mirror of https://github.com/llvm/torch-mlir
96 lines
3.1 KiB
Python
96 lines
3.1 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 SoftmaxBackwardModule(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, grad_output, output):
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return torch.ops.aten._softmax_backward_data(grad_output,
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output,
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dim=1,
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input_dtype=6)
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@register_test_case(module_factory=lambda: SoftmaxBackwardModule())
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def SoftmaxBackwardModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4), torch.randn(3, 2, 4))
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# ==============================================================================
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class TanhBackwardModule(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, grad_out, output):
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return torch.ops.aten.tanh_backward(grad_out, output)
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@register_test_case(module_factory=lambda: TanhBackwardModule())
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def TanhBackward_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 3), torch.randn(3, 3))
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# ==============================================================================
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class GeluBackwardModule(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, grad, input):
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return torch.ops.aten.gelu_backward(grad, input)
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@register_test_case(module_factory=lambda: GeluBackwardModule())
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def GeluBackwardModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(5, 3), tu.rand(5, 3))
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class LogSoftmaxBackwardModule(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, grad_output, output):
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return torch.ops.aten._log_softmax_backward_data(grad_output,
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output,
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dim=1,
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input_dtype=6)
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@register_test_case(module_factory=lambda: LogSoftmaxBackwardModule())
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def LogSoftmaxBackwardModule_basic(module, tu: TestUtils):
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module.forward(torch.randn(3, 2, 4), torch.randn(3, 2, 4))
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