from copy import deepcopy
from functools import partial

import pytest
import torch
import torch.multiprocessing as mp
from common import CONFIG, allclose

import colossalai
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor
from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
from colossalai.zero.legacy.sharded_param import ShardedTensor
from colossalai.zero.legacy.sharded_param.sharded_param import ShardedParamV2


@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
def run_shard_tensor_with_strategy(shard_strategy_class, world_size):
    t = ShardedTensor(tensor=torch.randn(world_size * 2, 3))
    assert list(t.origin_shape) == [world_size * 2, 3]
    assert list(t.shape) == [world_size * 2, 3]

    shard_strategy = shard_strategy_class()

    # test shard strategy
    shard_strategy.shard([t])
    assert list(t.shape) == [6], f"{list(t.shape)} vs 6"
    shard_strategy.gather([t])
    assert list(t.shape) == [world_size * 2, 3], f"{list(t.shape)} vs {[world_size * 2, 3]}"


def _run_shard_tensor(rank, world_size, port):
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    run_shard_tensor_with_strategy(world_size=world_size)


@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@rerun_if_address_is_in_use()
def test_shard_tensor(world_size):
    run_func = partial(_run_shard_tensor, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


def _run_shard_param_v2(rank, world_size, port):
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')

    param = torch.nn.Parameter(torch.randn(2, 3))
    param_ref = deepcopy(param)
    sparam = ShardedParamV2(param=param)

    allclose(sparam.data_payload, param_ref.data)

    # Test get memory usage
    sparam.saved_grad = StatefulTensor(torch.randn(2, 3))
    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
    assert cpu_mem_use == 2 * 3 * 4 * 2, f"cpu_mem_use: {cpu_mem_use}"

    sparam.set_data_none()
    assert (param.data.numel() == 0)
    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
    # 4 is size of dummy tensor of param.data
    assert cpu_mem_use == 2 * 3 * 4 * 2

    sparam.saved_grad = StatefulTensor(torch.randn(2, 3))
    sparam.set_data_none()
    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
    assert cpu_mem_use == 2 * 3 * 4 * 2
    assert cuda_mem_use == 0

    # append a grad to torch param
    param.data = sparam.data_payload
    param.grad = torch.randn(2, 3)
    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
    assert cpu_mem_use == 2 * 3 * 4 * 2 + 2 * 3 * 4, f"cpu_mem_use {cpu_mem_use}"
    assert cuda_mem_use == 0

    # reuse torch grad for sparam
    sparam.saved_grad = StatefulTensor(param.grad)
    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
    assert cpu_mem_use == 2 * 3 * 4 * 2
    assert cuda_mem_use == 0


@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@rerun_if_address_is_in_use()
def test_shard_param_v2(world_size):
    run_func = partial(_run_shard_param_v2, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    # test_shard_tensor(2)
    test_shard_param_v2(2)
