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Commit c25a91b6 authored by aiss's avatar aiss
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Merge branch 'ds-v0.9.2-rocm' into 'main' 

Ds v0.9.2 rocm

See merge request dcutoolkit/deeplearing/deepspeed!2
parents d1596c94 af82b300
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deepspeed/runtime/activation_checkpointing/checkpointing.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
'''
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
Use to partition the activations stored for backward propagation
Therefore reduces the memory consumption
Also implements CPU checkpointing and contiguous memory checkpointing
......@@ -10,7 +10,7 @@ Reduces memory consumption and memory fragmentation
Code for rng checkpointing taken from NVIDIA Megatron-LM mpu/random.py
b886b7bb972afe72bac0f5de4f42a4a7bae8ebef
'''
"""
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
......@@ -82,9 +82,7 @@ def detach_variable(inputs, device=None):
out.append(x)
return tuple(out)
else:
raise RuntimeError(
"Only tuple of tensors is supported. Got Unsupported input type: ",
type(inputs).__name__)
raise RuntimeError("Only tuple of tensors is supported. Got Unsupported input type: ", type(inputs).__name__)
def _set_cuda_rng_state(new_state, device=-1):
......@@ -92,7 +90,7 @@ def _set_cuda_rng_state(new_state, device=-1):
Arguments:
new_state (torch.ByteTensor): The desired state
This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
This function is adapted from PyTorch repo (torch.cuda.set_rng_state) #ignore-cuda
with a single change: the input state is not cloned. Cloning caused
major performance issues for +4 GPU cases.
"""
......@@ -128,6 +126,7 @@ class CudaRNGStatesTracker:
rng state, we can perform operations and return to our starting
cuda state.
"""
def __init__(self):
# Map from a string name to the cuda rng state.
self.states_ = {}
......@@ -227,13 +226,9 @@ def model_parallel_cuda_manual_seed(seed):
logger.info(
'> initializing model parallel cuda seeds on global rank {}, '
'model parallel rank {}, and data parallel rank {} with '
'model parallel seed: {} and data parallel seed: {}'.format(
dist.get_rank(),
tp_rank,
mpu.get_data_parallel_rank(),
model_parallel_seed,
data_parallel_seed),
)
'model parallel seed: {} and data parallel seed: {}'.format(dist.get_rank(), tp_rank,
mpu.get_data_parallel_rank(),
model_parallel_seed, data_parallel_seed), )
_CUDA_RNG_STATE_TRACKER.reset()
# Set the default state.
get_accelerator().manual_seed(data_parallel_seed)
......@@ -282,9 +277,7 @@ def gather_partitioned_activations(tensors, device=None):
if device is not None:
flat_tensor = torch.zeros([tensor_size], dtype=item.dtype, device=device)
else:
flat_tensor = torch.zeros([tensor_size],
dtype=item.dtype,
device=item.device)
flat_tensor = torch.zeros([tensor_size], dtype=item.dtype, device=item.device)
partitions = []
for i in range(mp_size):
part_i = flat_tensor.narrow(0, partition_size * i, partition_size)
......@@ -384,28 +377,21 @@ def partition_activations(args, cpu_checkpoint, contiguous_checkpoint):
i = arg_index - num_non_fp_tensors
partition_size = get_partition_size(item)
partition = item.detach().contiguous().view(-1).narrow(
0,
get_partition_start(item),
partition_size).clone()
partition = item.detach().contiguous().view(-1).narrow(0, get_partition_start(item), partition_size).clone()
buffer_device = torch.device('cpu') if cpu_checkpoint else partition.device
if contiguous_checkpoint:
if i >= len(contiguous_data_buffers):
tensor_list = [
torch.tensor(()).new_empty([partition_size],
dtype=partition.dtype,
device=buffer_device)
torch.tensor(()).new_empty([partition_size], dtype=partition.dtype, device=buffer_device)
for _ in range(num_layers)
]
contiguous_data_buffers.append(tensor_list)
data_offsets.append(0)
elif contiguous_data_buffers[i] is None:
tensor_list = [
torch.tensor(()).new_empty([partition_size],
dtype=partition.dtype,
device=buffer_device)
torch.tensor(()).new_empty([partition_size], dtype=partition.dtype, device=buffer_device)
for _ in range(num_layers)
]
contiguous_data_buffers[i] = tensor_list
......@@ -419,14 +405,10 @@ def partition_activations(args, cpu_checkpoint, contiguous_checkpoint):
# previously launched GPU kernels, there is a small
# window of time here for CPUs to populate pages asynchronously.
contiguous_data_buffers[i][data_offsets[i]].data[range(
0,
contiguous_data_buffers[i][data_offsets[i]].data.shape[0],
int(mmap.PAGESIZE /
contiguous_data_buffers[i][data_offsets[i]].data.element_size())
)] = 0
contiguous_partition = contiguous_data_buffers[i][
data_offsets[i]].data.copy_(partition.data)
0, contiguous_data_buffers[i][data_offsets[i]].data.shape[0],
int(mmap.PAGESIZE / contiguous_data_buffers[i][data_offsets[i]].data.element_size()))] = 0
contiguous_partition = contiguous_data_buffers[i][data_offsets[i]].data.copy_(partition.data)
data_offsets[i] = data_offsets[i] + 1
inputs.append(contiguous_partition)
else:
......@@ -459,21 +441,14 @@ def get_partitioned_activations_for_backward(args, inputs, contiguous_checkpoint
if i >= len(contiguous_size_buffers):
tmp = torch.tensor(())
contiguous_size_buffers.append(
tmp.new_empty([numel * num_layers],
dtype=size.dtype,
device=size.device))
tmp.new_empty([numel * num_layers], dtype=size.dtype, device=size.device))
size_offsets.append(0)
elif contiguous_size_buffers[i] is None:
tmp = torch.tensor(())
contiguous_size_buffers[i] = tmp.new_empty([numel * num_layers],
dtype=size.dtype,
device=size.device)
contiguous_size_buffers[i] = tmp.new_empty([numel * num_layers], dtype=size.dtype, device=size.device)
size_offsets[i] = 0
contiguous_size = contiguous_size_buffers[i].narrow(
0,
size_offsets[i],
numel).data.copy_(size.data)
contiguous_size = contiguous_size_buffers[i].narrow(0, size_offsets[i], numel).data.copy_(size.data)
contiguous_size = contiguous_size.view_as(size)
size_offsets[i] = size_offsets[i] + numel
new_args.append(contiguous_size)
......@@ -499,13 +474,14 @@ def get_cpu_activations_for_backward(args, inputs):
class CheckpointFunction(torch.autograd.Function):
"""This function is adapted from torch.utils.checkpoint with
two main changes:
1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state` #ignore-cuda
2) the states in the model parallel tracker are also properly
tracked/set/reset.
3) Performance activation partitioning, contiguous memory optimization
4) CPU Checkpointing
5) Profile forward and backward functions
"""
@staticmethod
def forward(ctx, run_function, all_outputs, *args):
global mpu, timers, SYNCHRONIZE, PROFILE_TIME
......@@ -551,12 +527,9 @@ class CheckpointFunction(torch.autograd.Function):
see_memory_usage("First Forward Beginning", force=False)
if dist.get_rank() == 0:
logger.info(f"Activation Checkpointing Information")
logger.info(f"----Partition Activations {PARTITION_ACTIVATIONS}, CPU CHECKPOINTING {CPU_CHECKPOINT}")
logger.info(
f"----Partition Activations {PARTITION_ACTIVATIONS}, CPU CHECKPOINTING {CPU_CHECKPOINT}"
)
logger.info(
f"----contiguous Memory Checkpointing {CONTIGUOUS_CHECKPOINTING} with {num_layers} total layers"
)
f"----contiguous Memory Checkpointing {CONTIGUOUS_CHECKPOINTING} with {num_layers} total layers")
logger.info(f"----Synchronization {SYNCHRONIZE}")
logger.info(f"----Profiling time in checkpointing {PROFILE_TIME}")
......@@ -564,18 +537,12 @@ class CheckpointFunction(torch.autograd.Function):
transport_stream = get_accelerator().Stream(device=cuda_device)
if PARTITION_ACTIVATIONS:
inputs = partition_activations(args,
CPU_CHECKPOINT,
CONTIGUOUS_CHECKPOINTING)
inputs = partition_activations(args, CPU_CHECKPOINT, CONTIGUOUS_CHECKPOINTING)
elif CPU_CHECKPOINT:
inputs = copy_to_device(args,
device=torch.device('cpu'),
criterion_func=is_activation_to_checkpoint)
inputs = copy_to_device(args, device=torch.device('cpu'), criterion_func=is_activation_to_checkpoint)
# just in case something funky is happening such as reuse of inputs
inputs_cuda = copy_to_device(args,
device=cuda_device,
criterion_func=is_activation_to_checkpoint)
inputs_cuda = copy_to_device(args, device=cuda_device, criterion_func=is_activation_to_checkpoint)
# Copy the rng states.
ctx.fwd_cpu_rng_state = torch.get_rng_state()
......@@ -591,10 +558,7 @@ class CheckpointFunction(torch.autograd.Function):
del inputs_cuda
if PARTITION_ACTIVATIONS:
new_args = get_partitioned_activations_for_backward(
args,
inputs,
CONTIGUOUS_CHECKPOINTING)
new_args = get_partitioned_activations_for_backward(args, inputs, CONTIGUOUS_CHECKPOINTING)
assert len(new_args) % 2 == 0, f'save_for_backward called with odd number of args, {len(new_args)}'
save_args_for_backward(*new_args)
elif CPU_CHECKPOINT:
......@@ -613,9 +577,7 @@ class CheckpointFunction(torch.autograd.Function):
if torch.is_tensor(outputs):
non_grad_outputs = [outputs] if not outputs.is_floating_point() else []
else:
non_grad_outputs = [
o for o in outputs if torch.is_tensor(o) and not o.is_floating_point()
]
non_grad_outputs = [o for o in outputs if torch.is_tensor(o) and not o.is_floating_point()]
ctx.mark_non_differentiable(*non_grad_outputs)
if torch.is_tensor(outputs):
......@@ -661,14 +623,11 @@ class CheckpointFunction(torch.autograd.Function):
if PARTITION_ACTIVATIONS:
# with get_accelerator().stream(transport_stream):
inputs = gather_partitioned_activations(
ctx.deepspeed_saved_tensors,
device=cuda_device if CPU_CHECKPOINT else None)
inputs = gather_partitioned_activations(ctx.deepspeed_saved_tensors,
device=cuda_device if CPU_CHECKPOINT else None)
detached_inputs = detach_variable(inputs)
elif CPU_CHECKPOINT:
inputs = move_to_device(ctx.deepspeed_saved_tensors,
cuda_device,
is_activation_to_checkpoint)
inputs = move_to_device(ctx.deepspeed_saved_tensors, cuda_device, is_activation_to_checkpoint)
detached_inputs = detach_variable(inputs)
else:
inputs = ctx.deepspeed_saved_tensors
......@@ -762,8 +721,7 @@ 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}************")
logger.info(f"**************Partition Activations {PARTITION_ACTIVATIONS}************")
def set_num_layers(nlayers):
......
