mirror of https://github.com/llvm/torch-mlir
126 lines
3.3 KiB
Python
126 lines
3.3 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 TensorToIntZeroRank(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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([], torch.int64, True),
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])
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def forward(self, x):
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return int(x)
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@register_test_case(module_factory=lambda: TensorToIntZeroRank())
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def TensorToIntZeroRank_basic(module, tu: TestUtils):
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module.forward(torch.randint(10, ()))
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# ==============================================================================
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class TensorToInt(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.int64, True),
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])
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def forward(self, x):
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return int(x)
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@register_test_case(module_factory=lambda: TensorToInt())
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def TensorToInt_basic(module, tu: TestUtils):
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module.forward(torch.randint(10, (1, 1)))
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# ==============================================================================
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class TensorToFloatZeroRank(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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([], torch.float64, True),
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])
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def forward(self, x):
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return float(x)
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@register_test_case(module_factory=lambda: TensorToFloatZeroRank())
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def TensorToFloatZeroRank_basic(module, tu: TestUtils):
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module.forward(torch.rand((), dtype=torch.float64))
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# ==============================================================================
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class TensorToFloat(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.float64, True),
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])
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def forward(self, x):
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return float(x)
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@register_test_case(module_factory=lambda: TensorToFloat())
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def TensorToFloat_basic(module, tu: TestUtils):
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module.forward(torch.rand((1, 1), dtype=torch.float64))
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# ==============================================================================
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class TensorToBoolZeroRank(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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([], torch.bool, True),
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])
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def forward(self, x):
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return bool(x)
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@register_test_case(module_factory=lambda: TensorToBoolZeroRank())
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def TensorToBoolZeroRank_basic(module, tu: TestUtils):
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module.forward(torch.tensor(1, dtype=torch.bool))
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# ==============================================================================
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class TensorToBool(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.bool, True),
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])
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def forward(self, x):
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return bool(x)
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@register_test_case(module_factory=lambda: TensorToBool())
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def TensorToBool_basic(module, tu: TestUtils):
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module.forward(torch.tensor([[1]], dtype=torch.bool))
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