from typing import List, Optional, Tuple

import numpy
import torch

from vllm import _custom_ops as ops
from vllm.platforms import current_platform

from .quant_utils import pack_cols, unpack_cols

GPTQ_MARLIN_TILE = 16
GPTQ_MARLIN_MIN_THREAD_N = 64
GPTQ_MARLIN_MIN_THREAD_K = 128
GPTQ_MARLIN_MAX_PARALLEL = 16

MARLIN_SUPPORTED_NUM_BITS = [4, 8]
MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]

# In case there is a performance issue with Marlin, the variable below can be
# changed to False, which allows Marlin to perform global reductions in fp16
# precision (instead of fp32), and therefore, save on some memory movements.
USE_FP32_REDUCE_DEFAULT = True


def _check_marlin_supported(num_bits: int, group_size: int, is_sym: bool,
                            min_capability: Optional[int],
                            has_zp: bool) -> Tuple[bool, Optional[str]]:
    if min_capability is not None:
        major, minor = current_platform.get_device_capability()
        device_capability = major * 10 + minor
        if device_capability < min_capability:
            return (False, "Marlin does not support device_capability = {}"
                    ", the min_capability required is {}".format(
                        device_capability, min_capability))

    if num_bits not in MARLIN_SUPPORTED_NUM_BITS:
        return (False, "Marlin does not support weight_bits = {}. "
                "Only weight_bits = {} are supported.".format(
                    num_bits, MARLIN_SUPPORTED_NUM_BITS))

    if group_size not in MARLIN_SUPPORTED_GROUP_SIZES:
        return (False, "Marlin does not support group_size = {}. Only "
                "group_sizes = {} are supported.".format(
                    group_size, MARLIN_SUPPORTED_GROUP_SIZES))

    if not has_zp and not is_sym:
        return (False,
                "Marlin without zero_points must have symmetric quantization")

    return True, None


def check_gptq_marlin_supported(num_bits: int, group_size: int, is_sym: bool,
                                min_capability: int) -> bool:
    cond, _ = _check_marlin_supported(num_bits,
                                      group_size,
                                      is_sym,
                                      min_capability,
                                      has_zp=False)
    return cond


def check_awq_marlin_supported(num_bits: int, group_size: int, has_zp: bool,
                               min_capability: int) -> bool:
    cond, _ = _check_marlin_supported(num_bits,
                                      group_size,
                                      False,
                                      min_capability,
                                      has_zp=has_zp)
    return cond


def verify_gptq_marlin_supported(num_bits: int, group_size: int,
                                 is_sym: bool) -> None:
    cond, err_msg = _check_marlin_supported(num_bits,
                                            group_size,
                                            is_sym,
                                            min_capability=None,
                                            has_zp=False)
    if not cond:
        assert err_msg is not None
        raise ValueError("GPTQ" + err_msg)


def verify_awq_marlin_supported(num_bits: int, group_size: int,
                                has_zp: bool) -> None:
    cond, err_msg = _check_marlin_supported(num_bits,
                                            group_size,
                                            False,
                                            min_capability=None,
                                            has_zp=has_zp)
    if not cond:
        assert err_msg is not None
        raise ValueError("AWQ" + err_msg)


def verify_marlin_supports_shape(output_size_per_partition: int,
                                 input_size_per_partition: int,
                                 input_size: int, group_size: int) -> None:

    # Validate output_size_per_partition
    if output_size_per_partition % GPTQ_MARLIN_MIN_THREAD_N != 0:
        raise ValueError(f"Weight output_size_per_partition = "
                         f"{output_size_per_partition} is not divisible by "
                         f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. "
                         "Consider reducing tensor_parallel_size or running "
                         "with --quantization gptq.")

    # Validate input_size_per_partition
    if input_size_per_partition % GPTQ_MARLIN_MIN_THREAD_K != 0:
        raise ValueError(f"Weight input_size_per_partition = "
                         f"{input_size_per_partition} is not divisible "
                         f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. "
                         "Consider reducing tensor_parallel_size or running "
                         "with --quantization gptq.")

    if (group_size < input_size
            and input_size_per_partition % group_size != 0):
        raise ValueError(
            f"Weight input_size_per_partition = {input_size_per_partition}"
            f" is not divisible by group_size = {group_size}."
            "Consider reducing tensor_parallel_size or running "
            "with --quantization gptq.")


def marlin_make_workspace(output_size_per_partition: int,
                          device: torch.device) -> torch.Tensor:
    max_workspace_size = (output_size_per_partition //
                          GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL

    return torch.zeros(max_workspace_size,
                       dtype=torch.int,
                       device=device,
                       requires_grad=False)


def marlin_is_k_full(act_order: bool, is_row_parallel: bool) -> bool:
    return (not act_order) or (act_order and not is_row_parallel)


def marlin_repeat_scales_on_all_ranks(act_order: bool, group_size: int,
                                      is_row_parallel: bool) -> bool:
    # Need to repeat scales on every rank if act_ordering or
    # channelwise and RowParallelLinear
    is_channelwise = group_size == -1
    return act_order or (is_channelwise and is_row_parallel)


def marlin_make_empty_g_idx(device: torch.device) -> torch.Tensor:
    return torch.nn.Parameter(torch.empty(0, dtype=torch.int, device=device),
                              requires_grad=False)


