[PIR] Refine and fix pir exe (PaddlePaddle#60443)

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc

@@ -71,9 +71,8 @@ IfInstruction::IfInstruction(size_t id,
   GetInputIds(op, *value_exec_info, &inputs);
   auto true_outside_inputs =
       GetExternalInputs(&true_branch_block, *value_exec_info, &inputs);
-  std::vector<pir::Value> false_outside_inputs;
   auto& false_branch_block = if_op.false_block();
-  false_outside_inputs =
+  auto false_outside_inputs =
       GetExternalInputs(&false_branch_block, *value_exec_info, &inputs);
   // NOTE(chenxi67): the variable corresponding to container value if a
   // <VariableRefArray> Type. It will recursively get the ID of internal
@@ -107,9 +106,14 @@ IfInstruction::IfInstruction(size_t id,
     }
   }
   InsertTuplePushContinerToOuts(&true_branch_block, *value_exec_info, &outputs);
-
   InsertTuplePushContinerToOuts(
       &if_op.false_block(), *value_exec_info, &outputs);
+
+  InsertInplacedExternalInputsToOuts(
+      &true_branch_block, true_outside_inputs, *value_exec_info, &outputs);
+  InsertInplacedExternalInputsToOuts(
+      &false_branch_block, false_outside_inputs, *value_exec_info, &outputs);
+
   for (auto& item : outputs) {
     auto& var_vec = item.second;
     for (auto it = var_vec.begin(); it != var_vec.end();) {

paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc

@@ -47,33 +47,25 @@ WhileInstruction::WhileInstruction(
     ValueExecutionInfo* parent_exe_info,
     interpreter::ExecutionConfig execution_config)
     : InstructionBase(id, place) {
-  op_ = op;
-  VLOG(6) << "finish process dist attributes";
-
-  SetKernelType(AnalyseOpFuncType(op, place));
-  VLOG(6) << "finish process analyse kernel type";
-
-  VLOG(6) << "finish process inputs outputs index";
-
   PADDLE_ENFORCE(op->isa<paddle::dialect::WhileOp>(),
                  phi::errors::PreconditionNotMet(
                      "While instruction only support While op"));
-
+  op_ = op;
   auto while_op = op->dyn_cast<paddle::dialect::WhileOp>();
+  body_block_ = &while_op.body();
 
-  cond_var_ = parent_exe_info->GetVarByValue(while_op.operand_source(0));
+  SetKernelType(AnalyseOpFuncType(op, place));
+  VLOG(6) << "finish process analyse kernel type";
 
+  cond_var_ = parent_exe_info->GetVarByValue(while_op.operand_source(0));
   for (size_t i = 1; i < while_op.num_operands(); ++i) {
     inputs_.push_back(
         parent_exe_info->GetVarByValue(while_op.operand_source(i)));
   }
-
   for (size_t i = 0; i < while_op.num_results(); ++i) {
     outputs_.push_back(parent_exe_info->GetVarByValue(while_op.result(i)));
   }
 
-  body_block_ = &while_op.body();
-
   std::unordered_map<pir::Value, std::vector<int>> inputs;
   GetInputIds(op, *parent_exe_info, &inputs);
   auto body_outside_inputs =
@@ -94,8 +86,10 @@ WhileInstruction::WhileInstruction(
       std::vector<int> outputs_id = GetValueIds(value, *parent_exe_info);
       outputs.emplace(value, outputs_id);
     }
-    InsertTuplePushContinerToOuts(body_block_, *parent_exe_info, &outputs);
   }
+  InsertTuplePushContinerToOuts(body_block_, *parent_exe_info, &outputs);
+  InsertInplacedExternalInputsToOuts(
+      body_block_, body_outside_inputs, *parent_exe_info, &outputs);
   SetOutputs(outputs);
 
   Scope* body_scope = &(parent_exe_info->GetScope()->NewScope());

