Robert Shaw
683e3cb9c4
[ Misc ] fbgemm checkpoints ( #6559 )
2024-07-20 09:36:57 -07:00
Dipika Sikka
7836fdcc11
[Misc] Fix get_min_capability ( #5971 )
2024-06-30 20:15:16 +00:00
SangBin Cho
df29793dc7
[mypy][5/N] Support all typing on model executor ( #4427 )
2024-04-28 19:01:26 -07:00
Cody Yu
a62aaf1df5
[Misc][Refactor] Generalize linear_method to be quant_method ( #4373 )
2024-04-26 16:41:14 -04:00
James Fleming and mgoin
2b7949c1c2
AQLM CUDA support ( #3287 )
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Co-authored-by: mgoin <michael@neuralmagic.com >
2024-04-23 13:59:33 -04:00
Antoni Baum
a10d3056da
[Core] Set linear_weights directly on the layer ( #3977 )
2024-04-11 16:35:51 -04:00
Kunshang Ji
e9da5a40c6
[Misc] Add indirection layer for custom ops ( #3913 )
2024-04-10 20:26:07 -07:00
Zhuohan Li
2f8844ba08
Re-enable the 80 char line width limit ( #3305 )
2024-03-10 19:49:14 -07:00
Kunshang Ji and Jiang Li
96b6f475dd
Remove hardcoded device="cuda" to support more devices ( #2503 )
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Co-authored-by: Jiang Li <jiang1.li@intel.com >
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com >
2024-02-01 15:46:39 -08:00
CHU Tianxiang
0fbfc4b81b
Add GPTQ support ( #916 )
2023-12-15 03:04:22 -08:00
6ccc0bfffb
Merge EmbeddedLLM/vllm-rocm into vLLM main ( #1836 )
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Co-authored-by: Philipp Moritz <pcmoritz@gmail.com >
Co-authored-by: Amir Balwel <amoooori04@gmail.com >
Co-authored-by: root <kuanfu.liu@akirakan.com >
Co-authored-by: tjtanaa <tunjian.tan@embeddedllm.com >
Co-authored-by: kuanfu <kuanfu.liu@embeddedllm.com >
Co-authored-by: miloice <17350011+kliuae@users.noreply.github.com >
2023-12-07 23:16:52 -08:00
Yanming W
e0c6f556e8
[Build] Avoid building too many extensions ( #1624 )
2023-11-23 16:31:19 -08:00
Woosuk Kwon
8d17774f92
Add AWQ support for all models ( #1714 )
2023-11-18 17:56:47 -08:00
Zhuohan Li
7076fa1c9f
TP/quantization/weight loading refactor part 2 - Refactor quantized linear logic and extend quantization support to all models ( #1622 )
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Refactor the tensor parallelism, quantization, and weight-loading codes.
Summary of the new features enabled by this PR:
- **All models** are able to be quantized with AWQ and SqueezeLLM, and [soon GPTQ](https://github.com/vllm-project/vllm/pull/1580 ).
- Model loading code became much simpler.
- Support model parallelism for all MQA/GQA models when the number of key/value heads is smaller than the tensor parallel size.
2023-11-15 22:50:41 -08:00
Robert Shaw