Adding functional batch_norm to BatchNorm2d substitution (sony#868)

* Adding functional batch_norm to BatchNorm2d substitution

model_compression_toolkit/core/common/graph/base_graph.py

@@ -299,19 +299,24 @@ def get_next_nodes(self,
         return [edges_list.sink_node for edges_list in self.out_edges(node_obj)]
 
     def get_prev_nodes(self,
-                       node_obj: BaseNode) -> List[BaseNode]:
+                       node_obj: BaseNode,
+                       sink_index_sorted: bool = False) -> List[BaseNode]:
         """
         Get previous nodes (in a topological order) of a node.
 
         Args:
             node_obj: Node to get its previous nodes.
+            sink_index_sorted: Whether to sort the returned list by the sink_index of the edges.
 
         Returns:
             List of input nodes objects.
 
         """
-
-        return [edges_list.source_node for edges_list in self.incoming_edges(node_obj)]
+        if sink_index_sorted:
+            sort_attr = 'sink_index'
+        else:
+            sort_attr = None
+        return [edges_list.source_node for edges_list in self.incoming_edges(node_obj, sort_by_attr=sort_attr)]
 
     def reconnect_out_edges(self,
                             current_node: BaseNode,
@@ -705,3 +710,19 @@ def is_single_activation_cfg(self):
 
         """
         return all([n.is_all_activation_candidates_equal() for n in self.nodes])
+
+    def replace_node(self, node_to_replace: BaseNode, new_node: BaseNode):
+        """
+        Replaces a node in the graph with a new node.
+
+        Args:
+            node_to_replace: The node to replace.
+            new_node: The new node to replace with.
+
+        """
+        self.add_node(new_node)
+        self.reconnect_out_edges(node_to_replace, new_node)
+        self.reconnect_in_edges(node_to_replace, new_node)
+        self.replace_output_node(node_to_replace, new_node)
+        self.replace_input_node(node_to_replace, new_node)
+        self.remove_node(node_to_replace)

model_compression_toolkit/core/pytorch/graph_substitutions/substitutions/functional_batch_norm.py

@@ -0,0 +1,94 @@
+# Copyright 2023 Sony Semiconductor Israel, Inc. 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.
+# ==============================================================================
+from torch import nn
+import torch.nn.functional as F
+
+from model_compression_toolkit.core.common.graph.graph_matchers import NodeOperationMatcher
+from model_compression_toolkit.core import common
+from model_compression_toolkit.core.common import BaseNode, Graph
+from model_compression_toolkit.core.pytorch.constants import *
+from model_compression_toolkit.logger import Logger
+
+
+class FunctionalBatchNorm(common.BaseSubstitution):
+    """
+    Replace functional batch_norm with BatchNorm2d.
+    """
+
+    def __init__(self):
+        """
+        Matches: functional batch_norm
+        """
+        bn_node = NodeOperationMatcher(F.batch_norm)
+        super().__init__(matcher_instance=bn_node)
+
+    def get_attributes_from_inputs(self, graph: Graph, node: BaseNode) -> dict:
+        input_nodes = graph.get_prev_nodes(node, sink_index_sorted=True)
+
+        if len(input_nodes) == 5:
+            return {
+                MOVING_MEAN: list(input_nodes[1].weights.values())[0],
+                MOVING_VARIANCE: list(input_nodes[2].weights.values())[0],
+                GAMMA: list(input_nodes[3].weights.values())[0],
+                BETA: list(input_nodes[4].weights.values())[0]
+            }
+        else:
+            Logger.warning(f'functional batch_norm is only folded in the 5 inputs case (input, mean, var, gamma, beta),'
+                           f'got {len(input_nodes)}')
+            return {}
+
+    def substitute(self,
+                   graph: Graph,
+                   node: BaseNode) -> Graph:
+        """
+        Substitute functional.batch_norm and its inputs with BatchNorm2d.
+        Args:
+            graph: Graph we apply the substitution on.
+            node: node that match the pattern in the substitution init.
+
+        Returns:
+            Graph after applying the substitution.
+        """
+        # if the input is not a 4D tensor, we can't substitute it with BatchNorm2d
+        if len(node.input_shape[0]) != 4:
+            return graph
+        out_channels = node.output_shape[0][1]
+
+        bn_node_weights = self.get_attributes_from_inputs(graph, node)
+        if not bn_node_weights:
+            return graph
+        new_batchnorm2d = BaseNode(name=node.name + '_into_BatchNorm2d',
+                                   framework_attr={NUM_FEATURES: out_channels,
+                                                   EPSILON: EPSILON_VAL,
+                                                   MOMENTUM: MOMENTUM_VAL},
+                                   input_shape=node.output_shape,
+                                   output_shape=node.output_shape,
+                                   weights=bn_node_weights,
+                                   layer_class=nn.BatchNorm2d)
+
+        num_nodes_before_substitution = len(graph.nodes)
+        num_edges_before_substitution = len(graph.edges)
+
+        batch_norm_consts = graph.get_prev_nodes(node)[1:]
+        for const in batch_norm_consts:
+            graph.remove_edge(const, node)
+            graph.remove_node(const)
+
+        graph.replace_node(node, new_batchnorm2d)
+
+        assert num_nodes_before_substitution - len(graph.nodes) == len(batch_norm_consts)
+        assert num_edges_before_substitution - len(graph.edges) == len(batch_norm_consts)
+
+        return graph

