Add More Observers (#1690)

paddleslim/quant/observers/__init__.py

@@ -14,5 +14,10 @@
 
 from .hist import HistObserver
 from .kl import KLObserver
+from .mse import MSEObserver
+from .emd import EMDObserver
+from .avg import AVGObserver
 
-__all__ = ["HistObserver", "KLObserver"]
+__all__ = [
\ No newline at end of file
+    "HistObserver", "KLObserver", "MSEObserver", "EMDObserver", "AVGObserver"
+]

paddleslim/quant/observers/avg.py

+# Copyright (c) 2023 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
+# limitations under the License.
+
+import numpy as np
+import paddle
+from .uniform import UniformObserver
+from paddle.quantization.factory import ObserverFactory
+
+
+class AVGObserver(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(AVGObserver, self).__init__(quant_bits=quant_bits)
+
+    def _get_class(self):
+        return AVGObserverLayer
+
+
+class AVGObserverLayer(UniformObserver):
+    def __init__(
+            self,
+            layer,
+            quant_bits=8, ):
+        super(AVGObserverLayer, self).__init__(quant_bits=quant_bits)
+        self._quant_bits = quant_bits
+        self._avg_list = []
+
+    def forward(self, inputs):
+        """ Calculate forward pass.
+        """
+        self._scale = None
+        self._zero_point = None
+        self._min = None
+        self._max = None
+        self._avg_min, self._avg_max = self.cal_min_max(inputs)
+        self._avg_list.append(self._avg_max)
+
+        return inputs
+
+    def cal_min_max(self, inputs):
+        abs_avg_value = paddle.abs(inputs.reshape((inputs.shape[0], -1)))
+        abs_avg_value = float(paddle.mean(paddle.max(abs_avg_value, axis=(1))))
+        return 0, abs_avg_value
+
+    def cal_thresholds(self):
+        """ Compute thresholds for MAX function.
+        """
+        self._min, self._max = self._avg_min, paddle.mean(
+            paddle.to_tensor(self._avg_list))
+        self._scale, self._zero_point = self.cal_scales_zero_points()
+
+    def min_value(self) -> float:
+        return self._min
+
+    def max_value(self) -> float:
+        return self._max
+
+    def bit_length(self):
+        """ Return the bit length of quantized data.
+        """
+        return self._quant_bits
+
+    def quant_axis(self):
+        """ Return quantization axis.
+        """
+        return -1
+
+    def scales(self):
+        """ Return output scales.
+        """
+        if self._scale is None:
+            self.cal_thresholds()
+        return self._scale
+
+    def zero_points(self):
+        """ Return output zero points.
+        """
+        if self._zero_point is None:
+            self.cal_thresholds()
+        return self._zero_point

paddleslim/quant/observers/emd.py

+# Copyright (c) 2023 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
+# limitations under the License.
+
+import numpy as np
+import paddle
+from .uniform import UniformObserver
+from paddle.quantization.factory import ObserverFactory
+
+
+class EMDObserver(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(EMDObserver, self).__init__(quant_bits=quant_bits)
+
+    def _get_class(self):
+        return EMDObserverLayer
+
+
+class EMDObserverLayer(UniformObserver):
+    def __init__(self, layer, quant_bits=8):
+        super(EMDObserverLayer, self).__init__(quant_bits=quant_bits)
+        self._quant_bits = quant_bits
+        self._calibration_loss = float('inf')
+        self.qmin, self.qmax = self.qmin_qmax
+
+    def forward(self, inputs):
+        """ Calculate forward pass.
+        """
+        self._scale = None
+        self._zero_point = None
+        self._min = None
+        self._max = None
+        self._emd_min, self._emd_max = self.cal_min_max(inputs)
+
+        return inputs
+
+    def cal_min_max(self, inputs):
+        abs_max_value = float(paddle.max(paddle.flatten(inputs)))
+        abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
+        s = 0.3
+        while s <= 1.0:
+            scale = s * abs_max_value
+            s += 0.02
+            bins = 2**(self._quant_bits - 1) - 1
+            quant_var = paddle.clip(
+                paddle.round(inputs / scale * self.qmax), -self.qmax - 1,
+                self.qmax)
+            quant_dequant_var = quant_var / self.qmax * scale
+
+            emd_loss = paddle.abs(
+                paddle.mean(inputs) - paddle.mean(quant_dequant_var)
+            ) + paddle.abs(paddle.std(inputs) - paddle.std(quant_dequant_var))
+            emd_loss = float(emd_loss)
+            if emd_loss <= self._calibration_loss:
+                self._calibration_loss = emd_loss
+
+        return 0, scale
+
+    def cal_thresholds(self):
+        """ Compute thresholds for MAX function.
+        """
+        self._min, self._max = self._emd_min, self._emd_max
+        self._scale, self._zero_point = self.cal_scales_zero_points()
+
+    def min_value(self) -> float:
+        return self._min
+
+    def max_value(self) -> float:
+        return self._max
+
+    def bit_length(self):
+        """ Return the bit length of quantized data.
+        """
+        return self._quant_bits
+
+    def quant_axis(self):
+        """ Return quantization axis.
+        """
+        return -1
+
+    def scales(self):
+        """ Return output scales.
+        """
+        if self._scale is None:
+            self.cal_thresholds()
+        return self._scale
+
+    def zero_points(self):
+        """ Return output zero points.
+        """
+        if self._zero_point is None:
+            self.cal_thresholds()
+        return self._zero_point

