remove activation_quantization_params_fn from NodeActivationQuantizationConf

Author: irenab

model_compression_toolkit/core/common/network_editors/__init__.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ==============================================================================
 
-from model_compression_toolkit.core.common.network_editors.actions import ChangeCandidatesWeightsQuantConfigAttr, ChangeFinalWeightsQuantConfigAttr, ChangeCandidatesActivationQuantConfigAttr, ChangeQuantizationParamFunction, ChangeCandidatesActivationQuantizationMethod, ChangeFinalWeightsQuantizationMethod, ChangeCandidatesWeightsQuantizationMethod, ChangeFinalActivationQuantConfigAttr
 from model_compression_toolkit.core.common.network_editors.actions import EditRule
 from model_compression_toolkit.core.common.network_editors.node_filters import NodeTypeFilter, NodeNameScopeFilter, \
     NodeNameFilter

model_compression_toolkit/core/common/network_editors/actions.py

@@ -22,10 +22,9 @@
 from model_compression_toolkit.logger import Logger
 
 
-from model_compression_toolkit.core.common.framework_info import get_fw_info
 from model_compression_toolkit.core.common.graph.base_node import BaseNode
 from model_compression_toolkit.core.common.quantization.quantization_params_fn_selection import \
-    get_activation_quantization_params_fn, get_weights_quantization_params_fn
+    get_weights_quantization_params_fn
 from model_compression_toolkit.core.common.quantization.quantization_fn_selection import \
     get_weights_quantization_fn
 
@@ -174,47 +173,6 @@ def apply(self, node: BaseNode, graph):
                 node.final_activation_quantization_cfg.set_quant_config_attr(parameter_name, parameter_value)
 
 
-class ChangeQuantizationParamFunction(BaseAction):
-    """
-    Class ChangeQuantizationParamFunction to change a node's weights/activations quantization params function.
-    """
-
-    def __init__(self,
-                 attr_name: str = None,
-                 activation_quantization_params_fn: Callable = None,
-                 weights_quantization_params_fn: Callable = None):
-        """
-        Init a ChangeQuantizationParamFunction object.
-
-        Args:
-            attr_name: The weights attribute's name to set the weights quantization params function for (if setting weights params).
-            activation_quantization_params_fn: a params function for a node's activations.
-            weights_quantization_params_fn: a params function for a node's weights.
-        """
-        self.activation_quantization_params_fn = activation_quantization_params_fn
-        self.weights_quantization_params_fn = weights_quantization_params_fn
-        self.attr_name = attr_name
-
-    def apply(self, node: BaseNode, graph):
-        """
-        Change the node's weights/activations quantization params function.
-
-        Args:
-            node: Node object to change its quantization params function.
-            graph: Graph to apply the action on.
-
-        Returns:
-            The node after its quantization params function has been modified.
-        """
-        for nqc in node.candidates_quantization_cfg:
-            if self.activation_quantization_params_fn is not None:
-                nqc.activation_quantization_cfg.set_activation_quantization_params_fn(
-                    self.activation_quantization_params_fn)
-            if self.weights_quantization_params_fn is not None:
-                attr_config = nqc.weights_quantization_cfg.get_attr_config(self.attr_name)
-                attr_config.override_weights_quantization_params_fn(self.weights_quantization_params_fn)
-
-
 class ChangeFinalActivationQuantizationMethod(BaseAction):
     """
     Class ChangeFinalActivationQuantizationMethod to change a node's weights/activations quantizer function.
@@ -243,13 +201,6 @@ def apply(self, node: BaseNode, graph):
         """
 
         if self.activation_quantization_method is not None and node.final_activation_quantization_cfg is not None:
-
-            activation_quantization_params_fn = get_activation_quantization_params_fn(
-                self.activation_quantization_method)
-
-            node.final_activation_quantization_cfg.set_activation_quantization_params_fn(
-                activation_quantization_params_fn)
-
             node.final_activation_quantization_cfg.activation_quantization_method = self.activation_quantization_method
 
