<gh_stars>0
# Copyright 2022 Sony Semiconductors 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 typing import List, Any, Tuple, Callable, Type
import numpy as np
import torch
from torch.nn import Module

from model_compression_toolkit import QuantizationConfig, FrameworkInfo, common, GradientPTQConfig, \
    MixedPrecisionQuantizationConfig
from model_compression_toolkit.common import Graph, BaseNode
from model_compression_toolkit.common.collectors.statistics_collector import BaseStatsCollector
from model_compression_toolkit.common.collectors.statistics_collector_generator import create_stats_collector_for_node
from model_compression_toolkit.common.framework_implementation import FrameworkImplementation
from model_compression_toolkit.common.gptq.gptq_training import GPTQTrainer
from model_compression_toolkit.common.model_builder_mode import ModelBuilderMode
from model_compression_toolkit.common.node_prior_info import NodePriorInfo
from model_compression_toolkit.common.user_info import UserInformation
from model_compression_toolkit.pytorch.back2framework.model_builder import model_builder
from model_compression_toolkit.pytorch.default_framework_info import DEFAULT_PYTORCH_INFO
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.batchnorm_folding import \
    pytorch_batchnorm_folding
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.relu_bound_to_power_of_2 import \
    ReLUBoundToPowerOfTwo
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.mark_activation import MarkActivation
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.reshape_with_static_shapes import \
    ReshapeWithStaticShapes
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.scale_equalization import ScaleEqualization, \
    ScaleEqualizationWithPad
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.shift_negative_activation import \
    pytorch_apply_shift_negative_correction
from model_compression_toolkit.pytorch.mixed_precision.sensitivity_evaluation import get_sensitivity_evaluation
from model_compression_toolkit.pytorch.pytorch_node_prior_info import create_node_prior_info
from model_compression_toolkit.pytorch.reader.reader import model_reader
import model_compression_toolkit.pytorch.constants as pytorch_constants
from model_compression_toolkit.pytorch.utils import to_torch_tensor, torch_tensor_to_numpy
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.softmax_shift import \
    pytorch_softmax_shift


class PytorchImplementation(FrameworkImplementation):
    """
    An class with implemented methods to support optimizing Pytorch models.
    """

    def __init__(self):
        super().__init__()

    @property
    def constants(self):
        """
        Returns: Module of Pytorch constants.
        """
        return pytorch_constants

    def to_numpy(self,
                 tensor: torch.Tensor) -> np.ndarray:
        """
        Convert a Pytorch tensor to a Numpy array.
        Args:
            tensor: Pytorch tensor.
        Returns:
            Numpy array converted from the input tensor.
        """
        return torch_tensor_to_numpy(tensor)

    def to_tensor(self, tensor: Any) -> torch.Tensor:
        """
        Convert a Numpy array to a framework's tensor.
        Args:
            tensor: Numpy array.
        Returns:
            Framework's tensor converted from the input Numpy array.
        """
        return to_torch_tensor(tensor)

    def model_reader(self,
                     module: Module,
                     representative_data_gen: Callable) -> Graph:
        """
        Convert a framework's module into a graph.
        Args:
            module: Framework's module.
            representative_data_gen (Callable): Dataset used for calibration.
        Returns:
            Graph representing the input module.
        """
        return model_reader(module, representative_data_gen, self.to_numpy, self.to_tensor)

    def model_builder(self,
                      graph: Graph,
                      mode: ModelBuilderMode,
                      append2output: List[Any] = None,
                      fw_info: FrameworkInfo = DEFAULT_PYTORCH_INFO,
                      gptq_config: GradientPTQConfig = None) -> Tuple[Module, UserInformation]:
        """
        Build a Pytorch module from a graph.
        The mode determines how the module should be build. append2output is a list of Nodes
        to set as the module outputs.
        Args:
            graph: Graph to build the module from it.
            mode: Mode for how to build the module.
            append2output: List of Nodes to set as the module's outputs.
            fw_info: FrameworkInfo object with information about the specific framework's module
            gptq_config: GPTQ configuration class
        Returns:
            A tuple of the Pytorch module that was built and an UserInformation object.
        """
        return model_builder(graph,
                             mode,
                             append2output,
                             fw_info,
                             gptq_config)

    def run_model_inference(self,
                            model: Any,
                            input_list: List[Any]) -> Tuple[Any]:
        """
        Run the model logic on the given the inputs.
        Args:
            model: Pytorch model.
            input_list: List of inputs for the model.
        Returns:
            The Pytorch model's output in numpy format.
        """
        return torch_tensor_to_numpy(model(*to_torch_tensor(input_list)))

