Elias Ellison de4fe7a38c 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 Differential Revision: D31732414 Pulled By: eellison fbshipit-source-id: 290a94a667c20467717202a43c60e4f9ca4c00e2		2021-10-19 16:41:49 -07:00
..
api	use irange for loops 5 (#66744)	2021-10-18 21:59:50 -07:00
c10d	use irange for loops 5 (#66744)	2021-10-18 21:59:50 -07:00
common
dist_autograd	Fix distributed autograd gradients synchronization (#57792)	2021-05-09 17:32:59 -07:00
jit	Add Initial NNC Dynamic Shapes Flow (#66136)	2021-10-19 16:41:49 -07:00
lazy	Add lazy Node base and associated infra (#66601)	2021-10-18 19:09:42 -07:00
lite_interpreter_runtime	Back out "Revert D30710710: [Pytorch Edge] Support profiling kineto events from external source" (#66421)	2021-10-12 10:55:29 -07:00
rpc	use irange for loops 5 (#66744)	2021-10-18 21:59:50 -07:00
tensorexpr	use irange for loops 5 (#66744)	2021-10-18 21:59:50 -07:00
__init__.py	remediation of S205607	2020-07-17 17:19:47 -07:00