deepspeed/runtime/activation_checkpointing/config.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
"""
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from deepspeed.runtime.config_utils import get_scalar_param, DeepSpeedConfigObject
......@@ -48,16 +47,15 @@ ACT_CHKPT = 'activation_checkpointing'
ACT_CHKPT_DEFAULT = {
ACT_CHKPT_PARTITION_ACTIVATIONS: ACT_CHKPT_PARTITION_ACTIVATIONS_DEFAULT,
ACT_CHKPT_NUMBER_CHECKPOINTS: ACT_CHKPT_NUMBER_CHECKPOINTS_DEFAULT,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION:
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION_DEFAULT,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY:
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY_DEFAULT,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION: ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION_DEFAULT,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY: ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY_DEFAULT,
ACT_CHKPT_PROFILE: ACT_CHKPT_PROFILE_DEFAULT,
ACT_CHKPT_CPU_CHECKPOINTING: ACT_CHKPT_CPU_CHECKPOINTING_DEFAULT
}
class DeepSpeedActivationCheckpointingConfig(DeepSpeedConfigObject):
def __init__(self, param_dict):
super(DeepSpeedActivationCheckpointingConfig, self).__init__()
......@@ -76,29 +74,21 @@ class DeepSpeedActivationCheckpointingConfig(DeepSpeedConfigObject):
self._initialize(act_chkpt_config_dict)
def _initialize(self, act_chkpt_config_dict):
self.partition_activations = get_scalar_param(
act_chkpt_config_dict,
ACT_CHKPT_PARTITION_ACTIVATIONS,
ACT_CHKPT_PARTITION_ACTIVATIONS_DEFAULT)
self.contiguous_memory_optimization = get_scalar_param(
act_chkpt_config_dict,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION_DEFAULT)
self.cpu_checkpointing = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_CPU_CHECKPOINTING,
self.partition_activations = get_scalar_param(act_chkpt_config_dict, ACT_CHKPT_PARTITION_ACTIVATIONS,
ACT_CHKPT_PARTITION_ACTIVATIONS_DEFAULT)
self.contiguous_memory_optimization = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION_DEFAULT)
self.cpu_checkpointing = get_scalar_param(act_chkpt_config_dict, ACT_CHKPT_CPU_CHECKPOINTING,
ACT_CHKPT_CPU_CHECKPOINTING_DEFAULT)
self.number_checkpoints = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_NUMBER_CHECKPOINTS,
self.number_checkpoints = get_scalar_param(act_chkpt_config_dict, ACT_CHKPT_NUMBER_CHECKPOINTS,
ACT_CHKPT_NUMBER_CHECKPOINTS_DEFAULT)
self.profile = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_PROFILE,
ACT_CHKPT_PROFILE_DEFAULT)
self.profile = get_scalar_param(act_chkpt_config_dict, ACT_CHKPT_PROFILE, ACT_CHKPT_PROFILE_DEFAULT)
self.synchronize_checkpoint_boundary = get_scalar_param(
act_chkpt_config_dict,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY_DEFAULT)
self.synchronize_checkpoint_boundary = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY_DEFAULT)
deepspeed/runtime/bf16_optimizer.py
View file @ c25a91b6
"""
Copyright 2022 The Microsoft DeepSpeed Team
"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from collections import OrderedDict
import torch
......@@ -13,29 +14,21 @@ from deepspeed.runtime import ZeROOptimizer
from packaging import version as pkg_version
from deepspeed.git_version_info import version
from deepspeed.runtime.utils import (get_global_norm_of_tensors,
clip_tensors_by_global_norm,
DummyOptim,
align_dense_tensors,
all_gather_dp_groups,
bwc_tensor_model_parallel_rank,
is_model_parallel_parameter,
see_memory_usage)
from deepspeed.runtime.utils import (get_global_norm_of_tensors, clip_tensors_by_global_norm, DummyOptim,
align_dense_tensors, all_gather_dp_groups, bwc_tensor_model_parallel_rank,
is_model_parallel_parameter, see_memory_usage)
from deepspeed.utils import link_hp_params, fragment_address
from deepspeed.checkpoint import enable_universal_checkpoint
from deepspeed.checkpoint.constants import (DS_VERSION,
PARTITION_COUNT,
BASE_OPTIMIZER_STATE,
SINGLE_PARTITION_OF_FP32_GROUPS,
CLIP_GRAD,
GROUP_PADDINGS,
from deepspeed.checkpoint.constants import (DS_VERSION, PARTITION_COUNT, BASE_OPTIMIZER_STATE,
SINGLE_PARTITION_OF_FP32_GROUPS, CLIP_GRAD, GROUP_PADDINGS,
PARAM_SLICE_MAPPINGS)
setattr(sys.modules[__name__], 'fragment_address', fragment_address)
class BF16_Optimizer(ZeROOptimizer):
def __init__(self,
init_optimizer,
param_names,
......@@ -58,9 +51,7 @@ class BF16_Optimizer(ZeROOptimizer):
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))
]
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()
......@@ -95,9 +86,7 @@ class BF16_Optimizer(ZeROOptimizer):
def _setup_for_real_optimizer(self):
dp_world_size = dist.get_world_size(group=self.dp_process_group)
self.partition_count = [
dp_world_size for i in range(len(self.optimizer.param_groups))
]
self.partition_count = [dp_world_size for i in range(len(self.optimizer.param_groups))]
for i, param_group in enumerate(self.optimizer.param_groups):
see_memory_usage(f'before initializing group {i}', force=True)
......@@ -105,69 +94,55 @@ class BF16_Optimizer(ZeROOptimizer):
partition_id = dist.get_rank(group=self.real_dp_process_group[i])
# grab the original list
self.bf16_groups.append(param_group['params'])
trainable_parameters = [param for param in param_group['params'] if param.requires_grad]
self.bf16_groups.append(trainable_parameters)
# create flat bf16 params
self.bf16_groups_flat.append(
self._flatten_dense_tensors_aligned(
self.bf16_groups[i],
self.nccl_start_alignment_factor * dp_world_size))
self._flatten_dense_tensors_aligned(self.bf16_groups[i],
self.nccl_start_alignment_factor * dp_world_size))
# Make bf16 params point to flat tensor storage
self._update_storage_to_flattened_tensor(
tensor_list=self.bf16_groups[i],
flat_tensor=self.bf16_groups_flat[i])
self._update_storage_to_flattened_tensor(tensor_list=self.bf16_groups[i],
flat_tensor=self.bf16_groups_flat[i])
# divide flat weights into equal sized partitions
partition_size = self.bf16_groups_flat[i].numel() // dp_world_size
bf16_dp_partitions = [
self.bf16_groups_flat[i].narrow(0,
dp_index * partition_size,
partition_size)
self.bf16_groups_flat[i].narrow(0, dp_index * partition_size, partition_size)
for dp_index in range(dp_world_size)
]
self.bf16_partitioned_groups.append(bf16_dp_partitions)
# create fp32 params partition
self.fp32_groups_flat_partition.append(
bf16_dp_partitions[partition_id].clone().float().detach())
self.fp32_groups_flat_partition.append(bf16_dp_partitions[partition_id].clone().float().detach())
self.fp32_groups_flat_partition[i].requires_grad = True
num_elem_list = [t.numel() for t in self.bf16_groups[i]]
# create fp32 gradients
self.fp32_groups_gradients_flat.append(
torch.zeros_like(self.bf16_groups_flat[i],
dtype=torch.float32))
self.fp32_groups_gradients_flat.append(torch.zeros_like(self.bf16_groups_flat[i], dtype=torch.float32))
# track individual fp32 gradients for entire model
fp32_gradients = self._split_flat_tensor(
flat_tensor=self.fp32_groups_gradients_flat[i],
num_elem_list=num_elem_list)
fp32_gradients = self._split_flat_tensor(flat_tensor=self.fp32_groups_gradients_flat[i],
num_elem_list=num_elem_list)
self.fp32_groups_gradients.append(fp32_gradients)
self.fp32_groups_gradient_dict[i] = fp32_gradients
# flat tensor corresponding to actual fp32 gradients (i.e., minus alignment padding)
length_without_padding = sum(num_elem_list)
self.fp32_groups_actual_gradients_flat.append(
torch.narrow(self.fp32_groups_gradients_flat[i],
0,
0,
length_without_padding))
torch.narrow(self.fp32_groups_gradients_flat[i], 0, 0, length_without_padding))
# flat tensor corresponding to gradient partition
self.fp32_groups_gradient_flat_partition.append(
torch.narrow(self.fp32_groups_gradients_flat[i],
0,
partition_id * partition_size,
partition_size))
torch.narrow(self.fp32_groups_gradients_flat[i], 0, partition_id * partition_size, partition_size))
# track fp32 gradient updates
self.fp32_groups_has_gradients.append([False] * len(self.bf16_groups[i]))
# Record padding required for alignment
if partition_id == dist.get_world_size(
group=self.real_dp_process_group[i]) - 1:
if partition_id == dist.get_world_size(group=self.real_dp_process_group[i]) - 1:
padding = self.bf16_groups_flat[i].numel() - length_without_padding
else:
padding = 0
......@@ -199,8 +174,7 @@ class BF16_Optimizer(ZeROOptimizer):
for lp in self.bf16_groups[i]:
if lp._hp_mapping is not None:
lp_name = self.param_names[lp]
param_mapping_per_group[
lp_name] = lp._hp_mapping.get_hp_fragment_address()
param_mapping_per_group[lp_name] = lp._hp_mapping.get_hp_fragment_address()
param_mapping.append(param_mapping_per_group)
return param_mapping
......@@ -212,17 +186,16 @@ class BF16_Optimizer(ZeROOptimizer):
partition_id = dist.get_rank(group=self.real_dp_process_group[i])
partition_size = self.bf16_groups_flat[i].numel() // dp_world_size
flat_hp_partition = self.fp32_groups_flat_partition[i]
link_hp_params(
lp_param_list=self.bf16_groups[i],
flat_hp_partition=flat_hp_partition,
gradient_dict=self.fp32_groups_gradient_dict,
offload_gradient_dict=None,
use_offload=False,
param_group_index=i,
partition_start=partition_id * partition_size,
partition_size=partition_size,
partition_optimizer_state=self.optimizer.state[flat_hp_partition],
dp_group=self.real_dp_process_group[i])
link_hp_params(lp_param_list=self.bf16_groups[i],
flat_hp_partition=flat_hp_partition,
gradient_dict=self.fp32_groups_gradient_dict,
offload_gradient_dict=None,
use_offload=False,
param_group_index=i,
partition_start=partition_id * partition_size,
partition_size=partition_size,
partition_optimizer_state=self.optimizer.state[flat_hp_partition],
dp_group=self.real_dp_process_group[i])
def initialize_optimizer_states(self):
"""Take an optimizer step with zero-valued gradients to allocate internal
......@@ -231,7 +204,8 @@ class BF16_Optimizer(ZeROOptimizer):
This helps prevent memory fragmentation by allocating optimizer state at the
beginning of training instead of after activations have been allocated.
"""
for param_partition, grad_partition in zip(self.fp32_groups_flat_partition, self.fp32_groups_gradient_flat_partition):
for param_partition, grad_partition in zip(self.fp32_groups_flat_partition,
self.fp32_groups_gradient_flat_partition):
param_partition.grad = grad_partition
self.optimizer.step()
......@@ -262,19 +236,17 @@ class BF16_Optimizer(ZeROOptimizer):
if closure is not None:
raise NotImplementedError(f'{self.__class__} does not support closure.')
all_groups_norm = get_global_norm_of_tensors(
input_tensors=self.get_grads_for_norm(),
mpu=self.mpu,
norm_type=self.norm_type)
all_groups_norm = get_global_norm_of_tensors(input_tensors=self.get_grads_for_norm(),
mpu=self.mpu,
norm_type=self.norm_type)
self._global_grad_norm = all_groups_norm
assert all_groups_norm > 0.
if self.clip_grad > 0.:
clip_tensors_by_global_norm(
input_tensors=self.get_grads_for_norm(for_clipping=True),
max_norm=self.clip_grad,
global_norm=all_groups_norm,
mpu=self.mpu)
clip_tensors_by_global_norm(input_tensors=self.get_grads_for_norm(for_clipping=True),
max_norm=self.clip_grad,
global_norm=all_groups_norm,
mpu=self.mpu)
self.optimizer.step()
......@@ -343,7 +315,8 @@ class BF16_Optimizer(ZeROOptimizer):
@torch.no_grad()
def update_lp_params(self):
for i, (bf16_partitions, fp32_partition) in enumerate(zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition)):
for i, (bf16_partitions,
fp32_partition) in enumerate(zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition)):
partition_id = dist.get_rank(group=self.real_dp_process_group[i])
bf16_partitions[partition_id].data.copy_(fp32_partition.data)
# print_rank_0(f'update_lp_params {i=} {partition_id=}', force=True)
......@@ -395,18 +368,11 @@ class BF16_Optimizer(ZeROOptimizer):
load_optimizer_states=True,
load_from_fp32_weights=False):
if checkpoint_folder:
self._load_universal_checkpoint(checkpoint_folder,
load_optimizer_states,
load_from_fp32_weights)
self._load_universal_checkpoint(checkpoint_folder, load_optimizer_states, load_from_fp32_weights)
else:
self._load_legacy_checkpoint(state_dict_list,
load_optimizer_states,
load_from_fp32_weights)
self._load_legacy_checkpoint(state_dict_list, load_optimizer_states, load_from_fp32_weights)
def _load_legacy_checkpoint(self,
state_dict_list,
load_optimizer_states=True,
load_from_fp32_weights=False):
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]
......@@ -421,17 +387,15 @@ class BF16_Optimizer(ZeROOptimizer):
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]):
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 _load_universal_checkpoint(self,
checkpoint_folder,
load_optimizer_states,
load_from_fp32_weights):
def _load_universal_checkpoint(self, checkpoint_folder, load_optimizer_states, load_from_fp32_weights):
self._load_hp_checkpoint_state(checkpoint_folder)
@property
......@@ -448,11 +412,8 @@ class BF16_Optimizer(ZeROOptimizer):
for lp in self.bf16_groups[i]:
if lp._hp_mapping is not None:
#print(f"Loading {self.param_names[lp]} {tp_rank=} {tp_world_size=}")
lp.load_hp_checkpoint_state(
os.path.join(checkpoint_dir,
self.param_names[lp]),
tp_rank,
tp_world_size)
lp.load_hp_checkpoint_state(os.path.join(checkpoint_dir, self.param_names[lp]), tp_rank,
tp_world_size)
def _get_padded_tensor(src_tensor, size):
......
