# Copyright (c) 2021 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 copy
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import framework as framework
from paddle.fluid import core, unique_name
from paddle.fluid.framework import Program, Parameter, Variable, program_guard
from paddle.fluid.data_feeder import check_variable_and_dtype, check_dtype
from paddle.fluid.backward import append_backward, _some_in_set_, _append_grad_suffix_
from paddle.distributed.auto_parallel.operators.common import get_distributed_operator
from paddle.distributed.auto_parallel.operators.common import find_best_compatible_distributed_operator_impl
from paddle.fluid.clip import GradientClipBase, GradientClipByNorm, error_clip_callback, append_gradient_clip_ops, ClipGradByGlobalNorm
from paddle.distributed.fleet.base.distributed_strategy import DistributedStrategy
from paddle.distributed.auto_parallel.context import DistributedContext
from paddle.distributed.fleet.meta_optimizers.common import is_loss_grad_op, is_backward_op, is_optimizer_op
from paddle.distributed.fleet.meta_optimizers.common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY
from .process import new_process_group
from .interface import _g_process_mesh_map
from .utils import _get_comm_group

__varname_not_in_block__ = ["lod_tensor_blocking_queue_0"]


class Partitioner(object):
    """
    warning:: Partitioner is experimental and subject to change.

    Partitioner convert a program into another program.
    Given a serial program which has been auto completed with shard annotation, the Partitioner 
    convert the serial program into a "distributed" program. The Partitioner will  modify the serial
    program in following two ways, which is also the major difference between serial and distributed program:
        1. partition op: replace a serial op into its corresponding dist op infered from the shard annotation
        2. partition var: if a var is sharded, modify the shape of var according to its shard annotation

    Partitioner is supposed to be call by the auto parallel framework, and not supposed to be directly called by user.

    Example:
        ....
            import paddle.distributed.auto_parallel as auto
            from paddle.fluid.distributed_attribute import get_default_distributed_context
            from paddle.distributed import fleet
            from paddle.distributed.auto_parallel.partitioner import Partitioner

            # create serial program with forward only 
            with static.program_guard(serial_main_program, serial_start_program):
                model = create_model(config)
                tokens = static.data(name="tokens", shape=[batch_size, sequence_len], dtype='int64')
                labels = static.data(name="labels", shape=[batch_size, sequence_len], dtype='int64')
                loss_mask = static.data(name="loss_mask", shape=[batch_size, sequence_len], dtype='int64')
                preds = model(tokens)
                loss = criterion(preds, labels, loss_mask)

            # auto completion
            auto.ProcessMesh(shape=[2, 4], process_group=[0, 1, 2, 3, 4, 5, 6, 7])
            annotated_main_program = auto.complete_annotation(serial_main_program)
            auto_paralle_context = get_default_distributed_context()
                
            # distributed strategy & rank info
            rank_id = paddle.distributed.get_rank()
            dist_strategy = fleet.DistributedStrategy()
    
            # create partitioner
            Partitioner = Partitioner(dist_strategy, auto_paralle_context, rank_id)

            # create dist program with forward only
            # for distributed inference, using partitioned_main_prog from here
            partitioned_main_prog, partitioned_startup_prog = Partitioner.transpile_forward(complete_train_program, start_program)

            # create dist program with forward/backward/update
            # for distributed training, using partitioned_main_prog from here
            dist_params_grads = Partitioner.apply_backward(loss, complete_train_program, start_program, partitioned_main_prog, partitioned_startup_prog)
            optimizer = paddle.fluid.optimizer.AdamOptimizer(
                learning_rate=0.00001,
                beta1=0.9,
                beta2=0.999,
                epsilon=1e-08,
                grad_clip=None)
            opt_ops = Partitioner.apply_optimize(optimizer, dist_params_grads, partitioned_main_prog, partitioned_startup_prog)
    """

    def __init__(self, dist_strategy, auto_parallel_context, rank_id=0):
        """
        Args:
            dist_strategy (paddle.fleet.distributed_strategy): used to determine the user defined distributed strategy.
            auto_parallel_context (paddle.fluid.DistributedContext): used to access the distributed_attr of var & op, every Partitioner object could maintain its own DistributedContext member, and partition program base on that shard scenario.
            rank_id (int): global rank id to which the partitioned distributed program belong.
        """

        if not isinstance(dist_strategy, DistributedStrategy):
            raise TypeError(
                "dist_strategy be paddle.fleet.base.DistributedStrategy, got %s here"
                % type(dist_strategy))

        if not isinstance(auto_parallel_context, DistributedContext):
            raise TypeError(
                "auto_parallel_context be paddle.fluid.DistributedContext, got %s here"
                % type(auto_parallel_context))

        self._dist_strategy = dist_strategy
        self._auto_parallel_context = auto_parallel_context
        self._rank_id = rank_id
        self._serial2dist_varname_mapping = {}
        self._dist_varname_suffix = ""

        # TODO if there is some dist op that is not compatible 
        # with auto_backward in forward, the following flag 
        # should be set to False
        self._compatible_with_auto_backward = True

        # data parallelism        
        self._enable_data_parallel = False
        self._dp_degree = 0
        self._dp_group = None

        # tensor parallelism        
        self._enable_tensor_parallel = False
        self._tp_degree = 0
        self._tp_group = None

    def transpile_forward(self, serial_main_program, serial_startup_program):
        """
        take serial forward programs with shard annotation, create a new distributed forward programs based on the serial ones.
        instead of modify the input programs inplace, this function will preserve the inputs and create new program for output.

        beside replace the serial op with its dist op, if user has defined other strategy in fleet.distributed_strategy, and if 
        those strategy need to transpile (modify) the forward network program, those forward program modification should also be done within this
        function in auto parallel scenario, in order to facilitate distributed inference/evaluation which need to DECOUPLE strategy specific forward transpilation with fleet.distributed_optimizer.minimize().

        by now the fleet.distributed_strategy that need transpile forward program are following: 
            1. (optimizer) sharding

        Args:
            main_program (paddle.fluid.framework.program): serial main program with forward network only
            startup_program (paddle.fluid.framework.program): serial startup program with forward network only
        
        return:
            main_program (paddle.fluid.framework.program): distributed main program with forward network only
            startup_program (paddle.fluid.framework.program): distributed startup program with forward network only
        """

        dist_main_program, dist_startup_program = self.transpile_forward_impl(
            serial_main_program, serial_startup_program)
        return dist_main_program, dist_startup_program

    def apply_backward(self,
                       serial_loss,
                       serial_main_program,
                       serial_startup_program,
                       dist_main_program,
                       dist_startup_program,
                       parameter_list=None,
                       no_grad_set=None,
                       callbacks=None):
        """
        A complete training neural network is made up of forward and backward propagation. 
        This function is to generate the dist backward program for the distributed forward program.

        By now, the current automatical backward mechanism in paddle framework might NOT handle the backward generation for 
        some dist ops correctly, some so we now have two ways to genenate the backward program:
            1. dist_forward_program --> auto_backward --> dist_backward_program (if auto_backward could handle all dist op)
            2. serial_forward_program --> auto_backward --> serial_backward_program --> dist_op_backward_transpile --> dist_backward_program (if auto_backward could not handle all dist op)
        
        the backprogram is append the input dist program inplaced.

        Args:
            serial_loss (Variable) the loss in serial program that to be minimized 
            serial_main_program (paddle.fluid.framework.program): serial main program with forward network only
            serial_startup_program (paddle.fluid.framework.program): serial startup program with forward network only
            dist_main_program (paddle.fluid.framework.program): dist main program with forward network only
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward network only
            parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
                to minimize ``loss``. The default value is None, at this time all parameters
                will be updated.
            no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need
                to be updated. The default value is None.
            callbacks (list, optional): list of callable objects to run when appending backward
                operator for one parameter. The default value is None.
        
        return:
            params_grads (list) list of tuple that contain param and its grad variable
        """
        params_grads = self.apply_backward_impl(
            serial_loss, serial_main_program, serial_startup_program,
            dist_main_program, dist_startup_program)
        return params_grads

    def apply_optimize(self, user_define_optimizer, params_grads,
                       dist_main_program, dist_startup_program):
        """
        append update related ops to the program: clip, weight decay, ops
        filter optimize op if sharding is enable
        naive gradient synchronization before update

        Args:
            user_define_optimizer (paddle.fluid.optimizer): 
            params_grads (list) list of tuple that contain param and its grad variable
            dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network 
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network 
        """

        optimize_ops = self.apply_optimize_impl(user_define_optimizer,
                                                params_grads, dist_main_program,
                                                dist_startup_program)

        return optimize_ops

    def transpile_forward_impl(self, main_program, startup_program):

        if not isinstance(main_program, (Program)):
            raise TypeError(
                "dist_strategy be paddle.fluid.framework.program, got %s here" %
                type(main_program))

        if not isinstance(startup_program, (Program)):
            raise TypeError(
                "auto_parallel_context be paddle.fluid.framework.program, got %s here"
                % type(startup_program))

        # check if shard annotated serial program valid
        if not self._is_valid_annotated_program(main_program):
            raise RuntimeError(
                "Not all vars or ops are annotated in main program !")

