[chat] polish performance evaluator (#3647)

applications/Chat/coati/trainer/callbacks/performance_evaluator.py

@@ -19,6 +19,14 @@ def print_rank_0(*args, **kwargs) -> None:
         print(*args, **kwargs)
 
 
+def divide(x: float, y: float) -> float:
+    if y == 0:
+        return float('inf')
+    elif y == float('inf'):
+        return float('nan')
+    return x / y
+
+
 @torch.no_grad()
 def all_reduce_mean(x: float, world_size: int) -> float:
     if world_size == 1:
@@ -29,6 +37,24 @@ def all_reduce_mean(x: float, world_size: int) -> float:
     return tensor.item()
 
 
+class Timer:
+
+    def __init__(self) -> None:
+        self.start_time: Optional[float] = None
+        self.duration: float = 0.
+
+    def start(self) -> None:
+        self.start_time = time()
+
+    def end(self) -> None:
+        assert self.start_time is not None
+        self.duration += time() - self.start_time
+        self.start_time = None
+
+    def reset(self) -> None:
+        self.duration = 0.
+
+
 class PerformanceEvaluator(Callback):
     """
         Callback for valuate the performance of the model.
@@ -58,27 +84,34 @@ class PerformanceEvaluator(Callback):
         self.ignore_episodes = ignore_episodes
         self.disable: bool = False
 
-        self.make_experience_duration: float = 0.
+        self.overall_timer = Timer()
-        self.make_experience_start_time: Optional[float] = None
+        self.make_experience_timer = Timer()
+        self.learn_timer = Timer()
         self.make_experience_num_samples: int = 0
         self.make_experience_flop: int = 0
-        self.learn_duration: float = 0.
-        self.learn_start_time: Optional[float] = None
         self.learn_num_samples: int = 0
         self.learn_flop: int = 0
 
     def on_episode_start(self, episode: int) -> None:
         self.disable = self.ignore_episodes > 0 and episode < self.ignore_episodes
+        if self.disable:
+            return
+        self.overall_timer.start()
+
+    def on_episode_end(self, episode: int) -> None:
+        if self.disable:
+            return
+        self.overall_timer.end()
 
     def on_make_experience_start(self) -> None:
         if self.disable:
             return
-        self.make_experience_start_time = time()
+        self.make_experience_timer.start()
 
     def on_make_experience_end(self, experience: Experience) -> None:
         if self.disable:
             return
-        self.make_experience_duration += time() - self.make_experience_start_time
+        self.make_experience_timer.end()
 
         batch_size, seq_len = experience.sequences.shape
 
@@ -101,12 +134,12 @@ class PerformanceEvaluator(Callback):
     def on_learn_batch_start(self) -> None:
         if self.disable:
             return
-        self.learn_start_time = time()
+        self.learn_timer.start()
 
     def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None:
         if self.disable:
             return
-        self.learn_duration += time() - self.learn_start_time
+        self.learn_timer.end()
 
         batch_size, seq_len = experience.sequences.shape
 
@@ -118,16 +151,33 @@ class PerformanceEvaluator(Callback):
         self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint))
 
     def on_fit_end(self) -> None:
-        avg_make_experience_duration = all_reduce_mean(self.make_experience_duration, self.world_size)
+        avg_make_experience_duration = all_reduce_mean(self.make_experience_timer.duration, self.world_size)
-        avg_learn_duration = all_reduce_mean(self.learn_duration, self.world_size)
+        avg_learn_duration = all_reduce_mean(self.learn_timer.duration, self.world_size)
+        avg_overall_duration = all_reduce_mean(self.overall_timer.duration, self.world_size)
 
-        avg_make_experience_throughput = self.make_experience_num_samples / (avg_make_experience_duration + 1e-12)
+        avg_make_experience_throughput = self.make_experience_num_samples * \
+            self.world_size / (avg_make_experience_duration + 1e-12)
         avg_make_experience_tflops = self.make_experience_flop / 1e12 / (avg_make_experience_duration + 1e-12)
 
-        avg_learn_throughput = self.learn_num_samples / (avg_learn_duration + 1e-12)
+        avg_learn_throughput = self.learn_num_samples * self.world_size / (avg_learn_duration + 1e-12)
         avg_learn_tflops = self.learn_flop / 1e12 / (avg_learn_duration + 1e-12)
 
+        num_effective_samples = min(self.learn_num_samples, self.make_experience_num_samples) * self.world_size
+
+        avg_overall_throughput = num_effective_samples / (avg_overall_duration + 1e-12)
+
+        overall_time_per_sample = divide(1, avg_overall_throughput)
+        make_experience_time_per_sample = divide(avg_make_experience_duration, num_effective_samples)
+        learn_time_per_sample = divide(avg_learn_duration, num_effective_samples)
+
         print_rank_0(
-            f'Making experience throughput: {avg_make_experience_throughput:.3f} samples/sec, TFLOPS: {avg_make_experience_tflops:.3f}'
+            f'Performance summary:\n' +
+            f'Generate {self.make_experience_num_samples * self.world_size} samples, throughput: {avg_make_experience_throughput:.2f} samples/s, TFLOPS per GPU: {avg_make_experience_tflops:.2f}\n'
+            +
+            f'Train {self.learn_num_samples * self.world_size} samples, throughput: {avg_learn_throughput:.2f} samples/s, TFLOPS per GPU: {avg_learn_tflops:.2f}\n'
+            + f'Overall throughput: {avg_overall_throughput:.2f} samples/s\n' +
+            f'Overall time per sample: {overall_time_per_sample:.2f} s\n' +
+            f'Make experience time per sample: {make_experience_time_per_sample:.2f} s, {make_experience_time_per_sample/overall_time_per_sample*100:.2f}%\n'
+            +
+            f'Learn time per sample: {learn_time_per_sample:.2f} s, {learn_time_per_sample/overall_time_per_sample*100:.2f}%'
         )
-        print_rank_0(f'Learning throughput: {avg_learn_throughput:.3f} samples/sec, TFLOPS: {avg_learn_tflops:.3f}')