[Auto parallel] Make sure the id semantics of every var and op unique (#38132)

* [Auto parallel] Make the id of var and op unique

* [Auto Parallel] Rename back dist_context to distop_context

paddle/fluid/framework/ir/node.h

@@ -122,6 +122,10 @@ class Node {
   // Please don't use this API!
   int id() const { return id_; }
 
+  // Only use this for auto parallel.
+  // A node does not have original desc if the return is zero.
+  uint64_t OriginalDescId() const { return original_desc_id_; }
+
   bool IsOp() const { return type_ == Type::kOperation; }
   bool IsVar() const { return type_ == Type::kVariable; }
   bool IsCtrlVar() const {
@@ -239,6 +243,10 @@ class Node {
   int desc_order_;
   int block_id_{-1};
 
+  // Store the original id of var desc or op desc.
+  // Only use this for auto parallel.
+  uint64_t original_desc_id_{0};
+
  private:
   // ID can only set by a Graph.
   void SetId(int id) { id_ = id; }
@@ -267,14 +275,16 @@ class Node {
         op_desc_(nullptr),
         type_(Type::kVariable),
         desc_order_(NO_DESC_ORDER),
-        block_id_(block_id) {}
+        block_id_(block_id),
+        original_desc_id_(var_desc->OriginalId()) {}
 
   explicit Node(OpDesc* op_desc)
       : name_(op_desc->Type()),
         var_desc_(nullptr),
         op_desc_(new OpDesc(*op_desc, op_desc->Block())),
         type_(Type::kOperation),
-        desc_order_(NO_DESC_ORDER) {}
+        desc_order_(NO_DESC_ORDER),
+        original_desc_id_(op_desc->OriginalId()) {}
 
   Node() = delete;
 

paddle/fluid/framework/op_desc.cc

@@ -352,15 +352,9 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
   inputs_ = op_desc.inputs_;
   outputs_ = op_desc.outputs_;
   attrs_ = op_desc.attrs_;
+  // The record of original_id_ is only for auto parallel.
+  original_id_ = op_desc.original_id_;
   need_update_ = true;
-  // When creating graph from program, the creation of op node will create a new
-  // OpDesc instead of
-  // referring to the original one. To find the original OpDesc of the op node,
-  // the id have to be
-  // copied to the new OpDesc. The var node has the same situation, but the
-  // default copy constructor
-  // can copy the id automatically.
-  id_ = op_desc.id_;
 }
 
 OpDesc::OpDesc(const proto::OpDesc &desc, BlockDesc *block)

paddle/fluid/framework/op_desc.h

@@ -154,17 +154,10 @@ class OpDesc {
 
   const BlockDesc *Block() const { return this->block_; }
 
-  // This thread-safe implementation seems to be redudent since the neural
-  // networks
-  // are usually constructed in a single thread
-  static uint64_t GenerateId() {
-    static std::atomic<std::uint64_t> id{0};
-    return ++id;
-  }
-
-  // Note: the identity only used as a key for referring to its
-  // distributed attribute now.
+  // The Id() and OrignalId() are only used for auto parallel.
   uint64_t Id() const { return id_; }
+  uint64_t OriginalId() const { return original_id_; }
+  void SetOriginalId(uint64_t original_id) { original_id_ = original_id; }
 
  private:
   template <typename MapType>
@@ -177,6 +170,14 @@ class OpDesc {
     return ret_val;
   }
 
+  // This thread-safe implementation seems to be redudent since the neural
+  // networks are usually constructed in a single thread
+  static uint64_t GenerateId() {
+    static std::atomic<std::uint64_t> uid{0};
+    // Must start from one
+    return ++uid;
+  }
+
   proto::OpDesc desc_;
   BlockDesc *block_{nullptr};  // not_own
   // input arg name => input variable names
@@ -189,7 +190,13 @@ class OpDesc {
   // local changes should be synchronized, need_update_ should be set to true.
   bool need_update_{false};
 
+  // Note: the id_ is unique (only for auto parallel).
   uint64_t id_ = GenerateId();
+  // Note: the orignal_id_ is used for referring to the original OpDesc
+  // that the current OpDesc is built from (only for auto parallel).
+  // The default original_id_ is same as the id_, which means the
+  // current OpDesc is not built from the other one.
+  uint64_t original_id_ = id_;
 };
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/var_desc.h