 
@@ -278,9 +229,6 @@ def apply(self, node: BaseNode, graph):
         """
         if self.activation_quantization_method is not None:
             for qc in node.candidates_quantization_cfg:
-                activation_quantization_params_fn = get_activation_quantization_params_fn(
-                    self.activation_quantization_method)
-                qc.activation_quantization_cfg.set_activation_quantization_params_fn(activation_quantization_params_fn)
                 qc.activation_quantization_cfg.activation_quantization_method = self.activation_quantization_method
 
 

model_compression_toolkit/core/common/quantization/node_quantization_config.py

@@ -81,22 +81,14 @@ class NodeActivationQuantizationConfig(BaseNodeQuantizationConfig):
     """
     Attributes for configuring the quantization of the activations of a node.
     """
-    def __init__(self,
-                 op_cfg: OpQuantizationConfig,
-                 activation_quantization_params_fn: Callable):
+    def __init__(self, op_cfg: OpQuantizationConfig):
         """
 
         Args:
             op_cfg: OpQuantizationConfig of the node with quantizers types to use when creating node quantization configuration.
-            activation_quantization_params_fn: Function to use when computing the threshold for quantizing a node's activations.
         """
-
-        self.activation_quantization_params_fn = activation_quantization_params_fn
         self.activation_quantization_method = op_cfg.activation_quantization_method
         self.activation_n_bits = op_cfg.activation_n_bits
-        self.activation_quantization_params = {}
-        # TODO irena: computed by compute_activation_bias_correction. shouldnt really be here
-        self.activation_bias_correction_term = None
         if op_cfg.enable_activation_quantization and op_cfg.quantization_preserving:
             raise ValueError("An OpQuantizationConfig can't have both enable_activation_quantization and quantization_preserving enabled.")
         if op_cfg.enable_activation_quantization:
@@ -107,6 +99,10 @@ def __init__(self,
             self.quant_mode = ActivationQuantizationMode.NO_QUANT
         self.signedness = op_cfg.signedness
 
+        self.activation_quantization_params = {}
+        # TODO irena: computed by compute_activation_bias_correction. shouldnt really be here
+        self.activation_bias_correction_term = None
+
         # TODO irena remove along with set_qc. Keeping for eq and hash to work without set_qc being called
         self.activation_error_method = None
         self.relu_bound_to_power_of_2 = None
@@ -146,16 +142,6 @@ def quantization_preserving(self):
     def fln_quantization(self):
         return self.quant_mode == ActivationQuantizationMode.FLN_QUANT
 
-    def set_activation_quantization_params_fn(self, activation_quantization_params_fn:Callable):
-        """
-        Sets activation params function for the node.
-
-        Args:
-            activation_quantization_params_fn: Function for calculating activation params.
-
-        """
-        self.activation_quantization_params_fn = activation_quantization_params_fn
-
     def set_activation_quantization_param(self,
                                           activation_params: dict):
         """
@@ -182,8 +168,7 @@ def __eq__(self, other: Any) -> bool:
         if not isinstance(other, NodeActivationQuantizationConfig):
             return False  # pragma: no cover
 
-        return self.activation_quantization_params_fn == other.activation_quantization_params_fn and \
-               self.activation_error_method == other.activation_error_method and \
+        return self.activation_error_method == other.activation_error_method and \
                self.activation_quantization_method == other.activation_quantization_method and \
                self.activation_n_bits == other.activation_n_bits and \
                self.quant_mode == other.quant_mode and \
@@ -197,8 +182,7 @@ def __eq__(self, other: Any) -> bool:
                self.shift_negative_threshold_recalculation == other.shift_negative_threshold_recalculation
 
     def __hash__(self):
-        return hash((self.activation_quantization_params_fn,
-                     self.activation_error_method,
+        return hash((self.activation_error_method,
                      self.activation_quantization_method,
                      self.activation_n_bits,
                      self.quant_mode,

model_compression_toolkit/core/common/quantization/quantization_params_fn_selection.py

