import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_fwd

import triton
import triton.language as tl
from . import custom_autotune


# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
    configs=[
        triton.Config(
            {
                "BLOCK_SIZE_M": 64,
                "BLOCK_SIZE_N": 256,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=4,
            num_warps=4,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 128,
                "BLOCK_SIZE_N": 128,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=4,
            num_warps=4,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 64,
                "BLOCK_SIZE_N": 128,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=4,
            num_warps=4,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 128,
                "BLOCK_SIZE_N": 32,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=4,
            num_warps=4,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 64,
                "BLOCK_SIZE_N": 64,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=4,
            num_warps=4,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 64,
                "BLOCK_SIZE_N": 128,
                "BLOCK_SIZE_K": 32,
                "GROUP_SIZE_M": 8,
            },
            num_stages=2,
            num_warps=8,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 64,
                "BLOCK_SIZE_N": 64,
                "BLOCK_SIZE_K": 64,
                "GROUP_SIZE_M": 8,
            },
            num_stages=3,
            num_warps=8,
        ),
        triton.Config(
            {
                "BLOCK_SIZE_M": 32,
                "BLOCK_SIZE_N": 32,
                "BLOCK_SIZE_K": 128,
                "GROUP_SIZE_M": 8,
            },
            num_stages=2,
            num_warps=4,
        ),
    ],
    key=["M", "N", "K"],
    nearest_power_of_two=True,
    prune_configs_by={
        "early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
        "perf_model": None,
        "top_k": None,
    },
)
@triton.jit
def matmul_248_kernel(
    a_ptr,
    b_ptr,
    c_ptr,
    scales_ptr,
    zeros_ptr,
    g_ptr,
    M,
    N,
    K,
    bits,
    maxq,
    stride_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    stride_scales,
    stride_zeros,
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
    GROUP_SIZE_M: tl.constexpr,
):
    """
    Compute the matrix multiplication C = A x B.
    A is of shape (M, K) float16
    B is of shape (K//8, N) int32
    C is of shape (M, N) float16
    scales is of shape (G, N) float16
    zeros is of shape (G, N) float16
    g_ptr is of shape (K) int32
    """
    infearure_per_bits = 32 // bits

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + (pid % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (
        offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    )  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
    a_mask = offs_am[:, None] < M
    # b_ptrs is set up such that it repeats elements along the K axis 8 times
    b_ptrs = b_ptr + (
        (offs_k[:, None] // infearure_per_bits) * stride_bk
        + offs_bn[None, :] * stride_bn
    )  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
    g_ptrs = g_ptr + offs_k
    # shifter is used to extract the N bits of each element in the 32-bit word from B
    scales_ptrs = scales_ptr + offs_bn[None, :]
    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)

    shifter = (offs_k % infearure_per_bits) * bits
    zeros_shifter = (offs_bn % infearure_per_bits) * bits
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

    for k in range(0, num_pid_k):
        g_idx = tl.load(g_ptrs)

        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
        scales = tl.load(
            scales_ptrs + g_idx[:, None] * stride_scales
        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        zeros = tl.load(
            zeros_ptrs + g_idx[:, None] * stride_zeros
        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)

        zeros = (zeros >> zeros_shifter[None, :]) & maxq
        zeros = (zeros + 1) & maxq  # eventually avoid overflow

        a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
        b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

        # Now we need to unpack b (which is N-bit values) into 32-bit values
        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
        b = (b - zeros) * scales  # Scale and shift

        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_K
        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
        g_ptrs += BLOCK_SIZE_K

    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
    tl.store(c_ptrs, accumulator, mask=c_mask)


def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
    with torch.cuda.device(input.device):
        output = torch.empty(
            (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
        )

        def grid(META):
            return (
                triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
                * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
            )

        matmul_248_kernel[grid](
            input,
            qweight,
            output,
            scales,
            qzeros,
            g_idx,
            input.shape[0],
            qweight.shape[1],
            input.shape[1],
            bits,
            maxq,
            input.stride(0),
            input.stride(1),
            qweight.stride(0),
            qweight.stride(1),
            output.stride(0),
            output.stride(1),
            scales.stride(0),
            qzeros.stride(0),
        )
        return output


class QuantLinearFunction(torch.autograd.Function):
    @staticmethod
    @custom_fwd(cast_inputs=torch.float16)
    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
        return output


class QuantLinear(nn.Module):
    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
        super().__init__()
        self.register_buffer("qweight", qweight)
        self.register_buffer("qzeros", qzeros)
        self.register_buffer("scales", scales)
        self.register_buffer("g_idx", g_idx)
        if bias is not None:
            self.register_buffer("bias", bias)
        else:
            self.bias = None
        if bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")
        self.bits = bits
        self.maxq = 2**self.bits - 1
        self.groupsize = groupsize

        self.outfeatures = qweight.shape[1]
        self.infeatures = qweight.shape[0] * 32 // bits

    @classmethod
    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
        if bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")

        qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
        qzeros = torch.zeros(
            (math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
            dtype=torch.int32,
        )
        scales = torch.zeros(
            (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
        )
        g_idx = torch.tensor(
            [i // groupsize for i in range(infeatures)], dtype=torch.int32
        )
        if bias:
            bias = torch.zeros((outfeatures), dtype=torch.float16)
        else:
            bias = None
        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)

    def pack(self, linear, scales, zeros, g_idx=None):
        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx

        scales = scales.t().contiguous()
        zeros = zeros.t().contiguous()
        scale_zeros = zeros * scales
        self.scales = scales.clone().half()
        if linear.bias is not None:
            self.bias = linear.bias.clone().half()

        intweight = []
        for idx in range(self.infeatures):
            intweight.append(
                torch.round(
                    (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
                    / self.scales[self.g_idx[idx]]
                ).to(torch.int)[:, None]
            )
        intweight = torch.cat(intweight, dim=1)
        intweight = intweight.t().contiguous()
        intweight = intweight.numpy().astype(np.uint32)
        qweight = np.zeros(
            (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
        )
        i = 0
        row = 0
        while row < qweight.shape[0]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qweight[row] |= intweight[j] << (self.bits * (j - i))
                i += 32 // self.bits
                row += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qweight = qweight.astype(np.int32)
        self.qweight = torch.from_numpy(qweight)

        zeros -= 1
        zeros = zeros.numpy().astype(np.uint32)
        qzeros = np.zeros(
            (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
        )
        i = 0
        col = 0
        while col < qzeros.shape[1]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
                i += 32 // self.bits
                col += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qzeros = qzeros.astype(np.int32)
        self.qzeros = torch.from_numpy(qzeros)

    def forward(self, x):
        out_shape = x.shape[:-1] + (self.outfeatures,)
        out = QuantLinearFunction.apply(
            x.reshape(-1, x.shape[-1]),
            self.qweight,
            self.scales,
            self.qzeros,
            self.g_idx,
            self.bits,
            self.maxq,
        )
        out = out + self.bias if self.bias is not None else out
        return out.reshape(out_shape)