# Copyright 2020 Xilinx Inc.
#
# 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.

"""Module for partitioning XGraph objects"""

import copy
import logging

from typing import List

from pyxir.graph import XGraph
from pyxir.shapes import TupleShape, TensorShape
from pyxir.shared.vector import StrVector

from ..xgraph_factory import XGraphFactory
from ..layer import xlayer
from ..optimization.optimizers.transposes_optimizer import \
    XGraphTransposesOptimizer

logger = logging.getLogger('pyxir')


class XGraphPartitioner(object):

    xgraph_factory = XGraphFactory()

    def __init__(self):
        pass

    def partition(self, xgraph: XGraph, targets: List[str], last_layer: str = None) -> XGraph:
        """
        Partition the provided XGraph according to the provided targets

        NOTE: This is a naive approach which only handles one target
        and just partitions everything it can

        Arguments
        ---------
        xgraph: XGraph
            the XGraph to be partitioned
        targets: List[int]
            the targets to be partitioned for, for now only one target can
            be passed
        last_layer: str
            the last layer to be partitioned
        """
        # TODO Add Base partitioning support for multiple algorithms
        if len(targets) != 1:
            raise NotImplementedError("XGraph partitioning is only supported"
                                      " for one target at the moment but got:"
                                      " {}".format(len(targets)))
        target = targets[0]

        # ALGO:
        # 1. loop through all the XLayers
        # 2. For every layer, if it's a target layer, either add it to the
        #   corresponding Partition layer if a bottom is already added to
        #   this layer, else start a new Partition layer
        # TODO set bottoms and tops to [] for partition input respectively
        #   output layers
        name, idx = 'xp', 0
        name_2_pars = {}
        par_2_names = {}
        xlayers = []
        # Keep track of layers that have a non-target bottom
        #  e.g. T1 --> NT --> T3 --> T4
        #        `--->  T2 ----^
        # Here, T3 has a non-target bottom layer and a new partition has to be
        #   started
        non_target_bottom_layers = set([])
        # Keep track of layer partition dependencies
        #  e.g. T1 --> NT --> T2 --> 
        #        `------------^
        # Here, T1 and T2 can't belong to the same partition because of NT in between
        # partition_dep_map = {}

        logger.debug("Partition for target: {}".format(target))

        stop_partitioning = False

        for X in xgraph.get_layers():

            logger.debug("----------------")
            logger.debug(X.name)

            # partition_dependencies = set([e for b in X.bottoms
            #                               if b in partition_dep_map
            #                               for e in partition_dep_map[b]])
            # if X.name in partition_dep_map:
            #     partition_dep_map[X.name] |= partition_dependencies
            # else:
            #     partition_dep_map[X.name] = partition_dependencies

            if target not in X.targets or stop_partitioning:

                if X.name in name_2_pars:
                    # Cap partition(s)

                    for p in name_2_pars[X.name]:
                        par_2_names[p].remove(X.name)

                    del name_2_pars[X.name]

                for t in X.tops:
                    non_target_bottom_layers.add(t)

                # partition_dependencies = set([e for b in X.bottoms
                #                               if b in name_2_pars
                #                               for e in name_2_pars[b]])
                # if X.name in partition_dep_map:
                #     partition_dep_map[X.name] |= partition_dependencies

                continue

            elif X.name in name_2_pars\
                    and X.name not in non_target_bottom_layers:
                    # and not partition_dep_map[X.name]\
                    #     .isdisjoint(set(name_2_pars[X.name])):

                # Check whether only one partition added this element
                if len(name_2_pars[X.name]) == 1:
                    # -- p_0 --> A
                    pass
                else:
                    # -- p_0 --> A
                    # -- p_1 ----^
                    new_p_name = name + str(idx)
                    idx += 1
                    logger.debug("Merge into new partition: {}"
                                 .format(new_p_name))

                    par_2_names[new_p_name] = []

                    for p in name_2_pars[X.name]:

                        par_2_names[new_p_name].extend(par_2_names[p])

                        for layer in par_2_names[p]:
                            name_2_pars[layer] = [new_p_name]