@@ -19,38 +19,13 @@
 from mct_quantizers import QuantizationMethod
 from model_compression_toolkit.logger import Logger
 from model_compression_toolkit.core.common.quantization.quantization_params_generation.lut_kmeans_params import \
-    lut_kmeans_tensor, lut_kmeans_histogram
+    lut_kmeans_tensor
 from model_compression_toolkit.core.common.quantization.quantization_params_generation.symmetric_selection import \
-    symmetric_selection_tensor, symmetric_selection_histogram
+    symmetric_selection_tensor
 from model_compression_toolkit.core.common.quantization.quantization_params_generation.uniform_selection import \
-    uniform_selection_histogram, uniform_selection_tensor
+    uniform_selection_tensor
 from model_compression_toolkit.core.common.quantization.quantization_params_generation.power_of_two_selection import \
-    power_of_two_selection_tensor, power_of_two_selection_histogram
-
-
-def get_activation_quantization_params_fn(activation_quantization_method: QuantizationMethod) -> Callable:
-    """
-    Generate a function for finding activation quantization parameters.
-
-    Args:
-        activation_quantization_method: Which quantization method to use for activations.
-    Returns:
-        A function to find the quantization parameters.
-
-    """
-    if activation_quantization_method == QuantizationMethod.POWER_OF_TWO:
-        params_fn = power_of_two_selection_histogram
-    elif activation_quantization_method == QuantizationMethod.SYMMETRIC:
-        params_fn = symmetric_selection_histogram
-    elif activation_quantization_method == QuantizationMethod.UNIFORM:
-        params_fn = uniform_selection_histogram
-    elif activation_quantization_method == QuantizationMethod.LUT_POT_QUANTIZER:
-        params_fn = lut_kmeans_histogram
-    else:
-        Logger.critical(
-            f"No parameter function found for the specified quantization method: {activation_quantization_method}")  # pragma: no cover
-    return params_fn
-
+    power_of_two_selection_tensor
 
 weights_quant_params_fns = {
     QuantizationMethod.POWER_OF_TWO: power_of_two_selection_tensor,

model_compression_toolkit/core/common/quantization/quantization_params_generation/__init__.py

@@ -12,9 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-from model_compression_toolkit.core.common.quantization.quantization_params_generation.power_of_two_selection import power_of_two_no_clipping_selection_min_max, \
-    power_of_two_selection_histogram, power_of_two_selection_tensor
-from model_compression_toolkit.core.common.quantization.quantization_params_generation.lut_kmeans_params import lut_kmeans_tensor
-from model_compression_toolkit.core.common.quantization.quantization_params_generation.symmetric_selection import symmetric_no_clipping_selection_min_max
-from model_compression_toolkit.core.common.quantization.quantization_params_generation.uniform_selection import uniform_no_clipping_selection_min_max
+from model_compression_toolkit.core.common.quantization.quantization_params_generation.power_of_two_selection import (
+    power_of_two_no_clipping_selection_min_max, power_of_two_selection_histogram, power_of_two_selection_tensor)
+from model_compression_toolkit.core.common.quantization.quantization_params_generation.lut_kmeans_params import (
+    lut_kmeans_tensor, lut_kmeans_histogram)
+from model_compression_toolkit.core.common.quantization.quantization_params_generation.symmetric_selection import (
+    symmetric_no_clipping_selection_min_max, symmetric_selection_histogram)
+from model_compression_toolkit.core.common.quantization.quantization_params_generation.uniform_selection import (
+    uniform_no_clipping_selection_min_max, uniform_selection_histogram)
 from model_compression_toolkit.core.common.quantization.quantization_params_generation.outlier_filter import z_score_filter