from functools import partial
import pytest
import torch
import torch.nn as nn
import torch.multiprocessing as mp
import colossalai
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.utils import free_port, get_current_device
from colossalai.nn.layer.moe import Top1Router, MoeLayer
from colossalai.global_variables import moe_env

BATCH_SIZE = 32
NUM_EXPERTS = 4
CONFIG = dict(parallel=dict(moe=dict(size=4)))


def check_equal(A, B, atol=1e-06):
    assert torch.allclose(A, B, rtol=0, atol=atol) is True


def run_routing(rank, world_size, port, rs=2, hidden_size=128, data_type=torch.float32):
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')

    # torch.set_printoptions(precision=30)
    torch.backends.cuda.matmul.allow_tf32 = False
    local_rank = gpc.get_local_rank(ParallelMode.GLOBAL)
    torch.manual_seed(rs + local_rank)
    moe_env.reset_loss()
    tokens = torch.randn(BATCH_SIZE, hidden_size, dtype=data_type, device=get_current_device(), requires_grad=True)
    # print(f"tokens:\n{tokens}")
    router = Top1Router(1)
    layer = MoeLayer(hidden_size, NUM_EXPERTS, router, nn.Identity())
    if data_type == torch.float16:
        layer = layer.half()
    layer.cuda_mode = False

    old_out = layer(tokens)
    # print(f"old output:\n{old_out}")

    ech = old_out.shape
    grad = torch.randn(ech, device=get_current_device())
    old_out.backward(grad)

    o_tk_grad = tokens.grad.data.clone()
    o_gt_grad = layer.gate.weight.grad.data.clone()

    tokens.grad.zero_()
    layer.gate.weight.grad.zero_()

    layer.cuda_mode = True
    new_out = layer(tokens)

    # print(torch.max(torch.abs(old_out - new_out)))
    if data_type == torch.float32:
        check_equal(old_out, new_out)
    else:
        check_equal(old_out, new_out, 1e-2)
    # print(f"forward functions passed")

    # print(f"new output:\n{new_out}")
    new_out.backward(grad)
    n_tk_grad = tokens.grad.data.clone()
    n_gt_grad = layer.gate.weight.grad.data.clone()

    # print(torch.max(torch.abs(o_tk_grad - n_tk_grad)))
    if data_type == torch.float32:
        check_equal(o_tk_grad, n_tk_grad)
    else:
        check_equal(o_tk_grad, o_tk_grad, 1e-2)
    # print(f"tokens gradient passed")

    # print(torch.max(torch.abs(o_gt_grad - n_gt_grad)))
    if data_type == torch.float32:
        check_equal(o_gt_grad, n_gt_grad, 5e-05)
    else:
        check_equal(o_gt_grad, n_gt_grad, 2e-01)
    # print(f"linear weight gradient passed")


@pytest.mark.skip(reason="Should be activated for detailed tests")
@pytest.mark.parametrize("rs", [2, 42, 60])
@pytest.mark.parametrize("hidden_size", [128, 256, 512, 768, 1024, 2048])
@pytest.mark.parametrize("data_type", [torch.float32, torch.float16])
def test_moe_top2(rs, hidden_size, data_type):
    world_size = 4
    run_func = partial(run_routing,
                       world_size=world_size,
                       port=free_port(),
                       rs=rs,
                       hidden_size=hidden_size,
                       data_type=data_type)
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_moe_top2(60, 512, torch.float16)
