# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import torch

import vllm._custom_ops as ops
from tests.kernels.quant_utils import per_block_cast_to_int8
from tests.kernels.quantization.nvfp4_utils import FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe.activation import MoEActivation
from vllm.model_executor.layers.fused_moe.all2all_utils import (
    maybe_make_prepare_finalize,
)
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
    FusedMoEParallelConfig,
    FusedMoEQuantConfig,
    RoutingMethodType,
)
from vllm.model_executor.layers.fused_moe.experts.fused_batched_moe import (
    BatchedTritonExperts,
    NaiveBatchedExperts,
)
from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
    TritonExperts,
)
from vllm.model_executor.layers.fused_moe.fused_moe import (
    fused_experts,
)
from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEKernel
from vllm.model_executor.layers.fused_moe.prepare_finalize.batched import (
    BatchedPrepareAndFinalize,
)
from vllm.model_executor.layers.fused_moe.router.fused_topk_router import fused_topk
from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
from vllm.utils.deep_gemm import per_block_cast_to_fp8
from vllm.utils.math_utils import round_up


def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
    """Fold weights to adjacent locations for Triton MoE / SwiGLU kernel layout."""
    shape = w.shape
    n = shape[-1]
    first = w[..., : n // 2]
    second = w[..., n // 2 :]
    stacked = torch.stack((first, second), dim=-1)
    return stacked.reshape(shape)


def make_dummy_moe_config(
    num_experts: int = 1,
    experts_per_token: int = 1,
    hidden_dim: int = 1,
    intermediate_size_per_partition: int = 1,
    in_dtype: torch.dtype = torch.bfloat16,
) -> FusedMoEConfig:
    """
    This is a dummy config for the mk constructor interface
    as most kernels like DeepGEMM, CUTLASSFp4, Triton, MARLIN
    do not actually use this config.

    CUTLASSFp8 needs to set some params for workshapes.
    """
    return FusedMoEConfig(
        num_experts=num_experts,
        experts_per_token=experts_per_token,
        hidden_dim=hidden_dim,
        intermediate_size_per_partition=intermediate_size_per_partition,
        num_local_experts=num_experts,
        num_logical_experts=num_experts,
        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
        activation=MoEActivation.SILU,
        in_dtype=in_dtype,
        device="cuda",
        routing_method=RoutingMethodType.TopK,
        max_num_tokens=512,
    )


def triton_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    quant_dtype: torch.dtype | None = None,
    per_act_token_quant=False,
    block_shape: list[int] | None = None,
) -> torch.Tensor:
    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )

    return fused_experts(a, w1, w2, topk_weight, topk_ids, quant_config=quant_config)


def batched_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    quant_dtype: torch.dtype | None = None,
    per_act_token_quant: bool = False,
    block_shape: list[int] | None = None,
) -> torch.Tensor:
    max_num_tokens = round_up(a.shape[0], 64)

    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )

    moe_config = make_dummy_moe_config()

    fused_experts = FusedMoEKernel(
        BatchedPrepareAndFinalize(
            max_num_tokens, num_dispatchers=1, num_local_experts=w1.shape[0], rank=0
        ),
        BatchedTritonExperts(
            max_num_tokens=max_num_tokens,
            num_dispatchers=1,
            quant_config=quant_config,
            moe_config=moe_config,
        ),
        inplace=False,
    )

    return fused_experts.apply(
        a,
        w1,
        w2,
        topk_weight,
        topk_ids,
        global_num_experts=w1.shape[0],
        activation=moe_config.activation,
        apply_router_weight_on_input=False,
        expert_map=None,
    )


def naive_batched_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    quant_dtype: torch.dtype | None = None,
    per_act_token_quant: bool = False,
    block_shape: list[int] | None = None,
) -> torch.Tensor:
    max_num_tokens = round_up(a.shape[0], 64)