@@ -69,6 +69,12 @@ class VarDesc {
 
   explicit VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
 
+  // Explicitly implement the copy constructor for auto parallel
+  VarDesc(const VarDesc &other)
+      : desc_(other.desc_),
+        attrs_(other.attrs_),
+        original_id_(other.original_id_) {}
+
   proto::VarDesc *Proto() { return &desc_; }
 
   const proto::VarDesc *Proto() const { return &desc_; }
@@ -153,16 +159,10 @@ class VarDesc {
 
   Attribute GetAttr(const std::string &name) const;
 
-  // This thread-safe implementation seems to be redudent since the neural
-  // networks are usually constructed in a single thread.
-  static uint64_t GenerateId() {
-    static std::atomic<std::uint64_t> uid{0};
-    return ++uid;
-  }
-
-  // Note: the identity only used as a key for referring to its
-  // distributed attribute now.
+  // The Id() and OriginalId() are only used for auto parallel.
   uint64_t Id() const { return id_; }
+  uint64_t OriginalId() const { return original_id_; }
+  void SetOriginalId(uint64_t original_id) { original_id_ = original_id; }
 
  private:
   const proto::VarType::TensorDesc &tensor_desc() const;
@@ -170,9 +170,23 @@ class VarDesc {
   proto::VarType::TensorDesc *mutable_tensor_desc();
   std::vector<proto::VarType::TensorDesc *> mutable_tensor_descs();
 
+  // This thread-safe implementation seems to be redudent since the neural
+  // networks are usually constructed in a single thread.
+  static uint64_t GenerateId() {
+    static std::atomic<std::uint64_t> uid{0};
+    return ++uid;
+  }
+
   proto::VarDesc desc_;
   AttributeMap attrs_;
+
+  // Note: the id_ is unique for all VarDesc (only for auto parallel).
   uint64_t id_ = GenerateId();
+  // Note: the orignal_id_ is used for referring to the original VarDesc
+  // that the current VarDesc is built from (only for auto parallel).
+  // The default original_id_ is same as the id_, which means the
+  // current VarDesc is not built from the other one.
+  uint64_t original_id_ = id_;
 };
 
 bool operator==(const VarDesc &left, const VarDesc &right);

paddle/fluid/pybind/ir.cc

@@ -143,6 +143,7 @@ void BindNode(py::module *m) {
       .def("var", &Node::Var, return_value_policy::reference)
       .def("op", &Node::Op, return_value_policy::reference)
       .def("id", &Node::id)
+      .def("original_desc_id", &Node::OriginalDescId)
       .def("is_op", &Node::IsOp)
       .def("is_var", &Node::IsVar)
       .def("is_ctrl_var", &Node::IsCtrlVar)

paddle/fluid/pybind/protobuf.cc

@@ -208,6 +208,8 @@ void BindVarDsec(pybind11::module *m) {
       .def("_set_attr", &pd::VarDesc::SetAttr)
       .def("remove_attr", &pd::VarDesc::RemoveAttr)
       .def("id", &pd::VarDesc::Id)
+      .def("original_id", &pd::VarDesc::OriginalId)
+      .def("set_original_id", &pd::VarDesc::SetOriginalId)
       .def("attr", &pd::VarDesc::GetAttr);
 
   pybind11::enum_<pd::proto::VarType::Type> vartype(var_desc, "VarType", "");
@@ -305,6 +307,8 @@ void BindOpDesc(pybind11::module *m) {
       .def("block", [](pd::OpDesc &self) { return self.Block(); },
            pybind11::return_value_policy::reference)
       .def("id", &pd::OpDesc::Id)
+      .def("original_id", &pd::OpDesc::OriginalId)
+      .def("set_original_id", &pd::OpDesc::SetOriginalId)
       .def("inputs", &pd::OpDesc::Inputs)
       .def("outputs", &pd::OpDesc::Outputs);
 }

python/paddle/distributed/auto_parallel/completion.py

@@ -698,13 +698,13 @@ def complete_backward_annotation(auto_parallel_main_prog, dist_context=None):
             continue
 
