# SPDX-License-Identifier: Apache-2.0

# Adapted from https://github.com/sgl-project/sglang/pull/2575
from typing import Optional, Tuple

import torch
import triton
import triton.language as tl


@triton.jit
def _per_token_group_quant_fp8(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    group_size,
    # Num columns of y
    y_num_columns,
    y_row_stride,
    # Avoid to divide zero
    eps,
    # Information for float8
    fp8_min,
    fp8_max,
    # Meta-parameters
    BLOCK: tl.constexpr,
):
    """A Triton-accelerated function to perform per-token-group
    quantization on a tensor.
    This function converts the tensor values into float8 values.
    """
    groups_per_row = y_num_columns // group_size

    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    row = g_id // groups_per_row
    row_g_id = g_id % groups_per_row

    y_ptr += (row * y_row_stride) + (row_g_id * group_size)
    y_q_ptr += g_id * group_size
    y_s_ptr += g_id

    cols = tl.arange(0, BLOCK)  # N <= BLOCK
    mask = cols < group_size

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    y_s = _absmax / fp8_max
    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)

    tl.store(y_q_ptr + cols, y_q, mask=mask)
    tl.store(y_s_ptr, y_s)


@triton.jit
def _per_token_group_quant_fp8_colmajor(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    group_size,
    # Num columns of y
    y_num_columns,
    y_row_stride,
    # Stride from one column to the next of y_s
    y_s_col_stride,
    # Avoid to divide zero
    eps,
    # Information for float8
    fp8_min,
    fp8_max,
    # Meta-parameters
    BLOCK: tl.constexpr,
):
    """A Triton-accelerated function to perform per-token-group
    quantization on a tensor.
    This function converts the tensor values into float8 values.
    """
    groups_per_row = y_num_columns // group_size

    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    row = g_id // groups_per_row
    row_g_id = g_id % groups_per_row

    y_ptr += (row * y_row_stride) + (row_g_id * group_size)
    y_q_ptr += g_id * group_size

    # Convert g_id the flattened block coordinate to 2D so we can index
    # into the output y_scales matrix
    blocks_per_row = y_num_columns // group_size
    scale_col = g_id % blocks_per_row
    scale_row = g_id // blocks_per_row
    y_s_ptr += scale_col * y_s_col_stride + scale_row

    cols = tl.arange(0, BLOCK)  # group_size <= BLOCK
    mask = cols < group_size

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    y_s = _absmax / fp8_max
    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)

    tl.store(y_q_ptr + cols, y_q, mask=mask)
    tl.store(y_s_ptr, y_s)


def per_token_group_quant_fp8(
    x: torch.Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: Optional[torch.dtype] = None,
    column_major_scales: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Function to perform per-token-group quantization on an input tensor `x`.
    It converts the tensor values into signed float8 values and returns the
    quantized tensor along with the scaling factor used for quantization.
    Args:
        x: The input tensor with ndim >= 2.
        group_size: The group size used for quantization.
        eps: The minimum to avoid dividing zero.
        dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`
        is supported for now.
    Returns:
        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the
        scaling factor for quantization.
    """
    assert x.shape[-1] % group_size == 0, f"the last dimension of `x` {x.shape[-1]} must be divisible by `group_size` {group_size}"
    assert x.stride(-1) == 1, "`x` groups must be contiguous"

    finfo = torch.finfo(dtype)
    fp8_min = finfo.min
    fp8_max = finfo.max

    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
    M = x.numel() // group_size
    N = group_size
    if column_major_scales:
        shape = (x.shape[-1] // group_size,) + x.shape[:-1]
        x_s = torch.empty(shape, device=x.device, dtype=torch.float32).permute(-1, -2)
    else:
        shape = x.shape[:-1] + (x.shape[-1] // group_size,)
        x_s = torch.empty(shape, device=x.device, dtype=torch.float32)

    BLOCK = triton.next_power_of_2(N)
    # heuristics for number of warps
    num_warps = min(max(BLOCK // 256, 1), 8)
    num_stages = 1
    if column_major_scales:
        _per_token_group_quant_fp8_colmajor[(M,)](
            x,
            x_q,
            x_s,
            group_size,
            x.shape[1],
            x.stride(0),
            x_s.stride(1),
            eps,
            fp8_min=fp8_min,
            fp8_max=fp8_max,
            BLOCK=BLOCK,
            num_warps=num_warps,
            num_stages=num_stages,
        )
    else:
        _per_token_group_quant_fp8[(M,)](
            x,
            x_q,
            x_s,
            group_size,
            x.shape[1],
            x.stride(0),
            eps,
            fp8_min=fp8_min,
            fp8_max=fp8_max,
            BLOCK=BLOCK,
            num_warps=num_warps,
            num_stages=num_stages,
        )

    return x_q, x_s