deepspeed/runtime/checkpoint_engine/__init__.py
View file @ c25a91b6
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
'''Copyright The Microsoft DeepSpeed Team'''
deepspeed/runtime/checkpoint_engine/checkpoint_engine.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import os
class CheckpointEngine(object):
......@@ -11,6 +16,9 @@ class CheckpointEngine(object):
# create checkpoint on give tag for save/load.
pass
def makedirs(self, path, exist_ok=False):
os.makedirs(path, exist_ok=exist_ok)
def save(self, state_dict, path: str):
pass
......
deepspeed/runtime/checkpoint_engine/nebula_checkpoint_engine.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import os
import torch
......@@ -15,6 +18,7 @@ def _get_tag_from_path(path):
class NebulaCheckpointEngine(CheckpointEngine):
def __init__(self, config_params=None):
super().__init__(config_params)
self.checkpoint = None
......@@ -27,8 +31,7 @@ class NebulaCheckpointEngine(CheckpointEngine):
nebula_config_params = {
NEBULA_PERSISTENT_STORAGE_PATH: config_params.persistent_storage_path,
NEBULA_PERSISTENT_TIME_INTERVAL: config_params.persistent_time_interval,
NEBULA_NUM_OF_VERSION_IN_RETENTION:
config_params.num_of_version_in_retention,
NEBULA_NUM_OF_VERSION_IN_RETENTION: config_params.num_of_version_in_retention,
}
torch_nebula.init(**nebula_config_params)
......@@ -54,16 +57,13 @@ class NebulaCheckpointEngine(CheckpointEngine):
first_load_flag = self.tag_flag is None or self.tag_flag == tag
if not self.enable_nebula_load and first_load_flag:
self.tag_flag = tag
logger.info(
f"[Nebula] Disable nebula load. Loading checkpoint from {path} ...")
logger.info(f"[Nebula] Disable nebula load. Loading checkpoint from {path} ...")
partition = torch.load(path, map_location=map_location)
logger.info(f"[Nebula] Disable nebula load. Loaded checkpoint from {path} .")
return partition
partititon_name = os.path.basename(path)
logger.info(
f"[Nebula] Loading {path} under tag {tag} from nebula path {self.nebula_load_path}..."
)
logger.info(f"[Nebula] Loading {path} under tag {tag} from nebula path {self.nebula_load_path}...")
checkpoint = None
if tag in (None, 'latest', 'latest_universal'):
......@@ -72,44 +72,36 @@ class NebulaCheckpointEngine(CheckpointEngine):
# will try to load the valid latest checkpoint from nebula(tier3 > tier1). So, in summary
# when met failure loading for given tag, the loading priority would be like:
# nebula tier3 latest > nebula tier1 latest.
checkpoint = torch_nebula.get_latest_checkpoint(
persist_path=self.nebula_load_path)
checkpoint = torch_nebula.get_latest_checkpoint(persist_path=self.nebula_load_path)
else:
checkpoint = torch_nebula.get_checkpoint(tag=tag,
persist_path=self.nebula_load_path)
checkpoint = torch_nebula.get_checkpoint(tag=tag, persist_path=self.nebula_load_path)
if checkpoint is None or (checkpoint is not None and checkpoint.tag == ''):
logger.info(
f"Unable to find valid checkpoint tag:{tag} from Nebula, try to get latest checkpoint again from nebula {self.nebula_load_path} path!"
)
# nebula tier3 latest
checkpoint = torch_nebula.get_latest_checkpoint(
persist_path=self.nebula_load_path)
checkpoint = torch_nebula.get_latest_checkpoint(persist_path=self.nebula_load_path)
if checkpoint is None or (checkpoint is not None and checkpoint.tag == ''):
logger.info(
f"Unable to find latest checkpoint from Nebula tier3, try to get latest checkpoint again from nebula tier1 path!"
)
# nebula tier1 latest
checkpoint = torch_nebula.get_latest_checkpoint()
logger.warning(
f"Unable to find valid checkpoint from Nebula under tag:{tag}.")
logger.warning(f"Unable to find valid checkpoint from Nebula under tag:{tag}.")
return None
tag = checkpoint.tag
self.tag_flag = -1
partition = checkpoint.load(partititon_name, map_location=map_location)
logger.info(
f"[Nebula] Loaded {path} under tag {tag} from {self.nebula_load_path}.")
logger.info(f"[Nebula] Loaded {path} under tag {tag} from {self.nebula_load_path}.")
return partition
def commit(self, tag):
# nebula commit will be call when all files under give tag are ready to be persisted in the async way.
logger.info(
f"[Nebula] all files for {tag} are saved in tier1. It is ready to start persisting"
)
logger.info(f"[Nebula] all files for {tag} are saved in tier1. It is ready to start persisting")
commit_rls = self.checkpoint.commit()
if not commit_rls:
logger.error(
f"[Nebula] failed to commit the checkpoint, please check the log.")
logger.error(f"[Nebula] failed to commit the checkpoint, please check the log.")
return False
return commit_rls
deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from deepspeed.utils import logger, log_dist
......@@ -7,11 +10,12 @@ from deepspeed.runtime.checkpoint_engine.checkpoint_engine import \
class TorchCheckpointEngine(CheckpointEngine):
def __init__(self, config_params=None):
super().__init__(config_params)
def create(self, tag):
log_dist(f"[Torch] Checkpoint {tag} is begin to save!", ranks=[0])
log_dist(f"[Torch] Checkpoint {tag} is about to be saved!", ranks=[0])
def save(self, state_dict, path: str):
logger.info(f"[Torch] Saving {path}...")
......
deepspeed/runtime/comm/__init__.py
View file @ c25a91b6
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
'''Copyright The Microsoft DeepSpeed Team'''
deepspeed/runtime/comm/coalesced_collectives.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
"""batched collective operations for overhead amortization and better
bandwidth utilization"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
batched collective operations for overhead amortization and better
bandwidth utilization
"""
import math
from typing import List
......@@ -15,15 +20,8 @@ import torch.nn.functional
from deepspeed.utils import instrument_w_nvtx
def _torch_reduce_scatter_fn(input_tensor: Tensor,
output_tensor: Tensor,
group=None,
async_op=False,
prof=False):
return instrument_w_nvtx(dist.reduce_scatter_fn)(output_tensor,
input_tensor,
group=group,
async_op=async_op)
def _torch_reduce_scatter_fn(input_tensor: Tensor, output_tensor: Tensor, group=None, async_op=False, prof=False):
return instrument_w_nvtx(dist.reduce_scatter_fn)(output_tensor, input_tensor, group=group, async_op=async_op)
@instrument_w_nvtx
......@@ -45,13 +43,10 @@ def reduce_scatter_coalesced(
flattened_tensor = tensor.view(-1)
chunk_sz = math.ceil(tensor.numel() / world_sz)
partition_lst_for_each_tensor[tensor_idx] = [
flattened_tensor[rank * chunk_sz:rank * chunk_sz + chunk_sz]
for rank in range(0,
world_sz)
flattened_tensor[rank * chunk_sz:rank * chunk_sz + chunk_sz] for rank in range(0, world_sz)
]
padded_partition_sz_for_each_tensor = tuple(
math.ceil(t.numel() / world_sz) for t in tensors)
padded_partition_sz_for_each_tensor = tuple(math.ceil(t.numel() / world_sz) for t in tensors)
if len(tensors) == 1 and tensors[0].numel() % world_sz == 0:
# if there's only one tensor being reduced and we don't need to pad
......@@ -68,21 +63,15 @@ def reduce_scatter_coalesced(
tensor_partitions_lst_with_padding.append(tensor_chunk)
# add padding if necessary
padding_sz = padded_partition_sz_for_each_tensor[
tensor_idx] - tensor_chunk.numel()
padding_sz = padded_partition_sz_for_each_tensor[tensor_idx] - tensor_chunk.numel()
if padding_sz > 0:
tensor_partitions_lst_with_padding.append(
torch.empty(padding_sz,
dtype=tensor_chunk.dtype,
device=tensor_chunk.device))
torch.empty(padding_sz, dtype=tensor_chunk.dtype, device=tensor_chunk.device))
tensor_partition_flat_buffer = instrument_w_nvtx(
torch.cat)(tensor_partitions_lst_with_padding)
tensor_partition_flat_buffer = instrument_w_nvtx(torch.cat)(tensor_partitions_lst_with_padding)
tensor_partition_flat_buffer.div_(world_sz) # pre-divide
tensor_partition_buffer_for_each_rank: List[Tensor] = torch.chunk(
tensor_partition_flat_buffer,
world_sz)
tensor_partition_buffer_for_each_rank: List[Tensor] = torch.chunk(tensor_partition_flat_buffer, world_sz)
# batched reduce-scatter call
_torch_reduce_scatter_fn(tensor_partition_flat_buffer,
......@@ -95,9 +84,7 @@ def reduce_scatter_coalesced(
offset = 0
for tensor_idx in range(len(tensors)):
output_lst[tensor_idx] = tensor_partition_buffer_for_each_rank[this_rank].narrow(
0,
offset,
partition_lst_for_each_tensor[tensor_idx][this_rank].numel())
0, offset, partition_lst_for_each_tensor[tensor_idx][this_rank].numel())
offset += padded_partition_sz_for_each_tensor[tensor_idx]
......