    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )
    moe_config = make_dummy_moe_config()

    fused_experts = FusedMoEKernel(
        BatchedPrepareAndFinalize(
            max_num_tokens, num_dispatchers=1, num_local_experts=w1.shape[0], rank=0
        ),
        NaiveBatchedExperts(
            max_num_tokens=max_num_tokens,
            num_dispatchers=1,
            quant_config=quant_config,
            moe_config=moe_config,
        ),
        inplace=False,
    )

    return fused_experts.apply(
        a,
        w1,
        w2,
        topk_weight,
        topk_ids,
        global_num_experts=w1.shape[0],
        activation=moe_config.activation,
        apply_router_weight_on_input=False,
        expert_map=None,
    )


def chunk_scales(
    scales: torch.Tensor | None, start: int, end: int
) -> torch.Tensor | None:
    if scales is not None:
        if scales.numel() == 1:
            return scales
        else:
            return scales[start:end]
    return None


def make_quantized_test_activations(
    E: int,
    m: int,
    k: int,
    in_dtype: torch.dtype,
    quant_dtype: torch.dtype | None = None,
    block_shape: list[int] | None = None,
    per_act_token_quant: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
    a = torch.randn((E, m, k), device="cuda", dtype=in_dtype) / 10
    a_q = a
    a_scale = None

    if quant_dtype is not None:
        assert quant_dtype == torch.float8_e4m3fn or quant_dtype == torch.int8, (
            "only fp8/int8 supported"
        )
        a_q = torch.zeros_like(a, dtype=quant_dtype)
        a_scale_l = [None] * E
        for e in range(E):
            a_q[e], a_scale_l[e] = moe_kernel_quantize_input(
                a[e], None, quant_dtype, per_act_token_quant, block_shape
            )
        a_scale = torch.stack(a_scale_l)

        if not per_act_token_quant and block_shape is None:
            a_scale = a_scale.view(E, 1, 1)

    return a, a_q, a_scale


def moe_quantize_weights_2d(
    w: torch.Tensor,
    w_s: torch.Tensor | None,
    quant_dtype: torch.dtype | str | None,
    per_token_quant: bool,
    block_shape: list[int] | None,
) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]:
    assert (
        quant_dtype == torch.float8_e4m3fn
        or quant_dtype == torch.int8
        or quant_dtype == "nvfp4"
    ), "only fp8/int8/nvfp4 supported"

    w_gs = None

    if block_shape is not None:
        assert not per_token_quant
        if quant_dtype == torch.int8:
            w, w_s = per_block_cast_to_int8(w, block_shape)
        elif quant_dtype == torch.float8_e4m3fn:
            w, w_s = per_block_cast_to_fp8(w, block_shape)
        elif quant_dtype == "nvfp4":
            raise RuntimeError("blocked quantization not supported for nvfp4")
        else:
            raise RuntimeError(f"Unsupported quant type {quant_dtype}")
    else:
        if quant_dtype == torch.int8:
            w, w_s = ops.scaled_int8_quant(
                w, w_s, use_per_token_if_dynamic=per_token_quant
            )
        elif quant_dtype == torch.float8_e4m3fn:
            w, w_s = ops.scaled_fp8_quant(
                w, w_s, use_per_token_if_dynamic=per_token_quant
            )
        elif quant_dtype == "nvfp4":
            assert not per_token_quant
            w_amax = torch.abs(w).max().to(torch.float32)
            w_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w_amax
            w, w_s = ops.scaled_fp4_quant(w, w_gs)
        else:
            raise RuntimeError(f"Unsupported quant type {quant_dtype}")

    return w, w_s, w_gs


def moe_quantize_weights(
    w: torch.Tensor,
    w_s: torch.Tensor | None,
    quant_dtype: torch.dtype | str | None,
    per_token_quant: bool,
    block_shape: list[int] | None,
) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]:
    assert w.dim() == 3
    e, rows, cols = w.shape
    w_l = [None] * e
    w_s_l = [None] * e
    w_gs_l = [None] * e
    for idx in range(e):
        w_l[idx], w_s_l[idx], w_gs_l[idx] = moe_quantize_weights_2d(
            w[idx], None, quant_dtype, per_token_quant, block_shape
        )

    w = torch.stack(w_l)
    w_s = torch.stack(w_s_l)
    w_gs = torch.stack(w_gs_l) if e > 0 and w_gs_l[0] is not None else None

    if w_s.ndim == 2:
        assert w_s.shape[-1] == 1
        w_s = w_s.view(-1, 1, 1)

