Revert "Revert "[Dy2Stat] Refactor ExecutorCache logic and pre-support...

Revert "Revert "[Dy2Stat] Refactor ExecutorCache logic and pre-support BuildStrategy for pass (#34181)" (#34348)" (#34384)

This reverts commit 577fdde5.

paddle/fluid/framework/executor_cache.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/executor_cache.h"
+#include "paddle/fluid/framework/op_info.h"
 
 namespace paddle {
 namespace framework {
@@ -25,11 +26,11 @@ namespace framework {
 
 namespace details {
 
-static ExecutionStrategy GetExecutionStrategy(
-    const ExecutorInfoCache::CacheKey &cache_key) {
+static ExecutionStrategy GetExecutionStrategy(const platform::Place &place) {
   framework::ExecutionStrategy execution_strategy;
 
-  switch (cache_key.device_type_) {
+  auto device_type = platform::Place2DeviceType(place);
+  switch (device_type) {
     case platform::DeviceType::CPU: {
       execution_strategy.num_threads_ = 2;
       break;
@@ -46,9 +47,9 @@ static ExecutionStrategy GetExecutionStrategy(
     }
     default:
       PADDLE_THROW(platform::errors::Unavailable("Unsupported Device type %d.",
-                                                 cache_key.device_type_));
+                                                 device_type));
   }
-  execution_strategy.use_device_ = cache_key.device_type_;
+  execution_strategy.use_device_ = device_type;
 
   return execution_strategy;
 }
@@ -136,20 +137,14 @@ ExecutorInfoCache &ExecutorInfoCache::Instance() {
   return g_exe_cache_info_map;
 }
 
-void ExecutorInfoCache::Finalize() {
-  // NOTE(Aurelius84): DO NOT perform finalize in destructor
-  // to avoid problems caused by destructor order of static
-  // object.
-  info_map_.clear();
-}
-
-CacheInfo GetExecutorInfoFromCache(const ExecutorInfoCache::CacheKey &cache_key,
+CacheInfo GetExecutorInfoFromCache(const ProgramDesc &program_desc,
+                                   const platform::Place &place,
+                                   int64_t start_op_index, int64_t end_op_index,
+                                   bool is_grad, int64_t program_id,
                                    framework::Scope *scope) {
   auto &cached_exe_info = framework::ExecutorInfoCache::Instance();
 
-  if (!cached_exe_info.Has(cache_key)) {
-    VLOG(1) << "create exe_info for " << cache_key.DebugString();
-
+  if (!cached_exe_info.Has(program_id, is_grad)) {
     // TODO(Aurelius84): Consider to use LRU algorithm to replace this.
     if (cached_exe_info.Size() > 4u /* max_cached_size*/) {
       VLOG(2) << "The cached info size has exceeded max_cached_size: 4, clear "
@@ -157,29 +152,28 @@ CacheInfo GetExecutorInfoFromCache(const ExecutorInfoCache::CacheKey &cache_key,
       cached_exe_info.Finalize();
     }
 
-    framework::BuildStrategy build_strategy;
-    auto execution_strategy = details::GetExecutionStrategy(cache_key);
+    VLOG(1) << "create exe_info for " << program_id << " is_grad: " << is_grad;
+    auto execution_strategy = details::GetExecutionStrategy(place);
+    auto &build_strategy = cached_exe_info.GetBuildStrategy(program_id);
 
+    // 2. Construct Graph and ParallelExecutor.
     auto graph = std::make_shared<framework::ir::Graph>(
-        *cache_key.program_desc_, cache_key.start_op_index_,
-        cache_key.end_op_index_);
+        program_desc, start_op_index, end_op_index);
     auto parallel_executor = std::make_shared<framework::ParallelExecutor>(
-        cache_key.place_, scope, execution_strategy, build_strategy,
-        graph.get());
+        place, scope, execution_strategy, build_strategy, graph.get());
     parallel_executor->PrepareVariables(scope);
 
