# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.

# Copyright 2019 Kakao Brain
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from copy import deepcopy
from itertools import chain

import pytest
import torch
from torch import nn, optim

from fairscale.nn.pipe.batchnorm import DeferredBatchNorm

CHUNKS = 4


def tilt_dist(input):
    # Tilt variance by channel.
    rgb = input.transpose(0, 1)
    rgb[0] *= 1
    rgb[1] *= 10
    rgb[2] *= 100

    # Tilt mean by single batch.
    for i, single in enumerate(input):
        single += 2 ** i

    return input


def chunked_forward(model, input, chunks=CHUNKS):
    output_chunks = []

    for chunk in input.chunk(chunks):
        output_chunks.append(model(chunk))

    return torch.cat(output_chunks)


@pytest.mark.parametrize("chunks", [1, 4])
@pytest.mark.parametrize("input_requires_grad", [True, False])
def test_transparency(chunks, input_requires_grad):
    bn = nn.BatchNorm2d(3)
    dbn = DeferredBatchNorm.convert_deferred_batch_norm(deepcopy(bn), chunks=chunks)

    input1 = torch.rand(16, 3, 224, 224)
    input1 = tilt_dist(input1)
    input2 = input1.clone()
    input1.requires_grad = input_requires_grad
    input2.requires_grad = input_requires_grad

    output1 = chunked_forward(bn, input1, chunks=chunks)
    output2 = chunked_forward(dbn, input2, chunks=chunks)

    assert torch.allclose(output1, output2, atol=1e-4)

    output1.mean().backward()
    output2.mean().backward()

    assert torch.allclose(bn.weight.grad, dbn.weight.grad, atol=1e-4)

    if input_requires_grad:
        assert input1.grad is not None
        assert input2.grad is not None
        assert torch.allclose(input1.grad, input2.grad, atol=1e-4)


@pytest.mark.parametrize("momentum", [0.1, None])
def test_running_stats(momentum):
    bn = nn.BatchNorm2d(3, momentum=momentum)
    dbn = DeferredBatchNorm.convert_deferred_batch_norm(deepcopy(bn), chunks=CHUNKS)

    input = torch.rand(16, 3, 224, 224)
    input = tilt_dist(input)

    bn(input)
    chunked_forward(dbn, input)

    assert torch.allclose(bn.running_mean, dbn.running_mean, atol=1e-4)
    assert torch.allclose(bn.running_var, dbn.running_var, atol=1e-4)


def test_convert_deferred_batch_norm():
    bn = nn.BatchNorm2d(3, track_running_stats=False)
    bn = DeferredBatchNorm.convert_deferred_batch_norm(bn, chunks=CHUNKS)
    assert type(bn) is nn.BatchNorm2d  # because of track_running_stats=False

    dbn = DeferredBatchNorm(3, chunks=CHUNKS)
    dbn_again = DeferredBatchNorm.convert_deferred_batch_norm(dbn, chunks=CHUNKS)
    assert dbn is dbn_again

    dbn_again = DeferredBatchNorm.convert_deferred_batch_norm(dbn, chunks=CHUNKS + 1)
    assert dbn is not dbn_again  # because of different chunks


def test_eval():
    bn = nn.BatchNorm2d(3)
    dbn = DeferredBatchNorm.convert_deferred_batch_norm(deepcopy(bn), chunks=CHUNKS)

    input = torch.rand(16, 3, 224, 224)
    input = tilt_dist(input)

    bn(input)
    chunked_forward(dbn, input)

    bn.eval()
    dbn.eval()

    assert torch.allclose(bn(input), dbn(input), atol=1e-4)


def test_optimize():
    bn = nn.BatchNorm2d(3)
    dbn = DeferredBatchNorm.convert_deferred_batch_norm(deepcopy(bn), chunks=CHUNKS)

    opt = optim.SGD(chain(bn.parameters(), dbn.parameters()), lr=1.0)

    for i in range(5):
        input = torch.rand(16, 3, 224, 224)
        input = tilt_dist(input)

        # train
        y = bn(input)
        a = y.sum()
        a.backward()

        y = chunked_forward(dbn, input)
        b = y.sum()
        b.backward()

        opt.step()

        # eval
        bn.eval()
        dbn.eval()

        with torch.no_grad():
            assert torch.allclose(bn(input), dbn(input), atol=1e-1 * (10 ** i))


def test_conv_bn():
    bn = nn.Sequential(nn.Conv2d(3, 3, 1), nn.BatchNorm2d(3))
    dbn = DeferredBatchNorm.convert_deferred_batch_norm(deepcopy(bn), chunks=CHUNKS)

    input = torch.rand(16, 3, 224, 224)
    input = tilt_dist(input)

    opt = optim.SGD(chain(bn.parameters(), dbn.parameters()), lr=0.1)

    # 1st step
    a = bn(input)
    b = chunked_forward(dbn, input)

    # Outputs are different. (per-mini-batch vs. per-micro-batch)
    assert not torch.allclose(a, b)

    a.sum().backward()
    b.sum().backward()
    opt.step()
    opt.zero_grad()

    # Conv layers are also trained differently because of their different outputs.
    assert not torch.allclose(bn[0].weight, dbn[0].weight)

    # But BNs track identical running stats.
    assert torch.allclose(bn[1].running_mean, dbn[1].running_mean, atol=1e-4)
    assert torch.allclose(bn[1].running_var, dbn[1].running_var, atol=1e3)

    # 2nd step
    a = bn(input)
    b = chunked_forward(dbn, input)
    a.sum().backward()
    b.sum().backward()

    # BNs can't track identical running stats due to the different conv layers.
    assert not torch.allclose(bn[1].running_mean, dbn[1].running_mean, atol=1e-4)
    assert not torch.allclose(bn[1].running_var, dbn[1].running_var, atol=1e3)


def test_input_requiring_grad():
    dbn = DeferredBatchNorm(3, chunks=CHUNKS)

    input = torch.rand(16, 3, 224, 224)
    input = tilt_dist(input)
    input.requires_grad = True

    chunked_forward(dbn, input)

    assert not dbn.sum.requires_grad
    assert dbn.sum.grad_fn is None
