Route tuner and assessor commands to 2 seperate queues (#891)

1. Route tuner and assessor commands to 2 seperate queues  issue #841
2. Allow tuner to leverage intermediate result when trial is early stopped.  issue #843

docs/en_US/ExperimentConfig.md

@@ -231,12 +231,17 @@ machineList:
     * __classArgs__
 
       __classArgs__ specifies the arguments of tuner algorithm.
-    * __gpuNum__
+
+  * __gpuNum__
 
       __gpuNum__ specifies the gpu number to run the tuner process. The value of this field should be a positive number.
 
       Note: users could only specify one way to set tuner, for example, set {tunerName, optimizationMode} or {tunerCommand, tunerCwd}, and could not set them both.
 
+  * __includeIntermediateResults__
+
+      If __includeIntermediateResults__ is true, the last intermediate result of the trial that is early stopped by assessor is sent to tuner as final result. The default value of __includeIntermediateResults__ is false.
+
 * __assessor__
 
   * Description

src/nni_manager/common/manager.ts

@@ -46,6 +46,7 @@ interface ExperimentParams {
         classFileName?: string;
         checkpointDir: string;
         gpuNum?: number;
+        includeIntermediateResults?: boolean;
     };
     assessor?: {
         className: string;

src/nni_manager/core/nnimanager.ts

@@ -277,11 +277,17 @@ class NNIManager implements Manager {
             newCwd = cwd;
         }
         // TO DO: add CUDA_VISIBLE_DEVICES
+        let includeIntermediateResultsEnv: boolean | undefined = false;
+        if (this.experimentProfile.params.tuner !== undefined) {
+            includeIntermediateResultsEnv = this.experimentProfile.params.tuner.includeIntermediateResults;
+        }
+
         let nniEnv = {
             NNI_MODE: mode,
             NNI_CHECKPOINT_DIRECTORY: dataDirectory,
             NNI_LOG_DIRECTORY: getLogDir(),
-            NNI_LOG_LEVEL: getLogLevel()
+            NNI_LOG_LEVEL: getLogLevel(),
+            NNI_INCLUDE_INTERMEDIATE_RESULTS: includeIntermediateResultsEnv
         };
         let newEnv = Object.assign({}, process.env, nniEnv);
         const tunerProc: ChildProcess = spawn(command, [], {

src/nni_manager/core/test/ipcInterfaceTerminate.test.ts

@@ -106,7 +106,7 @@ describe('core/ipcInterface.terminate', (): void => {
                 assert.ok(!procError);
                 deferred.resolve();
             },
-            2000);
+            5000);
 
         return deferred.promise;
     });

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -159,7 +159,8 @@ export namespace ValidationSchemas {
                 className: joi.string(),
                 classArgs: joi.any(),
                 gpuNum: joi.number().min(0),
-                checkpointDir: joi.string().allow('')
+                checkpointDir: joi.string().allow(''),
+                includeIntermediateResults: joi.boolean()
             }),
             assessor: joi.object({
                 builtinAssessorName: joi.string().valid('Medianstop', 'Curvefitting'),

src/sdk/pynni/nni/msg_dispatcher.py

@@ -18,14 +18,15 @@
 # OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 # ==================================================================================================
 
+import os
 import logging
 from collections import defaultdict
 import json_tricks
-import threading
 
 from .protocol import CommandType, send
 from .msg_dispatcher_base import MsgDispatcherBase
 from .assessor import AssessResult
+from .common import multi_thread_enabled
 
 _logger = logging.getLogger(__name__)
 
