def train_cifar(model,
config,
num_steps=400,
average_dp_losses=True,
fp16=True,
seed=123):
with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
ds_utils.set_random_seed(seed)
# disable dropout
model.eval()
trainset = cifar_trainset(fp16=fp16)
config['local_rank'] = dist.get_rank()
engine, _, _, _ = deepspeed.initialize(
config=config,
model=model,
model_parameters=[p for p in model.parameters()],
training_data=trainset)
losses = []
for step in range(num_steps):
loss = engine.train_batch()
losses.append(loss.item())
if step % 50 == 0 and dist.get_rank() == 0:
print(f'STEP={step} LOSS={loss.item()}')
if average_dp_losses:
loss_tensor = torch.tensor(losses).cuda()
dist.all_reduce(loss_tensor)
loss_tensor /= dist.get_world_size()
losses = loss_tensor.tolist()
return losses
def _create_ep_parallel_group(self, moe_experts):
# Call the init process
self.ep_group = {}
self.expert_mp_group = {}
moe_experts = moe_experts if type(moe_experts) is list else [moe_experts]
for e in moe_experts:
self.ep_group.update({e: None})
self.expert_mp_group.update({e: None})
for moe_ep_size in self.ep_group.keys():
num_ep_groups = dist.get_world_size() // moe_ep_size
for i in range(num_ep_groups):
ep_cnt = i * moe_ep_size
size = dist.get_world_size(
) if moe_ep_size > dist.get_world_size() else moe_ep_size
ranks = list(range(ep_cnt, ep_cnt + size))
_ep_group = dist.new_group(ranks)
if dist.get_rank() in ranks:
self.ep_group.update({moe_ep_size: _ep_group})
if dist.get_world_size() > moe_ep_size:
num_expert_mp_groups = dist.get_world_size() // num_ep_groups
expert_mp_size = dist.get_world_size() // moe_ep_size
for i in range(num_expert_mp_groups):
expert_mp_comm_ranks = [
i + nr * moe_ep_size for nr in range(expert_mp_size)
]
_expert_mp_group = dist.new_group(expert_mp_comm_ranks)
if dist.get_rank() in expert_mp_comm_ranks:
self.expert_mp_group.update({moe_ep_size: _expert_mp_group})
def load_state_dict(self, state_dict):
"""
Overrides load_state_dict() to add special handling when loading checkpoints
"""
# Because at different stage exp_avg_mask may change (e.g.,
# BERT pre-training seqlen 128 and 512 ), we don't use the exp_avg_mask
# in checkpoints but always use the one user provided in training script.
# (See example in DeepSpeedExamples/bing_bert/deepspeed_train.py.)
# Thus here we keep the exp_avg_mask unchanged when loading checkpoint
for i, group in enumerate(self.param_groups):
if 'exp_avg_mask' in group:
state_dict['param_groups'][i]['exp_avg_mask'] = group[
'exp_avg_mask']
elif 'exp_avg_mask' not in group and 'exp_avg_mask' in state_dict[
'param_groups'][i]:
state_dict['param_groups'][i].pop('exp_avg_mask')
super().load_state_dict(state_dict)
if self.state[self.param_groups[0]['params']
[0]]['step'] < self.freeze_step:
if dist.get_rank() == 0:
print(
"Checkpoint loaded and OnebitAdam warmup stage starts/continues."
)
if self.adam_freeze_key is True:
self.adam_freeze_key = False
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = True
else:
self.deepspeed.enable_backward_allreduce = True
else:
if dist.get_rank() == 0:
print(
"Checkpoint loaded and OnebitAdam compression stage starts/continues."
)
if self.adam_freeze_key is False:
self.adam_freeze_key = True
if self.using_pipeline:
self.deepspeed.pipeline_enable_backward_allreduce = False
else:
