compiler.py

#   Copyright (c) 2018 PaddlePaddle Authors. 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.

import multiprocessing
import os
import six
import sys
import warnings
from .. import compat as cpt
from . import framework
from .framework import _get_paddle_place, _get_paddle_place_list
from .framework import cuda_places, cpu_places, xpu_places
from . import core

__all__ = [
    'CompiledProgram', 'ExecutionStrategy', 'BuildStrategy',
    'IpuCompiledProgram', 'IpuStrategy'
]

ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
InferNativeConfig = core.NativeConfig
InferAnalysisConfig = core.AnalysisConfig
DeviceType = core.DeviceType


def _place_obj(place):
    p = core.Place()
    p.set_place(place)
    return p


def _is_pserver_mode(main_program):
    main = main_program if main_program \
        else framework.default_main_program()
    for op in main.global_block().ops:
        if op.type in ["send", "recv"]:
            return True
    return False


def _has_backward_op(graph):
    for node in graph.nodes():
        if node.is_op() and node.op() is not None and \
                node.op().type().endswith("_grad"):
            return True
    return False


def _prune_feed_ops(program):
    # prune the feed ops in the program.
    pop_idx = []
    for i, op in enumerate(program.global_block().ops):
        if op.type == "feed": pop_idx.append(i)
    for index in pop_idx[::-1]:
        program.global_block()._remove_op(index)


def _has_optimize_op(block):
    for op in block.ops:
        op_maker = core.op_proto_and_checker_maker
        optimize = core.op_proto_and_checker_maker.OpRole.Optimize
        if op_maker.kOpRoleVarAttrName() in op.attr_names and \
                int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize):
            return True
    return False


def _has_optimizer_in_control_flow(program):
    if not program:
        program = framework.default_main_program()
    for op in program.global_block().ops:
        if op.type == "conditional_block_grad":
            sub_block = program.block(op._block_attr_id("sub_block"))
            if _has_optimize_op(sub_block):
                return True

    return False


def _should_broadcast_or_not_exists(program, var_name):
    block = program.global_block()
    var = block.vars.get(var_name, None)
    if var is None:
        return True
    is_distributed = getattr(var, '_is_distributed', False) or getattr(
        var, 'is_distributed', False)
    return not is_distributed


class CompiledProgram(object):
    """
    :api_attr: Static Graph
    
    The CompiledProgram is used to transform a program or graph for
    various optimizations according to the configuration of build_strategy,
    for example, the operators' fusion in the computation graph, memory
    optimization during the execution of the computation graph, etc.
    For more information about build_strategy, please refer to
    :code:`paddle.static.BuildStrategy`.

    Args:
        program_or_graph (Graph|Program): This argument is the Program or Graph
            being executed.
        build_strategy(BuildStrategy): This argument is used to compile the
            program or graph with the specified options, such as operators' fusion
            in the computational graph and memory optimization during the execution
            of the computational graph. For more information about build_strategy,
            please refer to :code:`paddle.static.BuildStrategy`. The default is None.

    Returns:
        CompiledProgram

    Example:
        .. code-block:: python

            import numpy
            import paddle
            import paddle.static as static

            paddle.enable_static()

            place = paddle.CUDAPlace(0) # paddle.CPUPlace()
            exe = static.Executor(place)

            data = static.data(name='X', shape=[None, 1], dtype='float32')
            hidden = static.nn.fc(x=data, size=10)
            loss = paddle.mean(hidden)
            paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)

            exe.run(static.default_startup_program())
            compiled_prog = static.CompiledProgram(
                static.default_main_program())

            x = numpy.random.random(size=(10, 1)).astype('float32')
            loss_data, = exe.run(compiled_prog,
                                feed={"X": x},
                                fetch_list=[loss.name])
    """

