# Copyright 2019 The Microsoft DeepSpeed Team

import time
import logging
import copy
import os

from types import MethodType

from numpy import prod

import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist

from deepspeed.utils.logging import logger
from deepspeed.utils.timer import SynchronizedWallClockTimer, ThroughputTimer

from ..engine import DeepSpeedEngine, MEMORY_OPT_ALLREDUCE_SIZE
from ..utils import PartitionedTensor, ensure_directory_exists
from ..dataloader import RepeatingLoader

from .module import PipelineModule, PipelineError, TiedLayerSpec
from . import p2p
from . import schedule

TARGET_ID = -2
LOG_STAGE = -2
DATA_PARALLEL_ID = -2


def is_even(number):
    return number % 2 == 0


mem_alloced = 0
mem_cached = 0


def _tensor_bytes(tensor):
    return tensor.numel() * tensor.element_size()


class PipelineEngine(DeepSpeedEngine):
    """ A training engine hybrid pipeline, data, and model parallel training.

    This engine is created by ``deepspeed.initialize()`` when a :class:`PipelineModule`
    is provided.
    """
    def __init__(self, *super_args, **super_kwargs):
        super().__init__(*super_args, **super_kwargs)
        assert isinstance(self.module, PipelineModule), "model must base PipelineModule"

        # We schedule the all-reduces, so disable it in super().backward()
        self.enable_backward_allreduce = False

        # pipeline step for logging
        self.log_batch_step_id = -1

        self.micro_batch_size = self.train_micro_batch_size_per_gpu()
        self.micro_batches = self.gradient_accumulation_steps()

        # Set Grid and Communication Groups
        self.grid = self.module._grid
        if self.grid.get_global_rank() == 0:
            logger.info(f'CONFIG: micro_batches={self.micro_batches} '
                        f'micro_batch_size={self.micro_batch_size}')

        self.global_rank = self.grid.get_global_rank()

        assert self.dp_world_size == self.grid.data_parallel_size
        assert self.train_batch_size() == \
            self.micro_batch_size * self.micro_batches * self.grid.data_parallel_size

        #  Set Stage Inf
        self.num_stages = self.grid.pipe_parallel_size
        self.stage_id = self.grid.get_stage_id()
        self.prev_stage = self.stage_id - 1
        self.next_stage = self.stage_id + 1

        self.data_iterator = None
        self.batch_fn = None

        self._force_grad_boundary = False

        self.batch_timer = ThroughputTimer(batch_size=self.micro_batch_size *
                                           self.micro_batches,
                                           num_workers=self.dp_world_size,
                                           logging_fn=self.tput_log,
                                           monitor_memory=False,
                                           steps_per_output=self.steps_per_print())

        # PipelineEngine needs to handle data loading specially due to only the first
        # and last stages loading inputs/labels. We construct a sampler that uses
        if self.training_data:
            self._build_data_iter(self.training_data)

        self.is_pipe_parallel = self.grid.pipe_parallel_size > 1
        self.is_data_parallel = self.grid.data_parallel_size > 1
        self.is_model_parallel = self.grid.model_parallel_size > 1

        # Partition input/output buffers
        self.is_pipe_partitioned = self.is_model_parallel
        self.is_grad_partitioned = False

        model_parameters = filter(lambda p: p.requires_grad, self.module.parameters())
        num_params = sum([p.numel() for p in model_parameters])
        unique_params = num_params
        # Subtract tied parameters if we don't own them
        if self.module.tied_comms:
            tied_params = 0
            for key, d in self.module.tied_comms.items():
                if self.global_rank != min(d['ranks']):
                    tied_params += sum(p.numel() for p in d['module'].parameters())
            unique_params -= tied_params
        params_tensor = torch.LongTensor(data=[num_params,
                                               unique_params]).to(self.device)
        dist.all_reduce(params_tensor, group=self.grid.get_model_parallel_group())
        params_tensor = params_tensor.tolist()
        total_params = params_tensor[0]
        unique_params = params_tensor[1]
        if self.grid.data_parallel_id == 0:
            logger.info(f'RANK={self.global_rank} '
                        f'STAGE={self.stage_id} '
                        f'LAYERS={self.module._local_stop - self.module._local_start} '
                        f'[{self.module._local_start}, {self.module._local_stop}) '
                        f'STAGE_PARAMS={num_params} ({num_params/1e6:0.3f}M) '
                        f'TOTAL_PARAMS={total_params} ({total_params/1e6:0.3f}M) '
                        f'UNIQUE_PARAMS={unique_params} ({unique_params/1e6:0.3f}M)')

        #intialize peer-2-peer communication and allreduce groups
        if self.is_pipe_parallel:
            p2p.init_process_groups(self.grid)

        # Pipeline buffers
        self.num_pipe_buffers = 0
        self.pipe_buffers = {
            'inputs' : [],   # batch input and received activations
            'labels' : [],   # labels from batch input
            'outputs' : [],  # activations
            'output_tensors' : [], # tensor object to preserve backward graph
        }
        self.pipe_recv_buf = None
        self.grad_layer = None

        self.meta_buffer = None

        self.first_output_send = True
        self.first_gradient_send = True

        #stores the loss for the current micro batch being processed
        self.loss = torch.tensor(0.0).to(self.device)