deepspeed/runtime/comm/mpi.py
View file @ c25a91b6
'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
import cupy
......@@ -12,6 +13,7 @@ from deepspeed.runtime.compression.cupy import CupyBackend
class MpiBackend(object):
def __init__(self, cuda_aware):
self.comm = MPI.COMM_WORLD
self.rank = self.comm.Get_rank()
......@@ -31,49 +33,26 @@ class MpiBackend(object):
req.append(comm.Isend(sendbuf, dest=root))
return req
def gather_cuda(self,
rank,
world_size,
comm,
cupy_sign_list_packed,
cupy_recvbuf_sign,
cupy_worker_scale,
def gather_cuda(self, rank, world_size, comm, cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale,
cupy_recvbuf_scale):
# We do in-place operations on cupy buffers so we do not return any buffers
requests = []
for idx in range(world_size):
req_sign = self.my_igather(rank,
world_size,
comm,
cupy_sign_list_packed[idx],
cupy_recvbuf_sign,
root=idx)
req_sign = self.my_igather(rank, world_size, comm, cupy_sign_list_packed[idx], cupy_recvbuf_sign, root=idx)
requests += req_sign
for idx in range(world_size):
req_scale = self.my_igather(rank,
world_size,
comm,
cupy_worker_scale,
cupy_recvbuf_scale,
root=idx)
req_scale = self.my_igather(rank, world_size, comm, cupy_worker_scale, cupy_recvbuf_scale, root=idx)
requests += req_scale
MPI.Request.Waitall(requests)
def gather_host(self,
rank,
world_size,
comm,
cupy_sign_list_packed,
cupy_recvbuf_sign,
cupy_worker_scale,
def gather_host(self, rank, world_size, comm, cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale,
cupy_recvbuf_scale):
# In-place operations are not possible for newly created cupy arrays
# so we need to return the new buffers
numpy_recvbuf_sign = np.zeros([world_size,
cupy_sign_list_packed[rank].size],
numpy_recvbuf_sign = np.zeros([world_size, cupy_sign_list_packed[rank].size],
dtype=cupy_sign_list_packed[0].dtype)
numpy_recvbuf_scale = np.zeros([world_size, 1], dtype=cupy_worker_scale.dtype)
......@@ -101,12 +80,7 @@ class MpiBackend(object):
requests += req_sign
for idx in range(world_size):
req_scale = self.my_igather(rank,
world_size,
comm,
numpy_worker_scale,
numpy_recvbuf_scale,
root=idx)
req_scale = self.my_igather(rank, world_size, comm, numpy_worker_scale, numpy_recvbuf_scale, root=idx)
requests += req_scale
MPI.Request.Waitall(requests)
......@@ -122,30 +96,18 @@ class MpiBackend(object):
return cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale, cupy_recvbuf_scale
def allgather_cuda(self,
comm,
cupy_server_sign_packed,
cupy_recvbuf_sign_server,
cupy_server_scale,
def allgather_cuda(self, comm, cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server):
comm.Allgather(cupy_server_sign_packed, cupy_recvbuf_sign_server)
comm.Allgather(cupy_server_scale, cupy_recvbuf_scale_server)
def allgather_host(self,
comm,
cupy_server_sign_packed,
cupy_recvbuf_sign_server,
cupy_server_scale,
def allgather_host(self, comm, cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server):
# 1. Convert cupy to numpy
numpy_recvbuf_sign_server = np.zeros(
[comm.Get_size(),
cupy_server_sign_packed.size],
dtype=cupy_server_sign_packed.dtype)
numpy_recvbuf_scale_server = np.zeros([comm.Get_size(),
1],
dtype=cupy_server_scale.dtype)
numpy_recvbuf_sign_server = np.zeros([comm.Get_size(), cupy_server_sign_packed.size],
dtype=cupy_server_sign_packed.dtype)
numpy_recvbuf_scale_server = np.zeros([comm.Get_size(), 1], dtype=cupy_server_scale.dtype)
numpy_server_sign_packed = cupy.asnumpy(cupy_server_sign_packed)
numpy_recvbuf_sign_server = cupy.asnumpy(cupy_recvbuf_sign_server)
......@@ -167,11 +129,7 @@ class MpiBackend(object):
return cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale, cupy_recvbuf_scale_server
def compressed_allreduce(self,
buffer_m: torch.tensor,
worker_error,
server_error,
local_rank):
def compressed_allreduce(self, buffer_m: torch.tensor, worker_error, server_error, local_rank):
all_start_time = time.time()
original_shape = buffer_m.size()
......@@ -182,104 +140,71 @@ class MpiBackend(object):
cupy.cuda.Device(local_rank).use()
if original_size != worker_error_size:
empty_tensor = torch.zeros(worker_error_size - original_size,
device=buffer_m.device)
empty_tensor = torch.zeros(worker_error_size - original_size, device=buffer_m.device)
buffer_m = torch.cat([buffer_m, empty_tensor])
buffer_m.add_(worker_error)
worker_scale = torch.norm(buffer_m) / np.sqrt(torch.numel(buffer_m))
worker_error.set_(buffer_m - worker_scale *
buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
worker_error.set_(buffer_m - worker_scale * buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
cupy_sign_list_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool()),
self.size)
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool()), self.size)
cupy_worker_scale = self.compression_backend.torch2cupy(worker_scale)
cupy_recvbuf_sign = cupy.zeros(
[self.size,
cupy_sign_list_packed[self.rank].size],
dtype=cupy_sign_list_packed[0].dtype)
cupy_recvbuf_sign = cupy.zeros([self.size, cupy_sign_list_packed[self.rank].size],
dtype=cupy_sign_list_packed[0].dtype)
cupy_recvbuf_scale = cupy.zeros([self.size, 1], dtype=cupy_worker_scale.dtype)
# Communication Phase 1
gather_start = time.time()
if self.cuda_aware:
self.gather_cuda(self.rank,
self.size,
self.comm,
cupy_sign_list_packed,
cupy_recvbuf_sign,
cupy_worker_scale,
cupy_recvbuf_scale)
self.gather_cuda(self.rank, self.size, self.comm, cupy_sign_list_packed, cupy_recvbuf_sign,
cupy_worker_scale, cupy_recvbuf_scale)
else:
_, cupy_recvbuf_sign, _, cupy_recvbuf_scale = self.gather_host(self.rank,
self.size,
self.comm,
cupy_sign_list_packed,
cupy_recvbuf_sign,
cupy_worker_scale,
cupy_recvbuf_scale)
_, cupy_recvbuf_sign, _, cupy_recvbuf_scale = self.gather_host(self.rank, self.size, self.comm,
cupy_sign_list_packed, cupy_recvbuf_sign,
cupy_worker_scale, cupy_recvbuf_scale)
gather_end = time.time()
# cupy_sign_list_packed, cupy_worker_scale, worker_scale = None, None, None
cupy_sign_list_packed = None
compensated_server_m = self.compression_backend.cupy2torch(
(cupy.unpackbits(cupy_recvbuf_sign.flatten())).reshape(
self.size,
-1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(cupy_recvbuf_scale).mul_(
1 / self.size)).sum(0)
(cupy.unpackbits(cupy_recvbuf_sign.flatten())).reshape(self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(cupy_recvbuf_scale).mul_(1 / self.size)).sum(0)
compensated_server_m.add_(server_error)
server_scale = torch.norm(compensated_server_m) / np.sqrt(
compensated_server_m.numel())
server_error.set_(
compensated_server_m - server_scale *
compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
server_scale = torch.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel())
server_error.set_(compensated_server_m -
server_scale * compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
cupy_server_scale = self.compression_backend.torch2cupy(server_scale)
cupy_server_sign_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(
compensated_server_m.sign_().add_(1).bool()),
1)
self.compression_backend.torch2cupy(compensated_server_m.sign_().add_(1).bool()), 1)
compensated_server_m = None
cupy_recvbuf_sign_server = cupy.zeros(
[self.size,
cupy_server_sign_packed[0].size],
dtype=cupy_recvbuf_sign.dtype)
cupy_recvbuf_scale_server = cupy.zeros([self.size,
1],
dtype=cupy_recvbuf_scale.dtype)
cupy_recvbuf_sign_server = cupy.zeros([self.size, cupy_server_sign_packed[0].size],
dtype=cupy_recvbuf_sign.dtype)
cupy_recvbuf_scale_server = cupy.zeros([self.size, 1], dtype=cupy_recvbuf_scale.dtype)
# cupy_recvbuf_sign, cupy_recvbuf_scale = None, None
cupy_recvbuf_sign = None
# Communication Phase 2
if self.cuda_aware:
self.allgather_cuda(self.comm,
cupy_server_sign_packed[0],
cupy_recvbuf_sign_server,
cupy_server_scale,
self.allgather_cuda(self.comm, cupy_server_sign_packed[0], cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server)
else:
_, cupy_recvbuf_sign_server, _, cupy_recvbuf_scale_server = self.allgather_host(self.comm,
cupy_server_sign_packed[0],
cupy_recvbuf_sign_server,
cupy_server_scale,
cupy_recvbuf_scale_server)
_, cupy_recvbuf_sign_server, _, cupy_recvbuf_scale_server = self.allgather_host(
self.comm, cupy_server_sign_packed[0], cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server)
# cupy_server_sign_packed, cupy_server_scale, server_scale = None, None, None
cupy_server_sign_packed = None
buffer_m.data.copy_(
self.compression_backend.cupy2torch(
(cupy.unpackbits(cupy_recvbuf_sign_server.flatten())).reshape(
self.size,
-1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(
cupy_recvbuf_scale_server)).flatten().data)
self.compression_backend.cupy2torch((cupy.unpackbits(cupy_recvbuf_sign_server.flatten())).reshape(
self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server)).flatten().data)
if original_size != worker_error_size:
buffer_m = buffer_m[0:original_size]
if len(original_shape) > 1:
......
deepspeed/runtime/comm/nccl.py
View file @ c25a91b6
'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from deepspeed import comm as dist
......@@ -12,6 +13,7 @@ from deepspeed.accelerator import get_accelerator
class NcclBackend(object):
def __init__(self, mpu=None):
if mpu is None:
self.world_group = dist.new_group(ranks=range(dist.get_world_size()))
......@@ -24,7 +26,7 @@ class NcclBackend(object):
self.bool_not_supported = False
TORCH_MAJOR = int(torch.__version__.split('.')[0])
TORCH_MINOR = int(torch.__version__.split('.')[1])
if TORCH_MAJOR >= 1 and TORCH_MINOR >= 10:
if (TORCH_MAJOR == 1 and TORCH_MINOR >= 10) or TORCH_MAJOR == 2:
self.bool_not_supported = True
def my_igather(self, rank, size, group, sendbuf, recvbuf, root):
......@@ -49,11 +51,7 @@ class NcclBackend(object):
else:
dist.send(sendbuf, group=group, dst=root)
def compressed_allreduce(self,
buffer_m: torch.tensor,
worker_error,
server_error,
local_rank):
def compressed_allreduce(self, buffer_m: torch.tensor, worker_error, server_error, local_rank):
# all_start_time = time.time()
original_shape = buffer_m.size()
......@@ -64,53 +62,41 @@ class NcclBackend(object):
cupy.cuda.Device(local_rank).use()
if original_size != worker_error_size:
empty_tensor = torch.zeros(worker_error_size - original_size,
device=buffer_m.device)
empty_tensor = torch.zeros(worker_error_size - original_size, device=buffer_m.device)
buffer_m = torch.cat([buffer_m, empty_tensor])
buffer_m.add_(worker_error)
worker_scale = torch.norm(buffer_m) / np.sqrt(buffer_m.numel())
worker_error.set_(buffer_m - worker_scale *
buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
worker_error.set_(buffer_m - worker_scale * buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
if self.bool_not_supported:
cupy_sign_list_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(
buffer_m.sign_().add_(1).bool().to(dtype=torch.uint8)),
self.size)
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool().to(dtype=torch.uint8)), self.size)
else:
cupy_sign_list_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool()),
self.size)
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool()), self.size)
cupy_worker_scale = self.compression_backend.torch2cupy(worker_scale)
cupy_recvbuf_sign = cupy.zeros(
[self.size,
cupy_sign_list_packed[self.rank].size],
dtype=cupy_sign_list_packed[0].dtype)
cupy_recvbuf_sign = cupy.zeros([self.size, cupy_sign_list_packed[self.rank].size],
dtype=cupy_sign_list_packed[0].dtype)
# cupy_recvbuf_scale = cupy.zeros([self.size, 1], dtype=cupy_worker_scale.dtype)
sign_list_packed = [
self.compression_backend.cupy2torch(cupy_sign_list_packed[idx])
for idx in range(self.size)
self.compression_backend.cupy2torch(cupy_sign_list_packed[idx]) for idx in range(self.size)
]
# worker_scale = self.compression_backend.cupy2torch(cupy_worker_scale)
recvbuf_sign = self.compression_backend.cupy2torch(cupy_recvbuf_sign)
#recvbuf_scale = self.compression_backend.cupy2torch(cupy_recvbuf_scale)
recvbuf_scale = [
torch.zeros(1,
dtype=worker_scale.dtype,
device=torch.device(get_accelerator().device_name(local_rank)))
torch.zeros(1, dtype=worker_scale.dtype, device=torch.device(get_accelerator().device_name(local_rank)))
for i in range(self.size)
]
# communication phase 1
# gather_start = time.time()
# Alltoall for sign
dist.all_to_all_single(recvbuf_sign,
torch.stack(sign_list_packed),
group=self.world_group)
dist.all_to_all_single(recvbuf_sign, torch.stack(sign_list_packed), group=self.world_group)
# Allgather for scale
dist.all_gather(recvbuf_scale, worker_scale, group=self.world_group)
......@@ -123,61 +109,44 @@ class NcclBackend(object):
#cupy_recvbuf_scale = self.compression_backend.torch2cupy(torch.stack(recvbuf_scale))
compensated_server_m = self.compression_backend.cupy2torch(
(cupy.unpackbits(cupy_recvbuf_sign.flatten())).reshape(
self.size,
-1)).float().add_(-0.5).mul_(2.0).mul_(
torch.stack(recvbuf_scale).mul_(1 / self.size)).sum(0)
(cupy.unpackbits(cupy_recvbuf_sign.flatten())).reshape(self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
torch.stack(recvbuf_scale).mul_(1 / self.size)).sum(0)
compensated_server_m.add_(server_error)
server_scale = torch.norm(compensated_server_m) / np.sqrt(
compensated_server_m.numel())
server_error.set_(
compensated_server_m - server_scale *
compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
server_scale = torch.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel())
server_error.set_(compensated_server_m -
server_scale * compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
# cupy_server_scale = self.compression_backend.torch2cupy(server_scale)
if self.bool_not_supported:
cupy_server_sign_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(
compensated_server_m.sign_().add_(1).bool().to(dtype=torch.uint8)),
self.compression_backend.torch2cupy(compensated_server_m.sign_().add_(1).bool().to(dtype=torch.uint8)),
1)
else:
cupy_server_sign_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(
compensated_server_m.sign_().add_(1).bool()),
1)
self.compression_backend.torch2cupy(compensated_server_m.sign_().add_(1).bool()), 1)
compensated_server_m = None
cupy_recvbuf_sign_server = cupy.zeros(
[self.size,
cupy_server_sign_packed[0].size],
dtype=cupy_recvbuf_sign.dtype)
cupy_recvbuf_sign_server = cupy.zeros([self.size, cupy_server_sign_packed[0].size],
dtype=cupy_recvbuf_sign.dtype)
# cupy_recvbuf_sign, recvbuf_sign = None, None
cupy_recvbuf_sign = None
server_sign_packed = [
self.compression_backend.cupy2torch(cupy_server_sign_packed[0])
]
server_sign_packed = [self.compression_backend.cupy2torch(cupy_server_sign_packed[0])]
recvbuf_sign_server = [
self.compression_backend.cupy2torch(cupy_recvbuf_sign_server[idx])
for idx in range(self.size)
self.compression_backend.cupy2torch(cupy_recvbuf_sign_server[idx]) for idx in range(self.size)
]
# server_scale = self.compression_backend.cupy2torch(cupy_server_scale)
cupy_recvbuf_scale_server = cupy.zeros([self.size,
1],
dtype=cupy_worker_scale.dtype)
cupy_recvbuf_scale_server = cupy.zeros([self.size, 1], dtype=cupy_worker_scale.dtype)
# cupy_recvbuf_scale, recvbuf_scale = None, None
recvbuf_scale_server = [
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server[idx])
for idx in range(self.size)
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server[idx]) for idx in range(self.size)
]
# Communication Phase 2
dist.all_gather(recvbuf_sign_server,
server_sign_packed[0],
group=self.world_group)
dist.all_gather(recvbuf_sign_server, server_sign_packed[0], group=self.world_group)
dist.all_gather(recvbuf_scale_server, server_scale, group=self.world_group)
cupy_server_sign_packed = None
......@@ -186,16 +155,12 @@ class NcclBackend(object):
# dist.all_gather only provides a tensor list as the recv/output buffer
recvbuf_sign_server = torch.stack(recvbuf_sign_server)
cupy_recvbuf_sign_server = self.compression_backend.torch2cupy(
recvbuf_sign_server)
cupy_recvbuf_sign_server = self.compression_backend.torch2cupy(recvbuf_sign_server)
buffer_m.data.copy_(
self.compression_backend.cupy2torch(
(cupy.unpackbits(cupy_recvbuf_sign_server.flatten())).reshape(
self.size,
-1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(
cupy_recvbuf_scale_server)).flatten().data)
self.compression_backend.cupy2torch((cupy.unpackbits(cupy_recvbuf_sign_server.flatten())).reshape(
self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server)).flatten().data)
if original_size != worker_error_size:
buffer_m = buffer_m[0:original_size]
if len(original_shape) > 1:
......