    def __init__(self, program_or_graph, build_strategy=None):
        if isinstance(program_or_graph, core.Graph):
            self._graph = program_or_graph
            # don't not create a new program here.
            self._program = None
        elif isinstance(program_or_graph, framework.Program):
            _prune_feed_ops(program_or_graph)
            self._graph = core.Graph(program_or_graph.desc)
            self._program = program_or_graph
        else:
            raise TypeError(
                "The type of program_to_graph parameter is wrong, expected Graph or Program, but received %s"
                % type(program_or_graph))

        self._scope = None
        self._place = None
        self._executor = None
        self._compiled = False
        self._is_data_parallel = False
        self._is_inference = False
        self._loss_name = None
        self._share_vars_from = None
        self._places = None
        self._build_strategy = build_strategy
        self._exec_strategy = None

    def with_data_parallel(self,
                           loss_name=None,
                           build_strategy=None,
                           exec_strategy=None,
                           share_vars_from=None,
                           places=None):
        """
        This interface is used to transform the input Program or Graph to a multi-graph
        to run the model in data parallel mode. Users can use the build_strategy and
        exec_strategy to set some optimizations that can be applied during the construction
        and computation of the Graph, such as reducing the number of AllReduce operations,
        specifying the size of the thread pool used in the computation Graph running the model,
        and so on. 
        
        .. note::
            If build_strategy is specified when building CompiledProgram and calling 
            with_data_parallel, build_strategy in CompiledProgram will be overwritten, therefore, 
            if it is data parallel training, it is recommended to set build_strategy when calling 
            with_data_parallel interface.

        Args:
            loss_name (str): This parameter is the name of the loss Tensor of the model.
                **Note: If it is model training, you must set loss_name, otherwise the
                result may be problematic**. The default is None.
            build_strategy(BuildStrategy): This parameter is used to compile the
                program or graph with the specified options, such as operators' fusion
                in the computational graph and memory optimization during the execution
                of the computational graph. For more information about build_strategy,
                please refer to :code:`fluid.BuildStrategy`. The default is None.
            exec_strategy(ExecutionStrategy): exec_strategy specifies the options that can
                be changed when running the current model, such as the thread pool size.
                For more information about exec_strategy, please refer to :code:`fluid.ExecutionStrategy`.
                The default is None.
            share_vars_from(CompiledProgram): If share_vars_from is set, the current
                CompiledProgram will share the parameter value with the CompiledProgram
                specified by share_vars_from. This parameter needs to be set when model testing
                is required during model training, and the data parallel mode is used for
                training and testing. Since CompiledProgram will only distribute parameter
                Tensors to other devices when it is first executed, the CompiledProgram
                specified by share_vars_from must be run before the current CompiledProgram.
                The default is None.
            places(list(CUDAPlace)|list(CPUPlace)|list(str)|None): This parameter specifies the device
                on which the model is running. If you want to run on GPU0 and GPU1, places are
                [fluid.CUDAPlace(0), fluid.CUDAPlace(1)]; if you want to run with 2 CPUs, places are
                [fluid.CPUPlace()] * 2. If the parameter is not set, i.e. the parameter is None,
                the available device will be obtained from the environment variable when the model
                is executed: If the GPU is used, the currently available device ID is obtained
                from the environment variable FLAGS_selected_gpus or CUDA_VISIBLE_DEVICES when
                the model is executed; CPU, when the model is executed, the currently available
                CPU number is obtained from the environment variable CPU_NUM. For example,
                export CPU_NUM=4, if the environment variable is not set, the executor will
                add the variable to the environment variable and set its value to 1.
                The default is None. If ``places`` is the list of string, the string in the list
                can be ``cpu``, ``gpu:x``, where ``x`` is the index of the GPUs. 

