mirror of https://github.com/llvm/torch-mlir
966 lines
25 KiB
Python
966 lines
25 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 MmModule(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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([-1, -1], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.mm(lhs, rhs)
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@register_test_case(module_factory=lambda: MmModule())
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def MmModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 4), tu.rand(4, 4))
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@register_test_case(module_factory=lambda: MmModule())
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def MmModule_chained(module, tu: TestUtils):
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res = module.forward(tu.rand(4, 4), tu.rand(4, 4))
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module.forward(res, res)
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# ==============================================================================
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class BmmModule(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], torch.float32, True),
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([-1, -1, -1], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.bmm(lhs, rhs)
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@register_test_case(module_factory=lambda: BmmModule())
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def BmmModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5), tu.rand(3, 5, 4))
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# ==============================================================================
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# A subgraph with multiple mm ops.
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class MmDagModule(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, 4], torch.float32, True),
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([4, 4], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.mm(lhs, torch.mm(lhs, rhs))
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@register_test_case(module_factory=lambda: MmDagModule())
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def MmDagModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 4), tu.rand(4, 4))
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# ==============================================================================
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class MmTanhModule(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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([-1, -1], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.tanh(self.matmul(lhs, rhs))
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def matmul(self, lhs, rhs):
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return torch.mm(lhs, rhs)
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# ==============================================================================
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@register_test_case(module_factory=lambda: MmTanhModule())
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def MmTanhModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 2), tu.rand(2, 4))
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# ==============================================================================
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class AddmmModuleFloat(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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([-1, -1], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, M, mat1, mat2):
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return torch.addmm(M, mat1, mat2, beta=3.0, alpha=7.0)
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@register_test_case(module_factory=lambda: AddmmModuleFloat())
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def AddmmModuleFloat_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 4), tu.rand(4, 2), tu.rand(2, 4))
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# ==============================================================================
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class AddmmModuleBroadcastable(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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([-1, -1], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, M, mat1, mat2):
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return torch.addmm(M, mat1, mat2, beta=2.0, alpha=7.0)
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@register_test_case(module_factory=lambda: AddmmModuleBroadcastable())
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def AddmmModule_broadcastable(module, tu: TestUtils):
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module.forward(tu.rand(1, 2), tu.rand(3, 2), tu.rand(2, 2))
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# ==============================================================================
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class AddmmModuleDifferentRankBroadcastable(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], torch.float32, True),
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([-1, -1], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, M, mat1, mat2):
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return torch.addmm(M, mat1, mat2, beta=11.0, alpha=7.0)
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@register_test_case(module_factory=lambda: AddmmModuleDifferentRankBroadcastable())
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def AddmmModule_differentRankBroadcastable(module, tu: TestUtils):
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module.forward(tu.rand(3), tu.rand(3, 2), tu.rand(2, 3))
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# ==============================================================================
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class AdaptiveAvgPool2dModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.aap2d = torch.nn.AdaptiveAvgPool2d((1, 1))
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.aap2d(x)
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@register_test_case(module_factory=lambda: AdaptiveAvgPool2dModule())
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def AdaptiveAvgPool2dModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(10, 3, 8, 9))
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# ==============================================================================
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class FlattenStaticModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.flat = torch.nn.Flatten(2, 4)
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@export
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@annotate_args([
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None,
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([10, 3, 8, 9, 3, 4], torch.float32, True),
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])
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def forward(self, x):
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return self.flat(x)
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@register_test_case(module_factory=lambda: FlattenStaticModule())
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def FlattenStaticModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(10, 3, 8, 9, 3, 4))
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# ==============================================================================