        #stores the loss for the entire batch
        self.total_loss = None
        self.agg_loss = torch.tensor(0.0, requires_grad=False).to(self.device)
        self.dp_group_loss = torch.tensor(0.0, requires_grad=False).to(self.device)

        if self._config.pipeline['activation_checkpoint_interval'] > 0:
            self.module.activation_checkpoint_interval = self._config.pipeline[
                'activation_checkpoint_interval']

        if self.is_last_stage():
            self.loss_model = self.module.loss_fn

        # Initialize pipeline communicators. Just send a 0.
        if is_even(self.stage_id):
            if not self.is_last_stage():
                p2p.send(self.loss, self.next_stage)
            if not self.is_first_stage():
                p2p.recv(self.loss, self.prev_stage)
        else:
            if not self.is_first_stage():
                p2p.recv(self.loss, self.prev_stage)
            if not self.is_last_stage():
                p2p.send(self.loss, self.next_stage)

        # XXX look into timer reporting timing
        # Initialize some timers because of early weirdness.
        if self.wall_clock_breakdown():
            self.timers('forward_microstep').start()
            self.timers('forward_microstep').stop()
            self.timers('backward_microstep').start()
            self.timers('backward_microstep').stop()
            self.timers('backward_inner_microstep').start()
            self.timers('backward_inner_microstep').stop()
            self.timers('backward_allreduce_microstep').start()
            self.timers('backward_allreduce_microstep').stop()
            self.timers('backward_allreduce').start()
            self.timers('backward_allreduce').stop()
            self.timers('step_microstep').start()
            self.timers('step_microstep').stop()

    def _build_data_iter(self, dataset):
        sampler = torch.utils.data.distributed.DistributedSampler(
            dataset,
            num_replicas=self.dp_world_size,
            rank=self.mpu.get_data_parallel_rank(),
            shuffle=False)
        # Build a loader and make it repeating.
        pipe_dataloader = self.deepspeed_io(dataset, data_sampler=sampler)
        pipe_dataloader = RepeatingLoader(pipe_dataloader)
        self.set_dataloader(pipe_dataloader)

    def _exec_reduce_tied_grads(self):
        self.module.allreduce_tied_weight_gradients()

    def _exec_reduce_grads(self):
        self._force_grad_boundary = True
        if self.is_data_parallel:
            self.buffered_allreduce_fallback(
                elements_per_buffer=MEMORY_OPT_ALLREDUCE_SIZE)
        self._force_grad_boundary = False

    def _reserve_pipe_buffers(self, num_buffers):
        """Ensure that each pipeline buffer has at least ``num_buffers`` slots.

        This method only reserves slots and does not allocate tensors.

        Args:
            num_buffers (int): The number of buffers to reserve.
        """
        if self.num_pipe_buffers >= num_buffers:
            return

        num_added = num_buffers - self.num_pipe_buffers
        for key in self.pipe_buffers:
            self.pipe_buffers[key].extend([None] * num_added)
        self.num_pipe_buffers = num_buffers

    def train_batch(self, data_iter=None):
        """Progress the pipeline to train the next batch of data. The engine will ingest
        ``self.train_batch_size()`` total samples collectively across all workers.


        An iterator that over training data should be provided as an argument
        unless ``deepspeed.initialize()`` was provided a training set. In that event,
        the training data will automatically be read.


        .. warning::
            A total of ``self.gradient_accumulation_steps()`` entries will be pulled
            from ``data_iter`` by each pipeline. There must be sufficient
            data left in ``data_iter`` or else a ``StopIteration`` will halt training.

            DeepSpeed provides a convenience class :class:`deepspeed.utils.RepeatingLoader`
            that wraps data loaders to automatically restart upon a ``StopIteration``.

        Args:
            data_iter (Iterator, optional): Iterator of training data.

        Returns:
            The arithmetic mean of the losses computed this batch.
        """
        if not torch._C.is_grad_enabled():
            raise RuntimeError(
                f'train_batch() requires gradients enabled. Use eval_batch() instead.')

        if data_iter:
            self.set_dataiterator(data_iter)

        self.module.train()
        self.total_loss = None

        # Do the work
        self.timers('train_batch').start()
        sched = schedule.TrainSchedule(micro_batches=self.micro_batches,
                                       stages=self.num_stages,
                                       stage_id=self.stage_id)
        self._exec_schedule(sched)
        self.agg_train_loss = self._aggregate_total_loss()
        self.timers('train_batch').stop()

        if self.global_steps % self.steps_per_print() == 0:
            if self.global_rank == 0:
                elapsed = self.timers('train_batch').elapsed(reset=True)
                iter_time = elapsed / self.steps_per_print()
                tput = self.train_batch_size() / iter_time
                print(f'steps: {self.global_steps} '
                      f'loss: {self.agg_train_loss:0.4f} '
                      f'iter time (s): {iter_time:0.3f} '
                      f'samples/sec: {tput:0.3f}')

        # Tensorboard
        if self.tensorboard_enabled():
            if self.global_rank == 0:
                self.summary_events = [(f'Train/Samples/train_loss',
                                        self.agg_train_loss.mean().item(),
                                        self.global_samples)]
                for event in self.summary_events:  # write_summary_events
                    self.summary_writer.add_scalar(event[0], event[1], event[2])
                if self.global_steps % self.steps_per_print() == 0:
                    self.summary_writer.flush()

        if self.wall_clock_breakdown(
        ) and self.global_steps % self.steps_per_print() == 0:
            self.timers.log([
                'pipe_send_output',
                'pipe_send_grad',
                'pipe_recv_input',
                'pipe_recv_grad'
            ])

        # TODO: should return precisely what loss returned and allow others to be queried?
        return self.agg_train_loss

    def eval_batch(self, data_iter):
        """Evaluate the pipeline on a batch of data from ``data_iter``. The
        engine will evaluate ``self.train_batch_size()`` total samples
        collectively across all workers.

