mirror of https://github.com/llvm/torch-mlir
448 lines
12 KiB
Python
448 lines
12 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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@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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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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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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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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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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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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@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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@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 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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@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 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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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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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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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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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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class SoftmaxIntModule(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: 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 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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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
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@annotate_args([
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None,
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([-1, -1, -1], torch.float64, 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: 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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class BroadcastToModule(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, x):
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return torch.broadcast_to(x, [1, -1, -1, 4])
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@register_test_case(module_factory=lambda: BroadcastToModule())
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def BroadcastToModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 1, 1))
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class OnesModuleInt(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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])
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def forward(self):
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return torch.ones(3, 4, dtype=torch.int64)
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@register_test_case(module_factory=lambda: OnesModuleInt())
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def OnesModuleInt_basic(module, tu: TestUtils):
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module.forward()
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class OnesModuleFloat(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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])
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def forward(self):
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return torch.ones(3, 4, dtype=torch.float32)
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@register_test_case(module_factory=lambda: OnesModuleFloat())
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def OnesModuleFloat_basic(module, tu: TestUtils):
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module.forward()
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