MegEngine/imperative/python/test/unit/module/test_module.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from collections import OrderedDict
from io import BytesIO

import numpy as np
import pytest

import megengine as mge
import megengine.functional as F
from megengine import Parameter, Tensor, tensor
from megengine.experimental.traced_module import TracedModule, trace_module
from megengine.module import (
    BatchNorm1d,
    BatchNorm2d,
    Conv1d,
    Conv2d,
    Dropout,
    Linear,
    MaxPool2d,
    Module,
    Sequential,
    Softmax,
)
from megengine.module.module import _access_structure
from megengine.quantization.quantize import quantize, quantize_qat
from megengine.utils.module_utils import get_expand_structure, set_expand_structure


class MLP(Module):
    def __init__(self):
        super().__init__()
        self.dense0 = Linear(28, 50)
        self.dense1 = Linear(50, 20)

    def forward(self, x):
        x = self.dense0(x)
        x = F.relu(x)
        x = self.dense1(x)
        return x


class MyModule(Module):
    class InnerModule(Module):
        def __init__(self):
            super().__init__()
            self.bn = BatchNorm2d(4)

        def forward(self, x):
            return self.bn(x)

    def __init__(self):
        super().__init__()
        self.i = self.InnerModule()
        self.bn = BatchNorm2d(4)
        self.param = Parameter(np.ones(1, dtype=np.float32))
        self.buff = Tensor(np.ones(1, dtype=np.float32))

    def forward(self, x):
        x = self.i(x)
        x = self.bn(x)
        return x


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_module_api(test_traced_module):
    m = MyModule()
    if test_traced_module:
        buff = m.buff
        param = m.param
        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
        assert "buff" not in m.__dict__
        assert "param" not in m.__dict__
        m.buff = buff
        m.param = param

    assert list(m.children()) == [m.bn, m.i]
    assert list(m.named_children()) == [("bn", m.bn), ("i", m.i)]
    assert list(m.modules()) == [m, m.bn, m.i, m.i.bn]
    assert list(m.named_modules()) == [
        ("", m),
        ("bn", m.bn),
        ("i", m.i),
        ("i.bn", m.i.bn),
    ]
    assert list(m.named_modules(prefix="x")) == [
        ("x", m),
        ("x.bn", m.bn),
        ("x.i", m.i),
        ("x.i.bn", m.i.bn),
    ]
    assert list(m.buffers()) == [
        m.bn.running_mean,
        m.bn.running_var,
        m.buff,
        m.i.bn.running_mean,
        m.i.bn.running_var,
    ]
    assert list(m.buffers(recursive=False)) == [m.buff]
    assert list(m.named_buffers()) == [
        ("bn.running_mean", m.bn.running_mean),
        ("bn.running_var", m.bn.running_var),
        ("buff", m.buff),
        ("i.bn.running_mean", m.i.bn.running_mean),
        ("i.bn.running_var", m.i.bn.running_var),
    ]
    assert list(m.parameters()) == [
        m.bn.bias,
        m.bn.weight,
        m.i.bn.bias,
        m.i.bn.weight,
        m.param,
    ]
    assert list(m.named_parameters()) == [
        ("bn.bias", m.bn.bias),
        ("bn.weight", m.bn.weight),
        ("i.bn.bias", m.i.bn.bias),
        ("i.bn.weight", m.i.bn.weight),
        ("param", m.param),
    ]
    m.eval()
    assert (
        m.training == False
        and m.bn.training == False
        and m.i.training == False
        and m.i.bn.training == False
    )
    m.bn.train()
    assert m.training == False and m.bn.training == True and m.i.bn.training == False
    m.eval()
    m.i.train()
    assert (
        m.training == False
        and m.bn.training == False
        and m.i.training == True
        and m.i.bn.training == True
    )
    m.eval()
    m.train()
    assert m.training == True and m.bn.training == True and m.i.bn.training == True

    def fn(m):
        m.training = False

    m.apply(fn)
    assert m.bn.training == False and m.i.bn.training == False


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_module_api_reuse_submodule(test_traced_module):
    m = MyModule()
    if test_traced_module:
        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
    m.h = m.i  # pylint: disable=attribute-defined-outside-init
    assert list(m.modules()) == [m, m.bn, m.i, m.i.bn]
    assert list(m.named_modules()) == [
        ("", m),
        ("bn", m.bn),
        ("h", m.i),
        ("h.bn", m.i.bn),
    ]