model_compression_toolkit/core/pytorch/pytorch_implementation.py

@@ -48,6 +48,8 @@
     pytorch_batchnorm_reconstruction
 from model_compression_toolkit.core.pytorch.graph_substitutions.substitutions.batchnorm_refusing import \
     pytorch_batchnorm_refusing
+from model_compression_toolkit.core.pytorch.graph_substitutions.substitutions.functional_batch_norm import \
+    FunctionalBatchNorm
 from model_compression_toolkit.core.pytorch.graph_substitutions.substitutions.linear_collapsing import \
     pytorch_linear_collapsing
 from model_compression_toolkit.core.pytorch.graph_substitutions.substitutions.multi_head_attention_decomposition \
@@ -243,7 +245,8 @@ def get_substitutions_prepare_graph(self, fw_info: FrameworkInfo = None) -> List
         return [ReshapeWithStaticShapes(),
                 MultiHeadAttentionDecomposition(),
                 PermuteCallMethod(),
-                ConstantHolderConv(fw_info)]
+                ConstantHolderConv(fw_info),
+                FunctionalBatchNorm()]
 
     def get_substitutions_pre_statistics_collection(self,
                                                     quant_config: QuantizationConfig

tests/pytorch_tests/model_tests/feature_models/bn_folding_test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 import torch
+from torch import nn
 import numpy as np
 from model_compression_toolkit.core.pytorch.utils import set_model, to_torch_tensor, \
     torch_tensor_to_numpy
@@ -22,16 +23,21 @@
 """
 This test checks the BatchNorm folding feature, plus adding a residual connection.
 """
-class BNFoldingNet(torch.nn.Module):
-    def __init__(self, test_layer, fold_applied):
+class BNFoldingNet(nn.Module):
+    def __init__(self, test_layer, functional, fold_applied):
         super(BNFoldingNet, self).__init__()
         self.conv1 = test_layer
         self.fold_applied = fold_applied
-        self.bn = torch.nn.BatchNorm2d(test_layer.out_channels)
+        self.bn = nn.BatchNorm2d(test_layer.out_channels)
+        self.functional = functional
 
     def forward(self, inp):
         x1 = self.conv1(inp)
-        x = self.bn(x1)
+        if self.functional:
+            x = nn.functional.batch_norm(x1, self.bn.running_mean, self.bn.running_var, self.bn.weight, self.bn.bias,
+                             training=self.bn.training, momentum=self.bn.momentum, eps=self.bn.eps)
+        else:
+            x = self.bn(x1)
         x = torch.relu(x)
         if not self.fold_applied:
             x = x + x1
@@ -42,13 +48,14 @@ class BNFoldingNetTest(BasePytorchTest):
     """
     This test checks the BatchNorm folding feature, plus adding a residual connection.
     """
-    def __init__(self, unit_test, test_layer, fold_applied=True, float_reconstruction_error=1e-6):
+    def __init__(self, unit_test, test_layer, functional, fold_applied=True, float_reconstruction_error=1e-6):
         super().__init__(unit_test, float_reconstruction_error)
         self.test_layer = test_layer
         self.fold_applied = fold_applied
+        self.functional = functional
 
     def create_feature_network(self, input_shape):
-        return BNFoldingNet(self.test_layer, self.fold_applied)
+        return BNFoldingNet(self.test_layer, self.functional, self.fold_applied)
 
     def get_tpc(self):
         return {'no_quantization': super().get_tpc()['no_quantization']}
@@ -63,29 +70,29 @@ def compare(self, quantized_models, float_model, input_x=None, quantization_info
         out_float = torch_tensor_to_numpy(float_model(*input_x))
         out_quant = torch_tensor_to_numpy(quant_model(*input_x))
 