                        # xlayers.remove(pX)
                        del par_2_names[p]

                    name_2_pars[X.name] = [new_p_name]

            else:
                # Create new partition
                # Also,
                # -- p_0 --> A --> p_1 --> B
                #     `--------------------^

                new_p_name = name + str(idx)
                idx += 1
                logger.debug("Create new partition: {}".format(new_p_name))

                par_2_names[new_p_name] = [X.name]
                name_2_pars[X.name] = [new_p_name]

            p = name_2_pars[X.name][0]
            for t in X.tops:
                if t in name_2_pars and p not in name_2_pars[t]:
                    name_2_pars[t].append(p)
                else:
                    name_2_pars[t] = [p]

                if t not in par_2_names[p]:
                    par_2_names[p].append(t)

            if X.name == last_layer:
                stop_partitioning = True

            # logger.debug(name_2_pars)
            # logger.debug(par_2_names)

        logger.debug("----------------")

        # ALGO: keep only largest subgraph, prune all others
        # TODO Make more generic and support multiple criteria

        largest_xp, largest_xp_size = '', 0
        for xp in sorted(par_2_names.keys()):
            if len(par_2_names[xp]) > largest_xp_size:
                largest_xp = xp
                largest_xp_size = len(par_2_names[xp])

        for xp in list(par_2_names.keys()):
            if xp != largest_xp:
                del par_2_names[xp]

        for xname, xp_lst in list(name_2_pars.items()):
            if xp_lst[0] != largest_xp:
                del name_2_pars[xname]

        # Set target and group attributes
        for X in xgraph.get_layers():

            if X.name in name_2_pars:
                xlayers.append(X._replace(
                    target=target,
                    subgraph=name_2_pars[X.name][0]
                ))
            else:
                xlayers.append(copy.deepcopy(X))

        # TODO Sort xlayers in topological order
        xgraph = XGraphPartitioner.xgraph_factory.build_from_xlayer(
            net=xlayers,
            name=xgraph.get_name(),
            output_png='tvm_partitioned_graph.png' if
            logger.getEffectiveLevel() <= 10 else None
        )

        # Transpose optimizer
        optimizer = XGraphTransposesOptimizer(xgraph,
                                              target=target,
                                              opt_name='partitioning')
        optimizer.optimize()

        return xgraph

    def get_subgraphs(self, xgraph):
        # type: (XGraph) -> List[XGraph]
        """ Return a list of subgraphs for the given xgraph in XGraph format.
        """

        # ALGO:
        # 1. loop through all the XLayers
        # 2. For every layer, if it's a target layer, either add it to the
        #   corresponding partition if a bottom is already added to
        #   this layer, else start a new partition
        # TODO set bottoms and tops to [] for partition input respectively
        #   output layers

        in_idx = 0

        visited = {}
        subgraphs = {}

        for X in xgraph.get_layers():

            if X.subgraph is not None:

                X_copy = copy.deepcopy(X)

                if X.subgraph not in subgraphs:
                    new_subgraph = xlayer.defaultXLayer()
                    new_subgraph = new_subgraph._replace(
                        name=X.subgraph,
                        type=['SubGraph'],
                        data=[],
                        shapes=TupleShape([]),
                        sizes=[],
                        internal=1,
                        attrs={
                            'target': X.target,
                            '__bottom_tensors': {},
                            'orig_bottom_tensors': {},
                            '__top_tensors': {},
                            'orig_top_tensors': {},
                        }
                    )
                    subgraphs[X.subgraph] = new_subgraph
                    visited[X.subgraph] = set()

                # First check if this layer is a subgraph input layer
                #   by looking at the visited subgraph layers
                for b in X.bottoms:

                    if b not in visited[X.subgraph]:

                        bX = xgraph.get(b)

                        x_in_name = 'xinput' + str(in_idx)

                        def find_original_bottom_layers(rX):
                            if not bool(rX.internal):
                                return [rX.name]

                            bottom_layers = []
                            for r_bottom_name in rX.bottoms:
                                rbX = xgraph.get(r_bottom_name)
                                rec_bottom_layers = \
                                    find_original_bottom_layers(rbX)
                                bottom_layers.extend(rec_bottom_layers)

