No public description

PiperOrigin-RevId: 711792163
Change-Id: I5118482d2fbd0c4a722ab65ca01a2016aa4ce44b

qkeras/base_quantizer.py

@@ -0,0 +1,93 @@
+# Copyright 2025 Google LLC
+#
+#
+# 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 tensorflow.compat.v2 as tf
+import tensorflow.keras.backend as K
+
+
+def _create_variable_name(attr_name, var_name=None):
+  """Creates variable name.
+
+  Arguments:
+    attr_name: string. attribute name
+    var_name: string. variable name
+
+  Returns:
+    string. variable name
+  """
+
+  if var_name:
+    return var_name + "/" + attr_name
+
+  # This naming scheme is to solve a problem of a layer having more than
+  # one quantizer can have multiple qnoise_factor variables with the same
+  # name of "qnoise_factor".
+  return attr_name + "_" + str(K.get_uid(attr_name))
+
+
+class BaseQuantizer(tf.Module):
+  """Base quantizer.
+
+  Defines behavior all quantizers should follow.
+  """
+
+  def __init__(self):
+    self.built = False
+
+  def build(self, var_name=None, use_variables=False):
+    if use_variables:
+      if hasattr(self, "qnoise_factor"):
+        self.qnoise_factor = tf.Variable(
+            lambda: tf.constant(self.qnoise_factor, dtype=tf.float32),
+            name=_create_variable_name("qnoise_factor", var_name=var_name),
+            dtype=tf.float32,
+            trainable=False,
+        )
+    self.built = True
+
+  def _set_trainable_parameter(self):
+    pass
+
+  def update_qnoise_factor(self, qnoise_factor):
+    """Update qnoise_factor."""
+    if isinstance(self.qnoise_factor, tf.Variable):
+      # self.qnoise_factor is a tf.Variable.
+      # This is to update self.qnoise_factor during training.
+      self.qnoise_factor.assign(qnoise_factor)
+    else:
+      if isinstance(qnoise_factor, tf.Variable):
+        # self.qnoise_factor is a numpy variable, and qnoise_factor is a
+        # tf.Variable.
+        self.qnoise_factor = qnoise_factor.eval()
+      else:
+        # self.qnoise_factor and qnoise_factor are numpy variables.
+        # This is to set self.qnoise_factor before building
+        # (creating tf.Variable) it.
+        self.qnoise_factor = qnoise_factor
+
+  # Override not to expose the quantizer variables.
+  @property
+  def variables(self):
+    return ()
+
+  # Override not to expose the quantizer variables.
+  @property
+  def trainable_variables(self):
+    return ()
+
+  # Override not to expose the quantizer variables.
+  @property
+  def non_trainable_variables(self):
+    return ()

qkeras/qtools/DnC/divide_and_conquer.py

@@ -27,15 +27,16 @@
 
 import enum
 import logging
-from typing import List, Any, Union
+from typing import Any, List, Union
 
 import numpy as np
 import tensorflow as tf
 
+from qkeras import base_quantizer
 from qkeras import quantizers
+from qkeras.qtools import generate_layer_data_type_map
 from qkeras.qtools import qgraph
 from qkeras.qtools import qtools_util
-from qkeras.qtools import generate_layer_data_type_map
 from qkeras.qtools.DnC import dnc_layer_cost_ace
 
 
@@ -49,8 +50,11 @@ class CostMode(enum.Enum):
 class DivideConquerGraph:
   """This class creates model graph structure and methods to access layers."""
 
-  def __init__(self, model: tf.keras.Model,
-               source_quantizers: quantizers.BaseQuantizer = None):
+  def __init__(
+      self,
+      model: tf.keras.Model,
+      source_quantizers: base_quantizer.BaseQuantizer = None,
+  ):
     self._model = model
     self._source_quantizer_list = source_quantizers or [
         quantizers.quantized_bits(8, 0, 1)]

qkeras/qtools/quantized_operators/fused_bn_factory.py

@@ -23,13 +23,13 @@
 import math
 
 import numpy as np
-import copy
+
+from qkeras import base_quantizer
+from qkeras.qtools import qtools_util
 from qkeras.qtools.quantized_operators import adder_factory
 from qkeras.qtools.quantized_operators import divider_factory
 from qkeras.qtools.quantized_operators import multiplier_factory
 from qkeras.qtools.quantized_operators import quantizer_impl
-from qkeras.qtools import qtools_util
-from qkeras import quantizers
 
 class FusedBNFactory:
   """determine which quantizer implementation to use.
@@ -48,14 +48,15 @@ class FusedBNFactory:
   """
 
   def make_quantizer(
-      self, prev_output_quantizer: quantizer_impl.IQuantizer,
+      self,
+      prev_output_quantizer: quantizer_impl.IQuantizer,
       beta_quantizer: quantizer_impl.IQuantizer,
       mean_quantizer: quantizer_impl.IQuantizer,
       inverse_quantizer: quantizer_impl.IQuantizer,
       prev_bias_quantizer: quantizer_impl.IQuantizer,
       use_beta: bool,
       use_bias: bool,
-      qkeras_inverse_quantizer:quantizers.BaseQuantizer
+      qkeras_inverse_quantizer: base_quantizer.BaseQuantizer,
   ):
     """Makes a fused_bn quantizer.
 

qkeras/quantizer_imports.py

@@ -0,0 +1,31 @@
+# Copyright 2025 Google LLC
+#
+#
+# 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.
+# ==============================================================================
+"""Imports for QKeras quantizers."""
+
+from .quantizers import bernoulli
+from .quantizers import binary
+from .quantizers import quantized_bits
+from .quantizers import quantized_hswish
+from .quantizers import quantized_linear
+from .quantizers import quantized_po2
+from .quantizers import quantized_relu
+from .quantizers import quantized_relu_po2
+from .quantizers import quantized_sigmoid
+from .quantizers import quantized_tanh
+from .quantizers import quantized_ulaw
+from .quantizers import stochastic_binary
+from .quantizers import stochastic_ternary
+from .quantizers import ternary