# -*- coding: utf-8 -*-
# File: distributed.py

import re
import tensorflow as tf

from ..tfutils.common import get_global_step_var, get_op_tensor_name
from ..utils import logger
from ..utils.argtools import memoized
from .training import DataParallelBuilder, GraphBuilder
from .utils import OverrideCachingDevice, split_grad_list, allreduce_grads_naive, override_to_local_variable

__all__ = []


class DistributedBuilderBase(GraphBuilder):

    _sync_queue_counter = 0

    def __init__(self, server):
        self.server = server
        server_def = server.server_def
        self.cluster = tf.train.ClusterSpec(server_def.cluster)
        self.task_index = server_def.task_index

        self.num_ps = self.cluster.num_tasks('ps')
        self.num_worker = self.cluster.num_tasks('worker')

    def _add_sync_queues_and_barrier(self, name, dependencies):
        """Adds ops to enqueue on all worker queues.

        Args:
            name: prefixed for the shared_name of ops.
            dependencies: control dependency from ops.

        Returns:
            an op that should be used as control dependency before starting next step.
        """
        self._sync_queue_counter += 1
        with tf.device(self.sync_queue_devices[self._sync_queue_counter % len(self.sync_queue_devices)]):
            sync_queues = [
                tf.FIFOQueue(self.num_worker, [tf.bool], shapes=[[]],
                             shared_name='%s%s' % (name, i))
                for i in range(self.num_worker)]
            queue_ops = []
            # For each other worker, add an entry in a queue, signaling that it can finish this step.
            token = tf.constant(False)
            with tf.control_dependencies(dependencies):
                for i, q in enumerate(sync_queues):
                    if i != self.task_index:
                        queue_ops.append(q.enqueue(token))

            # Drain tokens off queue for this worker, one for each other worker.
            queue_ops.append(
                sync_queues[self.task_index].dequeue_many(len(sync_queues) - 1))

            return tf.group(*queue_ops, name=name)


class DistributedParameterServerBuilder(DataParallelBuilder, DistributedBuilderBase):
    """
    Distributed parameter server training.
    A single copy of parameters are scattered around PS.
    Gradients across GPUs are averaged within the worker, and applied to PS.
    Each worker also caches the variables for reading.

    It is an equivalent of ``--variable_update=parameter_server`` in
    `tensorflow/benchmarks <https://github.com/tensorflow/benchmarks>`_.
    However this implementation hasn't been well tested.
    It probably still has issues in model saving, etc.
    Also, TensorFlow team is not actively maintaining distributed training features.
    Check :class:`HorovodTrainer` and
    `ResNet-Horovod <https://github.com/tensorpack/benchmarks/tree/master/ResNet-Horovod>`_
    for better distributed training support.

    Note:
        1. Gradients are not averaged across workers, but applied to PS variables
           directly (either with or without locking depending on the optimizer).
    """

    def __init__(self, towers, server, caching_device):
        """
        Args:
            towers (list[int]): list of GPU ids.
            server (tf.train.Server): the server with ps and workers.
                job_name must be 'worker'.
            caching_device (str): either 'cpu' or 'gpu'
        """
        DataParallelBuilder.__init__(self, towers)
        DistributedBuilderBase.__init__(self, server)

        assert caching_device in ['cpu', 'gpu'], caching_device
        self.caching_device = caching_device

        self.is_chief = (self.task_index == 0)

        worker_prefix = '/job:worker/task:%s' % self.task_index
        self.param_server_device = tf.train.replica_device_setter(
            worker_device=worker_prefix + '/cpu:0', cluster=self.cluster)
        self.cpu_device = '%s/cpu:0' % worker_prefix
        self.raw_devices = ['{}/gpu:{}'.format(worker_prefix, k) for k in self.towers]

        self.sync_queue_devices = ['/job:ps/task:%s/cpu:0' % i for i in range(self.num_ps)]

    def build(self, get_grad_fn, get_opt_fn):
        ps_strategy = tf.contrib.training.GreedyLoadBalancingStrategy(
            self.num_ps, tf.contrib.training.byte_size_load_fn)
        devices = [
            tf.train.replica_device_setter(
                worker_device=d,
                cluster=self.cluster,
                ps_strategy=ps_strategy) for d in self.raw_devices]

        if self.caching_device == 'gpu':
            caching_devices = self.raw_devices
        else:
            caching_devices = [self.cpu_device]
        custom_getter = OverrideCachingDevice(
            caching_devices, self.cpu_device, 1024 * 64)

        with tf.variable_scope(tf.get_variable_scope(), custom_getter=custom_getter):
            grad_list = DataParallelBuilder.build_on_towers(self.towers, get_grad_fn, devices)
        DataParallelBuilder._check_grad_list(grad_list)

        with tf.device(self.param_server_device):
            all_grads, all_vars = split_grad_list(grad_list)
            all_grads = allreduce_grads_naive(all_grads)
            grads = [(g, v) for g, v in zip(all_grads, all_vars[0])]
            opt = get_opt_fn()
            train_op = opt.apply_gradients(grads, name='train_op')
        train_op = self._add_sync_queues_and_barrier('all_workers_sync_barrier', [train_op])
        return train_op


class DistributedReplicatedBuilder(DataParallelBuilder, DistributedBuilderBase):
    """
    Distributed replicated training.
    Each worker process builds the same model on one or more GPUs.
    Gradients across GPUs are averaged within the worker,
    and get synchronously applied to the global copy of variables located on PS.
    Then each worker copy the latest variables from PS back to local.