         # complete the annotation of grad op (xxx_grad op or sum op)
-        # xxx_grad op will have a corresponding forward op in gradopidx2opidx
+        # xxx_grad op will have a corresponding forward op in grad_op_id_to_op_id
         grad_op = ops[idx]
-        if grad_op.desc.id() in dist_op_context.gradopidx2opidx:
+        if grad_op.desc.id() in dist_op_context.grad_op_id_to_op_id:
             # TODO support the case where one forward op corresponding to multiple xxx_grad op
             forward_op = _get_op_by_id(
                 ops[:first_backward_op_idx],
-                dist_op_context.gradopidx2opidx[grad_op.desc.id()])
+                dist_op_context.grad_op_id_to_op_id[grad_op.desc.id()])
             assert forward_op is not None
 
             # op dist attr
@@ -769,7 +769,7 @@ def complete_backward_annotation(auto_parallel_main_prog, dist_context=None):
             dist_context.set_op_dist_attr_for_program(grad_op,
                                                       grad_op_dist_attr)
 
-        # only sum op for merge mutiple version grad has no a corresponding mapping in gradopidx2opidx
+        # only sum op for merge mutiple version grad has no a corresponding mapping in grad_op_id_to_op_id
         else:
             assert grad_op.type == "sum", "got unexpect op [{}]".format(
                 str(grad_op.type))

python/paddle/distributed/auto_parallel/dist_context.py

@@ -46,14 +46,19 @@ class DistributedContext:
     """
 
     def __init__(self, program=None):
+        # Program related data members
         self._serial_program = program
-        self._serial_graph = None
         self._is_initialized_for_program = False
-        self._is_initialized_for_graph = False
         self._dist_tensors_for_program = {}
         self._dist_ops_for_program = {}
+        # Graph related data members
+        self._is_initialized_for_graph = False
+        self._serial_graph = None
         self._dist_tensors_for_graph = {}
         self._dist_ops_for_graph = {}
+        self._node_id_to_tensor_id = {}
+        self._node_id_to_op_id = {}
+        # Other data members
         self._dist_op_context = DistributedOperatorContext()
         self._process_meshes = []
 
@@ -97,23 +102,48 @@ class DistributedContext:
 
     def get_dist_tensor_for_program(self, serial_tensor):
         serial_tensor_id = serial_tensor.desc.id()
-        return self._dist_tensors_for_program.get(serial_tensor_id, None)
+        dist_tensor = self._dist_tensors_for_program.get(serial_tensor_id, None)
+        if dist_tensor:
+            return dist_tensor
+        else:
+            serial_tensor_id = serial_tensor.desc.original_id()
+            dist_tensor = self._dist_tensors_for_program.get(serial_tensor_id,
+                                                             None)
+            if dist_tensor:
+                return dist_tensor
+            else:
+                return None
 
     def get_dist_tensor_for_graph(self, serial_tensor_node):
         serial_tensor_node_id = serial_tensor_node.id()
         return self._dist_tensors_for_graph.get(serial_tensor_node_id, None)
 
-    def get_dist_op_for_program(self, serial_tensor):
-        serial_tensor_id = serial_tensor.desc.id()
-        return self._dist_ops_for_program.get(serial_tensor_id, None)
+    def get_dist_op_for_program(self, serial_op):
+        serial_op_id = serial_op.desc.id()
+        dist_op = self._dist_ops_for_program.get(serial_op_id, None)
+        if dist_op:
+            return dist_op
+        else:
+            serial_op_id = serial_op.desc.original_id()
+            dist_op = self._dist_ops_for_program.get(serial_op_id, None)
+            if dist_op:
+                return dist_op
+            else:
+                return None
 
