support sharding stage1 (#54069)

* support sharding stage1

* fix unittest

* format

* pass sharded sharding params_and_grads to inner_opt apply_pptimize

* change sharding gradient allreduce to reduce

* support save state_dict adptively and support sharding with mp

* fix sharding test

* test set_state_dict

* add more unit test

* fix global norm of mp case

* polish

* hack to calculate global norm in order to remove diff in calculating global norm values in HybridParallelClipGrad compared to dp

* remove print

paddle/phi/infermeta/multiary.cc

@@ -2867,7 +2867,7 @@ void StackInferMeta(const std::vector<const MetaTensor*>& x,
           rank,
           axis));
   if (axis < 0) axis += (rank + 1);
-  auto vec = phi::vectorize<int>(out_dim);
+  auto vec = phi::vectorize<int64_t>(out_dim);
   vec.insert(vec.begin() + axis, input_dims.size());
   out->set_dims(phi::make_ddim(vec));
   out->set_dtype(x.at(0)->dtype());

python/paddle/amp/auto_cast.py

@@ -553,7 +553,7 @@ def _set_multi_precision(optimizer, multi_precision):
     )
 
     optimizer = (
-        optimizer._inner_optimizer
+        optimizer._inner_opt
         if isinstance(optimizer, DygraphShardingOptimizer)
         else optimizer
     )

python/paddle/distributed/fleet/base/topology.py

@@ -183,6 +183,11 @@ class HybridCommunicateGroup:
             "data"
         )
 
+        (
+            self.sharding_check_group,
+            self.sharding_check_comm_group,
+        ) = self._set_check_group("sharding")
+
         # create p2p group
         self.is_first_stage = self.stage_id == 0
         self.is_last_stage = self.stage_id == (self._pp_degree - 1)
@@ -376,8 +381,11 @@ class HybridCommunicateGroup:
         return self._sharding_comm_group.ranks[0]
 
     # check parallel group
-    def get_check_parallel_group(self):
-        return self._check_comm_group
+    def get_check_parallel_group(self, sharding=False):
+        if sharding:
+            return self.sharding_check_comm_group
+        else:
+            return self._check_comm_group
 
     def get_rank_from_stage(self, stage_id, **kwargs):
         return self._topo.get_rank_from_stage(

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/dygraph_sharding_optimizer.py

@@ -18,6 +18,7 @@ from functools import reduce
 
 import paddle
 from paddle import framework
+from paddle.fluid.dygraph import base as imperative_base
 
 from ...utils.log_util import logger
 
@@ -43,55 +44,51 @@ class DygraphShardingOptimizer:
     # 3. dynamic trainable params, which is the case bewteen pretraining and finetuning
     # 4. option to choose fuse comm (more GPU MEM need) or un-fuse comm
 
-    def __init__(
-        self,
-        hcg,
-        user_defined_strategy,
-        params,
-        inner_optimizer_class,
-        **inner_optimizer_kargs
-    ):
-
-        if not isinstance(params, list):
+    def __init__(self, optimizer, hcg):
+        # TODO(pangengzheng): support param_groups
+        if isinstance(optimizer._parameter_list[0], dict):
             raise TypeError(
-                "`parameters` argument given to the DygraphShardingOptimizer should be "
-                "an iterable of paddle Tensors, but got argument type is `{}`.".format(
-                    type(params)
-                )
+                "Do not support param_groups now, please set optimizer._parameter_list as a list of Parameter"
+            )
+        if not hasattr(optimizer, '_apply_optimize') or not callable(
+            optimizer._apply_optimize
+        ):
+            raise ValueError(
+                "the optimzier object should have _apply_optimize function"
             )
-        self._parameter_list = params
-        self._reference_is_trainable_params = list(
-            map(_is_trainable, self._parameter_list)
-        )
-
-        self._inner_optimizer_class = inner_optimizer_class
-        self._inner_optimizer_kargs = inner_optimizer_kargs
 
-        # sharding parallel information
-        # TODO better way to get the hcg & user_defined_strategy
+        # the self._parameter_list holds the whole model paramters
+        self._parameter_list = optimizer._parameter_list
+        self._inner_opt = optimizer
         self._hcg = hcg
-        self._user_defined_strategy = user_defined_strategy
         self._sharding_world_size = self._hcg.get_sharding_parallel_world_size()
         self._sharding_rank = self._hcg.get_sharding_parallel_rank()
 
