未验证 提交 b9defb4f 编写于 作者: Z zhaoyingli 提交者: GitHub

[AutoParallel] Save&Load Module (#36558)

* AutoParallel Save&Load

* tiny modi

* update func name

* tiny fix

* add NotImplementedError

* fix doc

* update func name

* update func param

* update interface

* add unitest & modi make_data_unshard

* update unittest

* update unittest

* fix unittest

* fix cmakelist

* update unittest
上级 53b3f40f
......@@ -111,21 +111,6 @@ def find_best_compatible_distributed_operator_impl(name, dist_op, fwd=True):
return best_compatible_impl, idx
# def copy_distributed_attr_for_var(src_op_dist_attr, dst_var, src_var):
# """
# copy src var's dist_attr to dst var
# """
# import copy
# auto_paralle_context = src_op_dist_attr.get_owner_context()
# dist_attr = copy.deepcopy(
# auto_paralle_context.get_tensor_distributed_attr_for_program(src_var))
# dist_attr._owner_tensor = var
# dist_attr._owner_context = auto_paralle_context.get_tensor_distributed_attr_for_program(
# src_var)._owner_context
# auto_paralle_context.set_tensor_distributed_attr_for_program(var, dist_attr)
def copy_distributed_attr_for_var(dist_context, dst_var, src_var):
"""
copy src var's dist_attr to dst var
......@@ -134,38 +119,6 @@ def copy_distributed_attr_for_var(dist_context, dst_var, src_var):
dist_context.set_tensor_dist_attr_for_program(dst_var, dist_attr)
# def copy_distributed_attr_for_dist_op(dist_op, dst_block, src_op_dist_attr):
# """
# copy src op's dist_attr to dst dist op
# """
# from ..attribute import OperatorDistributedAttribute
# auto_paralle_context = src_op_dist_attr.get_owner_context()
# op_dist_attr = OperatorDistributedAttribute(dist_op, auto_paralle_context)
# auto_paralle_context._copy_distributed_attr_from_op_desc(dist_op.desc,
# op_dist_attr)
# auto_paralle_context.set_op_distributed_attr_for_program(dist_op,
# op_dist_attr)
# op_dist_attr.set_process_mesh(src_op_dist_attr.get_process_mesh())
# op_dist_attr.set_impl_idx(src_op_dist_attr.get_impl_idx())
# for input_varname in dist_op.desc.input_arg_names():
# input_var = dst_block.var(input_varname)
# tensor_dist_attr = auto_paralle_context.get_tensor_distributed_attr_for_program(
# input_var)
# tensor_dims_mapping = tensor_dist_attr.get_dims_mapping()
# op_dist_attr.set_input_dims_mapping(input_varname, tensor_dims_mapping)
# for output_varname in dist_op.desc.output_arg_names():
# output_var = dst_block.var(output_varname)
# tensor_dist_attr = auto_paralle_context.get_tensor_distributed_attr_for_program(
# output_var)
# tensor_dims_mapping = tensor_dist_attr.get_dims_mapping()
# op_dist_attr.set_output_dims_mapping(output_varname,
# tensor_dims_mapping)
def copy_distributed_attr_for_dist_op(dist_context, dist_op, dst_block,
src_op_dist_attr):
"""
......
......@@ -17,6 +17,7 @@ from paddle.distributed.fleet import cloud_utils
import paddle.fluid.core as core
from .dist_context import DistributedContext
from .dist_context import get_default_distributed_context
from .dist_context import set_default_distributed_context
from .completion import complete_annotation, complete_backward_annotation
from .partitioner import Partitioner
from .process_group import get_all_process_groups
......@@ -38,8 +39,7 @@ class AutoParallelizer:
self._fleet = fleet
self._optimizer = self._fleet.user_defined_optimizer
self._dist_strategy = self._fleet._user_defined_strategy
# self._dist_context = DistributedContext()
self._dist_context = get_default_distributed_context()
self._dist_context = DistributedContext()
def _remove_distributed_attrs(self, main_program):
suffix = core.kAutoParallelSuffix()
......@@ -53,23 +53,15 @@ class AutoParallelizer:
def parallelize(self,
loss,
startup_program=None,
startup_program,
parameter_list=None,
no_grad_set=None):
self._original_main_program = loss.block.program
# For now, we only allow user to use the default startup and main program
assert startup_program is not None
if startup_program == None:
self._original_startup_program = \
paddle.static.default_startup_program().clone(for_test=False)
