'''Copyright The Microsoft DeepSpeed Team'''

import torch
import deepspeed
import pytest
from unit.common import DistributedTest
from unit.util import required_torch_version
from deepspeed.moe.layer import MoE


class MPU():
    def __init__(self, tp_world_size):
        self.rank = deepspeed.comm.get_rank()
        self.world_size = deepspeed.comm.get_world_size()
        self.tp_world_size = tp_world_size

        for i in range(0, self.world_size, tp_world_size):
            ranks = range(i, i + tp_world_size)
            group = deepspeed.comm.new_group(ranks)
            if self.rank in ranks:
                self.tp_group = group

        for i in range(0, tp_world_size):
            ranks = range(i, self.world_size, tp_world_size)
            group = deepspeed.comm.new_group(ranks)
            if self.rank in ranks:
                self.dp_group = group

    def get_model_parallel_rank(self):
        return self.rank % self.tp_world_size

    def get_model_parallel_world_size(self):
        return self.tp_world_size

    def get_data_parallel_rank(self):
        return self.rank // self.tp_world_size

    def get_data_parallel_world_size(self):
        return self.world_size // self.tp_world_size

    def get_data_parallel_group(self):
        return self.dp_group

    def get_model_parallel_group(self):
        return self.tp_group


@pytest.mark.parametrize("ep_size, tp_size", [(1, 2), (1, 4), (2, 2)])
@pytest.mark.parametrize("enable_expert_tp", [True, False])
@pytest.mark.parametrize("use_residual", [True, False])
class TestMOETensorParallel(DistributedTest):
    world_size = 4

    def test(self, ep_size, tp_size, enable_expert_tp, use_residual):
        # TODO: replace this with a true parallel mlp in the future
        # and run convergence tests
        if not required_torch_version():
            pytest.skip("DeepSpeed MoE tests need torch 1.8 or higher to run correctly")

        config_dict = {
            "train_batch_size": 8,
            "steps_per_print": 1,
            "fp16": {
                "enabled": True
            }
        }
        hidden_dim = 16

        tensor_parallel_expert = torch.nn.Sequential(
            torch.nn.Linear(hidden_dim,
                            4 * hidden_dim // tp_size),
            torch.nn.ReLU(),
            torch.nn.Linear(4 * hidden_dim // tp_size,
                            hidden_dim))

        # set num experts to world size
        world_size = deepspeed.comm.get_world_size()
        model = MoE(
            hidden_size=hidden_dim,
            expert=tensor_parallel_expert,
            num_experts=world_size,
            ep_size=ep_size,
            use_residual=use_residual,
            enable_expert_tensor_parallelism=enable_expert_tp,
        )
        optimizer = torch.optim.AdamW(params=model.parameters())
        model, _, _, _ = deepspeed.initialize(config=config_dict,
                                              model=model,
                                              optimizer=optimizer,
                                              dist_init_required=False,
                                              mpu=MPU(tp_size))

        assert model.num_local_experts == world_size // ep_size
        if enable_expert_tp:
            assert deepspeed.utils.groups._get_expert_model_parallel_world_size(
            ) == tp_size
        else:
            assert deepspeed.utils.groups._get_expert_model_parallel_world_size() == 1