@@ -70,7 +71,7 @@ def _pack_parameter(parameter_id, params, customized=False):
 
 class MsgDispatcher(MsgDispatcherBase):
     def __init__(self, tuner, assessor=None):
-        super().__init__()
+        super(MsgDispatcher, self).__init__()
         self.tuner = tuner
         self.assessor = assessor
         if assessor is None:
@@ -87,9 +88,8 @@ class MsgDispatcher(MsgDispatcherBase):
             self.assessor.save_checkpoint()
 
     def handle_initialize(self, data):
-        '''
-        data is search space
-        '''
+        """Data is search space
+        """
         self.tuner.update_search_space(data)
         send(CommandType.Initialized, '')
         return True
@@ -126,12 +126,7 @@ class MsgDispatcher(MsgDispatcherBase):
                     - 'type': report type, support {'FINAL', 'PERIODICAL'}
         """
         if data['type'] == 'FINAL':
-            id_ = data['parameter_id']
-            value = data['value']
-            if id_ in _customized_parameter_ids:
-                self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value)
-            else:
-                self.tuner.receive_trial_result(id_, _trial_params[id_], value)
+            self._handle_final_metric_data(data)
         elif data['type'] == 'PERIODICAL':
             if self.assessor is not None:
                 self._handle_intermediate_metric_data(data)
@@ -157,7 +152,19 @@ class MsgDispatcher(MsgDispatcherBase):
                 self.assessor.trial_end(trial_job_id, data['event'] == 'SUCCEEDED')
         return True
 
+    def _handle_final_metric_data(self, data):
+        """Call tuner to process final results
+        """
+        id_ = data['parameter_id']
+        value = data['value']
+        if id_ in _customized_parameter_ids:
+            self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value)
+        else:
+            self.tuner.receive_trial_result(id_, _trial_params[id_], value)
+
     def _handle_intermediate_metric_data(self, data):
+        """Call assessor to process intermediate results
+        """
         if data['type'] != 'PERIODICAL':
             return True
         if self.assessor is None:
@@ -187,5 +194,20 @@ class MsgDispatcher(MsgDispatcherBase):
         if result is AssessResult.Bad:
             _logger.debug('BAD, kill %s', trial_job_id)
             send(CommandType.KillTrialJob, json_tricks.dumps(trial_job_id))
+            # notify tuner
+            _logger.debug('env var: NNI_INCLUDE_INTERMEDIATE_RESULTS: [%s]', os.environ.get('NNI_INCLUDE_INTERMEDIATE_RESULTS'))
+            if os.environ.get('NNI_INCLUDE_INTERMEDIATE_RESULTS') == 'true':
+                self._earlystop_notify_tuner(data)
         else:
             _logger.debug('GOOD')
+
+    def _earlystop_notify_tuner(self, data):
+        """Send last intermediate result as final result to tuner in case the
+        trial is early stopped.
+        """
+        _logger.debug('Early stop notify tuner data: [%s]', data)
+        data['type'] = 'FINAL'
+        if multi_thread_enabled():
+            self._handle_final_metric_data(data)
+        else:
+            self.enqueue_command(CommandType.ReportMetricData, data)

src/sdk/pynni/nni/msg_dispatcher_base.py

@@ -19,14 +19,13 @@
 # ==================================================================================================
 
 #import json_tricks
-import logging
 import os
-from queue import Queue
-import sys
-
+import threading
+import logging
 from multiprocessing.dummy import Pool as ThreadPool
-
+from queue import Queue, Empty
 import json_tricks
+
 from .common import init_logger, multi_thread_enabled
 from .recoverable import Recoverable
 from .protocol import CommandType, receive
@@ -34,57 +33,109 @@ from .protocol import CommandType, receive
 init_logger('dispatcher.log')
 _logger = logging.getLogger(__name__)
 