        Returns:
            CompiledProgram

        Example:
            .. code-block:: python

                import numpy
                import os
                import paddle
                import paddle.static as static

                paddle.enable_static()

                use_cuda = True
                place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
                parallel_places = [paddle.CUDAPlace(0), paddle.CUDAPlace(1)] if use_cuda else [paddle.CPUPlace()] * 2

                # NOTE: If you use CPU to run the program, you need
                # to specify the CPU_NUM, otherwise, paddle will use
                # all the number of the logic core as the CPU_NUM,
                # in that case, the batch size of the input should be
                # greater than CPU_NUM, if not, the process will be
                # failed by an exception.
                if not use_cuda:
                    os.environ['CPU_NUM'] = str(2)

                exe = static.Executor(place)

                data = static.data(name='X', shape=[None, 1], dtype='float32')
                hidden = static.nn.fc(x=data, size=10)
                loss = paddle.mean(hidden)

                test_program = static.default_main_program().clone(for_test=True)
                paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)

                exe.run(static.default_startup_program())
                compiled_train_prog = static.CompiledProgram(
                    static.default_main_program()).with_data_parallel(
                            loss_name=loss.name, places=parallel_places)
                # NOTE: if not set share_vars_from=compiled_train_prog,
                # the parameters used in test process are different with 
                # the parameters used by train process
                compiled_test_prog = static.CompiledProgram(
                    test_program).with_data_parallel(
                            share_vars_from=compiled_train_prog,
                            places=parallel_places)

                train_data = numpy.random.random(size=(10, 1)).astype('float32')
                loss_data, = exe.run(compiled_train_prog,
                                feed={"X": train_data},
                                fetch_list=[loss.name])
                test_data = numpy.random.random(size=(10, 1)).astype('float32')
                loss_data, = exe.run(compiled_test_prog,
                                feed={"X": test_data},
                                fetch_list=[loss.name])
        """
        assert not self._is_data_parallel, "Already compiled with parallel, cannot be recompiled."
        assert not self._is_inference, "Cannot compile with both data parallel and inference."
        self._is_data_parallel = True
        # FIXME(zcd): Currently, the build_strategy can be set during creating
        # CompiledProgram or calling with_data_parallel, and it may be confusing,
        # but in the long run, we should set up build_strategy only when creating
        # CompiledProgram, and exec_strategy should be deprecated.
        if build_strategy is not None: self._build_strategy = build_strategy
        self._exec_strategy = exec_strategy
        self._loss_name = loss_name
        self._share_vars_from = share_vars_from
        if isinstance(places, (list, tuple)):
            self._places = _get_paddle_place_list(places)
        else:
            self._places = _get_paddle_place(places)

        if _has_backward_op(self._graph):
            assert self._loss_name is not None, "The loss name of CompiledProgram is None. The loss name should be set if CompiledProgram contains backward part."

        if self._places is not None:
            if not isinstance(self._places, (list, tuple)):
                self._places = [self._places]

        return self

    def _with_inference_optimize(self, config):
        """ Add inference optimize

        Args:
            config: instance of `NativeConfig` or `AnalysisConfig` to create predictor
        Returns:
            self
        """
        assert not self._is_data_parallel, "Cannot compile with both data parallel and inference"
        assert not self._is_inference, "Already compiled with inference, cannot be recompiled."

        assert any([
            isinstance(config, InferNativeConfig),
            isinstance(config, InferAnalysisConfig)
        ])
        self._is_inference = True
        self._infer_config = config
        return self

    def _with_distributed(self):
        raise NotImplementedError(
            "Subclass of CompiledProgram should implement _with_distributed method."
        )

    def _compile_data_parallel(self, places, use_device, scope=None):
        if self._share_vars_from:
            if scope:
                sys.stderr.write("share_vars_from is set, scope is ignored.\n")
            if not self._share_vars_from._is_data_parallel:
                raise ValueError(
                    "The shared Program is not data parallel, cannot "
                    "share variables from it.")
            if self._share_vars_from._executor is None:
                raise ValueError(
                    "The shared Program is not compiled and executed, so there is no "
                    "variables to share.")
            self._local_scopes = self._share_vars_from._executor.local_scopes()
        else:
            assert scope is not None, ""
            self._local_scopes = []

