# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# SPDX-License-Identifier: Apache-2.0
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/kernels/test_moe.py
"""Tests for the MOE layers.

Run `pytest tests/ops/test_fused_moe.py`.
"""

import pytest
import torch
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.activation import SiluAndMul

from vllm_ascend.ops.fused_moe import fused_experts

NUM_EXPERTS = [8, 64]
EP_SIZE = [1, 4]
TOP_KS = [2, 6]
DEVICE = ["npu"]


def torch_moe(a, w1, w2, topk_weights, topk_ids, topk, expert_map):
    B, D = a.shape
    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
    topk_weights = topk_weights.view(-1)
    topk_ids = topk_ids.view(-1)
    if expert_map is not None:
        topk_ids = expert_map[topk_ids]
    for i in range(w1.shape[0]):
        mask = topk_ids == i
        if mask.sum():
            out[mask] = SiluAndMul()(
                a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
    return (out.view(B, -1, w2.shape[1]) *
            topk_weights.view(B, -1, 1).to(out.dtype)).sum(dim=1)


@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 511, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("device", DEVICE)
def test_fused_experts(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    ep_size: int,
    dtype: torch.dtype,
    device: str,
):
    vllm_config = VllmConfig()
    with set_current_vllm_config(vllm_config):
        a = torch.randn((m, k), device=device, dtype=dtype) / 10
        w1 = torch.randn((e, 2 * n, k), device=device, dtype=dtype) / 10
        w2 = torch.randn((e, k, n), device=device, dtype=dtype) / 10

        score = torch.randn((m, e), device=device, dtype=dtype)

        if ep_size > 1:
            local_e = e // ep_size
            e_ids = torch.randint(0,
                                  e, (local_e, ),
                                  device=device,
                                  dtype=torch.int32)
            e_map = torch.full((e, ), -1, device=device, dtype=torch.int32)
            e_map[e_ids] = torch.arange(local_e,
                                        device=device,
                                        dtype=torch.int32)
            w1 = w1[e_ids]
            w2 = w2[e_ids]
        else:
            e_map = None

        score = torch.softmax(score, dim=-1, dtype=dtype)
        topk_weights, topk_ids = torch.topk(score, topk)
        topk_ids = topk_ids.to(torch.int32)

        output = fused_experts(a, w1, w2, topk_weights, topk_ids, topk, e_map)
        torch_output = torch_moe(a, w1, w2, topk_weights, topk_ids, topk,
                                 e_map)
        # TODO: The native params are: atol=2e-2, rtol=0, maybe related to the nan problem
        torch.testing.assert_close(output, torch_output, atol=4e-2, rtol=1)
    torch.npu.empty_cache()