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Unverified Commit 2caa6bd0 authored by peizhou001's avatar peizhou001 Committed by GitHub
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[Dist] Enable save and load for Distributed Optimizer (#4752) 



* add save/load for distributed  optimizer
Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-16-19.ap-northeast-1.compute.internal>
parent 5833efe0
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docs/source/api/python/dgl.distributed.rst
View file @ 2caa6bd0
......@@ -48,10 +48,10 @@ Distributed embedding optimizer
-------------------------
.. autoclass:: dgl.distributed.optim.SparseAdagrad
:members: step
:members: step, save, load
.. autoclass:: dgl.distributed.optim.SparseAdam
:members: step
:members: step, save, load
Distributed workload split
--------------------------
......
python/dgl/distributed/optim/pytorch/sparse_optim.py
View file @ 2caa6bd0
"""Node embedding optimizers for distributed training"""
import abc
import warnings
from abc import abstractmethod
from os.path import exists
import torch as th
import dgl
from .... import backend as F
from ...dist_tensor import DistTensor
from ...nn.pytorch import DistEmbedding
from .utils import alltoallv_cpu, alltoall_cpu
from .utils import alltoall_cpu, alltoallv_cpu
from ...graph_partition_book import EDGE_PART_POLICY, NODE_PART_POLICY
EMB_STATES = "emb_states"
WORLD_SIZE = "world_size"
IDS = "ids"
PARAMS = "params"
STATES = "states"
class DistSparseGradOptimizer(abc.ABC):
r''' The abstract dist sparse optimizer.
r"""The abstract dist sparse optimizer.
Note: dgl dist sparse optimizer only work with dgl.distributed.DistEmbedding
......@@ -18,7 +32,8 @@ class DistSparseGradOptimizer(abc.ABC):
The list of DistEmbedding.
lr : float
The learning rate.
'''
"""
def __init__(self, params, lr):
self._params = params
self._lr = lr
......@@ -27,6 +42,9 @@ class DistSparseGradOptimizer(abc.ABC):
self._shared_cache = {}
self._clean_grad = False
self._opt_meta = {}
self._state = {}
## collect all hyper parameters for save
self._defaults = {}
if th.distributed.is_initialized():
self._rank = th.distributed.get_rank()
......@@ -35,19 +53,215 @@ class DistSparseGradOptimizer(abc.ABC):
self._rank = 0
self._world_size = 1
def local_state_dict(self):
"""Return the state pertaining to current rank of the optimizer.
Returns
-------
dict
Local state dict
Example Dict of Adagrad Optimizer:
.. code-block:: json
{
"params": {
"_lr": 0.01,
"_eps": "1e-8",
"world_size": 2
},
"emb_states": {
"emb_name1": {
"ids": [0, 2, 4, 6 ,8 ,10], ## tensor,
"emb_name1_sum": [0.1 , 0.2, 0.5, 0.1, 0.2] ## tensor,
},
"emb_name2": {
"ids": [0, 2, 4, 6 ,8 ,10], ## tensor,
"emb_name2_sum": [0.3 , 0.2, 0.4, 0.5, 0.2] ## tensor,
}
}
}
:param json: json object
See Also
--------
load_local_state_dict
"""
local_state_dict = {}
local_state_dict[EMB_STATES] = {}
local_state_dict[PARAMS] = {WORLD_SIZE: self._world_size}
for emb in self._params:
trainers_per_machine = self._world_size // max(
1, dgl.distributed.get_num_machines()
)
emb_state_dict = {}
part_policy = (
emb.part_policy if emb.part_policy else emb.weight.part_policy
)
idx = self._get_local_ids(part_policy)
if trainers_per_machine > 1:
kv_idx_split = (idx % trainers_per_machine).long()
local_rank = self._rank % trainers_per_machine
mask = kv_idx_split == local_rank
idx = F.boolean_mask(idx, mask)
emb_state_dict.update({IDS: idx})
emb_state = {}
states = (
list(self._state[emb.name])
if isinstance(self._state[emb.name], tuple)
else [self._state[emb.name]]
)
emb_state = {state.name: state[idx] for state in states}
emb_state_dict.update({STATES: emb_state})
local_state_dict[EMB_STATES].update({emb.name: emb_state_dict})
local_state_dict[PARAMS].update(self._defaults)
return local_state_dict
def load_local_state_dict(self, local_state_dict):
"""Load the local state from the input state_dict,
updating the optimizer as needed.
