(flash_llama_awq, response_snapshot):
response = await flash_llama_awq.generate(
"What is Deep Learning?",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_load(
flash_llama_awq, generate_load, response_snapshot
):
responses = await generate_load(
flash_llama_awq, "What is Deep Learning?", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == "\nWhat is the difference between Deep Learning and Machine" for r in responses])
assert responses == response_snapshot

36
integration-tests/models/test_flash_awq_sharded.py Normal file
View File

@ -0,0 +1,36 @@
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle_sharded(launcher):
with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=2, quantize="awq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq_sharded(flash_llama_awq_handle_sharded):
await flash_llama_awq_handle_sharded.health(300)
return flash_llama_awq_handle_sharded.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_sharded(flash_llama_awq_sharded, response_snapshot):
response = await flash_llama_awq_sharded.generate(
"What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_load_sharded(
flash_llama_awq_sharded, generate_load, response_snapshot
):
responses = await generate_load(
flash_llama_awq_sharded, "What is Deep Learning?", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == "\nWhat is the difference between Deep Learning and Machine" for r in responses])
assert responses == response_snapshot

4
launcher/src/main.rs
View File

@ -25,6 +25,7 @@ enum Quantization {
BitsandbytesNF4,
BitsandbytesFP4,
Gptq,
Awq,
}
impl std::fmt::Display for Quantization {
@ -43,6 +44,9 @@ impl std::fmt::Display for Quantization {
Quantization::Gptq => {
write!(f, "gptq")
}
Quantization::Awq => {
write!(f, "awq")
}
}
}
}

1
server/.gitignore vendored
View File

@ -159,3 +159,4 @@ safetensors
flash-attention/
flash-attention-v2/
vllm/
llm-awq/

1
server/Makefile
View File

@ -1,6 +1,7 @@
include Makefile-flash-att
include Makefile-flash-att-v2
include Makefile-vllm
include Makefile-awq
unit-tests:
pytest -s -vv -m "not private" tests

13
server/Makefile-awq Normal file
View File

@ -0,0 +1,13 @@
awq_commit := f084f40bd996f3cf3a0633c1ad7d9d476c318aaa
awq:
rm -rf llm-awq
git clone https://github.com/mit-han-lab/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && python setup.py build
install-awq: build-awq
pip uninstall awq_inference_engine -y || true
cd llm-awq/awq/kernels && python setup.py install

