Remove experimental from keras kpi data name (#955)

---------

Co-authored-by: reuvenp <reuvenp@altair-semi.com>

model_compression_toolkit/core/__init__.py

@@ -23,5 +23,5 @@
 from model_compression_toolkit.core.common.quantization.core_config import CoreConfig
 from model_compression_toolkit.core.common.mixed_precision.kpi_tools.kpi import KPI
 from model_compression_toolkit.core.common.mixed_precision.mixed_precision_quantization_config import MixedPrecisionQuantizationConfig, MixedPrecisionQuantizationConfigV2
-from model_compression_toolkit.core.keras.kpi_data_facade import keras_kpi_data, keras_kpi_data_experimental
+from model_compression_toolkit.core.keras.kpi_data_facade import keras_kpi_data
 from model_compression_toolkit.core.pytorch.kpi_data_facade import pytorch_kpi_data

model_compression_toolkit/core/keras/kpi_data_facade.py

@@ -15,15 +15,13 @@
 
 from typing import Callable
 
-from model_compression_toolkit.core import MixedPrecisionQuantizationConfig, CoreConfig, MixedPrecisionQuantizationConfigV2
+from model_compression_toolkit.core import CoreConfig, MixedPrecisionQuantizationConfigV2
 from model_compression_toolkit.core.common.mixed_precision.kpi_tools.kpi import KPI
 from model_compression_toolkit.logger import Logger
 from model_compression_toolkit.constants import TENSORFLOW
 from model_compression_toolkit.target_platform_capabilities.target_platform import TargetPlatformCapabilities
 from model_compression_toolkit.core.common.mixed_precision.kpi_tools.kpi_data import compute_kpi_data
 from model_compression_toolkit.core.common.framework_info import FrameworkInfo
-from model_compression_toolkit.core.common.mixed_precision.mixed_precision_quantization_config import \
-    DEFAULT_MIXEDPRECISION_CONFIG
 from model_compression_toolkit.constants import FOUND_TF
 
 if FOUND_TF:
@@ -36,75 +34,13 @@
 
     KERAS_DEFAULT_TPC = get_target_platform_capabilities(TENSORFLOW, DEFAULT_TP_MODEL)
 
-
     def keras_kpi_data(in_model: Model,
                        representative_data_gen: Callable,
-                       quant_config: MixedPrecisionQuantizationConfig = DEFAULT_MIXEDPRECISION_CONFIG,
+                       core_config: CoreConfig,
                        fw_info: FrameworkInfo = DEFAULT_KERAS_INFO,
                        target_platform_capabilities: TargetPlatformCapabilities = KERAS_DEFAULT_TPC) -> KPI:
         """
         Computes KPI data that can be used to calculate the desired target KPI for mixed-precision quantization.
-        Builds the computation graph from the given model and target platform modeling, and uses it to compute the KPI data.
-
-        Args:
-            in_model (Model): Keras model to quantize.
-            representative_data_gen (Callable): Dataset used for calibration.
-            quant_config (MixedPrecisionQuantizationConfig): MixedPrecisionQuantizationConfig containing parameters of how the model should be quantized.
-            fw_info (FrameworkInfo): Information needed for quantization about the specific framework (e.g., kernel channels indices, groups of layers by how they should be quantized, etc.). `Default Keras info <https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/core/keras/default_framework_info.py>`_
-            target_platform_capabilities (TargetPlatformCapabilities): TargetPlatformCapabilities to optimize the Keras model according to.
-
-        Returns:
-            A KPI object with total weights parameters sum, max activation tensor and total kpi.
-
-        Examples:
-
-            Import a Keras model:
-
-            >>> from tensorflow.keras.applications.mobilenet import MobileNet
-            >>> model = MobileNet()
-
-            Create a random dataset generator, for required number of calibration iterations (num_calibration_batches):
-            In this example a random dataset of 10 batches each containing 4 images is used.
-
-            >>> import numpy as np
-            >>> num_calibration_batches = 10
-            >>> def repr_datagen():
-            >>>     for _ in range(num_calibration_batches):
-            >>>         yield [np.random.random((4, 224, 224, 3))]
-
-            Import MCT and call for KPI data calculation:
-
-            >>> import model_compression_toolkit as mct
-            >>> kpi_data = mct.core.keras_kpi_data(model, repr_datagen)
-
-
-        """
-
-        if not isinstance(quant_config, MixedPrecisionQuantizationConfig):
-            Logger.error("KPI data computation can't be executed without MixedPrecisionQuantizationConfig object."
-                         "Given quant_config is not of type MixedPrecisionQuantizationConfig.")
-
-        fw_impl = KerasImplementation()
-
-        quantization_config, mp_config = quant_config.separate_configs()
-        core_config = CoreConfig(quantization_config=quantization_config,
-                                 mixed_precision_config=mp_config)
-
-        return compute_kpi_data(in_model,
-                                representative_data_gen,
-                                core_config,
-                                target_platform_capabilities,
-                                fw_info,
-                                fw_impl)
-
-
-    def keras_kpi_data_experimental(in_model: Model,
-                                    representative_data_gen: Callable,
-                                    core_config: CoreConfig,
-                                    fw_info: FrameworkInfo = DEFAULT_KERAS_INFO,
-                                    target_platform_capabilities: TargetPlatformCapabilities = KERAS_DEFAULT_TPC) -> KPI:
-        """
-        Computes KPI data that can be used to calculate the desired target KPI for mixed-precision quantization.
         Builds the computation graph from the given model and hw modeling, and uses it to compute the KPI data.
 
