Merge pull request #2519 from bartvm/values_eq_approx_move

theano/sandbox/cuda/opt.py

@@ -1195,6 +1195,23 @@ def local_conv_fft_full(node):
         return
 
 
+def values_eq_approx_high_tol(a, b):
+    """This fct is needed to don't have DebugMode raise useless
+    error due to ronding error.
+
+    This happen as We reduce on the two last dimensions, so this
+    can raise the absolute error if the number of element we
+    reduce on is significant.
+
+    """
+    assert a.ndim == 4
+    atol = None
+    if a.shape[-1] * a.shape[-2] > 100:
+        # For float32 the default atol is 1e-5
+        atol = 3e-5
+    return CudaNdarrayType.values_eq_approx(a, b, atol=atol)
+
+
 @local_optimizer([gpu_from_host, conv.ConvOp])
 def local_gpu_conv(node):
     """
@@ -1244,22 +1261,6 @@ def local_gpu_conv(node):
                 return make_graph
         return ret
 
-    def values_eq_approx(a, b):
-        """This fct is needed to don't have DebugMode raise useless
-        error due to ronding error.
-
-        This happen as We reduce on the two last dimensions, so this
-        can raise the absolute error if the number of element we
-        reduce on is significant.
-
-        """
-        assert a.ndim == 4
-        atol = None
-        if a.shape[-1] * a.shape[-2] > 100:
-            #For float32 the default atol is 1e-5
-            atol = 3e-5
-        return CudaNdarrayType.values_eq_approx(a, b, atol=atol)
-
     if isinstance(node.op, GpuFromHost):
         #gpu_from_host(conv) -> gpu_conv(gpu_from_host)
         host_input = node.inputs[0]
@@ -1272,7 +1273,7 @@ def local_gpu_conv(node):
                            gpu_from_host(kern))
             out = tensor.patternbroadcast(out,
                                           node.outputs[0].broadcastable)
-            out.values_eq_approx = values_eq_approx
+            out.values_eq_approx = values_eq_approx_high_tol
             # in some case the ConvOp broadcast the last 2 dimensions
             # differently then the gpu ConvOp
             return [out]
@@ -1291,7 +1292,7 @@ def local_gpu_conv(node):
             out = tensor.patternbroadcast(
                 host_from_gpu(out),
                 node.outputs[0].broadcastable)
-            out.values_eq_approx = values_eq_approx
+            out.values_eq_approx = values_eq_approx_high_tol
             # in some case the ConvOp broadcast the last 2 dimensions
             # differently then the gpu ConvOp
             return [out]