# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from types import MethodType
from collections import OrderedDict
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.utils.timer import ThroughputTimer
from deepspeed.accelerator import get_accelerator
from deepspeed.runtime.bf16_optimizer import BF16_Optimizer
from ..engine import DeepSpeedEngine, MEMORY_OPT_ALLREDUCE_SIZE
from deepspeed.utils.timer import FORWARD_MICRO_TIMER, FORWARD_GLOBAL_TIMER, BACKWARD_MICRO_TIMER, \
BACKWARD_GLOBAL_TIMER, BACKWARD_INNER_MICRO_TIMER, BACKWARD_INNER_GLOBAL_TIMER, \
BACKWARD_REDUCE_MICRO_TIMER, BACKWARD_REDUCE_GLOBAL_TIMER, \
STEP_MICRO_TIMER, STEP_GLOBAL_TIMER
from ..utils import PartitionedTensor
from ..dataloader import RepeatingLoader
from ..zero.config import ZeroStageEnum
from ..activation_checkpointing import checkpointing as ds_checkpointing
from .module import PipelineModule, PipelineError
from . import p2p
from . import schedule
TARGET_ID = -2
LOG_STAGE = -2
DATA_PARALLEL_ID = -2
BATCH_INPUT_TIMER = 'batch_input'
TRAIN_BATCH_TIMER = 'train_batch'
PIPE_SEND_OUTPUT_TIMER = 'pipe_send_output'
PIPE_SEND_GRAD_TIMER = 'pipe_send_grad'
PIPE_RECV_INPUT_TIMER = 'pipe_recv_input'
PIPE_RECV_GRAD_TIMER = 'pipe_recv_grad'
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) \
or (hasattr(self.module, 'wrapped') and isinstance(self.module.wrapped, PipelineModule)), \
"model must base PipelineModule"
assert self.zero_optimization_stage(
) < ZeroStageEnum.gradients, "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
self.eval_return_logits = False
self.outputs = None
# BF16 Optimizer is hardcoded for fp32 gradient accumulation
self.using_bf16_optimizer = type(self.optimizer) == BF16_Optimizer
# used to disable the pipeline all-reduce when used with 1-bit Adam/1-bit LAMB
self.pipeline_enable_backward_allreduce = True
if self.elasticity_enabled():
if not self.is_elastic_model_parallel_supported():
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.train_batch_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.
assert isinstance(self._config.pipeline['pipe_partitioned'], bool)
assert isinstance(self._config.pipeline['grad_partitioned'], bool)
self.is_pipe_partitioned = self.is_model_parallel and self._config.pipeline['pipe_partitioned']
self.is_grad_partitioned = self.is_model_parallel and self._config.pipeline['grad_partitioned']
logger.info(f'is_pipe_partitioned= {self.is_pipe_partitioned} '
f'is_grad_partitioned= {self.is_grad_partitioned}')
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
self.pipe_partition_input_meta_cache = None
self.pipe_partition_output_meta_cache = None
self.pipe_partition_grad_meta_cache = None
self.grad_partition_grad_layer_meta_cache = None
#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.total_additional_losses = 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)
# stores aggregated-DP train final loss and aggregated-DP additional losses, if any
# additional losses are stored as dict: {loss-name: agg-loss}
self.agg_train_loss = None
self.agg_additional_losses = None
if self._config.pipeline['activation_checkpoint_interval'] > 0:
self.module.activation_checkpoint_interval = self._config.pipeline['activation_checkpoint_interval']
# set use_reentrant default to True.
if self._config.pipeline.get('use_reentrant') is None:
self._config.pipeline['use_reentrant'] = True
if self._config.pipeline['use_reentrant'] is False:
