Merge changes in pytorch tests workflow

.github/workflows/run_pytorch_tests.yml

@@ -24,10 +24,11 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements.txt          
           pip install torch==${{ inputs.torch-version }} torchvision onnx onnxruntime onnxruntime-extensions
+          pip install pytest
       - name: Run unittests
         run: |
           python -m unittest discover tests/pytorch_tests -v
-
+          pytest tests_pytest/pytorch
 
 
 

.github/workflows/run_tests_suite_coverage.yml

@@ -24,6 +24,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install coverage
+          pip install pytest
       - name: Prepare TF env
         run: pip install tensorflow==2.13.*
       - name: Run tensorflow testsuite
@@ -32,6 +33,8 @@ jobs:
         run: pip uninstall tensorflow -y && pip install torch==2.0.* torchvision onnx onnxruntime onnxruntime-extensions
       - name: Run torch testsuite
         run: coverage run --parallel-mode -m --omit "*__init__.py" --include "model_compression_toolkit/**/*.py" unittest tests/test_suite.py -v
+      - name: Run torch pytest
+        run: coverage run --parallel-mode -m --omit "*__init__.py" --include "model_compression_toolkit/**/*.py" pytest tests_pytest/pytorch
       - name: Combine Multiple Coverage Files
         run: coverage combine
       - name: Run Coverage HTML

model_compression_toolkit/core/common/graph/base_node.py

@@ -40,6 +40,7 @@ def __init__(self,
                  layer_class: type,
                  reuse: bool = False,
                  reuse_group: str = None,
+                 inputs_as_list: bool = False,
                  quantization_attr: Dict[str, Any] = None,
                  has_activation: bool = True,
                  is_custom: bool = False
@@ -58,6 +59,7 @@ def __init__(self,
             layer_class: Class path of the layer this node represents.
             reuse: Whether this node was duplicated and represents a reused layer.
             reuse_group: Name of group of nodes from the same reused layer.
+            inputs_as_list: Whether to pass the node its input tensors as a list or not when calling the layer.
             quantization_attr: Attributes the node holds regarding how it should be quantized.
             has_activation: Whether the node has activations that we might want to quantize.
             is_custom: Whether the node is custom layer or not.
@@ -71,6 +73,7 @@ def __init__(self,
         self.layer_class = layer_class
         self.reuse = reuse
         self.reuse_group = reuse_group
+        self.inputs_as_list = inputs_as_list
         self.final_weights_quantization_cfg = None
         self.final_activation_quantization_cfg = None
         self.candidates_quantization_cfg = None

model_compression_toolkit/core/common/graph/functional_node.py

@@ -55,13 +55,13 @@ def __init__(self,
                          layer_class,
                          reuse,
                          reuse_group,
+                         inputs_as_list,
                          quantization_attr,
                          has_activation=has_activation)
 
         self.op_call_kwargs = op_call_kwargs
         self.op_call_args = list(op_call_args)
         self.functional_op = functional_op
-        self.inputs_as_list = inputs_as_list
         self.tensor_input_allocs = [] if tensor_input_allocs is None else tensor_input_allocs
 
     @property

model_compression_toolkit/core/keras/back2framework/keras_model_builder.py

@@ -308,7 +308,7 @@ def _run_operation(self,
             else:
                 # If operator expects a single input tensor, it cannot be a list as it should
                 # have a dtype field.
-                if len(input_tensors) == 1:
+                if len(input_tensors) == 1 and not n.inputs_as_list:
                     input_tensors = input_tensors[0]
                 out_tensors_of_n_float = op_func(input_tensors)
 

model_compression_toolkit/core/keras/reader/node_builder.py

@@ -30,10 +30,12 @@
     from keras.src.layers.core import TFOpLambda, SlicingOpLambda
     from keras.src.engine.keras_tensor import KerasTensor
     from keras.src.engine.node import Node as KerasNode
+    from keras.src.layers.merging.base_merge import _Merge
 else:
     from keras.layers.core import TFOpLambda, SlicingOpLambda
     from keras.engine.keras_tensor import KerasTensor
     from keras.engine.node import Node as KerasNode
+    from keras.layers.merging.base_merge import _Merge
 
 from model_compression_toolkit.core.common.graph.base_node import BaseNode
 from model_compression_toolkit.core.common.graph.functional_node import FunctionalNode
@@ -287,6 +289,7 @@ def build_node(node: KerasNode,
             for i, arg in enumerate(op_call_args[0]):
                 if is_const(arg):
                     weights.update({i: to_numpy(arg, is_single_tensor=True)})
+        inputs_as_list = __is_node_inputs_a_list(op_call_args, keras_layer)
 
