torch-mlir/e2e_testing/torchscript/backprop.py

96 lines
3.1 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 SoftmaxBackwardModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1, -1], torch.float32, True),
([-1, -1, -1], torch.float32, True),
])
def forward(self, grad_output, output):
return torch.ops.aten._softmax_backward_data(grad_output,
output,
dim=1,
input_dtype=6)
@register_test_case(module_factory=lambda: SoftmaxBackwardModule())
def SoftmaxBackwardModule_basic(module, tu: TestUtils):
module.forward(torch.randn(3, 2, 4), torch.randn(3, 2, 4))
# ==============================================================================
class TanhBackwardModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1, -1], torch.float32, True),
])
def forward(self, grad_out, output):
return torch.ops.aten.tanh_backward(grad_out, output)
@register_test_case(module_factory=lambda: TanhBackwardModule())
def TanhBackward_basic(module, tu: TestUtils):
module.forward(torch.randn(3, 3), torch.randn(3, 3))
# ==============================================================================
class GeluBackwardModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
([-1, -1], torch.float32, True),
])
def forward(self, grad, input):
return torch.ops.aten.gelu_backward(grad, input)
@register_test_case(module_factory=lambda: GeluBackwardModule())
def GeluBackwardModule_basic(module, tu: TestUtils):
module.forward(tu.rand(5, 3), tu.rand(5, 3))
class LogSoftmaxBackwardModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1, -1], torch.float32, True),
([-1, -1, -1], torch.float32, True),
])
def forward(self, grad_output, output):
return torch.ops.aten._log_softmax_backward_data(grad_output,
output,
dim=1,
input_dtype=6)
@register_test_case(module_factory=lambda: LogSoftmaxBackwardModule())
def LogSoftmaxBackwardModule_basic(module, tu: TestUtils):
module.forward(torch.randn(3, 2, 4), torch.randn(3, 2, 4))