[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

python/paddle/distributed/auto_parallel/operators/common.py

@@ -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):
     """

python/paddle/distributed/auto_parallel/parallelizer.py

@@ -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

python/paddle/distributed/auto_parallel/utils.py

@@ -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):
@@ -338,8 +345,9 @@ 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
+    if dist_context is None:
         dist_context = get_default_distributed_context()
 
     for var in dist_main_prog.list_vars():
@@ -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"]

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -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)

python/paddle/fluid/tests/unittests/auto_parallel_save_load.py

+# 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()

python/paddle/fluid/tests/unittests/test_auto_parallel_save_load.py

+# 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()