self.deepspeed.enable_backward_allreduce = False
# We reset the compression errors when loading checkpoints for 3 reasons:
# 1) The worker and server error at each GPU are distinct, so in current implementation
# only rank 0's errors are saved in the checkpoint. Thus we have to reset the errors.
# If we want to save them correctly we need O(num_gpu*model_size) memory in order to
# gather all the error, which is a very large memory requirement. It's possible to save
# them in a distributed way, but it will make the checkpoint saving/loading much more complicated.
# 2) Even if we are able to save the compression errors correctly, you need to have the
# exact same number of GPUs in order to load them correctly.
# 3) We verified on BERT pre-training that occasionally resetting the compression error
# at checkpoint loading does not affect the convergence.
# However, please avoid frequent checkpoint loading which could break the error
# compensation mechanism thus affect the convergence.
for group in self.param_groups:
for p in group['params']:
if 'worker_error' in self.state[p]:
self.state[p].pop('worker_error')
if 'server_error' in self.state[p]:
self.state[p].pop('server_error')
def test_reduce_scatter_coalesced_tensor_smaller_than_world_sz():
input = torch.zeros((1, ),
dtype=torch.half,
device=torch.cuda.current_device())
(output, ) = reduce_scatter_coalesced([input], dist.get_world_group())
if dist.get_rank() == 0:
assert output.shape == (1, )
assert torch.allclose(output, torch.zeros_like(output))
elif dist.get_rank() == 1:
assert output.shape == (0, )
def _initialize_parameters(self, parameters, src_tensors, aio_handle):
assert len(parameters) == len(src_tensors)
swap_paths = self._get_swap_paths(
parameters=parameters,
num_elems=[src.numel() for src in src_tensors])
SWAP_INIT_TIMER = "swap_init_write"
self._start_timer(SWAP_INIT_TIMER)
pinned_buffers = self.swap_buffer_manager.allocate_all(
num_elems=self.largest_numel, dtype=self.dtype)
assert pinned_buffers is not None
self._swap_out_unpinned_tensors(aio_handle=aio_handle,
unpinned_tensors=src_tensors,
dest_paths=swap_paths,
pinned_buffers=pinned_buffers)
if dist.get_rank() == 0 and SWAPPER_DEBUG_MODE:
for i, tensor in enumerate(src_tensors):
logger.info(
f'copy_in_fp16_param: fp32_id = {id(parameters[i])} index = {i}, swap_num_elem = {src_tensors[i].numel()}'
)
self.swap_buffer_manager.free(pinned_buffers)
self._stop_timer(SWAP_INIT_TIMER)
self._log_timers([SWAP_INIT_TIMER])
def write_events(self, event_list, flush=True):
if self.enabled and self.summary_writer is not None and dist.get_rank(
) == 0:
for event in event_list:
self.summary_writer.add_scalar(*event)
if flush:
self.summary_writer.flush()
def __init__(self, swap_config, aio_config, base_folder, optimizer,
largest_numel, device, dtype, timers):
super(PartitionedOptimizerSwapper,
self).__init__(swap_config, aio_config, base_folder, optimizer,
largest_numel, device, dtype, timers)
aio_op = AsyncIOBuilder().load()
self.aio_handle = aio_op.aio_handle(aio_config[AIO_BLOCK_SIZE],
aio_config[AIO_QUEUE_DEPTH],
aio_config[AIO_SINGLE_SUBMIT],
aio_config[AIO_OVERLAP_EVENTS],
aio_config[AIO_THREAD_COUNT])
# Overlap swapping out
self.gradient_swapper = AsyncTensorSwapper(
aio_handle=self.aio_handle,
numel_alignment=self.numel_alignment,
timers=self.timers)
self.print_exclude_list += [
'aio_handle', 'gradient_swapper', 'print_exclude_list'
]
if dist.get_rank() == 0:
print_object(obj=self,
name='PartitionedOptimizerSwapper',
exclude_list=self.print_exclude_list)
def _swap_in_parameter(self, aio_handle, parameter, dest_buffers):
swap_info = self._get_param_swap_info(parameter)
if swap_info is None:
return
assert len(swap_info.tensors) <= len(dest_buffers)
swap_lengths = [self._io_aligned_numel(swap_info.numel())] * len(
swap_info.tensors)
swap_buffers = get_sized_buffers(dest_buffers, swap_lengths)
READ_TIMER = 'swap_submit_read_param'
WAIT_TIMER = 'swap_wait_read_param'
self._start_timer(READ_TIMER)
swap_in_tensors(aio_handle, swap_buffers, swap_info.swap_paths)
self._stop_timer(READ_TIMER)
swap_bytes = sum([
buffer.numel() * buffer.element_size() for buffer in swap_buffers
])
self._start_timer(WAIT_TIMER)
aio_handle.wait()
self._stop_timer(WAIT_TIMER)
compute_lengths = [swap_info.numel()] * len(swap_info.tensors)
compute_buffers = get_sized_buffers(dest_buffers, compute_lengths)
for t, buffer in zip(swap_info.tensors, compute_buffers):
t.data = buffer.data
self._log_timers([READ_TIMER, WAIT_TIMER])