-    framework::ExecutorInfoCache::ValueType cache_val = {parallel_executor,
-                                                         graph};
-    cached_exe_info.Insert(cache_key, cache_val);
-
-    bool is_new_created = true;
-    return std::make_pair(parallel_executor, is_new_created);
+    // 3. Insert value into cached map.
+    auto &cached_value = cached_exe_info.GetMutable(program_id, is_grad);
+    cached_value.executor_ = parallel_executor;
+    cached_value.graph_ = std::move(graph);
+    return std::make_pair(parallel_executor, /*is_new_created=*/true);
   } else {
-    VLOG(1) << "get exe_info from cache by: " << cache_key.DebugString();
-    bool is_new_created = false;
-    auto cache_val = cached_exe_info.GetMutable(cache_key);
-    auto parallel_executor = cache_val.first;
+    VLOG(1) << "get exe_info from cache by: " << program_id
+            << " is_grad: " << is_grad;
+    auto &cached_value = cached_exe_info.GetMutable(program_id, is_grad);
 
+    auto &parallel_executor = cached_value.executor_;
     // update op_handle scope_map in pe->executor_->Graph
     std::unordered_map<Scope *, Scope *> scope_map = {
         {parallel_executor->GetLocalScopes().front(), scope}};
@@ -187,7 +181,7 @@ CacheInfo GetExecutorInfoFromCache(const ExecutorInfoCache::CacheKey &cache_key,
     // need to recreate tmp variables in new scope
     parallel_executor->PrepareVariables(scope);
 
-    return std::make_pair(parallel_executor, is_new_created);
+    return std::make_pair(parallel_executor, /*is_new_created=*/false);
   }
 }
 

paddle/fluid/framework/executor_cache.h

@@ -45,121 +45,92 @@ void ParseSafeEagerDeletionSkipVars(
     std::vector<std::string>* skip_eager_delete_vars);
 
 }  // namespace details
-class ExecutorInfoCache {
+
+class ExecutorInfo {
  public:
-  struct CacheKey {
-    CacheKey(const ProgramDesc* program_desc, const platform::Place& place,
-             int64_t start_op_index, int64_t end_op_index, bool is_grad)
-        : program_desc_(program_desc),
-          place_(place),
-          start_op_index_(start_op_index),
-          end_op_index_(end_op_index),
-          is_grad_(is_grad) {
-      device_type_ = platform::Place2DeviceType(place);
-      PADDLE_ENFORCE_NOT_NULL(program_desc_,
-                              "program_desc should not be null.");
-    }
-
-    std::string DebugString() const {
-      std::stringstream ss;
-
-      ss << "\n CacheKey(program_desc: " << program_desc_;
-      ss << ", start_op_index: " << start_op_index_;
-      ss << ", end_op_index: " << end_op_index_;
-      ss << ", is_grad: " << is_grad_;
-      ss << ", device_type: " << device_type_ << ")";
-
-      return ss.str();
-    }
-
-    const ProgramDesc* program_desc_;
-    platform::Place place_;
-    int64_t start_op_index_;
-    int64_t end_op_index_;
-    bool is_grad_;
-    platform::DeviceType device_type_;
-  };
+  struct CacheValue {
+    std::shared_ptr<ParallelExecutor> executor_{nullptr};
+    std::shared_ptr<ir::Graph> graph_{nullptr};
 
-  using KeyType = size_t;
-  using ValueType =
-      std::pair<std::shared_ptr<ParallelExecutor>, std::shared_ptr<ir::Graph>>;
-
-  struct KeyHasher {
-    size_t operator()(const CacheKey& key) const noexcept {
-      size_t seed = 10;
-      auto* prog_desc = key.program_desc_;
-      /*
-       * Note(Aurelius84): DO NOT use only ProgramDesc* to calculate hash value
-       * because a new program will hold same pointer address after an older
-       * program is destructed with a small probability. Add op size while
-       * hashing because program may contains at least one block.
-       */
-      hash_combine(&seed, prog_desc);
-      for (size_t i = 0; i < prog_desc->Size(); ++i) {
-        hash_combine(&seed, &prog_desc->Block(i));
-        hash_combine(&seed, prog_desc->Block(i).OpSize());
-      }
-      hash_combine(&seed, static_cast<int>(key.device_type_));
-      hash_combine(&seed, key.start_op_index_);
-      hash_combine(&seed, key.end_op_index_);
-      hash_combine(&seed, key.is_grad_);
-      VLOG(3) << "hash value is : " << seed
-              << " of key:  " << key.DebugString();
-      return seed;
-    }
-
-    template <typename T>
-    void hash_combine(size_t* seed, const T& val) const {
-      std::hash<T> hasher;
-      (*seed) ^= hasher(val) + 0x9e3779b9 + ((*seed) << 6) + ((*seed >> 2));
-    }
+    std::vector<std::string> skip_eager_delete_vars_;
   };
 
