pytorch

aac36a89 - [model transform] tuple to arglist jit pass (#36093)

[model transform] tuple to arglist jit pass (#36093) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36093 Unwrap any tuples (including NamedTuples) in the module forward function input list to be arglist. 1. Supports multiple tuple inputs, and traces their use through CallMethods and TupleIndex 2. Does not unwrap inner use of other tuples that did not show up in the original toplevel graph inputs We work from the ScriptModule level instead of the Graph level because: 1. If the ScriptModule was previously called with the original set of inputs, the GraphExecutor caches the ExecutionPlan (specifically, ArgumentSpecCreator is derived from the Graph and type check the inputs passed in) 2. Since we are changing this graph's inputs, we clone the module and clear the GraphExecutor. Since we work from ScriptModule level, we cannot take advantage of jit level syntactic sugar like run_pass(), so I jit exposed this as a cpp extension. Let me know if there are other ideas about this. Test Plan: buck test caffe2/torch/fb/model_transform:signature_translation_test Todo: Verify use in bento Untranslated graph: ``` > graph(%self : __torch__.test_jit.SparseNNWrapper, > %inputs.1 : NamedTuple(dense : Tensor, sparse : Dict(int, Tensor))): > %2 : __torch__.test_jit.SparseNN = prim::GetAttr[name="main_module"](%self) > %4 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23 > return (%4) ``` Translated graph: ``` > graph(%self : __torch__.test_jit.___torch_mangle_1.SparseNNWrapper, > %inputs.1_0 : Tensor, > %inputs.1_1 : Dict(int, Tensor)): > %2 : __torch__.test_jit.___torch_mangle_2.SparseNN = prim::GetAttr[name="main_module"](%self) > %3 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1_0, %inputs.1_1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23 > return (%3) ``` Reviewed By: houseroad Differential Revision: D20313673 fbshipit-source-id: fddd07c9537dc8b6f480a14d697bea10ecc74470