abs_max.py

# Copyright (c) 2022 PaddlePaddle Authors. 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
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import paddle

from ..base_observer import BaseObserver
from ..factory import ObserverFactory


class AbsmaxObserver(ObserverFactory):
    r"""
    It collects maximum absolute values of target tensor.

    Args:
        bit_length(int, optional): Number of bits to represent an quantized integer in binary.
        dtype(str, optional): The data type of input tensor.
        name (str, optional): This parameter is used by developers to print debugging information. \
            For details, please refer to :ref:`api_guide_Name`. Default is None.

    Examples:
       .. code-block:: python

            from paddle.quantization import QuantConfig
            from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
            quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.99)
            q_config = QuantConfig(activation=quanter, weight=quanter)
    """

    def __init__(self, quant_bits=8):
        super(AbsmaxObserver, self).__init__(quant_bits=quant_bits)

    def _get_class(self):
        return AbsmaxObserverLayer


class AbsmaxObserverLayer(BaseObserver):
    """
    Per-tensor abs max quantizer.
    """

    INIT_ABS_MAX = 1e-7

    def __init__(self, layer, quant_bits=8):
        super(AbsmaxObserverLayer, self).__init__()
        self._quant_bits = quant_bits
        self.abs_max_val = paddle.to_tensor(AbsmaxObserverLayer.INIT_ABS_MAX)

    def forward(self, input):
        abs_max_val = paddle.max(paddle.abs(input))
        self.abs_max_val = paddle.maximum(abs_max_val, self.abs_max_val)
        return input

    def cal_thresholds(self):
        self.thresholds = self.abs_max_val

    def bit_length(self):
        return self._quant_bits

    def quant_axis(self):
        return -1

    def scales(self):
        return self.abs_max_val

    def zero_points(self):
        return None