deepspeed/runtime/compression/__init__.py
View file @ c25a91b6
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
'''Copyright The Microsoft DeepSpeed Team'''
deepspeed/runtime/compression/cupy.py
View file @ c25a91b6
'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import cupy
from torch.utils.dlpack import to_dlpack
......@@ -8,6 +9,7 @@ from torch.utils.dlpack import from_dlpack
class CupyBackend(object):
def __init__(self):
pass
......
deepspeed/runtime/config.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
"""
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import os
from typing import Union
from enum import Enum
import torch
import json
......@@ -30,6 +31,7 @@ from ..comm.config import DeepSpeedCommsConfig
from ..monitor.config import get_monitor_config
from deepspeed import comm as dist
from deepspeed.runtime.config_utils import DeepSpeedConfigModel
from ..git_version_info import version as __version__
from ..utils import logger
......@@ -71,12 +73,7 @@ ONEBIT_ADAM_OPTIMIZER = 'onebitadam'
ZERO_ONE_ADAM_OPTIMIZER = 'zerooneadam'
ONEBIT_LAMB_OPTIMIZER = 'onebitlamb'
DEEPSPEED_OPTIMIZERS = [
ADAGRAD_OPTIMIZER,
ADAM_OPTIMIZER,
ADAMW_OPTIMIZER,
LAMB_OPTIMIZER,
ONEBIT_ADAM_OPTIMIZER,
ONEBIT_LAMB_OPTIMIZER,
ADAGRAD_OPTIMIZER, ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, ONEBIT_LAMB_OPTIMIZER,
ZERO_ONE_ADAM_OPTIMIZER
]
......@@ -92,11 +89,36 @@ class DeepSpeedConfigError(Exception):
pass
class DtypeEnum(Enum):
# The torch dtype must always be the first value (so we return torch.dtype)
fp16 = torch.float16, "torch.float16", "fp16", "float16", "half"
fp32 = torch.float32, "torch.float32", "fp32", "float32", "float"
int8 = torch.int8, "torch.int8", "int8"
bf16 = torch.bfloat16, "torch.bfloat16", "bf16", "bfloat16"
# Copied from https://stackoverflow.com/a/43210118
# Allows us to use multiple values for each Enum index and returns first
# listed value when Enum is called
def __new__(cls, *values):
obj = object.__new__(cls)
# first value is canonical value
obj._value_ = values[0]
for other_value in values[1:]:
cls._value2member_map_[other_value] = obj
obj._all_values = values
return obj
def __repr__(self):
return "<%s.%s: %s>" % (
self.__class__.__name__,
self._name_,
", ".join([repr(v) for v in self._all_values]),
)
def get_pld_enabled(param_dict):
if PROGRESSIVE_LAYER_DROP in param_dict.keys():
return get_scalar_param(param_dict[PROGRESSIVE_LAYER_DROP],
PLD_ENABLED,
PLD_ENABLED_DEFAULT)
return get_scalar_param(param_dict[PROGRESSIVE_LAYER_DROP], PLD_ENABLED, PLD_ENABLED_DEFAULT)
else:
return False
......@@ -136,17 +158,13 @@ def get_fp16_enabled(param_dict):
def get_bfloat16_enabled(param_dict):
for key in [BFLOAT16, BFLOAT16_OLD]:
if key in param_dict.keys():
return get_scalar_param(param_dict[key],
BFLOAT16_ENABLED,
BFLOAT16_ENABLED_DEFAULT)
return get_scalar_param(param_dict[key], BFLOAT16_ENABLED, BFLOAT16_ENABLED_DEFAULT)
return False
def get_fp16_master_weights_and_grads_enabled(param_dict):
if get_fp16_enabled(param_dict):
return get_scalar_param(param_dict[FP16],
FP16_MASTER_WEIGHTS_AND_GRADS,
FP16_MASTER_WEIGHTS_AND_GRADS_DEFAULT)
return get_scalar_param(param_dict[FP16], FP16_MASTER_WEIGHTS_AND_GRADS, FP16_MASTER_WEIGHTS_AND_GRADS_DEFAULT)
else:
return False
......@@ -158,9 +176,7 @@ def get_fp16_auto_cast(param_dict):
def get_loss_scale(param_dict):
if get_fp16_enabled(param_dict):
return get_scalar_param(param_dict[FP16],
FP16_LOSS_SCALE,
FP16_LOSS_SCALE_DEFAULT)
return get_scalar_param(param_dict[FP16], FP16_LOSS_SCALE, FP16_LOSS_SCALE_DEFAULT)
elif get_bfloat16_enabled(param_dict):
return 1.0
else:
......@@ -169,8 +185,7 @@ def get_loss_scale(param_dict):
def get_initial_dynamic_scale(param_dict):
if get_fp16_enabled(param_dict):
initial_scale_power = get_scalar_param(param_dict[FP16],
FP16_INITIAL_SCALE_POWER,
initial_scale_power = get_scalar_param(param_dict[FP16], FP16_INITIAL_SCALE_POWER,
FP16_INITIAL_SCALE_POWER_DEFAULT)
elif get_bfloat16_enabled(param_dict):
initial_scale_power = 0
......@@ -191,18 +206,10 @@ def get_dynamic_loss_scale_args(param_dict):
FP16_HYSTERESIS,
]
if any(arg in list(fp16_dict.keys()) for arg in dynamic_loss_args):
init_scale = get_scalar_param(fp16_dict,
FP16_INITIAL_SCALE_POWER,
FP16_INITIAL_SCALE_POWER_DEFAULT)
scale_window = get_scalar_param(fp16_dict,
FP16_LOSS_SCALE_WINDOW,
FP16_LOSS_SCALE_WINDOW_DEFAULT)
delayed_shift = get_scalar_param(fp16_dict,
FP16_HYSTERESIS,
FP16_HYSTERESIS_DEFAULT)
min_loss_scale = get_scalar_param(fp16_dict,
FP16_MIN_LOSS_SCALE,
FP16_MIN_LOSS_SCALE_DEFAULT)
init_scale = get_scalar_param(fp16_dict, FP16_INITIAL_SCALE_POWER, FP16_INITIAL_SCALE_POWER_DEFAULT)
scale_window = get_scalar_param(fp16_dict, FP16_LOSS_SCALE_WINDOW, FP16_LOSS_SCALE_WINDOW_DEFAULT)
delayed_shift = get_scalar_param(fp16_dict, FP16_HYSTERESIS, FP16_HYSTERESIS_DEFAULT)
min_loss_scale = get_scalar_param(fp16_dict, FP16_MIN_LOSS_SCALE, FP16_MIN_LOSS_SCALE_DEFAULT)
loss_scale_args = {
INITIAL_LOSS_SCALE: 2**init_scale,
SCALE_WINDOW: scale_window,
......@@ -214,9 +221,7 @@ def get_dynamic_loss_scale_args(param_dict):
def get_gradient_accumulation_steps(param_dict):
return get_scalar_param(param_dict,
GRADIENT_ACCUMULATION_STEPS,
GRADIENT_ACCUMULATION_STEPS_DEFAULT)
return get_scalar_param(param_dict, GRADIENT_ACCUMULATION_STEPS, GRADIENT_ACCUMULATION_STEPS_DEFAULT)
def get_sparse_gradients_enabled(param_dict):
......@@ -224,9 +229,7 @@ def get_sparse_gradients_enabled(param_dict):
def get_communication_data_type(param_dict):
val = get_scalar_param(param_dict,
COMMUNICATION_DATA_TYPE,
COMMUNICATION_DATA_TYPE_DEFAULT)
val = get_scalar_param(param_dict, COMMUNICATION_DATA_TYPE, COMMUNICATION_DATA_TYPE_DEFAULT)
val = val.lower() if val is not None else val
if val is None:
return val # we must determine it by other parameters
......@@ -237,9 +240,7 @@ def get_communication_data_type(param_dict):
elif val == "bfp16":
return torch.bfloat16
raise ValueError(
f"Invalid communication_data_type. Supported data types: ['fp16', 'bfp16', 'fp32']. Got: {val}"
)
raise ValueError(f"Invalid communication_data_type. Supported data types: ['fp16', 'bfp16', 'fp32']. Got: {val}")
def get_prescale_gradients(param_dict):
......@@ -247,9 +248,7 @@ def get_prescale_gradients(param_dict):
def get_gradient_predivide_factor(param_dict):
return get_scalar_param(param_dict,
GRADIENT_PREDIVIDE_FACTOR,
GRADIENT_PREDIVIDE_FACTOR_DEFAULT)
return get_scalar_param(param_dict, GRADIENT_PREDIVIDE_FACTOR, GRADIENT_PREDIVIDE_FACTOR_DEFAULT)
def get_steps_per_print(param_dict):
......@@ -284,8 +283,7 @@ def get_sparse_attention(param_dict):
elif mode == SPARSE_BSLONGFORMER_MODE:
return get_sparse_bslongformer_config(sparsity)
else:
raise NotImplementedError(
f"Given sparsity mode, {mode}, has not been implemented yet!")
raise NotImplementedError(f"Given sparsity mode, {mode}, has not been implemented yet!")