        assert isinstance(places, tuple) or isinstance(places, list), \
            "Currently , The places type can only be list or tuple, but the input type is {}.".format(type(places))

        if self._build_strategy is None:
            self._build_strategy = BuildStrategy()
        self._build_strategy.is_distribution = _is_pserver_mode(self._program)

        if self._exec_strategy is None:
            self._exec_strategy = ExecutionStrategy()
        self._exec_strategy._use_device = use_device

        if self._exec_strategy.num_threads == 0:
            if self._exec_strategy._use_device == DeviceType.CUDA:
                # Experiments on se-resnext shows that too many threads hurt
                # performance. Worth tunning for other models in the future.
                self._exec_strategy.num_threads = len(places) * 4
            elif self._exec_strategy._use_device == DeviceType.XPU:
                # Currently only single thread is supported in Kunlun XPU.
                self._exec_strategy.num_threads = 1
            else:
                self._exec_strategy.num_threads = len(places) * 2

        if "FLAGS_use_cinn" in core.globals() and core.globals(
        )["FLAGS_use_cinn"] and self._exec_strategy.num_threads != 1:
            warnings.warn("At present, when CINN is turned on, each process can " \
                  "only contain one thread, so reset the number of threads to 1 here.")
            self._exec_strategy.num_threads = 1

        if self._build_strategy.num_trainers > 1:
            assert self._is_data_parallel, \
                "If you use multi-trainer to train the model, you should use "\
                "the data parallel model, i.e. calling with_data_parallel function."

        # TODO(wuyi): trainer endpoings should be passed in through
        # build_strategy, not program.xxx.
        # TODO(gongwb): let user to set them once.
        if self._program and self._build_strategy.num_trainers > 1 and \
                self._program._trainers_endpoints:
            tps = self._program._trainers_endpoints

            assert self._build_strategy.num_trainers == len(
                tps), "The trainer numbers is not equal to endpoint numbers."
            self._build_strategy.trainers_endpoints = tps

        if self._program:
            self._build_strategy.nccl_comm_num = self._program._nccl_comm_num
            self._build_strategy.use_hierarchical_allreduce = self._program._use_hierarchical_allreduce
            self._build_strategy.hierarchical_allreduce_inter_nranks = self._program._hierarchical_allreduce_inter_nranks

        if self._build_strategy.sync_batch_norm:
            self._build_strategy.enable_sequential_execution = True

        if self._program is not None and self._program._enable_dgc:
            assert self._exec_strategy._use_device == DeviceType.CUDA, "DGC only used under CUDA environment."
            assert self._build_strategy.num_trainers * len(
                places) > 1, "DGC is not avaliable for single card training."
            assert self._build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy.AllReduce, "DGC \
                only can be used for AllReduce BuildStrategy."

            # DGC doesn't support fuse for now, close fuse.
            self._build_strategy.fuse_all_reduce_ops = False

        self._persistable_vars = []
        for node in self._graph.nodes():
            if node.is_var() and node.var() is not None and node.var().persistable() and \
                    node.var().type() != core.VarDesc.VarType.RAW:
                name = cpt.to_text(node.name())
                if self._program is not None and _should_broadcast_or_not_exists(
                        self._program, name):
                    self._persistable_vars.append(cpt.to_text(node.name()))

        places = list(map(_place_obj, places))

        # ParallelExecutor would broadcast all the parameters during initializing.
        # The parameters of each process should be in the same ordered for the data-parallelism
        # distributed training to keep the broadcast correct.
        self._persistable_vars = list(set(self._persistable_vars))
        self._persistable_vars.sort()

        return core.ParallelExecutor(
            places, self._persistable_vars,
            cpt.to_text(self._loss_name) if self._loss_name else six.u(''),
            self._scope, self._local_scopes, self._exec_strategy,
            self._build_strategy, self._graph)

    def _compile_inference(self):
        return core.create_paddle_predictor(self._infer_config)

    def _compile(self, scope, place):
        """Compile the program based on the configs.