if DEBUG_MODE and dist.get_rank() == 0:
logger.info(
f'optimizer_param_swap_in: {(swap_bytes/(1024**3)):5.2f} GB')
def _report_statistics(self, message):
if dist.get_rank() == 0:
element_size = torch.tensor([], dtype=self.dtype).element_size()
swapped_GB = (self.num_elements_swapped * element_size) / (1024**3)
logger.debug(
f'{message} num_elems = {self.num_elements_swapped}, {swapped_GB:5.2f} GB'
)
def test(self, check_using_norm):
groups._create_expert_and_data_parallel(2)
param1 = torch.nn.Parameter(torch.Tensor([0]))
param1.grad = torch.Tensor([1])
param2 = torch.nn.Parameter(torch.Tensor([0]))
if dist.get_rank() == 0:
param2.grad = torch.Tensor([1])
else:
param2.grad = torch.Tensor([float("inf")])
param2.allreduce = False
# param2 is now MoE parameter
parameters = [param1, param2]
if check_using_norm:
grads_group_flat = [
_flatten_dense_tensors([p.grad for p in parameters])
]
norm = ds_utils.get_weight_norm(grads_group_flat)
overflow_checker = ds_utils.CheckOverflow([parameters])
overflow = overflow_checker.check_using_norm([norm],
reduce_overflow=False)
else:
overflow_checker = ds_utils.CheckOverflow([parameters])
overflow = overflow_checker.check()
assert overflow
def partition_activations_in_checkpoint(partition_activation):
global PARTITION_ACTIVATIONS
PARTITION_ACTIVATIONS = partition_activation
if dist.get_rank() == 0:
logger.info(
f"**************Partition Activations {PARTITION_ACTIVATIONS}************"
)
def _load_checkpoint(self, load_dir, load_module_strict=True, tag=None):
is_pipe_parallel = isinstance(self.module, PipelineModule)
if is_pipe_parallel:
raise RuntimeError(
'pipeline parallelism is currently not supported in inference.')
if os.path.isdir(load_dir):
if tag is None:
latest_path = os.path.join(load_dir, "latest")
if os.path.isfile(latest_path):
with open(latest_path, "r") as fd:
tag = fd.read().strip()
ckpt_list = self._get_all_ckpt_names(load_dir, tag)
sd_loader = SDLoaderFactory.get_sd_loader(ckpt_list, self.checkpoint_engine)
else:
sd_loader = SDLoaderFactory.get_sd_loader_json(load_dir)
if type(sd_loader) is list:
self.sd = torch.load(sd_loader[0], map_location='cpu')
self.key_list = list(self.sd.keys())
self.load_model_with_checkpoint(self.module)
for i in range(1, len(sd_loader)):
if not dist.is_initialized() or dist.get_rank() == 0:
print(f"loading checkpoint ({i})")
self.sd = torch.load(sd_loader[i], map_location='cuda')
self.key_list = list(self.sd.keys())
self.load_model_with_checkpoint(self.module)
else:
mp_rank = 0 if self.mpu is None else self.mpu.get_model_parallel_rank()
load_path, checkpoint, quantize_config = sd_loader.load(self.mp_world_size,
mp_rank,
is_pipe_parallel=is_pipe_parallel,
quantize=(self.dtype is torch.int8),
quantize_groups=self.quantize_groups,
mlp_extra_grouping=self.mlp_extra_grouping)
self.quantization_scales, self.quantize_merge_count = quantize_config
moe, _ = has_moe_layers(self.module)
if moe:
from deepspeed.runtime.engine import DeepSpeedEngine
old_moe_load = False
if not isinstance(checkpoint['num_experts'], list):
old_moe_load = True
DeepSpeedEngine.load_moe_state_dict(
load_dir,
tag,
state_dict=checkpoint[self._choose_module_key(checkpoint)],
old_moe_load=old_moe_load,
model=self.module,
mpu=self.mpu,
checkpoint_engine=self.checkpoint_engine)
self.module.load_state_dict(
state_dict=checkpoint[self._choose_module_key(checkpoint)],
checkpoint_engine=self.checkpoint_engine,
strict=load_module_strict)
def _apply_to_tensors_only(module, functional, backward_function, outputs):
if isinstance(outputs, (tuple, list)):
touched_outputs = []
for output in outputs:
touched_output = _apply_to_tensors_only(module, functional,
backward_function, output)
touched_outputs.append(touched_output)
return outputs.__class__(touched_outputs)
elif isinstance(outputs, dict):
# apply inplace to avoid recreating dict inherited objects
for key in outputs.keys():
outputs[key] = _apply_to_tensors_only(module, functional,
backward_function,
outputs[key])
return outputs
elif type(outputs) is torch.Tensor:
return functional.apply(module, backward_function, outputs)
else:
if not is_builtin_type(outputs):
global warned
if not warned and dist.get_rank() == 0:
logger.warning(
f"A module has unknown inputs or outputs type ({type(outputs)}) and the tensors embedded in it cannot be detected. "
"The ZeRO-3 hooks designed to trigger before or after backward pass of the module relies on knowing the input and "
"output tensors and therefore may not get triggered properly."
