Refactor fetch handler (#21264) (#21537)

* fix fetch handler problem and refactor
when a user define FetchHandler class, he or she should initialize a handler
with variable dict. the key of a variable dict is a user defined name,
the value of a variable dict is a Varaible generated from python API.

For each fetching, a user should implement handler function in which
fetched_result_dict will be available and the user can access the fetched value
with user defined keys.

Author: seiriosPlus

paddle/fluid/framework/executor.cc

@@ -186,6 +186,9 @@ void Executor::RunFromDataset(std::shared_ptr<TrainerBase> trainer) {
   // training and finalize training
   VLOG(3) << "Trainer starts to run";
   trainer->Run();
+}
+
+void Executor::ReleaseTrainer(std::shared_ptr<TrainerBase> trainer) {
   VLOG(3) << "Trainer going to finalize";
   trainer->Finalize();
 }

paddle/fluid/framework/executor.h

@@ -126,6 +126,8 @@ class Executor {
       Scope* scope, Dataset* dataset);
   void RunFromDataset(std::shared_ptr<TrainerBase> trainer);
 
+  void ReleaseTrainer(std::shared_ptr<TrainerBase> trainer);
+
   const platform::Place GetPlace() const { return place_; }
 
  private:

paddle/fluid/framework/multi_trainer.cc

@@ -77,14 +77,12 @@ void MultiTrainer::Run() {
                                      workers_[thidx].get()));
     }
   }
-}
-
-void MultiTrainer::Finalize() {
   for (auto& th : threads_) {
     th.join();
   }
-  root_scope_->DropKids();
 }
 
+void MultiTrainer::Finalize() { root_scope_->DropKids(); }
+
 }  // end namespace framework
 }  // end namespace paddle

paddle/fluid/pybind/pybind.cc

@@ -1364,10 +1364,13 @@ All parameter, weight, gradient are variables in Paddle.
       .def("close", &Executor::Close)
       .def("run_from_dataset", &Executor::RunFromDataset,
            py::call_guard<py::gil_scoped_release>())
+      .def("release_trainer", &Executor::ReleaseTrainer,
+           py::call_guard<py::gil_scoped_release>())
       .def("init_for_dataset",
            [](Executor &self, const ProgramDesc &prog,
               const std::string &trainer_desc, Scope *scope,
               Dataset *dataset) -> std::shared_ptr<TrainerBase> {
+             pybind11::gil_scoped_release release;
              return self.InitForDataset(prog, trainer_desc, scope, dataset);
            })
       .def("run_from_dataset",

python/paddle/fluid/executor.py

@@ -395,23 +395,28 @@ def _as_lodtensor(data, place):
 
 
 class FetchHandler(object):
-    def __init__(self, fetch_target_names, period_secs=60, return_np=True):
-        self.fetch_target_names = fetch_target_names
+    def __init__(self, var_dict=None, period_secs=60):
+        assert var_dict != None
+        self.var_dict = var_dict
         self.period_secs = period_secs
-        self.return_np = return_np
 
-    def handler(self, fetch_target_vars):
-        return
+    def handler(self, res_dict):
+        for key in res_dict:
+            if type(res_dict[key]) is np.ndarray:
+                sys.stdout.write("{}[0]: {} ".format(key, res_dict[key][0]))
+        sys.stdout.write("\n")
 
     @staticmethod
     def help():
         print("""
-class FetchHandlerExamlpe(FetchHandler):
-    def handler(self, fetch_target_vars):
-        b_auc = fetch_target_vars[0]
-        g_auc = fetch_target_vars[1]
-                        
-        print("b_auc: {}, g_auc: {} at time: {}".format(b_auc, g_auc, time.ctime()))
+class FetchHandlerExample(FetchHandler):
+    def handler(self, res_dict):
+        print(res_dict["auc"])
+        print("auc: {}, {}".format(res_dict["auc"], time.ctime()))
+
+auc = Variable()
+var_dict = {"auc": auc}
+handler = FetchHandlerExample(var_dict=var_dict)
 """)
 
 
@@ -1019,13 +1024,13 @@ def _run_from_dataset(self,
             scope0 = trainer_instance.get_worker_scope(0)
             fetch_monitor = FetchHandlerMonitor(scope0, fetch_handler)
             fetch_monitor.start()
-
             self._default_executor.run_from_dataset(trainer_instance)
-
             fetch_monitor.stop()
+            self._default_executor.release_trainer(trainer_instance)
         else:
 
             self._default_executor.run_from_dataset(trainer_instance)
+            self._default_executor.release_trainer(trainer_instance)
 
         dataset._dynamic_adjust_after_train()
         dataset._finish_to_run()

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

@@ -17,6 +17,7 @@
 import time
 import unittest
 import numpy as np
+from paddle.fluid.framework import Program
 
 import paddle.fluid.core as core
 import paddle.fluid as fluid
@@ -29,20 +30,35 @@ def test_fetch_handler(self):
 
         table = np.random.random((3, 10)).astype("float32")
 
+        prog = Program()
+        block = prog.current_block()
+        var_emb = block.create_var(name='emb', type=core.VarDesc.VarType.FP32)
+        var_emb3 = block.create_var(name='emb3', type=core.VarDesc.VarType.FP32)
+
         class FH(fluid.executor.FetchHandler):
-            def handler(self, fetch_target_vars):
-                assert len(fetch_target_vars) == 1
+            def handler(self, fetch_dict):
+                assert len(fetch_dict) == 1
 
         table_var = scope.var('emb').get_tensor()
         table_var.set(table, place)
-
-        fh = FH(['emb'], period_secs=2, return_np=True)
+        fh = FH(var_dict={'emb': var_emb}, period_secs=2)
         fm = fluid.trainer_factory.FetchHandlerMonitor(scope, fh)
 
         fm.start()
-        time.sleep(10)
+        time.sleep(3)
         fm.stop()
 