    It is an equivalent of ``--variable_update=distributed_replicated`` in
    `tensorflow/benchmarks <https://github.com/tensorflow/benchmarks>`_.
    Note that the performance of this trainer is still not satisfactory,
    and TensorFlow team is not actively maintaining distributed training features.
    Check :class:`HorovodTrainer` and
    `ResNet-Horovod <https://github.com/tensorpack/benchmarks/tree/master/ResNet-Horovod>`_
    for better distributed training support.

    Note:
        1. Gradients are not averaged across workers, but applied to PS variables
           directly (either with or without locking depending on the optimizer).
        2. Some details about collections: all variables created inside tower
           will become local variables,
           and a clone will be made in global variables for all trainable/model variables.

    Example:

        .. code-block:: python

            # Create the server object like this:
            hosts = ['host1.com', 'host2.com']
            cluster_spec = tf.train.ClusterSpec({
                'ps': [h + ':2222' for h in hosts],
                'worker': [h + ':2223' for h in hosts]
            })
            server = tf.train.Server(
                cluster_spec, job_name=args.job, task_index=args.task,
                config=get_default_sess_config())
            # initialize trainer with this server object

        .. code-block:: none

            # Start training like this:
            (host1)$ ./train.py --job worker --task 0
            (host1)$ CUDA_VISIBLE_DEVICES= ./train.py --job ps --task 0
            (host2)$ ./train.py --job worker --task 1
            (host2)$ CUDA_VISIBLE_DEVICES= ./train.py --job ps --task 1
    """

    def __init__(self, towers, server):
        """
        Args:
            towers (list[int]): list of GPU ids.
            server (tf.train.Server): the server with ps and workers.
                job_name must be 'worker'.
        """
        DataParallelBuilder.__init__(self, towers)
        DistributedBuilderBase.__init__(self, server)

        self.is_chief = (self.task_index == 0)

        worker_prefix = '/job:worker/task:%s' % self.task_index
        self.param_server_device = tf.train.replica_device_setter(
            worker_device=worker_prefix + '/cpu:0', cluster=self.cluster)

        self.nr_gpu = len(self.towers)
        self.cpu_device = '%s/cpu:0' % worker_prefix
        self.raw_devices = ['%s/gpu:%i' % (worker_prefix, i) for i in towers]

        # Device for queues for managing synchronization between servers
        self.sync_queue_devices = ['/job:ps/task:%s/cpu:0' % i for i in range(self.num_ps)]

    @staticmethod
    def _apply_shadow_vars(avg_grads):
        """
        Create shadow variables on PS, and replace variables in avg_grads
        by these shadow variables.

        Args:
            avg_grads: list of (grad, var) tuples
        """
        ps_var_grads = []
        for grad, var in avg_grads:
            assert var.name.startswith('tower'), var.name
            my_name = '/'.join(var.name.split('/')[1:])
            my_name = get_op_tensor_name(my_name)[0]
            new_v = tf.get_variable(my_name, dtype=var.dtype.base_dtype,
                                    initializer=var.initial_value,
                                    trainable=True)
            # (g, v) to be applied, where v is global (ps vars)
            ps_var_grads.append((grad, new_v))
        return ps_var_grads

    @staticmethod
    def _shadow_model_variables(shadow_vars):
        """
        Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.

        Returns:
            list of (shadow_model_var, local_model_var) used for syncing.
        """
        G = tf.get_default_graph()
        curr_shadow_vars = {v.name for v in shadow_vars}
        model_vars = tf.model_variables()
        shadow_model_vars = []
        for v in model_vars:
            assert v.name.startswith('tower'), "Found some MODEL_VARIABLES created outside of the tower function!"
            stripped_op_name, stripped_var_name = get_op_tensor_name(re.sub('^tower[0-9]+/', '', v.name))
            if stripped_op_name in curr_shadow_vars:
                continue
            try:
                G.get_tensor_by_name(stripped_var_name)
                logger.warn("Model Variable {} also appears in other collections.".format(stripped_var_name))
                continue
            except KeyError:
                pass
            new_v = tf.get_variable(stripped_op_name, dtype=v.dtype.base_dtype,
                                    initializer=v.initial_value,
                                    trainable=False)

            curr_shadow_vars.add(stripped_op_name)  # avoid duplicated shadow_model_vars
            shadow_vars.append(new_v)
            shadow_model_vars.append((new_v, v))  # only need to sync model_var from one tower
        return shadow_model_vars

    def build(self, get_grad_fn, get_opt_fn):
        """
        Args:
            get_grad_fn (-> [(grad, var)]):
            get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer

        Returns:
            (tf.Operation, tf.Operation, tf.Operation):

            1. the training op.