    def shift_negative_correction(self,
                                  graph: Graph,
                                  qc: QuantizationConfig,
                                  fw_info: FrameworkInfo) -> Graph:
        """
        Apply shift negative correction (SNC) on a graph.
        Args:
            graph: Graph to apply SNC on.
            qc: Quantization configuration.
            fw_info: FrameworkInfo object with information about the specific framework's module.
        Returns:
            Graph after SNC.
        """
        return pytorch_apply_shift_negative_correction(graph,
                                                       qc,
                                                       fw_info)

    def attach_sc_to_node(self,
                          node: BaseNode,
                          output_channel_index: int) -> BaseStatsCollector:
        """
        Return a statistics collector that should be attached to a node's output
        during statistics collection.
        Args:
            node: Node to return its collector.
            output_channel_index: Index of output channels of layers in the model's framework.
        Returns:
            Statistics collector for the node.
        """
        return create_stats_collector_for_node(node,
                                               output_channel_index=output_channel_index)

    def get_substitutions_marking(self) -> List[common.BaseSubstitution]:
        """
        Returns: A list of the framework substitutions used for marking
        points we fuse.
        """
        return [MarkActivation()]

    def get_substitutions_channel_equalization(self,
                                               quant_config: QuantizationConfig,
                                               fw_info: FrameworkInfo) -> List[common.BaseSubstitution]:
        """
        Return a list of the framework substitutions used for channel equalization.

        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.
            fw_info: FrameworkInfo object with information about the specific framework's model.

        Returns:
            A list of the framework substitutions used after we collect statistics.
        """
        substitutions_list = []
        if quant_config.activation_channel_equalization:
            substitutions_list.extend([ScaleEqualization(quant_config, fw_info),
                                       ScaleEqualizationWithPad(quant_config, fw_info)])
        return substitutions_list

    def get_substitutions_prepare_graph(self) -> List[common.BaseSubstitution]:
        """

        Returns: A list of the framework substitutions used before we collect the prior information.

        """
        return [ReshapeWithStaticShapes()]

    def get_substitutions_pre_statistics_collection(self,
                                                    quant_config: QuantizationConfig
                                                    ) -> List[common.BaseSubstitution]:
        """
        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.

        Returns: A list of the framework substitutions used before we build a quantized module.
        """
        substitutions_list = [pytorch_batchnorm_folding()]
        if quant_config.relu_bound_to_power_of_2:
            substitutions_list.append(ReLUBoundToPowerOfTwo())
        return substitutions_list

    def get_substitutions_post_statistics_collection(self,
                                                     quant_config: QuantizationConfig) -> List[common.BaseSubstitution]:
        """
        Return a list of the framework substitutions used after we collect statistics.
        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.
        Returns:
            A list of the framework substitutions used after we collect statistics.
        """
        substitutions_list = []
        if quant_config.softmax_shift:
            substitutions_list.append(pytorch_softmax_shift())
        if quant_config.input_scaling:
            raise Exception('Input scaling is currently not supported for Pytorch.')
        return substitutions_list

    def get_substitutions_pre_build(self) -> List[common.BaseSubstitution]:
        """
        Returns: A list of the framework substitutions used before we build a quantized module.
        """
        return []

    def get_gptq_trainer_obj(self) -> Type[GPTQTrainer]:
        """
        Returns: GPTQTrainer object
        """
        raise Exception('This feature is currently not yet available for Pytorch models. Work in progress.')

    def get_sensitivity_evaluation_fn(self,
                                      graph: Graph,
                                      quant_config: MixedPrecisionQuantizationConfig,
                                      metrics_weights: np.ndarray,
                                      representative_data_gen: Callable,
                                      fw_info: FrameworkInfo) -> Callable:
        """
        Create and return a function to compute a sensitivity metric for a mixed-precision
        configuration (comparing to the float Pytorch module).
        Args:
            graph: Graph to build it's float and mixed-precision Pytorch models.
            quant_config: QuantizationConfig of how the module should be quantized.
            metrics_weights: Array of weights to weight the sensitivity among different layers.
            representative_data_gen: Dataset to use for retrieving images for the models inputs.
            fw_info: FrameworkInfo object with information about the specific framework's module.
        Returns:
            A function that computes the metric.
        """
        return get_sensitivity_evaluation(graph,
                                          quant_config,
                                          metrics_weights,
                                          representative_data_gen,
                                          fw_info)

    def get_node_prior_info(self,
                            node: BaseNode,
                            fw_info: FrameworkInfo,
                            graph: Graph) -> NodePriorInfo:
        """
        Get a NodePriorInfo object for a node that represents a Pytorch layer.
        Args:
            node: Node to get its prior info.
            fw_info: Framework specific information needed to create the prior info of the node.
            graph: Graph to check the next node type.
        Returns:
            NodePriorInfo with information about the node.
        """

        return create_node_prior_info(node=node,
                                      fw_info=fw_info,
                                      graph=graph)

