fix loss normalization for ddp training

haoneng.lhn

2023-12-08 acb9a0fec8d8a4dabeedcbb8e08c26f66d7083f0

 funasr/models/e2e_asr_paraformer.py

@@ -255,7 +255,7 @@

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

@@ -867,7 +867,7 @@

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

@@ -1494,7 +1494,7 @@

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

@@ -1765,7 +1765,7 @@

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

@@ -1967,7 +1967,7 @@

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight