mirror of https://github.com/llvm/torch-mlir
125 lines
3.6 KiB
Python
125 lines
3.6 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 ReduceSumModule(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, a):
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return torch.sum(a)
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@register_test_case(module_factory=lambda: ReduceSumModule())
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def ReduceSumModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5))
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# ==============================================================================
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class ReduceSumDtypeModule(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.float64, True),
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])
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def forward(self, a):
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return torch.sum(a, dtype=torch.float32)
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@register_test_case(module_factory=lambda: ReduceSumDtypeModule())
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def ReduceSumDtypeModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5).to(torch.float64))
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# ==============================================================================
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class ReduceSumDimIntListModule(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, a):
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return torch.sum(a, (0, 1))
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@register_test_case(module_factory=lambda: ReduceSumDimIntListModule())
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def ReduceSumDimIntListModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5))
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# ==============================================================================
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class ReduceSumDimIntListDtypeModule(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.float64, True),
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])
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def forward(self, a):
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return torch.sum(a, (0, 1), dtype=torch.float32)
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@register_test_case(module_factory=lambda: ReduceSumDimIntListDtypeModule())
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def ReduceSumDimIntListDtypeModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5).to(torch.float64))
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# ==============================================================================
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class ReduceSumDimIntListKeepDimModule(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, a):
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return torch.sum(a, (1, 2), keepdim=True)
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@register_test_case(module_factory=lambda: ReduceSumDimIntListKeepDimModule())
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def ReduceSumDimIntListKeepDimModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5))
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# ==============================================================================
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class ReduceMeanDtypeModule(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.float64, True),
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])
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def forward(self, a):
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return torch.mean(a, dtype=torch.float32)
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@register_test_case(module_factory=lambda: ReduceMeanDtypeModule())
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def ReduceMeanDtypeModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5).to(torch.float64))
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