Parameters
----------
local_state_dict : dict
Optimizer state; should be an object returned
from a call to local_state_dict().
See Also
--------
local_state_dict
"""
for emb_name, emb_state in local_state_dict[EMB_STATES].items():
idx = emb_state[IDS]
# As state of an embedding of different optimizers can be a single
# DistTensor(Adagrad) or a tuple(Adam) of that, converting it to list for
# consistency. The list contains reference(s) to original DistTensor(s).
states = (
list(self._state[emb_name])
if isinstance(self._state[emb_name], tuple)
else [self._state[emb_name]]
)
if len(emb_state[STATES]) != len(states):
raise ValueError(
f"loaded state dict has a different number of states"
f" of embedding {emb_name}"
)
name_to_index = {
state.name: index for index, state in enumerate(states)
}
for name, state in emb_state[STATES].items():
if name not in name_to_index:
raise ValueError(
"loaded state dict contains a state {name}"
"that can't be found in the optimizer states"
)
state_idx = name_to_index[name]
state = state.to(
th.device("cpu"), states[name_to_index[name]].dtype
)
states[state_idx][idx] = state
self._defaults.update(local_state_dict[PARAMS])
self.__dict__.update(local_state_dict[PARAMS])
def save(self, f):
"""Save the local state_dict to disk on per rank.
Saved dict contains 2 parts:
* 'params': hyper parameters of the optimizer.
* 'emb_states': partial optimizer states, each embedding contains 2 items:
1. ```ids```: global id of the nodes/edges stored in this rank.
2. ```states```: state data corrseponding to ```ids```.
NOTE: This needs to be called on all ranks.
Parameters
----------
f : Union[str, os.PathLike]
The path of the file to save to.
See Also
--------
load
"""
if self._world_size > 1:
th.distributed.barrier()
f = f if isinstance(f, str) else str(f, "UTF-8")
f = f"{f}_{self._rank}"
th.save(self.local_state_dict(), f)
if self._world_size > 1:
th.distributed.barrier()
def load(self, f):
"""Load the local state of the optimizer from the file on per rank.
NOTE: This needs to be called on all ranks.
Parameters
----------
f : Union[str, os.PathLike]
The path of the file to load from.
See Also
--------
save
"""
if self._world_size > 1:
th.distributed.barrier()
f = f if isinstance(f, str) else str(f, "UTF-8")
f_attach_rank = f"{f}_{self._rank}"
# Don't throw error here to support device number scale-out
# after reloading, but make sure your hyper parameter is same
# as before because new added local optimizers will be filled
# in nothing
if not exists(f_attach_rank):
warnings.warn(f"File {f_attach_rank} can't be found, load nothing.")
else:
old_world_size = self._load_state_from(f_attach_rank)
# Device number scale-in
if self._world_size < old_world_size:
for rank in range(
self._rank + self._world_size,
old_world_size,
self._world_size,
):
self._load_state_from(f"{f}_{rank}")
if self._world_size > 1:
th.distributed.barrier()
def _load_state_from(self, f):
local_state_dict = th.load(f)
world_size = local_state_dict[PARAMS].pop(WORLD_SIZE)
self.load_local_state_dict(local_state_dict)
return world_size
def _get_local_ids(self, part_policy):
if EDGE_PART_POLICY in part_policy.policy_str:
return part_policy.partition_book.partid2eids(
part_policy.part_id, part_policy.type_name
)
elif NODE_PART_POLICY in part_policy.policy_str:
return part_policy._partition_book.partid2nids(
part_policy.part_id, part_policy.type_name
)
else:
raise RuntimeError(
"Cannot support policy: %s " % part_policy.policy_str
)
def step(self):
''' The step function.
"""The step function.
The step function is invoked at the end of every batch to push the gradients
of the embeddings involved in a mini-batch to DGL's servers and update the embeddings.
