PyTorch 2.1 - New Developments
summary
PyTorch 2.1 shows concrete speedups by combining torch.compile, new SDPA kernels, export-based quantization and sparsity: benchmarked pipelines report up to ~8.5x for Segment Anything and ~5.6x for Llama2 using a mix of FP16, INT8 and 4‑bit (GPDQ) techniques. Added tools include torch.export for backend‑agnostic IR, GPU INT8 and 4‑bit runtimes, and two4 sparsity plus nested tensors for batched variable inputs; the implemented kernels and APIs run and reproduce the reported throughput and memory reductions.
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chapters (17)
- 0:00Introduction to PyTorch 2.1
- 1:06PyTorch growth and metrics
- 3:16PyTorch 2.1 new features
- 4:20Dynamic shape support in TorchCompile
- 5:02Compiling NumPy operations
- 5:43TorchExport for model export
- 7:28Quantization in PyTorch 2.1
- 10:142:4 semi-structured sparsity
- 11:57Accelerating generative AI models
- 13:02SDPA for attention acceleration
- 14:26Nested tensors for variable shapes
- 15:50GPU quantization techniques
- 18:202:4 sparsity on GPU
- 20:474-bit quantization with GPTQ
- 22:35Performance results on Segment Anything
- 24:43Results on Llama 2 models
- 26:46Q&A on hardware agnostic deployment
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