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torch-mlir/frontends/pytorch/test/module_import/object-identity.py

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Handle object identity correctly. This required some careful considerations when defining object identity for tensors. See the comments for how we do it. This also tracks some basic information for diagnostics.
2021-02-06 09:57:38 +08:00
# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
import typing
import torch
import torch_mlir
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
mb = torch_mlir.ModuleBuilder()
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
# CHECK: %[[A:.*]] = numpy.create_array_from_tensor
# CHECK: torch.nn_module {
Implement GlobalizeObjectGraph transformation. This required restructuring of how we model TorchScript on import. The main difference is that now we split out a `torch.class_type` that holds methods and declarations of the types of each slot. This is more consistent with TorchScript (our previous representation was "denormalized"). Recommended reading order: 1. check out the description of `torch.class_type` in `TorchOps.td` and look at `test/Dialect/Torch/ops.mlir` and `frontends/pytorch/test/module_import/` to familiarize with the new representation. - Just look at the new IR. The diff between the old names and new names is confusing. 2. check out `test/Dialect/Torch/globalize-object-graph*.mlir` and read along with the pass description in `include/npcomp/Dialect/Torch/Transforms/Passes.td` 3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.
2021-02-18 03:28:51 +08:00
# CHECK: torch.slot "t1", %[[A]]
# CHECK: torch.slot "t2", %[[A]]
Handle object identity correctly. This required some careful considerations when defining object identity for tensors. See the comments for how we do it. This also tracks some basic information for diagnostics.
2021-02-06 09:57:38 +08:00
self.t1 = self.t2 = torch.tensor([10., 20.])
test_module = TestModule()
recursivescriptmodule = torch.jit.script(test_module)
# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
mb.import_module(recursivescriptmodule._c)
mb.module.operation.print()