        # determine parallelism mode
        self._determine_parallel_mode(main_program)

        # dist op & partition vars
        new_main_prog, new_startup_program = self._dist_var_op_forward_transpile(
            main_program, startup_program)

        # Sharding
        if self._dist_strategy.sharding:
            new_main_prog, new_startup_program = self._sharding_forward_transpile(
                new_main_prog, new_startup_program)

        return new_main_prog, new_startup_program

    def apply_backward_impl(self,
                            serial_loss,
                            serial_main_program,
                            serial_startup_program,
                            dist_main_program,
                            dist_startup_program,
                            parameter_list=None,
                            no_grad_set=None,
                            callbacks=None):
        """
        """

        params_grads = self._dist_var_op_backward_transpile(
            serial_loss, serial_main_program, serial_startup_program,
            dist_main_program, dist_startup_program)
        # Sharding
        if self._dist_strategy.sharding:
            self._sharding_backward_transpile(new_main_prog,
                                              new_startup_program)

        # Data Parallel pass
        if self._enable_data_parallel:
            self._gradient_sync_transpile(dist_main_program,
                                          dist_startup_program)

        return params_grads

    def apply_optimize_impl(self, user_define_optimizer, params_grads,
                            dist_main_program, dist_startup_program):
        """
        append update related ops to the program: clip, weight decay, ops
        filter optimize op if sharding is enable
        naive gradient synchronization before update

        Args:
            user_define_optimizer (paddle.fluid.optimizer): 
            params_grads (list) list of tuple that contain param and its grad variable
            dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network 
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network 
        """

        if self._dist_strategy.sharding:
            params_grads = sharding_optimize_transpile(
                params_grads, dist_main_program, dist_startup_program)

        optimize_ops = self._optimize_transpile(user_define_optimizer,
                                                params_grads, dist_main_program,
                                                dist_startup_program)

        return optimize_ops

    def _dist_var_op_forward_transpile(self,
                                       serial_main_program,
                                       serial_startup_program=None):
        """
        1. partition variables
        2. replace local op with corresponding dist op
        """

        partitioned_main_prog = fluid.Program()
        partitioned_global_block = partitioned_main_prog.global_block()
        serial_global_block = serial_main_program.global_block()
        serial_ops = serial_main_program.global_block().ops

        # transpile main program
        for op in serial_ops:

            # partititon input variables
            for serial_input_varname in op.desc.input_arg_names():
                if serial_input_varname not in self._serial2dist_varname_mapping:
                    new_varname = serial_input_varname + self._dist_varname_suffix
                    if serial_global_block.has_var(serial_input_varname):
                        _partition_var(self._auto_parallel_context,
                                       serial_global_block,
                                       partitioned_global_block,
                                       serial_input_varname, new_varname)
                    else:
                        assert serial_input_varname in __varname_not_in_block__

                    self._serial2dist_varname_mapping[
                        serial_input_varname] = new_varname

            # partition output vars
            for serial_output_varname in op.desc.output_arg_names():
                if serial_output_varname not in self._serial2dist_varname_mapping:
                    new_varname = serial_output_varname + self._dist_varname_suffix
                    _partition_var(self._auto_parallel_context,
                                   serial_global_block,
                                   partitioned_global_block,
                                   serial_output_varname, new_varname)
                    self._serial2dist_varname_mapping[
                        serial_output_varname] = new_varname

            # partition op
            if _found_match_dist_op(self._auto_parallel_context, op):
                # replace with corresponding dist op
                _insert_dist_op(op, partitioned_global_block,
                                self._serial2dist_varname_mapping,
                                self._auto_parallel_context, self._rank_id)
            else:
                # replicate op
                _insert_src_op(op, partitioned_global_block,
                               self._serial2dist_varname_mapping)

        # transpile startup program
        if serial_startup_program == None:
            partitioned_startup_prog = None
        else:
            partitioned_startup_prog = fluid.Program()
            # create parameter
            partitioned_startup_global_block = partitioned_startup_prog.global_block(
            )
            param2shape = {}
            for var in partitioned_main_prog.list_vars():
                if isinstance(var, Parameter):
                    _partition_parameter(self._auto_parallel_context, var,
                                         partitioned_startup_global_block,
                                         var.name, var.shape)
                    param2shape[var.name] = var.shape

            # copy initializer
            for op in serial_startup_program.global_block().ops:
                output_vars = op.desc.output_arg_names()
                assert len(
                    output_vars
                ) == 1, "initializer should output only ONE variable, but got [{}]".format(
                    str(op.desc))
                assert self._serial2dist_varname_mapping[output_vars[
                    0]] in param2shape, "try to initialize [{}] which is not a Parameter".format(
                        output_vars[0])
                new_op_desc = partitioned_startup_global_block.desc.append_op()
                new_op_desc.copy_from(op.desc)
                new_op_desc._rename_output(
                    output_vars[0],
                    self._serial2dist_varname_mapping[output_vars[0]])
                new_op_desc._set_attr("shape", param2shape[
                    self._serial2dist_varname_mapping[output_vars[0]]])
                partitioned_startup_global_block._sync_with_cpp()

            # MP broadcast not split parameter
            # NOTE Theoretically, the MP param init broadcast should be handled by
            # each dist op itself. but if we insert the broadcast op at that moment, the broadcast
            # will before the initializer, which lead to a undertermined case.
            if self._enable_tensor_parallel:
                param_to_sync = []
                for param in partitioned_startup_prog.all_parameters():
                    if not self._is_var_distributed(param):
                        param_to_sync.append(param)
                        # FIXME the ring id should be set by autoparallel.mapping module
                        # it should be determined by dp groups butfixed it here for hacking
                        partitioned_startup_global_block.append_op(
                            type='c_broadcast',
                            inputs={'X': param},
                            outputs={'Out': param},
                            attrs={
                                'ring_id': self._tp_group.id,
                                'root': 0,
                                'use_calc_stream': True,
                                OP_ROLE_KEY: OpRole.Forward
                            })
                partitioned_startup_global_block.append_op(
                    type='c_sync_comm_stream',
                    inputs={'X': param_to_sync},
                    outputs={'Out': param_to_sync},
                    attrs={
                        'ring_id': self._tp_group.id,
                        OP_ROLE_KEY: OpRole.Forward
                    })
                partitioned_startup_global_block._sync_with_cpp()

            # DP init param broadcast
            if self._enable_data_parallel:
                # parameters initialization synchronization 
                param_to_sync = []

                for param in partitioned_startup_global_block.all_parameters():
                    param_to_sync.append(param)

                    # FIXME the ring id should be set by autoparallel.mapping module
                    # it should be determined by dp groups butfixed it here for hacking
                    partitioned_startup_global_block.append_op(
                        type='c_broadcast',
                        inputs={'X': param},
                        outputs={'Out': param},
                        attrs={
                            'ring_id': self._dp_group.id,
                            'root': 0,
                            'use_calc_stream': True,
                            OP_ROLE_KEY: OpRole.Forward
                        })
                partitioned_startup_global_block.append_op(
                    type='c_sync_comm_stream',
                    inputs={'X': param_to_sync},
                    outputs={'Out': param_to_sync},
                    attrs={
                        'ring_id': self._dp_group.id,
                        OP_ROLE_KEY: OpRole.Forward
                    })
                partitioned_startup_global_block._sync_with_cpp()

        return partitioned_main_prog, partitioned_startup_prog

    def _dist_var_op_backward_transpile(self,
                                        serial_loss,
                                        serial_main_program,
                                        serial_startup_program,
                                        dist_main_program,
                                        dist_startup_program,
                                        parameter_list=None,
                                        no_grad_set=None,
                                        callbacks=None):
        """
        so far, the auto_backward case only guarantee the correcotness of backward ops for curtain Dist ops:
            1. NV-Megatron-like parallel embedding
            2. NV-Megatron-like row parallel linear
            3. NV-Megatron-like col parallel linear
        """

        if self._compatible_with_auto_backward:
            assert isinstance(
                serial_loss, Variable), "The target loss should be an Variable."
            dist_loss = self._serial_varname2dist_var(serial_loss.name,
                                                      dist_main_program)

            assert len(dist_loss.shape) == 1 and dist_loss.shape[0] == 1, \
                "The dist loss.shape should be (1L,), but the current dist loss.shape is {}. " \
                "Maybe that you should call fluid.layers.mean to process the current loss.".format(
                    dist_loss.shape)

            # update parameter list
            if parameter_list:
                parameter_list = [
                    self._serial_varname2dist_var(param.name, dist_main_program)
                    for param in parameter_list
                ]

            # update parameter no_grad_set
            if no_grad_set:
                no_grad_set = [
                    self._serial_varname2dist_var(param.name, dist_main_program)
                    for param in no_grad_set
                ]

            return _auto_backward(
                dist_loss,
                dist_startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set,
                callbacks=callbacks)
        # replace dist grad ops
        else:
            raise RuntimeError("transpile NOT implemented !")

    def _optimize_transpile(self, user_define_optimizer, params_grads,
                            main_program, startup_program):

        with program_guard(main_program, startup_program):
            optimize_ops = user_define_optimizer.apply_gradients(params_grads)

        return optimize_ops

    def _is_valid_annotated_program(self, program):

