torch-mlir/frontends/pytorch/examples/torchscript_tanh_e2e.py

50 lines
1.4 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
import npcomp
from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
from npcomp.compiler.utils import logging
import test_utils
#logging.enable()
# 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):
return torch.tanh(x)
test_module = TestModule()
class_annotator = torch_mlir.ClassAnnotator()
recursivescriptmodule = torch.jit.script(test_module)
torch.jit.save(recursivescriptmodule, '/tmp/foo.pt')
class_annotator.exportNone(recursivescriptmodule._c._type())
class_annotator.exportPath(recursivescriptmodule._c._type(), ['forward'])
class_annotator.annotateArgs(recursivescriptmodule._c._type(), ['forward'], [
None,
([2, 3, -1], torch.float32)
])
# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
mb.import_module(recursivescriptmodule._c, class_annotator)
#mb.module.operation.print()
backend = iree.IreeNpcompBackend()
compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
jit_module = backend.load(compiled)
torch.manual_seed(0)
input = torch.rand(2, 3, 1)
test_utils.compare_outputs(test_module.forward, jit_module.forward, input)