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Author SHA1 Message Date
Nicolas Patry 6c2c44b84c
Upgrade EETQ (Fixes the cuda graphs). (#1729)
# What does this PR do?

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## Before submitting
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2024-04-12 08:15:28 +02:00
dtlzhuangz 0595bf3e9a
feat: eetq gemv optimization when batch_size <= 4 (#1502)
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Add TensorRT-LLM weight-only GEMV kernel support. We extract GEMV kernel
from
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv)
to accelerate the decode speed of EETQ when batch_size is smaller or
equal to 4.

- Features

1. There is almost no loss of quantization accuracy.
2. The speed of decoding is 13% - 27% faster than original EETQ which
utilizes GEMM kernel.

- Test
Below is our test on 3090. Environment: torch=2.0.1, cuda=11.8, nvidia
driver: 525.78.01
prompt=1024, max_new_tokens=50

![image](https://github.com/huggingface/text-generation-inference/assets/139844877/98e63b23-23cd-452f-91bd-55ccdc9b7021)


![image](https://github.com/huggingface/text-generation-inference/assets/139844877/5c3132ff-fc1c-4b20-a83f-59b3d5f586b7)



## Before submitting
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2024-01-31 12:05:49 +01:00
Nicolas Patry 95a4bb696a
Support eetq weight only quantization (#1068)
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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Here are the
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[here are tips on formatting
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---------

Co-authored-by: zhaosida <zhaosida@corp.netease.com>
2023-09-27 11:42:57 +02:00