        # TODO (ZJ-LIANG) should check all block
        ops = program.global_block().ops
        vars_ = program.list_vars()
        op_dist_attrs = [
            self._auto_parallel_context.get_op_distributed_attr_for_program(op)
            for op in ops
        ]
        var_dist_attrs = [
            self._auto_parallel_context.get_tensor_distributed_attr_for_program(
                var) for var in vars_
        ]

        all_ops_annotated = all(dist_attr is not None
                                for dist_attr in op_dist_attrs)
        all_vars_annotated = all(dist_attr is not None
                                 for dist_attr in var_dist_attrs)

        return all_ops_annotated and all_vars_annotated

    def _serial_varname2dist_var(self, serial_varname, dist_program):
        assert serial_varname in self._serial2dist_varname_mapping, "The serial var [{}] is not found in var name mapping".format(
            serial_varname)
        dist_varname = self._serial2dist_varname_mapping[serial_varname]

        assert dist_program.global_block().has_var(
            dist_varname
        ), "The dist var [{}] is not found in dist program".format(dist_varname)
        dist_var = dist_program.global_block().var(dist_varname)

        return dist_var

    def _determine_parallel_mode(self, program):
        """
        determine the parallelism that is enabled
        NOTE a hard rule and should be updated in future
        """

        for param in program.all_parameters():
            if self._is_var_distributed(param):
                self._enable_tensor_parallel = True
                break

        for var in program.list_vars():
            var_dist_attr = self._auto_parallel_context.get_tensor_distributed_attr_for_program(
                var)
            if not var_dist_attr.is_parameter():
                mapping = var_dist_attr.get_dims_mapping()
                mesh = var_dist_attr.get_process_mesh().topology
                if mapping and mapping[0] >= 0 and mesh[mapping[0]] > 1:
                    self._enable_data_parallel = True
                    break

        # tensor parallelism
        if self._enable_tensor_parallel:
            model_parallel_axis, process_mesh = self._auto_parallel_context._get_model_parallel_info(
            )
            group_ranks = _get_comm_group(process_mesh.process_group,
                                          process_mesh.topology,
                                          model_parallel_axis, self._rank_id)
            self._tp_degree = len(group_ranks)
            self._tp_group = new_process_group(group_ranks)

        # data parallelism
        data_parallel_axis, process_mesh = self._auto_parallel_context._get_data_parallel_info(
        )
        if self._enable_data_parallel:
            group_ranks = _get_comm_group(process_mesh.process_group,
                                          process_mesh.topology,
                                          data_parallel_axis, self._rank_id)
            self._dp_degree = len(group_ranks)
            self._dp_group = new_process_group(group_ranks)

    def _is_var_distributed(self, var):

        dist_attr = self._auto_parallel_context.get_tensor_distributed_attr_for_program(
            var)
        assert dist_attr is not None, "dist_attr of var [{}] is None".format(
            var.name)
        return _is_distributed(dist_attr)

    def _sharding_forward_transpile(self, main_prog, startup_program):
        """
        this transpile conduct the modification in forward program need by sharding strategy
        which majorly include:
            1. partition the parameter
            2. insert broadcast op
            3. insert sync op 

        NOTE the transpile modification is inplace on the input program
        """

        raise NotImplementedError(
            "Sharding is NOT support in AutoParallel yet!")

    def _sharding_backward_transpile(self, main_prog, startup_program):
        """
        this transpile conduct the modification in backward program need by sharding strategy
        which majorly include:
            1. partition the gradient
            2. insert broadcast op
            3. insert sync op 

        NOTE the transpile modification is inplace on the input program
        """

        raise NotImplementedError(
            "Sharding is NOT support in AutoParallel yet!")

    def _sharding_optimize_transpile(self, params_grads, dist_main_program,
                                     dist_startup_program):
        """
        shard params_grads
        append the broadcast to sync parameters 
        """
        raise RuntimeError("sharding transpile is NOT implemented !")

    def _gradient_sync_transpile(self, main_program, startup_program):
        """
        append the gradient allreduce ops for all parameters' grad in case of Data Parallel
        """

        # scale loss by dp degree
        main_global_block = main_program.global_block()
        for idx, op in reversed(list(enumerate(main_global_block.ops))):
            if is_loss_grad_op(op):
                loss_grad_var = main_global_block.vars[op.output_arg_names[0]]
                main_global_block._insert_op_without_sync(
                    idx + 1,
                    type='scale',
                    inputs={'X': loss_grad_var},
                    outputs={'Out': loss_grad_var},
                    attrs={
                        'scale': 1.0 / self._dp_degree,
                        OP_ROLE_KEY: OpRole.Backward
                    })
                break
        main_global_block._sync_with_cpp()

        # gradient synchronization
        # NOTE naive gradient sync without overlapping
        # so there is not need to sync between calc and comm
        # collecting grad var
        grad_to_sync = []
        for idx, op in reversed(list(enumerate(main_global_block.ops))):
            if is_backward_op(op) and \
                    OP_ROLE_VAR_KEY in op.attr_names:
                op_role_var = op.all_attrs()[OP_ROLE_VAR_KEY]
                if len(op_role_var) != 0:
                    assert len(op_role_var) % 2 == 0
                    for i in range(0, len(op_role_var), 2):
                        param, reduced_grad = op_role_var[i], op_role_var[i + 1]
                        assert (reduced_grad not in grad_to_sync)
                        grad_to_sync.append(reduced_grad)
            if is_optimizer_op(op):
                first_optimize_op_idx = idx

        # insert allreduce
        for grad in grad_to_sync:
            # FIXME the ring id should be set by autoparallel.mapping module
            # it should be determined by dp groups butfixed it here for hacking
            main_global_block.append_op(
                type='c_allreduce_sum',
                inputs={'X': grad},
                outputs={'Out': grad},
                attrs={
                    'ring_id': self._dp_group.id,
                    'root': 0,
                    'use_calc_stream': True,
                    OP_ROLE_KEY: OpRole.Backward
                })
        main_global_block.append_op(
            type='c_sync_comm_stream',
            inputs={'X': grad_to_sync},
            outputs={'Out': grad_to_sync},
            attrs={'ring_id': self._dp_group.id,
                   OP_ROLE_KEY: OpRole.Backward})
        main_global_block._sync_with_cpp()


def _get_no_grad_set_name(no_grad_set):
    no_grad_set_name = set()
    if no_grad_set is not None:
        if isinstance(no_grad_set, (set, list, tuple)):
            for i, no_grad_var in enumerate(no_grad_set):
                if isinstance(no_grad_var, framework.Variable):
                    no_grad_set_name.add(no_grad_var.name)
                elif isinstance(no_grad_var, six.string_types):
                    no_grad_set_name.add(no_grad_var)
                else:
                    raise TypeError(
                        "The type of no_grad_set's member must be paddle.fluid.Variable or str, but received %s."
                        % (type(no_grad_var)))
        else:
            raise TypeError(
                "The type of no_grad_set should be set or list or tuple, but received {}".
                format(type(no_grad_set)))
    return no_grad_set_name


def _get_no_grad_set(loss, no_grad_set=None):
    no_grad_set = _get_no_grad_set_name(no_grad_set)
    parameters = loss.block.program.global_block().all_parameters()
    param_no_trainable = set(
        [param.name for param in parameters if param.trainable is False])
    # If the parameter is no trainable, it should not have a gradient.
    no_grad_set.update(param_no_trainable)

    return no_grad_set


def _found_match_dist_op(auto_paralle_context, op):
    dist_attr = auto_paralle_context.get_op_distributed_attr_for_program(op)
    dist_ops = get_distributed_operator(op.type)

    return dist_ops and dist_attr.get_impl_idx() >= 0 and dist_ops.get_impl( \
        dist_attr.get_impl_idx())._forward_implemented


def _auto_backward(loss,
                   startup_program=None,
                   parameter_list=None,
                   no_grad_set=None,
                   callbacks=None):
    """
    modification is inplaced
    """
    act_no_grad_set = _get_no_grad_set(loss, no_grad_set)
    assert isinstance(loss, Variable), "The target loss should be an Variable."

