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

# Adapted from https://github.com/sgl-project/sglang/blob/main/test/srt/test_triton_moe_channel_fp8_kernel.py
import itertools

import pytest
import torch

from tests.kernels.moe.utils import fused_moe
from vllm import _custom_ops as ops
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe.config import (
    fp8_w8a8_moe_quant_config)
from vllm.platforms import current_platform

if current_platform.get_device_capability() < (9, 0):
    pytest.skip("FP8 Triton requires CUDA 9.0 or higher",
                allow_module_level=True)

vllm_config = VllmConfig()
vllm_config.scheduler_config.max_num_seqs = 128
vllm_config.scheduler_config.max_model_len = 8192


def native_w8a8_per_token_matmul(A, B, As, Bs, output_dtype=torch.float16):
    """Matrix multiplication function that supports per-token input
    quantization and per-column weight quantization"""
    A = A.to(torch.float32)
    B = B.to(torch.float32)

    assert A.shape[-1] == B.shape[-1], "Dimension mismatch"
    assert B.ndim == 2 and B.is_contiguous(
    ), "B must be a 2D contiguous tensor"

    # Reshape input
    M = A.numel() // A.shape[-1]
    B = B.t()  # Transpose weight matrix
    N, K = B.shape
    origin_C_shape = A.shape[:-1] + (K, )
    A = A.reshape(M, N)

    # As is per-token [M, 1], Bs is per-column [1, K]
    C = torch.matmul(A, B)  # [M, K]
    C = As * C * Bs.view(1, -1)  # Broadcast per-column scale

    return C.reshape(origin_C_shape).to(output_dtype)


def fp8_mask(a, mask):
    dtype = a.dtype
    return a.view(torch.int8)[mask].view(dtype)


def torch_w8a8_per_column_moe(a, w1, w2, w1_s, w2_s, score, topk):
    """This function performs fused moe with per-column int8
    quantization using native torch."""

    B, D = a.shape
    # Perform per-token quantization
    a_q, a_s = ops.scaled_fp8_quant(a, use_per_token_if_dynamic=True)
    # Repeat tokens to match topk
    a_q = a_q.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    # Also repeat the scale
    a_s = a_s.view(B, -1, 1).repeat(1, topk, 1).reshape(-1, 1)  # [B*topk, 1]

    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)

    # Calculate routing
    score = torch.softmax(score, dim=-1, dtype=torch.float32)
    topk_weight, topk_ids = torch.topk(score, topk)
    topk_weight = topk_weight.view(-1)
    topk_ids = topk_ids.view(-1)
    # Process each expert
    for i in range(w1.shape[0]):
        mask = topk_ids == i
        if mask.sum():
            # First MLP layer: note that a_s is now per-token
            inter_out = native_w8a8_per_token_matmul(
                fp8_mask(a_q, mask),
                w1[i],
                fp8_mask(a_s, mask),
                w1_s[i],
                output_dtype=a.dtype,
            )
            # Activation function
            act_out = SiluAndMul().forward_native(inter_out)
            # Quantize activation output with per-token
            act_out_q, act_out_s = ops.scaled_fp8_quant(
                act_out, use_per_token_if_dynamic=True)

            # Second MLP layer
            out[mask] = native_w8a8_per_token_matmul(act_out_q,
                                                     w2[i],
                                                     act_out_s,
                                                     w2_s[i],
                                                     output_dtype=a.dtype)
    # Apply routing weights and sum
    return (out.view(B, -1, w2.shape[1]) *
            topk_weight.view(B, -1, 1).to(out.dtype)).sum(dim=1)


@pytest.fixture(autouse=True, scope="module")
def setup_cuda():
    """Sets the default CUDA device for all tests in this module."""
    torch.set_default_device("cuda")


DTYPES = [torch.half, torch.bfloat16]
M = [1, 33]
N = [128, 1024]
K = [256, 4096]
E = [8]
TOP_KS = [2, 6]
SEEDS = [0]


@pytest.mark.parametrize("M, N, K, E, topk, dtype, seed",
                         itertools.product(M, N, K, E, TOP_KS, DTYPES, SEEDS))
@torch.inference_mode()
def test_w8a8_fp8_fused_moe(M, N, K, E, topk, dtype, seed):
    torch.manual_seed(seed)
    # Initialize int8 quantization parameters
    factor_for_scale = 1e-2
    finfo = torch.finfo(torch.float8_e4m3fn)
    fp8_max = finfo.max
    fp8_min = finfo.min

    # Input tensor
    # M * K
    a = torch.randn((M, K), dtype=dtype) / 10

    # Generate int8 weights
    w1_fp32 = (torch.rand((E, 2 * N, K), dtype=torch.float32) - 0.5) * 2
    w1 = (w1_fp32 * fp8_max).clamp(min=fp8_min,
                                   max=fp8_max).to(torch.float8_e4m3fn)

    w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32) - 0.5) * 2
    w2 = (w2_fp32 * fp8_max).clamp(min=fp8_min,
                                   max=fp8_max).to(torch.float8_e4m3fn)

    # Generate scale for each column (per-column quantization)
    w1_s = torch.rand(E, 2 * N, device=w1_fp32.device) * factor_for_scale
    w2_s = torch.rand(E, K, device=w2_fp32.device) * factor_for_scale
    score = torch.randn((M, E), dtype=dtype)

    with set_current_vllm_config(vllm_config):
        ref_out = torch_w8a8_per_column_moe(a, w1, w2, w1_s, w2_s, score, topk)
        out = fused_moe(
            a,
            w1,
            w2,
            score,
            topk,
            renormalize=False,
            quant_config=fp8_w8a8_moe_quant_config(
                per_act_token_quant=True,
                w1_scale=w1_s,
                w2_scale=w2_s,
                block_shape=None,  # Not using block quantization
            ),
        )

    # Check results
    rel_diff = (torch.mean(
        torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /
                torch.mean(torch.abs(ref_out.to(torch.float32))))
    assert rel_diff < 0.05