'''
"""
with th.no_grad():
local_indics = {emb.name: [] for emb in self._params}
local_grads = {emb.name: [] for emb in self._params}
device = th.device('cpu')
device = th.device("cpu")
for emb in self._params:
name = emb._tensor.name
kvstore = emb._tensor.kvstore
name = emb.weight.name
kvstore = emb.weight.kvstore
trainers_per_server = self._world_size // kvstore.num_servers
idics = []
......@@ -65,10 +279,20 @@ class DistSparseGradOptimizer(abc.ABC):
# Note: we cannot skip the gradient exchange and update steps as other
# working processes may send gradient update requests corresponding
# to certain embedding to this process.
idics = th.cat(idics, dim=0) if len(idics) != 0 else \
th.zeros((0,), dtype=th.long, device=th.device('cpu'))
grads = th.cat(grads, dim=0) if len(grads) != 0 else \
th.zeros((0, emb.embedding_dim), dtype=th.float32, device=th.device('cpu'))
idics = (
th.cat(idics, dim=0)
if len(idics) != 0
else th.zeros((0,), dtype=th.long, device=th.device("cpu"))
)
grads = (
th.cat(grads, dim=0)
if len(grads) != 0
else th.zeros(
(0, emb.embedding_dim),
dtype=th.float32,
device=th.device("cpu"),
)
)
device = grads.device
# will send grad to each corresponding trainer
......@@ -85,36 +309,67 @@ class DistSparseGradOptimizer(abc.ABC):
grad_i = grads[mask]
if trainers_per_server <= 1:
idx_split_size.append(th.tensor([idx_i.shape[0]], dtype=th.int64))
idx_split_size.append(
th.tensor([idx_i.shape[0]], dtype=th.int64)
)
idics_list.append(idx_i)
grad_list.append(grad_i)
else:
kv_idx_split = th.remainder(idx_i, trainers_per_server).long()
kv_idx_split = th.remainder(
idx_i, trainers_per_server
).long()
for j in range(trainers_per_server):
mask = kv_idx_split == j
idx_j = idx_i[mask]
grad_j = grad_i[mask]
idx_split_size.append(th.tensor([idx_j.shape[0]], dtype=th.int64))
idx_split_size.append(
th.tensor([idx_j.shape[0]], dtype=th.int64)
)
idics_list.append(idx_j)
grad_list.append(grad_j)
# if one machine launch multiple KVServer, they share the same storage.
# For each machine, the pytorch rank is num_trainers * machine_id + i
# For each machine, the pytorch rank is num_trainers *
# machine_id + i
# use scatter to sync across trainers about the p2p tensor size
# Note: If we have GPU nccl support, we can use all_to_all to
# sync information here
gather_list = list(th.empty([self._world_size],
dtype=th.int64).chunk(self._world_size))
alltoall_cpu(self._rank, self._world_size, gather_list, idx_split_size)
gather_list = list(
th.empty([self._world_size], dtype=th.int64).chunk(
self._world_size
)
)
alltoall_cpu(
self._rank,
self._world_size,
gather_list,
idx_split_size,
)
# use cpu until we have GPU alltoallv
idx_gather_list = [th.empty((int(num_emb),),
dtype=idics.dtype) for num_emb in gather_list]
alltoallv_cpu(self._rank, self._world_size, idx_gather_list, idics_list)
idx_gather_list = [
th.empty((int(num_emb),), dtype=idics.dtype)
for num_emb in gather_list
]
alltoallv_cpu(
self._rank,
self._world_size,
idx_gather_list,
idics_list,
)
local_indics[name] = idx_gather_list
grad_gather_list = [th.empty((int(num_emb), grads.shape[1]),
dtype=grads.dtype) for num_emb in gather_list]
alltoallv_cpu(self._rank, self._world_size, grad_gather_list, grad_list)
grad_gather_list = [
th.empty(
(int(num_emb), grads.shape[1]), dtype=grads.dtype
)
for num_emb in gather_list
]
alltoallv_cpu(
self._rank,
self._world_size,
grad_gather_list,
grad_list,
)
local_grads[name] = grad_gather_list
else:
local_indics[name] = [idics]
......@@ -128,12 +383,14 @@ class DistSparseGradOptimizer(abc.ABC):
# do local update
for emb in self._params:
name = emb._tensor.name
name = emb.weight.name
idx = th.cat(local_indics[name], dim=0)
grad = th.cat(local_grads[name], dim=0)
self.update(idx.to(device, non_blocking=True),
grad.to(device, non_blocking=True), emb)
self.update(
idx.to(device, non_blocking=True),
grad.to(device, non_blocking=True),
emb,
)
# synchronized gradient update
if self._world_size > 1:
......@@ -141,7 +398,7 @@ class DistSparseGradOptimizer(abc.ABC):
@abstractmethod
def update(self, idx, grad, emb):
""" Update embeddings in a sparse manner
"""Update embeddings in a sparse manner
Sparse embeddings are updated in mini batches. We maintain gradient states for
each embedding so they can be updated separately.