        Args:
            scope: The variables (resources) that are associated with
               this compiled program.
            place: The location that the compiled program will be run on.

        Returns:
            self
        """
        if self._compiled:
            if scope and self._scope != scope:
                raise ValueError("Cannot compile program with different scope.")
            if place and not self._place._equals(place):
                raise ValueError("Cannot compile program with different place.")
            return self
        self._compiled = True

        self._scope = scope
        self._place = place

        if self._is_inference:
            self._executor = self._compile_inference()
        else:
            if self._is_data_parallel:
                self._places = self._get_places(self._place, self._places)
            else:
                self._places = [self._place]

            # Todo(liym27):If optimizer is used in control flow,
            #  training on multi-places is not supported now, will
            #  be supported later.
            if len(self._places) > 1 and \
                    _has_optimizer_in_control_flow(self._program):
                raise NotImplementedError(
                    "If optimizer is used in control flow, "
                    "training on multi-places is not supported now.")
            if isinstance(self._place, core.CUDAPlace):
                use_device = DeviceType.CUDA
            elif isinstance(self._place, core.XPUPlace):
                use_device = DeviceType.XPU
            else:
                use_device = DeviceType.CPU
            self._executor = self._compile_data_parallel(use_device=use_device,
                                                         scope=self._scope,
                                                         places=self._places)
        return self

    def _get_places(self, place, place_list):
        has_set_place = (place_list is not None)
        if has_set_place:
            for p in place_list:
                assert p._type() == place._type(), \
                    "Place type not match. You may set wrong type of places."
        else:
            if isinstance(place, core.CUDAPlace):
                place_list = cuda_places()
            elif isinstance(place, core.XPUPlace):
                place_list = xpu_places()
            else:
                place_list = cpu_places()
        assert place_list, "No places for execution."
        return place_list


class IpuStrategy(object):
    """
    Help users precisely control the graph building in :code:`paddle.static.IpuCompiledProgram` .

    Returns:
        The IpuStrategy instance.

    Examples:
        .. code-block:: python
	
            # required: ipu

            import paddle
            import paddle.static as static

            paddle.enable_static()

            ipu_strategy = static.IpuStrategy()
    """

    def __init__(self):
        if core.is_compiled_with_ipu():
            self._ipu_strategy = core.IpuStrategy()
            default_options = {
                'location_optimizer': {
                    'on_chip': 0,
                    'use_replicated_tensor_sharding': 1,
                },  # set optimizer location
                'accumulation_and_replication_reduction_type':
                1,  # popart::ReductionType::Mean
                'mean_accumulation_and_replication_reduction_strategy':
                1,  # popart::MeanReductionStrategy::Post
            }
            self._ipu_strategy.set_options(default_options)
            self.has_custom_ops = False
            self.custom_op_names = []
        else:
            raise RuntimeError(
                "Can not use IpuStrategy in non IPU compiled environment, please re-compile with WITH_IPU=ON."
            )

    def set_graph_config(self,
                         num_ipus=1,
                         is_training=True,
                         micro_batch_size=1,
                         enable_manual_shard=False):
        """
        Set graph configuration to the IpuStrategy instance.

        Args:
            num_ipus (int, optional): Number of IPU devices. Default 1, which means only use 1 IPU.
            is_training (bool, optional): True is training graph, False is inference graph. Default True, which means is training mode.
            batch_size (int, optional): The batch-size in the graph. Used to make the graph batch-size fixed,
                if the batch-size in the graph is dynamic. Default 1, which means the batch-size would be set 1, if the batch-size is dynamice.
            enable_manual_shard (bool, optional): Enable graph sharding or not. Only if num_ipus > 1, enable_manual_shard is able to be set True. 
                Default False, which means disabled.    
            
        Returns:
            None.