594
server/poetry.lock generated
View File

@ -323,19 +323,19 @@ files = [
[[package]]
name = "datasets"
version = "2.14.4"
version = "2.14.5"
description = "HuggingFace community-driven open-source library of datasets"
optional = true
python-versions = ">=3.8.0"
files = [
{file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
{file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
{file = "datasets-2.14.5-py3-none-any.whl", hash = "sha256:dd4155091034cba04d5a28711f2ed3944275ed15c5d0c5a2d0b6b9ea34a2bdfe"},
{file = "datasets-2.14.5.tar.gz", hash = "sha256:b738a86540ab8e1a7806c8a3790b67be0056318d0c5d5a58a1b0dbdd76c0f568"},
]
[package.dependencies]
aiohttp = "*"
dill = ">=0.3.0,<0.3.8"
fsspec = {version = ">=2021.11.1", extras = ["http"]}
fsspec = {version = ">=2023.1.0,<2023.9.0", extras = ["http"]}
huggingface-hub = ">=0.14.0,<1.0.0"
multiprocess = "*"
numpy = ">=1.17"
@ -421,21 +421,19 @@ test = ["pytest (>=6)"]
[[package]]
name = "filelock"
version = "3.12.3"
version = "3.12.4"
description = "A platform independent file lock."
optional = false
python-versions = ">=3.8"
files = [
{file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
{file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
{file = "filelock-3.12.4-py3-none-any.whl", hash = "sha256:08c21d87ded6e2b9da6728c3dff51baf1dcecf973b768ef35bcbc3447edb9ad4"},
{file = "filelock-3.12.4.tar.gz", hash = "sha256:2e6f249f1f3654291606e046b09f1fd5eac39b360664c27f5aad072012f8bcbd"},
]
[package.dependencies]
typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
[package.extras]
docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
typing = ["typing-extensions (>=4.7.1)"]
[[package]]
name = "frozenlist"
@ -582,148 +580,148 @@ testing = ["protobuf (>=4.21.9)"]
[[package]]
name = "grpcio"
version = "1.57.0"
version = "1.58.0"
description = "HTTP/2-based RPC framework"
optional = false
python-versions = ">=3.7"
files = [
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{file = "grpcio-1.57.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40b72effd4c789de94ce1be2b5f88d7b9b5f7379fe9645f198854112a6567d9a"},
{file = "grpcio-1.57.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f708a6a17868ad8bf586598bee69abded4996b18adf26fd2d91191383b79019"},
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agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
codecarbon = ["codecarbon (==1.2.0)"]
deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "timeout-decorator"]
dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
docs-specific = ["hf-doc-builder"]
fairscale = ["fairscale (>0.3)"]
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
@ -2147,15 +2161,15 @@ sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"]
tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (<10.0.0)", "torchvision"]
torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (<10.0.0)"]
@ -2181,13 +2195,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6.
[[package]]
name = "typing-extensions"
version = "4.7.1"
description = "Backported and Experimental Type Hints for Python 3.7+"
version = "4.8.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
{file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
{file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
{file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
]
[[package]]
@ -2203,13 +2217,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.0.4"
version = "2.0.5"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
{file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
{file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
{file = "urllib3-2.0.5-py3-none-any.whl", hash = "sha256:ef16afa8ba34a1f989db38e1dbbe0c302e4289a47856990d0682e374563ce35e"},
{file = "urllib3-2.0.5.tar.gz", hash = "sha256:13abf37382ea2ce6fb744d4dad67838eec857c9f4f57009891805e0b5e123594"},
]
[package.extras]

4
server/pyproject.toml
View File

@ -54,5 +54,7 @@ priority = "explicit"
markers = ["private: marks tests as requiring an admin hf token (deselect with '-m \"not private\"')"]
[build-system]
requires = ["poetry-core>=1.0.0"]
requires = [
"poetry-core>=1.0.0",
]
build-backend = "poetry.core.masonry.api"

1
server/text_generation_server/cli.py
View File

@ -17,6 +17,7 @@ class Quantization(str, Enum):
bitsandbytes_nf4 = "bitsandbytes-nf4"
bitsandbytes_fp4 = "bitsandbytes-fp4"
gptq = "gptq"
awq = "awq"
class Dtype(str, Enum):

4
server/text_generation_server/models/__init__.py
View File

@ -268,6 +268,10 @@ def get_model(
raise ValueError(
"gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
if quantize == "awq":
raise ValueError(
"awq quantization is not supported for AutoModel"
)
elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
raise ValueError(
"4bit quantization is not supported for AutoModel"

2
server/text_generation_server/models/flash_llama.py
View File

@ -62,7 +62,7 @@ class FlashLlama(FlashCausalLM):
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize == "gptq":
if config.quantize in ["gptq", "awq"]:
weights._set_gptq_params(model_id)
model = FlashLlamaForCausalLM(config, weights)

48
server/text_generation_server/utils/awq/quantize/qmodule.py Normal file
View File

@ -0,0 +1,48 @@
# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
import math
import torch
import torch.nn as nn
import awq_inference_engine # with CUDA kernels
# class ScaledActivation(nn.Module):
# def __init__(self, module, scales):
# super().__init__()
# self.act = module
# self.scales = nn.Parameter(scales.data)
#
# def forward(self, x):
# return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
class WQLinear(nn.Module):
def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
super().__init__()
if w_bit not in [4]:
raise NotImplementedError("Only 4-bit are supported for now.")
self.in_features = qweight.shape[0]
self.out_features = qweight.shape[1] * 32 // w_bit
self.w_bit = w_bit
self.group_size = group_size if group_size != -1 else self.in_features
# quick sanity check (make sure aligment)
assert self.in_features % self.group_size == 0
assert self.out_features % (32 // self.w_bit) == 0
self.qweight = qweight
self.qzeros = qzeros
self.scales = scales
if bias:
self.bias = bias
else:
self.bias = None
@torch.no_grad()
def forward(self, x):
out_shape = x.shape[:-1] + (self.out_features, )
out = awq_inference_engine.gemm_forward_cuda(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8)
out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape)