# set activation_checkpoint_func to non_reentrant_checkpoint func.
self.module.activation_checkpoint_func = ds_checkpointing.non_reentrant_checkpoint
if self.grid.get_global_rank() == 0:
logger.info(f'CONFIG: activation_checkpoint_func=non_reentrant_checkpoint')
self.module.checkpoint_parallel_write_pipeline = self._config.checkpoint_parallel_write_pipeline
if self.is_last_stage():
self.loss_model = self.module.loss_fn
self.has_attention_mask = self.module.__class__.__name__ == 'GPT2ModelPipe'
# 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_MICRO_TIMER).start()
self.timers(FORWARD_MICRO_TIMER).stop()
self.timers(BACKWARD_MICRO_TIMER).start()
self.timers(BACKWARD_MICRO_TIMER).stop()
self.timers(BACKWARD_INNER_MICRO_TIMER).start()
self.timers(BACKWARD_INNER_MICRO_TIMER).stop()
self.timers(BACKWARD_REDUCE_MICRO_TIMER).start()
self.timers(BACKWARD_REDUCE_MICRO_TIMER).stop()
self.timers(BACKWARD_REDUCE_GLOBAL_TIMER).start()
self.timers(BACKWARD_REDUCE_GLOBAL_TIMER).stop()
self.timers(STEP_MICRO_TIMER).start()
self.timers(STEP_MICRO_TIMER).stop()
def set_has_attention_mask(self, value):
assert isinstance(value, bool)
self.has_attention_mask = value
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()
weight_group_list = self.module.get_tied_weights_and_groups()
for weight, group in weight_group_list:
grad = weight._hp_grad if self.using_bf16_optimizer else weight.grad
dist.all_reduce(grad, group=group)
def _exec_reduce_grads(self):
self._force_grad_boundary = True
if self.pipeline_enable_backward_allreduce:
if self.using_bf16_optimizer:
# PP+BF16 work for ZeRO Stage 1
self._bf16_reduce_grads()
else:
self.allreduce_gradients(bucket_size=MEMORY_OPT_ALLREDUCE_SIZE)
self._force_grad_boundary = False
def _bf16_reduce_grads(self):
self.buffered_allreduce_fallback(grads=None, elements_per_buffer=MEMORY_OPT_ALLREDUCE_SIZE)
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
self.pipe_partition_input_meta_cache = None
self.pipe_partition_output_meta_cache = None
self.pipe_partition_grad_meta_cache = None
self.grad_partition_grad_layer_meta_cache = 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_legacy():
new_difficulty = self.curriculum_scheduler_legacy.update_difficulty( \
self.global_steps + 1)
if self.global_steps == 0 or self.curriculum_scheduler_legacy.first_step:
self.reset_activation_shape()
self.curriculum_scheduler_legacy.first_step = False
elif new_difficulty != self.curriculum_scheduler_legacy.get_difficulty( \
self.global_steps):
self.reset_activation_shape()
if data_iter is not None:
self.set_dataiterator(data_iter)
self.module.train()
self.total_loss = None
self.total_additional_losses = None
self._compute_loss = True
# Do the work
self.timers(TRAIN_BATCH_TIMER).start()
sched = schedule.TrainSchedule(micro_batches=self.micro_batches,
stages=self.num_stages,
stage_id=self.stage_id)
self._exec_schedule(sched)
with torch.no_grad():
self.agg_train_loss = self._aggregate_total_loss()
self.timers(TRAIN_BATCH_TIMER).stop()
if self.global_steps % self.steps_per_print() == 0:
if self.global_rank == 0:
elapsed = self.timers(TRAIN_BATCH_TIMER).elapsed(reset=True) / 1000.0
iter_time = elapsed / self.steps_per_print()
tput = self.train_batch_size() / iter_time
log_str = f'steps: {self.global_steps} loss: {self.agg_train_loss:0.4f} '
if self.agg_additional_losses is not None:
for loss_name, loss_value in self.agg_additional_losses.items():
log_str += f'{loss_name}: {loss_value.item():0.4f} '
log_str += f'iter time (s): {iter_time:0.3f} samples/sec: {tput:0.3f}'
print(log_str)
else:
self.timers(TRAIN_BATCH_TIMER).elapsed(reset=True)
# Monitoring
if self.global_rank == 0 and self.monitor.enabled:
self.summary_events = [(f'Train/Samples/train_loss', self.agg_train_loss.mean().item(),
self.global_samples)]
self.monitor.write_events(self.summary_events)
if self.wall_clock_breakdown() and self.global_steps % self.steps_per_print() == 0:
self.timers.log([
PIPE_SEND_OUTPUT_TIMER,
PIPE_SEND_GRAD_TIMER,
PIPE_RECV_INPUT_TIMER,
PIPE_RECV_GRAD_TIMER,
])
# 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,
return_logits=False,
compute_loss=True,
reduce_output='avg',
bcast_loss=True,
num_micro_batches=None):
"""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.eval_return_logits = return_logits
self.module.eval()
# Curriculum learning could change activation shape
if self.curriculum_enabled_legacy():
new_difficulty = self.curriculum_scheduler_legacy.update_difficulty( \
self.global_steps + 1)
