#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import copy
from functools import partial

import colossalai
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from colossalai.nn.optimizer import CPUAdam
from colossalai.utils import free_port
from colossalai.zero.shard_utils import TensorShardStrategy
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_optim import ShardedOptimizerV2
from tests.components_to_test.registry import non_distributed_component_funcs
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import Adam

from common import CONFIG, check_sharded_params_padding


def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
    model.train()
    optimizer.zero_grad()
    with torch.cuda.amp.autocast(enabled=enable_autocast):
        y = model(data)
        loss = criterion(y, label)
    loss = loss.float()
    if isinstance(model, ShardedModelV2):
        optimizer.backward(loss)
    else:
        loss.backward()
    optimizer.step()


def run_step_no_criterion(model, optimizer, data, label, enable_autocast=False):
    model.train()
    optimizer.zero_grad()
    with torch.cuda.amp.autocast(enabled=enable_autocast):
        loss = model(data, label)
    if isinstance(model, ShardedModelV2):
        optimizer.backward(loss)
    else:
        loss.backward()
    optimizer.step()


def run_dist(rank, world_size, port):
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    test_models = ['repeated_computed_layers', 'resnet18', 'bert']
    for model_name in test_models:
        get_components_func = non_distributed_component_funcs.get_callable(model_name)
        shard_strategy = TensorShardStrategy()
        model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
        model = model(checkpoint=True).cuda()
        zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy, offload_config={'device': 'cpu'})
        if dist.get_world_size() > 1:
            model = DDP(model)
        optim = Adam(model.parameters(), lr=1e-3)
        sharded_optim = ShardedOptimizerV2(CPUAdam(zero_model.parameters(), lr=1e-3),
                                           zero_model,
                                           shard_strategy,
                                           initial_scale=2**5,
                                           cpu_offload=True)
        for i, (data, label) in enumerate(train_dataloader):
            if i > 2:
                break
            data, label = data.cuda(), label.cuda()
            if criterion is None:
                run_step_no_criterion(model, optim, data, label, False)
                run_step_no_criterion(zero_model, sharded_optim, data, label, False)
            else:
                run_step(model, optim, data, label, criterion, False)
                run_step(zero_model, sharded_optim, data, label, criterion, False)
            check_sharded_params_padding(model, zero_model, loose=True)


@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
def test_sharded_optim_v2(world_size):
    run_func = partial(run_dist, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_sharded_optim_v2(world_size=2)
