Comments (2)
YixinSong-e
commented on June 11, 2024
这是考虑到了神经元的激活在下投影层是按列的，导致了下投影层加载时的不连续性，decoding阶段LLM推理是memory bandwidth bound的，实现思路就是尽可能保证连续的load，并且跳过不必要的memory access。尽管GPU上shared memory是可以从HBM先不连续的load进来再进行计算，但是CPU上的cache没办法显式控制，AXPY可以保证CPU/GPU都能做到连续的memory access，因此设计了AXPY。
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richardweii
commented on June 11, 2024
很好的设计思路，非常感谢解答！
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Related Issues (20)
- Will we have instruct fine-tuned model support in the future? HOT 1
- Clarification on Output Neuron Pruning Method in "Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time HOT 2
- Segmentation fault (core dumped) in ggml test
- two questions that i want to solve HOT 2
- How to assign the specified CUDA_VISIBLE_DEVICE?
- invalid device symbol
- Where is the definition or addition location of GGML_USE_HYBRID_THREADING? HOT 2
- convert.py: error: the following arguments are required: mlp_model HOT 4
- Unable to generate constant output HOT 2
- The code about the figures in paper HOT 1
- Any plans to support llamafied Qwen1.5？有支持llama化qwen的计划吗？ HOT 2
- 在A100-80G上无法找到cuda的情况 HOT 2
- 请问大神有支持LLama 3 70B 的计划吗？
- 关于在A100显卡上测得的效果异常的疑问 HOT 1
- Will this work with Falcon 2?
- Need quite a long time to load the model
- ReluFalcon 40B 在llama.cpp上无效输出 HOT 2
- 推理报错
- ggml-cuda.cu:8949: invalid argument无效参数问题 HOT 2
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