mirror of https://github.com/llvm/torch-mlir
122 lines
3.0 KiB
Python
122 lines
3.0 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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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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class SqueezeStaticModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([1, 7, 1, 3, 1], torch.float32, True),
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])
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def forward(self, a):
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return torch.squeeze(a)
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@register_test_case(
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module_factory=lambda: SqueezeStaticModule())
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def SqueezeModule_static(module, tu: TestUtils):
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module.forward(tu.rand(1, 7, 1, 3, 1))
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# ==============================================================================
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class SqueezeDynamicModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([1, -1, 1, 384, -1, 1, 1], torch.float32, True),
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])
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def forward(self, a):
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return torch.squeeze(a)
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@register_test_case(
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module_factory=lambda: SqueezeDynamicModule())
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def SqueezeModule_dynamic(module, tu: TestUtils):
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module.forward(tu.rand(1, 8, 1, 384, 12, 1, 1))
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# ==============================================================================
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class SqueezeNoUnitDimModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([4, -1, -1], torch.float32, True),
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])
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def forward(self, a):
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return torch.squeeze(a)
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@register_test_case(
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module_factory=lambda: SqueezeNoUnitDimModule())
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def SqueezeModule_noUnitDim(module, tu: TestUtils):
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module.forward(tu.rand(4, 2, 3))
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# ==============================================================================
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class SqueezeAllUnitDimModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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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, a):
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return torch.squeeze(a)
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@register_test_case(
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module_factory=lambda: SqueezeAllUnitDimModule())
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def SqueezeModule_allUnitDim(module, tu: TestUtils):
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module.forward(tu.rand(1, 1))
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# ==============================================================================
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class SqueezeBroadcastModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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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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([], torch.float32, True),
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])
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def forward(self, a, b):
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return a * b.squeeze()
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@register_test_case(
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module_factory=lambda: SqueezeBroadcastModule())
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def SqueezeModule_broadcast(module, tu: TestUtils):
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module.forward(tu.rand(4, 3), tu.rand())
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