Only warn once in LossScaler constructor

apex/amp/scaler.py

@@ -31,13 +31,15 @@ class LossScaler(object):
         try:
             import amp_C
             LossScaler.has_fused_kernel = True
-            LossScaler.scale_check_overflow = amp_C.scale_check_overflow
+            LossScaler.scale_check_overflow_cuda = amp_C.scale_check_overflow
             self._overflow_buf = torch.cuda.ByteTensor(1024,)
         except ImportError as err:
-            print("Warning:  Amp fused downscale kernel is unavailable, possibly because apex "
-                  "was installed without --cuda_ext.  Using Python fallback.  ImportError was: ", err)
+            if not LossScaler.warned_no_fused_kernel:
+                print("Warning:  Amp fused downscale kernel is unavailable, possibly because apex "
+                      "was installed without --cuda_ext.  Using Python fallback.  ImportError was: ",
+                      err)
             LossScaler.has_fused_kernel = False
-            LossScaler.scale_check_overflow = scale_check_overflow_python
+            LossScaler.warned_no_fused_kernel = True
 
     def loss_scale(self):
         return self._loss_scale
@@ -49,19 +51,19 @@ class LossScaler(object):
         for p in iter_params(param_groups):
             if p.grad is not None:
                 if LossScaler.has_fused_kernel and p.grad.data.type() == "torch.cuda.FloatTensor":
-                    LossScaler.scale_check_overflow(p.grad.data,
-                                                    1. / scale,
-                                                    self._overflow_buf)
+                    LossScaler.scale_check_overflow_cuda(p.grad.data,
+                                                         1./scale,
+                                                         self._overflow_buf)
                 else:
-                    if p.grad.data.type() != "torch.cuda.FloatTensor"
-                            and not LossScaler.warned_fp16_grad:
+                    if (p.grad.data.type() != "torch.cuda.FloatTensor"
+                            and not LossScaler.warned_fp16_grad):
                         logger = logging.getLogger("apex.amp")
                         logger.warning("Incoming grads are not fp32 (not master grads). "
                                        "Downscaling non-fp32 grads may indicate an error. "
                                        "When using Amp, you don't need to call .half() on your model.")
                         LossScaler.warned_fp16_grad = True
-                    self._has_overflow = LossScaler.scale_check_overflow(p.grad.data,
-                                                                         1. / scale)
+                    self._has_overflow = scale_check_overflow_python(p.grad.data,
+                                                                     1./scale)
                     if self._has_overflow:
                         break