        This method is equivalent to:

        .. code-block:: python

            module.eval()
            with torch.no_grad():
                output = module(batch)

        .. warning::
            A total of ``self.gradient_accumulation_steps()`` entries will be pulled
            from ``data_iter`` by each pipeline. There must be sufficient
            data left in ``data_iter`` or else a ``StopIteration`` will halt training.

            DeepSpeed provides a convenience class :class:`deepspeed.utils.RepeatingLoader`
            that wraps data loaders to automatically restart upon a ``StopIteration``.

        Args:
            data_iter (Iterator): Iterator of data to evaluate.

        Returns:
            The arithmetic mean of the losses computed this batch.
        """

        self.module.eval()
        self.total_loss = None

        # Use the provided data iterator
        train_iterator = self.data_iterator
        self.set_dataiterator(data_iter)

        # Do the work
        sched = schedule.InferenceSchedule(micro_batches=self.micro_batches,
                                           stages=self.num_stages,
                                           stage_id=self.stage_id)
        with torch.no_grad():
            self._exec_schedule(sched)

        self.agg_eval_loss = self._aggregate_total_loss()
        if self.tensorboard_enabled():
            if self.global_rank == 0:
                self.summary_events = [(f'Train/Samples/eval_loss',
                                        self.agg_eval_loss.mean().item(),
                                        self.global_samples)]
                for event in self.summary_events:  # write_summary_events
                    self.summary_writer.add_scalar(event[0], event[1], event[2])
                self.summary_writer.flush()

        # Restore the training iterator
        self.set_dataiterator(train_iterator)

        # Reset any buffers that may have been populated during the forward passes.
        #ds_checkpointing.reset()

        return self.agg_eval_loss

    def is_first_stage(self):
        """True if this process is in the first stage in the pipeline."""
        return self.stage_id == 0

    def is_last_stage(self):
        """True if this process is in the last stage in the pipeline."""
        return self.stage_id == self.num_stages - 1

    def _aggregate_total_loss(self):
        # Scale loss, average among DP ranks, and bcast loss to the rest of my DP group
        if self.is_last_stage():
            loss = self._scale_loss(self.total_loss)
            self.dp_group_loss = loss.clone().detach()

            ## Average loss across all data-parallel groups
            agg_loss = self.dp_group_loss.clone().detach()
            #print(f'RANK={self.global_rank} bcast SENDER src={self.global_rank} group={self.grid.pp_group}', flush=True)
            if self.is_data_parallel:
                dist.all_reduce(agg_loss, group=self.mpu.get_data_parallel_group())
                agg_loss /= self.dp_world_size

            assert self.global_rank in self.grid.pp_group
            losses = torch.Tensor([self.dp_group_loss, agg_loss]).to(self.device)
            dist.broadcast(tensor=losses,
                           src=self.global_rank,
                           group=self.mpu.get_pipe_parallel_group())

        else:
            # Get loss from last stage
            src_rank = self.grid.stage_to_global(self.num_stages - 1)
            assert src_rank in self.grid.pp_group
            losses = torch.Tensor([0., 0.]).to(self.device)
            dist.broadcast(tensor=losses,
                           src=src_rank,
                           group=self.grid.get_pipe_parallel_group())
            self.dp_group_loss = losses[0].clone().detach()
            agg_loss = losses[1].clone().detach()

        return agg_loss

    def set_dataloader(self, loader):
        """"""
        if self.is_first_stage() or self.is_last_stage():
            self.training_dataloader = loader
            self.data_iterator = iter(self.training_dataloader)

    def set_dataiterator(self, iterator):
        """ Store an iterator to sample for training data. """
        if self.is_first_stage() or self.is_last_stage():
            self.training_dataloader = None
            self.data_iterator = iterator

    def set_batch_fn(self, fn):
        self.batch_fn = fn

    def is_gradient_accumulation_boundary(self):
        """True if the engine is executing a gradient reduction or optimizer step instruction.

        This is overridden from :class:`DeepSpeedEngine` to force reductions
        and steps when the pipeline engine is instructed to do so.

        Returns:
            bool: whether reductions and optimizer steps should occur.
        """
        return self._force_grad_boundary

    def log_for_device(self, *msg):
        if LOG_STAGE == self.stage_id or LOG_STAGE == -1:
            if DATA_PARALLEL_ID == self.grid.data_parallel_id or DATA_PARALLEL_ID == -1:
                print(
                    f'RANK={dist.get_rank()} '
                    f'PIPE-ID={self.stage_id} '
                    f'DATA-ID={self.grid.data_parallel_id} '
                    f'MBATCH-ID={self.microbatch_id} '
                    f'STEP-ID={self.log_batch_step_id} '
                    '::',
                    *msg,
                    flush=True)

    def tput_log(self, *msg):
        if self.global_rank == 0 and self.global_steps % self.steps_per_print() == 0:
            print(*msg)

    def _next_batch(self):
        if self.is_model_parallel:
            mp_rank = self.grid.get_slice_parallel_rank()
        else:
            mp_rank = 0

        batch = None

        # Only MP rank 0 loads the data.
        if mp_rank == 0:
            if self.data_iterator is None:
                raise ValueError(f"RANK={self.global_rank} no data iterator provided.")
            batch = next(self.data_iterator)