    if block_shape is not None:
        block_n, block_k = block_shape
        n_tiles = (rows + block_n - 1) // block_n
        k_tiles = (cols + block_k - 1) // block_k
        assert w_s.shape == (e, n_tiles, k_tiles)

    return w, w_s, w_gs


def make_test_weight(
    e: int,
    rows: int,
    cols: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: torch.dtype | str | None = None,
    block_shape: list[int] | None = None,
    per_out_ch_quant: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None]:
    w_16 = torch.randn((e, rows, cols), device="cuda", dtype=in_dtype) / 15

    if quant_dtype is not None:
        w, w_s, w_gs = moe_quantize_weights(
            w_16, None, quant_dtype, per_out_ch_quant, block_shape
        )
    else:
        w = w_16
        w_s = None
        w_gs = None

    return w_16, w, w_s, w_gs


def make_test_weights(
    e: int,
    n: int,
    k: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: torch.dtype | str | None = None,
    block_shape: list[int] | None = None,
    per_out_ch_quant: bool = False,
    make_gate: bool = True,
) -> tuple[
    tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None],
    tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None],
]:
    return (
        make_test_weight(
            e,
            (2 if make_gate else 1) * n,
            k,
            in_dtype,
            quant_dtype,
            block_shape,
            per_out_ch_quant,
        ),
        make_test_weight(e, k, n, in_dtype, quant_dtype, block_shape, per_out_ch_quant),
    )


def per_token_cast_to_fp8(
    x: torch.Tensor, block_size: int = 128
) -> tuple[torch.Tensor, torch.Tensor]:
    assert x.dim() == 2
    m, n = x.shape
    pad_size = (block_size - (n % block_size)) % block_size
    x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
    x_view = x.view(m, -1, block_size)
    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
    fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
    return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)


def make_test_quant_config(
    e: int,
    n: int,
    k: int,
    in_dtype: torch.dtype,
    quant_dtype: torch.dtype | str | None = None,
    per_act_token_quant: bool = False,
    block_shape: list[int] | None = None,
    make_gate: bool = True,
    is_scale_swizzled: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, FusedMoEQuantConfig]:
    (_, w1, w1_s, w1_gs), (_, w2, w2_s, w2_gs) = make_test_weights(
        e,
        n,
        k,
        in_dtype,
        quant_dtype,
        per_out_ch_quant=per_act_token_quant,
        block_shape=block_shape,
        make_gate=make_gate,
    )

    # Hacky/trivial scales for nvfp4.
    a1_gscale: torch.Tensor | None = None
    a2_gscale: torch.Tensor | None = None
    if quant_dtype == "nvfp4":
        a1_gscale = torch.ones((e,), device="cuda", dtype=torch.float32)
        a2_gscale = torch.ones((e,), device="cuda", dtype=torch.float32)
        a1_scale = a1_gscale
        a2_scale = a2_gscale
    else:
        a1_scale = None
        a2_scale = None

    return (
        w1,
        w2,
        FusedMoEQuantConfig.make(
            quant_dtype,
            per_act_token_quant=per_act_token_quant,
            block_shape=block_shape,
            w1_scale=w1_s,
            w2_scale=w2_s,
            a1_gscale=a1_gscale,
            a2_gscale=a2_gscale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            # TODO: make sure this is handled properly
            g1_alphas=(1 / w1_gs) if w1_gs is not None else None,
            g2_alphas=(1 / w2_gs) if w2_gs is not None else None,
            is_scale_swizzled=is_scale_swizzled,
        ),
    )


def fused_moe(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    score: torch.Tensor,
    topk: int,
    renormalize: bool = False,
    quant_config: FusedMoEQuantConfig | None = None,
    global_num_experts: int = -1,
    expert_map: torch.Tensor | None = None,
) -> torch.Tensor:
    topk_weights, topk_ids, _ = fused_topk(
        hidden_states, score.float(), topk, renormalize
    )
    return fused_experts(
        hidden_states,
        w1,
        w2,
        topk_weights,
        topk_ids,
        global_num_experts=global_num_experts,
        expert_map=expert_map,
        quant_config=quant_config,
    )


class BaselineMM(torch.nn.Module):
    def __init__(
        self,
        b: torch.Tensor,
        out_dtype: torch.dtype,
    ):
        super().__init__()
        self.b = torch.nn.Parameter(b.to(dtype=torch.float32))
        self.out_dtype = out_dtype