)
warned = True
return outputs
def all_gather_dp_groups(partitioned_param_groups, dp_process_group,
start_alignment_factor, allgather_bucket_size):
for group_id, partitioned_params in enumerate(partitioned_param_groups):
# Sequential AllGather Best of both worlds
partition_id = dist.get_rank(group=dp_process_group[group_id])
dp_world_size = dist.get_world_size(group=dp_process_group[group_id])
num_shards = max(
1, partitioned_params[partition_id].numel() * dp_world_size //
allgather_bucket_size)
shard_size = partitioned_params[partition_id].numel() // num_shards
# Enforce nccl/rccl alignment of start location of each shard
shard_size = shard_size - (shard_size % start_alignment_factor)
num_elements = shard_size
assert shard_size * num_shards <= partitioned_params[
partition_id].numel()
for shard_id in range(num_shards):
if shard_id == (num_shards - 1):
num_elements = partitioned_params[partition_id].numel(
) - shard_id * shard_size
shard_list = []
for dp_id in range(dp_world_size):
curr_shard = partitioned_params[dp_id].narrow(
0, shard_id * shard_size, num_elements).detach()
shard_list.append(curr_shard)
dist.all_gather(shard_list, shard_list[partition_id],
dp_process_group[group_id])
def test(self):
x = torch.ones(1, 3).cuda() * (dist.get_rank() + 1)
sum_of_ranks = (dist.get_world_size() *
(dist.get_world_size() + 1)) // 2
result = torch.ones(1, 3).cuda() * sum_of_ranks
dist.all_reduce(x)
assert torch.all(x == result)
def see_memory_usage(message, force=False):
if not force:
return
if dist.is_initialized() and not dist.get_rank() == 0:
return
# python doesn't do real-time garbage collection so do it explicitly to get the correct RAM reports
gc.collect()
# Print message except when distributed but not rank 0
logger.info(message)
logger.info(
f"MA {round(torch.cuda.memory_allocated() / (1024 * 1024 * 1024),2 )} GB \
Max_MA {round(torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024),2)} GB \
CA {round(torch_memory_reserved() / (1024 * 1024 * 1024),2)} GB \
Max_CA {round(torch_max_memory_reserved() / (1024 * 1024 * 1024))} GB "
)
vm_stats = psutil.virtual_memory()
used_GB = round(((vm_stats.total - vm_stats.available) / (1024**3)), 2)
logger.info(
f'CPU Virtual Memory: used = {used_GB} GB, percent = {vm_stats.percent}%'
)
# get the peak memory to report correct data, so reset the counter for the next call
if hasattr(torch.cuda, "reset_peak_memory_stats"): # pytorch 1.4+
torch.cuda.reset_peak_memory_stats()
def write_events(self, event_list):
if self.enabled and dist.get_rank() == 0:
import csv
# We assume each event_list element is a tensorboard-style tuple in the format: (log_name: String, value, step: Int)
for event in event_list:
log_name = event[0]
value = event[1]
step = event[2]
# Set the header to the log_name
# Need this check because the deepspeed engine currently formats log strings to separate with '/'
if '/' in log_name:
record_splits = log_name.split('/')
header = record_splits[len(record_splits) - 1]
else:
header = log_name
# sanitize common naming conventions into filename
filename = log_name.replace('/', '_').replace(' ', '_')
fname = self.log_dir + '/' + filename + '.csv'
# Open file and record event. Insert header if this is the first time writing
with open(fname, 'a+') as csv_monitor_file:
csv_monitor_writer = csv.writer(csv_monitor_file)
if filename not in self.filenames:
