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
Casper
beb89f68b4
AWQ: Up to 2.66x higher throughput ( #2566 )
2024-01-26 23:53:17 -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
Simon Mo
5ffc0d13a2
Migrate linter from pylint to ruff ( #1665 )
2023-11-20 11:58:01 -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
