This RFC proposes an enhancement to torch.compile to improve its backend agnosticism. The goal is to enable a more seamless experience for users working with devices that may not be well-supported by the current default backend, Inductor. By allowing for the customization of the default backend,...
torch.compileraisestorch._dynamo.exc.BackendCompilerFailed importtorchtorch.manual_seed(420)classModel(torch.nn.Module):def__init__(self):super().__init__()self.conv=torch.nn.Conv2d(3,1,1)defforward(self,x):h=self.conv(x)h=torch.mul(h,3)a=torch.clamp_min(torch.clamp_max(h,6.0)...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [cudagraph] torch.compile(backend="cudagraphs") + StableDiffusion2.1 doesn't work · pytorch/pytorch@d3a11a0
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [cudagraph] torch.compile(backend="cudagraphs") + StableDiffusion2.1 doesn't work · pytorch/pytorch@f6838d5
🐛 Describe the bug For float16 (the repro passes if dtype is torch.float32), the two implementations differ tangibly when passed through dynamo when they are the same in eager. Example results shown below. One thing to note is that if we...
backend eager takes 8 minutes to compile TORCH_LOGS="+dynamo": 61MB unzipped: simple_train_logs.zip repro: xmfan/Jamba@be6b5c1, add backend="eager" Versions 2.3.0 (pinned by zeta) cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 ...
os.environ['TORCH_COMPILE_DISABLE'] = 'True' if current_platform.is_hpu(): # NOTE(kzawora): PT HPU lazy backend (PT_HPU_LAZY_MODE = 1) # does not support torch.compile # Eager backend (PT_HPU_LAZY_MODE = 0) must be selected for # torch.compile support is_lazy = os.environ...
🐛 Describe the bug My repro, note that it works fine without @torch._dynamo.optimize("eager"): import torch._dynamo import torch import torch.nn as nn class Model(nn.Module): export = False def __init__(self, linear): super().__init__() ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Support static method of torchbind attributes in torch.compile with inductor backend · pytorch/pytorch@0030a68
Similar to #10747, but applied specifically to PT HPU lazy backend. While PyTorch for Gaudi has torch.compile support, it currently needs to be enabled explicitly, and best performance is achieved with HPUGraphs instead. This patch disables torch.compile for PT lazy mode and HPUGraphs (HPU ex...