-    def get_dist_op_for_graph(self, serial_tensor_node):
-        serial_tensor_node_id = serial_tensor_node.id()
-        return self._dist_ops_for_graph.get(serial_tensor_node_id, None)
+    def get_dist_op_for_graph(self, serial_op_node):
+        serial_op_node_id = serial_op_node.id()
+        return self._dist_ops_for_graph.get(serial_op_node_id, None)
 
     def get_tensor_dist_attr_for_program(self, serial_tensor):
         serial_tensor_id = serial_tensor.desc.id()
         dist_tensor = self._dist_tensors_for_program.get(serial_tensor_id, None)
+        if dist_tensor:
+            return dist_tensor.dist_attr
+        else:
+            serial_tensor_id = serial_tensor.desc.original_id()
+            dist_tensor = self._dist_tensors_for_program.get(serial_tensor_id,
+                                                             None)
             if dist_tensor:
                 return dist_tensor.dist_attr
             else:
@@ -132,21 +162,14 @@ class DistributedContext:
         else:
             return None
 
-    def set_tensor_dist_attr_for_graph(self, serial_tensor_node, dist_attr):
-        assert serial_tensor_node.is_var() and \
-            serial_tensor_node.var() is not None
-        serial_tensor_id = serial_tensor_node.var().id()
-        dist_tensor = self._dist_tensors_for_program.get(serial_tensor_id, None)
-        assert dist_tensor is not None, \
-            "The distributed tensor of the program has not been added to this context."
-        serial_tensor_node_id = serial_tensor_node.id()
-        new_dist_tensor = DistributedTensor(dist_tensor.serial_tensor,
-                                            dist_attr)
-        self._dist_tensors_for_graph[serial_tensor_node_id] = new_dist_tensor
-
     def get_op_dist_attr_for_program(self, serial_op):
         serial_op_id = serial_op.desc.id()
         dist_op = self._dist_ops_for_program.get(serial_op_id, None)
+        if dist_op:
+            return dist_op.dist_attr
+        else:
+            serial_op_id = serial_op.desc.original_id()
+            dist_op = self._dist_ops_for_program.get(serial_op_id, None)
             if dist_op:
                 return dist_op.dist_attr
             else:
@@ -164,17 +187,6 @@ class DistributedContext:
         else:
             return None
 
-    def set_op_dist_attr_for_graph(self, serial_op_node, dist_attr):
-        assert serial_op_node.is_op() and \
-            serial_op_node.op() is not None
-        serial_op_id = serial_op_node.op().id()
-        dist_op = self._dist_ops_for_program.get(serial_op_id, None)
-        assert dist_op is not None, \
-            "The distributed operator of the program has not been added to this context."
-        serial_op_node_id = serial_op_node.id()
-        new_dist_op = DistributedOperator(dist_op.serial_op, dist_attr)
-        self._dist_ops_for_graph[serial_op_node_id] = new_dist_op
-
     def init_dist_attr_for_program(self):
         assert self._serial_program, \
             "Please set the program of this context before initializing its distribute attributes."
@@ -216,20 +228,36 @@ class DistributedContext:
         all_nodes = self._serial_graph.all_nodes()
         for node in all_nodes:
             if node.is_var() and node.var() is not None:
-                tensor_desc = node.var()
-                tensor_id = tensor_desc.id()
-                dist_tensor = self._dist_tensors_for_program.get(tensor_id,
-                                                                 None)
+                dist_tensor = None
+                tensor_id = node.node.original_desc_id()
+                for cur_tensor_id, cur_dist_tensor in self._dist_tensors_for_program.items(
+                ):
+                    if tensor_id == cur_tensor_id \
+                        or tensor_id == cur_dist_tensor.serial_tensor.desc.original_id():
+                        dist_tensor = cur_dist_tensor
+                        self._node_id_to_tensor_id[node.id()] = cur_tensor_id
                 assert dist_tensor is not None, \
                     "Tensor must have a distributed tensor after the initialization for program."
-                self.set_tensor_dist_attr_for_graph(node, dist_tensor.dist_attr)
+                serial_tensor_node_id = node.id()
+                new_dist_tensor = DistributedTensor(dist_tensor.serial_tensor,
+                                                    dist_tensor.dist_attr)
+                self._dist_tensors_for_graph[
+                    serial_tensor_node_id] = new_dist_tensor
             if node.is_op() and node.op() is not None:
-                op_desc = node.op()
-                op_id = op_desc.id()
-                dist_op = self._dist_ops_for_program.get(op_id, None)
+                dist_op = None
+                op_id = node.node.original_desc_id()
+                for cur_op_id, cur_dist_op in self._dist_ops_for_program.items(
+                ):
+                    if op_id == cur_op_id \
+                        or op_id == cur_dist_op.serial_op.desc.original_id():
+                        dist_op = cur_dist_op
+                        self._node_id_to_op_id[node.id()] = cur_op_id
                 assert dist_op is not None, \
                     "Operator must have a distributed operator after the initialization for program."
-                self.set_op_dist_attr_for_graph(node, dist_op.dist_attr)
+                serial_op_node_id = node.id()
+                new_dist_op = DistributedOperator(dist_op.serial_op,
+                                                  dist_op.dist_attr)
+                self._dist_ops_for_graph[serial_op_node_id] = new_dist_op
         self._is_initialized_for_graph = True
 