19
server/text_generation_server/utils/layers.py
View File

@ -18,6 +18,13 @@ from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
HAS_AWQ = True
try:
from text_generation_server.utils.awq.quantize.qmodule import WQLinear
except ImportError:
HAS_AWQ = False
try:
major, _minor = torch.cuda.get_device_capability()
except Exception:
@ -248,6 +255,14 @@ def get_linear(weight, bias, quantize):
bits,
groupsize,
)
elif quantize == "awq":
try:
qweight, qzeros, scales, _, bits, groupsize, _ = weight
except Exception:
raise NotImplementedError(
f"The passed weight is not `awq` compatible, loader needs to be updated."
)
linear = WQLinear(w_bit=bits, group_size=groupsize, qweight=qweight, qzeros=qzeros, scales=scales, bias=bias is not None)
else:
raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
return linear
@ -283,8 +298,8 @@ class TensorParallelHead(SuperLayer):
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
# GPTQ doesn't quantize heads (nor embeddings)
if config.quantize == "gptq":
# GPTQ and AWQ don't quantize heads (nor embeddings)
if config.quantize in ["gptq", "awq"]:
quantize = None
else:
quantize = config.quantize

54
server/text_generation_server/utils/weights.py
View File

@ -135,18 +135,21 @@ class Weights:
Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
already alternating Q,K,V within the main tensor
"""
if quantize == "gptq":
if quantize in ["gptq", "awq"]:
try:
qweight = self._get_qweight(f"{prefix}.qweight")
except RuntimeError:
raise RuntimeError(
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
f"Cannot load `{quantize}` weight, make sure the model is already quantized."
)
qzeros = self._get_qweight(f"{prefix}.qzeros")
scales = self._get_qweight(f"{prefix}.scales")
scales = scales.to(dtype=self.dtype)
g_idx = self.get_tensor(f"{prefix}.g_idx")
if quantize == "gptq":
g_idx = self.get_tensor(f"{prefix}.g_idx")
else:
g_idx = None
bits, groupsize = self._get_gptq_params()
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
@ -171,14 +174,14 @@ class Weights:
return weight
def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
if quantize == "gptq":
if quantize in ["gptq", "awq"]:
try:
qweight = torch.cat(
[self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
)
except RuntimeError:
raise RuntimeError(
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
f"Cannot load `{quantize}` weight, make sure the model is already quantized"
)
qzeros = torch.cat(
@ -187,10 +190,14 @@ class Weights:
scales = torch.cat(
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
if quantize == "gptq":
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
else:
g_idx = None
bits, groupsize = self._get_gptq_params()
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
@ -281,6 +288,22 @@ class Weights:
scales = self.get_tensor(f"{prefix}.scales")
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
elif quantize == "awq":
bits, groupsize = self._get_gptq_params()
try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `awq` weight, make sure the model is already quantized"
)
qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
scales = self.get_sharded(f"{prefix}.scales", dim=0)
g_idx = None
use_exllama = False
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
else:
weight = self.get_sharded(f"{prefix}.weight", dim=1)
@ -322,4 +345,15 @@ class Weights:
self.gptq_bits = data["bits"]
self.gptq_groupsize = data["group_size"]
except Exception:
pass
filename = "quant_config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(model_id, filename=filename)
with open(filename, "r") as f:
data = json.load(f)
self.gptq_bits = data["w_bit"]
self.gptq_groupsize = data["q_group_size"]
except Exception:
pass