Adjust torch.compile() best practices #3336
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1. Add best practice to prefer `mod.compile` over `torch.compile(mod)`, which avoids `_orig_` naming problems. Repro steps: - opt_mod = torch.compile(mod) - train opt_mod - save checkpoint In another script, potentially on a machine that does NOT support `torch.compile`: load checkpoint. This fails with an error, because the checkpoint on `opt_mod` got its params renamed by `torch.compile`: ``` RuntimeError: Error(s) in loading state_dict for VQVAE: Missing key(s) in state_dict: "embedding.weight", "encoder.encoder.net.0.weight", "encoder.encoder.net.0.bias", ... Unexpected key(s) in state_dict: "_orig_mod.embedding.weight", "_orig_mod.encoder.encoder.net.0.weight", "_orig_mod.encoder.encoder.net.0.bias", ... ``` - Add best practice to use, or at least try, `fullgraph=True`. This doesn't always work, but we should encourage it. Note: I'm not a PyTorch expert, these are just based on footguns I've encountered over the past week.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3336
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Description
mod.compileovertorch.compile(mod), which avoids_orig_naming problems. Repro steps:In another script, potentially on a machine that does NOT support
torch.compile: load checkpoint.This fails with an error, because the checkpoint on
opt_modgot its params renamed bytorch.compile:fullgraph=True. This doesn't always work, but we should encourage it.Note: I'm not a PyTorch expert, these are just based on footguns I've encountered over the past week.
Checklist
cc @williamwen42 @msaroufim @anijain2305