-        # logic partitioning
-        self._build_sharding_mapping()
+        self._rank2params = self._partition_parameters()
+        self._param2rank = self._map_param_to_rank()
 
-        # actually create opt ops
-        self._buid_inner_optimizer()
+        self._set_inner_opt_attr(
+            '_parameter_list', self._rank2params[self._sharding_rank]
+        )
+        self._set_inner_opt_attr(
+            '_param_groups', self._rank2params[self._sharding_rank]
+        )
 
-    def clear_grad(self):
+    def clear_grad(self, set_to_zero=True):
         """
         should clear grad for all parameters in model
         """
+        #
         for p in self._parameter_list:
-            if not p.stop_gradient:
-                p.clear_gradient()
-
-    def _build_sharding_mapping(self):
-
-        self._rank2params = self._partition_parameters()
-        self._param2rank = self._map_param_to_rank()
+            if hasattr(p, "main_grad") and p.main_grad is not None:
+                assert p._grad_ivar() is None
+                if set_to_zero:
+                    p.main_grad.zero_()
+                else:
+                    p.main_grad._clear()
+                    p.main_grad = None
+            elif not hasattr(p, "main_grad"):
+                p.clear_gradient(set_to_zero)
 
     def _partition_parameters(self):
         """
@@ -134,14 +131,35 @@ class DygraphShardingOptimizer:
                 mapping[param.name] = rank
         return mapping
 
-    def _buid_inner_optimizer(self):
-        # we rely on the inner opt to determine whether a parameter is stop_gradient or not:
-        # create moment
-        # update related ops: clip, regular, opt
-        self._inner_optimizer = self._inner_optimizer_class(
-            parameters=self._rank2params[self._sharding_rank],
-            **self._inner_optimizer_kargs
-        )
+    def reduce_gradients(self, parameter_list, hcg):
+        # TODO merge grad / nrank with dp
+        logger.debug("sharding start gradients sync")
+        with framework.no_grad():
+            sharding_nrank = hcg.get_sharding_parallel_group().nranks
+            for param in parameter_list:
+                g_var = None
+                if param.trainable and (param._grad_ivar() is not None):
+                    g_var = param._grad_ivar()
+                if param.trainable and hasattr(param, "main_grad"):
+                    assert (
+                        param._grad_ivar() is None
+                    ), "param.grad should be None when using main_grad"
+                    g_var = param.main_grad
+                if g_var is not None:
+                    g_var.scale_(1.0 / sharding_nrank)
+                    param_rank = self._param2rank[param.name]
+                    paddle.distributed.all_reduce(
+                        g_var,
+                        group=hcg.get_sharding_parallel_group(),
+                        sync_op=True,
+                    )
+                    # TODO(pangengzheng): change to reduce operation when there is no diff in calculating global norm values in HybridParallelClipGrad compared to dp.
+                    # paddle.distributed.reduce(
+                    #     g_var,
+                    #     dst=hcg.get_sharding_parallel_group().ranks[param_rank],
+                    #     group=hcg.get_sharding_parallel_group(),
+                    #     sync_op=True,
+                    # )
 
     def _sharding_sync_parameters(self):
         """
@@ -149,7 +167,6 @@ class DygraphShardingOptimizer:
         """
         # TODO speed up this functional
 
-        logger.debug("sharding start sync parameters")
         with framework.no_grad():
             # TODO detach not need (?)
             for rank, params in self._rank2params.items():
@@ -172,7 +189,6 @@ class DygraphShardingOptimizer:
     def minimize(
         self, loss, startup_program=None, parameters=None, no_grad_set=None
     ):
-
         # NOTE in dygraph mode, the only different between step and minimize is that minimize
         # allow user to customize the parameters for updating on each step
 
@@ -183,7 +199,7 @@ class DygraphShardingOptimizer:
                 self._rank2params[self._sharding_rank],
             )
         )
-        result = self._inner_optimizer.minimize(
+        result = self._inner_opt.minimize(
             loss, startup_program, parameters, no_grad_set
         )
 
@@ -192,22 +208,97 @@ class DygraphShardingOptimizer:
 
         return result
 
+    @imperative_base.no_grad
+    @framework.dygraph_only
     def step(self):
         # TODO Check whether the model trainable param changed and update state accordingly
 