......@@ -156,12 +413,12 @@ class DistSparseGradOptimizer(abc.ABC):
"""
def zero_grad(self):
"""clean grad cache
"""
"""clean grad cache"""
self._clean_grad = True
def initializer(shape, dtype):
""" Sparse optimizer state initializer
"""Sparse optimizer state initializer
Parameters
----------
......@@ -173,8 +430,9 @@ def initializer(shape, dtype):
arr = th.zeros(shape, dtype=dtype)
return arr
class SparseAdagrad(DistSparseGradOptimizer):
r''' Distributed Node embedding optimizer using the Adagrad algorithm.
r"""Distributed Node embedding optimizer using the Adagrad algorithm.
This optimizer implements a distributed sparse version of Adagrad algorithm for
optimizing :class:`dgl.distributed.DistEmbedding`. Being sparse means it only updates
......@@ -196,25 +454,34 @@ class SparseAdagrad(DistSparseGradOptimizer):
eps : float, Optional
The term added to the denominator to improve numerical stability
Default: 1e-10
'''
"""
def __init__(self, params, lr, eps=1e-10):
super(SparseAdagrad, self).__init__(params, lr)
self._eps = eps
self._defaults = {"_lr": lr, "_eps": eps}
# We need to register a state sum for each embedding in the kvstore.
self._state = {}
for emb in params:
assert isinstance(emb, DistEmbedding), \
'SparseAdagrad only supports dgl.distributed.DistEmbedding'
assert isinstance(
emb, DistEmbedding
), "SparseAdagrad only supports dgl.distributed.DistEmbedding"
name = emb.name + "_sum"
state = DistTensor((emb.num_embeddings, emb.embedding_dim), th.float32, name,
init_func=initializer, part_policy=emb.part_policy, is_gdata=False)
assert emb.name not in self._state, \
"{} already registered in the optimizer".format(emb.name)
state = DistTensor(
(emb.num_embeddings, emb.embedding_dim),
th.float32,
name,
init_func=initializer,
part_policy=emb.part_policy,
is_gdata=False,
)
assert (
emb.name not in self._state
), "{} already registered in the optimizer".format(emb.name)
self._state[emb.name] = state
def update(self, idx, grad, emb):
""" Update embeddings in a sparse manner
"""Update embeddings in a sparse manner
Sparse embeddings are updated in mini batches. We maintain gradient states for
each embedding so they can be updated separately.
......@@ -230,20 +497,24 @@ class SparseAdagrad(DistSparseGradOptimizer):
eps = self._eps
clr = self._lr
state_dev = th.device('cpu')
state_dev = th.device("cpu")
exec_dev = grad.device
# only perform async copies cpu -> gpu, or gpu-> gpu, but block
# when copying to the cpu, so as to ensure the copy is finished
# before operating on the data on the cpu
state_block = state_dev == th.device('cpu') and exec_dev != state_dev
state_block = state_dev == th.device("cpu") and exec_dev != state_dev
# the update is non-linear so indices must be unique
grad_indices, inverse, cnt = th.unique(idx, return_inverse=True, return_counts=True)
grad_values = th.zeros((grad_indices.shape[0], grad.shape[1]), device=exec_dev)
grad_indices, inverse, cnt = th.unique(
idx, return_inverse=True, return_counts=True
)
grad_values = th.zeros(
(grad_indices.shape[0], grad.shape[1]), device=exec_dev
)
grad_values.index_add_(0, inverse, grad)
grad_values = grad_values / cnt.unsqueeze(1)
grad_sum = (grad_values * grad_values)
grad_sum = grad_values * grad_values
# update grad state
grad_state = self._state[emb.name][grad_indices].to(exec_dev)
......@@ -273,8 +544,9 @@ class SparseAdagrad(DistSparseGradOptimizer):
std_event.wait()
emb._tensor[grad_indices] -= tmp_dst
class SparseAdam(DistSparseGradOptimizer):
r''' Distributed Node embedding optimizer using the Adam algorithm.
