mirror of https://github.com/llvm/torch-mlir
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
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# 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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import torch
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from torch_mlir.torchscript.e2e_test.framework import TestUtils
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from torch_mlir.torchscript.e2e_test.registry import register_test_case
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from torch_mlir.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),
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([-1, -1], torch.float32),
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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 TanhModule(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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([2, 3, -1], torch.float32),
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])
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def forward(self, x):
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return torch.tanh(x)
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@register_test_case(module_factory=lambda: TanhModule())
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def TanhModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 3, 1))
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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),
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([-1, -1], torch.float32),
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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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