torch-mlir/e2e_testing/torchscript/backprop.py

# 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, out_grad, output):
        return torch.ops.aten.tanh_backward(out_grad, output)

@register_test_case(module_factory=lambda: TanhBackwardModule())
def TanhBackward_basic(module, tu: TestUtils):
    module.forward(torch.randn(3, 3), torch.randn(3, 3))