Merge pull request #20 from NVIDIA/fp16_path

Fp16 patch, not path!

loss_scaler.py

@@ -51,11 +51,10 @@ class DynamicLossScaler:
 
     # `x` is a torch.Tensor
     def _has_inf_or_nan(x):
-        inf_count = torch.sum(x.abs() == float('inf'))
+        cpu_sum = float(x.float().sum())
-        if inf_count > 0:
+        if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
             return True
-        nan_count = torch.sum(x != x)
+        return False
-        return nan_count > 0
 
     # `overflow` is boolean indicating whether we overflowed in gradient
     def update_scale(self, overflow):

train.py

@@ -2,6 +2,7 @@ import os
 import time
 import argparse
 import math
+from numpy import finfo
 
 import torch
 from distributed import DistributedDataParallel
@@ -77,7 +78,9 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
 
 def load_model(hparams):
     model = Tacotron2(hparams).cuda()
-    model = batchnorm_to_float(model.half()) if hparams.fp16_run else model
+    if hparams.fp16_run:
+        model = batchnorm_to_float(model.half())
+        model.decoder.attention_layer.score_mask_value = float(finfo('float16').min)
 
     if hparams.distributed_run:
         model = DistributedDataParallel(model)
@@ -276,7 +279,7 @@ if __name__ == '__main__':
     torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
 
     print("FP16 Run:", hparams.fp16_run)
-    print("Dynamic Loss Scaling", hparams.dynamic_loss_scaling)
+    print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
     print("Distributed Run:", hparams.distributed_run)
     print("cuDNN Enabled:", hparams.cudnn_enabled)
     print("cuDNN Benchmark:", hparams.cudnn_benchmark)