                            return bottom_layers

                        orig_bottoms = find_original_bottom_layers(bX)

                        if 'input_names' not in subgraphs[X.subgraph].attrs:
                            subgraphs[X.subgraph].attrs['input_names'] =\
                                [x_in_name]
                        else:
                            subgraphs[X.subgraph].attrs['input_names']\
                                .append(x_in_name)

                        # Keep track of input - bottom connections
                        sg_bottoms_ext = \
                            subgraphs[X.subgraph].attrs['__bottom_tensors']
                        if X.name not in sg_bottoms_ext:
                            sg_bottoms_ext.update({x_in_name: [b]})
                        else:
                            new_bottoms_ext = sg_bottoms_ext[x_in_name] + [b]
                            sg_bottoms_ext.update({x_in_name: new_bottoms_ext})

                        # Keep track of input - original (model) bottom
                        #   connections, i.e. exclude internally added
                        #   operations here
                        sg_orig_bottoms_ext = \
                            subgraphs[X.subgraph].attrs['orig_bottom_tensors']
                        if X.name not in sg_orig_bottoms_ext:
                            sg_orig_bottoms_ext.update(
                                {x_in_name: orig_bottoms})
                        else:
                            new_orig_bottoms_ext = \
                                sg_orig_bottoms_ext[x_in_name] + orig_bottoms
                            sg_orig_bottoms_ext.update(
                                {x_in_name: new_orig_bottoms_ext})

                        new_in_X = xlayer.defaultXLayer()
                        new_in_X = new_in_X._replace(
                            name=x_in_name,
                            type=['Input'],
                            shapes=bX.shapes[:],
                            sizes=bX.sizes[:],
                            # Keep track of the first original layer of the
                            #   operation in front of which we are adding an
                            #   input layer
                            layer=[X.layer[0]],
                            tops=[X.name],
                            bottoms=[],
                            internal=1,
                            attrs={},
                            targets=[]
                        )
                        in_idx += 1

                        X_copy.bottoms[:] = \
                            [(bc if bc != b else new_in_X.name)
                             for bc in X_copy.bottoms]

                        subgraphs[X.subgraph].subgraph_data =\
                            subgraphs[X.subgraph].subgraph_data + [new_in_X]
                        # subgraphs[X.subgraph].shapes[:] = new_in_X.shapes[:]
                        # subgraphs[X.subgraph].sizes[:] = new_in_X.sizes[:]
                        subgraphs[X.subgraph].bottoms.append(b)

                        visited[X.subgraph].add(new_in_X.name)

                if X.tops == []:
                    sg_tops_ext = \
                        subgraphs[X.subgraph].attrs['__top_tensors']
                    sg_orig_tops_ext = \
                        subgraphs[X.subgraph].attrs['orig_top_tensors']
                    sg_tops_ext.update({X.name: []})
                    sg_orig_tops_ext.update({X.name: []})

                    if 'output_names' not in subgraphs[X.subgraph].attrs:
                        subgraphs[X.subgraph].attrs['output_names'] =\
                            [X.name]
                    else:
                        subgraphs[X.subgraph].attrs['output_names']\
                            .append(X.name)

                for t in X.tops:
                    tX = xgraph.get(t)

                    if tX.subgraph != X.subgraph:

                        def find_original_top_layers(rX):
                            if not bool(rX.internal):
                                return [rX.name]

                            top_layers = []
                            for r_top_name in rX.tops:
                                rtX = xgraph.get(r_top_name)
                                rec_top_layers = find_original_top_layers(rtX)
                                top_layers.extend(rec_top_layers)

                            return top_layers

                        orig_tops = find_original_top_layers(tX)

                        if 'output_names' not in subgraphs[X.subgraph].attrs:
                            subgraphs[X.subgraph].attrs['output_names'] =\
                                [X.name]
                        else:
                            subgraphs[X.subgraph].attrs['output_names']\
                                .append(X.name)