-        # actually updating
-        self._inner_optimizer.step()
+        # hack to grad_clip all parameters,
+        # otherwise the self._inner_opt will only grad_clip the self._rank2params[self._sharding_rank] params
+        # TODO(pangengzheng): remove the hacked grad_clip codes here when there is no diff in calculating global norm values in HybridParallelClipGrad compared to dp.
+        origin_clip = self._inner_opt._grad_clip
+        if not isinstance(self._parameter_list[0], dict):
+            params_grads = []
+            for param in self._parameter_list:
+                if (
+                    hasattr(param, "regularizer")
+                    and param.regularizer is not None
+                ):
+                    raise ValueError(
+                        "param {} should not has the regularizer attribute".format(
+                            param.name
+                        )
+                    )
+                if param.stop_gradient:
+                    continue
+                grad_var = param._grad_ivar()
+                if hasattr(param, "main_grad") and param.main_grad is not None:
+                    grad_var = param.main_grad
+                params_grads.append((param, grad_var))
+            if hasattr(self._inner_opt._grad_clip, 'not_sharding_stage1'):
+                self._inner_opt._grad_clip.not_sharding_stage1 = False
+            params_grads = self._inner_opt._grad_clip(params_grads)
+            # set inner_opt._grad_clip None to avoid repeatedly grad_clip gradients inside inner_opt._apply_optimize
+            self._set_inner_opt_attr('_grad_clip', None)
+            update_param_names = [
+                p.name for p in self._rank2params[self._sharding_rank]
+            ]
+            update_params_grads = [
+                (p, g) for p, g in params_grads if p.name in update_param_names
+            ]
+            self._apply_optimize(
+                loss=None,
+                startup_program=None,
+                params_grads=update_params_grads,
+            )
+            # restore the grad clip
+            self._set_inner_opt_attr('_grad_clip', origin_clip)
 
         # sync parameters across sharding ranks
         self._sharding_sync_parameters()
 
-    # TODO is it a good way to make _grad_clip a property
-    @property
-    def _grad_clip(self):
-        assert (
-            self._inner_optimizer is not None
-        ), "inner opt of sharding is not initiliazed."
-        return self._inner_optimizer._grad_clip
+    @framework.dygraph_only
+    def set_state_dict(self, state_dict):
+        inner_state = {}
+        parameters = self._rank2params[self._sharding_rank]
+
+        if "LR_Scheduler" in state_dict:
+            inner_state["LR_Scheduler"] = state_dict.pop("LR_Scheduler")
+
+        if "master_weights" in state_dict:
+            master = state_dict.pop("master_weights")
+            inner_state["master_weights"] = {}
+            for p in parameters:
+                for k, v in master.items():
+                    if p.name == k:
+                        v.name = self._inner_opt._gen_master_weight_var_name(p)
+                        inner_state["master_weights"][k] = v
+
+        for p in parameters:
+            for k, v in state_dict.items():
+                if p.name in k:
+                    inner_state[k] = v
+
+        self._inner_opt.set_state_dict(inner_state)
+
+    def _set_inner_opt_attr(self, attr_name, value):
+        inner_opt = self._inner_opt
+        inner_opt_name = '_inner_opt'
+        if not isinstance(attr_name, str):
+            raise TypeError(
+                "attr_name should be str type, but is {}".format(
+                    type(attr_name)
+                )
+            )
+        while hasattr(inner_opt, attr_name):
+            setattr(inner_opt, attr_name, value)
+            if (
+                hasattr(inner_opt, inner_opt_name)
+                and getattr(inner_opt, inner_opt_name, None) is not None
+            ):
+                inner_opt = getattr(inner_opt, inner_opt_name, None)
+            else:
+                break
 
     def __getattr__(self, item):
-        return getattr(self._inner_optimizer, item)
+        return getattr(self._inner_opt, item)

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py

@@ -17,13 +17,19 @@ import paddle
 from paddle import framework
 from paddle.autograd import no_grad
 from paddle.distributed import fleet
+from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import (
+    DygraphShardingOptimizer,
+)
+from paddle.distributed.fleet.utils.hybrid_parallel_util import (
+    obtain_optimizer_parameters_list,
+)
 from paddle.framework import core
 from paddle.nn import ClipGradByGlobalNorm, clip
 
 from ...base.topology import ParallelMode
 from ...utils.hybrid_parallel_util import (
     fused_allreduce_gradients,
-    sharding_reduce_gradients,
+    unwrap_optimizer,
 )
 from ...utils.log_util import logger
 from ...utils.mix_precision_utils import MixPrecisionOptimizer
@@ -31,24 +37,11 @@ from ...utils.mix_precision_utils import MixPrecisionOptimizer
 __all__ = []
 