    if callbacks is None:
        callbacks = [error_clip_callback]
    else:
        assert (isinstance(callbacks, list))

    assert len(loss.shape) == 1 and loss.shape[0] == 1, \
        "The loss.shape should be (1L,), but the current loss.shape is {}. " \
        "Maybe that you should call fluid.layers.mean to process the current loss.".format(
            loss.shape)

    program = loss.block.program
    with program_guard(program, startup_program):
        params_grads = append_backward(loss, parameter_list, act_no_grad_set,
                                       callbacks)

    return params_grads


def _is_distributed(dist_attr):

    mapping = dist_attr.get_dims_mapping()
    mesh = dist_attr.get_process_mesh().topology
    for idx in range(len(mapping)):
        if mapping[idx] >= 0 and mesh[mapping[idx]] > 1:
            return True

    return False


def _get_dist_shape(var, dist_attr):

    var_shape = var.shape
    mapping = dist_attr.get_dims_mapping()
    mesh = dist_attr.get_process_mesh().topology
    assert len(var_shape) == len(
        mapping
    ), "variable shape [{}] and dim_mapping [{}] is NOT match !".format(
        var_shape, mapping)
    new_shape = []
    for idx in range(len(var_shape)):
        if var_shape[idx] == -1 or mapping[idx] == -1:
            new_shape.append(var_shape[idx])
        else:
            assert var_shape[idx] % mesh[mapping[
                idx]] == 0, "un-event partition: var_shape[idx]=[{}], mesh[{}]".format(
                    var_shape[idx], mesh[mapping[idx]])
            new_shape.append(var_shape[idx] // mesh[mapping[idx]])

    return new_shape


def _partition_parameter(auto_paralle_context, src_var, dst_block, dst_varname,
                         dst_shape):
    # NOTE hack to copied Parameter
    # not initialized parameter, need to initialize it 
    copied_kwargs = {}
    copied_kwargs['trainable'] = src_var.trainable
    copied_kwargs['optimize_attr'] = src_var.optimize_attr
    copied_kwargs['regularizer'] = src_var.regularizer
    copied_kwargs['do_model_average'] = src_var.do_model_average
    copied_kwargs['need_clip'] = src_var.need_clip

    param = Parameter(
        block=dst_block,
        type=src_var.type,
        name=dst_varname,
        shape=dst_shape,
        dtype=src_var.dtype,
        lod_level=src_var.lod_level,
        error_clip=src_var.error_clip,
        stop_gradient=src_var.stop_gradient,
        is_data=src_var.is_data,
        belong_to_optimizer=src_var.belong_to_optimizer,
        **copied_kwargs)

    # set dist attr uid
    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
    # param.desc.set_distributed_attr_uid(distributed_attr_uid)
    dist_attr = copy.deepcopy(
        auto_paralle_context.get_tensor_distributed_attr_for_program(src_var))
    dist_attr._owner_tensor = param
    dist_attr._owner_context = auto_paralle_context.get_tensor_distributed_attr_for_program(
        src_var)._owner_context
    auto_paralle_context.set_tensor_distributed_attr_for_program(param,
                                                                 dist_attr)


def _partition_intermediate_var(auto_paralle_context, src_var, dst_block,
                                dst_varname, dst_shape):
    var = dst_block.create_var(
        type=src_var.type,
        name=dst_varname,
        shape=dst_shape,
        dtype=src_var.dtype,
        lod_level=src_var.lod_level,
        persistable=src_var.persistable,
        error_clip=src_var.error_clip,
        stop_gradient=src_var.stop_gradient,
        is_data=src_var.is_data,
        belong_to_optimizer=src_var.belong_to_optimizer)

    # set dist attr uid
    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
    # var.desc.set_distributed_attr_uid(distributed_attr_uid)
    dist_attr = copy.deepcopy(
        auto_paralle_context.get_tensor_distributed_attr_for_program(src_var))
    dist_attr._owner_tensor = var
    dist_attr._owner_context = auto_paralle_context.get_tensor_distributed_attr_for_program(
        src_var)._owner_context
    auto_paralle_context.set_tensor_distributed_attr_for_program(var, dist_attr)


def _partition_var(auto_paralle_context, src_block, dst_block, src_varname,
                   dst_varname):
    """
    partition include: split + replicate
    """
    src_var = src_block.var(src_varname)

    if src_var.type == core.VarDesc.VarType.READER:
        dst_block.create_var(
            type=src_var.type,
            name=dst_varname,
            persistable=True,
            stop_gradient=True)
    else:
        dist_attr = auto_paralle_context.get_tensor_distributed_attr_for_program(
            src_var)
        target_shape = _get_dist_shape(src_var, dist_attr)

        if isinstance(src_var, Parameter):
            _partition_parameter(auto_paralle_context, src_var, dst_block,
                                 dst_varname, target_shape)
        else:
            _partition_intermediate_var(auto_paralle_context, src_var,
                                        dst_block, dst_varname, target_shape)


def _insert_src_op(src_op, dst_block, varname_mapping):

    new_op_desc = dst_block.desc.append_op()
    new_op_desc.copy_from(src_op.desc)
    for local_varname in src_op.desc.input_arg_names():
        new_op_desc._rename_input(local_varname, varname_mapping[local_varname])
    for local_varname in src_op.desc.output_arg_names():
        new_op_desc._rename_output(local_varname,
                                   varname_mapping[local_varname])
    dst_block._sync_with_cpp()


def _insert_dist_op(src_op, dst_block, varname_mapping, auto_paralle_context,
                    rank_id):

    # build input varname mapping
    input_mapping = {}
    for input_name in src_op.desc.input_names():
        varnames = []
        for varname in src_op.desc.input(input_name):
            varnames.append(varname_mapping[varname])
        input_mapping[input_name] = varnames

    # build output varname mapping
    output_mapping = {}
    for output_name in src_op.desc.output_names():
        varnames = []
        for varname in src_op.desc.output(output_name):
            varnames.append(varname_mapping[varname])
        output_mapping[output_name] = varnames

    # append dist op 
    dist_attr = auto_paralle_context.get_op_distributed_attr_for_program(src_op)
    dist_ops = get_distributed_operator(src_op.type)
    append_op_handle = dist_ops.get_impl(dist_attr.get_impl_idx()).forward(
        src_op)
    append_op_handle(
        dst_block,
        src_op,
        dist_attr,
        input_mapping,
        output_mapping,
        rank_id=rank_id)

# Copyright (c) 2021 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 copy
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import framework as framework
from paddle.fluid import core, unique_name
from paddle.fluid.framework import Program, Parameter, Variable, program_guard
from paddle.fluid.data_feeder import check_variable_and_dtype, check_dtype
from paddle.fluid.backward import append_backward, _some_in_set_, _append_grad_suffix_
from paddle.distributed.auto_parallel.operators.common import get_distributed_operator
from paddle.distributed.auto_parallel.operators.common import find_best_compatible_distributed_operator_impl
from paddle.fluid.clip import GradientClipBase, GradientClipByNorm, error_clip_callback, append_gradient_clip_ops, ClipGradByGlobalNorm
from paddle.distributed.fleet.base.distributed_strategy import DistributedStrategy
from paddle.distributed.auto_parallel.context import DistributedContext
from paddle.distributed.fleet.meta_optimizers.common import is_loss_grad_op, is_backward_op, is_optimizer_op
from paddle.distributed.fleet.meta_optimizers.common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY
from .process import new_process_group
from .interface import _g_process_mesh_map
from .utils import _get_comm_group

__varname_not_in_block__ = ["lod_tensor_blocking_queue_0"]


class Partitioner(object):
    """
    warning:: Partitioner is experimental and subject to change.

    Partitioner convert a program into another program.
    Given a serial program which has been auto completed with shard annotation, the Partitioner 
    convert the serial program into a "distributed" program. The Partitioner will  modify the serial
    program in following two ways, which is also the major difference between serial and distributed program:
        1. partition op: replace a serial op into its corresponding dist op infered from the shard annotation
        2. partition var: if a var is sharded, modify the shape of var according to its shard annotation

    Partitioner is supposed to be call by the auto parallel framework, and not supposed to be directly called by user.

    Example:
        ....
            import paddle.distributed.auto_parallel as auto
            from paddle.fluid.distributed_attribute import get_default_distributed_context
            from paddle.distributed import fleet
            from paddle.distributed.auto_parallel.partitioner import Partitioner

            # create serial program with forward only 
            with static.program_guard(serial_main_program, serial_start_program):
                model = create_model(config)
                tokens = static.data(name="tokens", shape=[batch_size, sequence_len], dtype='int64')
                labels = static.data(name="labels", shape=[batch_size, sequence_len], dtype='int64')
                loss_mask = static.data(name="loss_mask", shape=[batch_size, sequence_len], dtype='int64')
                preds = model(tokens)
                loss = criterion(preds, labels, loss_mask)

            # auto completion
            auto.ProcessMesh(shape=[2, 4], process_group=[0, 1, 2, 3, 4, 5, 6, 7])
            annotated_main_program = auto.complete_annotation(serial_main_program)
            auto_paralle_context = get_default_distributed_context()
                
            # distributed strategy & rank info
            rank_id = paddle.distributed.get_rank()
            dist_strategy = fleet.DistributedStrategy()
    
            # create partitioner
            Partitioner = Partitioner(dist_strategy, auto_paralle_context, rank_id)

            # create dist program with forward only
            # for distributed inference, using partitioned_main_prog from here
            partitioned_main_prog, partitioned_startup_prog = Partitioner.transpile_forward(complete_train_program, start_program)

            # create dist program with forward/backward/update
            # for distributed training, using partitioned_main_prog from here
            dist_params_grads = Partitioner.apply_backward(loss, complete_train_program, start_program, partitioned_main_prog, partitioned_startup_prog)
            optimizer = paddle.fluid.optimizer.AdamOptimizer(
                learning_rate=0.00001,
                beta1=0.9,
                beta2=0.999,
                epsilon=1e-08,
                grad_clip=None)
            opt_ops = Partitioner.apply_optimize(optimizer, dist_params_grads, partitioned_main_prog, partitioned_startup_prog)
    """

    def __init__(self, dist_strategy, auto_parallel_context, rank_id=0):
        """
        Args:
            dist_strategy (paddle.fleet.distributed_strategy): used to determine the user defined distributed strategy.
            auto_parallel_context (paddle.fluid.DistributedContext): used to access the distributed_attr of var & op, every Partitioner object could maintain its own DistributedContext member, and partition program base on that shard scenario.
            rank_id (int): global rank id to which the partitioned distributed program belong.
        """

        if not isinstance(dist_strategy, DistributedStrategy):
            raise TypeError(
                "dist_strategy be paddle.fleet.base.DistributedStrategy, got %s here"
                % type(dist_strategy))

        if not isinstance(auto_parallel_context, DistributedContext):
            raise TypeError(
                "auto_parallel_context be paddle.fluid.DistributedContext, got %s here"
                % type(auto_parallel_context))

        self._dist_strategy = dist_strategy
        self._auto_parallel_context = auto_parallel_context
        self._rank_id = rank_id
        self._serial2dist_varname_mapping = {}
        self._dist_varname_suffix = ""