self.filenames.append(filename)
csv_monitor_writer.writerow(['step', header])
csv_monitor_writer.writerow([step, value])
def print_json_dist(message, ranks=None, path=None):
from deepspeed import comm as dist
"""Print message when one of following condition meets
+ not dist.is_initialized()
+ dist.get_rank() in ranks if ranks is not None or ranks = [-1]
Args:
message (str)
ranks (list)
path (str)
"""
should_log = not dist.is_initialized()
ranks = ranks or []
my_rank = dist.get_rank() if dist.is_initialized() else -1
if ranks and not should_log:
should_log = ranks[0] == -1
should_log = should_log or (my_rank in set(ranks))
if should_log:
message['rank'] = my_rank
import json
with open(path, 'w') as outfile:
json.dump(message, outfile)
os.fsync(outfile)
def _restore_from_bit16_weights(self):
for i, group in enumerate(self.bf16_groups):
partition_id = dist.get_rank(group=self.real_dp_process_group[i])
for bf16_partitions, fp32_partition in zip(
self.bf16_partitioned_groups,
self.fp32_groups_flat_partition):
fp32_partition.data.copy_(bf16_partitions[partition_id].data)
def _load_legacy_checkpoint(self,
state_dict_list,
load_optimizer_states=True,
load_from_fp32_weights=False):
dp_rank = dist.get_rank(group=self.dp_process_group)
current_rank_sd = state_dict_list[dp_rank]
ckpt_version = current_rank_sd.get(DS_VERSION, False)
assert ckpt_version, f"Empty ds_version in checkpoint, not clear how to proceed"
ckpt_version = pkg_version.parse(ckpt_version)
self.clip_grad = current_rank_sd.get(CLIP_GRAD, self.clip_grad)
if load_optimizer_states:
self.optimizer.load_state_dict(
current_rank_sd[BASE_OPTIMIZER_STATE])
if load_from_fp32_weights:
for current, saved in zip(
self.fp32_groups_flat_partition,
current_rank_sd[SINGLE_PARTITION_OF_FP32_GROUPS]):
src_tensor = _get_padded_tensor(saved, current.numel())
current.data.copy_(src_tensor.data)
if load_optimizer_states:
self._link_all_hp_params()
def write_events(self, event_list):
if self.enabled and dist.get_rank() == 0:
for event in event_list:
label = event[0]
value = event[1]
step = event[2]
self.log({label: value}, step=step)
def load(module, state_dict, prefix):
args = (state_dict, prefix, {}, True, [], [], error_msgs)
if len(list(module.parameters())) > 0 and list(
module.parameters())[0].numel() == 0:
with GatheredParameters(list(module.parameters(recurse=False)),
modifier_rank=0):
if dist.get_rank() == 0:
module._load_from_state_dict(*args)
else:
if hasattr(module, 'weight'):
if 'query_key_value' in prefix:
module.weight = self.mp_replace.qkv_copy(
module.weight.data,
state_dict[prefix + 'weight'])
else:
module.weight = self.mp_replace.copy(
module.weight.data,
state_dict[prefix + 'weight'])
else:
module.norm.weight = self.mp_replace.copy(
module.norm.weight.data,
state_dict[prefix + 'weight'])
if prefix + 'bias' in self.key_list:
if hasattr(module, 'norm'):
module.norm.bias = self.mp_replace.copy(
module.norm.bias,
state_dict[prefix + 'bias'])
else:
data = state_dict[prefix + 'bias']
data = data.to(torch.cuda.current_device())
module.bias = self.mp_replace.copy(module.bias, data)
def _distributed_test():
ds_cfg = {
"train_micro_batch_size_per_gpu": 1,
"zero_optimization": {
"stage": 3,
"stage3_max_reuse_distance": 0,
"contiguous_gradients": True,
"overlap_comm": True,
},
"optimizer": {
"type": "Adam",
"params": {
"lr": 1.