 
-def _obtain_optimizer_parameters_list(optimizer):
-    if getattr(optimizer, '_param_groups', None) and isinstance(
-        optimizer._param_groups[0], dict
-    ):
-        parameters_list = []
-        for group in optimizer._param_groups:
-            for param in group['params']:
-                parameters_list.append(param)
-    else:
-        parameters_list = list(optimizer._parameter_list)
-
-    return parameters_list
-
-
 class HybridParallelClipGrad:
     def __init__(self, clip, hcg):
         self._clip = clip
         self._hcg = hcg
+        self.not_sharding_stage1 = True
 
     @no_grad()
     def _dygraph_clip(self, params_grads):
@@ -169,8 +162,15 @@ class HybridParallelClipGrad:
 
         # add all reduce to get global norm of distributed params_and_grads
         if self._hcg.get_model_parallel_world_size() > 1:
+            sharding_flag = False
+            if (
+                self._hcg.get_sharding_parallel_world_size() > 1
+                and self._hcg.get_data_parallel_world_size() == 1
+            ):
+                sharding_flag = True
             paddle.distributed.all_reduce(
-                global_norm_var_dist, group=self._hcg.get_check_parallel_group()
+                global_norm_var_dist,
+                group=self._hcg.get_check_parallel_group(sharding_flag),
             )
 
         # add all reduce to get global norm of non-distributed params_and_grads in groups of pp
@@ -182,7 +182,11 @@ class HybridParallelClipGrad:
 
         # In Sharding mode, param and grad is mapping different rank in optimizer.
         # ClipGradByGlobalNorm need allreduce to get globol norm
-        if self._hcg.get_sharding_parallel_world_size() > 1:
+        # TODO(pangengzheng): remove the self.not_sharding_stage1 flag when there is no diff in calculating global norm values in HybridParallelClipGrad compared to dp.
+        if (
+            self._hcg.get_sharding_parallel_world_size() > 1
+            and self.not_sharding_stage1
+        ):
             paddle.distributed.all_reduce(
                 global_norm_var_not_dist,
                 group=self._hcg.get_sharding_parallel_group(),
@@ -236,6 +240,10 @@ class HybridParallelClipGrad:
 class HybridParallelOptimizer:
     # adapter wrapper for optimizer
     def __init__(self, optimizer, hcg, strategy):
+        # Note: Only sharding stage 1 is considered in HybridParallelOptimizer.
+        # The sharding stage2 and stage3 optimizers are invoked in other api.
+        if hcg.get_sharding_parallel_world_size() > 1:
+            optimizer = DygraphShardingOptimizer(optimizer, hcg)
         self._inner_opt = optimizer
         self._strategy = strategy
         self._hcg = hcg
@@ -260,16 +268,11 @@ class HybridParallelOptimizer:
                 "While using ClipGradByGlobalNorm in TensorParallel, PipelineParallel "
                 "or Sharding, the grad clip of original optimizer will be changed."
             )
-
-            inner_opt = (
-                self._inner_opt._inner_optimizer
-                if self._sharding_enable
-                else self._inner_opt
+            inner_opt = unwrap_optimizer(
+                self._inner_opt,
+                (MixPrecisionOptimizer, DygraphShardingOptimizer),
             )
 
-            if isinstance(inner_opt, MixPrecisionOptimizer):
-                inner_opt = inner_opt._inner_opt
-
             if (
                 inner_opt._parameter_list
                 and not isinstance(inner_opt._parameter_list[0], dict)
@@ -413,9 +416,10 @@ class HybridParallelOptimizer:
     @no_grad()
     @framework.dygraph_only
     def step(self):
-        parameters_list = _obtain_optimizer_parameters_list(self._inner_opt)
+        parameters_list = obtain_optimizer_parameters_list(self._inner_opt)
         if self._sharding_enable:
-            sharding_reduce_gradients(list(parameters_list), self._hcg)
+            assert isinstance(self._inner_opt, DygraphShardingOptimizer)
+            self._inner_opt.reduce_gradients(list(parameters_list), self._hcg)
 
         if self._dp_enable:
             fused_allreduce_gradients(list(parameters_list), self._hcg)
@@ -435,7 +439,8 @@ class HybridParallelOptimizer:
 
