"""Tests for the MOE layers.

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

from typing import List

import pytest
import torch

from moe._ops import ops
from moe.fused_moe import fused_moe, fused_topk, moe_align_block_size
from moe.fused_marlin_moe import fused_marlin_moe
from moe.platforms import current_platform
from moe.scalar_type import scalar_types
from moe.utils.marlin_utils_test import marlin_quantize, quantize_weights

from .utils import compute_max_diff, opcheck, torch_moe


def stack_and_dev(tensors: List[torch.Tensor]):
    dev = tensors[0].device
    return torch.stack(tensors, dim=0).to(dev)


NUM_EXPERTS = [8, 64]
TOP_KS = [2, 6]


@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("dtype", [torch.float16, torch.bfloat16])
def test_fused_moe(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    dtype: torch.dtype,
):
    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10

    score = torch.randn((m, e), device="cuda", dtype=dtype)
    triton_output = fused_moe(a, w1, w2, score, topk, renormalize=False)
    torch_output = torch_moe(a, w1, w2, score, topk)
    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
    # iterative_output = iterative_moe(a, w1, w2, score, topk, renormalize=False)
    # torch.testing.assert_close(iterative_output, torch_output, atol=2e-2, rtol=0)


@pytest.mark.parametrize("m", [1, 32, 222])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.parametrize("has_zp", [True, False])
@pytest.mark.parametrize("weight_bits", [4, 8])
def test_fused_moe_wn16(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    dtype: torch.dtype,
    group_size: int,
    has_zp: bool,
    weight_bits: int,
):
    print(m, n, k, e, topk, dtype, group_size, has_zp, weight_bits)
    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
    score = torch.randn((m, e), device="cuda", dtype=dtype)

    if weight_bits == 4:
        pack_factor = 2
        quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
    elif weight_bits == 8:
        pack_factor = 1
        quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128

    w1_ref = w1.clone()
    w2_ref = w2.clone()
    w1_qweight = torch.empty(
        (e, 2 * n, k // pack_factor), device="cuda", dtype=torch.uint8
    )
    w2_qweight = torch.empty((e, k, n // pack_factor), device="cuda", dtype=torch.uint8)
    w1_scales = torch.empty((e, 2 * n, k // group_size), device="cuda", dtype=dtype)
    w2_scales = torch.empty((e, k, n // group_size), device="cuda", dtype=dtype)
    w1_qzeros = torch.empty(
        (e, 2 * n // pack_factor, k // group_size), device="cuda", dtype=torch.uint8
    )
    w2_qzeros = torch.empty(
        (e, k // pack_factor, n // group_size), device="cuda", dtype=torch.uint8
    )

    for i in range(e * 2):
        expert_id = i % e
        if i // e == 0:
            w, w_ref, w_qweight, w_scales, w_qzeros = (
                w1,
                w1_ref,
                w1_qweight,
                w1_scales,
                w1_qzeros,
            )
        else:
            w, w_ref, w_qweight, w_scales, w_qzeros = (
                w2,
                w2_ref,
                w2_qweight,
                w2_scales,
                w2_qzeros,
            )
        weight, qweight, scales, qzeros = quantize_weights(
            w[expert_id].T, quant_type, group_size, has_zp, False
        )
        weight = weight.T
        qweight = qweight.T.contiguous().to(torch.uint8)
        scales = scales.T
        if has_zp:
            qzeros = qzeros.T.contiguous().to(torch.uint8)
        if weight_bits == 4:
            qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
            if has_zp:
                qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]

        w_ref[expert_id] = weight
        w_qweight[expert_id] = qweight
        w_scales[expert_id] = scales
        if has_zp:
            w_qzeros[expert_id] = qzeros

    triton_output = fused_moe(
        a,
        w1_qweight,
        w2_qweight,
        score,
        topk,
        renormalize=False,
        use_int4_w4a16=weight_bits == 4,
        use_int8_w8a16=weight_bits == 8,
        w1_scale=w1_scales,
        w2_scale=w2_scales,
        w1_zp=w1_qzeros if has_zp else None,
        w2_zp=w2_qzeros if has_zp else None,
        block_shape=[0, group_size],
    )
    torch_output = torch_moe(a, w1_ref, w2_ref, score, topk)
    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)


@pytest.mark.parametrize("m", [1, 33, 64, 222])
@pytest.mark.parametrize("n", [128, 2048])
@pytest.mark.parametrize("k", [128, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("group_size", [-1, 32, 128])
@pytest.mark.parametrize("act_order", [True, False])
@pytest.mark.parametrize("num_bits", [4, 8])
@pytest.mark.parametrize("is_k_full", [True, False])
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
def test_fused_marlin_moe(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    group_size: int,
    act_order: bool,
    num_bits: int,
    is_k_full: bool,
):
    torch.manual_seed(7)