+  bool IsAvailable(bool is_grad) {
+    const auto& executor =
+        is_grad ? backward_info_.executor_ : forward_info_.executor_;
+    return executor != nullptr;
+  }
+
+  CacheValue& GetMutable(bool is_grad) {
+    return is_grad ? backward_info_ : forward_info_;
+  }
+
+ private:
+  CacheValue forward_info_;
+  CacheValue backward_info_;
+};
+
+class ExecutorInfoCache {
+ public:
   static ExecutorInfoCache& Instance();
 
-  ValueType GetMutable(const CacheKey& key) {
-    auto key_val = key_hash_func_(key);
+  const BuildStrategy& GetBuildStrategy(int64_t program_id) {
+    // If not found, insert build_strategy with default value.
+    return strategy_map_[program_id];
+  }
+
+  void SetBuildStrategy(int64_t program_id,
+                        const BuildStrategy& build_strategy) {
     PADDLE_ENFORCE_EQ(
-        Has(key_val), true,
-        platform::errors::NotFound("%s doesn't exist in ExecutorInfoCache",
-                                   key.DebugString()));
-    return info_map_[key_val];
+        strategy_map_.count(program_id), 0,
+        platform::errors::PreconditionNotMet(
+            "program_id: %s already exist in ExecutorInfoCache", program_id));
+    strategy_map_[program_id] = build_strategy;
   }
 
-  bool Has(const CacheKey& key) const {
-    auto key_val = key_hash_func_(key);
-    return Has(key_val);
+  bool Has(int64_t program_id, bool is_grad) {
+    return info_map_.find(program_id) != info_map_.end() &&
+           info_map_[program_id].IsAvailable(is_grad);
   }
 
-  bool Has(const KeyType& key) const {
-    return info_map_.find(key) != info_map_.end();
+  ExecutorInfo::CacheValue& GetMutable(int64_t program_id, bool is_grad) {
+    return info_map_[program_id].GetMutable(is_grad);
   }
 
-  void Insert(const CacheKey& key, ValueType value) {
-    auto key_val = key_hash_func_(key);
-    PADDLE_ENFORCE_EQ(
-        Has(key_val), false,
-        platform::errors::NotFound("%s has existed in ExecutorInfoCache",
-                                   key.DebugString()));
-    info_map_.insert({key_val, value});
+  void UpdateSkipEagerDeleteVars(int64_t program_id, bool is_grad,
+                                 const std::vector<std::string>& skip_vars) {
+    auto& cached_value = GetMutable(program_id, is_grad);
+    cached_value.skip_eager_delete_vars_ = std::move(skip_vars);
+  }
+
+  std::vector<std::string>& SkipEagerDeleteVars(int64_t program_id,
+                                                bool is_grad) {
+    auto& cached_value = GetMutable(program_id, is_grad);
+    return cached_value.skip_eager_delete_vars_;
   }
 
   size_t Size() const { return info_map_.size(); }
 
-  void Finalize();
+  void Finalize() {
+    // NOTE(Aurelius84): DO NOT perform finalize in destructor
+    // to avoid problems caused by destructor order of static
+    // object.
+    info_map_.clear();
+    strategy_map_.clear();
+  }
 
  private:
-  ExecutorInfoCache() = default;
-  DISABLE_COPY_AND_ASSIGN(ExecutorInfoCache);
-
-  KeyHasher key_hash_func_;
-  std::unordered_map<KeyType, ValueType> info_map_;
+  std::unordered_map<int64_t, ExecutorInfo> info_map_;
+  std::unordered_map<int64_t, BuildStrategy> strategy_map_;
 };
 
 using CacheInfo =
     std::pair<std::shared_ptr<ParallelExecutor>, bool /*is_new_created*/>;
 