        Examples:
            .. code-block:: python
	
                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.set_graph_config(num_ipus=1,
                                            is_training=True,
                                            micro_batch_size=1,
                                            enable_manual_shard=False)
        """
        if num_ipus == 1 and enable_manual_shard:
            raise RuntimeError(
                "Only if num_ipus > 1, enable_manual_shard is able to be set True."
            )
        options = {
            'num_ipus': num_ipus,
            'is_training': is_training,
            'micro_batch_size': micro_batch_size,
            'enable_manual_shard': enable_manual_shard,
        }
        self.set_options(options)

    def set_pipelining_config(self,
                              enable_pipelining=False,
                              batches_per_step=1,
                              enable_gradient_accumulation=False,
                              accumulation_factor=1):
        """
        Set pipelining configuration to the IpuStrategy instance. Used to optimize the throughput performance.

        Args:
            enable_pipelining (bool, optional): Enable data pipelining between subgraphs. Only if enable_manual_shard=True, enable_pipelining is able to be set True. 
                Default False, which means disabled.
            batches_per_step (int, optional): Set the batches per run in data pipelining mode. Only if enable_pipelining=True, batches_per_step is able to be set > 1.
                Default 1, which means no data pipelining.
            enable_gradient_accumulation (bool, optional): Enable to accumulate gradients before updating the weights in training mode. Only if enable_pipelining=True,
                enable_gradient_accumulation is able to be set True. Default False, which means no gradient accumulation. 
            accumulation_factor (int, optional): Specify the number of micro-batches to accumulate 
                before applying the varUpdate. Default 1, which means disable the accumulation.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.set_pipelining_config(enable_pipelining=False,
                                                    batches_per_step=1,
                                                    enable_gradient_accumulation=False,
                                                    accumulation_factor=1)
        """
        enable_manual_shard = self.get_option('enable_manual_shard')
        if not enable_manual_shard and enable_pipelining:
            raise RuntimeError(
                "Only if enable_manual_shard=True, enable_pipelining is able to be set True."
            )
        options = {
            'enable_pipelining': enable_pipelining,
            'batches_per_step': batches_per_step,
            'enable_gradient_accumulation': enable_gradient_accumulation,
            'accumulation_factor': accumulation_factor,
        }
        self.set_options(options)

    def set_precision_config(self, enable_fp16=False):
        """
        Set half computation configuration to the IpuStrategy instance. Used to optimize the performance.

        Args:
            enable_fp16 (bool, optional): Enable FLOAT16 mode and transform FLOAT32 to FLOAT16. Default False, which means disable FLOAT16 mode.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.set_precision_config(enable_fp16=False)
        """
        options = {
            'enable_fp16': enable_fp16,
        }
        self.set_options(options)

    def add_custom_op(self,
                      paddle_op,
                      popart_op=None,
                      domain='custom.ops',
                      version=1):
        """
        Add a mapping to use popart custom ops running on the IPU.

        Args:
            paddle_op(str): the name of custom op in paddle.

            popart_op(str): the name of custom op in popart.

            domain(str): domain name of custom op in popart.

            version(int): version of custom op in popart.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.add_custom_op('paddle_relu', 'popart_relu')
        """
        if popart_op is None:
            popart_op = paddle_op
        custom_op = {
            'paddle_op': paddle_op,
            'popart_op': popart_op,
            'domain': domain,
            'version': version,
        }
        self.set_options({'custom_op': custom_op})
        self.custom_op_names.append(paddle_op)
        if not self.has_custom_ops:
            self.has_custom_ops = True

    def set_options(self, options):
        """
        Set options from dict.

        Args:
            options(dict): dict of options.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                options = {'num_ipus':1, 'enable_fp16': True}
                ipu_strategy.set_options(options)
        """
        self._ipu_strategy.set_options(options)

    def get_option(self, option):
        """
        Get option.

        Args:
            option(str): name of option.
        