    # Filter act_order
    if act_order:
        if group_size == -1:
            return
        if group_size in (k, n):
            return
    else:
        if not is_k_full:
            return

    quant_type = scalar_types.uint4b8 if num_bits == 4 else scalar_types.uint8b128
    dtype = torch.float16
    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10

    w_ref1_l = []
    qweight1_l = []
    scales1_l = []
    g_idx1_l = []
    sort_indices1_l = []

    for i in range(w1.shape[0]):
        test_perm = torch.randperm(k)
        w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = marlin_quantize(
            w1[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
        )
        w_ref1_l.append(w_ref1)
        qweight1_l.append(qweight1)
        scales1_l.append(scales1)
        g_idx1_l.append(g_idx1)
        sort_indices1_l.append(sort_indices1)

    w_ref1 = stack_and_dev(w_ref1_l)
    qweight1 = stack_and_dev(qweight1_l).contiguous()
    scales1 = stack_and_dev(scales1_l)
    g_idx1 = stack_and_dev(g_idx1_l)
    sort_indices1 = stack_and_dev(sort_indices1_l)

    w_ref2_l = []
    qweight2_l = []
    scales2_l = []
    g_idx2_l = []
    sort_indices2_l = []

    for i in range(w2.shape[0]):
        test_perm = torch.randperm(n)
        w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = marlin_quantize(
            w2[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
        )
        w_ref2_l.append(w_ref2)
        qweight2_l.append(qweight2)
        scales2_l.append(scales2)
        g_idx2_l.append(g_idx2)
        sort_indices2_l.append(sort_indices2)

    w_ref2 = stack_and_dev(w_ref2_l)
    qweight2 = stack_and_dev(qweight2_l).contiguous()
    scales2 = stack_and_dev(scales2_l)
    g_idx2 = stack_and_dev(g_idx2_l)
    sort_indices2 = stack_and_dev(sort_indices2_l)

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

    topk_weights, topk_ids = fused_topk(a, score, topk, False)

    triton_output = fused_moe(
        a,
        w_ref1.transpose(1, 2).contiguous(),
        w_ref2.transpose(1, 2).contiguous(),
        score,
        topk,
        renormalize=False,
    )
    marlin_output = fused_marlin_moe(
        a,
        qweight1,
        qweight2,
        scales1,
        scales2,
        score,
        topk_weights,
        topk_ids,
        g_idx1=g_idx1,
        g_idx2=g_idx2,
        sort_indices1=sort_indices1,
        sort_indices2=sort_indices2,
        num_bits=num_bits,
        is_k_full=is_k_full,
    )

    assert compute_max_diff(marlin_output, triton_output) < 4e-2

    token_expert_indicies = torch.empty(m, topk, dtype=torch.int32, device=a.device)

    opcheck(
        ops.topk_softmax,
        (
            topk_weights,
            topk_ids,
            token_expert_indicies,
            score.float(),
        ),
    )

    block_size_m = 4

    sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m, e)

    max_workspace_size = ((m + 255) // 256) * (max(2 * n, k) // 64) * 16
    workspace = torch.zeros(
        max_workspace_size, dtype=torch.int, device="cuda", requires_grad=False
    )

    zp = torch.empty((0, 0), dtype=dtype, device="cuda", requires_grad=False)
    opcheck(
        ops.marlin_gemm_moe,
        (
            a,
            qweight1,
            sorted_token_ids,
            topk_weights,
            topk_ids,
            scales1,
            zp,
            g_idx1,
            sort_indices1,
            workspace,
            quant_type.id,
            m,
            2 * n,
            k,
            True,
            e,
            topk,
            block_size_m,
            True,
            False,
        ),
    )


def test_moe_align_block_size_opcheck():
    num_experts = 4
    block_size = 4
    topk_ids = torch.randint(0, num_experts, (3, 4), dtype=torch.int32, device="cuda")

    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
    sorted_ids = torch.empty(
        (max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
    )
    sorted_ids.fill_(topk_ids.numel())
    max_num_m_blocks = max_num_tokens_padded // block_size
    expert_ids = torch.empty(
        (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
    )
    num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)

    opcheck(
        ops.moe_align_block_size,
        (
            topk_ids,
            num_experts,
            block_size,
            sorted_ids,
            expert_ids,
            num_tokens_post_pad,
        ),
    )