import torch
from torch import nn
from torch import autograd
from torch.optim import SGD, Adam, RMSprop, Optimizer

from colossalai.pipeline.rpc._pipeline_schedule import FillDrainPipelineEngine, OneFOneBPipelineEngine
from colossalai.testing import assert_close
from rpc_test_utils import rpc_run, parse_args, RpcTestModel

# global variable for model created
feat_num = 100
h = 100


def partition(pp_rank: int, chunk: int, stage_num: int):
    torch.manual_seed(1024)
    partition = RpcTestModel(pp_rank, stage_num, feat_num, h)
    return partition


def run_master(args):
    torch.manual_seed(100)

    device = args.device
    stage_num = args.world_size
    chunk = args.chunk
    actual_stage_num = stage_num * chunk
    use_checkpoint = args.use_checkpoint
    num_microbatches = args.num_microbatches
    optimizer_class = globals()[args.optimizer]

    lr = 1e-3
    sample_num = 1024
    batch_size = 1024

    assert sample_num % batch_size == 0

    input_sample = torch.randn((sample_num, feat_num), device=device)

    engine = OneFOneBPipelineEngine(partition_fn=partition,
                                    stage_num=stage_num,
                                    num_microbatches=num_microbatches,
                                    device=device,
                                    chunk=chunk,
                                    checkpoint=use_checkpoint)

    engine.initialize_optimizer(optimizer_class, lr=lr)

    _ = engine.forward_backward(input_sample)

    cuda_rpc_result = []
    single_result = []
    actual_stage_num = engine._get_actual_stage_num()

    # compute parameters after updating in cuda rpc
    parameters = engine.remote_parameters()
    for stage_id in range(actual_stage_num):
        for p in parameters[stage_id]:
            cuda_rpc_result.append(p)

    # compute forward result and backward grad of parameters just in rank_0
    test_model = nn.Sequential(
        *[partition(pp_rank, chunk, actual_stage_num) for pp_rank in range(actual_stage_num)]).to(device)
    optimizer: Optimizer = optimizer_class(test_model.parameters(), lr=lr)
    input_sample = input_sample.requires_grad_()
    out_val = test_model(input_sample).sum()
    autograd.backward(out_val)
    optimizer.step()
    optimizer.zero_grad()

    for p in test_model.parameters():
        single_result.append(p)

    assert len(cuda_rpc_result) == len(single_result)
    for r_c, r_s in zip(cuda_rpc_result, single_result):
        assert_close(r_c, r_s, 0.001, 0.001)


if __name__ == "__main__":
    args = parse_args()
    rpc_run(args, run_master)