Sherlock Huang ee4b99cc3a Decomp for aten.dropout (#106274) ... When exporting dropout with cpu tensor, we get following graph module ``` class GraphModule(torch.nn.Module): def forward(self, arg0_1: f32[512, 10]): empty_memory_format: f32[512, 10] = torch.ops.aten.empty.memory_format([512, 10], dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False, memory_format = torch.contiguous_format) bernoulli_p: f32[512, 10] = torch.ops.aten.bernoulli.p(empty_memory_format, 0.9); empty_memory_format = None div_scalar: f32[512, 10] = torch.ops.aten.div.Scalar(bernoulli_p, 0.9); bernoulli_p = None mul_tensor: f32[512, 10] = torch.ops.aten.mul.Tensor(arg0_1, div_scalar); arg0_1 = div_scalar = None return (mul_tensor,) ``` In addition, if we export with eval() mode, we will have an empty graph. However, when exporting with cuda tensor, we got ``` class GraphModule(torch.nn.Module): def forward(self, arg0_1: f32[512, 10]): native_dropout_default = torch.ops.aten.native_dropout.default(arg0_1, 0.1, True); arg0_1 = None getitem: f32[512, 10] = native_dropout_default[0]; native_dropout_default = None return (getitem,) ``` and exporting under eval() mode will still have a dropout node in graph. This PR make exporting with CPU tensor also produce aten.native_dropout. Pull Request resolved: https://github.com/pytorch/pytorch/pull/106274 Approved by: https://github.com/ezyang		2023-08-23 21:12:37 +00:00
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__init__.py	Decomp for aten.dropout (#106274)	2023-08-23 21:12:37 +00:00