        # All MP ranks participate in batch_fn, where they might broadcast the data.
        if self.batch_fn:
            batch = self.batch_fn(batch)

        # Sanity check dimensions.
        # XXX: the last minibatch with size < micro_batch_size kills us
        if torch.is_tensor(batch[0]):
            if batch[0].size(0) != self.micro_batch_size:
                print(f'size mismatch: {batch[0].size(0)} mb: {self.micro_batch_size}')
                return self._next_batch()
        else:
            assert torch.is_tensor(batch[0][0])
            if batch[0][0].size(0) != self.micro_batch_size:
                return self._next_batch()

        return batch

    def _exec_forward_pass(self, buffer_id):
        self.tput_timer.start()
        self.mem_status('BEFORE FWD', reset_max=True)

        if isinstance(self.pipe_buffers['inputs'][buffer_id], tuple):
            inputs = tuple(t.clone() for t in self.pipe_buffers['inputs'][buffer_id])
        else:
            inputs = self.pipe_buffers['inputs'][buffer_id].clone()

        # collect the partitioned input from the previous stage
        if self.is_pipe_partitioned and not self.is_first_stage():
            part_input = PartitionedTensor.from_meta(
                meta=inputs[0],
                local_part=inputs[1],
                group=self.grid.get_slice_parallel_group())

            inputs = tuple([part_input.full(), inputs[2]])
            inputs[0].requires_grad = True
            # skip mask
            #inputs[1].requires_grad = True
            part_input = None
            self.pipe_buffers['inputs'][buffer_id] = inputs

        # Zero out the gradients each time we use the tensor because only the data in
        # tensor changes across batches
        self._zero_grads(inputs)

        outputs = super().forward(inputs)

        # Partition the outputs if we are not the last stage
        if self.is_pipe_partitioned and not self.is_last_stage():
            part = PartitionedTensor(tensor=outputs[0],
                                     group=self.grid.get_slice_parallel_group())
            # Clear the large output data, but save the computation graph
            outputs[0].data = torch.zeros(1)
            self.pipe_buffers['output_tensors'][buffer_id] = outputs[0]
            # Inject the partitioned tensor into the output before sending
            outputs = tuple([part.to_meta(), part.data(), outputs[1]])
            part = None

        self.pipe_buffers['outputs'][buffer_id] = outputs

        # Optionally compute loss on the last device
        if self.is_last_stage():
            if self.loss_model is not None:
                labels = self.pipe_buffers['labels'][buffer_id]
                self.loss = self.loss_model(outputs, labels)
            else:
                # Some models just return loss from forward()
                self.loss = outputs

            if isinstance(self.loss, torch.Tensor):
                if self.total_loss is None:
                    self.total_loss = torch.zeros_like(self.loss)
                self.total_loss += self.loss.detach()
            else:
                if self.total_loss is None:
                    self.total_loss = [torch.zeros_like(l) for l in self.loss]
                for idx, l in enumerate(self.loss):
                    self.total_loss[idx] += l.detach()

    def _exec_backward_pass(self, buffer_id):
        assert self.optimizer is not None, "must provide optimizer during " \
                                           "init in order to use backward"

        self.mem_status('BEFORE BWD', reset_max=True)

        # The last stage just runs backward on the loss using DeepSpeed's typical
        # mechanisms.
        if self.is_last_stage():
            super().backward(self.loss)
            self.mem_status('AFTER BWD')
            return

        outputs = self.pipe_buffers['outputs'][buffer_id]

        if self.wall_clock_breakdown():
            self.timers('backward_microstep').start()
            self.timers('backward').start()
            self.timers('backward_inner_microstep').start()
            self.timers('backward_inner').start()

        # Reconstruct if we previously partitioned the output. We must be
        # careful to also restore the computational graph of the tensors we partitioned.
        if self.is_pipe_partitioned:
            if self.is_grad_partitioned:
                part_output = PartitionedTensor.from_meta(
                    meta=outputs[0],
                    local_part=outputs[1],
                    group=self.grid.get_slice_parallel_group())
                self.pipe_buffers['output_tensors'][buffer_id].data = part_output.full()
                outputs = tuple(
                    [self.pipe_buffers['output_tensors'][buffer_id],
                     outputs[2]])
            else:
                # Already restored from partition
                self.pipe_buffers['output_tensors'][buffer_id].data = outputs[0]
                outputs = tuple(
                    [self.pipe_buffers['output_tensors'][buffer_id],
                     outputs[1]])

        grad_tensors = self.grad_layer
        if self.is_grad_partitioned:
            #print(f'RANK={self.global_rank} BEFORE-BWD restoring grad={self.grad_layer[0].size()} {self.grad_layer[1].size()}')
            part_grad = PartitionedTensor.from_meta(
                meta=self.grad_layer[0],
                local_part=self.grad_layer[1],
                group=self.grid.get_slice_parallel_group())
            grad_tensors = tuple([part_grad.full(), self.grad_layer[2]])
            part_grad = None
            #print(f'RANK={self.global_rank} BEFORE-BWD restored grad={self.grad_layer[0].size()} {self.grad_layer[1].size()}')

