torch.compile + CUDA Graph optimization for bs=1

[YJYJLee]

PR request for Pytorch blog post.

Summary:
This post is the fourth part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. In this blog, we’ll focus on speeding up FAIR’s Seamless M4T-v2 model resulting in 2x speedup for text decoder module and 30x for vocoder module, resulting in 2.7x speedup for end-to-end inference, with no loss of accuracy by using CUDA Graph and native PyTorch optimization: torch.compile.

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