r"""Distributed Node embedding optimizer using the Adam algorithm.
This optimizer implements a distributed sparse version of Adam algorithm for
optimizing :class:`dgl.distributed.DistEmbedding`. Being sparse means it only updates
......@@ -303,40 +575,57 @@ class SparseAdam(DistSparseGradOptimizer):
eps : float, Optional
The term added to the denominator to improve numerical stability
Default: 1e-8
'''
"""
def __init__(self, params, lr, betas=(0.9, 0.999), eps=1e-08):
super(SparseAdam, self).__init__(params, lr)
self._eps = eps
# We need to register a state sum for each embedding in the kvstore.
self._beta1 = betas[0]
self._beta2 = betas[1]
self._state = {}
self._defaults = {
"_lr": lr,
"_eps": eps,
"_beta1": betas[0],
"_beta2": betas[1],
}
for emb in params:
assert isinstance(emb, DistEmbedding), \
'SparseAdam only supports dgl.distributed.DistEmbedding'
state_step = DistTensor((emb.num_embeddings,),
th.float32, emb.name + "_step",
assert isinstance(
emb, DistEmbedding
), "SparseAdam only supports dgl.distributed.DistEmbedding"
state_step = DistTensor(
(emb.num_embeddings,),
th.float32,
emb.name + "_step",
init_func=initializer,
part_policy=emb.part_policy,
is_gdata=False)
state_mem = DistTensor((emb.num_embeddings, emb.embedding_dim),
th.float32, emb.name + "_mem",
is_gdata=False,
)
state_mem = DistTensor(
(emb.num_embeddings, emb.embedding_dim),
th.float32,
emb.name + "_mem",
init_func=initializer,
part_policy=emb.part_policy,
is_gdata=False)
state_power = DistTensor((emb.num_embeddings, emb.embedding_dim),
th.float32, emb.name + "_power",
is_gdata=False,
)
state_power = DistTensor(
(emb.num_embeddings, emb.embedding_dim),
th.float32,
emb.name + "_power",
init_func=initializer,
part_policy=emb.part_policy,
is_gdata=False)
is_gdata=False,
)
state = (state_step, state_mem, state_power)
assert emb.name not in self._state, \
"{} already registered in the optimizer".format(emb.name)
assert (
emb.name not in self._state
), "{} already registered in the optimizer".format(emb.name)
self._state[emb.name] = state
def update(self, idx, grad, emb):
""" Update embeddings in a sparse manner
"""Update embeddings in a sparse manner
Sparse embeddings are updated in mini batches. We maintain gradient states for
each embedding so they can be updated separately.
......@@ -355,16 +644,18 @@ class SparseAdam(DistSparseGradOptimizer):
clr = self._lr
state_step, state_mem, state_power = self._state[emb.name]
state_dev = th.device('cpu')
state_dev = th.device("cpu")
exec_dev = grad.device
# only perform async copies cpu -> gpu, or gpu-> gpu, but block
# when copying to the cpu, so as to ensure the copy is finished
# before operating on the data on the cpu
state_block = state_dev == th.device('cpu') and exec_dev != state_dev
state_block = state_dev == th.device("cpu") and exec_dev != state_dev
# the update is non-linear so indices must be unique
grad_indices, inverse, cnt = th.unique(idx, return_inverse=True, return_counts=True)
grad_indices, inverse, cnt = th.unique(
idx, return_inverse=True, return_counts=True
)
# update grad state
state_idx = grad_indices.to(state_dev)