        # This handles either a single tensor or tuple of tensors.
        if isinstance(outputs, tuple):
            out_tensors = [t for t in outputs if t.is_floating_point()]
            assert len(out_tensors) == len(grad_tensors)
            torch.autograd.backward(tensors=out_tensors, grad_tensors=grad_tensors)
        else:
            torch.autograd.backward(tensors=(outputs, ), grad_tensors=(grad_tensors, ))

        # Free up the memory from the output of forward()
        self.pipe_buffers['output_tensors'][buffer_id] = None
        self.pipe_buffers['outputs'][buffer_id] = None
        grad_tensors = None

        if self.wall_clock_breakdown():
            self.timers('backward_inner').stop()
            self.timers('backward_inner_microstep').stop()
            self.timers('backward').stop()
            self.timers('backward_microstep').stop()

        self.mem_status('AFTER BWD')

    def _exec_load_micro_batch(self, buffer_id):
        if self.wall_clock_breakdown():
            self.timers('batch_input').start()

        batch = self._next_batch()

        if self.is_first_stage():
            loaded = None
            if torch.is_tensor(batch[0]):
                loaded = batch[0].clone().to(self.device).detach()
                loaded.requires_grad = loaded.is_floating_point()
            else:
                assert isinstance(batch[0], tuple)
                # Assume list or tuple
                loaded = []
                for x in batch[0]:
                    assert torch.is_tensor(x)
                    mine = x.clone().detach().to(self.device)
                    mine.requires_grad = mine.is_floating_point()
                    loaded.append(mine)
                loaded = tuple(loaded)

            self.pipe_buffers['inputs'][buffer_id] = loaded

        if self.is_last_stage():
            loaded = batch[1]
            if torch.is_tensor(batch[1]):
                loaded = batch[1].to(self.device)
            elif isinstance(batch[1], tuple):
                loaded = []
                for x in batch[1]:
                    assert torch.is_tensor(x)
                    x = x.to(self.device).detach()
                    loaded.append(x)
                loaded = tuple(loaded)

            self.pipe_buffers['labels'][buffer_id] = loaded

        if self.wall_clock_breakdown():
            self.timers('batch_input').stop()

    def _send_tensor_meta(self, buffer, recv_stage):
        """ Communicate metadata about upcoming p2p transfers.

        Metadata is communicated in this order:
            * type (0: tensor, 1: list)
            * num_tensors if type=list
            foreach tensor in buffer:
                * ndims
                * shape
        """
        send_bytes = 0
        if isinstance(buffer, torch.Tensor):
            type_tensor = torch.LongTensor(data=[0]).to(self.device)
            p2p.send(type_tensor, recv_stage)
            send_shape = torch.LongTensor(data=buffer.size()).to(self.device)
            send_ndims = torch.LongTensor(data=[len(buffer.size())]).to(self.device)
            p2p.send(send_ndims, recv_stage)
            p2p.send(send_shape, recv_stage)
            send_bytes += _tensor_bytes(buffer)
        elif isinstance(buffer, list):
            assert (False)
            type_tensor = torch.LongTensor(data=[1]).to(self.device)
            p2p.send(type_tensor, recv_stage)
            count_tensor = torch.LongTensor(data=[len(buffer)]).to(self.device)
            p2p.send(count_tensor, recv_stage)
            for tensor in buffer:
                assert isinstance(tensor, torch.Tensor)
                send_shape = torch.LongTensor(data=tensor.size()).to(self.device)
                send_ndims = torch.LongTensor(data=[len(tensor.size())]).to(self.device)
                p2p.send(send_ndims, recv_stage)
                p2p.send(send_shape, recv_stage)
                send_bytes += _tensor_bytes(tensor)
        elif isinstance(buffer, tuple):
            type_tensor = torch.LongTensor(data=[2]).to(self.device)
            p2p.send(type_tensor, recv_stage)
            count_tensor = torch.LongTensor(data=[len(buffer)]).to(self.device)
            p2p.send(count_tensor, recv_stage)
            for idx, tensor in enumerate(buffer):
                assert isinstance(tensor, torch.Tensor)
                send_shape = torch.LongTensor(data=tensor.size()).to(self.device)
                send_ndims = torch.LongTensor(data=[len(tensor.size())]).to(self.device)
                p2p.send(send_ndims, recv_stage)
                p2p.send(send_shape, recv_stage)
                # Useful for performance debugging.
                '''
                new_bytes = _tensor_bytes(tensor)
                send_bytes += _tensor_bytes(tensor)
                # Useful for performance debugging.
                if self.grid.data_parallel_id == 0:
                    print(
                        f'STAGE={self.stage_id} pipe-send-volume[{idx}]: shape={send_shape} {new_bytes/1024**2:0.2f}MB'
                    )
                '''
        else:
            raise NotImplementedError(f'Could not send meta type {type(buffer)}')

        # Useful for performance debugging.
        '''
        if self.grid.data_parallel_id == 0:
            print(f'STAGE={self.stage_id} pipe-send-volume: {send_bytes/1024**2:0.2f}MB')
        '''

    def _recv_tensor_meta(self, send_stage):
        """Receive metadata about upcoming p2p transfers and return allocated buffers.