            2. the op which sync all the local variables from PS.
            This op should be run before training.

            3. the op which sync all the local `MODEL_VARIABLES` from PS.
            You can choose how often to run it by yourself.
        """
        with override_to_local_variable():
            get_global_step_var()

        get_opt_fn = memoized(get_opt_fn)
        # Build the optimizer first, before entering any tower.
        # This makes sure that learning_rate is a global variable (what we expect)
        get_opt_fn()    # TODO get_opt_fn called before main graph was built

        # Ngpu * Nvar * 2
        grad_list = DataParallelBuilder.build_on_towers(
            self.towers, get_grad_fn,
            devices=self.raw_devices,
            use_vs=[True] * len(self.towers))  # open vs at each tower
        DataParallelBuilder._check_grad_list(grad_list)

        all_grads, all_vars = split_grad_list(grad_list)
        avg_grads = allreduce_grads_naive(all_grads, devices=self.raw_devices)  # N
        avg_grads = [(g, v) for g, v in zip(all_grads, all_vars[0])]
        with tf.device(self.param_server_device):
            ps_var_grads = DistributedReplicatedBuilder._apply_shadow_vars(avg_grads)
            var_update_ops = self._apply_gradients_and_copy(
                get_opt_fn(), grad_list, ps_var_grads)
            self._shadow_vars = [v for (__, v) in ps_var_grads]
            self._shadow_model_vars = DistributedReplicatedBuilder._shadow_model_variables(self._shadow_vars)

        # TODO add options to synchronize less
        main_fetch = tf.group(*var_update_ops, name='main_fetches')
        train_op = self._add_sync_queues_and_barrier(
            'post_copy_barrier', [main_fetch])

        # initial local_vars syncing
        with tf.name_scope('initial_sync_variables'):
            initial_sync_op = self._get_initial_sync_op()
        if len(self._shadow_model_vars) and self.is_chief:
            with tf.name_scope('sync_model_variables'):
                model_sync_op = self._get_sync_model_vars_op()
        else:
            model_sync_op = None
        return train_op, initial_sync_op, model_sync_op

    def _apply_gradients_and_copy(self, opt, raw_grad_list, ps_var_grads):
        """
        Apply averaged gradients to ps vars, and then copy the updated
        variables back to each tower.

        Args:
            raw_grad_list: Ngpu x Nvar x 2 gradient list from all towers
            ps_var_grads: Nvar x 2 (grad, ps_var)

        Returns:
            list of copy ops
        """
        # TODO do this for variables together?
        with tf.name_scope('apply_gradients'):
            var_update_ops = []
            for vid, (g, v) in enumerate(ps_var_grads):
                # TODO do we put momentum variables into local or global?
                apply_gradient_op = opt.apply_gradients([(g, v)])
                barrier = self._add_sync_queues_and_barrier(
                    'param_update_barrier_{}'.format(vid), [apply_gradient_op])
                with tf.control_dependencies([barrier]), \
                        tf.device(self.cpu_device):
                    updated_value = v.read_value()
                    for towerid in range(self.nr_gpu):
                        var_update_ops.append(
                            raw_grad_list[towerid][vid][1].assign(updated_value))
            return var_update_ops

    def _get_initial_sync_op(self):
        """
        Get the op to copy-initialized all local variables from PS.
        """
        def strip_port(s):
            if s.endswith(':0'):
                return s[:-2]
            return s
        local_vars = tf.local_variables()
        local_var_by_name = {strip_port(v.name): v for v in local_vars}
        ops = []
        nr_shadow_vars = len(self._shadow_vars)
        for v in self._shadow_vars:
            vname = strip_port(v.name)
            for i in range(self.nr_gpu):
                name = 'tower%s/%s' % (i, vname)
                assert name in local_var_by_name, \
                    "Shadow variable {} doesn't match a corresponding local variable!".format(v.name)
                copy_to = local_var_by_name[name]
                # logger.info("{} -> {}".format(v.name, copy_to.name))
                ops.append(copy_to.assign(v.read_value()))
        return tf.group(*ops, name='sync_{}_variables_from_ps'.format(nr_shadow_vars))

    def _get_sync_model_vars_op(self):
        """
        Get the op to sync local model_variables to PS.
        """
        ops = []
        for (shadow_v, local_v) in self._shadow_model_vars:
            ops.append(shadow_v.assign(local_v.read_value()))
        assert len(ops)
        return tf.group(*ops, name='sync_{}_model_variables_to_ps'.format(len(ops)))