-CacheInfo GetExecutorInfoFromCache(const ExecutorInfoCache::CacheKey& cache_key,
+CacheInfo GetExecutorInfoFromCache(const ProgramDesc& program_desc,
+                                   const platform::Place& place,
+                                   int64_t start_op_index, int64_t end_op_index,
+                                   bool is_grad, int64_t program_id,
                                    framework::Scope* scope);
 
 }  // namespace framework

paddle/fluid/operators/run_program_op.cc

@@ -103,6 +103,10 @@ class RunProgramOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, default false) Set to true for inference only, false "
                   "for training.")
         .SetDefault(false);
+    AddAttr<int64_t>(
+        "program_id",
+        "(int64_t)"
+        "The unique hash id used as cache key for ExecutorInfoCache.");
     AddComment(R"DOC(
 RunProgram operator.
 

paddle/fluid/operators/run_program_op.h

@@ -188,6 +188,7 @@ class RunProgramOpKernel : public framework::OpKernel<T> {
     auto start_op_index = ctx.Attr<int64_t>("start_op_index");
     auto end_op_index = ctx.Attr<int64_t>("end_op_index");
     auto is_test = ctx.Attr<bool>("is_test");
+    auto program_id = ctx.Attr<int64_t>("program_id");
 
     // NOTE(chenweihang): In order not to add new variable type, use vector
     // here. Originally, here can use scope directly.
@@ -215,23 +216,27 @@ class RunProgramOpKernel : public framework::OpKernel<T> {
 
     if (end_op_index > start_op_index) {
       auto *program = ctx.Attr<BlockDesc *>("global_block")->Program();
-      auto cache_key = framework::ExecutorInfoCache::CacheKey(
-          program, ctx.GetPlace(), start_op_index, end_op_index,
-          /*is_grad=*/false);
-      auto cache_info = framework::GetExecutorInfoFromCache(cache_key, &scope);
+      auto cache_info = framework::GetExecutorInfoFromCache(
+          *program, ctx.GetPlace(), start_op_index, end_op_index,
+          /*is_grad=*/false, program_id, &scope);
       auto &parallel_executor = cache_info.first;
+      // all out_vars are skip_eager_var
+      auto &skip_eager_delete_vars =
+          framework::ExecutorInfoCache::Instance().SkipEagerDeleteVars(
+              program_id, false);
       if (cache_info.second /*is_new_created*/) {
         parallel_executor->SkipMemoryReuse(/*scope_idx=*/0, input_var_names);
+        skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
+                                      output_var_names.begin(),
+                                      output_var_names.end());
+        skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
+                                      dout_var_names.begin(),
+                                      dout_var_names.end());
+        framework::details::ParseSafeEagerDeletionSkipVars(
+            *program, end_op_index, output_var_names, &skip_eager_delete_vars);
       }
 
       // Step 3. run ops
-      // all out_vars are skip_eager_var
-      std::vector<std::string> skip_eager_delete_vars(output_var_names);
-      skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
-                                    dout_var_names.begin(),
-                                    dout_var_names.end());
-      framework::details::ParseSafeEagerDeletionSkipVars(
-          *program, end_op_index, output_var_names, &skip_eager_delete_vars);
       parallel_executor->RunWithoutFetch(skip_eager_delete_vars);
     }
     // Step 4. Get Output
@@ -280,6 +285,7 @@ class RunProgramGradOpKernel : public framework::OpKernel<T> {
 
     auto *block = ctx.Attr<BlockDesc *>("global_block");
     auto orig_end_op_index = ctx.Attr<int64_t>("end_op_index");
+    auto program_id = ctx.Attr<int64_t>("program_id");
     // NOTE: skip `shape` and `fill_constant` op created by
     // fluid.backward.gradients, one forward output will generate one `shape`
     // and `fill_constant`
@@ -305,24 +311,30 @@ class RunProgramGradOpKernel : public framework::OpKernel<T> {
     if (end_op_index > start_op_index) {
       // Step 2. prepare executor and scope
       auto *program = ctx.Attr<BlockDesc *>("global_block")->Program();
-      auto cache_key = framework::ExecutorInfoCache::CacheKey(
-          program, ctx.GetPlace(), start_op_index, end_op_index,
-          /*is_grad*/ true);
-      auto cache_info = framework::GetExecutorInfoFromCache(cache_key, &scope);
+      auto cache_info = framework::GetExecutorInfoFromCache(
+          *program, ctx.GetPlace(), start_op_index, end_op_index,
+          /*is_grad*/ true, program_id, &scope);
       auto &parallel_executor = cache_info.first;
 
