add zero

paddle/fluid/framework/distributed_strategy.proto

@@ -24,6 +24,14 @@ enum Mode {
 
 message RecomputeConfig { repeated string checkpoints = 1; }
 
+message ZeROConfig {
+  optional bool amp = 1 [ default = true ];
+  optional int32 nrings = 2 [ default = 3 ];
+  optional float fuse_broadcast_MB_bytes = 3 [ default = 64.0 ];
+  repeated string checkpoints = 4;
+  optional bool allreduce = 5 [ default = false ];
+}
+
 message AMPConfig {
   optional float init_loss_scaling = 1 [ default = 32768.0 ];
   optional int32 incr_every_n_steps = 2 [ default = 1000 ];
@@ -127,6 +135,7 @@ message DistributedStrategy {
   optional int32 conv_workspace_size_limit = 22 [ default = 4000 ];
   optional bool cudnn_batchnorm_spatial_persistent = 23 [ default = true ];
   optional bool adaptive_localsgd = 24 [ default = false ];
+  optional bool zero = 25 [ default = false ];
 
   optional RecomputeConfig recompute_configs = 101;
   optional AMPConfig amp_configs = 102;
@@ -138,6 +147,7 @@ message DistributedStrategy {
   optional LarsConfig lars_configs = 108;
   optional LambConfig lamb_configs = 109;
   optional AdaptiveLocalSGDConfig adaptive_localsgd_configs = 110;
+  optional ZeROConfig zero_configs = 111;
   optional BuildStrategy build_strategy = 201;
   optional ExecutionStrategy execution_strategy = 202;
 }

paddle/fluid/operators/collective/c_sync_comm_stream_op.cc

@@ -55,8 +55,10 @@ class CSyncCommStreamOp : public framework::OperatorBase {
 class CSyncCommStreamOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() {
-    AddInput("X", "(Tensor) Dependency of the variable need to sync");
-    AddOutput("Out", "(Tensor) Dependency of the variable need to sync");
+    AddInput("X", "(Tensor) Dependency of the variable need to sync")
+        .AsDuplicable();
+    AddOutput("Out", "(Tensor) Dependency of the variable need to sync")
+        .AsDuplicable();
     AddAttr<int>("ring_id", "(int default 0) ring id.").SetDefault(0);
     AddComment(R"DOC(
 CSyncCommStream Operator

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

@@ -611,6 +611,39 @@ class DistributedStrategy(object):
                           "checkpoint_configs")
         assign_configs_value(self.strategy.recompute_configs, configs)
 
+    @property
+    def zero(self):
+        """
+        Indicating whether we are using Zero Redundancy Optimizer for memory
+        optimization
+        Default value: False
+        Examples:
+          .. code-block:: python
+            import paddle.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.zero = True
+        """
+        return self.strategy.zero
+
+    @zero.setter
+    def zero(self, flag):
+        if isinstance(flag, bool):
+            self.strategy.zero = flag
+        else:
+            print("WARNING: zero should have value of bool type")
+
+    @property
+    def zero_configs(self):
+        """
+        Set zero configurations.
+        """
+        return get_msg_dict(self.strategy.zero_configs)
+
+    @zero_configs.setter
+    def zero_configs(self, configs):
+        check_configs_key(self.strategy.zero_configs, configs, "zero_configs")
+        assign_configs_value(self.strategy.zero_configs, configs)
+
     @property
     def pipeline(self):
         """

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

@@ -1086,6 +1086,9 @@ class Fleet(object):
         context["program_optimize_ops"] = optimize_ops
         context["program_params_grads"] = params_grads
 
+        if self.user_defined_strategy.zero:
+            graph_optimizer = None
+
         if graph_optimizer:
             optimize_ops, params_grads = graph_optimizer.minimize(
                 loss,

python/paddle/distributed/fleet/meta_optimizers/__init__.py

@@ -23,3 +23,4 @@ from .lars_optimizer import LarsOptimizer
 from .parameter_server_graph_optimizer import ParameterServerGraphOptimizer
 from .dgc_optimizer import DGCOptimizer
 from .lamb_optimizer import LambOptimizer
+from .zero_optimizer import ZeroOptimizer

python/paddle/fluid/clip.py

@@ -847,7 +847,7 @@ def append_gradient_clip_ops(param_grads):
         if g is None:
             continue
         with p.block.program._optimized_guard(
-            [p, g]), framework.name_scope('graident_clip_@CLIP'):
+            [p, g]), framework.name_scope('gradient_clip_@CLIP'):
             param, new_grad = clip_attr._create_operators(param=p, grad=g)
             param_new_grad_name_dict[param.name] = new_grad.name
             res.append([param, new_grad])

python/paddle/fluid/contrib/mixed_precision/decorator.py

@@ -16,6 +16,7 @@ from ... import default_main_program
 from ... import default_startup_program
 from ... import layers
 from ... import unique_name
+from ... import framework
 from . import fp16_utils
 from .fp16_utils import rewrite_program
 from .fp16_utils import update_role_var_grad
@@ -132,6 +133,7 @@ class OptimizerWithMixedPrecision(object):
             gradient respectively, and the scaled loss.
         """
         rewrite_program(self._train_program, self._amp_lists)
+        with framework.name_scope('mixed_precision'):
             self._scaled_loss = loss * self._loss_scaling
         self._params_grads = self._optimizer.backward(
             self._scaled_loss, startup_program, parameter_list, no_grad_set,
@@ -156,11 +158,13 @@ class OptimizerWithMixedPrecision(object):
 
         grads = [g for _, g in params_grads]
         with self._train_program._optimized_guard(grads):
+            with framework.name_scope('mixed_precision'):
                 grads, found_inf = check_finite_and_unscale(
                     grads, self._loss_scaling, name="find_infinite_scale")
 
         if self._use_dynamic_loss_scaling:
             with self._train_program._optimized_guard(grads):
+                with framework.name_scope('mixed_precision'):
                     grads = update_loss_scaling(
                         grads,
                         found_inf,

python/paddle/fluid/framework.py

@@ -2063,9 +2063,15 @@ class Operator(object):
                             % (out_proto.name, len(out_args)))
                     out_arg_names = []
                     for arg in out_args:
+                        if isinstance(arg, six.string_types):
+                            out_arg_names.append(arg)
+                        else:
                             out_arg_names.append(cpt.to_text(arg.name))
                         # TODO(minqiyang): could we remove variable's op in static mode?
                         if not in_dygraph_mode():
+                            if isinstance(arg, six.string_types):
+                                block.var(arg).op = self
+                            else:
                                 arg.op = self
                     self.desc.set_output(out_proto.name, out_arg_names)
 
@@ -2801,7 +2807,6 @@ class Block(object):
         return var
 
     def _remove_var(self, name):
-        self._sync_with_cpp()
         self.desc._remove_var(cpt.to_bytes(name))
         del self.vars[name]
 
@@ -2893,7 +2898,6 @@ class Block(object):
         Returns:
             Operator: the insert Operator.
         """
-        self._sync_with_cpp()
         op_desc = self.desc._insert_op(index)
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
         self.ops.insert(index, op)
@@ -2909,7 +2913,6 @@ class Block(object):
         Returns:
             None
         """
-        self._sync_with_cpp()
         self.desc._remove_op(index, index + 1)
         del self.ops[index]