+QUEUE_LEN_WARNING_MARK = 20
+_worker_fast_exit_on_terminate = True
+
 class MsgDispatcherBase(Recoverable):
     def __init__(self):
         if multi_thread_enabled():
             self.pool = ThreadPool()
             self.thread_results = []
+        else:
+            self.stopping = False
+            self.default_command_queue = Queue()
+            self.assessor_command_queue = Queue()
+            self.default_worker = threading.Thread(target=self.command_queue_worker, args=(self.default_command_queue,))
+            self.assessor_worker = threading.Thread(target=self.command_queue_worker, args=(self.assessor_command_queue,))
+            self.default_worker.start()
+            self.assessor_worker.start()
+            self.worker_exceptions = []
 
     def run(self):
         """Run the tuner.
         This function will never return unless raise.
         """
+        _logger.info('Start dispatcher')
         mode = os.getenv('NNI_MODE')
         if mode == 'resume':
             self.load_checkpoint()
 
         while True:
-            _logger.debug('waiting receive_message')
             command, data = receive()
+            if data:
+                data = json_tricks.loads(data)
+
             if command is None or command is CommandType.Terminate:
                 break
             if multi_thread_enabled():
-                result = self.pool.map_async(self.handle_request_thread, [(command, data)])
+                result = self.pool.map_async(self.process_command_thread, [(command, data)])
                 self.thread_results.append(result)
                 if any([thread_result.ready() and not thread_result.successful() for thread_result in self.thread_results]):
                     _logger.debug('Caught thread exception')
                     break
             else:
-                self.handle_request((command, data))
+                self.enqueue_command(command, data)
 
+        _logger.info('Dispatcher exiting...')
+        self.stopping = True
         if multi_thread_enabled():
             self.pool.close()
             self.pool.join()
+        else:
+            self.default_worker.join()
+            self.assessor_worker.join()
 
         _logger.info('Terminated by NNI manager')
 
-    def handle_request_thread(self, request):
+    def command_queue_worker(self, command_queue):
+        """Process commands in command queues.
+        """
+        while True:
+            try:
+                # set timeout to ensure self.stopping is checked periodically
+                command, data = command_queue.get(timeout=3)
+                try:
+                    self.process_command(command, data)
+                except Exception as e:
+                    _logger.exception(e)
+                    self.worker_exceptions.append(e)
+                    break
+            except Empty:
+                pass
+            if self.stopping and (_worker_fast_exit_on_terminate or command_queue.empty()):
+                break
+
+    def enqueue_command(self, command, data):
+        """Enqueue command into command queues
+        """
+        if command == CommandType.TrialEnd or (command == CommandType.ReportMetricData and data['type'] == 'PERIODICAL'):
+            self.assessor_command_queue.put((command, data))
+        else:
+            self.default_command_queue.put((command, data))
+
+        qsize = self.default_command_queue.qsize()
+        if qsize >= QUEUE_LEN_WARNING_MARK:
+            _logger.warning('default queue length: %d', qsize)
+
+        qsize = self.assessor_command_queue.qsize()
+        if qsize >= QUEUE_LEN_WARNING_MARK:
+            _logger.warning('assessor queue length: %d', qsize)
+
+    def process_command_thread(self, request):
+        """Worker thread to process a command.
+        """
+        command, data = request
         if multi_thread_enabled():
             try:
-                self.handle_request(request)
+                self.process_command(command, data)
             except Exception as e:
                 _logger.exception(str(e))
                 raise
         else:
             pass
 
-    def handle_request(self, request):
-        command, data = request
-
-        _logger.debug('handle request: command: [{}], data: [{}]'.format(command, data))
-
-        if data:
-            data = json_tricks.loads(data)
+    def process_command(self, command, data):
+        _logger.debug('process_command: command: [{}], data: [{}]'.format(command, data))
 
         command_handlers = {
             # Tunner commands:

src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py

@@ -75,7 +75,7 @@ def _pack_parameter(parameter_id, params, customized=False, trial_job_id=None, p
 
 class MultiPhaseMsgDispatcher(MsgDispatcherBase):
     def __init__(self, tuner, assessor=None):
-        super()
+        super(MultiPhaseMsgDispatcher, self).__init__()
         self.tuner = tuner
         self.assessor = assessor
         if assessor is None:

src/sdk/pynni/nni/protocol.py

@@ -42,11 +42,10 @@ class CommandType(Enum):
     NoMoreTrialJobs = b'NO'
     KillTrialJob = b'KI'
 