# Copyright 2022 Sony Semiconductors 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 typing import List, Any, Tuple, Callable, Type
import numpy as np
import torch
from torch.nn import Module

from model_compression_toolkit import QuantizationConfig, FrameworkInfo, common, GradientPTQConfig, \
    MixedPrecisionQuantizationConfig
from model_compression_toolkit.common import Graph, BaseNode
from model_compression_toolkit.common.collectors.statistics_collector import BaseStatsCollector
from model_compression_toolkit.common.collectors.statistics_collector_generator import create_stats_collector_for_node
from model_compression_toolkit.common.framework_implementation import FrameworkImplementation
from model_compression_toolkit.common.gptq.gptq_training import GPTQTrainer
from model_compression_toolkit.common.model_builder_mode import ModelBuilderMode
from model_compression_toolkit.common.node_prior_info import NodePriorInfo
from model_compression_toolkit.common.user_info import UserInformation
from model_compression_toolkit.pytorch.back2framework.model_builder import model_builder
from model_compression_toolkit.pytorch.default_framework_info import DEFAULT_PYTORCH_INFO
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.batchnorm_folding import \
    pytorch_batchnorm_folding
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.relu_bound_to_power_of_2 import \
    ReLUBoundToPowerOfTwo
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.mark_activation import MarkActivation
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.reshape_with_static_shapes import \
    ReshapeWithStaticShapes
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.scale_equalization import ScaleEqualization, \
    ScaleEqualizationWithPad
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.shift_negative_activation import \
    pytorch_apply_shift_negative_correction
from model_compression_toolkit.pytorch.mixed_precision.sensitivity_evaluation import get_sensitivity_evaluation
from model_compression_toolkit.pytorch.pytorch_node_prior_info import create_node_prior_info
from model_compression_toolkit.pytorch.reader.reader import model_reader
import model_compression_toolkit.pytorch.constants as pytorch_constants
from model_compression_toolkit.pytorch.utils import to_torch_tensor, torch_tensor_to_numpy
from model_compression_toolkit.pytorch.graph_substitutions.substitutions.softmax_shift import \
    pytorch_softmax_shift


class PytorchImplementation(FrameworkImplementation):
    """
    An class with implemented methods to support optimizing Pytorch models.
    """

    def __init__(self):
        super().__init__()

    @property
    def constants(self):
        """
        Returns: Module of Pytorch constants.
        """
        return pytorch_constants

    def to_numpy(self,
                 tensor: torch.Tensor) -> np.ndarray:
        """
        Convert a Pytorch tensor to a Numpy array.
        Args:
            tensor: Pytorch tensor.
        Returns:
            Numpy array converted from the input tensor.
        """
        return torch_tensor_to_numpy(tensor)

    def to_tensor(self, tensor: Any) -> torch.Tensor:
        """
        Convert a Numpy array to a framework's tensor.
        Args:
            tensor: Numpy array.
        Returns:
            Framework's tensor converted from the input Numpy array.
        """
        return to_torch_tensor(tensor)

    def model_reader(self,
                     module: Module,
                     representative_data_gen: Callable) -> Graph:
        """
        Convert a framework's module into a graph.
        Args:
            module: Framework's module.
            representative_data_gen (Callable): Dataset used for calibration.
        Returns:
            Graph representing the input module.
        """
        return model_reader(module, representative_data_gen, self.to_numpy, self.to_tensor)

    def model_builder(self,
                      graph: Graph,
                      mode: ModelBuilderMode,
                      append2output: List[Any] = None,
                      fw_info: FrameworkInfo = DEFAULT_PYTORCH_INFO,
                      gptq_config: GradientPTQConfig = None) -> Tuple[Module, UserInformation]:
        """
        Build a Pytorch module from a graph.
        The mode determines how the module should be build. append2output is a list of Nodes
        to set as the module outputs.
        Args:
            graph: Graph to build the module from it.
            mode: Mode for how to build the module.
            append2output: List of Nodes to set as the module's outputs.
            fw_info: FrameworkInfo object with information about the specific framework's module
            gptq_config: GPTQ configuration class
        Returns:
            A tuple of the Pytorch module that was built and an UserInformation object.
        """
        return model_builder(graph,
                             mode,
                             append2output,
                             fw_info,
                             gptq_config)

