Fix use of multi_tensor_l2norm, remove test using deprecated syntax

apex/amp/_process_optimizer.py

@@ -263,10 +263,11 @@ def post_backward_with_master_weights_FusedAdam(self, scaler):
     norm_groups = []
     skip = False
     for grad_group in stash.grads:
-        norm = multi_tensor_applier(
+        norm, _ = multi_tensor_applier(
             stash.multi_tensor_l2norm,
             stash.dummy_overflow_buf,
-            [grad_group])
+            [grad_group],
+            False)
         # Still syncing here for now.
         norm = float(norm)
         norm_groups.append(norm)

tests/L0/run_amp/test_basic_casts.py

@@ -137,26 +137,6 @@ class TestTensorCasts(unittest.TestCase):
         fn = lambda x: x.sum()
         run_layer_test(self, [fn], ALWAYS_FLOAT, (self.b, self.h))
 
-class TestDisabledCasts(unittest.TestCase):
-    def setUp(self):
-        self.handle = amp.init(enabled=False)
-        common_init(self)
-
-    def test_disabled_linear(self):
-        m = nn.Linear(self.h, self.h)
-        f = ft.partial(F.linear, weight=m.weight, bias=m.bias)
-        input_shape = (self.b, self.h)
-
-        for fn in [m, f]:
-            x = torch.randn(input_shape, dtype=torch.float).requires_grad_()
-            y = fn(x)
-            self.assertEqual(y.type(), FLOAT)
-            y.sum().backward()
-            self.assertEqual(x.grad.type(), FLOAT)
-
-            x = torch.randn(input_shape, dtype=torch.half).requires_grad_()
-            self.assertRaises(RuntimeError, fn, x)
-
     # TODO: maybe more tests on disabled casting?
 
 if __name__ == '__main__':

tests/L1/cross_product/run.sh

 #!/bin/bash
 
-DATADIR="/home/mcarilli/Desktop/pt18data/apex_stale/examples/imagenet/bare_metal_train_val/"
+# DATADIR="/home/mcarilli/Desktop/pt18data/apex_stale/examples/imagenet/bare_metal_train_val/"
 # DATADIR="/opt/home/apex/examples/imagenet/"
 cp ../common/* .
-bash run_test.sh single_gpu $1 $DATADIR yes
+bash run_test.sh single_gpu $1