def marlin_sort_g_idx(
        g_idx: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    g_idx_sort_indices = torch.argsort(g_idx).to(torch.int)
    return g_idx[g_idx_sort_indices], g_idx_sort_indices


def get_scale_perms():
    scale_perm: List[int] = []
    for i in range(8):
        scale_perm.extend([i + 8 * j for j in range(8)])
    scale_perm_single: List[int] = []
    for i in range(4):
        scale_perm_single.extend(
            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
    return scale_perm, scale_perm_single


def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int,
                          group_size: int) -> torch.Tensor:

    scale_perm, scale_perm_single = get_scale_perms()
    if group_size < size_k and group_size != -1:
        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
    else:
        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
    s = s.reshape((-1, size_n)).contiguous()

    return s


def marlin_zero_points(zp: torch.Tensor, size_k: int, size_n: int,
                       num_bits: int) -> torch.Tensor:
    # Permute zero-points in a similar way to scales, but do not use the
    # "single" permutation, since zero-points are applied on every MMA
    scale_perm, _ = get_scale_perms()
    zp = zp.reshape((-1, len(scale_perm)))[:, scale_perm]

    # Interleave column dim (for the dequantize code) and pack it to int32
    if num_bits == 4:
        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
    elif num_bits == 8:
        interleave = numpy.array([0, 2, 1, 3])
    else:
        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))

    zp = zp.reshape((-1, len(interleave)))[:, interleave].ravel()
    zp = zp.reshape((-1, size_n)).contiguous()
    zp = pack_cols(zp, num_bits, size_k, size_n)

    return zp


def awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
                              size_n: int, num_bits: int) -> torch.Tensor:
    # AWQ zero-points are quantized and packed on the column dim.
    # In addition, the values are permuted based on dequantizer.
    # Here we undo both of these, and then apply marlin permutation
    # and pack it back.
    q_zp = unpack_cols(q_zp_packed, num_bits, size_k, size_n)

    # Undo interleaving (use argsort(..) to get inverse perm)
    if num_bits == 4:
        undo_interleave = numpy.argsort(numpy.array([0, 2, 4, 6, 1, 3, 5, 7]))
    elif num_bits == 8:
        undo_interleave = numpy.argsort(numpy.array([0, 2, 1, 3]))
    else:
        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))

    q_zp = q_zp.reshape((-1, len(undo_interleave)))[:, undo_interleave].ravel()
    q_zp = q_zp.reshape((-1, size_n)).contiguous()

    marlin_zp = marlin_zero_points(q_zp, size_k, size_n, num_bits)
    return marlin_zp


# Newly generated tensors need to replace existing tensors that are
# already registered as parameters by vLLM (and won't be freed)
def replace_tensor(layer: torch.nn.Module, name: str,
                   new_t: torch.Tensor) -> None:
    # It is important to use resize_() here since it ensures
    # the same buffer is reused
    getattr(layer, name).resize_(new_t.shape)
    getattr(layer, name).copy_(new_t)
    del new_t


def apply_gptq_marlin_linear(
        input: torch.Tensor,
        weight: torch.Tensor,
        weight_scale: torch.Tensor,
        weight_zp: torch.Tensor,
        g_idx: torch.Tensor,
        g_idx_sort_indices: torch.Tensor,
        workspace: torch.Tensor,
        num_bits: int,
        output_size_per_partition: int,
        input_size_per_partition: int,
        is_k_full: bool,
        bias: Optional[torch.Tensor] = None,
        use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT) -> torch.Tensor:
    reshaped_x = input.reshape(-1, input.shape[-1])
    out_shape = input.shape[:-1] + (output_size_per_partition, )

    output = ops.gptq_marlin_gemm(reshaped_x,
                                  weight,
                                  weight_scale,
                                  weight_zp,
                                  g_idx,
                                  g_idx_sort_indices,
                                  workspace,
                                  num_bits,
                                  size_m=reshaped_x.shape[0],
                                  size_n=output_size_per_partition,
                                  size_k=input_size_per_partition,
                                  is_k_full=is_k_full,
                                  has_zp=False,
                                  use_fp32_reduce=use_fp32_reduce)

    if bias is not None:
        output.add_(bias)  # In-place add

    return output.reshape(out_shape)


def apply_awq_marlin_linear(
        input: torch.Tensor,
        weight: torch.Tensor,
        weight_scale: torch.Tensor,
        weight_zp: torch.Tensor,
        g_idx: torch.Tensor,
        g_idx_sort_indices: torch.Tensor,
        workspace: torch.Tensor,
        num_bits: int,
        output_size_per_partition: int,
        input_size_per_partition: int,
        bias: Optional[torch.Tensor] = None,
        use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT) -> torch.Tensor:
    reshaped_x = input.reshape(-1, input.shape[-1])
    out_shape = input.shape[:-1] + (output_size_per_partition, )

    output = ops.gptq_marlin_gemm(reshaped_x,
                                  weight,
                                  weight_scale,
                                  weight_zp,
                                  g_idx,
                                  g_idx_sort_indices,
                                  workspace,
                                  num_bits,
                                  size_m=reshaped_x.shape[0],
                                  size_n=output_size_per_partition,
                                  size_k=input_size_per_partition,
                                  is_k_full=True,
                                  has_zp=True,
                                  use_fp32_reduce=use_fp32_reduce)

    if bias is not None:
        output.add_(bias)  # In-place add

    return output.reshape(out_shape)
