torch-mlir/frontends/pytorch/test/module_import/methods.py

45 lines
1.5 KiB
Python

# -*- 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__()
def forward(self, x, y):
return x * y
# The symbol name of the function is NOT load-bearing and cannot be relied upon.
# CHECK-LABEL: torch.class_type
# CHECK-SAME: @[[CLASSTYPE:.*]] {
# CHECK: torch.method "forward", @[[SYMNAME:.*]]
# CHECK: }
# CHECK-LABEL: func private
# CHECK-SAME: @[[SYMNAME]](
# CHECK-SAME: %[[SELF:.*]]: !torch.nn.Module<"[[CLASSTYPE]]">,
# CHECK-SAME: %[[X:.*]]: !numpy.ndarray<*:!numpy.any_dtype>,
# CHECK-SAME: %[[Y:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {
# CHECK: %[[RET:.*]] = torch.kernel_call "aten::mul" %[[X]], %[[Y]]
# CHECK: return %[[RET]] : !numpy.ndarray<*:!numpy.any_dtype>
# CHECK: %[[ROOT:.*]] = torch.nn_module {
# CHECK: } : !torch.nn.Module<"[[CLASSTYPE]]">
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()