        Metadata is communicated in this order:
            * type (0: tensor, 1: list)
            * num_tensors if type=list
            foreach tensor in buffer:
                * ndims
                * shape

        Returns:
            Allocated buffer for receiving from send_stage.
        """

        type_tensor = torch.LongTensor(data=[0]).to(self.device)
        p2p.recv(type_tensor, send_stage)
        recv_type = type_tensor.item()

        # A single tensor will be sent.
        if recv_type == 0:
            recv_ndims = torch.LongTensor(data=[0]).to(self.device)
            p2p.recv(recv_ndims, send_stage)
            recv_ndims = recv_ndims.item()
            recv_shape = torch.LongTensor([1] * recv_ndims).to(self.device)
            p2p.recv(recv_shape, send_stage)
            recv_shape = recv_shape.tolist()
            return self._allocate_buffer(recv_shape, num_buffers=1)[0]

        # List or tuple of tensors
        elif recv_type == 1 or recv_type == 2:
            count_tensor = torch.LongTensor(data=[0]).to(self.device)
            p2p.recv(count_tensor, send_stage)
            num_tensors = count_tensor.item()
            recv_shapes = []
            for idx in range(num_tensors):
                recv_ndims = torch.LongTensor(data=[0]).to(self.device)
                p2p.recv(recv_ndims, send_stage)
                recv_ndims = recv_ndims.item()
                recv_shape = torch.LongTensor([1] * recv_ndims).to(self.device)
                p2p.recv(recv_shape, send_stage)
                recv_shapes.append(recv_shape.tolist())

            buffers = self._allocate_buffers(recv_shapes, num_buffers=1)[0]
            # Convert to tuples if requested.
            if recv_type == 2:
                buffers = tuple(buffers)
            return buffers

        else:
            raise NotImplementedError(f'Could not receive type {type(recv_type)}')

    def _exec_send_activations(self, buffer_id):
        if self.wall_clock_breakdown():
            self.timers('pipe_send_output').start()

        outputs = self.pipe_buffers['outputs'][buffer_id]

        # NCCL does not like to send torch.BoolTensor types, so cast the mask to half().
        # We could do char, but with half() we can eventually flatten with other fp16
        # messages (TODO)
        if self.module.__class__.__name__ == 'GPT2ModelPipe':
            outputs = list(outputs)
            outputs[-1] = outputs[-1].half()
            outputs = tuple(outputs)

        if self.first_output_send:
            self.first_output_send = False
            self._send_tensor_meta(outputs, self.next_stage)

        if isinstance(outputs, torch.Tensor):
            p2p.send(outputs, self.next_stage)
        elif isinstance(outputs, tuple):
            for idx, buffer in enumerate(outputs):
                p2p.send(buffer, self.next_stage)
        else:
            raise NotImplementedError('Could not send output of type '
                                      f'{type(outputs)}')

        # Restore the boolean tensor
        if self.module.__class__.__name__ == 'GPT2ModelPipe':
            outputs = list(outputs)
            outputs[-1] = outputs[-1].bool()
            outputs = tuple(outputs)

        if self.wall_clock_breakdown():
            self.timers('pipe_send_output').stop()

    def _exec_send_grads(self, buffer_id):
        if self.wall_clock_breakdown():
            self.timers('pipe_send_grad').start()

        inputs = self.pipe_buffers['inputs'][buffer_id]

        # Partition the gradient
        if self.is_grad_partitioned:
            part = PartitionedTensor(tensor=inputs[0].grad,
                                     group=self.grid.get_slice_parallel_group())
            # Clear the large output data, but save the computation graph
            # Inject the partitoned tensor into the output before sending

            # XXX Hack
            inputs = tuple([part.to_meta(), part.data(), inputs[1]])

        # XXX Terrible hack
        # Drop the attention mask from the input buffer here. It does not have
        # a grad that needs to be communicated. We free the buffer immediately
        # after, so no need to restore it. The receiver also has a hack that skips
        # the recv. This is because NCCL does not let us send torch.BoolTensor :-(.
        if self.module.__class__.__name__ == 'GPT2ModelPipe':
            inputs = list(inputs)
            inputs.pop()
            inputs = tuple(inputs)

        if isinstance(inputs, torch.Tensor):
            assert inputs.grad is not None
            p2p.send(inputs.grad, self.prev_stage)
        else:
            # XXX terrible hacky branch
            if self.is_grad_partitioned:
                # First two sends are partitioned gradient
                p2p.send(inputs[0], self.prev_stage)
                p2p.send(inputs[1], self.prev_stage)
                # XXX hack hack hack
                #p2p.send(inputs[2].grad, self.prev_stage)
            else:
                for idx, buffer in enumerate(inputs):
                    # Skip tensors that will not produce a grad
                    if not buffer.is_floating_point():
                        assert buffer.grad is None
                        continue
                    assert buffer.grad is not None
                    p2p.send(buffer.grad, self.prev_stage)

        # We can free up the input buffer now
        self.pipe_buffers['inputs'][buffer_id] = None

        if self.wall_clock_breakdown():
            self.timers('pipe_send_grad').stop()

    def _exec_recv_activations(self, buffer_id):
        if self.wall_clock_breakdown():
            self.timers('pipe_recv_input').start()

        recvd = None

        # Allocate the buffer if necessary
        if self.pipe_recv_buf is None:
            self.pipe_recv_buf = self._recv_tensor_meta(self.prev_stage)

        if isinstance(self.pipe_recv_buf, torch.Tensor):
            p2p.recv(self.pipe_recv_buf, self.prev_stage)
            recvd = self.pipe_recv_buf.clone().detach()
            recvd.requires_grad = recvd.is_floating_point()
        else:
            assert isinstance(self.pipe_recv_buf, tuple)
            recvd = [None] * len(self.pipe_recv_buf)
            for idx, buffer in enumerate(self.pipe_recv_buf):
                assert torch.is_tensor(buffer)
                # XXX hardcode meta type
                if self.is_pipe_partitioned and idx == 0 and buffer.dtype != torch.long:
                    if self.meta_buffer is None:
                        self.meta_buffer = torch.zeros(buffer.size(),
                                                       dtype=torch.long,
                                                       device=self.device)
                    buffer = self.meta_buffer

                p2p.recv(buffer, self.prev_stage)
                recvd[idx] = buffer.clone().detach()