else:
return None
......@@ -303,15 +301,9 @@ def get_sparse_fixed_config(sparsity):
SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT,
)
num_local_blocks = get_scalar_param(sparsity,
SPARSE_NUM_LOCAL_BLOCKS,
SPARSE_NUM_LOCAL_BLOCKS_DEFAULT)
num_global_blocks = get_scalar_param(sparsity,
SPARSE_NUM_GLOBAL_BLOCKS,
SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
attention = get_scalar_param(sparsity,
SPARSE_ATTENTION_TYPE,
SPARSE_ATTENTION_TYPE_DEFAULT)
num_local_blocks = get_scalar_param(sparsity, SPARSE_NUM_LOCAL_BLOCKS, SPARSE_NUM_LOCAL_BLOCKS_DEFAULT)
num_global_blocks = get_scalar_param(sparsity, SPARSE_NUM_GLOBAL_BLOCKS, SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
attention = get_scalar_param(sparsity, SPARSE_ATTENTION_TYPE, SPARSE_ATTENTION_TYPE_DEFAULT)
horizontal_global_attention = get_scalar_param(
sparsity,
SPARSE_HORIZONTAL_GLOBAL_ATTENTION,
......@@ -342,23 +334,15 @@ def get_sparse_variable_config(sparsity):
SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT,
)
num_random_blocks = get_scalar_param(sparsity,
SPARSE_NUM_RANDOM_BLOCKS,
SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
local_window_blocks = get_scalar_param(sparsity,
SPARSE_LOCAL_WINDOW_BLOCKS,
SPARSE_LOCAL_WINDOW_BLOCKS_DEFAULT)
global_block_indices = get_scalar_param(sparsity,
SPARSE_GLOBAL_BLOCK_INDICES,
SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
num_random_blocks = get_scalar_param(sparsity, SPARSE_NUM_RANDOM_BLOCKS, SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
local_window_blocks = get_scalar_param(sparsity, SPARSE_LOCAL_WINDOW_BLOCKS, SPARSE_LOCAL_WINDOW_BLOCKS_DEFAULT)
global_block_indices = get_scalar_param(sparsity, SPARSE_GLOBAL_BLOCK_INDICES, SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
global_block_end_indices = get_scalar_param(
sparsity,
SPARSE_GLOBAL_BLOCK_END_INDICES,
SPARSE_GLOBAL_BLOCK_END_INDICES_DEFAULT,
)
attention = get_scalar_param(sparsity,
SPARSE_ATTENTION_TYPE,
SPARSE_ATTENTION_TYPE_DEFAULT)
attention = get_scalar_param(sparsity, SPARSE_ATTENTION_TYPE, SPARSE_ATTENTION_TYPE_DEFAULT)
horizontal_global_attention = get_scalar_param(
sparsity,
SPARSE_HORIZONTAL_GLOBAL_ATTENTION,
......@@ -385,17 +369,13 @@ def get_sparse_bigbird_config(sparsity):
SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT,
)
num_random_blocks = get_scalar_param(sparsity,
SPARSE_NUM_RANDOM_BLOCKS,
SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
num_random_blocks = get_scalar_param(sparsity, SPARSE_NUM_RANDOM_BLOCKS, SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
num_sliding_window_blocks = get_scalar_param(
sparsity,
SPARSE_NUM_SLIDING_WINDOW_BLOCKS,
SPARSE_NUM_SLIDING_WINDOW_BLOCKS_DEFAULT,
)
num_global_blocks = get_scalar_param(sparsity,
SPARSE_NUM_GLOBAL_BLOCKS,
SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
num_global_blocks = get_scalar_param(sparsity, SPARSE_NUM_GLOBAL_BLOCKS, SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
return {
SPARSE_MODE: SPARSE_BIGBIRD_MODE,
......@@ -419,9 +399,7 @@ def get_sparse_bslongformer_config(sparsity):
SPARSE_NUM_SLIDING_WINDOW_BLOCKS,
SPARSE_NUM_SLIDING_WINDOW_BLOCKS_DEFAULT,
)
global_block_indices = get_scalar_param(sparsity,
SPARSE_GLOBAL_BLOCK_INDICES,
SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
global_block_indices = get_scalar_param(sparsity, SPARSE_GLOBAL_BLOCK_INDICES, SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
global_block_end_indices = get_scalar_param(
sparsity,
SPARSE_GLOBAL_BLOCK_END_INDICES,
......@@ -474,8 +452,7 @@ def get_optimizer_name(param_dict):
def get_optimizer_params(param_dict):
if (get_optimizer_name(param_dict) is not None
and OPTIMIZER_PARAMS in param_dict[OPTIMIZER].keys()):
if (get_optimizer_name(param_dict) is not None and OPTIMIZER_PARAMS in param_dict[OPTIMIZER].keys()):
return param_dict[OPTIMIZER][OPTIMIZER_PARAMS]
else:
return None
......@@ -497,9 +474,11 @@ def get_optimizer_legacy_fusion(param_dict):
def get_zero_allow_untested_optimizer(param_dict):
return get_scalar_param(param_dict,
ZERO_ALLOW_UNTESTED_OPTIMIZER,
ZERO_ALLOW_UNTESTED_OPTIMIZER_DEFAULT)
return get_scalar_param(param_dict, ZERO_ALLOW_UNTESTED_OPTIMIZER, ZERO_ALLOW_UNTESTED_OPTIMIZER_DEFAULT)
def get_zero_force_ds_cpu_optimizer(param_dict):
return get_scalar_param(param_dict, ZERO_FORCE_DS_CPU_OPTIMIZER, ZERO_FORCE_DS_CPU_OPTIMIZER_DEFAULT)
def get_scheduler_name(param_dict):
......@@ -510,8 +489,7 @@ def get_scheduler_name(param_dict):
def get_scheduler_params(param_dict):
if (get_scheduler_name(param_dict) is not None
and SCHEDULER_PARAMS in param_dict[SCHEDULER].keys()):
if (get_scheduler_name(param_dict) is not None and SCHEDULER_PARAMS in param_dict[SCHEDULER].keys()):
return param_dict[SCHEDULER][SCHEDULER_PARAMS]
else:
return None
......@@ -530,15 +508,28 @@ def get_train_micro_batch_size_per_gpu(param_dict):
def get_wall_clock_breakdown(param_dict):
return get_scalar_param(param_dict,
WALL_CLOCK_BREAKDOWN,
WALL_CLOCK_BREAKDOWN_DEFAULT)
return get_scalar_param(param_dict, WALL_CLOCK_BREAKDOWN, WALL_CLOCK_BREAKDOWN_DEFAULT)
def get_memory_breakdown(param_dict):
return get_scalar_param(param_dict, MEMORY_BREAKDOWN, MEMORY_BREAKDOWN_DEFAULT)
class HybridEngineConfig(DeepSpeedConfigModel):
enabled: bool = False
max_out_tokens: int = 512
inference_tp_size: int = 1
release_inference_cache: bool = False
pin_parameters: bool = True
tp_gather_partition_size: int = 8
def get_hybrid_engine_config(param_dict):
hybrid_engine_config_dict = param_dict.get("hybrid_engine", {})
hybrid_engine_config = HybridEngineConfig(**hybrid_engine_config_dict)
return hybrid_engine_config
def get_eigenvalue_config(param_dict):
if get_quantize_enabled(param_dict):
param_dict = param_dict[QUANTIZE_TRAINING]
......@@ -568,45 +559,35 @@ def get_eigenvalue_config(param_dict):
def get_eigenvalue_enabled(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_ENABLED,
EIGENVALUE_ENABLED_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_ENABLED, EIGENVALUE_ENABLED_DEFAULT)
else:
return EIGENVALUE_ENABLED_DEFAULT
def get_eigenvalue_verbose(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_VERBOSE,
EIGENVALUE_VERBOSE_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_VERBOSE, EIGENVALUE_VERBOSE_DEFAULT)
else:
return EIGENVALUE_VERBOSE_DEFAULT
def get_eigenvalue_max_iter(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_MAX_ITER,
EIGENVALUE_MAX_ITER_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_MAX_ITER, EIGENVALUE_MAX_ITER_DEFAULT)
else:
return EIGENVALUE_MAX_ITER_DEFAULT
def get_eigenvalue_tol(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_TOL,
EIGENVALUE_TOL_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_TOL, EIGENVALUE_TOL_DEFAULT)
else:
return EIGENVALUE_TOL_DEFAULT
def get_eigenvalue_stability(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_STABILITY,
EIGENVALUE_STABILITY_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_STABILITY, EIGENVALUE_STABILITY_DEFAULT)
else:
return EIGENVALUE_STABILITY_DEFAULT
......@@ -624,18 +605,14 @@ def get_eigenvalue_gas_boundary_resolution(param_dict):
def get_eigenvalue_layer_name(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_LAYER_NAME,
EIGENVALUE_LAYER_NAME_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_LAYER_NAME, EIGENVALUE_LAYER_NAME_DEFAULT)
else:
return EIGENVALUE_LAYER_NAME_DEFAULT
def get_eigenvalue_layer_num(param_dict):
if EIGENVALUE in param_dict.keys():
return get_scalar_param(param_dict[EIGENVALUE],
EIGENVALUE_LAYER_NUM,
EIGENVALUE_LAYER_NUM_DEFAULT)
return get_scalar_param(param_dict[EIGENVALUE], EIGENVALUE_LAYER_NUM, EIGENVALUE_LAYER_NUM_DEFAULT)
else:
return EIGENVALUE_LAYER_NUM_DEFAULT
......@@ -649,35 +626,29 @@ def get_data_types_params(param_dict):
def get_checkpoint_tag_validation_mode(checkpoint_params):
tag_validation_mode = checkpoint_params.get(CHECKPOINT_TAG_VALIDATION,
CHECKPOINT_TAG_VALIDATION_DEFAULT)
tag_validation_mode = checkpoint_params.get(CHECKPOINT_TAG_VALIDATION, CHECKPOINT_TAG_VALIDATION_DEFAULT)
tag_validation_mode = tag_validation_mode.upper()
if tag_validation_mode in CHECKPOINT_TAG_VALIDATION_MODES:
return tag_validation_mode
else:
raise DeepSpeedConfigError(
"Checkpoint config contains invalid tag_validation "
f"value of {tag_validation_mode}, expecting one of {CHECKPOINT_TAG_VALIDATION_MODES}"
)
f"value of {tag_validation_mode}, expecting one of {CHECKPOINT_TAG_VALIDATION_MODES}")
def get_checkpoint_parallel_write_pipeline(checkpoint_params):
par_write_params = checkpoint_params.get(CHECKPOINT_PARALLEL_WRITE, {})
par_write_pipeline = par_write_params.get(
CHECKPOINT_PARALLEL_WRITE_PIPELINE_STAGE,
CHECKPOINT_PARALLEL_WRITE_PIPELINE_STAGE_DEFAULT)
par_write_pipeline = par_write_params.get(CHECKPOINT_PARALLEL_WRITE_PIPELINE_STAGE,
CHECKPOINT_PARALLEL_WRITE_PIPELINE_STAGE_DEFAULT)
if par_write_pipeline in [True, False]:
return par_write_pipeline
else:
raise DeepSpeedConfigError(
"checkpoint::parallel_write::pipeline_stage "
f"value of '{par_write_pipeline}' is invalid, expecting: true or false")
raise DeepSpeedConfigError("checkpoint::parallel_write::pipeline_stage "
f"value of '{par_write_pipeline}' is invalid, expecting: true or false")
def get_dataloader_drop_last(param_dict):
return get_scalar_param(param_dict,
DATALOADER_DROP_LAST,
DATALOADER_DROP_LAST_DEFAULT)
return get_scalar_param(param_dict, DATALOADER_DROP_LAST, DATALOADER_DROP_LAST_DEFAULT)
'''Write deepspeed config files by modifying basic templates.
......@@ -685,6 +656,7 @@ Can be used for quickly changing parameters via command line parameters.'''