    def run_model_inference(self,
                            model: Any,
                            input_list: List[Any]) -> Tuple[Any]:
        """
        Run the model logic on the given the inputs.
        Args:
            model: Pytorch model.
            input_list: List of inputs for the model.
        Returns:
            The Pytorch model's output in numpy format.
        """
        return torch_tensor_to_numpy(model(*to_torch_tensor(input_list)))

    def shift_negative_correction(self,
                                  graph: Graph,
                                  qc: QuantizationConfig,
                                  fw_info: FrameworkInfo) -> Graph:
        """
        Apply shift negative correction (SNC) on a graph.
        Args:
            graph: Graph to apply SNC on.
            qc: Quantization configuration.
            fw_info: FrameworkInfo object with information about the specific framework's module.
        Returns:
            Graph after SNC.
        """
        return pytorch_apply_shift_negative_correction(graph,
                                                       qc,
                                                       fw_info)

    def attach_sc_to_node(self,
                          node: BaseNode,
                          output_channel_index: int) -> BaseStatsCollector:
        """
        Return a statistics collector that should be attached to a node's output
        during statistics collection.
        Args:
            node: Node to return its collector.
            output_channel_index: Index of output channels of layers in the model's framework.
        Returns:
            Statistics collector for the node.
        """
        return create_stats_collector_for_node(node,
                                               output_channel_index=output_channel_index)

    def get_substitutions_marking(self) -> List[common.BaseSubstitution]:
        """
        Returns: A list of the framework substitutions used for marking
        points we fuse.
        """
        return [MarkActivation()]

    def get_substitutions_channel_equalization(self,
                                               quant_config: QuantizationConfig,
                                               fw_info: FrameworkInfo) -> List[common.BaseSubstitution]:
        """
        Return a list of the framework substitutions used for channel equalization.

        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.
            fw_info: FrameworkInfo object with information about the specific framework's model.

        Returns:
            A list of the framework substitutions used after we collect statistics.
        """
        substitutions_list = []
        if quant_config.activation_channel_equalization:
            substitutions_list.extend([ScaleEqualization(quant_config, fw_info),
                                       ScaleEqualizationWithPad(quant_config, fw_info)])
        return substitutions_list

    def get_substitutions_prepare_graph(self) -> List[common.BaseSubstitution]:
        """

        Returns: A list of the framework substitutions used before we collect the prior information.

        """
        return [ReshapeWithStaticShapes()]

    def get_substitutions_pre_statistics_collection(self,
                                                    quant_config: QuantizationConfig
                                                    ) -> List[common.BaseSubstitution]:
        """
        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.

        Returns: A list of the framework substitutions used before we build a quantized module.
        """
        substitutions_list = [pytorch_batchnorm_folding()]
        if quant_config.relu_bound_to_power_of_2:
            substitutions_list.append(ReLUBoundToPowerOfTwo())
        return substitutions_list

    def get_substitutions_post_statistics_collection(self,
                                                     quant_config: QuantizationConfig) -> List[common.BaseSubstitution]:
        """
        Return a list of the framework substitutions used after we collect statistics.
        Args:
            quant_config: QuantizationConfig to determine which substitutions to return.
        Returns:
            A list of the framework substitutions used after we collect statistics.
        """
        substitutions_list = []
        if quant_config.softmax_shift:
            substitutions_list.append(pytorch_softmax_shift())
        if quant_config.input_scaling:
            raise Exception('Input scaling is currently not supported for Pytorch.')
        return substitutions_list

    def get_substitutions_pre_build(self) -> List[common.BaseSubstitution]:
        """
        Returns: A list of the framework substitutions used before we build a quantized module.
        """
        return []

    def get_gptq_trainer_obj(self) -> Type[GPTQTrainer]:
        """
        Returns: GPTQTrainer object
        """
        raise Exception('This feature is currently not yet available for Pytorch models. Work in progress.')

