turn off cudnn by default

apache · Jan 27, 2019 · 8a7707c · 8a7707c
1 parent 54f6e23
commit 8a7707c
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Showing 2 changed files with 24 additions and 6 deletions.
diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
@@ -77,7 +77,7 @@ struct DropoutParam : public dmlc::Parameter<DropoutParam> {
     .describe("Whether to only turn on dropout during training or to also turn on for inference.");
     DMLC_DECLARE_FIELD(axes).set_default(TShape())
     .describe("Axes for variational dropout kernel.");
-    DMLC_DECLARE_FIELD(cudnn_off).set_default(dmlc::optional<bool>(false))
+    DMLC_DECLARE_FIELD(cudnn_off).set_default(dmlc::optional<bool>(true))
     .describe("Whether to turn off cudnn in dropout operator.");
   }
 };  // struct DropoutParam

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
@@ -5902,10 +5902,10 @@ def check_correctness(executor, input, ratio):
         elif ratio == 0:
             assert output_zeroes == 0
 
-    def check_dropout_ratio(ratio, shape):
+    def check_dropout_ratio(ratio, shape, cudnn_off=True):
         # test dropout
         x = mx.sym.var('data')
-        y = mx.sym.Dropout(x, p=ratio)
+        y = mx.sym.Dropout(x, p=ratio, cudnn_off=cudnn_off)
         exe = y.simple_bind(ctx=default_context(), data=shape)
 
         if ratio == 1:
@@ -5943,7 +5943,7 @@ def check_dropout_ratio(ratio, shape):
 
             # test permanent dropout
             x = mx.sym.var('data')
-            y = mx.sym.Dropout(x, p=ratio, mode='always')
+            y = mx.sym.Dropout(x, p=ratio, mode='always', cudnn_off=cudnn_off)
             exe = y.simple_bind(ctx=default_context(), data=shape)
 
             exe.arg_arrays[0][:] = 1
@@ -5968,13 +5968,13 @@ def get_slice(x, axis, idx):
                 ix += (slice(None, None, None),)
         return x[ix]
 
-    def check_dropout_axes(ratio, shape, axes):
+    def check_dropout_axes(ratio, shape, axes, cudnn_off=False):
         compactshape = list(shape)
         for axis in axes:
             compactshape[axis] = 1
         compactx = mx.random.uniform(shape=tuple(compactshape))
         broadcastx = compactx.broadcast_to(shape)
-        dropouty = mx.nd.Dropout(broadcastx, p=ratio, axes=axes)
+        dropouty = mx.nd.Dropout(broadcastx, p=ratio, axes=axes, cudnn_off=cudnn_off)
         for axis in axes:
             target = get_slice(dropouty, axis, 0).asnumpy()
             for i in range(1, shape[axis]):
@@ -5986,6 +5986,11 @@ def check_dropout_axes(ratio, shape, axes):
     check_dropout_ratio(1.0, shape)
     check_dropout_ratio(0.75, shape)
     check_dropout_ratio(0.25, shape)
+    check_dropout_ratio(0.5, shape, cudnn_off=False)
+    check_dropout_ratio(0.0, shape, cudnn_off=False)
+    check_dropout_ratio(1.0, shape, cudnn_off=False)
+    check_dropout_ratio(0.75, shape, cudnn_off=False)
+    check_dropout_ratio(0.25, shape, cudnn_off=False)
 
     nshape = (10, 10, 10, 10)
     with mx.autograd.train_mode():
@@ -6002,6 +6007,19 @@ def check_dropout_axes(ratio, shape, axes):
         check_dropout_axes(0.25, nshape, axes = (0, 1, 2))
         check_dropout_axes(0.25, nshape, axes = (0, 2, 3))
         check_dropout_axes(0.25, nshape, axes = (1, 2, 3))
+        check_dropout_axes(0.25, nshape, axes = (0,), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (1,), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (2,), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (3,), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (0, 1), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (0, 2), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (0, 3), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (1, 2), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (1, 3), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (2, 3), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (0, 1, 2), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (0, 2, 3), cudnn_off=False)
+        check_dropout_axes(0.25, nshape, axes = (1, 2, 3), cudnn_off=False)
 
 
 @unittest.skip("test fails intermittently. temporarily disabled till it gets fixed. tracked at /~https://github.com/apache/incubator-mxnet/issues/11290")