-        is_bn_in_model = torch.nn.BatchNorm2d in [type(module) for name, module in quant_model.named_modules()]
+        is_bn_in_model = nn.BatchNorm2d in [type(module) for name, module in quant_model.named_modules()]
         self.unit_test.assertTrue(self.fold_applied is not is_bn_in_model)
         self.unit_test.assertTrue(np.isclose(out_quant, out_float, atol=1e-6, rtol=1e-4).all())
 
 
-class BNForwardFoldingNet(torch.nn.Module):
+class BNForwardFoldingNet(nn.Module):
     def __init__(self, test_layer, add_bn=False, is_dw=False):
         super(BNForwardFoldingNet, self).__init__()
         if is_dw:
-            self.bn = torch.nn.Conv2d(3, 3, 1, groups=3)
+            self.bn = nn.Conv2d(3, 3, 1, groups=3)
         else:
-            self.bn = torch.nn.BatchNorm2d(3)
-            torch.nn.init.uniform_(self.bn.weight, 0.02, 1.05)
-            torch.nn.init.uniform_(self.bn.bias, -1.2, 1.05)
-            torch.nn.init.uniform_(self.bn.running_var, 0.02, 1.05)
-            torch.nn.init.uniform_(self.bn.running_mean, -1.2, 1.05)
+            self.bn = nn.BatchNorm2d(3)
+            nn.init.uniform_(self.bn.weight, 0.02, 1.05)
+            nn.init.uniform_(self.bn.bias, -1.2, 1.05)
+            nn.init.uniform_(self.bn.running_var, 0.02, 1.05)
+            nn.init.uniform_(self.bn.running_mean, -1.2, 1.05)
         self.conv = test_layer
         if add_bn:
-            self.bn2 = torch.nn.BatchNorm2d(test_layer.out_channels)
-            torch.nn.init.uniform_(self.bn2.weight, 0.02, 1.05)
-            torch.nn.init.uniform_(self.bn2.bias, -1.2, 1.05)
-            torch.nn.init.uniform_(self.bn2.running_var, 0.02, 1.05)
-            torch.nn.init.uniform_(self.bn2.running_mean, -1.2, 1.05)
+            self.bn2 = nn.BatchNorm2d(test_layer.out_channels)
+            nn.init.uniform_(self.bn2.weight, 0.02, 1.05)
+            nn.init.uniform_(self.bn2.bias, -1.2, 1.05)
+            nn.init.uniform_(self.bn2.running_var, 0.02, 1.05)
+            nn.init.uniform_(self.bn2.running_mean, -1.2, 1.05)
         else:
             self.bn2 = None
 
@@ -106,6 +113,7 @@ class BNForwardFoldingNetTest(BasePytorchTest):
     def __init__(self, unit_test, test_layer, fold_applied=True, add_bn=False, is_dw=False):
         super().__init__(unit_test, float_reconstruction_error=1e-6, val_batch_size=2)
         self.test_layer = test_layer
+        self.bn_layer = nn.BatchNorm2d
         self.fold_applied = fold_applied
         self.add_bn = add_bn
         self.is_dw = is_dw
@@ -125,10 +133,10 @@ def compare(self, quantized_models, float_model, input_x=None, quantization_info
         set_model(quant_model)
 
         if self.is_dw:
-            is_bn_in_model = (sum([type(module) is torch.nn.Conv2d for name, module in float_model.named_modules()]) ==
-                              sum([type(module) is torch.nn.Conv2d for name, module in quant_model.named_modules()]))
+            is_bn_in_model = (sum([type(module) is nn.Conv2d for name, module in float_model.named_modules()]) ==
+                              sum([type(module) is nn.Conv2d for name, module in quant_model.named_modules()]))
         else:
-            is_bn_in_model = torch.nn.BatchNorm2d in [type(module) for name, module in quant_model.named_modules()]
+            is_bn_in_model = nn.BatchNorm2d in [type(module) for name, module in quant_model.named_modules()]
 
         self.unit_test.assertTrue(self.fold_applied is not is_bn_in_model)