                        # Keep track of output - top connections
                        sg_tops_ext = \
                            subgraphs[X.subgraph].attrs['__top_tensors']
                        if X.name not in sg_tops_ext:
                            sg_tops_ext.update({X.name: [t]})  # X.tops[:]
                        else:
                            new_tops_ext = sg_tops_ext[X.name] + [t]  # X.tops
                            sg_tops_ext.update({X.name: new_tops_ext})

                        # Keep track of output - original (model) top
                        #   connections, i.e. exclude internally added
                        #   operations here
                        sg_orig_tops_ext = \
                            subgraphs[X.subgraph].attrs['orig_top_tensors']
                        if X.name not in sg_orig_tops_ext:
                            sg_orig_tops_ext.update({X.name: orig_tops})
                        else:
                            new_orig_tops_ext = \
                                sg_orig_tops_ext[X.name] + orig_tops
                            sg_orig_tops_ext.update(
                                {X.name: new_orig_tops_ext})

                        X_copy.tops.remove(t)
                        subgraphs[X.subgraph].tops.append(t)
                        subgraphs[X.subgraph].shapes.append(X.shapes[:])
                        subgraphs[X.subgraph].sizes.extend(X.sizes[:])

                # If no tops
                if X.tops == []:
                    subgraphs[X.subgraph].shapes.append(X.shapes[:])
                    subgraphs[X.subgraph].sizes.extend(X.sizes[:])

                subgraphs[X.subgraph].subgraph_data = \
                    subgraphs[X.subgraph].subgraph_data + [X_copy]
                visited[X.subgraph].add(X_copy.name)

        sg_list = []
        for sg, sgX in subgraphs.items():
            # (len(sgX.tops) == len(sgX.shapes))
            # if len(sgX.tops) != 1:
            #    raise ValueError("Subgraphs are only supported for one output"
            #        " but got: {}".format(sgX.tops))

            # TODO Sort xlayers in topological order
            # sub_xgraph = XGraphPartitioner.xgraph_factory.build_from_xlayer(
            #     net=sgX.data,
            #     name=sg
            # )

            sg_list.append(
                sgX._replace(
                    # shapes = sgX.shapes[0],
                    # sizes=[sum([s[0] for s in sgX.sizes])],
                    subgraph_data=sgX.subgraph_data
                )
            )

        return sg_list

# Copyright 2020 Xilinx Inc.
#
# 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.

"""Module for partitioning XGraph objects"""

import copy
import logging

from typing import List

from pyxir.graph import XGraph
from pyxir.shapes import TupleShape, TensorShape
from pyxir.shared.vector import StrVector

from ..xgraph_factory import XGraphFactory
from ..layer import xlayer
from ..optimization.optimizers.transposes_optimizer import \
    XGraphTransposesOptimizer

logger = logging.getLogger('pyxir')


class XGraphPartitioner(object):

    xgraph_factory = XGraphFactory()

    def __init__(self):
        pass

    def partition(self, xgraph: XGraph, targets: List[str], last_layer: str = None) -> XGraph:
        """
        Partition the provided XGraph according to the provided targets

        NOTE: This is a naive approach which only handles one target
        and just partitions everything it can

        Arguments
        ---------
        xgraph: XGraph
            the XGraph to be partitioned
        targets: List[int]
            the targets to be partitioned for, for now only one target can
            be passed
        last_layer: str
            the last layer to be partitioned
        """
        # TODO Add Base partitioning support for multiple algorithms
        if len(targets) != 1:
            raise NotImplementedError("XGraph partitioning is only supported"
                                      " for one target at the moment but got:"
                                      " {}".format(len(targets)))
        target = targets[0]

