from functools import partial

import pytest
import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F

from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler
from colossalai.auto_parallel.tensor_shard.node_handler.softmax_handler import SoftmaxHandler
from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
from colossalai.device.device_mesh import DeviceMesh
from colossalai.fx import ColoGraphModule, ColoTracer
from colossalai.initialize import launch
from colossalai.logging import disable_existing_loggers
from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
from colossalai.testing.pytest_wrapper import run_on_environment_flag
from colossalai.utils import free_port
from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy


class LinearSplitModel(nn.Module):

    def __init__(self, softmax_dim):
        super().__init__()
        self.softmax_dim = softmax_dim

    def forward(self, input, other):
        linear_node = F.linear(input, other, bias=None)
        softmax_node = F.softmax(linear_node, self.softmax_dim)
        return softmax_node


def check_split_handler(rank, softmax_dim, model_cls, world_size, port):
    disable_existing_loggers()
    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    model = model_cls(softmax_dim=softmax_dim).cuda()

    input = torch.rand(8, 16, 64, 32).to('cuda')
    other = torch.rand(64, 32).to('cuda')
    # index of linear node in computation graph
    node_index = 2
    # total number of linear strategies
    strategy_number = 23

    physical_mesh_id = torch.arange(0, 4)
    mesh_shape = (2, 2)
    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)

    numerical_test_for_node_strategy(model=model,
                                     device_mesh=device_mesh,
                                     node_index=node_index,
                                     strategy_number=strategy_number,
                                     input_args=[input, other],
                                     meta_arg_names=['input', 'other'],
                                     node_type='following')
    tracer = ColoTracer()

    # graph():
    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
    #     %other : torch.Tensor [#users=1] = placeholder[target=other]
    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None})
    #     %softmax : [#users=1] = call_method[target=split](args = (%linear,), kwargs = {})
    #     return split
    graph = tracer.trace(model,
                         meta_args={
                             "input": torch.rand(8, 16, 64, 32).to('meta'),
                             "other": torch.rand(64, 32).to('meta'),
                         })

    gm = ColoGraphModule(model, graph)

    previous_mod_node = list(graph.nodes)[2]
    split_node = list(graph.nodes)[3]
    split_strategies_vector = StrategiesVector(split_node)
    previous_strategies_vector = StrategiesVector(previous_mod_node)

    # build handler
    assert len(previous_strategies_vector) == 0
    linear_handler = LinearFunctionHandler(node=previous_mod_node,
                                           device_mesh=device_mesh,
                                           strategies_vector=previous_strategies_vector)
    linear_handler.register_strategy(compute_resharding_cost=False)
    setattr(previous_mod_node, 'strategies_vector', previous_strategies_vector)

    softmax_handler = SoftmaxHandler(node=split_node,
                                     device_mesh=device_mesh,
                                     strategies_vector=split_strategies_vector)

    softmax_handler.register_strategy(compute_resharding_cost=False)

    # check operation data mapping
    mapping = softmax_handler.get_operation_data_mapping()

    for name, op_data in mapping.items():
        op_data: OperationData
        # make sure they have valid values
        assert op_data.data is not None

    assert mapping['input'].name == "linear"
    assert mapping['input'].data.is_meta
    assert mapping['input'].data.shape == torch.Size([8, 16, 64, 64])
    assert mapping['input'].type == OperationDataType.ARG
    assert mapping['input'].logical_shape == torch.Size([8, 16, 64, 64])

    assert mapping['softmax_dim'].name == "softmax_dim"
    assert mapping['softmax_dim'].data == softmax_dim
    assert mapping['softmax_dim'].type == OperationDataType.ARG

    assert mapping['output'].name == "softmax"
    assert mapping['output'].data.shape == torch.Size([8, 16, 64, 64])
    assert mapping['output'].logical_shape == torch.Size([8, 16, 64, 64])
    assert mapping['output'].type == OperationDataType.OUTPUT

    # reshape handler is a following strategy handler, so the number of strategies is equal to the predecessor node.
    assert len(split_strategies_vector) == len(previous_strategies_vector)
    strategy_name_list = [strategy.name for strategy in split_strategies_vector]

    if softmax_dim == 0:
        assert '[R, R, R, S1] -> [R, R, R, S1]_11' in strategy_name_list
        assert '[R, S0, R, S1] -> [R, S0, R, S1]_12' in strategy_name_list
        assert '[R, R, S0, S1] -> [R, R, S0, S1]_13' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_14' in strategy_name_list
        assert '[R, S1, R, S0] -> [R, S1, R, S0]_15' in strategy_name_list
        assert '[R, R, S1, S0] -> [R, R, S1, S0]_16' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_17' in strategy_name_list
        assert '[R, S0, R, R] -> [R, S0, R, R]_18' in strategy_name_list
        assert '[R, R, S0, R] -> [R, R, S0, R]_19' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_20' in strategy_name_list
        assert '[R, S1, R, R] -> [R, S1, R, R]_21' in strategy_name_list
        assert '[R, R, S1, R] -> [R, R, S1, R]_22' in strategy_name_list
        assert '[R, R, R, S1] -> [R, R, R, S1]_10' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_9' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_8' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_6' in strategy_name_list
        assert '[R, R, R, S1] -> [R, R, R, S1]_5' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_0' in strategy_name_list
        assert '[R, S01, R, R] -> [R, S01, R, R]_1' in strategy_name_list
        assert '[R, R, S01, R] -> [R, R, S01, R]_2' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_3' in strategy_name_list
        assert '[R, R, R, S01] -> [R, R, R, S01]_4' in strategy_name_list

    if softmax_dim == 1:
        assert '[S0, R, R, S1] -> [S0, R, R, S1]_11' in strategy_name_list
        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
        assert '[R, R, S0, S1] -> [R, R, S0, S1]_13' in strategy_name_list
        assert '[S1, R, R, S0] -> [S1, R, R, S0]_14' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_15' in strategy_name_list
        assert '[R, R, S1, S0] -> [R, R, S1, S0]_16' in strategy_name_list
        assert '[S0, R, R, R] -> [S0, R, R, R]_17' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list
        assert '[R, R, S0, R] -> [R, R, S0, R]_19' in strategy_name_list
        assert '[S1, R, R, R] -> [S1, R, R, R]_20' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
        assert '[R, R, S1, R] -> [R, R, S1, R]_22' in strategy_name_list
        assert '[R, R, R, S1] -> [R, R, R, S1]_10' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_9' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_8' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list
        assert '[R, R, R, S0] -> [R, R, R, S0]_6' in strategy_name_list
        assert '[R, R, R, S1] -> [R, R, R, S1]_5' in strategy_name_list
        assert '[S01, R, R, R] -> [S01, R, R, R]_0' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_1' in strategy_name_list
        assert '[R, R, S01, R] -> [R, R, S01, R]_2' in strategy_name_list
        assert '[R, R, R, R] -> [R, R, R, R]_3' in strategy_name_list
        assert '[R, R, R, S01] -> [R, R, R, S01]_4' in strategy_name_list


@run_on_environment_flag(name='AUTO_PARALLEL')
@pytest.mark.dist
@rerun_if_address_is_in_use()
@parameterize('softmax_dim', [0, 1, 2, 3])
@parameterize('model_cls', [LinearSplitModel])
def test_split_handler(softmax_dim, model_cls):
    world_size = 4
    run_func = partial(check_split_handler,
                       softmax_dim=softmax_dim,
                       model_cls=model_cls,
                       world_size=world_size,
                       port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_split_handler()
