remove two useless flags: enable_subgraph_optimize, memory_optimize_debug, test=develop (#17491)

paddle/fluid/framework/ir/memory_optimize_pass/inplace_op_pass.cc

@@ -48,8 +48,6 @@ DEFINE_bool(
     "Such as scale, elementwise_add"
     "By default, it's turned off");
 
-DECLARE_string(memory_optimize_debug);
-
 namespace paddle {
 namespace framework {
 namespace ir {
@@ -461,13 +459,6 @@ void InplacePass::ApplyImpl(ir::Graph *graph) const {
         continue;
       }
 
-      // Debug Interface. Which would be skipped by the pass.
-      if (out_arg == FLAGS_memory_optimize_debug) {
-        VLOG(4) << "Skiped var by force. FLAGS_memory_optimize_debug="
-                << out_node->Name();
-        continue;
-      }
-
       VLOG(4) << "Rename " << out_node->Name() << " with " << in_node->Name()
               << " in " << op_type;
       RenameInOut(op_node, in_node, out_node);

paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_pass.cc

@@ -31,15 +31,6 @@
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
 
-DEFINE_bool(enable_subgraph_optimize, false,
-            "SubGraph also reuse global graph variables, it will reduce the "
-            "memory occupation"
-            "but a higher risk of memory reuse error. default disabled.");
-DEFINE_string(memory_optimize_debug, "",
-              "debug the operator output variable when do the variable reuse."
-              "memory reuse pass."
-              "only for debug, default disabled.");
-
 namespace paddle {
 namespace framework {
 namespace ir {
@@ -57,15 +48,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const {
     auto* op_desc = op->Op();
     // some op in graph has no op desc
     if (op_desc == nullptr) continue;
-    if (OpHasSubBlock(op_desc)) {
-      if (FLAGS_enable_subgraph_optimize) {
-        SubGraphOptimize(op_desc);
-      } else {
-        VLOG(3) << op->Name()
-                << " has subblock, but disable subgraph optimize. skipped.";
-        continue;
-      }
-    }
 
     for (auto& var : op->outputs) {
       if (var->IsVar() && !var->IsCtrlVar() && skip_set_.count(var->Name())) {
@@ -82,13 +64,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const {
                   << "replace it again. Skip this candidate.";
           cache = pool_.FindNextBestFitNode(var, cache);
         }
-        if (var->Name() == FLAGS_memory_optimize_debug) {
-          VLOG(3) << "start match var " << DebugString(var) << " of op "
-                  << op->Name();
-          VLOG(3) << pool_.ToString();
-          VLOG(3) << "matched in pool : "
-                  << ((cache == nullptr) ? "False" : "True");
-        }
 
         if (cache != nullptr) {
           int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
@@ -128,81 +103,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const {
   graph->ResolveHazard(var_nodes_);
 }
 
-void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
-  // conditional block, while op and their grad op
-  auto* sub_block_desc =
-      AttrReader(op_desc->GetAttrMap()).Get<BlockDesc*>("sub_block");
-
-  // create a mirror block to construct an IR Graph.
-  ProgramDesc prog;
-  auto* copy_block = prog.MutableBlock(0);
-  for (auto* op : sub_block_desc->AllOps()) {
-    auto* copy_op = copy_block->AppendOp();
-    copy_op->CopyFrom(*op);
-    copy_op->Flush();
-  }
-
-  for (auto* var : sub_block_desc->AllVars()) {
-    auto* copy_var = copy_block->Var(var->Name());
-    copy_var->SetDataType(var->GetDataType());
-    // only lod tensor can be reused. So ignore the multiple dims case.
-    copy_var->SetType(var->GetType());
-    copy_var->SetShape(var->GetShape());
-    copy_var->SetPersistable(var->Persistable());
-  }
-
-  ir::Graph sub_graph(prog);
-  std::unordered_set<ir::Node*> sub_graph_all_ops;
-  FilterVariables(sub_graph.Nodes(), [&](ir::Node* var) {
-    // sub_graph_all_ops.emplace(var);
-    if (var->IsVar() && !var->IsCtrlVar()) {
-      sub_graph_all_ops.emplace(var);
-    }
-  });
-  int sub_reuse_id = 0;
-  // subgraph nodes is unordered, reuse need to follow the desc order.
-  // find the right op node through the descs
-  for (auto* sub_op_desc : sub_block_desc->AllOps()) {
-    ir::Node* sub_op = nullptr;
-    for (auto* node : sub_graph_all_ops) {
-      if (node->Op() == sub_op_desc) {
-        sub_op = node;
-        break;
-      }
-    }
-    PADDLE_ENFORCE(sub_op != nullptr);
-    for (auto* var : sub_op->outputs) {
-      if (NodeCanReused(var)) {
-        ir::Node* cache = pool_.FindBestFitNode(var);
-        if (cache != nullptr) {
-          if (var->Var()->GetDataType() != cache->Var()->GetDataType()) {
-            continue;
-          }
-          int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
-          VLOG(3) << string::Sprintf(
-              "!!! %s,  %s => %s, cache idx %d, pool size %d",
-              std::to_string(sub_reuse_id++), DebugString(var),
-              DebugString(cache), node_idx_in_pool,
-              static_cast<int>(pool_.size()));
-          // NOTE(dzh): subblock is not in IR graph. Modify the block_desc
-          // immediately to make the subblock variable reuse strategy take
-          // effect. Because it is a single op in graph. No need to
-          // update the ir nodes.
-          // FIXME(liuwei1031): Graph is not aware of the existence of
-          // BlockDescs and ProgramDescs.
-          // The operations related to BlockDesc or ProgramDesc should perform
-          // on Graph or Node directly!
-          sub_op_desc->Rename(var->Name(), cache->Name());
-          if (sub_op_desc->Block() != nullptr &&
-              sub_op_desc->Block()->HasVar(var->Name())) {
-            sub_op_desc->Block()->RemoveVar(var->Name());
-          }
-        }
-      }
-    }
-  }
-}
-
 void MemoryOptimizePass::CollectSkipVarsSet(ir::Graph* graph) const {
   // fill skip_set_
   PADDLE_ENFORCE(graph->Has(kMemOptSkipVars));

python/paddle/fluid/__init__.py

@@ -139,8 +139,8 @@ def __bootstrap__():
         'allocator_strategy', 'reader_queue_speed_test_mode',
         'print_sub_graph_dir', 'pe_profile_fname', 'inner_op_parallelism',
         'enable_parallel_graph', 'fuse_parameter_groups_size',
-        'multiple_of_cupti_buffer_size', 'enable_subgraph_optimize',
-        'fuse_parameter_memory_size', 'tracer_profile_fname'
+        'multiple_of_cupti_buffer_size', 'fuse_parameter_memory_size',
+        'tracer_profile_fname'
     ]
     if 'Darwin' not in sysstr:
         read_env_flags.append('use_pinned_memory')
@@ -182,8 +182,8 @@ def __bootstrap__():
             'fraction_of_gpu_memory_to_use', 'initial_gpu_memory_in_mb',
             'reallocate_gpu_memory_in_mb', 'cudnn_deterministic',
             'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
-            'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
-            'sync_nccl_allreduce', 'limit_of_tmp_allocation',
+            'cudnn_exhaustive_search', 'selected_gpus', 'sync_nccl_allreduce',
+            'limit_of_tmp_allocation',
             'times_excess_than_required_tmp_allocation',
             'enable_inplace_whitelist', 'cudnn_batchnorm_spatial_persistent'
         ]