        # TODO if there is some dist op that is not compatible 
        # with auto_backward in forward, the following flag 
        # should be set to False
        self._compatible_with_auto_backward = True

        # data parallelism        
        self._enable_data_parallel = False
        self._dp_degree = 0
        self._dp_group = None

        # tensor parallelism        
        self._enable_tensor_parallel = False
        self._tp_degree = 0
        self._tp_group = None

    def transpile_forward(self, serial_main_program, serial_startup_program):
        """
        take serial forward programs with shard annotation, create a new distributed forward programs based on the serial ones.
        instead of modify the input programs inplace, this function will preserve the inputs and create new program for output.

        beside replace the serial op with its dist op, if user has defined other strategy in fleet.distributed_strategy, and if 
        those strategy need to transpile (modify) the forward network program, those forward program modification should also be done within this
        function in auto parallel scenario, in order to facilitate distributed inference/evaluation which need to DECOUPLE strategy specific forward transpilation with fleet.distributed_optimizer.minimize().

        by now the fleet.distributed_strategy that need transpile forward program are following: 
            1. (optimizer) sharding

        Args:
            main_program (paddle.fluid.framework.program): serial main program with forward network only
            startup_program (paddle.fluid.framework.program): serial startup program with forward network only
        
        return:
            main_program (paddle.fluid.framework.program): distributed main program with forward network only
            startup_program (paddle.fluid.framework.program): distributed startup program with forward network only
        """

        dist_main_program, dist_startup_program = self.transpile_forward_impl(
            serial_main_program, serial_startup_program)
        return dist_main_program, dist_startup_program

    def apply_backward(self,
                       serial_loss,
                       serial_main_program,
                       serial_startup_program,
                       dist_main_program,
                       dist_startup_program,
                       parameter_list=None,
                       no_grad_set=None,
                       callbacks=None):
        """
        A complete training neural network is made up of forward and backward propagation. 
        This function is to generate the dist backward program for the distributed forward program.

        By now, the current automatical backward mechanism in paddle framework might NOT handle the backward generation for 
        some dist ops correctly, some so we now have two ways to genenate the backward program:
            1. dist_forward_program --> auto_backward --> dist_backward_program (if auto_backward could handle all dist op)
            2. serial_forward_program --> auto_backward --> serial_backward_program --> dist_op_backward_transpile --> dist_backward_program (if auto_backward could not handle all dist op)
        
        the backprogram is append the input dist program inplaced.

        Args:
            serial_loss (Variable) the loss in serial program that to be minimized 
            serial_main_program (paddle.fluid.framework.program): serial main program with forward network only
            serial_startup_program (paddle.fluid.framework.program): serial startup program with forward network only
            dist_main_program (paddle.fluid.framework.program): dist main program with forward network only
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward network only
            parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
                to minimize ``loss``. The default value is None, at this time all parameters
                will be updated.
            no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need
                to be updated. The default value is None.
            callbacks (list, optional): list of callable objects to run when appending backward
                operator for one parameter. The default value is None.
        
        return:
            params_grads (list) list of tuple that contain param and its grad variable
        """
        params_grads = self.apply_backward_impl(
            serial_loss, serial_main_program, serial_startup_program,
            dist_main_program, dist_startup_program)
        return params_grads

    def apply_optimize(self, user_define_optimizer, params_grads,
                       dist_main_program, dist_startup_program):
        """
        append update related ops to the program: clip, weight decay, ops
        filter optimize op if sharding is enable
        naive gradient synchronization before update

        Args:
            user_define_optimizer (paddle.fluid.optimizer): 
            params_grads (list) list of tuple that contain param and its grad variable
            dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network 
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network 
        """

        optimize_ops = self.apply_optimize_impl(user_define_optimizer,
                                                params_grads, dist_main_program,
                                                dist_startup_program)

        return optimize_ops

    def transpile_forward_impl(self, main_program, startup_program):

        if not isinstance(main_program, (Program)):
            raise TypeError(
                "dist_strategy be paddle.fluid.framework.program, got %s here" %
                type(main_program))

        if not isinstance(startup_program, (Program)):
            raise TypeError(
                "auto_parallel_context be paddle.fluid.framework.program, got %s here"
                % type(startup_program))

        # check if shard annotated serial program valid
        if not self._is_valid_annotated_program(main_program):
            raise RuntimeError(
                "Not all vars or ops are annotated in main program !")

        # determine parallelism mode
        self._determine_parallel_mode(main_program)

        # dist op & partition vars
        new_main_prog, new_startup_program = self._dist_var_op_forward_transpile(
            main_program, startup_program)

        # Sharding
        if self._dist_strategy.sharding:
            new_main_prog, new_startup_program = self._sharding_forward_transpile(
                new_main_prog, new_startup_program)

        return new_main_prog, new_startup_program

    def apply_backward_impl(self,
                            serial_loss,
                            serial_main_program,
                            serial_startup_program,
                            dist_main_program,
                            dist_startup_program,
                            parameter_list=None,
                            no_grad_set=None,
                            callbacks=None):
        """
        """

        params_grads = self._dist_var_op_backward_transpile(
            serial_loss, serial_main_program, serial_startup_program,
            dist_main_program, dist_startup_program)
        # Sharding
        if self._dist_strategy.sharding:
            self._sharding_backward_transpile(new_main_prog,
                                              new_startup_program)

        # Data Parallel pass
        if self._enable_data_parallel:
            self._gradient_sync_transpile(dist_main_program,
                                          dist_startup_program)

        return params_grads

    def apply_optimize_impl(self, user_define_optimizer, params_grads,
                            dist_main_program, dist_startup_program):
        """
        append update related ops to the program: clip, weight decay, ops
        filter optimize op if sharding is enable
        naive gradient synchronization before update

        Args:
            user_define_optimizer (paddle.fluid.optimizer): 
            params_grads (list) list of tuple that contain param and its grad variable
            dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network 
            dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network 
        """

        if self._dist_strategy.sharding:
            params_grads = sharding_optimize_transpile(
                params_grads, dist_main_program, dist_startup_program)

        optimize_ops = self._optimize_transpile(user_define_optimizer,
                                                params_grads, dist_main_program,
                                                dist_startup_program)

        return optimize_ops

    def _dist_var_op_forward_transpile(self,
                                       serial_main_program,
                                       serial_startup_program=None):
        """
        1. partition variables
        2. replace local op with corresponding dist op
        """

        partitioned_main_prog = fluid.Program()
        partitioned_global_block = partitioned_main_prog.global_block()
        serial_global_block = serial_main_program.global_block()
        serial_ops = serial_main_program.global_block().ops

        # transpile main program
        for op in serial_ops:

            # partititon input variables
            for serial_input_varname in op.desc.input_arg_names():
                if serial_input_varname not in self._serial2dist_varname_mapping:
                    new_varname = serial_input_varname + self._dist_varname_suffix
                    if serial_global_block.has_var(serial_input_varname):
                        _partition_var(self._auto_parallel_context,
                                       serial_global_block,
                                       partitioned_global_block,
                                       serial_input_varname, new_varname)
                    else:
                        assert serial_input_varname in __varname_not_in_block__

                    self._serial2dist_varname_mapping[
                        serial_input_varname] = new_varname

            # partition output vars
            for serial_output_varname in op.desc.output_arg_names():
                if serial_output_varname not in self._serial2dist_varname_mapping:
                    new_varname = serial_output_varname + self._dist_varname_suffix
                    _partition_var(self._auto_parallel_context,
                                   serial_global_block,
                                   partitioned_global_block,
                                   serial_output_varname, new_varname)
                    self._serial2dist_varname_mapping[
                        serial_output_varname] = new_varname

            # partition op
            if _found_match_dist_op(self._auto_parallel_context, op):
                # replace with corresponding dist op
                _insert_dist_op(op, partitioned_global_block,
                                self._serial2dist_varname_mapping,
                                self._auto_parallel_context, self._rank_id)
            else:
                # replicate op
                _insert_src_op(op, partitioned_global_block,
                               self._serial2dist_varname_mapping)

        # transpile startup program
        if serial_startup_program == None:
            partitioned_startup_prog = None
        else:
            partitioned_startup_prog = fluid.Program()
            # create parameter
            partitioned_startup_global_block = partitioned_startup_prog.global_block(
            )
            param2shape = {}
            for var in partitioned_main_prog.list_vars():
                if isinstance(var, Parameter):
                    _partition_parameter(self._auto_parallel_context, var,
                                         partitioned_startup_global_block,
                                         var.name, var.shape)
                    param2shape[var.name] = var.shape