-      parallel_executor->SkipMemoryReuse(/*scope_idx=*/0,
-                                         output_grad_var_names);
+      auto &skip_eager_delete_vars =
+          framework::ExecutorInfoCache::Instance().SkipEagerDeleteVars(
+              program_id, true);
+      if (cache_info.second /*is_new_created*/) {
+        parallel_executor->SkipMemoryReuse(/*scope_idx=*/0,
+                                           output_grad_var_names);
+
+        skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
+                                      input_grad_var_names.begin(),
+                                      input_grad_var_names.end());
+        framework::details::AppendSkipDeletionVars(param_grad_names,
+                                                   &skip_eager_delete_vars);
+      }
 
       details::ShareVarsIntoScope(output_grad_vars, output_grad_var_names,
                                   &scope);
       // Debug info: scope info when run end
       VLOG(3) << framework::GenScopeTreeDebugInfo(out_scope_vec->front());
 
-      std::vector<std::string> skip_eager_delete_vars(input_grad_var_names);
-      framework::details::AppendSkipDeletionVars(param_grad_names,
-                                                 &skip_eager_delete_vars);
-
       // Step 3. run ops
       parallel_executor->RunWithoutFetch(
           /*skip_eager_delete_vars=*/skip_eager_delete_vars);

paddle/fluid/pybind/pybind.cc

@@ -3011,6 +3011,12 @@ All parameter, weight, gradient are variables in Paddle.
                 optimization passes should be defined in this way. BuildStrategy
                 cannot be updated after being finalized.)DOC");
 
+  m.def("_set_cached_executor_build_strategy",
+        [](int64_t program_id, const BuildStrategy &build_strategy) {
+          auto &cached_exe_info = framework::ExecutorInfoCache::Instance();
+          cached_exe_info.SetBuildStrategy(program_id, build_strategy);
+        });
+
   pe.def(py::init<const std::vector<platform::Place> &,
                   const std::vector<std::string> &, const std::string &,
                   Scope *, std::vector<Scope *> &, const ExecutionStrategy &,

python/paddle/fluid/core.py

@@ -273,6 +273,7 @@ if avx_supported():
         from .core_avx import _cuda_synchronize
         from .core_avx import _is_compiled_with_heterps
         from .core_avx import _promote_types_if_complex_exists
+        from .core_avx import _set_cached_executor_build_strategy
         from .core_avx import _device_synchronize
         from .core_avx import _get_current_stream
         if sys.platform != 'win32':
@@ -324,6 +325,7 @@ if load_noavx:
         from .core_noavx import _cuda_synchronize
         from .core_noavx import _is_compiled_with_heterps
         from .core_noavx import _promote_types_if_complex_exists
+        from .core_noavx import _set_cached_executor_build_strategy
         from .core_noavx import _device_synchronize
         from .core_noavx import _get_current_stream
         if sys.platform != 'win32':

python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py

@@ -24,6 +24,8 @@ from paddle.fluid.dygraph.dygraph_to_static import logging_utils
 from paddle.fluid.dygraph.dygraph_to_static.return_transformer import RETURN_NO_VALUE_MAGIC_NUM
 from paddle.fluid.layers.utils import flatten
 from paddle.fluid.layers.utils import pack_sequence_as
+from paddle.fluid.layers.utils import _hash_with_id
+from paddle.fluid.compiler import BuildStrategy
 import paddle.compat as cpt
 from paddle import _C_ops
 