     def clear_dist_info_for_program(self):
@@ -247,9 +275,8 @@ class DistributedContext:
         all_nodes = self._serial_graph.all_nodes()
         for node in all_nodes:
             if node.is_var() and node.var() is not None:
-                tensor_desc = node.var()
-                tensor_id = tensor_desc.id()
-                updated = updated_tensors.get(tensor_desc.name(), False)
+                tensor_id = self._node_id_to_tensor_id[node.id()]
+                updated = updated_tensors.get(tensor_id, False)
                 # If a var has multiples var nodes in graph, only use the first one for now
                 if not updated:
                     tensor_dist_attr_for_graph = self.get_tensor_dist_attr_for_graph(
@@ -257,10 +284,9 @@ class DistributedContext:
                     dist_tensor_for_program = self._dist_tensors_for_program[
                         tensor_id]
                     dist_tensor_for_program.dist_attr = tensor_dist_attr_for_graph
-                    updated_tensors[tensor_desc.name()] = True
+                    updated_tensors[tensor_id] = True
             if node.is_op() and node.op() is not None:
-                op_desc = node.op()
-                op_id = op_desc.id()
+                op_id = self._node_id_to_op_id[node.id()]
                 op_dist_attr_for_graph = self.get_op_dist_attr_for_graph(node)
                 dist_op_for_program = self._dist_ops_for_program[op_id]
                 dist_op_for_program.dist_attr = op_dist_attr_for_graph
@@ -360,7 +386,7 @@ class DistributedOperatorContext:
         self._rank_id = None
         self._cur_src_op = None
         self._cur_dist_attr = None
-        self.gradopidx2opidx = {}
+        self.grad_op_id_to_op_id = {}
         self.already_init_sync_vars = set()
 
     def __deepcopy__(self, memo):

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

@@ -22,6 +22,7 @@ from ..utils import is_valid_list_index
 from ..utils import compute_compatible_dim_mapping
 from ..utils import compute_compatible_dims_mapping
 from ..utils import compute_compatible_and_update_dim_mapping
+from ..utils import set_dist_op_desc_original_id
 from ..dist_attribute import OperatorDistributedAttribute
 from paddle.fluid import core, unique_name
 from paddle.fluid.framework import in_dygraph_mode
@@ -86,6 +87,7 @@ class DistributedDefaultImpl0(DistributedOperatorImpl):
         # replicate op in dist program
         dist_op_desc = main_block.desc.append_op()
         dist_op_desc.copy_from(src_op.desc)
+        set_dist_op_desc_original_id(dist_op_desc, src_op.desc, ctx)
         for input_name in src_op.desc.input_names():
             dist_op_desc.set_input(input_name, kwargs[input_name])
         for output_name in src_op.desc.output_names():
@@ -170,6 +172,8 @@ class DistributedDefaultImpl0(DistributedOperatorImpl):
         # replicate op in dist program
         dist_op_desc = main_block.desc.append_op()
         dist_op_desc.copy_from(backward_op.desc)
+        # Refer to the related dist op
+        set_dist_op_desc_original_id(dist_op_desc, backward_op.desc, ctx)
         for input_name in backward_op.desc.input_names():
             dist_op_desc.set_input(input_name, kwargs[input_name])
         for output_name in backward_op.desc.output_names():

