import torch
from torch import nn
import torch.autograd as autograd

from colossalai.pipeline.rpc import ChimeraPipelineEngine
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)

    epoch = args.epoch
    device = args.device
    stage_num = 4
    chunk = 1
    num_microbatches = 4
    actual_stage_num = 4
    use_checkpoint = False

    sample_num = 1024
    batch_size = 1024

    assert sample_num % batch_size == 0

    engine = ChimeraPipelineEngine(partition_fn=partition,
                                   stage_num=stage_num,
                                   num_microbatches=num_microbatches,
                                   device=device,
                                   checkpoint=use_checkpoint)
    engine.initialize_optimizer(torch.optim.Adam, lr=1e-3)

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

    forward_result = engine.forward_backward(input_sample)

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

    # compute forward result and backward grad of parameters in cuda rpc
    cuda_rpc_result.append(sum(forward_result[0]))
    grad = engine.remote_grad()
    for stage_id in range(actual_stage_num):
        for p in grad[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)
    # input_sample = input_sample[len(input_sample) // 2:]
    input_sample = input_sample.requires_grad_()
    out_val = test_model(input_sample).sum()
    autograd.backward(out_val)
    single_result.append(out_val)
    for p in test_model.parameters():
        single_result.append(p.grad)

    # print("my")
    # print(cuda_rpc_result[1])
    # print("answer:")
    # print(single_result[1])

    # 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()
    args.world_size = 4
    args.num_microbatches = 4
    rpc_run(args, run_master)
