Print IPS in auto parallel Engine (#46554)

python/paddle/distributed/auto_parallel/engine.py

@@ -23,7 +23,7 @@ from collections import defaultdict
 import paddle
 import paddle.utils as utils
 
-from paddle import fluid, static
+from paddle import fluid, profiler, static
 from paddle.jit import to_static
 from paddle.metric import Metric
 from paddle.static import InputSpec
@@ -570,7 +570,8 @@ class Engine:
                    step=None,
                    lr=None,
                    fetch_new_names=None,
-                   fetch_sections=None):
+                   fetch_sections=None,
+                   profiler_log=""):
         prefix = "[{}] ".format(mode)
         logs = {}
         if epoch is not None:
@@ -596,7 +597,7 @@ class Engine:
                 else:
                     for i in range(section_start, section_end):
                         logs[fetch_new_names[i] + ": {} "] = outs[i]
-        string = prefix + ''.join(list(logs.keys()))
+        string = prefix + ''.join(list(logs.keys())) + profiler_log
         self._logger.info(string.format(*list(logs.values())))
 
     def fit(self,
@@ -695,6 +696,7 @@ class Engine:
             mode=self.mode)
         lr_scheduler = self._get_lr_scheduler(self.main_program)
 
+        with profiler.Profiler(timer_only=True) as prof:
             for epoch in range(epochs):
                 for step, _ in enumerate(train_dataloader):
                     try:
@@ -708,8 +710,12 @@ class Engine:
                     if lr_scheduler and step % self._k_steps == 0:
                         lr_scheduler.step()
                     lr = self._get_lr(self._lr_optimizer)
+
+                    prof.step()
+
                     self._print_log(outs, self.mode, epoch, step, lr,
-                                fetch_new_names, fetch_sections)
+                                    fetch_new_names, fetch_sections,
+                                    prof.step_info())
 
                 if valid_data and epoch % valid_freq == 0:
                     self.evaluate(valid_data, valid_sample_split, batch_size,