    def get_sensitivity_evaluation_fn(self,
                                      graph: Graph,
                                      quant_config: MixedPrecisionQuantizationConfig,
                                      metrics_weights: np.ndarray,
                                      representative_data_gen: Callable,
                                      fw_info: FrameworkInfo) -> Callable:
        """
        Create and return a function to compute a sensitivity metric for a mixed-precision
        configuration (comparing to the float Pytorch module).
        Args:
            graph: Graph to build it's float and mixed-precision Pytorch models.
            quant_config: QuantizationConfig of how the module should be quantized.
            metrics_weights: Array of weights to weight the sensitivity among different layers.
            representative_data_gen: Dataset to use for retrieving images for the models inputs.
            fw_info: FrameworkInfo object with information about the specific framework's module.
        Returns:
            A function that computes the metric.
        """
        return get_sensitivity_evaluation(graph,
                                          quant_config,
                                          metrics_weights,
                                          representative_data_gen,
                                          fw_info)

    def get_node_prior_info(self,
                            node: BaseNode,
                            fw_info: FrameworkInfo,
                            graph: Graph) -> NodePriorInfo:
        """
        Get a NodePriorInfo object for a node that represents a Pytorch layer.
        Args:
            node: Node to get its prior info.
            fw_info: Framework specific information needed to create the prior info of the node.
            graph: Graph to check the next node type.
        Returns:
            NodePriorInfo with information about the node.
        """

        return create_node_prior_info(node=node,
                                      fw_info=fw_info,
                                      graph=graph)
# Copyright 2022 Sony Semiconductors 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. # ============================================================================== An class with implemented methods to support optimizing Pytorch models. Returns: Module of Pytorch constants. Convert a Pytorch tensor to a Numpy array.         Args:             tensor: Pytorch tensor.         Returns:             Numpy array converted from the input tensor. Convert a Numpy array to a framework's tensor.         Args:             tensor: Numpy array.         Returns:             Framework's tensor converted from the input Numpy array. Convert a framework's module into a graph.         Args:             module: Framework's module.             representative_data_gen (Callable): Dataset used for calibration.         Returns:             Graph representing the input module. Build a Pytorch module from a graph.         The mode determines how the module should be build. append2output is a list of Nodes         to set as the module outputs.         Args:             graph: Graph to build the module from it.             mode: Mode for how to build the module.             append2output: List of Nodes to set as the module's outputs.             fw_info: FrameworkInfo object with information about the specific framework's module             gptq_config: GPTQ configuration class         Returns:             A tuple of the Pytorch module that was built and an UserInformation object. Run the model logic on the given the inputs.         Args:             model: Pytorch model.             input_list: List of inputs for the model.         Returns:             The Pytorch model's output in numpy format. Apply shift negative correction (SNC) on a graph.         Args:             graph: Graph to apply SNC on.             qc: Quantization configuration.             fw_info: FrameworkInfo object with information about the specific framework's module.         Returns:             Graph after SNC. Return a statistics collector that should be attached to a node's output         during statistics collection.         Args:             node: Node to return its collector.             output_channel_index: Index of output channels of layers in the model's framework.         Returns:             Statistics collector for the node. Returns: A list of the framework substitutions used for marking         points we fuse. Return a list of the framework substitutions used for channel equalization.          Args:             quant_config: QuantizationConfig to determine which substitutions to return.             fw_info: FrameworkInfo object with information about the specific framework's model.          Returns:             A list of the framework substitutions used after we collect statistics. Returns: A list of the framework substitutions used before we collect the prior information. Args:             quant_config: QuantizationConfig to determine which substitutions to return.          Returns: A list of the framework substitutions used before we build a quantized module. Return a list of the framework substitutions used after we collect statistics.         Args:             quant_config: QuantizationConfig to determine which substitutions to return.         Returns:             A list of the framework substitutions used after we collect statistics. Returns: A list of the framework substitutions used before we build a quantized module. Returns: GPTQTrainer object Create and return a function to compute a sensitivity metric for a mixed-precision         configuration (comparing to the float Pytorch module).         Args:             graph: Graph to build it's float and mixed-precision Pytorch models.             quant_config: QuantizationConfig of how the module should be quantized.             metrics_weights: Array of weights to weight the sensitivity among different layers.             representative_data_gen: Dataset to use for retrieving images for the models inputs.             fw_info: FrameworkInfo object with information about the specific framework's module.         Returns:             A function that computes the metric. Get a NodePriorInfo object for a node that represents a Pytorch layer.         Args:             node: Node to get its prior info.             fw_info: Framework specific information needed to create the prior info of the node.             graph: Graph to check the next node type.         Returns:             NodePriorInfo with information about the node.