            # copy initializer
            for op in serial_startup_program.global_block().ops:
                output_vars = op.desc.output_arg_names()
                assert len(
                    output_vars
                ) == 1, "initializer should output only ONE variable, but got [{}]".format(
                    str(op.desc))
                assert self._serial2dist_varname_mapping[output_vars[
                    0]] in param2shape, "try to initialize [{}] which is not a Parameter".format(
                        output_vars[0])
                new_op_desc = partitioned_startup_global_block.desc.append_op()
                new_op_desc.copy_from(op.desc)
                new_op_desc._rename_output(
                    output_vars[0],
                    self._serial2dist_varname_mapping[output_vars[0]])
                new_op_desc._set_attr("shape", param2shape[
                    self._serial2dist_varname_mapping[output_vars[0]]])
                partitioned_startup_global_block._sync_with_cpp()

            # MP broadcast not split parameter
            # NOTE Theoretically, the MP param init broadcast should be handled by
            # each dist op itself. but if we insert the broadcast op at that moment, the broadcast
            # will before the initializer, which lead to a undertermined case.
            if self._enable_tensor_parallel:
                param_to_sync = []
                for param in partitioned_startup_prog.all_parameters():
                    if not self._is_var_distributed(param):
                        param_to_sync.append(param)
                        # FIXME the ring id should be set by autoparallel.mapping module
                        # it should be determined by dp groups butfixed it here for hacking
                        partitioned_startup_global_block.append_op(
                            type='c_broadcast',
                            inputs={'X': param},
                            outputs={'Out': param},
                            attrs={
                                'ring_id': self._tp_group.id,
                                'root': 0,
                                'use_calc_stream': True,
                                OP_ROLE_KEY: OpRole.Forward
                            })
                partitioned_startup_global_block.append_op(
                    type='c_sync_comm_stream',
                    inputs={'X': param_to_sync},
                    outputs={'Out': param_to_sync},
                    attrs={
                        'ring_id': self._tp_group.id,
                        OP_ROLE_KEY: OpRole.Forward
                    })
                partitioned_startup_global_block._sync_with_cpp()

            # DP init param broadcast
            if self._enable_data_parallel:
                # parameters initialization synchronization 
                param_to_sync = []

                for param in partitioned_startup_global_block.all_parameters():
                    param_to_sync.append(param)

                    # FIXME the ring id should be set by autoparallel.mapping module
                    # it should be determined by dp groups butfixed it here for hacking
                    partitioned_startup_global_block.append_op(
                        type='c_broadcast',
                        inputs={'X': param},
                        outputs={'Out': param},
                        attrs={
                            'ring_id': self._dp_group.id,
                            'root': 0,
                            'use_calc_stream': True,
                            OP_ROLE_KEY: OpRole.Forward
                        })
                partitioned_startup_global_block.append_op(
                    type='c_sync_comm_stream',
                    inputs={'X': param_to_sync},
                    outputs={'Out': param_to_sync},
                    attrs={
                        'ring_id': self._dp_group.id,
                        OP_ROLE_KEY: OpRole.Forward
                    })
                partitioned_startup_global_block._sync_with_cpp()

        return partitioned_main_prog, partitioned_startup_prog

    def _dist_var_op_backward_transpile(self,
                                        serial_loss,
                                        serial_main_program,
                                        serial_startup_program,
                                        dist_main_program,
                                        dist_startup_program,
                                        parameter_list=None,
                                        no_grad_set=None,
                                        callbacks=None):
        """
        so far, the auto_backward case only guarantee the correcotness of backward ops for curtain Dist ops:
            1. NV-Megatron-like parallel embedding
            2. NV-Megatron-like row parallel linear
            3. NV-Megatron-like col parallel linear
        """

        if self._compatible_with_auto_backward:
            assert isinstance(
                serial_loss, Variable), "The target loss should be an Variable."
            dist_loss = self._serial_varname2dist_var(serial_loss.name,
                                                      dist_main_program)

            assert len(dist_loss.shape) == 1 and dist_loss.shape[0] == 1, \
                "The dist loss.shape should be (1L,), but the current dist loss.shape is {}. " \
                "Maybe that you should call fluid.layers.mean to process the current loss.".format(
                    dist_loss.shape)

            # update parameter list
            if parameter_list:
                parameter_list = [
                    self._serial_varname2dist_var(param.name, dist_main_program)
                    for param in parameter_list
                ]

            # update parameter no_grad_set
            if no_grad_set:
                no_grad_set = [
                    self._serial_varname2dist_var(param.name, dist_main_program)
                    for param in no_grad_set
                ]

            return _auto_backward(
                dist_loss,
                dist_startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set,
                callbacks=callbacks)
        # replace dist grad ops
        else:
            raise RuntimeError("transpile NOT implemented !")

    def _optimize_transpile(self, user_define_optimizer, params_grads,
                            main_program, startup_program):

        with program_guard(main_program, startup_program):
            optimize_ops = user_define_optimizer.apply_gradients(params_grads)

        return optimize_ops

    def _is_valid_annotated_program(self, program):

        # TODO (ZJ-LIANG) should check all block
        ops = program.global_block().ops
        vars_ = program.list_vars()
        op_dist_attrs = [
            self._auto_parallel_context.get_op_distributed_attr_for_program(op)
            for op in ops
        ]
        var_dist_attrs = [
            self._auto_parallel_context.get_tensor_distributed_attr_for_program(
                var) for var in vars_
        ]

        all_ops_annotated = all(dist_attr is not None
                                for dist_attr in op_dist_attrs)
        all_vars_annotated = all(dist_attr is not None
                                 for dist_attr in var_dist_attrs)

        return all_ops_annotated and all_vars_annotated

    def _serial_varname2dist_var(self, serial_varname, dist_program):
        assert serial_varname in self._serial2dist_varname_mapping, "The serial var [{}] is not found in var name mapping".format(
            serial_varname)
        dist_varname = self._serial2dist_varname_mapping[serial_varname]

        assert dist_program.global_block().has_var(
            dist_varname
        ), "The dist var [{}] is not found in dist program".format(dist_varname)
        dist_var = dist_program.global_block().var(dist_varname)

        return dist_var

    def _determine_parallel_mode(self, program):
        """
        determine the parallelism that is enabled
        NOTE a hard rule and should be updated in future
        """

        for param in program.all_parameters():
            if self._is_var_distributed(param):
                self._enable_tensor_parallel = True
                break

        for var in program.list_vars():
            var_dist_attr = self._auto_parallel_context.get_tensor_distributed_attr_for_program(
                var)
            if not var_dist_attr.is_parameter():
                mapping = var_dist_attr.get_dims_mapping()
                mesh = var_dist_attr.get_process_mesh().topology
                if mapping and mapping[0] >= 0 and mesh[mapping[0]] > 1:
                    self._enable_data_parallel = True
                    break

        # tensor parallelism
        if self._enable_tensor_parallel:
            model_parallel_axis, process_mesh = self._auto_parallel_context._get_model_parallel_info(
            )
            group_ranks = _get_comm_group(process_mesh.process_group,
                                          process_mesh.topology,
                                          model_parallel_axis, self._rank_id)
            self._tp_degree = len(group_ranks)
            self._tp_group = new_process_group(group_ranks)

        # data parallelism
        data_parallel_axis, process_mesh = self._auto_parallel_context._get_data_parallel_info(
        )
        if self._enable_data_parallel:
            group_ranks = _get_comm_group(process_mesh.process_group,
                                          process_mesh.topology,
                                          data_parallel_axis, self._rank_id)
            self._dp_degree = len(group_ranks)
            self._dp_group = new_process_group(group_ranks)

    def _is_var_distributed(self, var):

        dist_attr = self._auto_parallel_context.get_tensor_distributed_attr_for_program(
            var)
        assert dist_attr is not None, "dist_attr of var [{}] is None".format(
            var.name)
        return _is_distributed(dist_attr)

    def _sharding_forward_transpile(self, main_prog, startup_program):
        """
        this transpile conduct the modification in forward program need by sharding strategy
        which majorly include:
            1. partition the parameter
            2. insert broadcast op
            3. insert sync op 

        NOTE the transpile modification is inplace on the input program
        """

        raise NotImplementedError(
            "Sharding is NOT support in AutoParallel yet!")

    def _sharding_backward_transpile(self, main_prog, startup_program):
        """
        this transpile conduct the modification in backward program need by sharding strategy
        which majorly include:
            1. partition the gradient
            2. insert broadcast op
            3. insert sync op 

        NOTE the transpile modification is inplace on the input program
        """

        raise NotImplementedError(
            "Sharding is NOT support in AutoParallel yet!")

    def _sharding_optimize_transpile(self, params_grads, dist_main_program,
                                     dist_startup_program):
        """
        shard params_grads
        append the broadcast to sync parameters 
        """
        raise RuntimeError("sharding transpile is NOT implemented !")

    def _gradient_sync_transpile(self, main_program, startup_program):
        """
        append the gradient allreduce ops for all parameters' grad in case of Data Parallel
        """