        Returns:
            option value.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                num_ipus = ipu_strategy.get_option('num_ipus')
        """
        return self._ipu_strategy.get_option(option)['value']

    def enable_pattern(self, pattern):
        """
        Enable PopART pattern to optimize the graph.

        Args:
            pattern(string): the name of the pattern.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.enable_pattern("ViewSimplifyPattern")
        """
        self._ipu_strategy.enable_pattern(pattern)

    def disable_pattern(self, pattern):
        """
        Disable PopART pattern.

        Args:
            pattern(string): the name of the pattern.
        
        Returns:
            None.

        Examples:
            .. code-block:: python

                # required: ipu

                import paddle
                import paddle.static as static

                paddle.enable_static()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.disable_pattern("ViewSimplifyPattern")
        """
        self._ipu_strategy.disable_pattern(pattern)

    @property
    def num_ipus(self):
        """
        Get the number of IPU devices from IpuStrategy instance.
        """
        return self.get_option('num_ipus')

    @property
    def is_training(self):
        """
        Get the boolean of training or inference from IpuStrategy instance.
        """
        return self.get_option('is_training')

    @property
    def enable_pipelining(self):
        """
        Get the boolean of enable pipelining or not from IpuStrategy instance.
        """
        return self.get_option('enable_pipelining')

    @property
    def enable_fp16(self):
        """
        Get the boolean of float16 mode or not from IpuStrategy instance.
        """
        return self.get_option('enable_fp16')


class IpuCompiledProgram(object):
    """
    The IpuCompiledProgram is used to transform a program to a ipu-target program,
    such as forward graph extraction, computing graph transformation, useless scale Ops clean, etc.

    Args:
        program(Program, optional): This parameter represents the :code:`Program`
            to be executed. Default is None, which means the program will be set to 
            the default program :code:`paddle.static.default_main_program()` .
        scope(Scope, optional): The scope used to run this program, you can switch
            it to different scope. Default is None, which means use the global 
            scope :code:`paddle.static.global_scope()` .
        ipu_strategy(IpuStrategy, optional): This argument is used to build the program with the
            specified options, such as half computation, training or inference session, the number of IPUs, etc.
            Default is None, which means build the program based on the default `ipu_strategy`. 

    Returns:
        IpuCompiledProgram

    Example:
        .. code-block:: python
	
            # required: ipu

            import paddle
            import paddle.static as static

            paddle.enable_static()

            a = static.data(name='data', shape=[None, 1], dtype='int32')
            b = a + 1
            main_prog = static.default_main_program()
            
            ipu_strategy = static.IpuStrategy()
            ipu_strategy.set_graph_config(num_ipus=1, is_training=True, micro_batch_size=1)
            ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, enable_gradient_accumulation=False, accumulation_factor=1)
            ipu_strategy.set_precision_config(enable_fp16=False)
            
            ipu_compiled_program = static.IpuCompiledProgram(
                main_prog,
                ipu_strategy=ipu_strategy)
    """

    def __init__(self, program=None, scope=None, ipu_strategy=None):
        if not core.is_compiled_with_ipu():
            raise ValueError(
                "Can not use this function since PaddlePaddle is not compiled with IPU"
            )

        if program is None:
            program = framework.default_main_program()

        if not isinstance(program, framework.Program):
            raise TypeError(
                "The type of program is wrong, expected Program, but got %s" %
                type(program))

        self._program = program
        self._compiled = False

        if scope is not None:
            self._scope = scope
        else:
            # import here to avoiding confused
            import paddle
            self._scope = paddle.static.global_scope()

        if ipu_strategy is not None:
            self._ipu_strategy = ipu_strategy
        else:
            self._ipu_strategy = IpuStrategy()

        if ipu_strategy.has_custom_ops:
            self._custom_op_names = set(ipu_strategy.custom_op_names)
        else:
            self._custom_op_names = ()

        self._backend = core.IpuBackend.get_instance()

    def compile(self, feed_list, fetch_list):
        """
        This interface is used to compile the input Program to a program
        to run the model on the ipu.
        