         # Here sharding should use global parameter list
         if self._sharding_enable:
-            sharding_reduce_gradients(list(parameter_list), self._hcg)
+            assert isinstance(self._inner_opt, DygraphShardingOptimizer)
+            self._inner_opt.reduce_gradients(list(parameter_list), self._hcg)
 
         if self._dp_enable:
             fused_allreduce_gradients(list(parameter_list), self._hcg)

python/paddle/distributed/fleet/utils/hybrid_parallel_util.py

@@ -29,6 +29,20 @@ from .log_util import logger
 __all__ = []
 
 
+def obtain_optimizer_parameters_list(optimizer):
+    if getattr(optimizer, '_param_groups', None) and isinstance(
+        optimizer._param_groups[0], dict
+    ):
+        parameters_list = []
+        for group in optimizer._param_groups:
+            for param in group['params']:
+                parameters_list.append(param)
+    else:
+        parameters_list = list(optimizer._parameter_list)
+
+    return parameters_list
+
+
 def _apply_collective_grads(parameters, comm_group, bucket_size, scale=None):
     grad_var_set = set()
     grad_vars = []
@@ -224,31 +238,6 @@ def fused_allreduce_gradients(parameter_list, hcg):
     fused_allreduce_gradients_with_group(parameter_list, data_parallel_group)
 
 
-def sharding_reduce_gradients(parameter_list, hcg):
-    # TODO allreduce --> reduce
-    # TODO merge grad / nrank with dp
-    logger.debug("sharding start gradients sync")
-    with framework.no_grad():
-
-        sharding_nrank = hcg.get_sharding_parallel_group().nranks
-        for param in parameter_list:
-            g_var = None
-            if param.trainable and (param._grad_ivar() is not None):
-                g_var = param._grad_ivar()
-            if param.trainable and hasattr(param, "main_grad"):
-                assert (
-                    param._grad_ivar() is None
-                ), "param.grad should be None when using main_grad"
-                g_var = param.main_grad
-            if g_var is not None:
-                g_var.scale_(1.0 / sharding_nrank)
-                paddle.distributed.all_reduce(
-                    g_var,
-                    group=hcg.get_sharding_parallel_group(),
-                    sync_op=True,
-                )
-
-
 def broadcast_sharding_parameters(model, hcg):
     # TODO TO save memory, use un-fused broadcast to avoid potentional OOM
     logger.debug("sharding start init parameters sync")
@@ -257,3 +246,10 @@ def broadcast_sharding_parameters(model, hcg):
     sync_params_buffers(
         model, sharding_parallel_group, src_rank, is_model_parallel=False
     )
+
+
+def unwrap_optimizer(optimizer, optimizer_instances=()):
+    _inner_opt = optimizer
+    while isinstance(_inner_opt, optimizer_instances):
+        _inner_opt = _inner_opt._inner_opt
+    return _inner_opt

python/paddle/distributed/fleet/utils/mix_precision_utils.py

@@ -21,6 +21,9 @@ import numpy as np
 import paddle
 from paddle import _legacy_C_ops, nn
 from paddle.distributed import fleet
+from paddle.distributed.fleet.utils.hybrid_parallel_util import (
+    obtain_optimizer_parameters_list,
+)
 from paddle.fluid import framework
 from paddle.fluid.dygraph import base as imperative_base
 from paddle.fluid.dygraph import to_variable
@@ -95,20 +98,7 @@ class MixPrecisionLayer(nn.Layer):
 class MixPrecisionOptimizer:
     def __init__(self, optimizer):
         self._inner_opt = optimizer
-        self._parameter_list = self._obtain_optimizer_parameters_list()
-
-    def _obtain_optimizer_parameters_list(self):
-        if getattr(self._inner_opt, '_param_groups', None) and isinstance(
-            self._inner_opt._param_groups[0], dict
-        ):
-            parameters_list = []
-            for group in self._inner_opt._param_groups:
-                for param in group['params']:
-                    parameters_list.append(param)
-        else:
-            parameters_list = list(self._inner_opt._parameter_list)
-
-        return parameters_list
+        self._parameter_list = obtain_optimizer_parameters_list(optimizer)
 
     @imperative_base.no_grad
     @framework.dygraph_only

python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_sharding_model.py

@@ -212,44 +212,21 @@ class TestDistMPTraning(unittest.TestCase):
         optimizer.clear_grad()
         return loss
 