startup_program = paddle.static.default_startup_program()
else:
self._original_startup_program = \
startup_program.clone(for_test=False)
main_program = loss.block.program
# Annotation completion
completed_main_program = complete_annotation(
self._original_main_program, self._dist_context)
completed_main_program = complete_annotation(main_program,
self._dist_context)
# Logical partition
rank = paddle.distributed.get_rank()
partitioner = Partitioner(self._dist_strategy, self._dist_context, rank)
......@@ -93,9 +85,13 @@ class AutoParallelizer:
# The last step: remove all distributed attributes to be compatiable
# with inference.
self._remove_distributed_attrs(partitioned_main_prog)
make_data_unshard(partitioned_main_prog, partitioned_startup_prog)
make_data_unshard(partitioned_main_prog, partitioned_startup_prog,
self._dist_context)
reshard(partitioned_main_prog, partitioned_startup_prog, rank,
self._dist_context)
# Copy distributed info to the default context
set_default_distributed_context(self._dist_context)
return dist_optimize_ops, dist_params_grads, partitioned_startup_prog, partitioned_main_prog
......@@ -12,9 +12,16 @@
# See the License for the specific language governing permissions and
# limitations under the License
import os
import paddle
import threading
import paddle.fluid.core as core
import numpy as np
import warnings
import logging
import paddle.fluid.core as core
from paddle.fluid.io import is_parameter, is_belong_to_optimizer
from paddle.framework.io import _to_LodTensor
def is_valid_list_index(list, index):
......@@ -338,9 +345,10 @@ def _get_unshard_dist_shape(var, dist_attr):
return new_shape
def make_data_unshard(dist_main_prog, dist_startup_prog):
def make_data_unshard(dist_main_prog, dist_startup_prog, dist_context=None):
from .dist_context import get_default_distributed_context
dist_context = get_default_distributed_context()
if dist_context is None:
dist_context = get_default_distributed_context()
for var in dist_main_prog.list_vars():
if var.is_data:
......@@ -352,3 +360,140 @@ def make_data_unshard(dist_main_prog, dist_startup_prog):
dim_mapping = [-1] * len(dim_mapping)
tensor_dist_attr.dims_mapping = dim_mapping
dist_context.set_tensor_dist_attr_for_program(var, tensor_dist_attr)
def _check_addition_info(addition_info):
"""
Validity check of additional information
"""
if not addition_info:
return addition_info
elif not isinstance(addition_info, dict):
raise TypeError(
"The type of addition_info should be 'dict', but got {}".format(
str(type(addition_info))))
else:
return addition_info
def _check_valid_path(file_path):
"""
Validity check of input file path
"""
if not file_path:
return file_path
elif isinstance(file_path, str):
if not os.path.exists(file_path):
raise ValueError("The file_path '{}' does not exist.".format(
file_path))
else:
return [file_path]
elif isinstance(file_path, list):
if not all(isinstance(file, str) for file in file_path):
raise ValueError("The type of each file_path should be str.")
if not all(os.path.exists(file) for file in file_path):
raise ValueError("The file_path's file does not exist.")
return file_path
else:
raise TypeError(
"The type of file_path should be 'str' or 'list', but got '{}'.".
format(str(type(file_path))))
def save_distributed_checkpoint(program,
checkpoint_path,
is_integrated=False,
addition_info=None,
dist_attr_path=None):
"""
Save model parameter state, optimzer state, distributed attribute and
additional information of each rank.
Args:
program(Program): The program to be saved.
checkpoint_path(str): The path of the checkpoint file to be saved.
is_integrated(bool, optional): Whether to integrate param before save. Default: False.
addition_info(dict, optional): Additional information. Default: None.
dist_attr_path(str, optional): The path of distributed attribute file to be saved. Default: None
Returns:
None
Examples:
.. code-block:: python
ckpt_path = os.path.join(args.output_dir, "step_%d" % step)
os.makedirs(ckpt_path, exist_ok=True)
save_distributed_checkpoint(program, ckpt_path)
