[Misc] Upgrade to torch==2.3.0

[mgoin]

Now that there is an xformers release and flash-attn release with PyTorch 2.3.0 support, we can consider upgrading to PyTorch 2.3.0.

PyTorch 2.3.0 is an useful version because it allows using torch._scaled_mm on Ada Lovelace GPUs (CUDA compute capability 8.9) for running native FP8 matmuls. (We will enable and test this in a follow-up PR)

Performance benchmarks using a Marlin quantized Llama 7B on RTX 4090:
https://docs.google.com/spreadsheets/d/1sYIzF7bLEvpcg-p9IS-6XvZhdv4h95GvjhLIbvpW1l4/edit?usp=sharing

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[youkaichao]

Be sure to check the blocks of memory we can use, before and after the upgrade.

e.g. # GPU blocks: 881, # CPU blocks: 0 in Basic Correctness Test

[youkaichao]

I feel we can parse these stats and present them in the dashboard, too, especially for the GPU blocks. @simon-mo

GPU blocks: 881, # CPU blocks: 0

[mgoin]

Thanks for the suggestion @youkaichao, here are my results for kv cache block allocation using an A6000.

There is a roughly a ~1 block difference for common 7B models. The worst case is facebook/opt-125m which can allocate 75937 GPU blocks on main and 75934 GPU blocks on this PR, a difference of 3 blocks. I think this is acceptable.

Using main's current HEAD commit 73c8d67:

> python -c 'from vllm import LLM;LLM("facebook/opt-125m")'
INFO 04-29 19:12:30 gpu_executor.py:118] # GPU blocks: 75937, # CPU blocks: 7281

> python -c 'from vllm import LLM;LLM("meta-llama/Meta-Llama-3-8B-Instruct")'
INFO 04-29 19:12:48 gpu_executor.py:118] # GPU blocks: 13248, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("teknium/OpenHermes-2.5-Mistral-7B")'
INFO 04-29 19:13:07 gpu_executor.py:118] # GPU blocks: 12735, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("TheBloke/Llama-2-7B-GPTQ")'
INFO 04-29 19:13:19 gpu_executor.py:118] # GPU blocks: 4877, # CPU blocks: 512

> python -c 'from vllm import LLM;LLM("TheBloke/zephyr-7B-beta-AWQ")'
INFO 04-29 19:13:37 gpu_executor.py:118] # GPU blocks: 17641, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("mgoin/phi-2-super-marlin")'
INFO 04-29 19:13:51 gpu_executor.py:118] # GPU blocks: 8214, # CPU blocks: 819

With this PR:

> python -c 'from vllm import LLM;LLM("facebook/opt-125m")'
INFO 04-29 19:03:36 gpu_executor.py:119] # GPU blocks: 75934, # CPU blocks: 7281

> python -c 'from vllm import LLM;LLM("meta-llama/Meta-Llama-3-8B-Instruct")'
INFO 04-29 19:04:43 gpu_executor.py:119] # GPU blocks: 13247, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("teknium/OpenHermes-2.5-Mistral-7B")'
INFO 04-29 19:09:23 gpu_executor.py:119] # GPU blocks: 12734, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("TheBloke/Llama-2-7B-GPTQ")'
INFO 04-29 19:10:47 gpu_executor.py:119] # GPU blocks: 4877, # CPU blocks: 512

> python -c 'from vllm import LLM;LLM("TheBloke/zephyr-7B-beta-AWQ")'
INFO 04-29 19:11:09 gpu_executor.py:119] # GPU blocks: 17640, # CPU blocks: 2048

> python -c 'from vllm import LLM;LLM("mgoin/phi-2-super-marlin")'
INFO 04-29 19:11:26 gpu_executor.py:119] # GPU blocks: 8213, # CPU blocks: 819

[zhouyuan]

Thanks for covering the CPU part - there's a minor performance increase in CPU backend also mostly due to torch.linear improvement in 2.3

[youkaichao]

LGTM, thanks!