            # NCCL does not like to send torch.BoolTensor types, so un-cast the
            # attention mask
            if self.module.__class__.__name__ == 'GPT2ModelPipe':
                recvd[-1] = recvd[-1].bool()

            recvd = tuple(recvd)

            for buffer in recvd:
                buffer.requires_grad = buffer.is_floating_point()

        self.pipe_buffers['inputs'][buffer_id] = recvd

        if self.wall_clock_breakdown():
            self.timers('pipe_recv_input').stop()

    def _exec_recv_grads(self, buffer_id):
        if self.wall_clock_breakdown():
            self.timers('pipe_recv_grad').start()

        outputs = self.pipe_buffers['outputs'][buffer_id]
        # XXX these shapes are hardcoded for Megatron
        # Restore partitioned output if it was partitioned and we are sending full gradients
        if self.is_pipe_partitioned and not self.is_grad_partitioned:
            part_output = PartitionedTensor.from_meta(
                meta=outputs[0],
                local_part=outputs[1],
                group=self.grid.get_slice_parallel_group())
            outputs[0].data = part_output.full()
            outputs = tuple([outputs[0], outputs[2]])
            # save for backward
            self.pipe_buffers['outputs'][buffer_id] = outputs

        # Allocate gradient if necessary
        if self.grad_layer is None:
            if isinstance(outputs, torch.Tensor):
                s = list(outputs.size())
                self.grad_layer = self._allocate_buffer(s, num_buffers=1)[0]
            else:
                sizes = [list(t.size()) for t in outputs if t.is_floating_point()]
                self.grad_layer = self._allocate_buffers(sizes, num_buffers=1)[0]

        if isinstance(self.grad_layer, torch.Tensor):
            p2p.recv(self.grad_layer, self.next_stage)
        else:
            assert isinstance(outputs, tuple)
            for idx, buffer in enumerate(self.grad_layer):
                # XXX GPT-2 hack
                if self.is_grad_partitioned and idx == 0 and buffer.dtype != torch.long:
                    buffer.data = torch.zeros(buffer.size(),
                                              dtype=torch.long,
                                              device=self.device)
                p2p.recv(buffer, self.next_stage)

        if self.wall_clock_breakdown():
            self.timers('pipe_recv_grad').stop()

    def _exec_optimizer_step(self, lr_kwargs=None):
        if self.wall_clock_breakdown():
            self.timers('step_microstep').start()
            self.timers('step').start()
        self.mem_status('BEFORE STEP', reset_max=True)

        self._force_grad_boundary = True
        self._take_model_step(lr_kwargs)
        self._force_grad_boundary = False

        self.mem_status('AFTER STEP')

        if self.tensorboard_enabled():
            if self.global_rank == 0:
                self.summary_events = [(f'Train/Samples/lr',
                                        self.get_lr()[0],
                                        self.global_samples)]
                if self.fp16_enabled() and hasattr(self.optimizer, 'cur_scale'):
                    self.summary_events.append((f'Train/Samples/loss_scale',
                                                self.optimizer.cur_scale,
                                                self.global_samples))
                for event in self.summary_events:  # write_summary_events
                    self.summary_writer.add_scalar(event[0], event[1], event[2])

        if self.wall_clock_breakdown():
            self.timers('step_microstep').stop()
            self.timers('step').stop()
            if self.global_steps % self.steps_per_print() == 0:
                self.timers.log([
                    'batch_input',
                    'forward_microstep',
                    'backward_microstep',
                    'backward_inner_microstep',
                    'backward_allreduce_microstep',
                    'backward_tied_allreduce_microstep',
                    'step_microstep'
                ])
            if self.global_steps % self.steps_per_print() == 0:
                self.timers.log([
                    'forward',
                    'backward',
                    'backward_inner',
                    'backward_allreduce',
                    'step'
                ])

    def _zero_grads(self, inputs):
        if isinstance(inputs, torch.Tensor):
            if inputs.grad is not None:
                inputs.grad.data.zero_()
        else:
            for t in inputs:
                if t.grad is not None:
                    t.grad.data.zero_()

    def _allocate_zeros(self, shape, fp16=None, **kwargs):
        """ Allocate a tensor of zeros on the engine's device.

