111815d482
[Kernel] Support Fp8 Checkpoints (Dynamic + Static) ( #4332 )
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Co-authored-by: Philipp Moritz <pcmoritz@gmail.com >
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu >
Co-authored-by: mgoin <michael@neuralmagic.com >
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com >
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com >
2024-04-30 21:46:12 +00:00
Kunshang Ji
26f2fb5113
[Core]Refactor gptq_marlin ops ( #4466 )
2024-04-30 08:14:47 -04:00
73c8d677e5
[Kernel] Marlin Expansion: Support AutoGPTQ Models with Marlin ( #3922 )
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Co-authored-by: alexm <alexm@neuralmagic.com >
Co-authored-by: mgoin <michael@neuralmagic.com >
2024-04-29 09:35:34 -07:00
SangBin Cho
df29793dc7
[mypy][5/N] Support all typing on model executor ( #4427 )
2024-04-28 19:01:26 -07:00
Philipp Moritz and Woosuk Kwon
12628d3c78
[Kernel] Optimize FP8 support for MoE kernel / Mixtral via static scales ( #4343 )
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Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu >
2024-04-27 04:49:59 +00:00
Cody Yu
a62aaf1df5
[Misc][Refactor] Generalize linear_method to be quant_method ( #4373 )
2024-04-26 16:41:14 -04:00
Kunshang Ji
f4bc4de1b1
[Core]refactor aqlm quant ops ( #4351 )
2024-04-25 15:03:56 -04:00
Robert Shaw
79a268c4ab
[BUG] fixed fp8 conflict with aqlm ( #4307 )
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Fixes fp8 iterface which broke in AQLM merge.
2024-04-23 18:26:33 -07:00
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
Noam Gat
cc74b2b232
Updating lm-format-enforcer version and adding links to decoding libraries in docs ( #4222 )
2024-04-20 08:33:16 +00:00
Cody Yu
a22cdea371
[Kernel][FP8] Initial support with dynamic per-tensor scaling ( #4118 )
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Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726
This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine.
Algorithm:
We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass.
Initial Results:
Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128:
BF16: 1.47s
FP8: 1.66s
I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
2024-04-20 04:28:57 +00:00
Michael Goin
53b018edcb
[Bugfix] Get available quantization methods from quantization registry ( #4098 )
2024-04-18 00:21:55 -07:00
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
+2
2ff767b513
Enable scaled FP8 (e4m3fn) KV cache on ROCm (AMD GPU) ( #3290 )
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Co-authored-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com >
Co-authored-by: HaiShaw <hixiao@gmail.com >
Co-authored-by: AdrianAbeyta <Adrian.Abeyta@amd.com >
Co-authored-by: Matthew Wong <Matthew.Wong2@amd.com >
Co-authored-by: root <root@gt-pla-u18-08.pla.dcgpu >
Co-authored-by: mawong-amd <156021403+mawong-amd@users.noreply.github.com >
Co-authored-by: ttbachyinsda <ttbachyinsda@outlook.com >
Co-authored-by: guofangze <guofangze@kuaishou.com >
Co-authored-by: Michael Goin <mgoin64@gmail.com >
Co-authored-by: jacobthebanana <50071502+jacobthebanana@users.noreply.github.com >
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu >
2024-04-03 14:15:55 -07:00
SangBin Cho
01bfb22b41
[CI] Try introducing isort. ( #3495 )
2024-03-25 07:59:47 -07:00
Enrique Shockwave
b983ba35bd
fix marlin config repr ( #3414 )
2024-03-14 16:26:19 -07:00
Zhuohan Li
2f8844ba08
Re-enable the 80 char line width limit ( #3305 )
2024-03-10 19:49:14 -07:00
c0c2335ce0
Integrate Marlin Kernels for Int4 GPTQ inference ( #2497 )
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Co-authored-by: Robert Shaw <114415538+rib-2@users.noreply.github.com >
Co-authored-by: alexm <alexm@neuralmagic.com >
2024-03-01 12:47:51 -08:00
CHU Tianxiang
01a5d18a53
Add Support for 2/3/8-bit GPTQ Quantization Models ( #2330 )
2024-02-28 21:52:23 -08:00
Rex and Chunan Zeng
563836496a
Refactor 2 awq gemm kernels into m16nXk32 ( #2723 )
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Co-authored-by: Chunan Zeng <chunanzeng@Chunans-Air.attlocal.net >
2024-02-12 11:02:17 -08:00
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
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
