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Reduce LLaMA memory usage #18181
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kunal-vaishnavi
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kunal-vaishnavi:kvaishnavi/llama-reduce-memory-usage
Nov 1, 2023
Merged
Reduce LLaMA memory usage #18181
kunal-vaishnavi
merged 7 commits into
microsoft:main
from
kunal-vaishnavi:kvaishnavi/llama-reduce-memory-usage
Nov 1, 2023
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onnxruntime/python/tools/transformers/models/llama/benchmark.py
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frank-dong-ms
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Oct 31, 2023
tianleiwu
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LGTM.
BTW, there is no need to convert torch.tensor->numpy for io_binding. You can directly use torch tensor in io binding. See example in
class CudaSession: |
tianleiwu
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### Description This PR reduces the memory usage when exporting and benchmarking LLaMA. ### Motivation and Context - Exporting: The PyTorch model is deleted from memory after a successful export instead of deleting it from memory after exporting + converting the ONNX model to the desired precision. - Benchmarking: In the ONNX model with GroupQueryAttention, the KV cache inputs use the same GPU memory for both the prompt and token generation benchmarks.
kleiti
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Mar 22, 2024
### Description This PR reduces the memory usage when exporting and benchmarking LLaMA. ### Motivation and Context - Exporting: The PyTorch model is deleted from memory after a successful export instead of deleting it from memory after exporting + converting the ONNX model to the desired precision. - Benchmarking: In the ONNX model with GroupQueryAttention, the KV cache inputs use the same GPU memory for both the prompt and token generation benchmarks.
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Description
This PR reduces the memory usage when exporting and benchmarking LLaMA.
Motivation and Context