-
+_lock = threading.Lock()
 try:
     _in_file = open(3, 'rb')
     _out_file = open(4, 'wb')
-    _lock = threading.Lock()
 except OSError:
     _msg = 'IPC pipeline not exists, maybe you are importing tuner/assessor from trial code?'
     import logging
@@ -60,8 +59,7 @@ def send(command, data):
     """
     global _lock
     try:
-        if multi_thread_enabled():
-            _lock.acquire()
+        _lock.acquire()
         data = data.encode('utf8')
         assert len(data) < 1000000, 'Command too long'
         msg = b'%b%06d%b' % (command.value, len(data), data)
@@ -69,8 +67,7 @@ def send(command, data):
         _out_file.write(msg)
         _out_file.flush()
     finally:
-        if multi_thread_enabled():
-            _lock.release()
+        _lock.release()
 
 
 def receive():

src/sdk/pynni/tests/test_assessor.py

@@ -73,11 +73,12 @@ class AssessorTestCase(TestCase):
 
         assessor = NaiveAssessor()
         dispatcher = MsgDispatcher(None, assessor)
-        try:
-            dispatcher.run()
-        except Exception as e:
-            self.assertIs(type(e), AssertionError)
-            self.assertEqual(e.args[0], 'Unsupported command: CommandType.NewTrialJob')
+        nni.msg_dispatcher_base._worker_fast_exit_on_terminate = False
+
+        dispatcher.run()
+        e = dispatcher.worker_exceptions[0]
+        self.assertIs(type(e), AssertionError)
+        self.assertEqual(e.args[0], 'Unsupported command: CommandType.NewTrialJob')
 
         self.assertEqual(_trials, ['A', 'B', 'A'])
         self.assertEqual(_end_trials, [('A', False), ('B', True)])
@@ -90,4 +91,4 @@ class AssessorTestCase(TestCase):
 
 
 if __name__ == '__main__':
-    main()
+    main()
\ No newline at end of file

src/sdk/pynni/tests/test_tuner.py

@@ -88,11 +88,12 @@ class TunerTestCase(TestCase):
 
         tuner = NaiveTuner()
         dispatcher = MsgDispatcher(tuner)
-        try:
-            dispatcher.run()
-        except Exception as e:
-            self.assertIs(type(e), AssertionError)
-            self.assertEqual(e.args[0], 'Unsupported command: CommandType.KillTrialJob')
+        nni.msg_dispatcher_base._worker_fast_exit_on_terminate = False
+
+        dispatcher.run()
+        e = dispatcher.worker_exceptions[0]
+        self.assertIs(type(e), AssertionError)
+        self.assertEqual(e.args[0], 'Unsupported command: CommandType.KillTrialJob')
 
         _reverse_io()  # now we are receiving from Tuner's outgoing stream
         self._assert_params(0, 2, [ ], None)

test/tuner_test.py

@@ -76,8 +76,8 @@ def run(dispatch_type):
     dipsatcher_list = TUNER_LIST if dispatch_type == 'Tuner' else ASSESSOR_LIST
     for dispatcher_name in dipsatcher_list:
         try:
-            # sleep 5 seconds here, to make sure previous stopped exp has enough time to exit to avoid port conflict
-            time.sleep(5)
+            # Sleep here to make sure previous stopped exp has enough time to exit to avoid port conflict
+            time.sleep(6)
             test_builtin_dispatcher(dispatch_type, dispatcher_name)
             print(GREEN + 'Test %s %s: TEST PASS' % (dispatcher_name, dispatch_type) + CLEAR)
         except Exception as error:

tools/nni_cmd/config_schema.py

@@ -58,6 +58,7 @@ Optional('tuner'): Or({
     Optional('classArgs'): {
         'optimize_mode': Or('maximize', 'minimize')
     },
+    Optional('includeIntermediateResults'): bool,
     Optional('gpuNum'): And(int, lambda x: 0 <= x <= 99999),
 },{
     'builtinTunerName': Or('BatchTuner', 'GridSearch'),