Source code for deepspeed.runtime.pipe.engine

# 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 deepspeed.inference.engine import InferenceEngine
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()


[docs]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. """ ID_TO_DTYPE = [ torch.float32, torch.float64, torch.complex64, torch.complex128, torch.float16, torch.bfloat16, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64, torch.bool ] DTYPE_TO_ID = {dtype: id_ for id_, dtype in enumerate(ID_TO_DTYPE)} def __init__(self, has_bool_tensors=False, *super_args, **super_kwargs): super().__init__(*super_args, **super_kwargs) assert isinstance(self.module, PipelineModule), "model must base PipelineModule" assert self.zero_optimization_stage() < 2, "ZeRO-2 and ZeRO-3 are incompatible with pipeline parallelism" # We schedule the all-reduces, so disable it in super().backward() self.enable_backward_allreduce = False self.has_bool_tensors = has_bool_tensors # used to disable the pipeline all-reduce when used with 1-bit Adam/1-bit LAMB self.pipeline_enable_backward_allreduce = True assert not self.elasticity_enabled(), "Elasticity is not currently supported" \ " with pipeline parallelism." # 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 # XXX temporarily disable while I revert some partition hacks. self.is_pipe_partitioned = self.is_model_parallel self.is_grad_partitioned = self.is_model_parallel 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)') #initialize 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): # We need to run this first to write to self.averaged_gradients; # since this class turns `enable_backward_allreduce` off, # `self.overlapping_partition_gradients_reduce_epilogue()` defined in the DeepSpeedEngine # never actually runs. I suspect this is because of efficiency problems; get_flat_partition in # stage2.py might do something expensive; someone will have to look into that later. But # in the meantime, this fixes ZeRO2 + Pipelining enough to run a demo. Further profiling # needed to decide if it actually breaks everything. # (see https://github.com/EleutherAI/gpt-neox/issues/62#issuecomment-761471944) if self.zero_optimization_partition_gradients(): self.optimizer.overlapping_partition_gradients_reduce_epilogue() self.module.allreduce_tied_weight_gradients() def _exec_reduce_grads(self): self._force_grad_boundary = True if self.pipeline_enable_backward_allreduce: self.allreduce_gradients(bucket_size=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
[docs] def reset_activation_shape(self): """Reset the buffers when the shape of activation and gradient change. For example, for curriculum learning that changes the seqlen of each sample, we need to call this whenever the seqlen is going to change. """ self.first_output_send = True self.pipe_recv_buf = None self.grad_layer = None self.meta_buffer = None
[docs] 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.') # Curriculum learning could change activation shape if self.curriculum_enabled(): new_difficulty = self.curriculum_scheduler.update_difficulty( \ self.global_steps + 1) if self.global_steps == 0 or self.curriculum_scheduler.first_step: self.reset_activation_shape() self.curriculum_scheduler.first_step = False elif new_difficulty != self.curriculum_scheduler.get_difficulty( \ self.global_steps): self.reset_activation_shape() if data_iter: self.set_dataiterator(data_iter) self.module.train() self.total_loss = None self._compute_loss = True # 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
[docs] def eval_batch(self, data_iter, compute_loss=True, reduce_output='avg'): """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() # Curriculum learning could change activation shape if self.curriculum_enabled(): new_difficulty = self.curriculum_scheduler.update_difficulty( \ self.global_steps + 1) if self.global_steps == 0 or self.curriculum_scheduler.first_step: self.reset_activation_shape() self.curriculum_scheduler.first_step = False elif new_difficulty != self.curriculum_scheduler.get_difficulty( \ self.global_steps): self.reset_activation_shape() eval_output = None self._compute_loss = compute_loss # 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) if self.is_last_stage(): eval_output = self._reduce_outputs(self.fwd_outputs, reduce=reduce_output) if compute_loss: eval_output = self._bcast_pipe_scalar(eval_output) if self.tensorboard_enabled(): if self.global_rank == 0: self.summary_events = [(f'Train/Samples/eval_loss', eval_output.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 eval_output