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_module_api_iterable_stability(test_traced_module):
    m = MyModule()
    if test_traced_module:
        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
    l = list(m.modules())
    for _ in range(100):
        assert list(m.modules()) == l


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_module_api_hooks(test_traced_module):
    net = MyModule()
    if test_traced_module:
        net = trace_module(net, Tensor(np.zeros((1, 4, 1, 1))))
    pre_hook_num = 0
    post_hook_num = 0
    hooks = []

    def pre_hook(_, inputs):
        nonlocal pre_hook_num
        pre_hook_num += 1
        modified_inputs = tuple(inp + 1 for inp in inputs)
        return modified_inputs

    def post_hook(_, __, outputs):
        nonlocal post_hook_num
        post_hook_num += 1
        outputs += 1
        return outputs

    net.apply(lambda module: hooks.append(module.register_forward_pre_hook(pre_hook)))
    net.apply(lambda module: hooks.append(module.register_forward_hook(post_hook)))

    shape = (1, 4, 1, 1)
    x = tensor(np.zeros(shape, dtype=np.float32))
    y = net(x)

    assert pre_hook_num == 4
    assert post_hook_num == 4
    mean1 = Parameter(np.zeros(shape), dtype=np.float32)
    bn1 = F.batch_norm(
        x + 3, mean1, Parameter(np.ones(shape), dtype=np.float32), training=True
    )
    np.testing.assert_allclose(
        net.i.bn.running_mean.numpy(), mean1.numpy(),
    )
    mean2 = Parameter(np.zeros(shape), dtype=np.float32)
    bn2 = F.batch_norm(
        bn1 + 3, mean2, Parameter(np.ones(shape), dtype=np.float32), training=True
    )
    np.testing.assert_allclose(
        net.bn.running_mean.numpy(), mean2.numpy(),
    )
    np.testing.assert_allclose((bn2 + 2).numpy(), y.numpy())

    assert len(hooks) == 8
    for handler in hooks:
        handler.remove()
    y = net(x)
    assert pre_hook_num == 4
    assert post_hook_num == 4


class MyModule2(Module):
    class InnerModule(Module):
        def __init__(self):
            super().__init__()
            self.bn = BatchNorm2d(4)
            self.test_bool_key = {True: 1, False: 0}

        def forward(self, x):
            x = self.bn(x)

    def __init__(self):
        super().__init__()
        self.bn = BatchNorm2d(4)
        self.a = [
            BatchNorm2d(4),
            {"x": BatchNorm2d(4), "y": [BatchNorm2d(4), self.InnerModule()], "z": 0},
            (self.InnerModule(),),
        ]

    def forward(self, x):
        return x


def test_expand_structure():
    m = MyModule2()
    rst = [
        ("", m),
        ("a.0", m.a[0]),
        ("a.1.x", m.a[1]["x"]),
        ("a.1.y.0", m.a[1]["y"][0]),
        ("a.1.y.1", m.a[1]["y"][1]),
        ("a.1.y.1.bn", m.a[1]["y"][1].bn),
        ("a.2.0", m.a[2][0]),
        ("a.2.0.bn", m.a[2][0].bn),
        ("bn", m.bn),
    ]
    assert list(m.named_modules()) == rst

    for item in rst[1:]:
        assert get_expand_structure(m, item[0]) == item[1]

    for item in reversed(rst[1:]):
        if _access_structure(m, item[0], lambda p, k, o: isinstance(p, tuple)):
            continue
        set_expand_structure(m, item[0], "TEST_VALUE")
        assert get_expand_structure(m, item[0]) == "TEST_VALUE"


def test_flatten_others():
    def be_others(obj):
        return not isinstance(obj, (Tensor, Module))

    m = MyModule2()
    assert len(list(m._flatten(with_key=True, predicate=be_others))) == 0