         node = BaseNode(node_name,
                         layer_config,
@@ -296,6 +299,7 @@ def build_node(node: KerasNode,
                         layer_class,
                         is_reused,
                         reuse_group,
+                        inputs_as_list,
                         is_custom=is_keras_custom_layer(layer_class))
 
     node_name_to_node[node_name] = node
@@ -316,6 +320,24 @@ def __is_functional_inputs_a_list(op_call_args: Any, keras_layer: Any) -> bool:
     """
 
     return (keras_layer.symbol in
-            [TFOpLambda(tf.concat).symbol, TFOpLambda(tf.stack).symbol,TFOpLambda(tf.add_n).symbol] and
+            [TFOpLambda(tf.concat).symbol, TFOpLambda(tf.stack).symbol, TFOpLambda(tf.add_n).symbol] and
             len(op_call_args) > 0 and
             isinstance(op_call_args[0], list))
+
+
+def __is_node_inputs_a_list(op_call_args: Any, keras_layer: Any) -> bool:
+    """
+    Check whether the input tensors should be passed as a list or not. This is relevant
+    only for layers that inherit from _Merge such as Concatenate and Add.
+
+    Args:
+        op_call_args: Arguments list to check.
+        keras_layer: Keras layer.
+
+    Returns:
+        Whether the input tensors should be passed as a list or not.
+    """
+
+    return (isinstance(keras_layer, _Merge) and
+            len(op_call_args) > 0 and
+            isinstance(op_call_args[0], (list, tuple)))

model_compression_toolkit/core/pytorch/back2framework/pytorch_model_builder.py

@@ -139,7 +139,11 @@ def _run_operation(n: BaseNode,
         _tensor_input_allocs = None
 
     if isinstance(n, FunctionalNode) and n.inputs_as_list:
-        out_tensors_of_n_float = op_func(input_tensors, *op_call_args, **functional_kwargs)
+        if isinstance(op_func, PytorchQuantizationWrapper):
+            # in wrapped nodes, the op args & kwargs are already in the PytorchQuantizationWrapper.
+            out_tensors_of_n_float = op_func(*input_tensors)
+        else:
+            out_tensors_of_n_float = op_func(input_tensors, *op_call_args, **functional_kwargs)
     else:
         merged_inputs, functional_kwargs = _merge_inputs(n, input_tensors, op_call_args, functional_kwargs.copy(),
                                                          tensor_input_allocs=_tensor_input_allocs)

model_compression_toolkit/core/pytorch/reader/graph_builders.py

@@ -232,10 +232,19 @@ def nodes_builder(model: GraphModule,
 
         # Add constants to weights dictionary.
         if node.op != PLACEHOLDER:
-            for i, input_node in enumerate(node.all_input_nodes):
-                if input_node in consts_dict:
-                    used_consts.add(input_node)
-                    weights.update({i: consts_dict[input_node]})
+            if len(node.args) and isinstance(node.args[0], (list, tuple)):
+                # handle weights in nodes with list input. Especially when there's a duplicate of a tensor
+                # in the input list (e.g. torch.concat([const1, x, const2, x, const3], 1)).
+                for input_node in node.all_input_nodes:
+                    for i, input_arg in enumerate(node.args[0]):
+                        if input_node is input_arg and input_node in consts_dict:
+                            used_consts.add(input_node)
+                            weights.update({i: consts_dict[input_node]})
+            else:
+                for i, input_node in enumerate(node.all_input_nodes):
+                    if input_node in consts_dict:
+                        used_consts.add(input_node)
+                        weights.update({i: consts_dict[input_node]})
 
         # Extract input and output shapes of the node.
         input_shape, output_shape = _extract_input_and_output_shapes(node)

model_compression_toolkit/exporter/model_wrapper/keras/builder/fully_quantized_model_builder.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-from typing import Tuple, Callable
+from typing import Tuple, Callable, Union
 from model_compression_toolkit.core import common
 from model_compression_toolkit.core.common import Graph
 from model_compression_toolkit.verify_packages import FOUND_TF
@@ -25,10 +25,12 @@
     import tensorflow as tf
     from tensorflow.keras.layers import Layer
     from model_compression_toolkit.core.keras.back2framework.keras_model_builder import KerasModelBuilder
+    from model_compression_toolkit.core.common.graph.functional_node import FunctionalNode
     from mct_quantizers import KerasQuantizationWrapper
     from mct_quantizers import KerasActivationQuantizationHolder
+    from mct_quantizers.common.constants import OP_CALL_ARGS, OP_CALL_KWARGS
 
