wehub-resource-sync
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import paddle
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from ..base_observer import BaseObserver
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from ..factory import ObserverFactory
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class GroupWiseWeightObserver(ObserverFactory):
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r"""
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It collects channel-wise maximum absolute values of target weights.
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Args:
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bit_length(int, optional): Number of bits to represent an quantized integer in binary.
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dtype(str, optional): The data type of input tensor.
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name (str, optional): This parameter is used by developers to print debugging information. \
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For details, please refer to :ref:`api_guide_Name`. Default is None.
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Examples:
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.. code-block:: pycon
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import AbsMaxChannelWiseWeightObserver
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>>> quanter = AbsMaxChannelWiseWeightObserver()
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>>> q_config = QuantConfig(activation=None, weight=quanter)
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"""
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def __init__(self, quant_bits=8, group_size=128):
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super().__init__(quant_bits=quant_bits)
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def _get_class(self):
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return GroupWiseWeightObserverLayer
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class GroupWiseWeightObserverLayer(BaseObserver):
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def __init__(self, layer, quant_bits=8, group_size=128):
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super().__init__()
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self._quant_bits = quant_bits
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self.group_size = group_size
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self._layer = layer
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self._max = None
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self._scale = None
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self._zero_point = None
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def forward(self, inputs):
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self._max = self._cal_abs_max(inputs)
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return inputs
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def _cal_abs_max(self, inputs):
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"""Use group_size to group the input, then use the
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absmax method to calculate the scale
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"""
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input_shape = inputs.shape
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assert self.group_size == 64 or self.group_size == 128, (
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"group_size only support 64 or 128"
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)
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assert inputs.shape[0] % self.group_size == 0, (
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"group_size must be a factor of input channels"
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)
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assert len(inputs.shape) == 2, "Currently only support 2D tensor"
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input_processed = inputs.transpose([1, 0]).reshape(
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[input_shape[1], input_shape[0] // self.group_size, self.group_size]
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)
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abs_max_values = paddle.max(paddle.abs(input_processed), axis=2).cast(
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"float32"
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)
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abs_max_values = paddle.where(
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abs_max_values == np.float32(0), np.float32(1e-8), abs_max_values
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)
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abs_max_values = abs_max_values.transpose([1, 0])
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return abs_max_values
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def min_value(self) -> float:
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return 0.0
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def max_value(self) -> float:
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return self._max
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def bit_length(self):
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return self._quant_bits
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def quant_axis(self):
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return -1
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def cal_thresholds(self):
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"""Compute thresholds for MAX function."""
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if self._scale is None:
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self._scale = self._max
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self._zero_point = paddle.zeros_like(self._scale)
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def scales(self):
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"""Return output scales."""
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if self._scale is None:
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self.cal_thresholds()
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return self._scale
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def zero_points(self):
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"""Return output zero points."""
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if self._zero_point is None:
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self.cal_thresholds()
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return self._zero_point
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