-    def build_optimizer(
-        self, model, strategy=None, is_sharding=True, Optimizer="adam"
-    ):
+    def build_optimizer(self, model, strategy=None, Optimizer="adam"):
         clip = paddle.nn.ClipGradByGlobalNorm(0.5)
         if Optimizer == "adam":
-            if is_sharding:
-                optimizer = DygraphShardingOptimizer(
-                    hcg=fleet.get_hybrid_communicate_group(),
-                    user_defined_strategy=strategy,
-                    params=model.parameters(),
-                    inner_optimizer_class=paddle.optimizer.AdamW,
-                    learning_rate=0.001,
-                    weight_decay=0.00001,
-                    grad_clip=clip,
-                )
-            else:
-                optimizer = paddle.optimizer.AdamW(
-                    parameters=model.parameters(),
-                    learning_rate=0.001,
-                    weight_decay=0.00001,
-                    grad_clip=clip,
-                )
+            optimizer = paddle.optimizer.AdamW(
+                parameters=model.parameters(),
+                learning_rate=0.001,
+                weight_decay=0.00001,
+                grad_clip=clip,
+            )
         else:
-            if is_sharding:
-                optimizer = DygraphShardingOptimizer(
-                    hcg=fleet.get_hybrid_communicate_group(),
-                    user_defined_strategy=strategy,
-                    params=model.parameters(),
-                    inner_optimizer_class=paddle.optimizer.Momentum,
-                    learning_rate=0.001,
-                    grad_clip=clip,
-                )
-            else:
-                optimizer = paddle.optimizer.Momentum(
-                    learning_rate=0.001,
-                    parameters=model.parameters(),
-                    grad_clip=clip,
-                )
+            optimizer = paddle.optimizer.Momentum(
+                learning_rate=0.001,
+                parameters=model.parameters(),
+                grad_clip=clip,
+            )
         return optimizer
 
     def build_model_optimizer(self, Optimizer="adam"):
@@ -268,7 +245,6 @@ class TestDistMPTraning(unittest.TestCase):
         optimizer_a = self.build_optimizer(
             model_a,
             strategy=self.strategy,
-            is_sharding=True,
             Optimizer=Optimizer,
         )
         model_a = fleet.distributed_model(model_a)
@@ -280,7 +256,6 @@ class TestDistMPTraning(unittest.TestCase):
         optimizer_b = self.build_optimizer(
             model_b,
             strategy=self.strategy,
-            is_sharding=False,
             Optimizer=Optimizer,
         )
 
@@ -299,9 +274,7 @@ class TestDistMPTraning(unittest.TestCase):
 
             if idx == 2 and paddle.distributed.get_rank() == 0:
                 self.assertTrue(
-                    set(
-                        optimizer_a._inner_opt._inner_optimizer.state_dict().keys()
-                    )
+                    set(optimizer_a._inner_opt._inner_opt.state_dict().keys())
                     == sharded_accumulators
                 )
 

