[wingman -> rllib] Improved stats changes in AsyncSamplesOptimizer

python/ray/rllib/optimizers/async_samples_optimizer.py

@@ -50,11 +50,14 @@ def _init(self,
               num_sgd_iter=1,
               minibatch_buffer_size=1,
               _fake_gpus=False):
-        self.learning_started = False
         self.train_batch_size = train_batch_size
         self.sample_batch_size = sample_batch_size
         self.broadcast_interval = broadcast_interval
 
+        self._stats_start_time = time.time()
+        self._last_stats_time = {}
+        self._last_stats_sum = {}
+
         if num_gpus > 1 or num_data_loader_buffers > 1:
             logger.info(
                 "Enabling multi-GPU mode, {} GPUs, {} parallel loaders".format(
@@ -82,10 +85,12 @@ def _init(self,
         assert len(self.remote_evaluators) > 0
 
         # Stats
-        self.timers = {k: TimerStat() for k in ["train", "sample"]}
+        self._optimizer_step_timer = TimerStat()
         self.num_weight_syncs = 0
         self.num_replayed = 0
-        self.learning_started = False
+        self._stats_start_time = time.time()
+        self._last_stats_time = {}
+        self._last_stats_val = {}
 
         # Kick off async background sampling
         self.sample_tasks = TaskPool()
@@ -108,19 +113,36 @@ def _init(self,
         self.replay_buffer_num_slots = replay_buffer_num_slots
         self.replay_batches = []
 
+    def add_stat_val(self, key, val):
+        if key not in self._last_stats_sum:
+            self._last_stats_sum[key] = 0
+            self._last_stats_time[key] = self._stats_start_time
+        self._last_stats_sum[key] += val
+
+    def get_mean_stats_and_reset(self):
+        now = time.time()
+        mean_stats = {
+            key: round(val / (now - self._last_stats_time[key]), 3)
+            for key, val in self._last_stats_sum.items()
+        }
+
+        for key in self._last_stats_sum.keys():
+            self._last_stats_sum[key] = 0
+            self._last_stats_time[key] = time.time()
+
+        return mean_stats
+
     @override(PolicyOptimizer)
     def step(self):
         assert self.learner.is_alive()
-        start = time.time()
-        sample_timesteps, train_timesteps = self._step()
-        time_delta = time.time() - start
-        self.timers["sample"].push(time_delta)
-        self.timers["sample"].push_units_processed(sample_timesteps)
+        with self._optimizer_step_timer:
+            sample_timesteps, train_timesteps = self._step()
+
+        if sample_timesteps > 0:
+            self.add_stat_val("sample_throughput", sample_timesteps)
         if train_timesteps > 0:
-            self.learning_started = True
-        if self.learning_started:
-            self.timers["train"].push(time_delta)
-            self.timers["train"].push_units_processed(train_timesteps)
+            self.add_stat_val("train_throughput", train_timesteps)
+
         self.num_steps_sampled += sample_timesteps
         self.num_steps_trained += train_timesteps
 
@@ -130,27 +152,24 @@ def stop(self):
 
     @override(PolicyOptimizer)
     def stats(self):
+        def timer_to_ms(timer):
+            return round(1000 * timer.mean, 3)
+
         timing = {
-            "{}_time_ms".format(k): round(1000 * self.timers[k].mean, 3)
-            for k in self.timers
+            "optimizer_step_time_ms": timer_to_ms(self._optimizer_step_timer),
+            "learner_grad_time_ms": timer_to_ms(self.learner.grad_timer),
+            "learner_load_time_ms": timer_to_ms(self.learner.load_timer),
+            "learner_load_wait_time_ms": timer_to_ms(
+                self.learner.load_wait_timer),
+            "learner_dequeue_time_ms": timer_to_ms(self.learner.queue_timer),
         }
-        timing["learner_grad_time_ms"] = round(
-            1000 * self.learner.grad_timer.mean, 3)
-        timing["learner_load_time_ms"] = round(
-            1000 * self.learner.load_timer.mean, 3)
-        timing["learner_load_wait_time_ms"] = round(
-            1000 * self.learner.load_wait_timer.mean, 3)
-        timing["learner_dequeue_time_ms"] = round(
-            1000 * self.learner.queue_timer.mean, 3)
-        stats = {
-            "sample_throughput": round(self.timers["sample"].mean_throughput,
-                                       3),
-            "train_throughput": round(self.timers["train"].mean_throughput, 3),
+        stats = dict({
             "num_weight_syncs": self.num_weight_syncs,
             "num_steps_replayed": self.num_replayed,
             "timing_breakdown": timing,
             "learner_queue": self.learner.learner_queue_size.stats(),
-        }
+        }, **self.get_mean_stats_and_reset())
+        self._last_stats_val.clear()
         if self.learner.stats:
             stats["learner"] = self.learner.stats
         return dict(PolicyOptimizer.stats(self), **stats)