paddle/fluid/framework/new_executor/instruction/instruction_util.cc

@@ -346,6 +346,55 @@ void InsertTuplePushContinerToOuts(
   }
 }
 
+void InsertInplacedExternalInputsToOuts(
+    pir::Block* block,
+    const std::vector<pir::Value>& external_inputs,
+    const ValueExecutionInfo& value_exec_info,
+    std::unordered_map<pir::Value, std::vector<int>>* outputs) {
+  for (auto& op : *block) {
+    if (op.attributes().count("is_inplace") != 0 &&
+        op.attributes()
+            .at("is_inplace")
+            .dyn_cast<pir::BoolAttribute>()
+            .data()) {
+      std::string op_name = op.name();
+      if (op.attributes().count("op_name")) {
+        op_name = op.attributes()
+                      .at("op_name")
+                      .dyn_cast<pir::StrAttribute>()
+                      .AsString();
+      }
+      pir::OpInfo op_info =
+          pir::IrContext::Instance()->GetRegisteredOpInfo(op_name);
+      paddle::dialect::OpYamlInfoParser yaml_parser(
+          op_info.GetInterfaceImpl<paddle::dialect::OpYamlInfoInterface>()
+              ->get_op_info_(op_name),
+          paddle::dialect::IsLegacyOp(op_name));
+
+      for (size_t i = 0; i < op.num_results(); ++i) {
+        pir::Value value = op.result(i);
+        if (!IsInvalid(value)) {
+          VLOG(8) << "Number " << i << " result of " << op_name
+                  << " is not invalid, so skip build a variable.";
+          continue;
+        }
+        std::string value_name = yaml_parser.OutputNames()[i];
+        if (yaml_parser.HasInplace(value_name)) {
+          const std::string& inplace_name = yaml_parser.InplaceName(value_name);
+          pir::Value inplace_value =
+              op.operand_source(yaml_parser.InputName2Id().at(inplace_name));
+          if (std::find(external_inputs.begin(),
+                        external_inputs.end(),
+                        inplace_value) != external_inputs.end()) {
+            outputs->emplace(value,
+                             GetValueIds(inplace_value, value_exec_info));
+          }
+        }
+      }
+    }
+  }
+}
+
 bool GetCondData(const phi::DenseTensor& cond) {
   if (paddle::platform::is_cpu_place(cond.place())) {
     return cond.data<bool>()[0];

paddle/fluid/framework/new_executor/instruction/instruction_util.h

@@ -59,6 +59,12 @@ void InsertTuplePushContinerToOuts(
     const ValueExecutionInfo& value_exec_info,
     std::unordered_map<pir::Value, std::vector<int>>* outputs);
 
+void InsertInplacedExternalInputsToOuts(
+    pir::Block* block,
+    const std::vector<pir::Value>& external_inputs,
+    const ValueExecutionInfo& value_exec_info,
+    std::unordered_map<pir::Value, std::vector<int>>* outputs);
+
 bool GetCondData(const phi::DenseTensor& cond);
 }  // namespace framework
 }  // namespace paddle

test/legacy_test/test_while_op.py

@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import unittest
 
 import numpy
 from utils import compare_legacy_with_pt
 
 import paddle
-from paddle import base, set_flags
+from paddle import base
 from paddle.base import core
 from paddle.base.backward import append_backward
 from paddle.base.executor import Executor
@@ -82,7 +81,6 @@ def simple_net(self):
         loss = paddle.mean(sum_result)
         return loss, sum_result
 
-    # TODO(winter-wang): Support pir test in (FLAGS_enable_pir_in_executor_trace_run = False && FLAGS_new_executor_serial_run == False).
     @test_with_pir_api
     def test_simple_net(self):
         main_program = base.Program()
@@ -92,14 +90,6 @@ def test_simple_net(self):
 
             append_backward(loss)
 
-            if in_pir_mode():
-                flag_1 = "FLAGS_enable_pir_in_executor_trace_run"
-                flag_2 = "FLAGS_new_executor_serial_run"
-                os.environ[flag_1] = 'True'
-                os.environ[flag_2] = 'True'
-                set_flags({flag_1: True})
-                set_flags({flag_2: True})
-
             cpu = core.CPUPlace()
             exe = Executor(cpu)
             d = []
@@ -111,14 +101,8 @@ def test_simple_net(self):
                 feed={'d0': d[0], 'd1': d[1], 'd2': d[2]},
                 fetch_list=[sum_result],
             )
-            if in_pir_mode():
-                del os.environ[flag_1]
-                del os.environ[flag_2]
-                set_flags({flag_1: False})
-                set_flags({flag_2: False})
             self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
 
-    # TODO(winter-wang): Support pir test in (FLAGS_enable_pir_in_executor_trace_run = False && FLAGS_new_executor_serial_run == False).
     @test_with_pir_api
     def test_simple_net_forward(self):
         main_program = base.Program()
@@ -136,20 +120,8 @@ def test_simple_net_forward(self):
             for i in range(3):
                 d.append(numpy.random.random(size=[10]).astype('float32'))
 
-            if in_pir_mode():
-                flag_1 = "FLAGS_enable_pir_in_executor_trace_run"
-                flag_2 = "FLAGS_new_executor_serial_run"
-                os.environ[flag_1] = 'True'
-                os.environ[flag_2] = 'True'
-                set_flags({flag_1: True})
-                set_flags({flag_2: True})
             for _ in range(2):
                 exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]})
-            if in_pir_mode():
-                del os.environ[flag_1]
-                del os.environ[flag_2]
-                set_flags({flag_1: False})
-                set_flags({flag_2: False})
 
     @compare_legacy_with_pt
     @test_with_pir_api