        Args:
            feed_list(list): This parameter represents the input Tensors of the model.

            fetch_list(list): This parameter represents the Tensors that need to be returned
                after the model.

        Returns:
            Program

        Example:
            .. code-block:: python
    	
                # required: ipu
    
                import paddle
                import paddle.static as static
    
                paddle.enable_static()
    
                a = static.data(name='data', shape=[None, 1], dtype='int32')
                b = a + 1
                main_prog = static.default_main_program()

                ipu_strategy = static.IpuStrategy()
                ipu_strategy.set_graph_config(num_ipus=1, is_training=True, micro_batch_size=1)
                ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, enable_gradient_accumulation=False, accumulation_factor=1)
                ipu_strategy.set_precision_config(enable_fp16=False)
                
                program = static.IpuCompiledProgram(
                    main_prog,
                    ipu_strategy=ipu_strategy).compile([a.name], [b.name])
        """
        self._backend.set_scope(self._scope)
        self._backend.set_ipu_strategy(self._ipu_strategy._ipu_strategy)

        # feed and fetch doesn't have corresponding popart op, so we rm both here
        global_block = self._program.global_block()
        need_to_remove_op_index = []
        for i, op in enumerate(global_block.ops):
            op.desc.set_is_target(False)
            if op.type == 'feed' or op.type == 'fetch':
                need_to_remove_op_index.append(i)

        for index in need_to_remove_op_index[::-1]:
            global_block._remove_op(index)

        for var in ['feed', 'fetch']:
            if global_block.has_var(var):
                global_block._remove_var(var)

        self._program.desc.flush()
        self._graph = core.Graph(self._program.desc)

        if self._ipu_strategy.is_training:
            passes = [
                'optimizer_extract_pass',
                'optimizer_state_align_pass',
            ]
            for pass_name in passes:
                a_pass = core.get_pass(pass_name)
                a_pass.apply(self._graph)

        passes = [
            'forward_graph_extract_pass',
            'infer_shape_pass',
            'avg_shard_pass',
            'delete_scale_op_pass',
        ]
        for pass_name in passes:
            a_pass = core.get_pass(pass_name)
            if pass_name == 'infer_shape_pass':
                a_pass.set('feed_list', feed_list)
            a_pass.apply(self._graph)

        a_pass = core.get_pass('popart_canonicalization_pass')
        if self._custom_op_names:
            a_pass.set('custom_ops', self._custom_op_names)
        a_pass.apply(self._graph)

        passes = [
            'ipu_inplace_pass',
            'ipu_graph_builder_pass',
            'ipu_runtime_replacer_pass',
        ]
        for pass_name in passes:
            a_pass = core.get_pass(pass_name)
            a_pass.set('feed_list', feed_list)
            a_pass.set('fetch_list', fetch_list)
            a_pass.apply(self._graph)

        convert_pass = core.get_pass('graph_to_program_pass')
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
        convert_pass.apply(self._graph)
        program = framework.Program._construct_from_desc(desc)

        if hasattr(self._program, 'lr_sheduler'):
            # how to share var between two different block ?
            lr_var_name = self._program.lr_sheduler._var_name

            program.lr_sheduler = self._program.lr_sheduler
            # Program.clone will clone lr_sheduler, so i set lr_var as
            # lr_sheduler attribute
            global_block = self._program.global_block()
            program.lr_sheduler.lr_var = global_block.vars[lr_var_name]

        # with popart, we need to support batches_per_step, what means
        # the shape of feed_var and feed_tensor(maybe numpy array) will
        # mismatch, so we set need_check_feed to False. Thus we can avoid
        # modify logic of run.
        program_global_block = program.global_block()
        for feed_name in feed_list:
            feed_var = program_global_block.var(feed_name)
            feed_var.desc.set_need_check_feed(False)

        if not hasattr(program, 'org_program'):
            program.org_program = self._program

        return program