Raise an error in any case if precision is float16 for grad convs.

theano/gpuarray/dnn.py

@@ -982,9 +982,6 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
 
     """
 
-    # Establish dtype in which to perform the computation of the convolution
-    precision = get_precision(precision, [img, kerns])
-
     if workmem is not None:
         if algo is not None:
             raise ValueError("You can't use both algo and workmem")
@@ -1008,8 +1005,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
                    shape_i(img, 3, fgraph) - shape_i(kerns, 3, fgraph) + 1)
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        if precision == 'float16':
-            precision = 'float32'
+        precision = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=(1, 1),
                               conv_mode='cross', precision=precision)(out.shape)
         conv = GpuDnnConvGradW()(img, kerns, out, desc)
@@ -1029,8 +1025,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
                    shape_i(img, 3, fgraph) + (shape_i(kerns, 3, fgraph) - 1) * dilation[1])
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        if precision == 'float16':
-            precision = 'float32'
+        precision = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=dilation,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
         return GpuDnnConvGradI()(kerns, img, out, desc)
@@ -1040,6 +1035,8 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
     # if the img contains negative strides
     img = gpu_contiguous(img)
     kerns = gpu_contiguous(kerns)
+    # Establish dtype in which to perform the computation of the convolution
+    precision = get_precision(precision, [img, kerns])
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample, dilation=dilation,
                           conv_mode=conv_mode, precision=precision,
                           num_groups=num_groups)(kerns.shape)
@@ -1113,9 +1110,6 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
 
     """
 
-    # Establish dtype in which to perform the computation of the convolution
-    precision = get_precision(precision, [img, kerns])
-
     fgraph = getattr(img, 'fgraph', None) or getattr(kerns, 'fgraph', None)
     ctx_name = infer_context_name(img, kerns)
     if (border_mode == 'valid' and subsample == (1, 1, 1) and dilation == (1, 1, 1) and
@@ -1135,8 +1129,7 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
                    shape_i(img, 4, fgraph) - shape_i(kerns, 4, fgraph) + 1)
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        if precision == 'float16':
-            precision = 'float32'
+        precision = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=(1, 1, 1),
                               conv_mode='cross', precision=precision)(out.shape)
         conv = GpuDnnConvGradW()(img, kerns, out, desc)
@@ -1157,8 +1150,7 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
                    shape_i(img, 4, fgraph) + (shape_i(kerns, 4, fgraph) - 1) * dilation[2])
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        if precision == 'float16':
-            precision = 'float32'
+        precision = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=dilation,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
         return GpuDnnConvGradI()(kerns, img, out, desc)
@@ -1168,6 +1160,8 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
     # if the img contains negative strides
     img = gpu_contiguous(img)
     kerns = gpu_contiguous(kerns)
+    # Establish dtype in which to perform the computation of the convolution
+    precision = get_precision(precision, [img, kerns])
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample, dilation=dilation,
                           conv_mode=conv_mode, precision=precision,
                           num_groups=num_groups)(kerns.shape)

theano/gpuarray/tests/test_dnn.py

@@ -171,17 +171,29 @@ def test_dnn_conv_inplace():
     assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
 
 
-def test_dnn_conv_invalid_precision():
-    img = T.tensor4()
-    kerns = T.tensor4()
-    topgrad = T.tensor4()
-    shape = (1, 2, 3, 4)
+def run_dnn_conv_invalid_precision(ndim):
+    bc = (False,) * (ndim + 2)
+    img = T.tensor(theano.config.floatX, broadcastable=bc)
+    kerns = T.tensor(theano.config.floatX, broadcastable=bc)
+    topgrad = T.tensor(theano.config.floatX, broadcastable=bc)
+    shape = np.arange(ndim + 2)
+    if ndim == 2:
+        dnn_conv_func = dnn.dnn_conv
+        dnn_gradw_func = dnn.dnn_gradweight
+        dnn_gradi_func = dnn.dnn_gradinput
+    elif ndim == 3:
+        dnn_conv_func = dnn.dnn_conv3d
+        dnn_gradw_func = dnn.dnn_gradweight3d
+        dnn_gradi_func = dnn.dnn_gradinput3d
 
     def dnn_gradw(precision):
-        return dnn.dnn_gradweight(img, topgrad, shape, precision=precision)
+        return dnn_gradw_func(img, topgrad, shape, precision=precision)
 
     def dnn_gradi(precision):
-        return dnn.dnn_gradinput(kerns, topgrad, shape, precision=precision)
+        return dnn_gradi_func(kerns, topgrad, shape, precision=precision)
+
+    def dnn_conv(precision, border_mode, direction_hint):
+        return dnn_conv_func(img, kerns, border_mode=border_mode, direction_hint=direction_hint, precision=precision)
 
     dnn_gradw('float64')
     dnn_gradw('float32')
@@ -191,6 +203,22 @@ def test_dnn_conv_invalid_precision():
     dnn_gradi('float32')
     assert_raises(TypeError, dnn_gradi, 'float16')
 
+    for precision in ('float64', 'float32'):
+        dnn_conv(precision, 'valid', None)
+        dnn_conv(precision, 'valid', 'bprop weights')
+        dnn_conv(precision, 'full', None)
+        dnn_conv(precision, 'full', 'forward!')
+
+    dnn_conv('float16', 'valid', None)
+    assert_raises(TypeError, dnn_conv, 'float16', 'valid', 'bprop weights')
+    assert_raises(TypeError, dnn_conv, 'float16', 'full', None)
+    dnn_conv('float16', 'full', 'forward!')
+
+
+def test_dnn_conv_invalid_precision():
+    yield (run_dnn_conv_invalid_precision, 2)
+    yield (run_dnn_conv_invalid_precision, 3)
+
 
 def test_pooling():
     if not dnn.dnn_available(test_ctx_name):