mirror of https://github.com/llvm/torch-mlir
121 lines
4.0 KiB
Python
121 lines
4.0 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 NllLossModule(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.int64, True),
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])
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# Here the 2nd index is ignored.
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def forward(self, x, y):
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return torch.ops.aten.nll_loss_forward(x,
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target=y,
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weight=None,
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reduction=0,
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ignore_index=2)[0]
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@register_test_case(module_factory=lambda: NllLossModule())
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def NllLossModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 3), torch.tensor([0, 1]))
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class NllLossModule_ignore_index_out_of_bounds(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.int64, True),
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])
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# None of the index is ignored here, since the ignored index is out of bounds.
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def forward(self, x, y):
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return torch.ops.aten.nll_loss_forward(x,
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target=y,
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weight=None,
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reduction=0,
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ignore_index=10)[0]
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@register_test_case(module_factory=lambda: NllLossModule_ignore_index_out_of_bounds())
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def NllLossModule_ignore_index(module, tu: TestUtils):
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module.forward(tu.rand(2, 3), torch.tensor([0, 1]))
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class NllLossModule_backward(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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([-1], torch.int64, True),
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([], torch.float32, True),
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])
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def forward(self, grad_output, input, target, total_weight):
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return torch.ops.aten.nll_loss_backward(grad_output=grad_output,
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self=input,
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target=target,
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weight=None,
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reduction=0,
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ignore_index=10,
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total_weight=total_weight)
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@register_test_case(module_factory=lambda: NllLossModule_backward())
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def NllLossModuleBackward_basic(module, tu: TestUtils):
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module.forward(tu.rand(3), tu.rand(3, 4), torch.tensor([2, 3, 0]),
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torch.tensor(3.))
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class NllLossModule_backward_ignore_index(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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([-1], torch.int64, True),
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([], torch.float32, True),
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])
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def forward(self, grad_output, input, target, total_weight):
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return torch.ops.aten.nll_loss_backward(grad_output=grad_output,
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self=input,
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target=target,
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weight=None,
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reduction=0,
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ignore_index=1,
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total_weight=total_weight)
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@register_test_case(
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module_factory=lambda: NllLossModule_backward_ignore_index())
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def NllLossModuleBackward_ignore_index(module, tu: TestUtils):
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module.forward(tu.rand(3), tu.rand(3, 4), torch.tensor([2, 3, 0]),
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torch.tensor(3.))
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