        # scale loss by dp degree
        main_global_block = main_program.global_block()
        for idx, op in reversed(list(enumerate(main_global_block.ops))):
            if is_loss_grad_op(op):
                loss_grad_var = main_global_block.vars[op.output_arg_names[0]]
                main_global_block._insert_op_without_sync(
                    idx + 1,
                    type='scale',
                    inputs={'X': loss_grad_var},
                    outputs={'Out': loss_grad_var},
                    attrs={
                        'scale': 1.0 / self._dp_degree,
                        OP_ROLE_KEY: OpRole.Backward
                    })
                break
        main_global_block._sync_with_cpp()

        # gradient synchronization
        # NOTE naive gradient sync without overlapping
        # so there is not need to sync between calc and comm
        # collecting grad var
        grad_to_sync = []
        for idx, op in reversed(list(enumerate(main_global_block.ops))):
            if is_backward_op(op) and \
                    OP_ROLE_VAR_KEY in op.attr_names:
                op_role_var = op.all_attrs()[OP_ROLE_VAR_KEY]
                if len(op_role_var) != 0:
                    assert len(op_role_var) % 2 == 0
                    for i in range(0, len(op_role_var), 2):
                        param, reduced_grad = op_role_var[i], op_role_var[i + 1]
                        assert (reduced_grad not in grad_to_sync)
                        grad_to_sync.append(reduced_grad)
            if is_optimizer_op(op):
                first_optimize_op_idx = idx

        # insert allreduce
        for grad in grad_to_sync:
            # FIXME the ring id should be set by autoparallel.mapping module
            # it should be determined by dp groups butfixed it here for hacking
            main_global_block.append_op(
                type='c_allreduce_sum',
                inputs={'X': grad},
                outputs={'Out': grad},
                attrs={
                    'ring_id': self._dp_group.id,
                    'root': 0,
                    'use_calc_stream': True,
                    OP_ROLE_KEY: OpRole.Backward
                })
        main_global_block.append_op(
            type='c_sync_comm_stream',
            inputs={'X': grad_to_sync},
            outputs={'Out': grad_to_sync},
            attrs={'ring_id': self._dp_group.id,
                   OP_ROLE_KEY: OpRole.Backward})
        main_global_block._sync_with_cpp()


def _get_no_grad_set_name(no_grad_set):
    no_grad_set_name = set()
    if no_grad_set is not None:
        if isinstance(no_grad_set, (set, list, tuple)):
            for i, no_grad_var in enumerate(no_grad_set):
                if isinstance(no_grad_var, framework.Variable):
                    no_grad_set_name.add(no_grad_var.name)
                elif isinstance(no_grad_var, six.string_types):
                    no_grad_set_name.add(no_grad_var)
                else:
                    raise TypeError(
                        "The type of no_grad_set's member must be paddle.fluid.Variable or str, but received %s."
                        % (type(no_grad_var)))
        else:
            raise TypeError(
                "The type of no_grad_set should be set or list or tuple, but received {}".
                format(type(no_grad_set)))
    return no_grad_set_name


def _get_no_grad_set(loss, no_grad_set=None):
    no_grad_set = _get_no_grad_set_name(no_grad_set)
    parameters = loss.block.program.global_block().all_parameters()
    param_no_trainable = set(
        [param.name for param in parameters if param.trainable is False])
    # If the parameter is no trainable, it should not have a gradient.
    no_grad_set.update(param_no_trainable)

    return no_grad_set


def _found_match_dist_op(auto_paralle_context, op):
    dist_attr = auto_paralle_context.get_op_distributed_attr_for_program(op)
    dist_ops = get_distributed_operator(op.type)

    return dist_ops and dist_attr.get_impl_idx() >= 0 and dist_ops.get_impl( \
        dist_attr.get_impl_idx())._forward_implemented


def _auto_backward(loss,
                   startup_program=None,
                   parameter_list=None,
                   no_grad_set=None,
                   callbacks=None):
    """
    modification is inplaced
    """
    act_no_grad_set = _get_no_grad_set(loss, no_grad_set)
    assert isinstance(loss, Variable), "The target loss should be an Variable."

    if callbacks is None:
        callbacks = [error_clip_callback]
    else:
        assert (isinstance(callbacks, list))

    assert len(loss.shape) == 1 and loss.shape[0] == 1, \
        "The loss.shape should be (1L,), but the current loss.shape is {}. " \
        "Maybe that you should call fluid.layers.mean to process the current loss.".format(
            loss.shape)

    program = loss.block.program
    with program_guard(program, startup_program):
        params_grads = append_backward(loss, parameter_list, act_no_grad_set,
                                       callbacks)

    return params_grads


def _is_distributed(dist_attr):

    mapping = dist_attr.get_dims_mapping()
    mesh = dist_attr.get_process_mesh().topology
    for idx in range(len(mapping)):
        if mapping[idx] >= 0 and mesh[mapping[idx]] > 1:
            return True

    return False


def _get_dist_shape(var, dist_attr):

    var_shape = var.shape
    mapping = dist_attr.get_dims_mapping()
    mesh = dist_attr.get_process_mesh().topology
    assert len(var_shape) == len(
        mapping
    ), "variable shape [{}] and dim_mapping [{}] is NOT match !".format(
        var_shape, mapping)
    new_shape = []
    for idx in range(len(var_shape)):
        if var_shape[idx] == -1 or mapping[idx] == -1:
            new_shape.append(var_shape[idx])
        else:
            assert var_shape[idx] % mesh[mapping[
                idx]] == 0, "un-event partition: var_shape[idx]=[{}], mesh[{}]".format(
                    var_shape[idx], mesh[mapping[idx]])
            new_shape.append(var_shape[idx] // mesh[mapping[idx]])

    return new_shape


def _partition_parameter(auto_paralle_context, src_var, dst_block, dst_varname,
                         dst_shape):
    # NOTE hack to copied Parameter
    # not initialized parameter, need to initialize it 
    copied_kwargs = {}
    copied_kwargs['trainable'] = src_var.trainable
    copied_kwargs['optimize_attr'] = src_var.optimize_attr
    copied_kwargs['regularizer'] = src_var.regularizer
    copied_kwargs['do_model_average'] = src_var.do_model_average
    copied_kwargs['need_clip'] = src_var.need_clip

    param = Parameter(
        block=dst_block,
        type=src_var.type,
        name=dst_varname,
        shape=dst_shape,
        dtype=src_var.dtype,
        lod_level=src_var.lod_level,
        error_clip=src_var.error_clip,
        stop_gradient=src_var.stop_gradient,
        is_data=src_var.is_data,
        belong_to_optimizer=src_var.belong_to_optimizer,
        **copied_kwargs)

    # set dist attr uid
    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
    # param.desc.set_distributed_attr_uid(distributed_attr_uid)
    dist_attr = copy.deepcopy(
        auto_paralle_context.get_tensor_distributed_attr_for_program(src_var))
    dist_attr._owner_tensor = param
    dist_attr._owner_context = auto_paralle_context.get_tensor_distributed_attr_for_program(
        src_var)._owner_context
    auto_paralle_context.set_tensor_distributed_attr_for_program(param,
                                                                 dist_attr)


def _partition_intermediate_var(auto_paralle_context, src_var, dst_block,
                                dst_varname, dst_shape):
    var = dst_block.create_var(
        type=src_var.type,
        name=dst_varname,
        shape=dst_shape,
        dtype=src_var.dtype,
        lod_level=src_var.lod_level,
        persistable=src_var.persistable,
        error_clip=src_var.error_clip,
        stop_gradient=src_var.stop_gradient,
        is_data=src_var.is_data,
        belong_to_optimizer=src_var.belong_to_optimizer)

    # set dist attr uid
    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
    # var.desc.set_distributed_attr_uid(distributed_attr_uid)
    dist_attr = copy.deepcopy(
        auto_paralle_context.get_tensor_distributed_attr_for_program(src_var))
    dist_attr._owner_tensor = var
    dist_attr._owner_context = auto_paralle_context.get_tensor_distributed_attr_for_program(
        src_var)._owner_context
    auto_paralle_context.set_tensor_distributed_attr_for_program(var, dist_attr)


def _partition_var(auto_paralle_context, src_block, dst_block, src_varname,
                   dst_varname):
    """
    partition include: split + replicate
    """
    src_var = src_block.var(src_varname)

    if src_var.type == core.VarDesc.VarType.READER:
        dst_block.create_var(
            type=src_var.type,
            name=dst_varname,
            persistable=True,
            stop_gradient=True)
    else:
        dist_attr = auto_paralle_context.get_tensor_distributed_attr_for_program(
            src_var)
        target_shape = _get_dist_shape(src_var, dist_attr)

        if isinstance(src_var, Parameter):
            _partition_parameter(auto_paralle_context, src_var, dst_block,
                                 dst_varname, target_shape)
        else:
            _partition_intermediate_var(auto_paralle_context, src_var,
                                        dst_block, dst_varname, target_shape)


def _insert_src_op(src_op, dst_block, varname_mapping):

    new_op_desc = dst_block.desc.append_op()
    new_op_desc.copy_from(src_op.desc)
    for local_varname in src_op.desc.input_arg_names():
        new_op_desc._rename_input(local_varname, varname_mapping[local_varname])
    for local_varname in src_op.desc.output_arg_names():
        new_op_desc._rename_output(local_varname,
                                   varname_mapping[local_varname])
    dst_block._sync_with_cpp()


def _insert_dist_op(src_op, dst_block, varname_mapping, auto_paralle_context,
                    rank_id):

