add timer info

deepspeech/training/gradclip.py

@@ -48,9 +48,8 @@ class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
             sum_square_list.append(sum_square)
 
             # debug log
-            if i < 10:
-                logger.debug(
-                    f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }")
+            logger.debug(
+                f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }")
 
         # all parameters have been filterd out
         if len(sum_square_list) == 0:
@@ -77,9 +76,8 @@ class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
             params_and_grads.append((p, new_grad))
 
             # debug log
-            if i < 10:
-                logger.debug(
-                    f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}"
-                )
+            logger.debug(
+                f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}"
+            )
 
         return params_and_grads

deepspeech/training/timer.py

@@ -27,7 +27,7 @@ class Timer():
             do some thing
     """
 
-    def __init__(self, message):
+    def __init__(self, message=None):
         self.message = message
 
     def duration(self) -> str:
@@ -40,7 +40,8 @@ class Timer():
         return self
 
     def __exit__(self, type, value, traceback):
-        logger.info(self.message.format(self.duration()))
+        if self.message:
+            logger.info(self.message.format(self.duration()))
 
     def __call__(self) -> float:
         return time.time() - self.start

deepspeech/training/trainer.py

@@ -185,46 +185,47 @@ class Trainer():
 
     def train(self):
         """The training process control by epoch."""
-        with Timer("Load/Init Model: {}"):
-            from_scratch = self.resume_or_scratch()
-            if from_scratch:
-                # save init model, i.e. 0 epoch
-                self.save(tag='init', infos=None)
-            self.lr_scheduler.step(self.epoch)
-            if self.parallel and hasattr(self.train_loader, "batch_sampler"):
-                self.train_loader.batch_sampler.set_epoch(self.epoch)
+        from_scratch = self.resume_or_scratch()
+        if from_scratch:
+            # save init model, i.e. 0 epoch
+            self.save(tag='init', infos=None)
+        self.lr_scheduler.step(self.epoch)
+        if self.parallel and hasattr(self.train_loader, "batch_sampler"):
+            self.train_loader.batch_sampler.set_epoch(self.epoch)
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.training.n_epoch:
-            self.model.train()
-            try:
-                data_start_time = time.time()
-                for batch_index, batch in enumerate(self.train_loader):
-                    dataload_time = time.time() - data_start_time
-                    msg = "Train: Rank: {}, ".format(dist.get_rank())
-                    msg += "epoch: {}, ".format(self.epoch)
-                    msg += "step: {}, ".format(self.iteration)
-                    msg += "batch : {}/{}, ".format(batch_index + 1,
-                                                    len(self.train_loader))
-                    msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
-                    msg += "data time: {:>.3f}s, ".format(dataload_time)
-                    self.train_batch(batch_index, batch, msg)
+            with Timer("Epoch-Train Time Cost: {}"):
+                self.model.train()
+                try:
                     data_start_time = time.time()
-            except Exception as e:
-                logger.error(e)
-                raise e
-
-            total_loss, num_seen_utts = self.valid()
-            if dist.get_world_size() > 1:
-                num_seen_utts = paddle.to_tensor(num_seen_utts)
-                # the default operator in all_reduce function is sum.
-                dist.all_reduce(num_seen_utts)
-                total_loss = paddle.to_tensor(total_loss)
-                dist.all_reduce(total_loss)
-                cv_loss = total_loss / num_seen_utts
-                cv_loss = float(cv_loss)
-            else:
-                cv_loss = total_loss / num_seen_utts
+                    for batch_index, batch in enumerate(self.train_loader):
+                        dataload_time = time.time() - data_start_time
+                        msg = "Train: Rank: {}, ".format(dist.get_rank())
+                        msg += "epoch: {}, ".format(self.epoch)
+                        msg += "step: {}, ".format(self.iteration)
+                        msg += "batch : {}/{}, ".format(batch_index + 1,
+                                                        len(self.train_loader))
+                        msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
+                        msg += "data time: {:>.3f}s, ".format(dataload_time)
+                        self.train_batch(batch_index, batch, msg)
+                        data_start_time = time.time()
+                except Exception as e:
+                    logger.error(e)
+                    raise e
+
+            with Timer("Eval Time Cost: {}"):
+                total_loss, num_seen_utts = self.valid()
+                if dist.get_world_size() > 1:
+                    num_seen_utts = paddle.to_tensor(num_seen_utts)
+                    # the default operator in all_reduce function is sum.
+                    dist.all_reduce(num_seen_utts)
+                    total_loss = paddle.to_tensor(total_loss)
+                    dist.all_reduce(total_loss)
+                    cv_loss = total_loss / num_seen_utts
+                    cv_loss = float(cv_loss)
+                else:
+                    cv_loss = total_loss / num_seen_utts
 
             logger.info(
                 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))