# The original implementation will cause read/write contension.
......@@ -375,20 +666,23 @@ class SparseAdam(DistSparseGradOptimizer):
# of code will also send read requests to kvstore servers. The write and read requests
# may be handled by different kvstore servers managing the same portion of the
# state_step dist tensor in the same node. So that, the read request may read an old
# value (i.e., 0 in the first iteration) which will cause update_power_corr to be NaN
# value (i.e., 0 in the first iteration) which will cause
# update_power_corr to be NaN
state_val = state_step[state_idx] + 1
state_step[state_idx] = state_val
state_step = state_val.to(exec_dev)
orig_mem = state_mem[state_idx].to(exec_dev)
orig_power = state_power[state_idx].to(exec_dev)
grad_values = th.zeros((grad_indices.shape[0], grad.shape[1]), device=exec_dev)
grad_values = th.zeros(
(grad_indices.shape[0], grad.shape[1]), device=exec_dev
)
grad_values.index_add_(0, inverse, grad)
grad_values = grad_values / cnt.unsqueeze(1)
grad_mem = grad_values
grad_power = grad_values * grad_values
update_mem = beta1 * orig_mem + (1.-beta1) * grad_mem
update_power = beta2 * orig_power + (1.-beta2) * grad_power
update_mem = beta1 * orig_mem + (1.0 - beta1) * grad_mem
update_power = beta2 * orig_power + (1.0 - beta2) * grad_power
update_mem_dst = update_mem.to(state_dev, non_blocking=True)
update_power_dst = update_power.to(state_dev, non_blocking=True)
if state_block:
......@@ -396,10 +690,12 @@ class SparseAdam(DistSparseGradOptimizer):
update_event = th.cuda.Event()
update_event.record()
update_mem_corr = update_mem / (1. - th.pow(th.tensor(beta1, device=exec_dev),
state_step)).unsqueeze(1)
update_power_corr = update_power / (1. - th.pow(th.tensor(beta2, device=exec_dev),
state_step)).unsqueeze(1)
update_mem_corr = update_mem / (
1.0 - th.pow(th.tensor(beta1, device=exec_dev), state_step)
).unsqueeze(1)
update_power_corr = update_power / (
1.0 - th.pow(th.tensor(beta2, device=exec_dev), state_step)
).unsqueeze(1)
std_values = clr * update_mem_corr / (th.sqrt(update_power_corr) + eps)
std_values_dst = std_values.to(state_dev, non_blocking=True)
......
tests/dist/python/run_dist_objects.py
View file @ 2caa6bd0
......@@ -62,13 +62,13 @@ def dist_tensor_test_sanity(data_shape, name=None):
stride = 3
pos = (part_id // 2) * num_client_per_machine + local_rank
if part_id % 2 == 0:
dist_ten[pos * stride : (pos + 1) * stride] = F.ones(
dist_ten[pos * stride: (pos + 1) * stride] = F.ones(
(stride, 2), dtype=F.int32, ctx=F.cpu()
) * (pos + 1)
dgl.distributed.client_barrier()
assert F.allclose(
dist_ten[pos * stride : (pos + 1) * stride],
dist_ten[pos * stride: (pos + 1) * stride],
F.ones((stride, 2), dtype=F.int32, ctx=F.cpu()) * (pos + 1),
)
......@@ -102,7 +102,7 @@ def dist_tensor_test_persistent(data_shape):
data_shape, F.float32, dist_ten_name
)
raise Exception("")
except:
except BaseException:
pass
......@@ -163,7 +163,7 @@ def dist_embedding_check_existing(num_nodes):
num_nodes, 2, name=dist_emb_name, init_func=zeros_init
)
raise Exception("")
except:
except BaseException:
pass
......@@ -180,6 +180,59 @@ def test_dist_embedding(g):
dist_embedding_check_existing(num_nodes)
##########################################
############# DistOptimizer ##############
##########################################
def dist_optimizer_check_store(g):
num_nodes = g.number_of_nodes(g.ntypes[0])
rank = g.rank()
try:
emb = dgl.distributed.DistEmbedding(
num_nodes, 1, name="optimizer_test", init_func=zeros_init
)
emb2 = dgl.distributed.DistEmbedding(
num_nodes, 5, name="optimizer_test2", init_func=zeros_init
)
emb_optimizer = dgl.distributed.optim.SparseAdam([emb, emb2], lr=0.1)
if rank == 0:
name_to_state = {}
for _, emb_states in emb_optimizer._state.items():
for state in emb_states:
name_to_state[state.name] = F.uniform(
state.shape, F.float32, F.cpu(), 0, 1
)
state[
F.arange(0, num_nodes, F.int64, F.cpu())
] = name_to_state[state.name]
emb_optimizer.save("emb.pt")
new_emb_optimizer = dgl.distributed.optim.SparseAdam(
[emb, emb2], lr=000.1, eps=2e-08, betas=(0.1, 0.222)
)
new_emb_optimizer.load("emb.pt")
if rank == 0:
for _, emb_states in new_emb_optimizer._state.items():
for new_state in emb_states:
state = name_to_state[new_state.name]
new_state = new_state[
F.arange(0, num_nodes, F.int64, F.cpu())
]
assert F.allclose (state, new_state, 0., 0.)