         Args:
@@ -154,12 +90,6 @@ def keras_kpi_data_experimental(in_model: Model,
     # If tensorflow is not installed,
     # we raise an exception when trying to use this function.
     def keras_kpi_data(*args, **kwargs):
-        Logger.critical('Installing tensorflow and is mandatory '
-                        'when using keras_kpi_data. '
-                        'Could not find Tensorflow package.')  # pragma: no cover
-
-
-    def keras_kpi_data_experimental(*args, **kwargs):
         Logger.critical('Installing tensorflow is mandatory '
                         'when using keras_kpi_data. '
                         'Could not find Tensorflow package.')  # pragma: no cover

tests/keras_tests/function_tests/test_kpi_data.py

@@ -95,6 +95,7 @@ def prep_test(model, mp_bitwidth_candidates_list, random_datagen):
 
     kpi_data = mct.core.keras_kpi_data(in_model=model,
                                        representative_data_gen=random_datagen,
+                                       core_config=mct.core.CoreConfig(mixed_precision_config=mct.core.MixedPrecisionQuantizationConfigV2()),
                                        target_platform_capabilities=tpc)
 
     return kpi_data

tutorials/notebooks/keras/gptq/example_keras_mobilenet_gptq_mixed_precision.py

@@ -125,10 +125,10 @@ def representative_data_gen() -> list:
     # Get KPI information to constraint your model's memory size.
     # Retrieve a KPI object with helpful information of each KPI metric,
     # to constraint the quantized model to the desired memory size.
-    kpi_data = mct.core.keras_kpi_data_experimental(model,
-                                               representative_data_gen,
-                                               config,
-                                               target_platform_capabilities=target_platform_cap)
+    kpi_data = mct.core.keras_kpi_data(model,
+                                       representative_data_gen,
+                                       config,
+                                       target_platform_capabilities=target_platform_cap)
 
     # Set a constraint for each of the KPI metrics.
     # Create a KPI object to limit our returned model's size. Note that this values affects only layers and attributes

tutorials/notebooks/keras/ptq/example_keras_mobilenet_mixed_precision.py

@@ -118,10 +118,10 @@ def representative_data_gen() -> list:
     # Get KPI information to constraint your model's memory size.
     # Retrieve a KPI object with helpful information of each KPI metric,
     # to constraint the quantized model to the desired memory size.
-    kpi_data = mct.core.keras_kpi_data_experimental(model,
-                                                    representative_data_gen,
-                                                    configuration,
-                                                    target_platform_capabilities=target_platform_cap)
+    kpi_data = mct.core.keras_kpi_data(model,
+                                       representative_data_gen,
+                                       configuration,
+                                       target_platform_capabilities=target_platform_cap)
 
     # Set a constraint for each of the KPI metrics.
     # Create a KPI object to limit our returned model's size. Note that this values affects only layers and attributes

tutorials/notebooks/keras/ptq/example_keras_mobilenet_mixed_precision_lut.py

@@ -123,10 +123,10 @@ def representative_data_gen() -> list:
     # Get KPI information to constraint your model's memory size.
     # Retrieve a KPI object with helpful information of each KPI metric,
     # to constraint the quantized model to the desired memory size.
-    kpi_data = mct.core.keras_kpi_data_experimental(model,
-                                                    representative_data_gen,
-                                                    configuration,
-                                                    target_platform_capabilities=target_platform_cap)
+    kpi_data = mct.core.keras_kpi_data(model,
+                                       representative_data_gen,
+                                       configuration,
+                                       target_platform_capabilities=target_platform_cap)
 
     # Set a constraint for each of the KPI metrics.
     # Create a KPI object to limit our returned model's size. Note that this values affects only layers and attributes

tutorials/quick_start/keras_fw/quant.py

@@ -59,7 +59,7 @@ def get_target_kpi(model, weights_compression, representative_data_gen, core_con
         A KPI object computed from MCT and contains info about the target model size.
 
     """
-    kpi_data = mct.core.keras_kpi_data_experimental(model, representative_data_gen, core_config=core_config, target_platform_capabilities=tpc)
+    kpi_data = mct.core.keras_kpi_data(model, representative_data_gen, core_config=core_config, target_platform_capabilities=tpc)
     weights_kpi = BYTES_TO_FP32 * kpi_data.weights_memory / weights_compression # (4 bytes for fp32) * weights memory(in Bytes) / compression rate
     return KPI(weights_memory=weights_kpi)