python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_sharding_state_dict.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.
+
+import copy
+import random
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.distributed import fleet
+from paddle.distributed.fleet.utils.mix_precision_utils import (
+    MixPrecisionOptimizer,
+)
+
+vocab_size = 20
+hidden_size = 10
+inner_size = 8
+output_size = 10
+seq_length = 2
+batch_size = 4
+STEPS = 10
+
+
+class SimpleDPNet(paddle.nn.Layer):
+    def __init__(
+        self, vocab_size, hidden_size, inner_size, output_size, np_fc1, np_fc2
+    ):
+
+        super().__init__()
+        self.linear1 = paddle.nn.Linear(
+            hidden_size,
+            inner_size,
+            weight_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Assign(np_fc1)
+            ),
+            bias_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(0.0)
+            ),
+        )
+
+        self.linear2 = paddle.nn.Linear(
+            inner_size,
+            hidden_size,
+            weight_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Assign(np_fc2)
+            ),
+            bias_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(0.0)
+            ),
+        )
+
+        self.linear3 = paddle.nn.Linear(
+            hidden_size,
+            output_size,
+            weight_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(0.0)
+            ),
+            bias_attr=paddle.framework.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(0.0)
+            ),
+        )
+
+        self.embedding = paddle.nn.Embedding(
+            vocab_size,
+            hidden_size,
+            weight_attr=paddle.nn.initializer.Constant(value=0.5),
+        )
+
+    def forward(self, x):
+        x = self.embedding(x)
+        x = self.linear1(x)
+        x = self.linear2(x)
+        x = self.linear3(x)
+        x = paddle.matmul(x, self.embedding.weight, transpose_y=True)
+        return x
+
+
+class TestDistShardingTraining(unittest.TestCase):
+    def setUp(self):
+        random.seed(2021)
+        np.random.seed(2021)
+        paddle.seed(2021)
+
+        self.strategy = fleet.DistributedStrategy()
+        self.strategy.hybrid_configs = {
+            "sharding_degree": 2,
+            "dp_degree": 1,
+            "mp_degree": 1,
+            "pp_degree": 1,
+        }
+        fleet.init(is_collective=True, strategy=self.strategy)
+        self.data = [
+            np.random.randint(
+                0,
+                vocab_size,
+                (
+                    batch_size,
+                    seq_length,
+                ),
+            )
+            for _ in range(STEPS)
+        ]
+
+    def build_adam_optimizer(self, model, lr=0.001):
+        clip = paddle.nn.ClipGradByGlobalNorm(0.5)
+        optimizer = paddle.optimizer.AdamW(
+            parameters=model.parameters(),
+            learning_rate=lr,
+            weight_decay=0.00001,
+            grad_clip=clip,
+        )
+        return optimizer
+
+    def test_set_state_dict(self):
+        np_fc1 = np.random.random_sample((hidden_size, inner_size))
+        np_fc2 = np.random.random_sample((inner_size, hidden_size))
+
+        model = SimpleDPNet(
+            vocab_size, hidden_size, inner_size, output_size, np_fc1, np_fc2
+        )
+        init_lr = 0.001
+        init_lr_scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
+            learning_rate=init_lr, T_max=1
+        )
+        local_optimizer = self.build_adam_optimizer(model, init_lr_scheduler)
+        dist_optimizer = fleet.distributed_optimizer(local_optimizer)
+        # prepare state_dict
+        state_dict = {}
+        # lr_scheduler
+        base_lr = 0.1
+        lr_scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
+            learning_rate=base_lr, T_max=1
+        )
+        state_dict["LR_Scheduler"] = lr_scheduler.state_dict()
+        # master_weights and accumulators
+        state_dict["master_weights"] = {}
+        all_param_names = []
+        accumulator_names = ["moment1", "moment2"]
+        #
+        local_params = dist_optimizer._rank2params[
+            dist_optimizer._sharding_rank
+        ]
+        local_param_names = [p.name for p in local_params]
+        local_acc_names = []
+        other_acc_names = []
+        for p in model.parameters():
+            var_name = dist_optimizer._gen_master_weight_var_name(p)
+            var = paddle.static.create_global_var(
+                name=var_name,
+                shape=p.shape,
+                value=0,
+                dtype='float32',
+                persistable=True,
+            )
+            var = paddle.randn(shape=var.shape, dtype=var.dtype, name=var.name)
+            state_dict["master_weights"][p.name] = var
+            # accumulator
+            for name in accumulator_names:
+                acc_name = p.name + '_' + name
+                state_dict[acc_name] = paddle.randn(
+                    shape=var.shape, dtype=var.dtype, name=acc_name
+                )
+                if p.name in local_param_names:
+                    local_acc_names.append(acc_name)
+                else:
+                    other_acc_names.append(acc_name)
+            all_param_names.append(p.name)
+        # test api