-    def _get_wrapper(node: common.BaseNode,
+    def _get_wrapper(node: Union[common.BaseNode, FunctionalNode],
                      layer: Layer,
                      fw_impl=None) -> Layer:
         """
@@ -45,9 +47,16 @@ def _get_wrapper(node: common.BaseNode,
             # for positional weights we need to extract the weight's value.
             weights_values = {attr: node.get_weights_by_keys(attr)
                               for attr in weights_quantizers if isinstance(attr, int)}
+            # When wrapping functional nodes, need to set call args\kwargs in wrapper, because they
+            # are used during wrapper call method.
+            func_node_kwargs = {OP_CALL_ARGS: node.op_call_args,
+                                OP_CALL_KWARGS: node.op_call_kwargs
+                                } if isinstance(node, FunctionalNode) else {}
             return KerasQuantizationWrapper(layer,
                                             weights_quantizers,
-                                            weights_values)
+                                            weights_values,
+                                            is_inputs_as_list=node.inputs_as_list,
+                                            **func_node_kwargs)
         return layer
 
 

model_compression_toolkit/exporter/model_wrapper/pytorch/builder/fully_quantized_model_builder.py

@@ -24,7 +24,9 @@
 if FOUND_TORCH:
     import torch
     from mct_quantizers import PytorchQuantizationWrapper, PytorchActivationQuantizationHolder
+    from mct_quantizers.common.constants import OP_CALL_ARGS, OP_CALL_KWARGS
     from model_compression_toolkit.core.pytorch.back2framework.pytorch_model_builder import PyTorchModelBuilder
+    from model_compression_toolkit.core.common.graph.functional_node import FunctionalNode
 
 
     def fully_quantized_wrapper(node: common.BaseNode,
@@ -46,7 +48,14 @@ def fully_quantized_wrapper(node: common.BaseNode,
             # for positional weights we need to extract the weight's value.
             weights_values = {attr: fw_impl.to_tensor(node.get_weights_by_keys(attr))
                               for attr in weight_quantizers if isinstance(attr, int)}
-            return PytorchQuantizationWrapper(module, weight_quantizers, weights_values)
+            # When wrapping functional nodes, need to set call args\kwargs in wrapper, because they
+            # are used during wrapper call method.
+            func_node_kwargs = {OP_CALL_ARGS: node.op_call_args,
+                                OP_CALL_KWARGS: node.op_call_kwargs
+                                } if isinstance(node, FunctionalNode) else {}
+            return PytorchQuantizationWrapper(module, weight_quantizers, weights_values,
+                                              is_inputs_as_list=node.inputs_as_list,
+                                              **func_node_kwargs)
         return module
 
 

model_compression_toolkit/gptq/__init__.py

@@ -13,8 +13,20 @@
 # limitations under the License.
 # ==============================================================================
 
-from model_compression_toolkit.gptq.common.gptq_config import GradientPTQConfig, RoundingType, GPTQHessianScoresConfig
-from model_compression_toolkit.gptq.keras.quantization_facade import keras_gradient_post_training_quantization
-from model_compression_toolkit.gptq.keras.quantization_facade import get_keras_gptq_config
-from model_compression_toolkit.gptq.pytorch.quantization_facade import pytorch_gradient_post_training_quantization
-from model_compression_toolkit.gptq.pytorch.quantization_facade import get_pytorch_gptq_config
+from model_compression_toolkit.gptq.common.gptq_config import (
+    GradientPTQConfig,
+    RoundingType,
+    GPTQHessianScoresConfig,
+    GradualActivationQuantizationConfig,
+    QFractionLinearAnnealingConfig
+)
+
+from model_compression_toolkit.verify_packages import FOUND_TF, FOUND_TORCH
+
+if FOUND_TF:
+    from model_compression_toolkit.gptq.keras.quantization_facade import keras_gradient_post_training_quantization
+    from model_compression_toolkit.gptq.keras.quantization_facade import get_keras_gptq_config
+
+if FOUND_TORCH:
+    from model_compression_toolkit.gptq.pytorch.quantization_facade import pytorch_gradient_post_training_quantization
+    from model_compression_toolkit.gptq.pytorch.quantization_facade import get_pytorch_gptq_config