    def forward(self, a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
        return torch.mm(a.to(dtype=torch.float32), self.b).to(self.out_dtype), None


class BaselineSiluAndMul(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        d = x.shape[-1] // 2
        return torch.nn.functional.silu(x[..., :d]) * x[..., d:]


class TestMLP(torch.nn.Module):
    def __init__(
        self,
        w1: torch.Tensor,
        w2: torch.Tensor,
        out_dtype: torch.dtype,
    ):
        super().__init__()
        self.gate_up_proj = BaselineMM(w1, out_dtype)
        self.down_proj = BaselineMM(w2, out_dtype)
        self.act_fn = BaselineSiluAndMul()

    def forward(self, x):
        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
        x, _ = self.down_proj(x)
        return x


def make_naive_shared_experts(
    N: int,
    K: int,
    in_dtype: torch.dtype = torch.bfloat16,
) -> torch.nn.Module:
    w1 = torch.randn((K, N * 2), device="cuda", dtype=in_dtype) / 15
    w2 = torch.randn((N, K), device="cuda", dtype=in_dtype) / 15
    return TestMLP(w1, w2, out_dtype=in_dtype)


class RealMLP(torch.nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        w1: torch.Tensor,
        w2: torch.Tensor,
        hidden_act: str = "silu",
        quant_config=None,
        reduce_results: bool = True,
        prefix: str = "",
        w1_s: torch.Tensor | None = None,
        w2_s: torch.Tensor | None = None,
    ) -> None:
        from vllm.model_executor.layers.linear import (
            MergedColumnParallelLinear,
            RowParallelLinear,
        )

        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.gate_up_proj.register_parameter(
            "weight", torch.nn.Parameter(w1, requires_grad=False)
        )
        self.gate_up_proj.register_parameter(
            "weight_scale", torch.nn.Parameter(w1_s, requires_grad=False)
        )
        self.gate_up_proj.register_parameter(
            "input_scale", None
        )  # torch.nn.Parameter(None, requires_grad=False))
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        self.down_proj.register_parameter(
            "weight", torch.nn.Parameter(w2, requires_grad=False)
        )
        self.down_proj.register_parameter(
            "weight_scale", torch.nn.Parameter(w2_s, requires_grad=False)
        )
        self.down_proj.register_parameter(
            "input_scale", None
        )  # torch.nn.Parameter(None, requires_grad=False))
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


def make_shared_experts_with_weights(
    N: int,
    K: int,
    in_dtype: torch.dtype,
    w1: torch.Tensor,
    w2: torch.Tensor,
    w1_s: torch.Tensor | None = None,
    w2_s: torch.Tensor | None = None,
    quant_dtype: torch.dtype | str | None = None,
) -> torch.nn.Module:
    old_dtype = torch.get_default_dtype()
    try:
        torch.set_default_dtype(in_dtype)
        if quant_dtype == torch.float8_e4m3fn:
            from vllm.model_executor.layers.quantization.fp8 import Fp8Config

            quant_config = Fp8Config(True)
        else:
            quant_config = None

        return RealMLP(K, N, w1, w2, "silu", quant_config, w1_s=w1_s, w2_s=w2_s)
    finally:
        torch.set_default_dtype(old_dtype)


def modular_triton_fused_moe(
    moe_config: FusedMoEConfig,
    quant_config: FusedMoEQuantConfig,
) -> FusedMoEKernel:
    return FusedMoEKernel(
        maybe_make_prepare_finalize(
            moe=moe_config,
            quant_config=quant_config,
            allow_new_interface=True,
            use_monolithic=False,
        ),
        TritonExperts(moe_config, quant_config),
        inplace=False,
    )


def make_shared_experts(
    N: int,
    K: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: torch.dtype | str | None = None,
) -> torch.nn.Module:
    (_, w1, w1_s, _), (_, w2, w2_s, _) = make_test_weights(
        1,
        N,
        K,
        in_dtype=in_dtype,
        quant_dtype=quant_dtype,
    )

    return make_shared_experts_with_weights(
        N, K, in_dtype, w1, w2, w1_s=w1_s, w2_s=w2_s, quant_dtype=quant_dtype
    )