def test_flatten_with_parent():
    m = MyModule2()
    assert list(m.named_modules(with_parent=True)) == [
        ("", m, None),
        ("a.0", m.a[0], m),
        ("a.1.x", m.a[1]["x"], m),
        ("a.1.y.0", m.a[1]["y"][0], m),
        ("a.1.y.1", m.a[1]["y"][1], m),
        ("a.1.y.1.bn", m.a[1]["y"][1].bn, m.a[1]["y"][1]),
        ("a.2.0", m.a[2][0], m),
        ("a.2.0.bn", m.a[2][0].bn, m.a[2][0]),
        ("bn", m.bn, m),
    ]
    assert list(m.modules(with_parent=True)) == [
        (m, None),
        (m.a[0], m),
        (m.a[1]["x"], m),
        (m.a[1]["y"][0], m),
        (m.a[1]["y"][1], m),
        (m.a[1]["y"][1].bn, m.a[1]["y"][1]),
        (m.a[2][0], m),
        (m.a[2][0].bn, m.a[2][0]),
        (m.bn, m),
    ]


class MyModule3(Module):
    class InnerModule(Module):
        def __init__(self):
            super().__init__()
            self.bn = BatchNorm2d(4)

        def forward(self, x):
            x = self.bn(x)

    def __init__(self):
        super().__init__()
        self.bn = BatchNorm2d(4)
        self.seq = Sequential(BatchNorm2d(4), self.InnerModule(),)

    def forward(self, x):
        return x


def test_module_api_with_sequential():
    m = MyModule3()
    assert list(m.named_modules()) == [
        ("", m),
        ("bn", m.bn),
        ("seq", m.seq),
        ("seq.0", m.seq[0]),
        ("seq.1", m.seq[1]),
        ("seq.1.bn", m.seq[1].bn),
    ]


def test_sequential_named_children():
    modules = OrderedDict()
    modules["name0"] = Linear(20, 10)
    modules["name1"] = Linear(10, 5)
    modules["name2"] = Linear(5, 1)
    m = Sequential(modules)
    l = list(m.named_children())
    assert l[0][0] == "name0"
    assert l[1][0] == "name1"
    assert l[2][0] == "name2"


def test_state_dict():
    data_shape = (2, 28)
    data = tensor(np.random.random(data_shape))
    mlp = MLP()
    pred0 = mlp(data)

    with BytesIO() as fout:
        mge.save(mlp.state_dict(), fout)
        fout.seek(0)
        state_dict = mge.load(fout)
        state_dict["extra"] = None
        mlp1 = MLP()
        mlp1.load_state_dict(state_dict, strict=False)
        pred1 = mlp1(data)
        np.testing.assert_allclose(pred0.numpy(), pred1.numpy(), atol=5e-6)
        with pytest.raises(KeyError):
            mlp1.load_state_dict(state_dict)
        del state_dict["extra"]
        del state_dict["dense0.bias"]
        with pytest.raises(KeyError):
            mlp1.load_state_dict(state_dict)


class AssertModule(Module):
    def __init__(self):
        super().__init__()
        self.error_tensor_key = {True: tensor([]), False: 0}

    def forward(self, x):
        return x


def test_assert_message():
    with pytest.raises(
        AssertionError, match="keys for Tensor and Module must be str, error key: True"
    ):
        m = AssertModule()
        list(m._flatten())


class Simple(Module):
    def __init__(self):
        super().__init__()
        self.conv0 = Conv2d(1, 1, kernel_size=3, bias=False)
        self.conv1 = Conv2d(1, 1, kernel_size=3, bias=False)
        self.conv1.weight = self.conv0.weight

    def forward(self, inputs):
        x = self.conv0(inputs)
        y = self.conv1(inputs)
        return x + y


