mirror of https://github.com/llvm/torch-mlir
133 lines
3.7 KiB
Python
133 lines
3.7 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 MatmulDot(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], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: MatmulDot())
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def Matmul_dot(module, tu: TestUtils):
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module.forward(tu.rand(3), tu.rand(3))
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# ==============================================================================
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class Matmul2D(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.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: Matmul2D())
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def Matmul_2d(module, tu: TestUtils):
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module.forward(tu.rand(3, 4), tu.rand(4, 5))
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# ==============================================================================
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class MatmulVecMat(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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])
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def forward(self, lhs, rhs):
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return torch.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: MatmulVecMat())
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def Matmul_vecmat(module, tu: TestUtils):
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module.forward(tu.rand(4), tu.rand(4, 5))
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# ==============================================================================
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class MatmulMatVec(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], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: MatmulMatVec())
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def Matmul_matvec(module, tu: TestUtils):
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module.forward(tu.rand(4, 5), tu.rand(5))
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# ==============================================================================
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class Matmul3D(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.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: Matmul3D())
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def Matmul_3d(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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class Matmul4d(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, -1], torch.float32, True),
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([-1, -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.matmul(lhs, rhs)
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@register_test_case(module_factory=lambda: Matmul4d())
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def Matmul_4d(module, tu: TestUtils):
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module.forward(tu.rand(4, 5, 6, 7), tu.rand(4, 5, 7, 6))
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# ==============================================================================
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