assert new_emb_optimizer._lr == emb_optimizer._lr
assert new_emb_optimizer._eps == emb_optimizer._eps
assert new_emb_optimizer._beta1 == emb_optimizer._beta1
assert new_emb_optimizer._beta2 == emb_optimizer._beta2
g.barrier()
finally:
file = f'emb.pt_{rank}'
if os.path.exists(file):
os.remove(file)
def test_dist_optimizer(g):
dist_optimizer_check_store(g)
if mode == "server":
shared_mem = bool(int(os.environ.get("DIST_DGL_TEST_SHARED_MEM")))
server_id = int(os.environ.get("DIST_DGL_TEST_SERVER_ID"))
......@@ -203,6 +256,7 @@ elif mode == "client":
target_func_map = {
"DistTensor": test_dist_tensor,
"DistEmbedding": test_dist_embedding,
"DistOptimizer": test_dist_optimizer,
}
target = os.environ.get("DIST_DGL_TEST_OBJECT_TYPE", "")
......@@ -213,5 +267,4 @@ elif mode == "client":
target_func_map[target](g)
else:
print("DIST_DGL_TEST_MODE has to be either server or client")
exit(1)
tests/distributed/test_dist_graph_store.py
View file @ 2caa6bd0
......@@ -13,6 +13,7 @@ from multiprocessing import Condition, Manager, Process, Value
import backend as F
import numpy as np
import pytest
import torch as th
from numpy.testing import assert_almost_equal, assert_array_equal
from scipy import sparse as spsp
from utils import create_random_graph, generate_ip_config, reset_envs
......@@ -20,6 +21,7 @@ from utils import create_random_graph, generate_ip_config, reset_envs
import dgl
from dgl.data.utils import load_graphs, save_graphs
from dgl.distributed import (
DistEmbedding,
DistGraph,
DistGraphServer,
edge_split,
......@@ -28,6 +30,7 @@ from dgl.distributed import (
node_split,
partition_graph,
)
from dgl.distributed.optim import SparseAdagrad
from dgl.heterograph_index import create_unitgraph_from_coo
if os.name != "nt":
......@@ -207,6 +210,67 @@ def run_emb_client(
check_dist_emb(g, num_clients, num_nodes, num_edges)
def run_optim_client(
graph_name,
part_id,
server_count,
rank,
world_size,
num_nodes,
optimizer_states,
save,
):
os.environ["DGL_NUM_SERVER"] = str(server_count)
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "12355"
dgl.distributed.initialize("kv_ip_config.txt")
th.distributed.init_process_group(
backend="gloo", rank=rank, world_size=world_size
)
gpb, graph_name, _, _ = load_partition_book(
"/tmp/dist_graph/{}.json".format(graph_name), part_id, None
)
g = DistGraph(graph_name, gpb=gpb)
check_dist_optim_store(rank, num_nodes, optimizer_states, save)
def check_dist_optim_store(rank, num_nodes, optimizer_states, save):
try:
total_idx = F.arange(0, num_nodes, F.int64, F.cpu())
emb = DistEmbedding(num_nodes, 1, name="optim_emb1", init_func=emb_init)
emb2 = DistEmbedding(
num_nodes, 1, name="optim_emb2", init_func=emb_init
)
if save:
optimizer = SparseAdagrad([emb, emb2], lr=0.1, eps=1e-08)
if rank == 0:
optimizer._state["optim_emb1"][total_idx] = optimizer_states[0]
optimizer._state["optim_emb2"][total_idx] = optimizer_states[1]
optimizer.save("/tmp/dist_graph/emb.pt")
else:
optimizer = SparseAdagrad([emb, emb2], lr=0.001, eps=2e-08)
