Reduce LLaMA memory usage - #18181
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kunal-vaishnavi merged 7 commits intoNov 1, 2023
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frank-dong-ms
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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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### 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