        # ALGO:
        # 1. loop through all the XLayers
        # 2. For every layer, if it's a target layer, either add it to the
        #   corresponding Partition layer if a bottom is already added to
        #   this layer, else start a new Partition layer
        # TODO set bottoms and tops to [] for partition input respectively
        #   output layers
        name, idx = 'xp', 0
        name_2_pars = {}
        par_2_names = {}
        xlayers = []
        # Keep track of layers that have a non-target bottom
        #  e.g. T1 --> NT --> T3 --> T4
        #        `--->  T2 ----^
        # Here, T3 has a non-target bottom layer and a new partition has to be
        #   started
        non_target_bottom_layers = set([])
        # Keep track of layer partition dependencies
        #  e.g. T1 --> NT --> T2 --> 
        #        `------------^
        # Here, T1 and T2 can't belong to the same partition because of NT in between
        # partition_dep_map = {}

        logger.debug("Partition for target: {}".format(target))

        stop_partitioning = False

        for X in xgraph.get_layers():

            logger.debug("----------------")
            logger.debug(X.name)

            # partition_dependencies = set([e for b in X.bottoms
            #                               if b in partition_dep_map
            #                               for e in partition_dep_map[b]])
            # if X.name in partition_dep_map:
            #     partition_dep_map[X.name] |= partition_dependencies
            # else:
            #     partition_dep_map[X.name] = partition_dependencies

            if target not in X.targets or stop_partitioning:

                if X.name in name_2_pars:
                    # Cap partition(s)

                    for p in name_2_pars[X.name]:
                        par_2_names[p].remove(X.name)

                    del name_2_pars[X.name]

                for t in X.tops:
                    non_target_bottom_layers.add(t)

                # partition_dependencies = set([e for b in X.bottoms
                #                               if b in name_2_pars
                #                               for e in name_2_pars[b]])
                # if X.name in partition_dep_map:
                #     partition_dep_map[X.name] |= partition_dependencies

                continue

            elif X.name in name_2_pars\
                    and X.name not in non_target_bottom_layers:
                    # and not partition_dep_map[X.name]\
                    #     .isdisjoint(set(name_2_pars[X.name])):

                # Check whether only one partition added this element
                if len(name_2_pars[X.name]) == 1:
                    # -- p_0 --> A
                    pass
                else:
                    # -- p_0 --> A
                    # -- p_1 ----^
                    new_p_name = name + str(idx)
                    idx += 1
                    logger.debug("Merge into new partition: {}"
                                 .format(new_p_name))

                    par_2_names[new_p_name] = []

                    for p in name_2_pars[X.name]:

                        par_2_names[new_p_name].extend(par_2_names[p])

                        for layer in par_2_names[p]:
                            name_2_pars[layer] = [new_p_name]

                        # xlayers.remove(pX)
                        del par_2_names[p]

                    name_2_pars[X.name] = [new_p_name]

            else:
                # Create new partition
                # Also,
                # -- p_0 --> A --> p_1 --> B
                #     `--------------------^

                new_p_name = name + str(idx)
                idx += 1
                logger.debug("Create new partition: {}".format(new_p_name))

                par_2_names[new_p_name] = [X.name]
                name_2_pars[X.name] = [new_p_name]

            p = name_2_pars[X.name][0]
            for t in X.tops:
                if t in name_2_pars and p not in name_2_pars[t]:
                    name_2_pars[t].append(p)
                else:
                    name_2_pars[t] = [p]

                if t not in par_2_names[p]:
                    par_2_names[p].append(t)

            if X.name == last_layer:
                stop_partitioning = True

            # logger.debug(name_2_pars)
            # logger.debug(par_2_names)

        logger.debug("----------------")

        # ALGO: keep only largest subgraph, prune all others
        # TODO Make more generic and support multiple criteria

        largest_xp, largest_xp_size = '', 0
        for xp in sorted(par_2_names.keys()):
            if len(par_2_names[xp]) > largest_xp_size:
                largest_xp = xp
                largest_xp_size = len(par_2_names[xp])

        for xp in list(par_2_names.keys()):
            if xp != largest_xp:
                del par_2_names[xp]

        for xname, xp_lst in list(name_2_pars.items()):
            if xp_lst[0] != largest_xp:
                del name_2_pars[xname]

        # Set target and group attributes
        for X in xgraph.get_layers():

            if X.name in name_2_pars:
                xlayers.append(X._replace(
                    target=target,
                    subgraph=name_2_pars[X.name][0]
                ))
            else:
                xlayers.append(copy.deepcopy(X))