paddleslim/quant/observers/mse.py

+# Copyright (c) 2023 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
+# limitations under the License.
+
+import numpy as np
+import paddle
+from .uniform import UniformObserver
+from paddle.quantization.factory import ObserverFactory
+
+
+class MSEObserver(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(MSEObserver, self).__init__(quant_bits=quant_bits)
+
+    def _get_class(self):
+        return MSEObserverLayer
+
+
+class MSEObserverLayer(UniformObserver):
+    def __init__(self, layer, quant_bits=8):
+        super(MSEObserverLayer, self).__init__(quant_bits=quant_bits)
+        self.quant_bits = quant_bits
+        self.calibration_loss = float('inf')
+        self.qmin, self.qmax = self.qmin_qmax
+
+    def forward(self, inputs):
+        """ Calculate forward pass.
+        """
+        self._scale = None
+        self._zero_point = None
+        self._min = None
+        self._max = None
+
+        self._mse_min, self._mse_max = self.cal_min_max(inputs)
+
+        return inputs
+
+    def cal_min_max(self, inputs):
+        abs_max_value = float(paddle.max(paddle.abs(inputs.flatten())))
+        abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
+        s = 0.3
+        while s <= 1.0:
+            scale = s * abs_max_value
+            s += 0.02
+            quant_var = paddle.clip(
+                paddle.round(inputs / scale * self.qmax), -self.qmax - 1,
+                self.qmax)
+            quant_dequant_var = quant_var / self.qmax * scale
+
+            mse_loss = float(((inputs - quant_dequant_var)**2).mean())
+            if mse_loss <= self.calibration_loss:
+                self.calibration_loss = mse_loss
+
+        return 0, scale
+
+    def cal_thresholds(self):
+        """ Compute thresholds for MAX function.
+        """
+        self._min, self._max = self._mse_min, self._mse_max
+        self._scale, self._zero_point = self.cal_scales_zero_points()
+
+    def min_value(self) -> float:
+        return self._min
+
+    def max_value(self) -> float:
+        return self._max
+
+    def bit_length(self):
+        """ Return the bit length of quantized data.
+        """
+        return self._quant_bits
+
+    def quant_axis(self):
+        """ Return quantization axis.
+        """
+        return -1
+
+    def scales(self):
+        """ Return output scales.
+        """
+        if self._scale is None:
+            self.cal_thresholds()
+        return self._scale
+
+    def zero_points(self):
+        """ Return output zero points.
+        """
+        if self._zero_point is None:
+            self.cal_thresholds()
+        return self._zero_point

tests/quantization/test_observers.py

@@ -22,15 +22,19 @@ from paddle.vision.models import resnet18
 from paddle.quantization import QuantConfig
 from paddle.quantization import PTQ
 
-from paddleslim.quant.observers import HistObserver, KLObserver
+from paddleslim.quant.observers import HistObserver, KLObserver, EMDObserver, MSEObserver, AVGObserver
 from paddleslim.quant.observers.hist import PercentHistObserverLayer
 from paddleslim.quant.observers.kl import KLObserverLayer
+from paddleslim.quant.observers.mse import MSEObserverLayer
+from paddleslim.quant.observers.avg import AVGObserverLayer
+from paddleslim.quant.observers.emd import EMDObserverLayer
+from paddleslim.quant.observers.kl import KLObserverLayer
 from paddle.nn.quant.format import LinearDequanter, LinearQuanter
 
 
-class TestPTQWithHistObserver(unittest.TestCase):
+class TestPTQObserver(unittest.TestCase):
     def __init__(self, observer, observer_type, *args, **kvargs):
-        super(TestPTQWithHistObserver, self).__init__(*args, **kvargs)
+        super(TestPTQObserver, self).__init__(*args, **kvargs)
         self.observer = observer
         self.observer_type = observer_type
 
@@ -49,8 +53,6 @@ class TestPTQWithHistObserver(unittest.TestCase):
         self.test_convert()
 
     def init_case(self):
-        # observer = HistObserver()
-        # self.observer_type = PercentHistObserverLayer
         self.q_config = QuantConfig(activation=None, weight=None)
         self.q_config.add_type_config(
             paddle.nn.Conv2D, activation=self.observer, weight=self.observer)
@@ -96,25 +98,31 @@ class TestPTQWithHistObserver(unittest.TestCase):
 
 observer_suite = unittest.TestSuite()
 observer_suite.addTest(
-    TestPTQWithHistObserver(
+    TestPTQObserver(
         observer=HistObserver(sign=True, symmetric=True),
         observer_type=PercentHistObserverLayer))
 observer_suite.addTest(
-    TestPTQWithHistObserver(
+    TestPTQObserver(
         observer=HistObserver(sign=False, symmetric=True),
         observer_type=PercentHistObserverLayer))
 observer_suite.addTest(
-    TestPTQWithHistObserver(
+    TestPTQObserver(
         observer=HistObserver(sign=True, symmetric=False),
         observer_type=PercentHistObserverLayer))
 observer_suite.addTest(
-    TestPTQWithHistObserver(
+    TestPTQObserver(
         observer=HistObserver(sign=False, symmetric=False),
         observer_type=PercentHistObserverLayer))
 observer_suite.addTest(
-    TestPTQWithHistObserver(
+    TestPTQObserver(
         observer=KLObserver(bins_count=256), observer_type=KLObserverLayer))
 
+observer_suite.addTest(
+    TestPTQObserver(observer=AVGObserver(), observer_type=AVGObserverLayer))
+observer_suite.addTest(
+    TestPTQObserver(observer=EMDObserver(), observer_type=EMDObserverLayer))
+observer_suite.addTest(
+    TestPTQObserver(observer=MSEObserver(), observer_type=MSEObserverLayer))
 if __name__ == '__main__':
     runner = unittest.TextTestRunner(verbosity=2)
     runner.run(observer_suite)