"""
if not is_integrated:
rank = paddle.distributed.get_rank()
ckpt_file_name = os.path.join(checkpoint_path,
"model_state_rank{}.pdmodel".format(rank))
state_dict = {
"model": program.state_dict(),
"ranks": paddle.distributed.get_world_size()
}
if _check_addition_info(addition_info):
state_dict["addition_info"] = addition_info
paddle.save(state_dict, ckpt_file_name)
logging.info("Already save model to {}".format(checkpoint_path))
if dist_attr_path:
raise NotImplementedError(
"Save distributed attribute has not been implemented.")
else:
# TODO: integrate param before save
raise NotImplementedError(
"Integrating parameter has not been implemented.")
def load_distributed_checkpoint(checkpoint_path,
program=None,
dist_attr_path=None):
"""
Load parameter, optimizer, distributed attribute and addition_info of model.
Args:
checkpoint_path(str|list[str]): checkpoint_path's type can be 'str' or 'list', \
which must be in order of rank id when type is 'list'.
program(Program, optional): The program to be updated with checkpoint_path. Default: None.
dist_attr_path(str|list[str], optional): dist_attr_path's type can be 'str' or 'list', \
which must be in order of rank id when type is 'list'. Default: None.
Returns:
None or addition_info which user saved in last train.
Examples:
.. code-block:: python
exe.run(startup_program)
ckpt_path = ['./output/step_10/model_state_rank0.pdmodel',
'./output/step_10/model_state_rank1.pdmodel']
load_distributed_checkpoint(ckpt_path, main_program)
"""
checkpoint_path = _check_valid_path(checkpoint_path)
dist_attr_path = _check_valid_path(dist_attr_path)
if checkpoint_path and dist_attr_path:
raise NotImplementedError(
"Merge&Slice parameter with dist_attr has not been implemented.")
elif checkpoint_path:
assert len(checkpoint_path) == paddle.distributed.get_world_size(), \
"The number of checkpoint_path must equal to the number of ranks"
rank = paddle.distributed.get_rank()
state_dict_info = paddle.load(checkpoint_path[rank])
state_dict = state_dict_info["model"]
else:
raise ValueError("'checkpoint_path' can not be None.")
program.set_state_dict(state_dict) if program else \
warnings.warn("'Program' is None, parameters will not be loaded.")
if "addition_info" not in state_dict_info:
return
return state_dict_info["addition_info"]
......@@ -38,6 +38,7 @@ list(APPEND DIST_TEST_OPS test_hybrid_parallel_inference_helper)
list(APPEND DIST_TEST_OPS test_parallel_class_center_sample)
list(APPEND DIST_TEST_OPS test_parallel_margin_cross_entropy)
list(APPEND DIST_TEST_OPS test_auto_parallel_data_unshard)
list(APPEND DIST_TEST_OPS test_auto_parallel_save_load)
set(MIXED_DIST_TEST_OPS ${DIST_TEST_OPS})
#remove distribute unittests.
list(APPEND MIXED_DIST_TEST_OPS test_dgc_op)
......@@ -253,6 +254,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM))
LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_dpmppp)
LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_cost_model)
LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_data_unshard)
LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_save_load)
elseif(WITH_GPU)
if (${CUDNN_VERSION} VERSION_LESS 7100)
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
......@@ -1032,6 +1034,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL)
set_tests_properties(test_parallel_class_center_sample PROPERTIES TIMEOUT 120)
set_tests_properties(test_parallel_margin_cross_entropy PROPERTIES TIMEOUT 120)
set_tests_properties(test_auto_parallel_data_unshard PROPERTIES TIMEOUT 120)
set_tests_properties(test_auto_parallel_save_load PROPERTIES TIMEOUT 120)
if(${NCCL_VERSION} VERSION_GREATER_EQUAL 2212)
set_tests_properties(test_parallel_dygraph_sparse_embedding PROPERTIES TIMEOUT 120)
set_tests_properties(test_parallel_dygraph_transformer PROPERTIES TIMEOUT 120)
......
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import random
import numpy as np
import os
import shutil
import paddle
import paddle.nn as nn
import paddle.utils as utils
import paddle.static as static