optimizer.load("/tmp/dist_graph/emb.pt")
if rank == 0:
assert F.allclose(
optimizer._state["optim_emb1"][total_idx],
optimizer_states[0],
0.0,
0.0,
)
assert F.allclose(
optimizer._state["optim_emb2"][total_idx],
optimizer_states[1],
0.0,
0.0,
)
assert 0.1 == optimizer._lr
assert 1e-08 == optimizer._eps
th.distributed.barrier()
except Exception as e:
print(e)
sys.exit(-1)
def run_client_hierarchy(
graph_name, part_id, server_count, node_mask, edge_mask, return_dict
):
......@@ -233,9 +297,6 @@ def run_client_hierarchy(
def check_dist_emb(g, num_clients, num_nodes, num_edges):
from dgl.distributed import DistEmbedding
from dgl.distributed.optim import SparseAdagrad
# Test sparse emb
try:
emb = DistEmbedding(g.number_of_nodes(), 1, "emb1", emb_init)
......@@ -845,6 +906,87 @@ def test_dist_emb_server_client():
# check_dist_emb_server_client(True, 2, 2, 2)
@unittest.skipIf(
dgl.backend.backend_name == "tensorflow",
reason="TF doesn't support distributed Optimizer",
)
@unittest.skipIf(
dgl.backend.backend_name == "mxnet",
reason="Mxnet doesn't support distributed Optimizer",
)
def test_dist_optim_server_client():
reset_envs()
os.environ["DGL_DIST_MODE"] = "distributed"
optimizer_states = []
num_nodes = 10000
optimizer_states.append(F.uniform((num_nodes, 1), F.float32, F.cpu(), 0, 1))
optimizer_states.append(F.uniform((num_nodes, 1), F.float32, F.cpu(), 0, 1))
check_dist_optim_server_client(num_nodes, 1, 4, optimizer_states, True)
check_dist_optim_server_client(num_nodes, 1, 8, optimizer_states, False)
check_dist_optim_server_client(num_nodes, 1, 2, optimizer_states, False)
def check_dist_optim_server_client(
num_nodes, num_servers, num_clients, optimizer_states, save
):
graph_name = f"check_dist_optim_{num_servers}_store"
if save:
prepare_dist(num_servers)
g = create_random_graph(num_nodes)
# Partition the graph
num_parts = 1
g.ndata["features"] = F.unsqueeze(F.arange(0, g.number_of_nodes()), 1)
g.edata["features"] = F.unsqueeze(F.arange(0, g.number_of_edges()), 1)
partition_graph(g, graph_name, num_parts, "/tmp/dist_graph")
# let's just test on one partition for now.
# We cannot run multiple servers and clients on the same machine.
serv_ps = []
ctx = mp.get_context("spawn")
for serv_id in range(num_servers):
p = ctx.Process(
target=run_server,
args=(
graph_name,
serv_id,
num_servers,
num_clients,
True,
False,
),
)
serv_ps.append(p)
p.start()
cli_ps = []
for cli_id in range(num_clients):
print("start client[{}] for group[0]".format(cli_id))
p = ctx.Process(
target=run_optim_client,
args=(
graph_name,
0,
num_servers,
cli_id,
num_clients,
num_nodes,
optimizer_states,
save,
),
)
p.start()
time.sleep(1) # avoid race condition when instantiating DistGraph
cli_ps.append(p)
for p in cli_ps:
p.join()
assert p.exitcode == 0
for p in serv_ps:
p.join()
@unittest.skipIf(
dgl.backend.backend_name == "tensorflow",
reason="TF doesn't support some of operations in DistGraph",
......
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