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fix: fix missing float type in shape analysis #1399

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Merged
merged 10 commits into from
Oct 12, 2022
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fix: fix missing float type in shape analysis #1399

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74dd00e
fix: fix missing float type in shape analysis
bowang007 Oct 6, 2022
8448dfa
chore: minor fixes
peri044 Oct 10, 2022
39fc2dd
chore: Moving threshold constants to utils
peri044 Oct 11, 2022
b73796b
chore: linter fixes
peri044 Oct 11, 2022
a558e2a
Merge pull request #1397 from pytorch/minor_fix
peri044 Oct 11, 2022
1011ac1
docs: [Automated] Regenerating documenation for a558e2a
Oct 11, 2022
6bef925
fix: `torch.std` and `torch.var` support multi-dimensional reductions…
gs-olive Oct 12, 2022
6c56aac
docs: [Automated] Regenerating documenation for 6bef925
Oct 12, 2022
5f3a5ae
fix: fix missing float type in shape analysis
bowang007 Oct 6, 2022
7330964
Merge branch 'fix_missing_type' of https://github.com/pytorch/TensorR…
bowang007 Oct 12, 2022
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4 changes: 2 additions & 2 deletions BUILD
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Expand Up @@ -22,9 +22,9 @@ pkg_tar(
"//core/lowering:include",
"//core/lowering/passes:include",
"//core/partitioning:include",
"//core/partitioning/segmentedblock:include",
"//core/partitioning/partitioninginfo:include",
"//core/partitioning/partitioningctx:include",
"//core/partitioning/partitioninginfo:include",
"//core/partitioning/segmentedblock:include",
"//core/plugins:impl_include",
"//core/plugins:include",
"//core/runtime:include",
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17 changes: 12 additions & 5 deletions core/lowering/passes/unpack_var.cpp
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Expand Up @@ -26,11 +26,18 @@ void UnpackVar(std::shared_ptr<torch::jit::Graph>& graph) {
%var: Tensor = aten::sub(%sqrdmean, %meansqrd, %1)
%varout : Tensor = prim::If(%unbiased)
block0():
%shape: int[] = aten::size(%input)
%shapet: Tensor = aten::tensor(%shape, %f32_dtype, %none, %false)
%dim: int = prim::ListUnpack(%dims)
%reduceddims: Tensor = aten::select(%shapet, %0, %dim)
%numel: Tensor = aten::prod(%reduceddims, %dim, %keepdim, %none)
# Compute number of elements in original input tensor
%originalshape: int[] = aten::size(%input)
%originalshapet: Tensor = aten::tensor(%originalshape, %f32_dtype, %none, %false)
%originalnumel: Tensor = aten::prod(%originalshapet, %0, %false, %none)
# Compute number of elements in resulting output tensor
%resultingshape: int[] = aten::size(%var)
%resultingshapet: Tensor = aten::tensor(%resultingshape, %f32_dtype, %none, %false)
%resultingnumel: Tensor = aten::prod(%resultingshapet, %0, %false, %none)
# Quotient of original number of elements and resulting number of elements
# is equal to the number of elements used per variance calculation
%numel: Tensor = aten::div(%originalnumel, %resultingnumel)
# Perform Bessel's correction on computed variance
%mul: Tensor = aten::mul(%var, %numel)
%sub: Tensor = aten::sub(%numel, %1, %1)
%v: Tensor = aten::div(%mul, %sub)
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2 changes: 2 additions & 0 deletions core/partitioning/shape_analysis.cpp
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Expand Up @@ -102,6 +102,8 @@ void getSegmentsOutputByRunning(
jit_inputs_ivalues.push_back(ivalues_maps[input].toInt());
} else if (input->type()->isSubtypeOf(torch::jit::BoolType::get())) {
jit_inputs_ivalues.push_back(ivalues_maps[input].toBool());
} else if (input->type()->isSubtypeOf(torch::jit::FloatType::get())) {
jit_inputs_ivalues.push_back(ivalues_maps[input].toDouble());
} else if (input->type()->kind() == torch::jit::TypeKind::ListType) {
// create list
jit_inputs_ivalues.push_back(ivalues_maps[input].toList());
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