import paddle.nn.functional as F
import paddle.distributed.auto_parallel as auto
from paddle.distributed import fleet
from paddle.fluid.initializer import NumpyArrayInitializer
from paddle.distributed.auto_parallel.utils import make_data_unshard
from paddle.distributed.auto_parallel.utils import save_distributed_checkpoint, load_distributed_checkpoint
from paddle.distributed.auto_parallel.reshard import reshard
from paddle.distributed.auto_parallel.partitioner import Partitioner
from paddle.distributed.auto_parallel.dist_context import DistributedContext
from paddle.distributed.auto_parallel.process_group import get_all_process_groups
paddle.enable_static()
_global_parallel_strategy = None
_global_process_mesh = None
PP_MESH_0 = None
PP_MESH_1 = None
class MLPLayer(nn.Layer):
def __init__(self,
hidden_size=64,
intermediate_size=4 * 64,
initializer_range=0.02):
super(MLPLayer, self).__init__()
d_model = hidden_size
dim_feedforward = intermediate_size
np.random.seed(2021)
arr = np.random.normal(0, 0.02, size=(d_model, dim_feedforward))
weight_attr = paddle.ParamAttr(initializer=NumpyArrayInitializer(arr))
bias_attr = None
self.linear0 = nn.Linear(
d_model, dim_feedforward, weight_attr, bias_attr=bias_attr)
self.linear1 = nn.Linear(
dim_feedforward, d_model, weight_attr, bias_attr=bias_attr)
self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
def forward(self, input):
if _global_parallel_strategy == "pp":
auto.shard_tensor(
self.linear0.weight,
dist_attr={
"process_mesh": PP_MESH_0,
"dims_mapping": [-1, -1]
})
auto.shard_tensor(
self.linear1.weight,
dist_attr={
"process_mesh": PP_MESH_1,
"dims_mapping": [-1, -1]
})
elif _global_parallel_strategy == "mp":
auto.shard_tensor(
self.linear0.weight,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [-1, 0]
})
auto.shard_tensor(
self.linear1.weight,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [0, -1]
})
elif _global_parallel_strategy == "dp":
auto.shard_tensor(
self.linear0.weight,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [-1, -1]
})
auto.shard_tensor(
self.linear1.weight,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [-1, -1]
})
out = self.norm(input)
out = self.linear0(out)
out = F.gelu(out, approximate=True)
out = self.linear1(out)
return out
def mlp_forward(train_program, start_program):
with static.program_guard(train_program,start_program), \
utils.unique_name.guard():
batch_size = 4
hidden_size = 64
input = static.data(
name="input", shape=[batch_size, hidden_size], dtype='float32')
label = static.data(
name="label", shape=[batch_size, 1], dtype='float32')
if _global_parallel_strategy == "pp":
auto.shard_tensor(
input,
dist_attr={
"process_mesh": PP_MESH_0,
"dims_mapping": [-1, -1]
})
auto.shard_tensor(
label,
dist_attr={
"process_mesh": PP_MESH_1,
"dims_mapping": [-1, -1]
})
elif _global_parallel_strategy == "dp":
auto.shard_tensor(
input,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [0, -1]
})
elif _global_parallel_strategy == "mp":
auto.shard_tensor(
input,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mapping": [-1, -1]
})
mlp = MLPLayer(
hidden_size=hidden_size,
intermediate_size=4 * hidden_size,
initializer_range=0.02)
predict = mlp(input)
error_cost = paddle.nn.functional.square_error_cost(predict, label)
loss = paddle.mean(error_cost)
return loss, train_program, start_program
def get_distributed_program():
train_program = static.Program()
startup_program = static.Program()
dist_strategy = fleet.DistributedStrategy()
dist_strategy.semi_auto = True
fleet.init(is_collective=True, strategy=dist_strategy)
loss, train_program, startup_program = mlp_forward(train_program,
startup_program)
optimizer = paddle.fluid.optimizer.SGDOptimizer(learning_rate=0.01)
optimizer = fleet.distributed_optimizer(optimizer)
_, _, dist_startup_prog, dist_main_prog = optimizer.minimize(
loss, startup_program)
return dist_main_prog, dist_startup_prog, loss
class TestMLPSaveLoad(unittest.TestCase):
def setUp(self):
paddle.seed(2021)
random.seed(2021)
