mirror of https://github.com/llvm/torch-mlir
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
# -*- Python -*-
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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"""
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Example of taking a moduled traced by TorchFX and compiling it using torch-mlir.
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To run the example, make sure the following are in your PYTHONPATH:
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1. /path/to/torch-mlir/examples
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2. /path/to/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir
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then, simply call `python torchfx_add_tanh_sigmoid.py`.
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"""
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import torch
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import numpy as np
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from torch.fx.experimental.fx_acc import acc_tracer
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from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend \
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import RefBackendLinalgOnTensorsBackend
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from torch_mlir.passmanager import PassManager
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from torchfx.builder import build_module
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from utils.annotator import annotate_forward_args
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from utils.torch_mlir_types import TorchTensorType
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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# TODO: Debug issue with RefBackend
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#return torch.tanh(x) + torch.sigmoid(y)
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return torch.tanh(x)
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module = MyModule()
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traced_module = acc_tracer.trace(module, [torch.Tensor(2,2),
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torch.Tensor(2,2)])
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print("TRACE")
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arg_type = TorchTensorType(shape=[None, None], dtype=torch.float)
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traced_module = annotate_forward_args(traced_module, [arg_type, arg_type])
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print(traced_module.graph)
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mlir_module = build_module(traced_module)
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print("\n\nTORCH MLIR")
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mlir_module.dump()
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print(mlir_module.operation.verify())
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with mlir_module.context:
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pm = PassManager.parse('torchscript-module-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline')
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pm.run(mlir_module)
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print("\n\nLOWERED MLIR")
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mlir_module.dump()
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backend = RefBackendLinalgOnTensorsBackend()
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compiled = backend.compile(mlir_module)
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jit_module = backend.load(compiled)
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print("\n\nRunning Forward Function")
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np_t = np.random.rand(2, 2).astype(dtype=np.float32)
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t = torch.tensor(np_t, dtype=torch.float)
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print("Compiled result:\n", jit_module.forward(np_t, np_t))
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print("\nExpected result:\n", module.forward(t, t))
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