class DeepSpeedConfigWriter:
def __init__(self, data=None):
self.data = data if data is not None else {}
......@@ -692,9 +664,7 @@ class DeepSpeedConfigWriter:
self.data[key] = value
def load_config(self, filename):
self.data = json.load(open(filename,
"r"),
object_pairs_hook=dict_raise_error_on_duplicate_keys)
self.data = json.load(open(filename, "r"), object_pairs_hook=dict_raise_error_on_duplicate_keys)
def write_config(self, filename):
with open(filename, "w") as outfile:
......@@ -702,15 +672,13 @@ class DeepSpeedConfigWriter:
class DeepSpeedConfig(object):
def __init__(self, config: Union[str, dict], mpu=None):
super(DeepSpeedConfig, self).__init__()
if isinstance(config, dict):
self._param_dict = config
elif os.path.exists(config):
self._param_dict = hjson.load(
open(config,
"r"),
object_pairs_hook=dict_raise_error_on_duplicate_keys)
self._param_dict = hjson.load(open(config, "r"), object_pairs_hook=dict_raise_error_on_duplicate_keys)
else:
try:
config_decoded = base64.urlsafe_b64decode(config).decode('utf-8')
......@@ -744,24 +712,18 @@ class DeepSpeedConfig(object):
# Ensure the resource scheduler saw the same elastic config we are using at runtime
ensure_immutable_elastic_config(runtime_elastic_config_dict=elastic_dict)
self.elastic_model_parallel_size = elastic_dict.get(
MODEL_PARLLEL_SIZE,
MODEL_PARLLEL_SIZE_DEFAULT)
self.elastic_model_parallel_size = elastic_dict.get(MODEL_PARLLEL_SIZE, MODEL_PARLLEL_SIZE_DEFAULT)
if self.elastic_model_parallel_size < 1:
raise ElasticityConfigError(
"Model-Parallel size cannot be less than 1, "
f"given model-parallel size: {self.elastic_model_parallel_size}")
raise ElasticityConfigError("Model-Parallel size cannot be less than 1, "
f"given model-parallel size: {self.elastic_model_parallel_size}")
self.num_gpus_per_node = elastic_dict.get(NUM_GPUS_PER_NODE,
NUM_GPUS_PER_NODE_DEFAULT)
self.num_gpus_per_node = elastic_dict.get(NUM_GPUS_PER_NODE, NUM_GPUS_PER_NODE_DEFAULT)
if self.num_gpus_per_node < 1:
raise ElasticityConfigError(
"NUmber of GPUs per node cannot be less than 1, "
f"given number of GPUs per node: {self.num_gpus_per_node}")
raise ElasticityConfigError("NUmber of GPUs per node cannot be less than 1, "
f"given number of GPUs per node: {self.num_gpus_per_node}")
ignore_non_elastic_batch_info = elastic_dict.get(
IGNORE_NON_ELASTIC_BATCH_INFO,
IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT)
ignore_non_elastic_batch_info = elastic_dict.get(IGNORE_NON_ELASTIC_BATCH_INFO,
IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT)
if not ignore_non_elastic_batch_info:
batch_params = [
......@@ -779,23 +741,17 @@ class DeepSpeedConfig(object):
# micro_bsz * world_size * gas = total_batch_size
# gas = total_batch_size // (micro_bsz * world_size)
gradient_accu_steps = final_batch_size // (micro_batch_size *
self.world_size)
gradient_accu_steps = final_batch_size // (micro_batch_size * self.world_size)
if TRAIN_BATCH_SIZE in self._param_dict:
logger.warning(
"[Elasticity] overriding training_batch_size: "
f"{self._param_dict[TRAIN_BATCH_SIZE]} -> {final_batch_size}")
logger.warning("[Elasticity] overriding training_batch_size: "
f"{self._param_dict[TRAIN_BATCH_SIZE]} -> {final_batch_size}")
if TRAIN_MICRO_BATCH_SIZE_PER_GPU in self._param_dict:
logger.warning(
"[Elasticity] overriding train_micro_batch_size_per_gpu: "
f"{self._param_dict[TRAIN_MICRO_BATCH_SIZE_PER_GPU]} -> {micro_batch_size}"
)
logger.warning("[Elasticity] overriding train_micro_batch_size_per_gpu: "
f"{self._param_dict[TRAIN_MICRO_BATCH_SIZE_PER_GPU]} -> {micro_batch_size}")
if GRADIENT_ACCUMULATION_STEPS in self._param_dict:
logger.warning(
"[Elasticity] overriding gradient_accumulation_steps: "
f"{self._param_dict[GRADIENT_ACCUMULATION_STEPS]} -> {gradient_accu_steps}"
)
logger.warning("[Elasticity] overriding gradient_accumulation_steps: "
f"{self._param_dict[GRADIENT_ACCUMULATION_STEPS]} -> {gradient_accu_steps}")
logger.info(f"[Elasticity] valid GPU counts: {valid_gpus}")
......@@ -811,8 +767,7 @@ class DeepSpeedConfig(object):
def _initialize_params(self, param_dict):
self.train_batch_size = get_train_batch_size(param_dict)
#print(f"beginning get_train_batch_size = {get_train_batch_size}")
self.train_micro_batch_size_per_gpu = get_train_micro_batch_size_per_gpu(
param_dict)
self.train_micro_batch_size_per_gpu = get_train_micro_batch_size_per_gpu(param_dict)
self.gradient_accumulation_steps = get_gradient_accumulation_steps(param_dict)
self.steps_per_print = get_steps_per_print(param_dict)
self.dump_state = get_dump_state(param_dict)
......@@ -824,11 +779,12 @@ class DeepSpeedConfig(object):
self.sparse_gradients_enabled = get_sparse_gradients_enabled(param_dict)
self.zero_config = get_zero_config(param_dict)
self.mics_shard_size = self.zero_config.mics_shard_size
self.mics_hierarchial_params_gather = self.zero_config.mics_hierarchical_params_gather
self.zero_optimization_stage = self.zero_config.stage
self.zero_enabled = self.zero_optimization_stage > 0
self.activation_checkpointing_config = DeepSpeedActivationCheckpointingConfig(
param_dict)
self.activation_checkpointing_config = DeepSpeedActivationCheckpointingConfig(param_dict)
self.comms_config = DeepSpeedCommsConfig(param_dict)
self.monitor_config = get_monitor_config(param_dict)
......@@ -837,9 +793,9 @@ class DeepSpeedConfig(object):
self.fp16_enabled = get_fp16_enabled(param_dict)
self.fp16_auto_cast = get_fp16_auto_cast(param_dict)
self.bfloat16_enabled = get_bfloat16_enabled(param_dict)
assert not (self.fp16_enabled and self.bfloat16_enabled), 'bfloat16 and fp16 modes cannot be simultaneously enabled'
self.fp16_master_weights_and_gradients = get_fp16_master_weights_and_grads_enabled(
param_dict)
assert not (self.fp16_enabled
and self.bfloat16_enabled), 'bfloat16 and fp16 modes cannot be simultaneously enabled'
self.fp16_master_weights_and_gradients = get_fp16_master_weights_and_grads_enabled(param_dict)
self.amp_enabled = get_amp_enabled(param_dict)
self.amp_params = get_amp_params(param_dict)
self.loss_scale = get_loss_scale(param_dict)
......@@ -849,22 +805,21 @@ class DeepSpeedConfig(object):
self.compression_config = get_compression_config(param_dict)
self.optimizer_name = get_optimizer_name(param_dict)
if (self.optimizer_name is not None
and self.optimizer_name.lower() in DEEPSPEED_OPTIMIZERS):
if (self.optimizer_name is not None and self.optimizer_name.lower() in DEEPSPEED_OPTIMIZERS):
self.optimizer_name = self.optimizer_name.lower()
self.optimizer_params = get_optimizer_params(param_dict)
self.optimizer_legacy_fusion = get_optimizer_legacy_fusion(param_dict)
self.zero_allow_untested_optimizer = get_zero_allow_untested_optimizer(
param_dict)
self.zero_allow_untested_optimizer = get_zero_allow_untested_optimizer(param_dict)
self.zero_force_ds_cpu_optimizer = get_zero_force_ds_cpu_optimizer(param_dict)
self.scheduler_name = get_scheduler_name(param_dict)
self.scheduler_params = get_scheduler_params(param_dict)
self.flops_profiler_config = DeepSpeedFlopsProfilerConfig(param_dict)
self.wall_clock_breakdown = (get_wall_clock_breakdown(param_dict)
| self.flops_profiler_config.enabled)
self.wall_clock_breakdown = (get_wall_clock_breakdown(param_dict) | self.flops_profiler_config.enabled)
self.memory_breakdown = get_memory_breakdown(param_dict)
self.autotuning_config = DeepSpeedAutotuningConfig(param_dict)
......@@ -879,6 +834,8 @@ class DeepSpeedConfig(object):
self.eigenvalue_layer_num,
) = get_eigenvalue_config(param_dict)
self.hybrid_engine = get_hybrid_engine_config(param_dict)
self.sparse_attention = get_sparse_attention(param_dict)
self.pipeline = get_pipeline_config(param_dict)
......@@ -893,20 +850,16 @@ class DeepSpeedConfig(object):
checkpoint_params = get_checkpoint_params(param_dict)
validation_mode = get_checkpoint_tag_validation_mode(checkpoint_params)
self.checkpoint_tag_validation_enabled = (validation_mode !=
ValidationMode.IGNORE)
self.checkpoint_tag_validation_enabled = (validation_mode != ValidationMode.IGNORE)
self.checkpoint_tag_validation_fail = validation_mode == ValidationMode.FAIL
self.load_universal_checkpoint = checkpoint_params.get(
LOAD_UNIVERSAL_CHECKPOINT,
LOAD_UNIVERSAL_CHECKPOINT_DEFAULT)
self.load_universal_checkpoint = checkpoint_params.get(LOAD_UNIVERSAL_CHECKPOINT,
LOAD_UNIVERSAL_CHECKPOINT_DEFAULT)
self.use_node_local_storage = checkpoint_params.get(
USE_NODE_LOCAL_STORAGE_CHECKPOINT,
USE_NODE_LOCAL_STORAGE_CHECKPOINT_DEFAULT)
self.use_node_local_storage = checkpoint_params.get(USE_NODE_LOCAL_STORAGE_CHECKPOINT,
USE_NODE_LOCAL_STORAGE_CHECKPOINT_DEFAULT)
data_types_params = get_data_types_params(param_dict)
self.grad_accum_dtype = data_types_params.get(GRAD_ACCUM_DTYPE,
GRAD_ACCUM_DTYPE_DEFAULT)
self.grad_accum_dtype = data_types_params.get(GRAD_ACCUM_DTYPE, GRAD_ACCUM_DTYPE_DEFAULT)
par_write_pipe = get_checkpoint_parallel_write_pipeline(checkpoint_params)
self.checkpoint_parallel_write_pipeline = par_write_pipe
......@@ -923,23 +876,16 @@ class DeepSpeedConfig(object):
micro_batch = self.train_micro_batch_size_per_gpu
grad_acc = self.gradient_accumulation_steps
assert (
train_batch > 0
), f"Train batch size: {train_batch} has to be greater than 0"
assert (train_batch > 0), f"Train batch size: {train_batch} has to be greater than 0"
assert (
micro_batch > 0
), f"Micro batch size per gpu: {micro_batch} has to be greater than 0"
assert (micro_batch > 0), f"Micro batch size per gpu: {micro_batch} has to be greater than 0"
assert (
grad_acc > 0
), f"Gradient accumulation steps: {grad_acc} has to be greater than 0"
assert (grad_acc > 0), f"Gradient accumulation steps: {grad_acc} has to be greater than 0"
assert train_batch == micro_batch * grad_acc * self.world_size, (
f"Check batch related parameters. train_batch_size is not equal "
"to micro_batch_per_gpu * gradient_acc_step * world_size "
f"{train_batch} != {micro_batch} * {grad_acc} * {self.world_size}"
)
f"{train_batch} != {micro_batch} * {grad_acc} * {self.world_size}")
def _set_batch_related_parameters(self):
......@@ -1002,8 +948,7 @@ class DeepSpeedConfig(object):
sort_keys=True,
indent=4,
cls=ScientificNotationEncoder,
separators=(",",
":"),
separators=(",", ":"),
)))
def print(self, name):
......@@ -1016,20 +961,16 @@ class DeepSpeedConfig(object):
self.print_user_config()
def _do_error_check(self):
assert (
self.train_micro_batch_size_per_gpu
), "DeepSpeedConfig: {} is not defined".format(TRAIN_MICRO_BATCH_SIZE_PER_GPU)
assert (self.train_micro_batch_size_per_gpu
), "DeepSpeedConfig: {} is not defined".format(TRAIN_MICRO_BATCH_SIZE_PER_GPU)
assert (
self.gradient_accumulation_steps
), "DeepSpeedConfig: {} is not defined".format(GRADIENT_ACCUMULATION_STEPS)
self.gradient_accumulation_steps), "DeepSpeedConfig: {} is not defined".format(GRADIENT_ACCUMULATION_STEPS)
if self.zero_enabled:
assert (
self.zero_optimization_stage <= ZeroStageEnum.max_stage
), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format(
ZeroStageEnum.max_stage
)
assert (self.zero_optimization_stage <=
ZeroStageEnum.max_stage), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format(
ZeroStageEnum.max_stage)
if self.fp16_master_weights_and_gradients:
assert self.zero_enabled and self.zero_optimization_stage == ZeroStageEnum.gradients, "Fp16_master_weights_and_grads is only supported with ZeRO Stage 2 for now."
......@@ -1040,19 +981,15 @@ class DeepSpeedConfig(object):
vocabulary_size = self._param_dict.get(VOCABULARY_SIZE, VOCABULARY_SIZE_DEFAULT)
if vocabulary_size and vocabulary_size % TENSOR_CORE_ALIGN_SIZE != 0:
logger.warning(
"DeepSpeedConfig: vocabulary size {} is not aligned to {}, may import tensor core utilization."
.format(vocabulary_size,
TENSOR_CORE_ALIGN_SIZE))
"DeepSpeedConfig: vocabulary size {} is not aligned to {}, may import tensor core utilization.".format(
vocabulary_size, TENSOR_CORE_ALIGN_SIZE))
if (self.optimizer_params is not None
and MAX_GRAD_NORM in self.optimizer_params.keys()
if (self.optimizer_params is not None and MAX_GRAD_NORM in self.optimizer_params.keys()
and self.optimizer_params[MAX_GRAD_NORM] > 0):
if fp16_enabled:
if self.global_rank == 0:
logger.warning(
"DeepSpeedConfig: In FP16 mode, DeepSpeed will pass {}:{} to FP16 wrapper"
.format(MAX_GRAD_NORM,
self.optimizer_params[MAX_GRAD_NORM]))
logger.warning("DeepSpeedConfig: In FP16 mode, DeepSpeed will pass {}:{} to FP16 wrapper".format(
MAX_GRAD_NORM, self.optimizer_params[MAX_GRAD_NORM]))
else:
if self.global_rank == 0:
logger.warning(
......
deepspeed/runtime/config_utils.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
"""
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
Collection of DeepSpeed configuration utilities
"""
......@@ -50,15 +49,10 @@ class DeepSpeedConfigModel(BaseModel):
new_param='my_new_field',
new_param_fn=(lambda x: int(x)))
"""
def __init__(self, strict=False, **data):
if (
not strict
): # This is temporary until we refactor all DS configs, allows HF to load models
data = {
k: v
for k,
v in data.items() if (v != "auto" or k == "replace_method")
}
if (not strict): # This is temporary until we refactor all DS configs, allows HF to load models
data = {k: v for k, v in data.items() if (v != "auto" or k == "replace_method")}
super().__init__(**data)
self._deprecated_fields_check(self)
......@@ -73,8 +67,7 @@ class DeepSpeedConfigModel(BaseModel):
dep_msg = kwargs.get("deprecated_msg", "")
if dep_param in fields_set:
logger.warning(f"Config parameter {dep_param} is deprecated" +
(f" use {new_param} instead" if new_param else "") +
(f". {dep_msg}" if dep_msg else ""))
(f" use {new_param} instead" if new_param else "") + (f". {dep_msg}" if dep_msg else ""))
# Check if there is a new param and if it should be set with a value
if new_param and kwargs.get("set_new_param", True):
# Remove the deprecate field if there is a replacing field
......@@ -89,9 +82,7 @@ class DeepSpeedConfigModel(BaseModel):
if len(new_param_nested) > 1:
# If the new param exists in a subconfig, we need to get
# the fields set for that subconfig
pydantic_config = reduce(getattr,
new_param_nested[:-1],
pydantic_config)
pydantic_config = reduce(getattr, new_param_nested[:-1], pydantic_config)
fields_set = pydantic_config.__fields_set__
new_param_name = new_param_nested[-1]
assert (
......@@ -101,9 +92,7 @@ class DeepSpeedConfigModel(BaseModel):
try:
setattr(pydantic_config, new_param_name, param_value)
except Exception as e:
logger.error(
f"Tried setting value for '{new_param}' with value from deprecated '{dep_param}'"
)
logger.error(f"Tried setting value for '{new_param}' with value from deprecated '{dep_param}'")
raise e
def _deprecated_fields_check(self, pydantic_config):
......@@ -121,12 +110,20 @@ class DeepSpeedConfigModel(BaseModel):
arbitrary_types_allowed = True
def get_config_default(config, field_name):
assert field_name in config.__fields__, f"'{field_name}' is not a field in {config}"
assert not config.__fields__.get(
field_name).required, f"'{field_name}' is a required field and does not have a default value"
return config.__fields__.get(field_name).default
class pp_int(int):
"""
A wrapper for integers that will return a custom string or comma-formatted
string of the integer. For example, print(pp_int(1e5)) will return
"10,000". This is useful mainly for auto-generated documentation purposes.
