mirror of https://github.com/llvm/torch-mlir
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
# 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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import torch
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import torch.nn as nn
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from torch_mlir_e2e_test.torchscript.framework import TestUtils
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from torch_mlir_e2e_test.torchscript.registry import register_test_case
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from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
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# ==============================================================================
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# Multi-layer perceptron (MLP) models.
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class Mlp1LayerModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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self.fc0 = nn.Linear(3, 5)
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self.tanh0 = nn.Tanh()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.tanh0(self.fc0(x))
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@register_test_case(module_factory=lambda: Mlp1LayerModule())
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def Mlp1LayerModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(5, 3))
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class Mlp2LayerModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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N_HIDDEN = 5
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self.fc0 = nn.Linear(3, N_HIDDEN)
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self.tanh0 = nn.Tanh()
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self.fc1 = nn.Linear(N_HIDDEN, 2)
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self.tanh1 = nn.Tanh()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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])
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def forward(self, x):
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x = self.tanh0(self.fc0(x))
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x = self.tanh1(self.fc1(x))
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return x
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@register_test_case(module_factory=lambda: Mlp2LayerModule())
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def Mlp2LayerModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(5, 3))
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