        # TODO Sort xlayers in topological order
        xgraph = XGraphPartitioner.xgraph_factory.build_from_xlayer(
            net=xlayers,
            name=xgraph.get_name(),
            output_png='tvm_partitioned_graph.png' if
            logger.getEffectiveLevel() <= 10 else None
        )

        # Transpose optimizer
        optimizer = XGraphTransposesOptimizer(xgraph,
                                              target=target,
                                              opt_name='partitioning')
        optimizer.optimize()

        return xgraph

    def get_subgraphs(self, xgraph):
        # type: (XGraph) -> List[XGraph]
        """ Return a list of subgraphs for the given xgraph in XGraph format.
        """

        # ALGO:
        # 1. loop through all the XLayers
        # 2. For every layer, if it's a target layer, either add it to the
        #   corresponding partition if a bottom is already added to
        #   this layer, else start a new partition
        # TODO set bottoms and tops to [] for partition input respectively
        #   output layers

        in_idx = 0

        visited = {}
        subgraphs = {}

        for X in xgraph.get_layers():

            if X.subgraph is not None:

                X_copy = copy.deepcopy(X)

                if X.subgraph not in subgraphs:
                    new_subgraph = xlayer.defaultXLayer()
                    new_subgraph = new_subgraph._replace(
                        name=X.subgraph,
                        type=['SubGraph'],
                        data=[],
                        shapes=TupleShape([]),
                        sizes=[],
                        internal=1,
                        attrs={
                            'target': X.target,
                            '__bottom_tensors': {},
                            'orig_bottom_tensors': {},
                            '__top_tensors': {},
                            'orig_top_tensors': {},
                        }
                    )
                    subgraphs[X.subgraph] = new_subgraph
                    visited[X.subgraph] = set()

                # First check if this layer is a subgraph input layer
                #   by looking at the visited subgraph layers
                for b in X.bottoms:

                    if b not in visited[X.subgraph]:

                        bX = xgraph.get(b)

                        x_in_name = 'xinput' + str(in_idx)

                        def find_original_bottom_layers(rX):
                            if not bool(rX.internal):
                                return [rX.name]

                            bottom_layers = []
                            for r_bottom_name in rX.bottoms:
                                rbX = xgraph.get(r_bottom_name)
                                rec_bottom_layers = \
                                    find_original_bottom_layers(rbX)
                                bottom_layers.extend(rec_bottom_layers)

                            return bottom_layers

                        orig_bottoms = find_original_bottom_layers(bX)

                        if 'input_names' not in subgraphs[X.subgraph].attrs:
                            subgraphs[X.subgraph].attrs['input_names'] =\
                                [x_in_name]
                        else:
                            subgraphs[X.subgraph].attrs['input_names']\
                                .append(x_in_name)

                        # Keep track of input - bottom connections
                        sg_bottoms_ext = \
                            subgraphs[X.subgraph].attrs['__bottom_tensors']
                        if X.name not in sg_bottoms_ext:
                            sg_bottoms_ext.update({x_in_name: [b]})
                        else:
                            new_bottoms_ext = sg_bottoms_ext[x_in_name] + [b]
                            sg_bottoms_ext.update({x_in_name: new_bottoms_ext})

                        # Keep track of input - original (model) bottom
                        #   connections, i.e. exclude internally added
                        #   operations here
                        sg_orig_bottoms_ext = \
                            subgraphs[X.subgraph].attrs['orig_bottom_tensors']
                        if X.name not in sg_orig_bottoms_ext:
                            sg_orig_bottoms_ext.update(
                                {x_in_name: orig_bottoms})
                        else:
                            new_orig_bottoms_ext = \
                                sg_orig_bottoms_ext[x_in_name] + orig_bottoms
                            sg_orig_bottoms_ext.update(
                                {x_in_name: new_orig_bottoms_ext})

                        new_in_X = xlayer.defaultXLayer()
                        new_in_X = new_in_X._replace(
                            name=x_in_name,
                            type=['Input'],
                            shapes=bX.shapes[:],
                            sizes=bX.sizes[:],
                            # Keep track of the first original layer of the
                            #   operation in front of which we are adding an
                            #   input layer
                            layer=[X.layer[0]],
                            tops=[X.name],
                            bottoms=[],
                            internal=1,
                            attrs={},
                            targets=[]
                        )
                        in_idx += 1

