import copy
from functools import partial
from types import MethodType

import pytest
import torch
import torch.distributed as dist
import torch.nn as nn

import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.interface import OptimizerWrapper
from colossalai.pipeline.schedule.one_f_one_b import OneForwardOneBackwardSchedule
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.testing.random import seed_all

DIM = 8
NUM_LAYER = 8


class MlpModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.ModuleList([nn.Linear(DIM, DIM) for _ in range(NUM_LAYER)])

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x


def pp_linear_fwd(
    forward,
    data: torch.Tensor = None,
    input_obj: torch.Tensor = None,
    stage_mgr: PipelineStageManager = None,
):
    if stage_mgr.is_first_stage():
        return {"input_obj": forward(data)}
    elif stage_mgr.is_last_stage():
        return forward(input_obj)
    else:
        return {"input_obj": forward(input_obj)}


def examine_pp(num_microbatch: int, batch_size: int):
    """
    This test is to examine the correctness of 1F1B, compared with torch.
    Be aware it contains some hardcodes.
    """
    world_size = dist.get_world_size()
    dist.get_rank()
    seed_all(1453)

    # create models
    torch_model = MlpModel().cuda()

    pp_model = copy.deepcopy(torch_model).cuda()

    pg_mesh = ProcessGroupMesh(world_size)
    stage_manager = PipelineStageManager(pg_mesh, pipeline_axis=0)
    schedule = OneForwardOneBackwardSchedule(stage_manager, num_microbatches=num_microbatch)

    rank = dist.get_rank()
    sharded_model = torch.nn.ModuleList()
    num_local_layer = NUM_LAYER // world_size
    for idx, sub_model in enumerate(pp_model.layers):
        if idx // num_local_layer == rank:
            sharded_model.append(sub_model.cuda())
    assert len(sharded_model) == num_local_layer

    def custom_fwd(self, x):
        for layer in self._modules.values():
            x = layer(x)
        return x

    sharded_model._forward = MethodType(custom_fwd, sharded_model)
    sharded_model.forward = MethodType(
        partial(
            pp_linear_fwd,
            stage_mgr=stage_manager,
        ),
        sharded_model._forward,
    )

    # create optimizer
    torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1)
    pp_optimizer = OptimizerWrapper(torch.optim.SGD(sharded_model.parameters(), lr=1))

    # create
    seed_all(1453)
    input_list = [torch.rand(batch_size, DIM).cuda()]
    dist.all_reduce(input_list[0])

    criterion = lambda x, *arg, **kwargs: (x * x).mean()

    # forward and backward
    torch_output = torch_model(input_list[0])
    torch_loss = criterion(torch_output)
    torch_loss.backward()
    pp_ret = schedule.forward_backward_step(
        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
    )

    # check loss
    if stage_manager.is_last_stage():
        assert torch.allclose(torch_loss, pp_ret["loss"])

    # check gradients
    for i in range(len(sharded_model)):
        idx = rank * num_local_layer + i
        assert torch.allclose(torch_model.layers[idx].weight.grad, sharded_model[i].weight.grad)
        assert torch.allclose(torch_model.layers[idx].bias.grad, sharded_model[i].bias.grad)

    # step
    torch_optimizer.step()
    pp_optimizer.step()
    pp_optimizer.zero_grad()

    # check updated param
    for i in range(len(sharded_model)):
        idx = rank * num_local_layer + i
        assert torch.allclose(torch_model.layers[idx].weight, sharded_model[i].weight)
        assert torch.allclose(torch_model.layers[idx].bias, sharded_model[i].bias)

    # forward only
    with torch.no_grad():
        torch_output = torch_model(input_list[0])
        torch_loss = criterion(torch_output)

        pp_ret = schedule.forward_backward_step(
            sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
        )
        if stage_manager.is_last_stage():
            assert torch.allclose(torch_loss, pp_ret["loss"])

        for layer in sharded_model:
            if layer.weight.grad is None:
                assert layer.weight.grad is None and layer.bias.grad is None
            else:
                assert torch.allclose(layer.weight.grad, torch.zeros_like(layer.weight.grad))
                assert torch.allclose(layer.bias.grad, torch.zeros_like(layer.bias.grad))


def run_dist(
    rank: int,
    world_size: int,
    port: int,
    num_microbatch: int,
    batch_size: int,
):
    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host="localhost")
    examine_pp(num_microbatch, batch_size)


@pytest.mark.dist
@pytest.mark.parametrize("num_microbatch", [4, 6])
@pytest.mark.parametrize("batch_size", [12])
@pytest.mark.parametrize("world_size", [2, 4])
@rerun_if_address_is_in_use()
def test_pp(num_microbatch: int, batch_size: int, world_size: int):
    assert NUM_LAYER % world_size == 0
    spawn(
        run_dist,
        world_size,
        num_microbatch=num_microbatch,
        batch_size=batch_size,
    )


if __name__ == "__main__":
    test_pp(num_microbatch=4, batch_size=4, world_size=4)