if self.global_steps == 0 or self.curriculum_scheduler_legacy.first_step:
self.reset_activation_shape()
self.curriculum_scheduler_legacy.first_step = False
elif new_difficulty != self.curriculum_scheduler_legacy.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)
# set the number micro batches in case the user chose value than training
micro_batches = self.micro_batches if num_micro_batches is None else num_micro_batches
# Do the work
sched = schedule.InferenceSchedule(micro_batches=self.micro_batches,
stages=self.num_stages,
stage_id=self.stage_id)
# prevent dead-lock with multiple evals sequence
dist.barrier()
with torch.no_grad():
self._exec_schedule(sched)
if self.is_last_stage():
eval_output = self._reduce_outputs(self.fwd_outputs, reduce=reduce_output, micro_batches=micro_batches)
if compute_loss and (bcast_loss or self.monitor.enabled):
eval_output = self._bcast_pipe_scalar(eval_output)
if self.global_rank == 0 and self.monitor.enabled:
self.summary_events = [(f'Train/Samples/eval_loss', eval_output.mean().item(), self.global_samples)]
self.monitor.write_events(self.summary_events)
# Restore the training iterator
self.set_dataiterator(train_iterator)
# Reset any buffers that may have been populated during the forward passes.
#ds_checkpointing.reset()
self.eval_return_logits = False
if return_logits:
outputs = self.outputs
self.outputs = None
return eval_output, outputs
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, micro_batches=None):
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, eval_micro_batches=micro_batches)
# 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().type(dtype).to(self.device)
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():
# Scale loss and additional losses, if any
loss = self._scale_loss_by_gas(self.total_loss)
self.agg_additional_losses = self.total_additional_losses
if self.agg_additional_losses is not None:
self.agg_additional_losses = OrderedDict({
loss_name: self._scale_loss_by_gas(_loss.clone().detach())
for loss_name, _loss in self.agg_additional_losses.items()
})
self.dp_group_loss = loss.clone().detach()
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)
# Average loss across all data-parallel groups
if self.is_data_parallel:
if self.agg_additional_losses is None:
dist.all_reduce(agg_loss, group=self.mpu.get_data_parallel_group())
agg_loss /= self.dp_world_size
else:
# use a single reduce op for agg_loss and additional losses, if any
assert '__train_loss__' not in self.agg_additional_losses.keys()
tensors = OrderedDict({'__train_loss__': agg_loss})
tensors.update(self.agg_additional_losses.items())
flat_tensor = torch.cat([t.clone().reshape(-1).detach() for t in tensors.values()])
dist.all_reduce(flat_tensor, group=self.mpu.get_data_parallel_group())
flat_tensor /= self.dp_world_size
offset = 0
reduced_tensor = {}
for name, t in tensors.items():
n_elem = t.numel()
reduced_tensor[name] = flat_tensor[offset:offset + n_elem].clone().detach().reshape(t.shape)
offset += n_elem
agg_loss = reduced_tensor['__train_loss__']
self.agg_additional_losses = OrderedDict(
{name: reduced_tensor[name]
for name in self.agg_additional_losses.keys()})
assert self.global_rank in self.grid.pp_group
losses = [self.dp_group_loss, agg_loss]
if self.agg_additional_losses is not None:
losses += list(self.agg_additional_losses.values())
losses = torch.stack(losses).float()
if self.is_pipe_parallel:
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 to reduce are: dp_group_loss, agg_loss, model additional losses
# therefore: 2 + n_additional_losses
additional_losses = self.module.get_additional_losses()
n_additional_losses = 0 if additional_losses is None else len(additional_losses)
losses = torch.Tensor([0.] * (2 + n_additional_losses)).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()
if additional_losses is not None:
self.agg_additional_losses = OrderedDict(
{name: losses[2 + i].clone().detach()
for i, name in enumerate(additional_losses.keys())})
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
[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
[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():
if self.pipe_partition_input_meta_cache is None:
self.pipe_partition_input_meta_cache = inputs[0].to('cpu')
part_input = PartitionedTensor.from_meta(meta=self.pipe_partition_input_meta_cache,
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
# inputs has no gradient because it is from a cloned tensor
outputs = super().forward(inputs)