@@ -162,6 +164,14 @@ class PartialProgramLayer:
 
         return train_program
 
+    @LazyInitialized
+    def _infer_program_id(self):
+        return _hash_with_id(self._infer_program, self)
+
+    @LazyInitialized
+    def _train_program_id(self):
+        return _hash_with_id(self._train_program, self)
+
     def _verify_program(self, main_program):
         """
         Verify that the program parameter is initialized, prune some unused params,
@@ -228,7 +238,7 @@ class PartialProgramLayer:
 
         attrs = ('global_block', self.program.desc.block(0), 'start_op_index',
                  0, 'end_op_index', self._infer_program.desc.block(0).op_size(),
-                 'is_test', not self.training)
+                 'is_test', not self.training, 'program_id', self.program_id)
         _C_ops.run_program(
             self._valid_vars(in_vars),
             self._valid_vars(self._params),
@@ -242,6 +252,10 @@ class PartialProgramLayer:
     def program(self):
         return self._train_program if self.training else self._infer_program
 
+    @property
+    def program_id(self):
+        return self._train_program_id if self.training else self._infer_program_id
+
     def _prepare(self, inputs):
         """
         Prepare inputs, outputs, attrs.

python/paddle/fluid/dygraph/io.py

@@ -27,6 +27,7 @@ from paddle.fluid import backward
 from paddle.fluid import unique_name
 from paddle.fluid.dygraph import layers
 from paddle.fluid.layers import nn
+from paddle.fluid.layers.utils import _hash_with_id
 from paddle.fluid.dygraph.base import switch_to_static_graph
 from paddle.fluid.framework import in_dygraph_mode
 
@@ -824,7 +825,8 @@ def _run_dygraph(instance, input, program_holder):
             'global_block': trace_program.block(0),
             'start_op_index': 0,
             'end_op_index': end_op_index,
-            'is_test': instance._is_test
+            'is_test': instance._is_test,
+            'program_id': _hash_with_id(trace_program)
         })
     # NOTE: [ why need set param's gradient type here ]
     # if user set sparse gradient mode, the param's gradient

python/paddle/fluid/layers/utils.py

@@ -78,6 +78,15 @@ def is_sequence(seq):
             not isinstance(seq, six.string_types))
 
 
+def _hash_with_id(*args):
+    """
+    Return int hash value calculated by id(arg) or tuple(id1,id2, ...).
+    """
+    assert len(args) > 0
+    info = tuple([id(v) for v in args])
+    return hash(info) & 0xfffffff
+
+
 def _sorted(dict_):
     """
     Returns a sorted list of the dict keys, with error if keys not sortable.

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

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+set(GC_ENVS FLAGS_eager_delete_tensor_gb=0.0)
 
 list(REMOVE_ITEM TEST_OPS test_lac)
 # NOTE(Aurelius84): In case of Windows CI, if open ON_INFER, RWLOCK of Scope will
@@ -10,7 +11,7 @@ if(NOT ON_INFER)
 endif()
 
 foreach(TEST_OP ${TEST_OPS})
-    py_test_modules(${TEST_OP} MODULES ${TEST_OP})
+    py_test_modules(${TEST_OP} MODULES ${TEST_OP} ENVS ${GC_ENVS})
 endforeach(TEST_OP)
 
 set_tests_properties(test_se_resnet PROPERTIES TIMEOUT 900)

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

@@ -23,6 +23,7 @@ import paddle
 import paddle.fluid as fluid
 from paddle import compat as cpt
 from paddle.fluid import core, framework, executor
+from paddle.fluid.layers.utils import _hash_with_id
 
 paddle.enable_static()
 
@@ -174,6 +175,7 @@ class RunProgramOpTest(unittest.TestCase):
     def calc_dygraph_output(self, place):
         self.program_desc, self.fwd_op_num = self.get_program_desc()
         self.attrs = self.prepare_attrs()
+        self.attrs['program_id'] = _hash_with_id(self.program_desc)
 
         with fluid.dygraph.guard(place):
             inputs = self.prepare_dygraph_input(place)
@@ -189,6 +191,7 @@ class RunProgramOpTest(unittest.TestCase):
     def calc_dygraph_grad(self, place):
         self.program_desc, self.fwd_op_num = self.get_program_desc()
         self.attrs = self.prepare_attrs()
+        self.attrs['program_id'] = _hash_with_id(self.program_desc)
 
         with fluid.dygraph.guard(place):
             # Step 1. run forward