}
},
"fp16": {
"enabled": True,
"loss_scale": 1.,
}
}
with deepspeed.zero.Init(config=ds_cfg,
mem_efficient_linear=False,
enabled=init_context_manager):
model = ManyParamModel()
ds_engine = _ds_initialize_for_param_partitioning_testing(
model, ds_cfg)
for _ in range(3): # test multiple iterations to cover prefetching
activations: List[Tensor] = ds_engine(
torch.ones((param_sz, ),
dtype=torch.float16,
device=ds_engine.device))
assert len(activations) == n_layers
partition_sz = math.ceil(param_sz / world_sz)
expected_activations = torch.empty(param_sz,
dtype=torch.float16,
device=ds_engine.device)
for start_idx in range(0, param_sz, partition_sz):
expected_activations[start_idx:start_idx +
partition_sz] = dist.get_rank()
for layer_num, activation in enumerate(activations):
expected_activations *= 2 * layer_num
assert torch.allclose(activation, expected_activations)
# TODO. finish writing this test
ds_engine.backward(activations[-1].sum())
avgd_gradients = ds_engine.optimizer.averaged_gradients
assert set(avgd_gradients.keys()) == {
0
}, "should only have one parameter group"
weight_gradients: List[Tensor] = avgd_gradients[0]
for layer_num, activation in enumerate(weight_gradients):
pass
def write_events(self, event_list):
if dist.get_rank() == 0:
if self.tb_monitor is not None:
self.tb_monitor.write_events(event_list)
if self.wandb_monitor is not None:
self.wandb_monitor.write_events(event_list)
if self.csv_monitor is not None:
self.csv_monitor.write_events(event_list)
def __init__(self, mp_group=None, mp_size=1, out_dim=1, in_dim=0):
if mp_group is not None:
self.gpu_index = dist.get_rank(group=mp_group)
else:
self.gpu_index = 0
self.out_dim = out_dim
self.in_dim = in_dim
self.mp_size = mp_size
def create_deepspeed_args():
parser = argparse.ArgumentParser()
args = parser.parse_args(args='')
args.deepspeed = True
if dist.is_initialized():
# We assume up to one full node executing unit tests
assert dist.get_world_size() <= torch.cuda.device_count()
args.local_rank = dist.get_rank()
return args
def __init__(self, tensor, group, partition_meta=None):
super().__init__()
self.group = group
self.num_parts = dist.get_world_size(group=self.group)
self.rank = dist.get_rank(group=self.group)
self.orig_size = list(tensor.size())
self.orig_device = tensor.device
self.local_data, self.partition = self._partition_tensor(tensor)
def test_reduce_scatter_coalesced_single_input():
input = torch.full((6, ),
dist.get_rank(),
dtype=torch.half,
device=torch.cuda.current_device())
(output, ) = reduce_scatter_coalesced([input], dist.get_world_group())
assert output.shape == (3, )
assert torch.allclose(output, torch.full_like(output, 0.5))
def _link_all_hp_params(self):
dp_world_size = dist.get_world_size(group=self.dp_process_group)
for i, param_group in enumerate(self.optimizer.param_groups):
# Link bf16 and fp32 params in partition
partition_id = dist.get_rank(group=self.real_dp_process_group[i])
partition_size = self.bf16_groups_flat[i].numel() // dp_world_size
self._link_hp_params(self.bf16_groups[i],
self.fp32_groups_flat_partition[i],
partition_id * partition_size, partition_size,
self.real_dp_process_group[i])
def __init__(self,
init_optimizer,
param_names,
mpu=None,
clip_grad=0.0,
norm_type=2,
allgather_bucket_size=5000000000,
dp_process_group=None,
timers=None):
super().__init__()
see_memory_usage('begin bf16_optimizer', force=True)
self.timers = timers
self.optimizer = init_optimizer
self.param_names = param_names
self.using_real_optimizer = not isinstance(self.optimizer, DummyOptim)
self.clip_grad = clip_grad
self.norm_type = norm_type
self.mpu = mpu
self.allgather_bucket_size = int(allgather_bucket_size)
self.dp_process_group = dp_process_group
self.dp_rank = dist.get_rank(group=self.dp_process_group)
self.real_dp_process_group = [
dp_process_group for i in range(len(self.optimizer.param_groups))
]
# Load pre-built or JIT compile (un)flatten ops
util_ops = UtilsBuilder().load()
self.flatten = util_ops.flatten
self.unflatten = util_ops.unflatten
#align nccl all-gather send buffers to 4-bye boundary
self.nccl_start_alignment_factor = 2 # 4-byte alignment/sizeof(fp16) = 2
# Build BF16/FP32 groups
self.bf16_groups = []
self.bf16_groups_flat = []
self.bf16_partitioned_groups = []
self.fp32_groups_flat_partition = []
# Maintain different fp32 gradients views for convenience
self.fp32_groups_gradients = []
self.fp32_groups_gradients_flat = []
self.fp32_groups_actual_gradients_flat = []
self.fp32_groups_gradient_flat_partition = []
self.fp32_groups_has_gradients = []
self.step_count = 0
self.group_paddings = []
if self.using_real_optimizer:
self._setup_for_real_optimizer()
see_memory_usage('end bf16_optimizer', force=True)