+        tmp_state_dict = copy.deepcopy(state_dict)
+        dist_optimizer.set_state_dict(state_dict)
+        # check result
+        other_param_names = [
+            p_name
+            for p_name in all_param_names
+            if p_name not in local_param_names
+        ]
+        inner_opt = dist_optimizer._inner_opt
+        self.assertEqual(inner_opt._learning_rate.last_lr, base_lr)
+        assert hasattr(inner_opt, "_master_weights")
+        for p_name, weight in inner_opt._master_weights.items():
+            assert p_name in local_param_names
+            assert p_name not in other_param_names
+            assert p_name in tmp_state_dict["master_weights"]
+            np.testing.assert_array_almost_equal(
+                weight.numpy(), tmp_state_dict["master_weights"][p_name].numpy()
+            )
+        for acc_name, val in inner_opt._accumulators_holder.items():
+            assert acc_name in local_acc_names
+            assert acc_name not in other_acc_names
+            assert acc_name in tmp_state_dict
+            np.testing.assert_array_almost_equal(
+                val.numpy(), tmp_state_dict[acc_name].numpy()
+            )
+
+    def test_clear_grad(self):
+        np_fc1 = np.random.random_sample((hidden_size, inner_size))
+        np_fc2 = np.random.random_sample((inner_size, hidden_size))
+
+        model = SimpleDPNet(
+            vocab_size, hidden_size, inner_size, output_size, np_fc1, np_fc2
+        )
+
+        local_optimizer = self.build_adam_optimizer(model)
+        dist_optimizer = fleet.distributed_optimizer(local_optimizer)
+
+        tmp_parameter_list = []
+        for p in dist_optimizer._inner_opt._parameter_list:
+            main_grad = paddle.randn(shape=p.shape, dtype=p.dtype, name=p.name)
+            p.main_grad = main_grad
+            tmp_parameter_list.append(p)
+
+        assert hasattr(
+            dist_optimizer._inner_opt._parameter_list[0], "main_grad"
+        )
+        # test set_to_zero True
+        dist_optimizer._inner_opt.clear_grad(set_to_zero=True)
+        for p in dist_optimizer._inner_opt._parameter_list:
+            np.testing.assert_array_almost_equal(
+                p.main_grad.numpy(), np.zeros(p.main_grad.numpy().shape)
+            )
+        # test set_to_zero False
+        dist_optimizer._inner_opt.clear_grad(set_to_zero=False)
+        for p in dist_optimizer._inner_opt._parameter_list:
+            self.assertTrue(p.main_grad is None)
+
+    def test_set_inner_opt_attr(self):
+        np_fc1 = np.random.random_sample((hidden_size, inner_size))
+        np_fc2 = np.random.random_sample((inner_size, hidden_size))
+
+        model = SimpleDPNet(
+            vocab_size, hidden_size, inner_size, output_size, np_fc1, np_fc2
+        )
+
+        local_optimizer = self.build_adam_optimizer(model)
+        local_optimizer = MixPrecisionOptimizer(local_optimizer)
+        dist_optimizer = fleet.distributed_optimizer(local_optimizer)
+        sharding_opt = dist_optimizer._inner_opt
+        sharding_opt._set_inner_opt_attr('_parameter_list', 123)
+        self.assertTrue(hasattr(sharding_opt._inner_opt, '_parameter_list'))
+        self.assertTrue(
+            hasattr(sharding_opt._inner_opt._inner_opt, '_parameter_list')
+        )
+        self.assertEqual(sharding_opt._inner_opt._parameter_list, 123)
+        self.assertEqual(
+            sharding_opt._inner_opt._inner_opt._parameter_list, 123
+        )
+
+        sharding_opt._set_inner_opt_attr('_param_groups', 123)
+        self.assertTrue(hasattr(sharding_opt._inner_opt, '_param_groups'))
+        self.assertTrue(
+            hasattr(sharding_opt._inner_opt._inner_opt, '_param_groups')
+        )
+        self.assertEqual(sharding_opt._inner_opt._param_groups, 123)
+        self.assertEqual(sharding_opt._inner_opt._inner_opt._param_groups, 123)
+
+        # test bad case
+        try:
+            sharding_opt._set_inner_opt_attr(123, 123)
+            self.assertTrue(False)
+        except:
+            pass
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/collective/fleet/test_parallel_dygraph_sharding_parallel.py

@@ -23,6 +23,9 @@ class TestHybridParallel(TestMultipleGpus):
     def test_hybrid_parallel_sharding_logic(self):
         self.run_mnist_2gpu('hybrid_parallel_sharding_model.py')
 
+    def test_hybrid_parallel_sharding_state_dict(self):
+        self.run_mnist_2gpu('hybrid_parallel_sharding_state_dict.py')
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/optimizer/optimizer.py

@@ -649,8 +649,7 @@ class Optimizer:
         else:
             assert isinstance(self.helper, LayerHelper)
 
-            var_name = param.name + "_fp32_master"
-            var_name = unique_name.generate(var_name)
+            var_name = self._gen_master_weight_var_name(param)
             var = paddle.static.create_global_var(
                 name=var_name,
                 shape=param.shape,
@@ -671,6 +670,10 @@ class Optimizer:
             self._master_weights[param.name] = var
         return var
 
+    def _gen_master_weight_var_name(self, param):
+        var_name = param.name + "_fp32_master"
+        return unique_name.generate(var_name)
+
     def _create_accumulators(self, block, parameters):
         """Create all accumulators needed by the parameters