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

@@ -25,6 +25,7 @@ from ..utils import is_valid_list_index
 from ..utils import compute_compatible_dim_mapping
 from ..utils import compute_compatible_dims_mapping
 from ..utils import compute_compatible_and_update_dim_mapping
+from ..utils import set_dist_op_desc_original_id
 from ..dist_attribute import OperatorDistributedAttribute
 from paddle.fluid import core, unique_name
 from paddle.fluid.framework import in_dygraph_mode
@@ -35,9 +36,10 @@ from ..process_group import new_process_group
 from ..utils import _get_comm_group, _get_corresponding_rank
 
 
-def copy_op_with_new_input_output(block, src_op, **kwargs):
+def copy_op_with_new_input_output(ctx, block, src_op, **kwargs):
     dist_op_desc = block.desc.append_op()
     dist_op_desc.copy_from(src_op.desc)
+    set_dist_op_desc_original_id(dist_op_desc, src_op.desc, ctx)
     for input_name in src_op.desc.input_names():
         assert input_name in kwargs
         dist_op_desc.set_input(input_name, kwargs[input_name])
@@ -253,7 +255,7 @@ def _right_operand_parameter_matmul_backward(ctx, *args, **kwargs):
             new_kwargs = copy.deepcopy(kwargs)
             new_kwargs['Out@GRAD'] = [intermediate_var_0.name]
             matmul_op_desc = copy_op_with_new_input_output(
-                main_block, backward_op, **new_kwargs)
+                ctx, main_block, backward_op, **new_kwargs)
         else:
             # col parallel: matmul + allreduce
             assert Y_var_dim_mapping[0] < 0
@@ -281,7 +283,7 @@ def _right_operand_parameter_matmul_backward(ctx, *args, **kwargs):
                 new_kwargs['X@GRAD'] = [intermediate_var_0.name]
 
             matmul_op_desc = copy_op_with_new_input_output(
-                main_block, backward_op, **new_kwargs)
+                ctx, main_block, backward_op, **new_kwargs)
 
             # NOTE (JZ-LIANG) trick to skip one allreduce if left operand has not grad
             if has_x_grad:
@@ -304,8 +306,8 @@ def _right_operand_parameter_matmul_backward(ctx, *args, **kwargs):
                                                   X_grad_dist_attr, ctx)
     else:
         # replicate
-        matmul_op_desc = copy_op_with_new_input_output(main_block, backward_op,
-                                                       **kwargs)
+        matmul_op_desc = copy_op_with_new_input_output(ctx, main_block,
+                                                       backward_op, **kwargs)
 
     main_block._sync_with_cpp()
 

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

@@ -22,6 +22,7 @@ from ..utils import is_valid_list_index
 from ..utils import compute_compatible_dim_mapping
 from ..utils import compute_compatible_dims_mapping
 from ..utils import compute_compatible_and_update_dim_mapping
+from ..utils import set_dist_op_desc_original_id
 from paddle.fluid import core, unique_name
 from paddle.fluid.framework import in_dygraph_mode
 from paddle.fluid.framework import Program, Parameter, Variable, program_guard
@@ -181,6 +182,7 @@ class DistributedReshapeImpl0(DistributedOperatorImpl):
         # create op
         new_op_desc = main_block.desc.append_op()
         new_op_desc.copy_from(src_op.desc)
+        set_dist_op_desc_original_id(new_op_desc, src_op.desc, ctx)
         new_op_desc.set_input('ShapeTensor', ShapeTensor_var_list)
         new_op_desc.set_input('Shape', Shape_var_list)
         new_op_desc.set_input('X', [X_var.name])
@@ -345,6 +347,7 @@ class DistributedReshapeImpl1(DistributedOperatorImpl):
         # create op
         new_op_desc = main_block.desc.append_op()
         new_op_desc.copy_from(src_op.desc)
+        set_dist_op_desc_original_id(new_op_desc, src_op.desc, ctx)
         new_op_desc.set_input('ShapeTensor', ShapeTensor_var_list)
         new_op_desc.set_input('Shape', Shape_var_list)
         new_op_desc.set_input('X', [X_var.name])

python/paddle/distributed/auto_parallel/parallelizer.py

@@ -39,6 +39,7 @@ from .process_group import get_world_process_groups
 from .process_group import _g_process_group_map, ProcessGroup
 from .utils import make_data_unshard
 from .utils import set_grad_var_shape
+from .utils import print_program_with_dist_attr
 from .utils import SerialProgramInfo
 from .reshard import reshard, HAS_SENT, HAS_RECV, HAS_ALLGATHER
 from .cluster import Cluster