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_shared_param(test_traced_module):
    net = Simple()
    if test_traced_module:
        net = trace_module(net, tensor(np.random.random((1, 1, 8, 8))))
    assert net.conv0.weight is net.conv1.weight
    data = tensor(np.random.random((1, 1, 8, 8)).astype(np.float32))
    np.testing.assert_allclose(net.conv0(data).numpy(), net.conv1(data).numpy())
    with BytesIO() as f:
        mge.save(net, f)
        f.seek(0)
        net1 = mge.load(f)
    assert net1.conv0.weight is net1.conv1.weight
    np.testing.assert_allclose(net1.conv0(data).numpy(), net1.conv1(data).numpy())

    with BytesIO() as f:
        mge.save(net.conv0, f)
        f.seek(0)
        conv0 = mge.load(f)

    with BytesIO() as f:
        mge.save(net.conv1, f)
        f.seek(0)
        conv1 = mge.load(f)

    assert conv0.weight is not conv1.weight
    np.testing.assert_allclose(conv0(data).numpy(), conv1(data).numpy())


class Simple2(Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv1d(1, 1, kernel_size=3, bias=False)
        self.conv0 = Conv1d(1, 1, kernel_size=3, bias=False)
        self.conv1.weight = self.conv0.weight

    def forward(self, inputs):
        pass


def test_shared_param_1d():
    net = Simple2()
    assert net.conv0.weight is net.conv1.weight
    data = tensor(np.random.random((1, 1, 8)).astype(np.float32))
    np.testing.assert_allclose(net.conv0(data).numpy(), net.conv1(data).numpy())
    with BytesIO() as f:
        mge.save(net, f)
        f.seek(0)
        net1 = mge.load(f)
    assert net1.conv0.weight is net1.conv1.weight
    np.testing.assert_allclose(net1.conv0(data).numpy(), net1.conv1(data).numpy())

    with BytesIO() as f:
        mge.save(net.conv0, f)
        f.seek(0)
        conv0 = mge.load(f)

    with BytesIO() as f:
        mge.save(net.conv1, f)
        f.seek(0)
        conv1 = mge.load(f)

    assert conv0.weight is not conv1.weight
    np.testing.assert_allclose(conv0(data).numpy(), conv1(data).numpy())


@pytest.mark.parametrize("test_traced_module", [True, False])
def test_pickle_module(test_traced_module):
    data_shape = (2, 28)
    data = tensor(np.random.random(data_shape))
    mlp = MLP()
    pred_gt = mlp(data)
    if test_traced_module:
        mlp = trace_module(mlp, data)
    # pickle before forward
    with BytesIO() as fout:
        mge.save(mlp, fout)
        fout.seek(0)
        mlp1 = mge.load(fout)
        if test_traced_module:
            assert type(mlp1) == TracedModule
        pred0 = mlp1(data)

    pred1 = mlp(data)

    # pickle after forward
    with BytesIO() as fout:
        mge.save(mlp, fout)
        fout.seek(0)
        mlp1 = mge.load(fout)
        if test_traced_module:
            assert type(mlp1) == TracedModule
        pred2 = mlp1(data)

    np.testing.assert_allclose(pred_gt.numpy(), pred1.numpy(), atol=5e-6)
    np.testing.assert_allclose(pred0.numpy(), pred1.numpy(), atol=5e-6)
    np.testing.assert_allclose(pred0.numpy(), pred2.numpy(), atol=5e-6)


def test_repr_basic():
    # test whether __repr__ can output correct information
    class ConvModel(Module):
        def __init__(self):
            super().__init__()
            self.conv1 = Conv2d(3, 128, 3, padding=1, bias=False)
            self.conv2 = Conv2d(3, 128, 3, dilation=2, bias=False)
            self.bn1 = BatchNorm1d(128)
            self.bn2 = BatchNorm2d(128)
            self.pooling = MaxPool2d(kernel_size=2, padding=0)
            modules = OrderedDict()
            modules["depthwise"] = Conv2d(256, 256, 3, 1, 0, groups=256, bias=False,)
            modules["pointwise"] = Conv2d(
                256, 256, kernel_size=1, stride=1, padding=0, bias=True,
            )
            self.submodule1 = Sequential(modules)
            self.list1 = [Dropout(drop_prob=0.1), [Softmax(axis=100)]]
            self.tuple1 = (
                Dropout(drop_prob=0.1),
                (Softmax(axis=100), Dropout(drop_prob=0.2)),
            )
            self.dict1 = {"Dropout": Dropout(drop_prob=0.1)}
            self.fc1 = Linear(512, 1024)

        def forward(self, inputs):
            pass

    ground_truth = (
        "ConvModel(\n"
        "  (conv1): Conv2d(3, 128, kernel_size=(3, 3), padding=(1, 1), bias=False)\n"
        "  (conv2): Conv2d(3, 128, kernel_size=(3, 3), dilation=(2, 2), bias=False)\n"
        "  (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)\n"
        "  (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)\n"
        "  (pooling): MaxPool2d(kernel_size=2, stride=2, padding=0)\n"
        "  (submodule1): Sequential(\n"
        "    (depthwise): Conv2d(256, 256, kernel_size=(3, 3), groups=256, bias=False)\n"
        "    (pointwise): Conv2d(256, 256, kernel_size=(1, 1))\n"
        "  )\n"
        "  (list1.0): Dropout(drop_prob=0.1)\n"
        "  (list1.1.0): Softmax(axis=100)\n"
        "  (tuple1.0): Dropout(drop_prob=0.1)\n"
        "  (tuple1.1.0): Softmax(axis=100)\n"
        "  (tuple1.1.1): Dropout(drop_prob=0.2)\n"
        "  (dict1.Dropout): Dropout(drop_prob=0.1)\n"
        "  (fc1): Linear(in_features=512, out_features=1024, bias=True)\n"
        ")"
    )
    net = ConvModel()
    output = net.__repr__()
    assert output == ground_truth


def test_repr_module_reassign():
    # test whether __repr__ can deal with module reassign
    class ConvModel1(Module):
        def __init__(self):
            super().__init__()
            self.conv1 = Conv2d(3, 128, 3, bias=False)
            self.conv2 = Conv2d(3, 128, 3, padding=1, bias=False)
            self.conv1 = Conv2d(3, 256, 3, dilation=2, bias=False)

        def forward(self, inputs):
            pass

    ground_truth = (
        "ConvModel1(\n"
        "  (conv1): Conv2d(3, 256, kernel_size=(3, 3), dilation=(2, 2), bias=False)\n"
        "  (conv2): Conv2d(3, 128, kernel_size=(3, 3), padding=(1, 1), bias=False)\n"
        ")"
    )
    net = ConvModel1()
    output = net.__repr__()
    assert output == ground_truth


def test_repr_module_rereference():
    # test whether __repr__ can deal with module re-reference
    class ConvModel2(Module):
        def __init__(self):
            super().__init__()
            self.conv1 = Conv2d(3, 128, 3, bias=False)
            self.conv2 = self.conv1
            self.conv3 = self.conv1

        def forward(self, inputs):
            pass

    ground_truth = (
        "ConvModel2(\n"
        "  (conv1): Conv2d(3, 128, kernel_size=(3, 3), bias=False)\n"
        "  (conv2): Conv2d(3, 128, kernel_size=(3, 3), bias=False)\n"
        "  (conv3): Conv2d(3, 128, kernel_size=(3, 3), bias=False)\n"
        ")"
    )
    net = ConvModel2()
    output = net.__repr__()
    assert output == ground_truth


def test_repr_module_delete():
    # test whether __repr__ can deal with module delete
    class ConvModel3(Module):
        def __init__(self):
            super().__init__()
            self.conv1 = Conv2d(3, 128, 3, bias=False)
            self.softmax = Softmax(100)

        def forward(self, inputs):
            pass

    ground_truth = (
        "ConvModel3(\n"
        "  (conv1): Conv2d(3, 128, kernel_size=(3, 3), bias=False)\n"
        ")"
    )
    net = ConvModel3()
    del net.softmax
    output = net.__repr__()
    assert output == ground_truth


def test_repr_module_reset_attr():
    class ResetAttrModule(Module):
        def __init__(self, flag):
            super().__init__()
            if flag:
                self.a = None
                self.a = Linear(3, 5)
            else:
                self.a = Linear(3, 5)
                self.a = None

        def forward(self, x):
            if self.a:
                x = self.a(x)
            return x

    ground_truth = [
        (
            "ResetAttrModule(\n"
            "  (a): Linear(in_features=3, out_features=5, bias=True)\n"
            ")"
        ),
        ("ResetAttrModule()"),
    ]

    m0 = ResetAttrModule(True)
    m1 = ResetAttrModule(False)
    output = [m0.__repr__(), m1.__repr__()]
    assert output == ground_truth