+        default_fh = fluid.executor.FetchHandler(
+            var_dict={'emb': var_emb,
+                      'emb2': None,
+                      'emb3': var_emb3},
+            period_secs=1)
+        default_fm = fluid.trainer_factory.FetchHandlerMonitor(scope,
+                                                               default_fh)
+        default_fm.start()
+        time.sleep(5)
+        default_fm.stop()
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/trainer_factory.py

@@ -15,11 +15,15 @@
 
 import threading
 import time
-
+import logging
 import numpy as np
 
+logging.basicConfig()
+
 from .trainer_desc import MultiTrainer, DistMultiTrainer, PipelineTrainer
 from .device_worker import Hogwild, DownpourSGD, Section
+from .framework import Variable
+from multiprocessing import Process, Manager
 
 __all__ = ["TrainerFactory", "FetchHandler", "FetchHandlerMonitor"]
 
@@ -93,68 +97,74 @@ class FetchHandlerMonitor(object):
     def __init__(self, scope, handler):
         self.fetch_instance = handler
         self.fetch_thread = threading.Thread(
-            target=self.handler_decorator,
-            args=(scope, self.fetch_instance.handler))
+            target=self.handler_launch_func, args=(scope, self.fetch_instance))
+        self.running_lock = threading.Lock()
         self.running = False
 
+    def handler_launch_func(self, scope, handler):
+        fetch_instance = handler
+        period_secs = fetch_instance.period_secs
+        var_name_to_key = {}
+        for key in fetch_instance.var_dict:
+            if isinstance(fetch_instance.var_dict[key], Variable):
+                var_name_to_key[fetch_instance.var_dict[key].name] = key
+            else:
+                logging.warning("the value of {} is not a Variable".format(key))
+                var_name_to_key["None.var"] = key
+        elapsed_secs = 0
+        while True:
+            self.running_lock.acquire()
+            if self.running == False:
+                break
+            if elapsed_secs < period_secs:
+                # TODO(guru4elephant): needs customized condition
+                time.sleep(1)
+                elapsed_secs += 1
+            else:
+                elapsed_secs = 0
+                fetch_dict = {}
+                for key in var_name_to_key:
+                    var = scope.find_var(key)
+                    fetch_dict[key] = var
+                    if var == None:
+                        logging.warning("{} value currently not available".
+                                        format(var_name_to_key[key]))
+                res_dict = {}
+                for key in fetch_dict:
+                    user_name = var_name_to_key[key]
+                    if fetch_dict[key] == None:
+                        res_dict[user_name] = None
+                        continue
+                    else:
+                        res_dict[user_name] = fetch_dict[key].get_tensor()
+
+                    lod = res_dict[user_name].lod()
+                    if len(lod) > 0:
+                        raise RuntimeError("Some of your fetched tensors \
+                                            hold LoD information. \
+                                            They can not be completely cast \
+                                            to Python ndarray. We can \
+                                            not return LoDTensor itself directly, \
+                                            please choose another targets")
+                    if res_dict[user_name]._is_initialized():
+                        res_dict[user_name] = np.array(res_dict[user_name])
+                    else:
+                        res_dict[user_name] = None
+                fetch_instance.handler(res_dict)
+            self.running_lock.release()
+
     def start(self):
         """
         start monitor,
         it will start a monitor thread.
         """
+        self.running_lock.acquire()
         self.running = True
+        self.running_lock.release()
         self.fetch_thread.setDaemon(True)
         self.fetch_thread.start()
 
-    def handler_decorator(self, fetch_scope, fetch_handler):
-        """
-        decorator of handler,
-        Args:
-            fetch_scope(Scope): fetch scope
-            fetch_handler(Handler): fetch handler
-        """
-        fetch_target_names = self.fetch_instance.fetch_target_names
-        period_secs = self.fetch_instance.period_secs
-
-        elapsed_secs = 0
-        while True:
-            while self.running and elapsed_secs >= period_secs:
-                elapsed_secs = 0
-
-                fetch_vars = [
-                    fetch_scope.find_var(varname)
-                    for varname in fetch_target_names
-                ]
-
-                if None in fetch_vars:
-                    continue
-
-                fetch_tensors = [var.get_tensor() for var in fetch_vars]
-
-                if self.fetch_instance.return_np:
-                    fetch_nps = []
-
-                    for tensor in fetch_tensors:
-                        lod = tensor.lod()
-
-                        if len(lod) > 0:
-                            raise RuntimeError(
-                                "Some of your fetched tensors hold LoD information. \
-                        They can not be completely cast to Python ndarray. We can not \
-                        return LoDTensor itself directly, please choose another targets"
-                            )
-
-                        if tensor._is_initialized():
-                            fetch_nps.append(np.array(tensor))
-                        else:
-                            fetch_nps.append(None)
-
-                    fetch_handler(fetch_nps)
-                else:
-                    fetch_handler(fetch_tensors)
-            else:
-                time.sleep(1)
-                elapsed_secs += 1
-
     def stop(self):
+        self.running_lock.acquire()
         self.running = False
+        self.running_lock.release()