"""
def __new__(cls, val, custom_print_str=None):
inst = super().__new__(cls, val)
inst.custom_print_str = custom_print_str
......@@ -148,6 +145,7 @@ class ScientificNotationEncoder(json.JSONEncoder):
Just pass ``cls=ScientificNotationEncoder`` to ``json.dumps`` to activate it
"""
def iterencode(self, o, _one_shot=False, level=0):
indent = self.indent if self.indent is not None else 4
prefix_close = " " * level * indent
......@@ -161,10 +159,7 @@ class ScientificNotationEncoder(json.JSONEncoder):
else:
return f"{o}"
elif isinstance(o, collections.abc.Mapping):
x = [
f'\n{prefix}"{k}": {self.iterencode(v, level=level)}' for k,
v in o.items()
]
x = [f'\n{prefix}"{k}": {self.iterencode(v, level=level)}' for k, v in o.items()]
return "{" + ", ".join(x) + f"\n{prefix_close}" + "}"
elif isinstance(o, collections.abc.Sequence) and not isinstance(o, str):
return f"[{ f', '.join(map(self.iterencode, o)) }]"
......@@ -175,6 +170,7 @@ class DeepSpeedConfigObject(object):
"""
For json serialization
"""
def repr(self):
return self.__dict__
......
deepspeed/runtime/constants.py
View file @ c25a91b6
'''Copyright The Microsoft DeepSpeed Team'''
"""
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
#############################################
# Routes
......@@ -73,6 +72,8 @@ MAX_GRAD_NORM = 'max_grad_norm'
#############################################
ZERO_ALLOW_UNTESTED_OPTIMIZER = "zero_allow_untested_optimizer"
ZERO_ALLOW_UNTESTED_OPTIMIZER_DEFAULT = False
ZERO_FORCE_DS_CPU_OPTIMIZER = "zero_force_ds_cpu_optimizer"
ZERO_FORCE_DS_CPU_OPTIMIZER_DEFAULT = True
# Steps
STEPS_PER_PRINT = "steps_per_print"
......@@ -368,11 +369,7 @@ class ValidationMode:
CHECKPOINT = "checkpoint"
CHECKPOINT_TAG_VALIDATION = "tag_validation"
CHECKPOINT_TAG_VALIDATION_DEFAULT = ValidationMode.WARN
CHECKPOINT_TAG_VALIDATION_MODES = [
ValidationMode.WARN,
ValidationMode.IGNORE,
ValidationMode.FAIL
]
CHECKPOINT_TAG_VALIDATION_MODES = [ValidationMode.WARN, ValidationMode.IGNORE, ValidationMode.FAIL]
LOAD_UNIVERSAL_CHECKPOINT = "load_universal"
LOAD_UNIVERSAL_CHECKPOINT_DEFAULT = False
......
deepspeed/runtime/csr_tensor.py deleted 100644 → 0
View file @ d1596c94
"""
Copyright 2020 The Microsoft DeepSpeed Team
Implementation of a compressed sparse row (CSR) tensor. Similar in
functionality to TensorFlow's IndexedSlices implementation.
"""
import torch
class CSRTensor(object):
""" Compressed Sparse Row (CSR) Tensor """
def __init__(self, dense_tensor=None):
self.orig_dense_tensor = dense_tensor
if dense_tensor is not None:
result = torch.sum(dense_tensor, dim=1)
self.indices = result.nonzero().flatten()
self.values = dense_tensor[self.indices]
self.dense_size = list(dense_tensor.size())
else:
self.indices = None
self.values = None
self.dense_size = None
@staticmethod
def type():
return "deepspeed.CSRTensor"
def to_dense(self):
it = self.indices.unsqueeze(1)
full_indices = torch.cat([it for _ in range(self.dense_size[1])], dim=1)
return self.values.new_zeros(self.dense_size).scatter_add_(
0,
full_indices,
self.values)
def sparse_size(self):
index_size = list(self.indices.size())
index_size = index_size[0]
value_size = list(self.values.size())
value_size = value_size[0] * value_size[1]
dense_size = self.dense_size[0] * self.dense_size[1]
return index_size + value_size, dense_size
def add(self, b):
assert self.dense_size == b.dense_size
self.indices = torch.cat([self.indices, b.indices])
self.values = torch.cat([self.values, b.values])
def __str__(self):
sparse_size, dense_size = self.sparse_size()
return "DeepSpeed.CSRTensor(indices_size={}, values_size={}, " \
"dense_size={}, device={}, reduction_factor={})".format(
self.indices.size(), self.values.size(), self.dense_size,
self.indices.get_device(), dense_size / sparse_size
)
def __repr__(self):
return self.__str__()
deepspeed/runtime/data_pipeline/__init__.py
View file @ c25a91b6
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
'''Copyright The Microsoft DeepSpeed Team'''
deepspeed/runtime/data_pipeline/config.py
View file @ c25a91b6
'''
Copyright 2022 The Microsoft DeepSpeed Team
'''
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from .constants import *
import copy
from ..config_utils import get_scalar_param
......@@ -24,18 +26,14 @@ def get_data_efficiency_config(param_dict):
def get_data_efficiency_enabled(param_dict):
if DATA_EFFICIENCY in param_dict.keys():
return get_scalar_param(param_dict[DATA_EFFICIENCY],
DATA_EFFICIENCY_ENABLED,
DATA_EFFICIENCY_ENABLED_DEFAULT)
return get_scalar_param(param_dict[DATA_EFFICIENCY], DATA_EFFICIENCY_ENABLED, DATA_EFFICIENCY_ENABLED_DEFAULT)
else:
return False
def get_data_efficiency_seed(param_dict):
if DATA_EFFICIENCY in param_dict.keys():
return get_scalar_param(param_dict[DATA_EFFICIENCY],
DATA_EFFICIENCY_SEED,
DATA_EFFICIENCY_SEED_DEFAULT)
return get_scalar_param(param_dict[DATA_EFFICIENCY], DATA_EFFICIENCY_SEED, DATA_EFFICIENCY_SEED_DEFAULT)
else:
return DATA_EFFICIENCY_SEED_DEFAULT
......@@ -55,26 +53,21 @@ def get_data_sampling(param_dict):
def get_data_sampling_enabled(param_dict):
if DATA_SAMPLING in param_dict.keys():
return get_scalar_param(param_dict[DATA_SAMPLING],
DATA_SAMPLING_ENABLED,
DATA_SAMPLING_ENABLED_DEFAULT)
return get_scalar_param(param_dict[DATA_SAMPLING], DATA_SAMPLING_ENABLED, DATA_SAMPLING_ENABLED_DEFAULT)
else:
return False
def get_data_sampling_num_epochs(param_dict):
if DATA_SAMPLING in param_dict.keys():
return get_scalar_param(param_dict[DATA_SAMPLING],
DATA_SAMPLING_NUM_EPOCHS,
DATA_SAMPLING_NUM_EPOCHS_DEFAULT)
return get_scalar_param(param_dict[DATA_SAMPLING], DATA_SAMPLING_NUM_EPOCHS, DATA_SAMPLING_NUM_EPOCHS_DEFAULT)
else:
return DATA_SAMPLING_NUM_EPOCHS_DEFAULT
def get_data_sampling_num_workers(param_dict):
if DATA_SAMPLING in param_dict.keys():
return get_scalar_param(param_dict[DATA_SAMPLING],
DATA_SAMPLING_NUM_WORKERS,
return get_scalar_param(param_dict[DATA_SAMPLING], DATA_SAMPLING_NUM_WORKERS,
DATA_SAMPLING_NUM_WORKERS_DEFAULT)
else:
return DATA_SAMPLING_NUM_WORKERS_DEFAULT
......@@ -87,7 +80,8 @@ def get_curriculum_learning(param_dict):
param_dict[CURRICULUM_LEARNING] = {}
sub_param_dict = param_dict[CURRICULUM_LEARNING]
if output[CURRICULUM_LEARNING_ENABLED]:
assert CURRICULUM_LEARNING_METRICS in sub_param_dict.keys(), f"Curriculum learning is enabled, {CURRICULUM_LEARNING_METRICS} must be specified"
assert CURRICULUM_LEARNING_METRICS in sub_param_dict.keys(
), f"Curriculum learning is enabled, {CURRICULUM_LEARNING_METRICS} must be specified"
for key, val in get_curriculum_learning_params(param_dict).items():
output[key] = val
return output
......@@ -95,8 +89,7 @@ def get_curriculum_learning(param_dict):
def get_curriculum_learning_enabled(param_dict):
if CURRICULUM_LEARNING in param_dict.keys():
return get_scalar_param(param_dict[CURRICULUM_LEARNING],
CURRICULUM_LEARNING_ENABLED,
return get_scalar_param(param_dict[CURRICULUM_LEARNING], CURRICULUM_LEARNING_ENABLED,
CURRICULUM_LEARNING_ENABLED_DEFAULT)
else:
return False
......@@ -113,8 +106,7 @@ def get_curriculum_learning_params(param_dict):
def get_curriculum_enabled_legacy(param_dict):
if CURRICULUM_LEARNING_LEGACY in param_dict.keys():
return get_scalar_param(param_dict[CURRICULUM_LEARNING_LEGACY],
CURRICULUM_ENABLED_LEGACY,
return get_scalar_param(param_dict[CURRICULUM_LEARNING_LEGACY], CURRICULUM_ENABLED_LEGACY,
CURRICULUM_ENABLED_DEFAULT_LEGACY)
else:
return False
......@@ -142,9 +134,7 @@ def get_data_routing(param_dict):
def get_data_routing_enabled(param_dict):
if DATA_ROUTING in param_dict.keys():
return get_scalar_param(param_dict[DATA_ROUTING],
DATA_ROUTING_ENABLED,
DATA_ROUTING_ENABLED_DEFAULT)
return get_scalar_param(param_dict[DATA_ROUTING], DATA_ROUTING_ENABLED, DATA_ROUTING_ENABLED_DEFAULT)
else:
return False
......@@ -164,9 +154,7 @@ def get_random_ltd(param_dict):
def get_random_ltd_enabled(param_dict):
if RANDOM_LTD in param_dict.keys():
return get_scalar_param(param_dict[RANDOM_LTD],
RANDOM_LTD_ENABLED,
RANDOM_LTD_ENABLED_DEFAULT)
return get_scalar_param(param_dict[RANDOM_LTD], RANDOM_LTD_ENABLED, RANDOM_LTD_ENABLED_DEFAULT)
else:
return False
......
deepspeed/runtime/data_pipeline/constants.py
View file @ c25a91b6
'''
Copyright 2022 The Microsoft DeepSpeed Team
'''
#########################################
# Data efficiency library
# See sample config at https://www.deepspeed.ai/docs/config-json/#data-efficiency
#########################################
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
Data efficiency library
See sample config at https://www.deepspeed.ai/docs/config-json/data-efficiency
"""
DATA_EFFICIENCY = "data_efficiency"
DATA_EFFICIENCY_ENABLED = "enabled"
DATA_EFFICIENCY_ENABLED_DEFAULT = False
......
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