Use resultElementType for tensor creation for AtenMemoryFormatOp with none dtype. #3941
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Hi @ramiro050, I see that I am reverting your change here. It's not clear to me why the |
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Hello @ramiro050 and @vivekkhandelwal1 can you please review this PR when you have a chance? Thanks! |
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| func.func @torch.aten.empty.memory_format$noneDtype() -> !torch.vtensor<[200,200,26],f64> attributes {torch.assume_strict_symbolic_shapes} { | ||
| %int200 = torch.constant.int 200 | ||
| %int26 = torch.constant.int 26 | ||
| %false = torch.constant.bool false | ||
| %none = torch.constant.none | ||
| %0 = torch.prim.ListConstruct %int200, %int200, %int26 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int> | ||
| %1 = torch.aten.empty.memory_format %0, %none, %none, %none, %false, %none : !torch.list<int>, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.none -> !torch.vtensor<[200,200,26],f64> | ||
| return %1 : !torch.vtensor<[200,200,26],f64> | ||
| } |
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Hi @sahas3, the above test case is an invalid test since the tensor element type is f32 when none is specifed for the aten.empty.memory_format op.
I just tried it. See:
>>> a = torch.ops.aten.empty.memory_format([2, 3])
>>> a
tensor([[-3.4054e+29, 4.2543e-41, -3.4085e+29],
[ 4.2543e-41, -3.4086e+29, 4.2543e-41]])
>>> a.dtype
torch.float32
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Also, the changes that you've done are not correct and hence this patch should not be merged.
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Prior to the change in this PR
torch-mlir-opt --convert-torch-to-linalgwas running into the following error:This is because when
dtypeof theaten.empty.memory_formatisnone, by defaultf32was being selected as the element type of the resulting tensor which doesn't match with the actual element type of the result.