python/paddle/distributed/auto_parallel/partitioner.py

@@ -24,6 +24,7 @@ from paddle.distributed.auto_parallel.operators.common import get_distributed_op
 from paddle.distributed.auto_parallel.dist_context import DistributedContext, DistributedOperatorContext
 from .dist_attribute import OperatorDistributedAttribute
 from .process_group import new_process_group
+from .utils import set_dist_op_desc_original_id
 from .utils import print_program_with_dist_attr, is_forward_op, is_backward_op, is_recompute_op
 from .operators.common import BACKWARD_ONLY_DIST_OPS
 
@@ -363,8 +364,9 @@ def _partition_var(dist_context, src_block, dst_block, src_varname,
 def _get_dist_op_backward_implement(backward_op, dist_context,
                                     forward_op_id2forward_op):
     dist_op_context = dist_context.dist_op_context
-    if backward_op.desc.id() in dist_op_context.gradopidx2opidx:
-        forward_op_id = dist_op_context.gradopidx2opidx[backward_op.desc.id()]
+    if backward_op.desc.id() in dist_op_context.grad_op_id_to_op_id:
+        forward_op_id = dist_op_context.grad_op_id_to_op_id[backward_op.desc.id(
+        )]
         forward_op = forward_op_id2forward_op[forward_op_id]
         forward_op_dist_attr = dist_context.get_op_dist_attr_for_program(
             forward_op)

python/paddle/distributed/auto_parallel/utils.py

@@ -1402,3 +1402,19 @@ def get_standalone_cost_data(distributed_programs):
         standalone_cost_data.append(cost_data)
 
     return standalone_cost_data
+
+
+def set_dist_op_desc_original_id(dist_op_desc, op_desc, dist_context):
+    op_id = op_desc.id()
+    op_original_id = op_desc.original_id()
+    # First, try to set the original id to the id of the op_desc
+    if op_id in dist_context._dist_ops_for_program:
+        dist_op_desc.set_original_id(op_id)
+        return
+    # Second, try to set the original id to the original_id of the op_desc
+    elif op_original_id in dist_context._dist_ops_for_program:
+        dist_op_desc.set_original_id(op_original_id)
+        return
+    # Third, print error infomation if we cannot find the original id
+    else:
+        assert False, "Cannot find the original id in the distributed context"

python/paddle/fluid/backward.py

@@ -1109,10 +1109,11 @@ def _append_backward_ops_(block,
         # Getting op's corresponding grad_op
         grad_op_desc, op_grad_to_var = core.get_grad_op_desc(
             op.desc, cpt.to_text(no_grad_dict[block.idx]), grad_sub_block_list)
+        # Build the mapping between the forward op and bacckward op (Only for auto parallel)
         if distop_context is not None:
             for op_desc in grad_op_desc:
-                assert op_desc.id() not in distop_context.gradopidx2opidx
-                distop_context.gradopidx2opidx[op_desc.id()] = op.desc.id()
+                assert op_desc.id() not in distop_context.grad_op_id_to_op_id
+                distop_context.grad_op_id_to_op_id[op_desc.id()] = op.desc.id()
 
         # Set device for grad_op according to forward Op
         device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
@@ -1197,6 +1198,12 @@ def _append_backward_ops_(block,
     for op_desc in grad_op_descs:
         new_op_desc = target_block.desc.append_op()
         new_op_desc.copy_from(op_desc)
+        # Rebuild the mapping because new_op_desc has a differnt id (Only for auto parallel)
+        if distop_context is not None:
+            if op_desc.id() in distop_context.grad_op_id_to_op_id:
+                distop_context.grad_op_id_to_op_id[new_op_desc.id(
+                )] = distop_context.grad_op_id_to_op_id[op_desc.id()]
+                distop_context.grad_op_id_to_op_id.pop(op_desc.id())
         new_op_desc._set_attr(op_role_attr_name, backward)
         grad_to_var["__current_op_desc__"] = new_op_desc
         if callbacks is not None: