optimum/quanto/tensor/optimizers/affine_optimizer.py

# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Optional, Tuple

import torch

from ..grouped import group
from ..qtype import qtype
from .optimizer import Optimizer


__all__ = ["AffineOptimizer"]


class AffineOptimizer(Optimizer):
    def __call__(
        self,
        base: torch.Tensor,
        qtype: qtype,
        axis: int,
        group_size: Optional[int] = None,
        zeropoint: bool = False,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            base (`torch.Tensor`): the weight Tensor to quantize
            qtype (`quanto.qtype`): The target quantization type
            axis ('int`): The quantization axis (0 or -1)
            group_size (`Optional[int]`): The quantization group size
            zeropoint (`bool`): Allow an exact representation of zero. If True, the shifts are stored as
                integer instead of float, which results in a slightly smaller model, but might also reduce
                the model performance. Defaults to False.
        Returns:
            A tuple of scale, shift Tensor.
        """
        if axis not in [0, -1]:
            raise ValueError("axis parameter must be 0 (first axis) or -1 (last axis)")
        if group_size is not None:
            base = group(base, axis, group_size)
        if axis is not None and base.shape[axis] == 1:
            axis = None
        scale, shift = self.optimize(base, qtype, axis)
        assert scale.dtype == base.dtype
        assert shift.dtype == base.dtype
        if zeropoint:
            # Round shift to make sure zero can be represented exactly using 'shift' as quantized value
            shift = torch.clamp(torch.round(shift / scale), 0, 2**qtype.bits - 1).to(torch.uint8)
        return scale, shift

    def optimize(self, base: torch.Tensor, qtype: qtype, axis: int) -> Tuple[torch.Tensor, torch.Tensor]:
        raise NotImplementedError