# Reset activation checkpointing buffers.
# Need to call this between evaluation iterations
if not self.module.training:
ds_checkpointing.reset()
# 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.module.loss_fn is not None:
labels = self.pipe_buffers['labels'][buffer_id]
self.loss = self.module.loss_fn(outputs, labels)
else:
# Some models just return loss from forward()
self.loss = outputs
if self.eval_return_logits:
self.outputs = outputs
if isinstance(self.loss, torch.Tensor):
self.fwd_outputs.append(self.loss.detach())
else:
self.fwd_outputs.append([l.detach() for l in self.loss])
def add_to_total_loss(_total_loss, _loss):
if isinstance(_loss, torch.Tensor):
if _total_loss is None:
_total_loss = torch.zeros_like(_loss)
_total_loss += _loss.detach()
else:
if _total_loss is None:
_total_loss = [torch.zeros_like(_l) for _l in _loss]
for _idx, _l in enumerate(_loss):
_total_loss[_idx] += _l.detach()
return _total_loss
self.total_loss = add_to_total_loss(self.total_loss, self.loss)
# aggregate additional losses across gradient accumulation steps
additional_losses = self.module.get_additional_losses()
if additional_losses is not None:
if self.total_additional_losses is None:
self.total_additional_losses = OrderedDict()
for name, loss in additional_losses.items():
total = self.total_additional_losses[name] if name in self.total_additional_losses else None
self.total_additional_losses[name] = add_to_total_loss(total, loss)
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_MICRO_TIMER).start()
self.timers(BACKWARD_GLOBAL_TIMER).start()
self.timers(BACKWARD_INNER_MICRO_TIMER).start()
self.timers(BACKWARD_INNER_GLOBAL_TIMER).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:
if self.pipe_partition_output_meta_cache is None:
self.pipe_partition_output_meta_cache = outputs[0].to('cpu')
part_output = PartitionedTensor.from_meta(meta=self.pipe_partition_output_meta_cache,
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()}')
if self.grad_partition_grad_layer_meta_cache is None:
self.grad_partition_grad_layer_meta_cache = self.grad_layer[0].to('cpu')
part_grad = PartitionedTensor.from_meta(meta=self.grad_partition_grad_layer_meta_cache,
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()}')
if self.using_bf16_optimizer and not self.is_last_stage():
# manually call because we don't call optimizer.backward()
self.optimizer.clear_lp_grads()
# 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, ))
if self.using_bf16_optimizer and not self.is_last_stage():
# manually call because we don't call optimizer.backward()
self.optimizer.update_hp_grads(clear_lp_grads=False)
# 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_MICRO_TIMER).stop()
self.timers(BACKWARD_INNER_GLOBAL_TIMER).stop()
self.timers(BACKWARD_MICRO_TIMER).stop()
self.timers(BACKWARD_GLOBAL_TIMER).stop()
self.mem_status('AFTER BWD')
def _exec_load_micro_batch(self, buffer_id):
if self.wall_clock_breakdown():
self.timers(BATCH_INPUT_TIMER).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()
if self._config.pipeline['activation_checkpoint_interval'] > 0 and self._config.pipeline[
'use_reentrant']:
loaded.requires_grad = loaded.is_floating_point()
else:
assert isinstance(batch[0], (tuple, list))
# Assume list or tuple
loaded = []
for x in batch[0]:
assert torch.is_tensor(x)
mine = x.clone().detach().to(self.device)
if self._config.pipeline['activation_checkpoint_interval'] > 0 and self._config.pipeline[
'use_reentrant']:
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)
# XXX: torch 1.6.0 DataLoader will auto convert tuple to list
elif isinstance(batch[1], (tuple, list)):
