mirror of https://github.com/llvm/torch-mlir
50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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import typing
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import torch
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import torch_mlir
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import npcomp
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from npcomp.compiler.pytorch.backend import refjit
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from npcomp.compiler.utils import logging
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import test_utils
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#logging.enable()
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# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
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mb = torch_mlir.ModuleBuilder()
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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return torch.tanh(x)
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test_module = TestModule()
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class_annotator = torch_mlir.ClassAnnotator()
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recursivescriptmodule = torch.jit.script(test_module)
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torch.jit.save(recursivescriptmodule, '/tmp/foo.pt')
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class_annotator.exportNone(recursivescriptmodule._c._type())
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class_annotator.exportPath(recursivescriptmodule._c._type(), ['forward'])
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class_annotator.annotateShapesAndDtypes(recursivescriptmodule._c._type(), ['forward'], [
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None,
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([2, 3, -1], torch.float32)
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])
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# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
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mb.import_module(recursivescriptmodule._c, class_annotator)
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#mb.module.operation.print()
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backend = refjit.CompilerBackend()
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compiled = backend.compile_object_graph(mb.module)
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jit_module = backend.load(compiled)
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torch.manual_seed(0)
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input = torch.rand(2, 3, 1)
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test_utils.compare_outputs(test_module.forward, jit_module.forward, input)
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