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class FlattenRank0Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.flat = torch.nn.Flatten(-1, -1)
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@export
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@annotate_args([
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None,
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([], torch.float32, True),
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])
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def forward(self, x):
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return self.flat(x)
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@register_test_case(module_factory=lambda: FlattenRank0Module())
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def FlattenRank0Module_basic(module, tu: TestUtils):
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module.forward(torch.tensor(4.0))
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# ==============================================================================
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class FlattenDynamicModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.flat = torch.nn.Flatten(2, 4)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, 9, 3, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.flat(x)
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@register_test_case(module_factory=lambda: FlattenDynamicModule())
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def FlattenDynamicModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(10, 3, 8, 9, 3, 4))
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# ==============================================================================
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class MaxPool2dModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mp2d = torch.nn.MaxPool2d(kernel_size=[6, 8],
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stride=[2, 2],
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padding=[3, 4],
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dilation=2)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.mp2d(x)
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# ==============================================================================
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@register_test_case(module_factory=lambda: MaxPool2dModule())
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def MaxPool2dModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(1, 1, 20, 20) - 0.5)
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class TransposeIntModule(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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([3, 4, 2], torch.float32, True),
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])
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def forward(self, x):
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return torch.transpose(x, 0, 1)
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# ==============================================================================
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@register_test_case(module_factory=lambda: TransposeIntModule())
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def TransposeIntModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 2))
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class PermuteModule(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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([3, 4, 2], torch.float32, True)
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])
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def forward(self, x):
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return x.permute(0, 2, 1)
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@register_test_case(module_factory=lambda: PermuteModule())
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def PermuteModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 2))
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# ==============================================================================
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class TransposeIntNegDimsModule(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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([3, 4, 2], torch.float32, True),
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])
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def forward(self, x):
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return torch.transpose(x, -1, -2)
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# ==============================================================================
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@register_test_case(module_factory=lambda: TransposeIntNegDimsModule())
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def TransposeIntNegDimsModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 2))
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class PermuteNegativeIndexModule(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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([3, 4, 2], torch.float32, True)
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])
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def forward(self, x):
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return x.permute(0, -1, 1)
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# ==============================================================================
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@register_test_case(module_factory=lambda: PermuteNegativeIndexModule())
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def PermuteNegativeIndexModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 2))
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class TensorsConcatModule(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], torch.float32, True),
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([-1, -1, -1], torch.float32, True),
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([-1, -1, -1], torch.float32, True),
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])
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def forward(self, x, y, z):
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return torch.cat([x, y, z], 1)
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@register_test_case(module_factory=lambda: TensorsConcatModule())
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def TensorsConcatModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 2, 4), tu.rand(2, 1, 4), tu.rand(2, 3, 4))
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# ==============================================================================
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class GatherModule(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], torch.float32, True),
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([-1, -1, -1], torch.int64, True),
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])
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def forward(self, tensor, indices):
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return torch.gather(tensor, 2, indices)
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@register_test_case(module_factory=lambda: GatherModule())
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def GatherModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 3, 4), torch.tensor([[[1, 2, 3], [1, 2, 3]]]))