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_TIMER).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_TIMER).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.has_attention_mask 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.has_attention_mask 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_TIMER).stop()
def _exec_send_grads(self, buffer_id):
if self.wall_clock_breakdown():
self.timers(PIPE_SEND_GRAD_TIMER).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:]]
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.has_attention_mask 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_TIMER).stop()
def _exec_recv_activations(self, buffer_id):
if self.wall_clock_breakdown():
self.timers(PIPE_RECV_INPUT_TIMER).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.has_attention_mask 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_TIMER).stop()
def _exec_recv_grads(self, buffer_id):
if self.wall_clock_breakdown():
self.timers(PIPE_RECV_GRAD_TIMER).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:
if self.pipe_partition_grad_meta_cache is None:
self.pipe_partition_grad_meta_cache = outputs[0].to('cpu')
part_output = PartitionedTensor.from_meta(meta=self.pipe_partition_grad_meta_cache,
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_TIMER).stop()
def _exec_optimizer_step(self, lr_kwargs=None):
if self.wall_clock_breakdown():
self.timers(STEP_MICRO_TIMER).start()
self.timers(STEP_GLOBAL_TIMER).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.global_rank == 0 and self.monitor.enabled:
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))
self.monitor.write_events(self.summary_events)
if self.wall_clock_breakdown():
self.timers(STEP_MICRO_TIMER).stop()
self.timers(STEP_GLOBAL_TIMER).stop()
if self.global_steps % self.steps_per_print() == 0:
self.timers.log([
BATCH_INPUT_TIMER,
FORWARD_MICRO_TIMER,
BACKWARD_MICRO_TIMER,
BACKWARD_INNER_MICRO_TIMER,
BACKWARD_REDUCE_MICRO_TIMER,
STEP_MICRO_TIMER,
])
if self.global_steps % self.steps_per_print() == 0:
self.timers.log([
FORWARD_GLOBAL_TIMER,
BACKWARD_GLOBAL_TIMER,
BACKWARD_INNER_GLOBAL_TIMER,
BACKWARD_REDUCE_GLOBAL_TIMER,
STEP_GLOBAL_TIMER,
])
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:
if self.fp16_enabled():
kwargs["dtype"] = torch.half
if self.bfloat16_enabled():
kwargs["dtype"] = torch.bfloat16
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
get_accelerator().synchronize()
if reset_max:
get_accelerator().reset_max_memory_cached()
get_accelerator().reset_max_memory_allocated()
new_alloced = get_accelerator().memory_allocated()
new_cached = get_accelerator().memory_cached()
delta_alloced = new_alloced - mem_alloced
delta_cached = new_cached - mem_cached
mem_cached = new_cached
mem_alloced = new_alloced
max_alloced = get_accelerator().max_memory_allocated()
max_cached = get_accelerator().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, exclude_frozen_parameters=False):
"""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,
checkpoint_engine=self.checkpoint_engine,
exclude_frozen_params=exclude_frozen_parameters)
return None
[docs] def load_module_state_dict(self, checkpoint, strict=True, custom_load_fn=None, fetch_z3_params=False):
"""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.
"""
assert custom_load_fn is None, "custom_load_fn not supported w. pipeline parallelism"
state_dict = checkpoint if self.has_moe_layers else checkpoint['module']
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,
checkpoint_engine=self.checkpoint_engine)
# 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)
def get_additional_losses(self):
return self.agg_additional_losses