pytorch

fc82ad18 - Add Initial NNC Dynamic Shapes Flow (#66136)

Add Initial NNC Dynamic Shapes Flow (#66136) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66136 FOR REVIEWERS: this is ready to review, test failures comes from somewhere else in stack.. Takes in a TensorExprGraph of static shapes and generalizes the input shapes to symbolic dimensions. Dimensions of value 1 will be preserved, otherwise dimensions with the same value will be bucketed to the same symbolic shape. E.g. `Tensor(5, 3), Tensor(3, 1) -> Tensor(SS(-1), SS(-2)), Tensor(SS(-2), 1)` From there, runs symbolic shape inference on the graph, and creates a versioning if in the graph with prim::TensorExprDynamicGuard checking if the inputs at runtime match the Generalized Symbolic Shapes that are inputs to the TE Kernel. The computate to calculate all symbolic dimensions is inlined in to the if block with the TE Kernel. All Sym Dim Value* are appended to the end of the TE Kernel Graph/Node inputs, and the Node is augmented with a integer list attr `symbolic_shape_inputs` that gives the mapping from Value * -> Symbolic Shape int64_t value. For more lengthy IR examples and walkthrough look at ShapeAnalysisTest.DynamicShapesFusion in `test_shape_analysis` Returns True on Success, False on Failure, can fail if shape propagation fails to propagate # of dims or if complete shapes on inputs not set. Example transformation ``` graph(%x_inp : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y_inp : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z_inp : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %3 : Tensor = prim::TensorExprGroup_0(%x_inp, %y_inp, %z_inp) return () with prim::TensorExprGroup_0 = graph(%x.1 : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y.1 : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %3 : int = prim::Constant[value=0]() %4 : Tensor = aten::tanh(%x.1) %5 : Tensor = aten::erf(%4) %6 : Tensor = aten::relu(%y.1) %7 : Tensor[] = prim::ListConstruct(%5, %6) %8 : Tensor = aten::cat(%7, %3) %9 : Tensor = aten::hardswish(%8) %10 : Tensor = aten::mul(%9, %z) return (%9) ``` -> ``` graph(%x_inp : Float(10, 5, strides=[5, 1], requires_grad=0, device=cpu), %y_inp : Float(4, 5, strides=[5, 1], requires_grad=0, device=cpu), %z_inp : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)): %4 : bool = prim::TensorExprDynamicGuard[types=[Float(SS(-2), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), Float(SS(-4), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu)]](%x_inp, %y_inp, %z_inp) %5 : Tensor = prim::If(%4) block0(): %15 : int[] = aten::size(%x_inp) %16 : int[] = aten::size(%y_inp) %17 : int = prim::Constant[value=1]() %18 : int = prim::Constant[value=0]() %elem.3 : int = aten::__getitem__(%15, %18) # <string>:40:10 %elem.5 : int = aten::__getitem__(%15, %17) # <string>:40:10 %elem.11 : int = aten::__getitem__(%16, %18) # <string>:40:10 %cat_dim_size.48 : int = aten::add(%elem.3, %elem.11) # <string>:321:29 %3 : Tensor = prim::TensorExprGroup_0[symbolic_shape_inputs=[-5, -4, -3, -2]](%x_inp, %y_inp, %z_inp, %cat_dim_size.48, %elem.11, %elem.5, %elem.3) -> (%3) block1(): %14 : Tensor = prim::FallbackGraph_1(%x_inp, %y_inp, %z_inp) -> (%14) return () with prim::TensorExprGroup_0 = graph(%x.1 : Float(SS(-2), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), %y.1 : Float(SS(-4), SS(-3), strides=[5, 1], requires_grad=0, device=cpu), %z : Float(1, 1, strides=[1, 1], requires_grad=0, device=cpu), %SS_5 : int, %SS_4 : int, %SS_3 : int, %SS_2 : int): %3 : int = prim::Constant[value=0]() %4 : Tensor(SS(-2), SS(-3)) = aten::tanh(%x.1) %5 : Tensor(SS(-2), SS(-3)) = aten::erf(%4) %6 : Tensor(SS(-4), SS(-3)) = aten::relu(%y.1) %7 : Tensor[] = prim::ListConstruct(%5, %6) %8 : Tensor(SS(-5), SS(-3)) = aten::cat(%7, %3) %9 : Tensor(SS(-5), SS(-3)) = aten::hardswish(%8) %10 : Tensor(SS(-5), SS(-3)) = aten::mul(%9, %z) return (%9) ``` Test Plan: Imported from OSS Reviewed By: navahgar, anjali411 Differential Revision: D31797466 Pulled By: eellison fbshipit-source-id: b508d2f5baef6e8e4020955ab1d4bc4b9c7bdfdd