import pytest
import torch
import torch.distributed as dist

import colossalai
from colossalai.nn.optimizer import HybridAdam
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from colossalai.utils import set_seed
from colossalai.zero import GeminiDDP, GeminiOptimizer
from colossalai.zero.gemini.chunk import search_chunk_configuration
from tests.components_to_test.registry import non_distributed_component_funcs

PLACEMENT_CONFIGS = [
    {
        'placement_policy': 'static',
        'shard_param_frac': 0.0,
        'offload_optim_frac': 0.0
    },    # zero2
    {
        'placement_policy': 'static',
        'shard_param_frac': 0.0,
        'offload_optim_frac': 1.0
    },    # zero2-offload
    {
        'placement_policy': 'static',
        'shard_param_frac': 0.0,
        'offload_optim_frac': 0.5
    },    # zero2-offload-half
    {
        'placement_policy': 'auto'
    }
]


@parameterize('placement_config', PLACEMENT_CONFIGS)
@parameterize('keep_gathered', [True, False])
def exam_zero_optim_state_dict(placement_config, keep_gathered):
    set_seed(431)
    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()

    model = model_builder()

    set_seed(451)

    world_size = torch.distributed.get_world_size()
    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
    config_dict[world_size]['chunk_size'] = 5000
    config_dict[world_size]['keep_gathered'] = keep_gathered

    model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True)

    optimizer = HybridAdam(model.parameters())
    optim = GeminiOptimizer(optimizer, model, initial_scale=32)    # initialize the link between chunk16 and chunk32

    set_seed(dist.get_rank() * 3 + 128)
    model.train()
    for i, (input_ids, label) in enumerate(train_dataloader):
        if i > 0:
            break
        optim.zero_grad()
        logits = model(input_ids)
        logits = logits.float()
        loss = criterion(logits, input_ids)
        optim.backward(loss)
        optim.step()

    optim_state_dict = optim.state_dict()
    optim.load_state_dict(optim_state_dict)
    new_state = optim.state_dict()['state']
    org_state = optim_state_dict['state']

    for k, v in org_state.items():
        w = new_state[k]
        for n, m in v.items():
            if isinstance(m, torch.Tensor):
                o = w[n]
                assert torch.equal(m, o)
            else:
                assert m == w[n]


def run_dist(rank, world_size, port):
    config = {}
    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    exam_zero_optim_state_dict()


@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_zero_optim(world_size):
    spawn(run_dist, world_size)


if __name__ == '__main__':
    test_zero_optim(1)