np.random.seed(2021)
def test_mlp_dp(self):
global _global_parallel_strategy
_global_parallel_strategy = "dp"
global _global_process_mesh
_global_process_mesh = auto.ProcessMesh([0, 1])
dist_main_prog, dist_start_prog, loss = get_distributed_program()
place = paddle.set_device("gpu")
exe = paddle.static.Executor(place)
exe.run(dist_start_prog)
input = np.random.random(size=(80, 64)).astype('float32')
label = np.random.random(size=(80, 1)).astype('float32')
for step in range(20):
if step == 10:
path = "./output_dp{}".format(paddle.distributed.get_rank())
os.makedirs(path, exist_ok=True)
save_distributed_checkpoint(dist_main_prog, path)
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
last_res = res[0]
ckpt_path = [
"./output_dp0/model_state_rank0.pdmodel",
"./output_dp1/model_state_rank1.pdmodel"
]
load_distributed_checkpoint(ckpt_path, dist_main_prog)
for step in range(10, 20):
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
self.assertEqual(last_res, res[0])
shutil.rmtree("./output_dp{}".format(paddle.distributed.get_rank()))
def test_mlp_mp(self):
global _global_parallel_strategy
_global_parallel_strategy = "mp"
global _global_process_mesh
_global_process_mesh = auto.ProcessMesh([0, 1])
dist_main_prog, dist_start_prog, loss = get_distributed_program()
place = paddle.set_device("gpu")
exe = paddle.static.Executor(place)
exe.run(dist_start_prog)
input = np.random.random(size=(80, 64)).astype('float32')
label = np.random.random(size=(80, 1)).astype('float32')
for step in range(20):
if step == 10:
path = "./output_mp{}".format(paddle.distributed.get_rank())
os.makedirs(path, exist_ok=True)
save_distributed_checkpoint(dist_main_prog, path)
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
last_res = res[0]
ckpt_path = [
"./output_mp0/model_state_rank0.pdmodel",
"./output_mp1/model_state_rank1.pdmodel"
]
load_distributed_checkpoint(ckpt_path, dist_main_prog)
for step in range(10, 20):
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
self.assertEqual(last_res, res[0])
shutil.rmtree("./output_mp{}".format(paddle.distributed.get_rank()))
def test_mlp_pp(self):
global _global_parallel_strategy
_global_parallel_strategy = "pp"
global _global_process_mesh
_global_process_mesh = auto.ProcessMesh([0, 1])
global PP_MESH_0
PP_MESH_0 = auto.ProcessMesh(mesh=[0])
global PP_MESH_1
PP_MESH_1 = auto.ProcessMesh(mesh=[1])
dist_main_prog, dist_start_prog, loss = get_distributed_program()
place = paddle.set_device("gpu")
exe = paddle.static.Executor(place)
exe.run(dist_start_prog)
input = np.random.random(size=(80, 64)).astype('float32')
label = np.random.random(size=(80, 1)).astype('float32')
for step in range(20):
if step == 10:
path = "./output_pp{}".format(paddle.distributed.get_rank())
os.makedirs(path, exist_ok=True)
save_distributed_checkpoint(dist_main_prog, path)
if paddle.distributed.get_rank() in [0]:
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
})
else:
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
if paddle.distributed.get_rank() in [1]:
last_res = res[0]
ckpt_path = [
"./output_pp0/model_state_rank0.pdmodel",
"./output_pp1/model_state_rank1.pdmodel"
]
load_distributed_checkpoint(ckpt_path, dist_main_prog)
for step in range(10, 20):
if paddle.distributed.get_rank() in [0]:
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
})
else:
res = exe.run(dist_main_prog,
feed={
"input": input[step * 4:(step + 1) * 4, :],
"label": label[step * 4:(step + 1) * 4, :]
},
fetch_list=[loss])
if paddle.distributed.get_rank() in [1]:
self.assertEqual(last_res, res[0])
shutil.rmtree("./output_pp{}".format(paddle.distributed.get_rank()))
if __name__ == "__main__":
unittest.main()
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
from test_parallel_dygraph_dataparallel import TestMultipleGpus
class TestAutoParallelSaveLoad(TestMultipleGpus):
def test_auto_parallel_save_load(self):
self.run_mnist_2gpu('auto_parallel_save_load.py')
if __name__ == "__main__":
unittest.main()
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