Fix TPC for stack layer

model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v1/tpc.py

@@ -169,7 +169,7 @@ def generate_tpc(default_config: OpQuantizationConfig,
     no_quantization_config = (default_configuration_options.clone_and_edit(enable_activation_quantization=False)
                               .clone_and_edit_weight_attribute(enable_weights_quantization=False))
 
-    operator_set.append(schema.OperatorsSet(name=schema.OperatorSetNames.STACK, qc_options=no_quantization_config))
+    operator_set.append(schema.OperatorsSet(name=schema.OperatorSetNames.STACK))
     operator_set.append(schema.OperatorsSet(name=schema.OperatorSetNames.UNSTACK, qc_options=no_quantization_config))
     operator_set.append(schema.OperatorsSet(name=schema.OperatorSetNames.DROPOUT, qc_options=no_quantization_config))
     operator_set.append(schema.OperatorsSet(name=schema.OperatorSetNames.FLATTEN, qc_options=no_quantization_config))

tests_pytest/common_tests/unit_tests/target_platform_capabilities/test_tpc_stack.py

@@ -0,0 +1,29 @@
+# Copyright 2025 Sony Semiconductor Solutions, 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 model_compression_toolkit.target_platform_capabilities.tpc_models.imx500_tpc.v1.tpc import get_tpc as get_tpc_imx500_v1
+
+def test_tpc_stack():
+
+    tpc = get_tpc_imx500_v1() # only imx500 supported
+    opset = tpc.operator_set[0]
+    assert opset.name == 'Stack'
+    assert opset.qc_options is None
+
+    qc = tpc.default_qco.quantization_configurations[0] # Stack layer applies default qc_options
+    assert qc.default_weight_attr_config.enable_weights_quantization == False
+    assert qc.attr_weights_configs_mapping == {}
+    assert qc.enable_activation_quantization == True
+    assert qc.activation_n_bits == 8

tests_pytest/keras_tests/e2e_tests/test_stack.py

@@ -0,0 +1,53 @@
+# Copyright 2025 Sony Semiconductor Solutions, 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 typing import Iterator, List
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+import keras
+import model_compression_toolkit as mct
+from mct_quantizers import KerasActivationQuantizationHolder
+
+
+def get_model():
+    inputs = keras.layers.Input((32, 32, 3))
+    out1 = layers.Conv2D(16, kernel_size=3, padding='same', activation='relu')(inputs)
+    out2 = layers.Conv2D(16, kernel_size=3, padding='same', activation='relu')(inputs)
+    outputs = tf.stack([out1, out2], -1)
+    return keras.Model(inputs, outputs)
+
+
+def get_representative_dataset(n_iter=1):
+    def representative_dataset() -> Iterator[List]:
+        for _ in range(n_iter):
+            yield [np.random.randn(1, 32, 32, 3)]
+    return representative_dataset
+
+
+def test_stack():
+
+    model = get_model()
+    tpc = mct.get_target_platform_capabilities('tensorflow', 'imx500') # only imx500 supported
+    q_model, _ = mct.ptq.keras_post_training_quantization(model,
+                                                          get_representative_dataset(n_iter=1),
+                                                          target_resource_utilization=None,
+                                                          core_config=mct.core.CoreConfig(),
+                                                          target_platform_capabilities=tpc)
+
+    assert getattr(q_model.layers[-2], "function") is tf.stack
+
+    stack_activation_holder = q_model.layers[-1] # activation holder for stack layer
+    assert isinstance(stack_activation_holder, KerasActivationQuantizationHolder)
+    assert stack_activation_holder.activation_holder_quantizer.num_bits == 8

tests_pytest/pytorch_tests/e2e_tests/test_stack.py

@@ -0,0 +1,63 @@
+# Copyright 2025 Sony Semiconductor Solutions, 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 typing import Iterator, List
+import torch
+import torch.nn as nn
+import model_compression_toolkit as mct
+from mct_quantizers import PytorchActivationQuantizationHolder
+
+
+def get_model():
+
+    class StackModel(nn.Module):
+        def __init__(self):    
+            super().__init__()
+            self.conv1 = nn.Sequential(
+                nn.Conv2d(3, 16, kernel_size=3, padding=1),
+                nn.ReLU()
+            )
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(3, 16, kernel_size=3, padding=1),
+                nn.ReLU()
+            )
+
+        def forward(self, x):
+            out1 = self.conv1(x)
+            out2 = self.conv2(x)
+            output = torch.stack([out1, out2], dim=-1)
+            return output
+    return StackModel()
+
+
+def get_representative_dataset(n_iter=1):
+    def representative_dataset() -> Iterator[List]:
+        for _ in range(n_iter):
+            yield [torch.randn(1, 3, 32, 32)]
+    return representative_dataset
+
+
+def test_stack():
+
+    model = get_model()
+    tpc = mct.get_target_platform_capabilities('pytorch', 'imx500') # only imx500 supported
+    q_model, _ = mct.ptq.pytorch_post_training_quantization(model, 
+                                                            get_representative_dataset(n_iter=1),
+                                                            target_resource_utilization=None,
+                                                            core_config=mct.core.CoreConfig(),
+                                                            target_platform_capabilities=tpc)
+
+    assert hasattr(q_model, 'stack_activation_holder_quantizer') # activation holder for stack layer
+    assert isinstance(q_model.stack_activation_holder_quantizer, PytorchActivationQuantizationHolder)
+    assert q_model.stack_activation_holder_quantizer.activation_holder_quantizer.num_bits == 8