[docs] def set_train_batch_size(self, train_batch_size): """Adjust the global batch size by increasing or decreasing the number of micro-batches (i.e., gradient accumulation steps). The size of each micro-batch (i.e., ``train_micro_batch_size_per_gpu``) is not changed. Args: train_batch_size (int): The new global batch size for training. Raises: ValueError: if ``train_batch_size`` is not divisible by the configured micro-batch size and data parallelism. """ super().set_train_batch_size(train_batch_size) self.micro_batches = self.gradient_accumulation_steps()
[docs] def is_first_stage(self): """True if this process is in the first stage in the pipeline.""" return self.stage_id == 0
[docs] 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 _reduce_outputs(self, outputs, reduce='avg', reduce_dp=True): if reduce is None: return outputs if reduce.lower() == 'avg': # first sum over all microbatches if torch.is_tensor(outputs[0]): reduced = sum(outputs) else: assert isinstance(outputs, (list, tuple)) reduced = [torch.zeros_like(o) for o in outputs[0]] for idx, out in outputs: reduced[idx] += out # Average over the microbatches reduced = self._scale_loss_by_gas(reduced) # Average over DP groups if reduce_dp and self.is_data_parallel: if torch.is_tensor(reduced): dist.all_reduce(reduced, group=self.mpu.get_data_parallel_group()) reduced /= self.dp_world_size else: for idx in range(len(reduced)): dist.all_reduce(reduced[idx], group=self.mpu.get_data_parallel_group()) reduced[idx] /= self.dp_world_size return reduced else: raise NotImplementedError(f'reduction type {reduce} not supported.') def _bcast_pipe_scalar(self, data, src_rank=None, dtype=torch.float32): # Default to last stage (e.g., for broadcasting loss) if src_rank is None: src_rank = self.grid.stage_to_global(self.num_stages - 1) assert src_rank in self.grid.pp_group if self.global_rank == src_rank: result = data.clone().detach() else: result = torch.Tensor([0.]).type(dtype).to(self.device) dist.broadcast(tensor=result, src=src_rank, group=self.mpu.get_pipe_parallel_group()) return result 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_by_gas(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)
[docs] 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
[docs] 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 using 3D parallelism, only some first-stage ranks may do IO batch = None if self.data_iterator is not None: batch = next(self.data_iterator) # Any post-processing, like broadcasting across a slice-parallel group. if self.batch_fn: batch = self.batch_fn(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 = (part_input.full(), *inputs[2:]) inputs[0].requires_grad = True # skip mask #inputs[1].requires_grad = True part_input = None inputs = inputs[0] if len(inputs) == 1 else inputs 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(): if isinstance(outputs, tuple): first_output = outputs[0] # TODO: Improve pipe partitioning to pass multiple tensors that require grads assert all([ torch.is_tensor(elt) and elt.requires_grad is False for elt in outputs[1:] ]) outputs_tail = outputs[1:] elif torch.is_tensor(outputs): first_output = outputs outputs_tail = [] else: raise ValueError("expecting a tensor or a tuple of tensors") part = PartitionedTensor(tensor=first_output, group=self.grid.get_slice_parallel_group()) # Clear the large output data, but save the computation graph first_output.data = torch.zeros(1) self.pipe_buffers['output_tensors'][buffer_id] = first_output # Inject the partitioned tensor into the output before sending outputs = (part.to_meta(), part.data(), *outputs_tail) part = None self.pipe_buffers['outputs'][buffer_id] = outputs # Optionally compute loss on the last device if self.is_last_stage(): if self._compute_loss and 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): self.fwd_outputs.append(self.loss.detach()) if self.total_loss is None: self.total_loss = torch.zeros_like(self.loss) self.total_loss += self.loss.detach() else: self.fwd_outputs.append([l.detach() for l in self.loss]) 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 = (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 = (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 = (part_grad.full(), *grad_tensors[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) send_dtype = torch.LongTensor(data=[self.DTYPE_TO_ID[tensor.dtype]]).to( self.device) p2p.send(send_dtype, recv_stage) 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_and_dtypes = [] for idx in range(num_tensors): recv_dtype = torch.LongTensor(data=[0]).to(self.device) p2p.recv(recv_dtype, send_stage) recv_dtype = self.ID_TO_DTYPE[recv_dtype.item()] 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_and_dtypes.append((recv_shape.tolist(), recv_dtype)) buffers = self._allocate_buffers(recv_shapes_and_dtypes, 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' or self.has_bool_tensors: 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' or self.has_bool_tensors: 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: if isinstance(inputs, tuple): first_input = inputs[0] assert all([torch.is_tensor(elt) for elt in inputs[1:]]) inputs_grad_tail = [ elt.grad for elt in inputs[1:] if elt.grad is not None ] elif torch.is_tensor(inputs): first_input = inputs inputs_grad_tail = [] else: raise ValueError("expecting a tensor or a tuple of tensors") assert torch.is_tensor(first_input) part = PartitionedTensor(tensor=first_input.grad, group=self.grid.get_slice_parallel_group()) inputs = (part.to_meta(), part.data(), *inputs_grad_tail) # 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' or self.has_bool_tensors: 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) 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' or self.has_bool_tensors: 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 = (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, dtype=outputs.dtype, num_buffers=1)[0] else: # XXX This is a HACK # When we exchange activations/gradients, the two pipe stages # need to issue the send/recv with the same buffer sizes or # else there is a deadlock. The is_floating_point() filter is # used to avoid sending gradients for tensors that do not # produce gradients. When TP>1, we partition the first # activations/gradients across TP ranks to save communication # volume and memory. That partitioned tensor is represented as # two tensors: a 1/TPth chunk of the original data and also a # small LongTensor storing the metadata used to reconstruct on # the other side. When combined, the floating point filter also # filtered out the metadata tensor. This quick (hacky) fix just # branches on is_grad_partitioned so we don't filter out the # metadata tensor. if self.is_grad_partitioned: sizes_and_dtypes = [ (list(t.size()), t.dtype) for t in outputs[:2] ] + [(list(t.size()), t.dtype) for t in outputs[2:] if t.is_floating_point()] else: sizes_and_dtypes = [(list(t.size()), t.dtype) for t in outputs if t.is_floating_point()] self.grad_layer = self._allocate_buffers(sizes_and_dtypes, 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, **kwargs): """ Allocate a tensor of zeros on the engine's device. Arguments: shape: the shape of the tensor to allocate kwargs: passed to torch.zeros() Returns: A tensor from torch.zeros() allocated on self.device. """ if "dtype" not in kwargs and self.fp16_enabled(): kwargs["dtype"] = torch.half 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_and_dtypes, 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, dtype in shapes_and_dtypes: buffer.append( self._allocate_zeros(shape, dtype=dtype, requires_grad=requires_grad)) buffers.append(buffer) return buffers
[docs] def forward(self, *args, **kwargs): """Disabled for pipeline parallel training. See ``train_batch()``. """ raise PipelineError("Only train_batch() is accessible in pipeline mode.")
[docs] def backward(self, *args, **kwargs): """Disabled for pipeline parallel training. See ``train_batch()``. """ raise PipelineError("Only train_batch() is accessible in pipeline mode.")
[docs] 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)' )
[docs] 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
[docs] 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): # Reserve and reset buffers. self._reserve_pipe_buffers(pipe_schedule.num_pipe_buffers()) self.fwd_outputs = [] # 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)
[docs] def set_batch_fn(self, fn): """Execute a post-processing function on input data. Args: fn (function): The function to run. """ self.batch_fn = fn