    # build input varname mapping
    input_mapping = {}
    for input_name in src_op.desc.input_names():
        varnames = []
        for varname in src_op.desc.input(input_name):
            varnames.append(varname_mapping[varname])
        input_mapping[input_name] = varnames

    # build output varname mapping
    output_mapping = {}
    for output_name in src_op.desc.output_names():
        varnames = []
        for varname in src_op.desc.output(output_name):
            varnames.append(varname_mapping[varname])
        output_mapping[output_name] = varnames

    # append dist op 
    dist_attr = auto_paralle_context.get_op_distributed_attr_for_program(src_op)
    dist_ops = get_distributed_operator(src_op.type)
    append_op_handle = dist_ops.get_impl(dist_attr.get_impl_idx()).forward(
        src_op)
    append_op_handle(
        dst_block,
        src_op,
        dist_attr,
        input_mapping,
        output_mapping,
        rank_id=rank_id)

# Copyright (c) 2021 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 warning:: Partitioner is experimental and subject to change.      Partitioner convert a program into another program.     Given a serial program which has been auto completed with shard annotation, the Partitioner      convert the serial program into a "distributed" program. The Partitioner will  modify the serial     program in following two ways, which is also the major difference between serial and distributed program:         1. partition op: replace a serial op into its corresponding dist op infered from the shard annotation         2. partition var: if a var is sharded, modify the shape of var according to its shard annotation      Partitioner is supposed to be call by the auto parallel framework, and not supposed to be directly called by user.      Example:         ....             import paddle.distributed.auto_parallel as auto             from paddle.fluid.distributed_attribute import get_default_distributed_context             from paddle.distributed import fleet             from paddle.distributed.auto_parallel.partitioner import Partitioner              # create serial program with forward only              with static.program_guard(serial_main_program, serial_start_program):                 model = create_model(config)                 tokens = static.data(name="tokens", shape=[batch_size, sequence_len], dtype='int64')                 labels = static.data(name="labels", shape=[batch_size, sequence_len], dtype='int64')                 loss_mask = static.data(name="loss_mask", shape=[batch_size, sequence_len], dtype='int64')                 preds = model(tokens)                 loss = criterion(preds, labels, loss_mask)              # auto completion             auto.ProcessMesh(shape=[2, 4], process_group=[0, 1, 2, 3, 4, 5, 6, 7])             annotated_main_program = auto.complete_annotation(serial_main_program)             auto_paralle_context = get_default_distributed_context()                              # distributed strategy & rank info             rank_id = paddle.distributed.get_rank()             dist_strategy = fleet.DistributedStrategy()                  # create partitioner             Partitioner = Partitioner(dist_strategy, auto_paralle_context, rank_id)              # create dist program with forward only             # for distributed inference, using partitioned_main_prog from here             partitioned_main_prog, partitioned_startup_prog = Partitioner.transpile_forward(complete_train_program, start_program)              # create dist program with forward/backward/update             # for distributed training, using partitioned_main_prog from here             dist_params_grads = Partitioner.apply_backward(loss, complete_train_program, start_program, partitioned_main_prog, partitioned_startup_prog)             optimizer = paddle.fluid.optimizer.AdamOptimizer(                 learning_rate=0.00001,                 beta1=0.9,                 beta2=0.999,                 epsilon=1e-08,                 grad_clip=None)             opt_ops = Partitioner.apply_optimize(optimizer, dist_params_grads, partitioned_main_prog, partitioned_startup_prog) Args:             dist_strategy (paddle.fleet.distributed_strategy): used to determine the user defined distributed strategy.             auto_parallel_context (paddle.fluid.DistributedContext): used to access the distributed_attr of var & op, every Partitioner object could maintain its own DistributedContext member, and partition program base on that shard scenario.             rank_id (int): global rank id to which the partitioned distributed program belong. # TODO if there is some dist op that is not compatible # with auto_backward in forward, the following flag # should be set to False # data parallelism # tensor parallelism take serial forward programs with shard annotation, create a new distributed forward programs based on the serial ones.         instead of modify the input programs inplace, this function will preserve the inputs and create new program for output.          beside replace the serial op with its dist op, if user has defined other strategy in fleet.distributed_strategy, and if          those strategy need to transpile (modify) the forward network program, those forward program modification should also be done within this         function in auto parallel scenario, in order to facilitate distributed inference/evaluation which need to DECOUPLE strategy specific forward transpilation with fleet.distributed_optimizer.minimize().          by now the fleet.distributed_strategy that need transpile forward program are following:              1. (optimizer) sharding          Args:             main_program (paddle.fluid.framework.program): serial main program with forward network only             startup_program (paddle.fluid.framework.program): serial startup program with forward network only                  return:             main_program (paddle.fluid.framework.program): distributed main program with forward network only             startup_program (paddle.fluid.framework.program): distributed startup program with forward network only A complete training neural network is made up of forward and backward propagation.          This function is to generate the dist backward program for the distributed forward program.          By now, the current automatical backward mechanism in paddle framework might NOT handle the backward generation for          some dist ops correctly, some so we now have two ways to genenate the backward program:             1. dist_forward_program --> auto_backward --> dist_backward_program (if auto_backward could handle all dist op)             2. serial_forward_program --> auto_backward --> serial_backward_program --> dist_op_backward_transpile --> dist_backward_program (if auto_backward could not handle all dist op)                  the backprogram is append the input dist program inplaced.          Args:             serial_loss (Variable) the loss in serial program that to be minimized              serial_main_program (paddle.fluid.framework.program): serial main program with forward network only             serial_startup_program (paddle.fluid.framework.program): serial startup program with forward network only             dist_main_program (paddle.fluid.framework.program): dist main program with forward network only             dist_startup_program (paddle.fluid.framework.program): dist startup program with forward network only             parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update                 to minimize ``loss``. The default value is None, at this time all parameters                 will be updated.             no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need                 to be updated. The default value is None.             callbacks (list, optional): list of callable objects to run when appending backward                 operator for one parameter. The default value is None.                  return:             params_grads (list) list of tuple that contain param and its grad variable append update related ops to the program: clip, weight decay, ops         filter optimize op if sharding is enable         naive gradient synchronization before update          Args:             user_define_optimizer (paddle.fluid.optimizer):              params_grads (list) list of tuple that contain param and its grad variable             dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network              dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network # check if shard annotated serial program valid # determine parallelism mode # dist op & partition vars # Sharding # Sharding # Data Parallel pass append update related ops to the program: clip, weight decay, ops         filter optimize op if sharding is enable         naive gradient synchronization before update          Args:             user_define_optimizer (paddle.fluid.optimizer):              params_grads (list) list of tuple that contain param and its grad variable             dist_main_program (paddle.fluid.framework.program): dist main program with forward & backward network              dist_startup_program (paddle.fluid.framework.program): dist startup program with forward & backward  network 1. partition variables         2. replace local op with corresponding dist op # transpile main program # partititon input variables # partition output vars # partition op # replace with corresponding dist op # replicate op # transpile startup program # create parameter # copy initializer # MP broadcast not split parameter # NOTE Theoretically, the MP param init broadcast should be handled by # each dist op itself. but if we insert the broadcast op at that moment, the broadcast # will before the initializer, which lead to a undertermined case. # FIXME the ring id should be set by autoparallel.mapping module # it should be determined by dp groups butfixed it here for hacking # DP init param broadcast # parameters initialization synchronization # FIXME the ring id should be set by autoparallel.mapping module # it should be determined by dp groups butfixed it here for hacking so far, the auto_backward case only guarantee the correcotness of backward ops for curtain Dist ops:             1. NV-Megatron-like parallel embedding             2. NV-Megatron-like row parallel linear             3. NV-Megatron-like col parallel linear # update parameter list # update parameter no_grad_set # replace dist grad ops # TODO (ZJ-LIANG) should check all block determine the parallelism that is enabled         NOTE a hard rule and should be updated in future # tensor parallelism # data parallelism this transpile conduct the modification in forward program need by sharding strategy         which majorly include:             1. partition the parameter             2. insert broadcast op             3. insert sync op           NOTE the transpile modification is inplace on the input program this transpile conduct the modification in backward program need by sharding strategy         which majorly include:             1. partition the gradient             2. insert broadcast op             3. insert sync op           NOTE the transpile modification is inplace on the input program shard params_grads         append the broadcast to sync parameters append the gradient allreduce ops for all parameters' grad in case of Data Parallel # scale loss by dp degree # gradient synchronization # NOTE naive gradient sync without overlapping # so there is not need to sync between calc and comm # collecting grad var # insert allreduce # FIXME the ring id should be set by autoparallel.mapping module # it should be determined by dp groups butfixed it here for hacking # If the parameter is no trainable, it should not have a gradient. modification is inplaced # NOTE hack to copied Parameter # not initialized parameter, need to initialize it # set dist attr uid # distributed_attr_uid = src_var.desc.get_distributed_attr_uid() # param.desc.set_distributed_attr_uid(distributed_attr_uid) # set dist attr uid # distributed_attr_uid = src_var.desc.get_distributed_attr_uid() # var.desc.set_distributed_attr_uid(distributed_attr_uid) partition include: split + replicate # build input varname mapping # build output varname mapping # append dist op