torch-mlir/e2e_testing/torchscript/conv.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 Conv2dNoPaddingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(0)
        self.conv = torch.nn.Conv2d(2, 10, 3, bias=False)
        self.train(False)

    @export
    @annotate_args([
        None,
        ([-1, -1, -1, -1], torch.float32, True),
    ])
    def forward(self, x):
        return self.conv(x)


@register_test_case(module_factory=lambda: Conv2dNoPaddingModule())
def Conv2dNoPaddingModule_basic(module, tu: TestUtils):
    t = tu.rand(5, 2, 10, 20)
    module.forward(t)


class Conv2dBiasNoPaddingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(0)
        self.conv = torch.nn.Conv2d(2, 10, 3, bias=True)
        self.train(False)

    @export
    @annotate_args([
        None,
        ([-1, -1, -1, -1], torch.float32, True),
    ])
    def forward(self, x):
        return self.conv(x)


@register_test_case(module_factory=lambda: Conv2dBiasNoPaddingModule())
def Conv2dBiasNoPaddingModule_basic(module, tu: TestUtils):
    t = tu.rand(5, 2, 10, 20)
    module.forward(t)


class Conv2dWithPaddingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(0)
        self.conv = torch.nn.Conv2d(2, 10, 3, bias=False, padding=3)
        self.train(False)

    @export
    @annotate_args([
        None,
        ([-1, -1, -1, -1], torch.float32, True),
    ])
    def forward(self, x):
        return self.conv(x)


@register_test_case(module_factory=lambda: Conv2dWithPaddingModule())
def Conv2dWithPaddingModule_basic(module, tu: TestUtils):
    t = tu.rand(5, 2, 10, 20)
    module.forward(t)


class Conv2dWithPaddingDilationStrideModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(0)
        self.conv = torch.nn.Conv2d(in_channels=2,
                                    out_channels=10,
                                    kernel_size=3,
                                    padding=3,
                                    stride=2,
                                    dilation=3,
                                    bias=False)
        self.train(False)

    @export
    @annotate_args([
        None,
        ([-1, -1, -1, -1], torch.float32, True),
    ])
    def forward(self, x):
        return self.conv(x)


@register_test_case(
    module_factory=lambda: Conv2dWithPaddingDilationStrideModule())
def Conv2dWithPaddingDilationStrideModule_basic(module, tu: TestUtils):
    t = tu.rand(5, 2, 10, 20)
    module.forward(t)


class Conv2dWithPaddingDilationStrideStaticModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(0)
        self.conv = torch.nn.Conv2d(in_channels=2,
                                    out_channels=10,
                                    kernel_size=3,
                                    padding=3,
                                    stride=2,
                                    dilation=3,
                                    bias=False)
        self.train(False)

    @export
    @annotate_args([
        None,
        ([5, 2, 10, 20], torch.float32, True),
    ])
    def forward(self, x):
        return self.conv(x)


@register_test_case(
    module_factory=lambda: Conv2dWithPaddingDilationStrideStaticModule())
def Conv2dWithPaddingDilationStrideStaticModule_basic(module, tu: TestUtils):
    t = tu.rand(5, 2, 10, 20)
    module.forward(t)