        Arguments:
            shape: the shape of the tensor to allocate
            fp16 (bool): whether to use FP16. default: defer to self.fp16_enabled()
            kwargs: passed to torch.zeros()

        Returns:
            A tensor from torch.zeros() allocated on self.device.
        """

        if fp16 is None:
            fp16 = self.fp16_enabled()

        if fp16:
            return torch.zeros(shape, dtype=torch.half, device=self.device, **kwargs)
        else:
            return torch.zeros(shape, device=self.device, **kwargs)

    def _allocate_buffer(self, shape, num_buffers=-1, **kwargs):
        buffers = []
        if num_buffers == -1:
            num_buffers = self.num_pipe_buffers
        for count in range(num_buffers):
            buffers.append(self._allocate_zeros(shape, **kwargs))
        return buffers

    def _allocate_buffers(self, shapes, requires_grad=False, num_buffers=-1):
        buffers = []
        if num_buffers == -1:
            num_buffers = self.num_pipe_buffers
        for count in range(num_buffers):
            buffer = []
            for shape in shapes:
                buffer.append(self._allocate_zeros(shape, requires_grad=requires_grad))
            buffers.append(buffer)
        return buffers

    def forward(self, *args, **kwargs):
        """Disabled for pipeline parallel training. See ``train_batch()``. """
        raise PipelineError("Only train_batch() is accessible in pipeline mode.")

    def backward(self, *args, **kwargs):
        """Disabled for pipeline parallel training. See ``train_batch()``. """
        raise PipelineError("Only train_batch() is accessible in pipeline mode.")

    def step(self, *args, **kwargs):
        """Disabled for pipeline parallel training. See ``train_batch()``. """
        raise PipelineError("Only train_batch() is accessible in pipeline mode.")

    def mem_status(self, msg, print_rank=-1, reset_max=False):
        return
        global mem_alloced, mem_cached
        if not self.global_steps == 0 or not self.global_steps == 9:
            #return
            pass
        if self.mpu.get_data_parallel_rank() != 0:
            return

        if self.global_rank != 0:
            return

        rank = self.global_rank
        if print_rank != -1 and rank != print_rank:
            return

        torch.cuda.synchronize()

        if reset_max:
            torch.cuda.reset_max_memory_cached()
            torch.cuda.reset_max_memory_allocated()

        new_alloced = torch.cuda.memory_allocated()
        new_cached = torch.cuda.memory_cached()

        delta_alloced = new_alloced - mem_alloced
        delta_cached = new_cached - mem_cached

        mem_cached = new_cached
        mem_alloced = new_alloced

        max_alloced = torch.cuda.max_memory_allocated()
        max_cached = torch.cuda.max_memory_cached()

        # convert to GB for printing
        new_alloced /= 1024**3
        new_cached /= 1024**3
        delta_alloced /= 1024**3
        delta_cached /= 1024**3
        max_alloced /= 1024**3
        max_cached /= 1024**3

        print(
            f'RANK={rank} STAGE={self.stage_id} STEP={self.global_steps} MEMSTATS',
            msg,
            f'current alloc={new_alloced:0.4f}GB (delta={delta_alloced:0.4f}GB max={max_alloced:0.4f}GB) '
            f'current cache={new_cached:0.4f}GB (delta={delta_cached:0.4f}GB max={max_cached:0.4f}GB)'
        )

    def module_state_dict(self):
        """Override hack to save a pipe model and return the directory path of the save.

        This method should only be called by DeepSpeed's ``save_checkpoint()``. The
        recommended way of saving a ``PipelineModule`` outside of ``save_checkpoint()``
        is ``save_state_dict()``.

        Returns:
            None
        """
        assert isinstance(self.module, PipelineModule)
        assert self._curr_ckpt_path is not None, \
            "PipelineEngine expects module_state_dict() to be called from save_checkpoint()"

        self.module.save_state_dict(self._curr_ckpt_path)
        return None

    def load_module_state_dict(self, state_dict, strict=True):
        """Override hack to instead use a directory path.

        This is important because pipeline models checkpoint by layer instead of rank.

        If ``state_dict`` is not ``None`` or a ``str``, we revert to ``super()`` expecting a ``dict``.

        Args:
            state_dict (str, None): unused
            strict (bool, optional): Strict state loading. Defaults to True.
        """
        if (state_dict is not None) and (not isinstance(state_dict, str)):
            super().load_module_state_dict(state_dict, strict)
            return

        self.module.load_state_dir(load_dir=self._curr_ckpt_path, strict=strict)

    # A map of PipeInstruction types to methods. Each method will be executed with the
    # kwargs provided to the PipeInstruction from the scheduler.
    _INSTRUCTION_MAP = {
        schedule.OptimizerStep: _exec_optimizer_step,
        schedule.ReduceGrads: _exec_reduce_grads,
        schedule.ReduceTiedGrads: _exec_reduce_tied_grads,
        schedule.LoadMicroBatch: _exec_load_micro_batch,
        schedule.ForwardPass: _exec_forward_pass,
        schedule.BackwardPass: _exec_backward_pass,
        schedule.SendActivation: _exec_send_activations,
        schedule.RecvActivation: _exec_recv_activations,
        schedule.SendGrad: _exec_send_grads,
        schedule.RecvGrad: _exec_recv_grads,
    }

    def _exec_schedule(self, pipe_schedule):
        self._reserve_pipe_buffers(pipe_schedule.num_pipe_buffers())
        # For each step in the schedule
        for step_cmds in pipe_schedule:
            # For each instruction in the step
            for cmd in step_cmds:
                if type(cmd) not in self._INSTRUCTION_MAP:
                    raise RuntimeError(
                        f'{self.__class__.__name__} does not understand instruction {repr(cmd)}'
                    )

                # Equivalent to: self._exec_forward_pass(buffer_id=0)
                self._exec_instr = MethodType(self._INSTRUCTION_MAP[type(cmd)], self)
                self._exec_instr(**cmd.kwargs)
