# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Tests for the MOE permute/unpermute kernel

Run `pytest tests/kernels/test_moe_permute_unpermute.py`.
"""

import numpy as np
import pytest
import torch

from vllm.model_executor.layers.fused_moe import fused_topk
from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
    moe_permute,
    moe_permute_unpermute_supported,
    moe_unpermute,
)
from vllm.platforms import current_platform
from vllm.utils.torch_utils import set_random_seed

NUM_EXPERTS = [16, 64, 256]
TOP_KS = [2, 6, 8]
EP_SIZE = [1, 4, 16]
set_random_seed(0)

if current_platform.is_rocm():
    pytest.skip(
        "moe_permute_unpermute_supported is not defined for ROCm",
        allow_module_level=True,
    )


def torch_permute(
    hidden_states: torch.Tensor,
    topk_ids: torch.Tensor,
    #   token_expert_indices: torch.Tensor,
    topk: int,
    n_expert: int,
    n_local_expert: int,
    start_expert: int,
    expert_map: torch.Tensor | None = None,
) -> list[torch.Tensor]:
    n_token = hidden_states.shape[0]
    if expert_map is not None:
        is_local_expert = expert_map[topk_ids] != -1
        not_local_expert = expert_map[topk_ids] == -1
        topk_ids = is_local_expert * (topk_ids - start_expert) + not_local_expert * (
            topk_ids + n_expert
        )
    token_expert_indices = torch.arange(
        0, n_token * topk, dtype=torch.int32, device=hidden_states.device
    ).reshape((n_token, topk))

    sorted_topk_ids, sorted_indices = torch.sort(topk_ids.flatten(), stable=True)
    dst_row_id2src_row_id_map = token_expert_indices.flatten()[sorted_indices]

    expert_first_token_offset = torch.zeros(
        n_local_expert + 1, dtype=torch.int64, device="cuda"
    )
    idx = 0
    for i in range(0, n_local_expert):
        cnt = 0
        while idx < sorted_topk_ids.numel() and sorted_topk_ids[idx] == i:
            cnt += 1
            idx += 1
        expert_first_token_offset[i + 1] = expert_first_token_offset[i] + cnt

    _, src2dst_idx = torch.sort(dst_row_id2src_row_id_map)
    valid_row_idx = []
    permuted_hidden_states = hidden_states[dst_row_id2src_row_id_map // topk, ...]
    src_row_id2dst_row_id_map = torch.arange(
        0, n_token * topk, device="cuda", dtype=torch.int32
    )[src2dst_idx].reshape((n_token, topk))
    valid_row_idx += [i for i in range(expert_first_token_offset[-1])]
    dst_row_id2src_row_id_map[expert_first_token_offset[-1] :] = n_token * topk
    return [
        permuted_hidden_states,
        expert_first_token_offset,
        src_row_id2dst_row_id_map,
        dst_row_id2src_row_id_map,
        valid_row_idx,
    ]


def torch_unpermute(
    permuted_hidden_states: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    token_expert_indices: torch.Tensor,
    src_row_id2dst_row_id_map: torch.Tensor,
    valid_row_idx: torch.Tensor,
    topk: int,
    n_expert: int,
) -> torch.Tensor:
    # ignore invalid row
    n_hidden = permuted_hidden_states.shape[1]
    mask = torch.zeros(permuted_hidden_states.shape[0], dtype=bool, device="cuda")
    mask[valid_row_idx] = True
    permuted_hidden_states[~mask] = 0

    permuted_hidden_states = permuted_hidden_states[
        src_row_id2dst_row_id_map.flatten(), ...
    ]
    permuted_hidden_states = permuted_hidden_states.view(-1, topk, n_hidden)
    output = (
        (permuted_hidden_states * topk_weights.unsqueeze(2))
        .sum(1)
        .to(permuted_hidden_states.dtype)
    )
    return output


@pytest.mark.parametrize("n_token", [1, 33, 1024, 5000])
@pytest.mark.parametrize("n_hidden", [2048, 7168])
@pytest.mark.parametrize("n_expert", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("ep_size", EP_SIZE)
def test_moe_permute_unpermute(
    n_token: int,
    n_hidden: int,
    topk: int,
    n_expert: int,
    ep_size: int,
    dtype: torch.dtype,
):
    if not moe_permute_unpermute_supported():
        pytest.skip("moe_permute_unpermute is not supported on this platform.")
    ep_rank = np.random.randint(0, ep_size)
    expert_map = None
    n_local_expert = n_expert
    if ep_size != 1:
        n_local_expert, expert_map, _ = determine_expert_map(ep_size, ep_rank, n_expert)
        expert_map = expert_map.cuda()
    start_expert = n_local_expert * ep_rank
    set_random_seed(0)
    hidden_states = torch.randn((n_token, n_hidden), device="cuda").to(dtype)
    gating_output = torch.randn((n_token, n_expert), device="cuda").to(dtype)
    topk_weights, topk_ids, token_expert_indices = fused_topk(
        hidden_states, gating_output, topk, False
    )
    (
        gold_permuted_hidden_states,
        gold_expert_first_token_offset,
        gold_inv_permuted_idx,
        gold_permuted_idx,
        valid_row_idx,
    ) = torch_permute(
        hidden_states,
        topk_ids,
        # token_expert_indices,
        topk,
        n_expert,
        n_local_expert,
        start_expert,
        expert_map=expert_map,
    )

    (
        permuted_hidden_states,
        _,
        expert_first_token_offset,
        inv_permuted_idx,
        _,
    ) = moe_permute(
        hidden_states=hidden_states,
        a1q_scale=None,
        topk_ids=topk_ids,
        n_expert=n_expert,
        n_local_expert=n_local_expert,
        expert_map=expert_map,
    )

    # check expert_first_token_offset
    torch.testing.assert_close(
        gold_expert_first_token_offset, expert_first_token_offset, atol=0, rtol=0
    )
    # check src_row_id2dst_row_id_map
    torch.testing.assert_close(
        gold_inv_permuted_idx.flatten(), inv_permuted_idx, atol=0, rtol=0
    )

    # check permuted_hidden_states, only valid token
    torch.testing.assert_close(
        gold_permuted_hidden_states[valid_row_idx],
        permuted_hidden_states[valid_row_idx],
        atol=0,
        rtol=0,
    )
    # add a random tensor to simulate group gemm
    result0 = 0.5 * permuted_hidden_states + torch.randn_like(permuted_hidden_states)
    result4 = torch.empty_like(hidden_states)
    moe_unpermute(
        result4, result0, topk_weights, inv_permuted_idx, expert_first_token_offset
    )

    gold4 = torch_unpermute(
        result0,
        topk_weights,
        topk_ids,
        token_expert_indices,
        inv_permuted_idx,
        valid_row_idx,
        topk,
        n_local_expert,
    )
    # check unpermuted hidden
    torch.testing.assert_close(result4, gold4, atol=2e-2, rtol=0)