torch-mlir/e2e_testing/torchscript/nll_loss.py

193 lines
6.1 KiB
Python

# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
import torch
from torch_mlir_e2e_test.torchscript.framework import TestUtils
from torch_mlir_e2e_test.torchscript.registry import register_test_case
from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
# ==============================================================================
class NllLossModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
])
# Here the 2nd index is ignored.
def forward(self, x, y):
return torch.ops.aten.nll_loss_forward(x,
target=y,
weight=None,
reduction=0,
ignore_index=2)[0]
@register_test_case(module_factory=lambda: NllLossModule())
def NllLossModule_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 3), torch.randint(0, 3, (2,)))
class NllLossModule_mean(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
])
# Here the 2nd index is ignored.
def forward(self, x, y):
return torch.ops.aten.nll_loss_forward(x,
target=y,
weight=None,
reduction=1,
ignore_index=2)[0]
@register_test_case(module_factory=lambda: NllLossModule_mean())
def NllLossModule_mean_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 3), torch.randint(0, 3, (2,)))
class NllLossModule_sum(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
])
# Here the 2nd index is ignored.
def forward(self, x, y):
return torch.ops.aten.nll_loss_forward(x,
target=y,
weight=None,
reduction=2,
ignore_index=2)[0]
@register_test_case(module_factory=lambda: NllLossModule_sum())
def NllLossModule_sum_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 3), torch.randint(0, 3, (2,)))
class NllLossModule_1D(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1], torch.float32, True),
([], torch.int64, True),
])
# Here the 2nd index is ignored.
def forward(self, x, y):
return torch.ops.aten.nll_loss_forward(x,
target=y,
weight=None,
reduction=0,
ignore_index=2)[0]
@register_test_case(module_factory=lambda: NllLossModule_1D())
def NllLossModule_1D_basic(module, tu: TestUtils):
module.forward(tu.rand(3), torch.randint(0, 3, ()))
class NllLossModule_ignore_index_out_of_bounds(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
])
# None of the index is ignored here, since the ignored index is out of bounds.
def forward(self, x, y):
return torch.ops.aten.nll_loss_forward(x,
target=y,
weight=None,
reduction=0,
ignore_index=10)[0]
@register_test_case(module_factory=lambda: NllLossModule_ignore_index_out_of_bounds())
def NllLossModule_ignore_index_out_of_bounds_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 3), torch.randint(0, 3, (2,)))
class NllLossModule_backward(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1], torch.float32, True),
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
([], torch.float32, True),
])
def forward(self, grad_output, input, target, total_weight):
return torch.ops.aten.nll_loss_backward(grad_output,
input,
target=target,
weight=None,
reduction=0,
ignore_index=10,
total_weight=total_weight)
@register_test_case(module_factory=lambda: NllLossModule_backward())
def NllLossModuleBackward_basic(module, tu: TestUtils):
module.forward(tu.rand(3), tu.rand(3, 4), torch.tensor([2, 3, 0]),
torch.tensor(3.))
class NllLossModule_backward_ignore_index(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1], torch.float32, True),
([-1, -1], torch.float32, True),
([-1], torch.int64, True),
([], torch.float32, True),
])
def forward(self, grad_output, input, target, total_weight):
return torch.ops.aten.nll_loss_backward(grad_output,
input,
target=target,
weight=None,
reduction=0,
ignore_index=1,
total_weight=total_weight)
@register_test_case(
module_factory=lambda: NllLossModule_backward_ignore_index())
def NllLossModuleBackward_ignore_index(module, tu: TestUtils):
module.forward(tu.rand(3), tu.rand(3, 4), torch.tensor([2, 3, 0]),
torch.tensor(3.))