                        X_copy.bottoms[:] = \
                            [(bc if bc != b else new_in_X.name)
                             for bc in X_copy.bottoms]

                        subgraphs[X.subgraph].subgraph_data =\
                            subgraphs[X.subgraph].subgraph_data + [new_in_X]
                        # subgraphs[X.subgraph].shapes[:] = new_in_X.shapes[:]
                        # subgraphs[X.subgraph].sizes[:] = new_in_X.sizes[:]
                        subgraphs[X.subgraph].bottoms.append(b)

                        visited[X.subgraph].add(new_in_X.name)

                if X.tops == []:
                    sg_tops_ext = \
                        subgraphs[X.subgraph].attrs['__top_tensors']
                    sg_orig_tops_ext = \
                        subgraphs[X.subgraph].attrs['orig_top_tensors']
                    sg_tops_ext.update({X.name: []})
                    sg_orig_tops_ext.update({X.name: []})

                    if 'output_names' not in subgraphs[X.subgraph].attrs:
                        subgraphs[X.subgraph].attrs['output_names'] =\
                            [X.name]
                    else:
                        subgraphs[X.subgraph].attrs['output_names']\
                            .append(X.name)

                for t in X.tops:
                    tX = xgraph.get(t)

                    if tX.subgraph != X.subgraph:

                        def find_original_top_layers(rX):
                            if not bool(rX.internal):
                                return [rX.name]

                            top_layers = []
                            for r_top_name in rX.tops:
                                rtX = xgraph.get(r_top_name)
                                rec_top_layers = find_original_top_layers(rtX)
                                top_layers.extend(rec_top_layers)

                            return top_layers

                        orig_tops = find_original_top_layers(tX)

                        if 'output_names' not in subgraphs[X.subgraph].attrs:
                            subgraphs[X.subgraph].attrs['output_names'] =\
                                [X.name]
                        else:
                            subgraphs[X.subgraph].attrs['output_names']\
                                .append(X.name)

                        # Keep track of output - top connections
                        sg_tops_ext = \
                            subgraphs[X.subgraph].attrs['__top_tensors']
                        if X.name not in sg_tops_ext:
                            sg_tops_ext.update({X.name: [t]})  # X.tops[:]
                        else:
                            new_tops_ext = sg_tops_ext[X.name] + [t]  # X.tops
                            sg_tops_ext.update({X.name: new_tops_ext})

                        # Keep track of output - original (model) top
                        #   connections, i.e. exclude internally added
                        #   operations here
                        sg_orig_tops_ext = \
                            subgraphs[X.subgraph].attrs['orig_top_tensors']
                        if X.name not in sg_orig_tops_ext:
                            sg_orig_tops_ext.update({X.name: orig_tops})
                        else:
                            new_orig_tops_ext = \
                                sg_orig_tops_ext[X.name] + orig_tops
                            sg_orig_tops_ext.update(
                                {X.name: new_orig_tops_ext})

                        X_copy.tops.remove(t)
                        subgraphs[X.subgraph].tops.append(t)
                        subgraphs[X.subgraph].shapes.append(X.shapes[:])
                        subgraphs[X.subgraph].sizes.extend(X.sizes[:])

                # If no tops
                if X.tops == []:
                    subgraphs[X.subgraph].shapes.append(X.shapes[:])
                    subgraphs[X.subgraph].sizes.extend(X.sizes[:])

                subgraphs[X.subgraph].subgraph_data = \
                    subgraphs[X.subgraph].subgraph_data + [X_copy]
                visited[X.subgraph].add(X_copy.name)

        sg_list = []
        for sg, sgX in subgraphs.items():
            # (len(sgX.tops) == len(sgX.shapes))
            # if len(sgX.tops) != 1:
            #    raise ValueError("Subgraphs are only supported for one output"
            #        " but got: {}".format(sgX.tops))

