add norm_by_times param to ctc_loss (#32490)

* add norm_by_times param to ctc_loss

* fix doc,test=develop

python/paddle/nn/functional/loss.py

@@ -1023,7 +1023,8 @@ def ctc_loss(log_probs,
              input_lengths,
              label_lengths,
              blank=0,
-             reduction='mean'):
+             reduction='mean',
+             norm_by_times=False):
     """
 
     An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc)
@@ -1038,6 +1039,7 @@ def ctc_loss(log_probs,
         label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64.
         blank (int, optional): The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default is 0.
         reduction (string, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output loss will be divided by the label_lengths, and then return the mean of quotient; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default is ``'mean'``.
+        norm_by_times (bool, default False) – Whether to normalize the gradients by the number of time-step, which is also the sequence’s length. There is no need to normalize the gradients if reduction mode is 'mean'.
 
     Returns:
         Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and  ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``.
@@ -1101,7 +1103,7 @@ def ctc_loss(log_probs,
 
     """
 
-    loss_out = fluid.layers.warpctc(log_probs, labels, blank, False,
+    loss_out = fluid.layers.warpctc(log_probs, labels, blank, norm_by_times,
                                     input_lengths, label_lengths)
 
     loss_out = fluid.layers.squeeze(loss_out, [-1])

python/paddle/nn/layer/loss.py

@@ -1060,6 +1060,7 @@ class CTCLoss(fluid.dygraph.Layer):
         labels (Tensor): The ground truth sequence with padding, which must be a 3-D Tensor. The tensor shape is [batch_size, max_label_length], where max_label_length is the longest length of label sequence. The data type must be int32.
         input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64.
         label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64.
+        norm_by_times (bool, default false) – Whether to normalize the gradients by the number of time-step, which is also the sequence’s length. There is no need to normalize the gradients if reduction mode is 'mean'.
 
     Returns:
         Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and  ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``.
@@ -1122,10 +1123,20 @@ class CTCLoss(fluid.dygraph.Layer):
         self.blank = blank
         self.reduction = reduction
 
-    def forward(self, log_probs, labels, input_lengths, label_lengths):
-        return paddle.nn.functional.ctc_loss(log_probs, labels, input_lengths,
-                                             label_lengths, self.blank,
-                                             self.reduction)
+    def forward(self,
+                log_probs,
+                labels,
+                input_lengths,
+                label_lengths,
+                norm_by_times=False):
+        return paddle.nn.functional.ctc_loss(
+            log_probs,
+            labels,
+            input_lengths,
+            label_lengths,
+            self.blank,
+            self.reduction,
+            norm_by_times=norm_by_times)
 
 
 class SmoothL1Loss(fluid.dygraph.Layer):