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# ==============================================================================
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class AddSizeIntModule(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, tensor):
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# This is a workaround for not supporting scalar arguments.
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# TODO: pass in dim as an argument to the forward method when scalar
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# arguments are supported.
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return tensor.add(tensor, alpha=tensor.size(1))
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@register_test_case(module_factory=lambda: AddSizeIntModule())
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def AddSizeIntModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 3))
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# ==============================================================================
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class AddSizeIntNegDimModule(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, tensor):
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# This is a workaround for not supporting scalar arguments.
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# TODO: pass in dim as an argument to the forward method when scalar
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# arguments are supported.
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return tensor.add(tensor, alpha=tensor.size(-2))
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@register_test_case(module_factory=lambda: AddSizeIntNegDimModule())
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def AddSizeIntNegDimModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 3))
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# ==============================================================================
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class EmbeddingModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.embed = torch.nn.Embedding(num_embeddings=100,
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embedding_dim=50,
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padding_idx=4)
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@export
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@annotate_args([
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None,
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([-1, -1], torch.int64, True),
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])
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def forward(self, indices):
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return self.embed.forward(indices)
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@register_test_case(module_factory=lambda: EmbeddingModule())
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def EmbeddingModule_basic(module, tu: TestUtils):
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module.forward(torch.randint(100, (3, 3)))
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# ==============================================================================
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class SoftmaxIntModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.softmax = torch.nn.Softmax(2)
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@export
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|
@annotate_args([
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None,
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([-1, -1, -1], torch.float32, True),
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])
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def forward(self, tensor):
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return self.softmax.forward(tensor)
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@register_test_case(module_factory=lambda: SoftmaxIntModule())
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def SoftmaxIntModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4))
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class _SoftmaxModule(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], torch.float32, True),
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])
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def forward(self, tensor):
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return torch.ops.aten._softmax(tensor, 0, False)
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@register_test_case(module_factory=lambda: _SoftmaxModule())
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def _SoftmaxModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4))
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# ==============================================================================
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class SoftmaxIntNegDimModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.softmax = torch.nn.Softmax(-2)
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|
@export
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|
@annotate_args([
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|
None,
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([-1, -1, -1], torch.float32, True),
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])
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|
def forward(self, tensor):
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return self.softmax.forward(tensor)
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@register_test_case(module_factory=lambda: SoftmaxIntNegDimModule())
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def SoftmaxIntNegDimModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4))
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# ==============================================================================
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class SoftmaxIntArgTypeF64Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.softmax = torch.nn.Softmax(2)
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|
|
@export
|
|
@annotate_args([
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|
None,
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|
([-1, -1, -1], torch.float64, True),
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])
|
|
def forward(self, tensor):
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return self.softmax.forward(tensor)
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@register_test_case(module_factory=lambda: SoftmaxIntArgTypeF64Module())
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def SoftmaxIntArgTypeF64Module_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4).double())
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|
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# ==============================================================================
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class BroadcastToModule(torch.nn.Module):
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|
def __init__(self):
|
|
super().__init__()
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|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1, 1], torch.float32, True),
|
|
])
|
|
def forward(self, x):
|
|
return torch.broadcast_to(x, [1, -1, -1, 4])
|
|
|
|
|
|
@register_test_case(module_factory=lambda: BroadcastToModule())
|
|
def BroadcastToModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3, 1, 1))
|
|
|
|
# ==============================================================================
|
|
|
|
class ExpandModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1, 1], torch.float32, True),
|
|
])
|
|
def forward(self, x):
|
|
return x.expand([1, -1, -1, 4])
|
|
|
|
|
|
@register_test_case(module_factory=lambda: ExpandModule())
|
|
def ExpandModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3, 1, 1))
|
|
|
|
# ==============================================================================
|
|
|
|
class ContiguousModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, x):
|
|
return x.contiguous()
|
|
|
|
|
|
@register_test_case(module_factory=lambda: ContiguousModule())
|
|
def ContiguousModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3, 1))
|
|
|
|
class TensorToInt(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([], torch.int64, True),
|
|
])
|
|
def forward(self, x):
|
|
return int(x)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: TensorToInt())
|
|
def TensorToInt_basic(module, tu: TestUtils):
|
|
module.forward(torch.randint(10,[]))
|
|
|
|
class LogSoftmaxIntModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.log_softmax = torch.nn.LogSoftmax(2)
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1, -1], torch.float64, True),
|
|
])
|
|
def forward(self, tensor):
|
|
return self.log_softmax.forward(tensor)
|
|
|
|
@register_test_case(module_factory=lambda: LogSoftmaxIntModule())
|
|
def LogSoftmaxIntModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.randn(3, 2, 4).double())
|
|
|
|
|
|
class NumToTensorIntModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
])
|
|
|
|
def forward(self):
|
|
return torch.ops.prim.NumToTensor(1)
|
|
|
|
@register_test_case(module_factory=lambda: NumToTensorIntModule())
|
|
def NumToTensorIntModule_basic(module, tu: TestUtils):
|
|
module.forward()
|
|
|
|
|
|
class NumToTensorFloatModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
])
|
|
|
|
def forward(self):
|
|
return torch.ops.prim.NumToTensor(1.0)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: NumToTensorFloatModule())
|
|
def NumToTensorFloatModule_basic(module, tu: TestUtils):
|
|
module.forward()
|
|
|
|
# ==============================================================================
|
|
|
|
# This test can be removed once we have one real op returning 3 float32 tensors
|
|
class ReturnThreeTensorFloat32(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, a, b, c):
|
|
return a, b, c
|
|
|
|
@register_test_case(module_factory=lambda: ReturnThreeTensorFloat32())
|
|
def ReturnThreeTensorFloat32_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(2, 3), tu.rand(2, 3), tu.rand(2, 3))
|
|
|
|
class AddCMulModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
|
|
def forward(self, input, tensor1, tensor2):
|
|
return torch.addcmul(input, tensor1, tensor2, value=1.0)
|
|
|
|
@register_test_case(module_factory=lambda: AddCMulModule())
|
|
def AddCMulModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1,3), tu.rand(1,3), tu.rand(1,3))
|
|
|
|
class AddCDivModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
|
|
def forward(self, input, tensor1, tensor2):
|
|
return torch.addcdiv(input, tensor1, tensor2, value=1.0)
|
|
|
|
@register_test_case(module_factory=lambda: AddCDivModule())
|
|
def AddCDivModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1,3), tu.rand(1,3), tu.rand(1,3))
|
|
|
|
# ==============================================================================
|
|
|
|
class tensorIntModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
])
|
|
|
|
def forward(self):
|
|
a = 1
|
|
return torch.tensor(a)
|
|
|
|
@register_test_case(module_factory=lambda: tensorIntModule())
|
|
def TensorIntModule_basic(module, tu: TestUtils):
|
|
module.forward()
|
|
|
|
class tensorFloatModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
])
|
|
|
|
def forward(self):
|
|
a = 1.0
|
|
return torch.tensor(a)
|
|
|
|
@register_test_case(module_factory=lambda: tensorFloatModule())
|
|
def TensorFloatModule_basic(module, tu: TestUtils):
|
|
module.forward()
|
|
|
|
# ==============================================================================
|
|
|
|
class DropoutModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
|
|
def forward(self, x):
|
|
return torch.dropout(x, 0.0, False)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: DropoutModule())
|
|
def DropoutModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3, 4))
|
|
|
|
|
|
class MeanModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([3, 4], torch.float32, True),
|
|
])
|
|
def forward(self, x):
|
|
return torch.mean(x)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: MeanModule())
|
|
def MeanModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.randn(3, 4))
|
|
|
|
|
|
class MeanDynamicSizesModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, x):
|
|
return torch.mean(x)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: MeanDynamicSizesModule())
|
|
def MeanDynamicSizesModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.randn(3, 4))
|
|
|
|
|
|
class NumelModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, input):
|
|
return torch.numel(input)
|
|
|
|
@register_test_case(module_factory=lambda: NumelModule())
|
|
def NumelModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(4, 3, 5))
|
|
|
|
|
|
class NumelZeroRankModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([], torch.int64, True),
|
|
])
|
|
def forward(self, input):
|
|
return torch.numel(input)
|
|
|
|
@register_test_case(module_factory=lambda: NumelZeroRankModule())
|
|
def NumelZeroRankModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.randint(10,[]))
|
|
|
|
|
|
class BoolTensorReturnFalseModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1], torch.bool, True),
|
|
])
|
|
def forward(self, a):
|
|
return a
|
|
|
|
|
|
@register_test_case(module_factory=lambda: BoolTensorReturnFalseModule())
|
|
def BoolTensorReturnFalseModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.tensor([0, 0], dtype=torch.bool))
|
|
|
|
|
|
class BoolTensorReturnTrueModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1], torch.bool, True),
|
|
])
|
|
def forward(self, a):
|
|
return a
|
|
|
|
|
|
@register_test_case(module_factory=lambda: BoolTensorReturnTrueModule())
|
|
def BoolTensorReturnTrueModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.tensor([1, 1, 1, 1, 1], dtype=torch.bool))
|
|
|
|
|
|
class BoolTensorReturnMixedModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.bool, True),
|
|
])
|
|
def forward(self, a):
|
|
return a
|
|
|
|
|
|
@register_test_case(module_factory=lambda: BoolTensorReturnMixedModule())
|
|
def BoolTensorReturnMixedModule_basic(module, tu: TestUtils):
|
|
module.forward(torch.tensor([[1, 0], [0,1]], dtype=torch.bool))
|
|
|
|
# ==============================================================================
|
|
|
|
class TModuleRank2(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, lhs):
|
|
return torch.t(lhs)
|
|
|
|
@register_test_case(module_factory=lambda: TModuleRank2())
|
|
def TModuleRank2_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3, 4))
|
|
|
|
|
|
class TModuleRank1(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1], torch.float32, True),
|
|
])
|
|
def forward(self, lhs):
|
|
return torch.t(lhs)
|
|
|
|
@register_test_case(module_factory=lambda: TModuleRank1())
|
|
def TModuleRank1_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(3))
|
|
|
|
|
|
class TModuleRank0(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([], torch.float32, True),
|
|
])
|
|
def forward(self, lhs):
|
|
return torch.t(lhs)
|
|
|
|
@register_test_case(module_factory=lambda: TModuleRank0())
|
|
def TModuleRank0_basic(module, tu: TestUtils):
|
|
module.forward(torch.tensor(7, dtype=torch.float32))
|
|
|