            # TODO Sort xlayers in topological order
            # sub_xgraph = XGraphPartitioner.xgraph_factory.build_from_xlayer(
            #     net=sgX.data,
            #     name=sg
            # )

            sg_list.append(
                sgX._replace(
                    # shapes = sgX.shapes[0],
                    # sizes=[sum([s[0] for s in sgX.sizes])],
                    subgraph_data=sgX.subgraph_data
                )
            )

        return sg_list

# Copyright 2020 Xilinx Inc. # # 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. Module for partitioning XGraph objects Partition the provided XGraph according to the provided targets          NOTE: This is a naive approach which only handles one target         and just partitions everything it can          Arguments         ---------         xgraph: XGraph             the XGraph to be partitioned         targets: List[int]             the targets to be partitioned for, for now only one target can             be passed         last_layer: str             the last layer to be partitioned # TODO Add Base partitioning support for multiple algorithms # ALGO: # 1. loop through all the XLayers # 2. For every layer, if it's a target layer, either add it to the #   corresponding Partition layer if a bottom is already added to #   this layer, else start a new Partition layer # TODO set bottoms and tops to [] for partition input respectively #   output layers # Keep track of layers that have a non-target bottom #  e.g. T1 --> NT --> T3 --> T4 #        `--->  T2 ----^ # Here, T3 has a non-target bottom layer and a new partition has to be #   started # Keep track of layer partition dependencies #  e.g. T1 --> NT --> T2 --> #        `------------^ # Here, T1 and T2 can't belong to the same partition because of NT in between # partition_dep_map = {} # partition_dependencies = set([e for b in X.bottoms #                               if b in partition_dep_map #                               for e in partition_dep_map[b]]) # if X.name in partition_dep_map: #     partition_dep_map[X.name] |= partition_dependencies # else: #     partition_dep_map[X.name] = partition_dependencies # Cap partition(s) # partition_dependencies = set([e for b in X.bottoms #                               if b in name_2_pars #                               for e in name_2_pars[b]]) # if X.name in partition_dep_map: #     partition_dep_map[X.name] |= partition_dependencies # and not partition_dep_map[X.name]\ #     .isdisjoint(set(name_2_pars[X.name])): # Check whether only one partition added this element # -- p_0 --> A # -- p_0 --> A # -- p_1 ----^ # xlayers.remove(pX) # Create new partition # Also, # -- p_0 --> A --> p_1 --> B #     `--------------------^ # logger.debug(name_2_pars) # logger.debug(par_2_names) # ALGO: keep only largest subgraph, prune all others # TODO Make more generic and support multiple criteria # Set target and group attributes # TODO Sort xlayers in topological order # Transpose optimizer # type: (XGraph) -> List[XGraph] Return a list of subgraphs for the given xgraph in XGraph format. # ALGO: # 1. loop through all the XLayers # 2. For every layer, if it's a target layer, either add it to the #   corresponding partition if a bottom is already added to #   this layer, else start a new partition # TODO set bottoms and tops to [] for partition input respectively #   output layers # First check if this layer is a subgraph input layer #   by looking at the visited subgraph layers # Keep track of input - bottom connections # Keep track of input - original (model) bottom #   connections, i.e. exclude internally added #   operations here # Keep track of the first original layer of the #   operation in front of which we are adding an #   input layer # subgraphs[X.subgraph].shapes[:] = new_in_X.shapes[:] # subgraphs[X.subgraph].sizes[:] = new_in_X.sizes[:] # Keep track of output - top connections # X.tops[:] # X.tops # Keep track of output - original (model) top #   connections, i.e. exclude internally added #   operations here # If no tops # (len(sgX.tops) == len(sgX.shapes)) # if len(sgX.tops) != 1: #    raise ValueError("Subgraphs are only supported for one output" #        " but got: {}".format(sgX.tops)) # TODO Sort xlayers in topological order # sub_xgraph = XGraphPartitioner.xgraph_factory.build_from_xlayer( #     net=sgX.data, #     name=sg # ) # shapes = sgX.shapes[0], # sizes=[sum([s[0] for s in sgX.sizes])],