moved inherit_stack_trace inside alpha_merge decorator

theano/gpuarray/dnn.py

@@ -3406,21 +3406,18 @@ optdb.register('local_dnna_conv_inplace',
 @register_opt('cudnn')
 @alpha_merge(GpuDnnConv, alpha_in=4, beta_in=5)
 def local_dnn_conv_alpha_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [GpuDnnConv(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 
 @register_opt('cudnn')
 @alpha_merge(GpuDnnConvGradW, alpha_in=4, beta_in=5)
 def local_dnn_convw_alpha_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [GpuDnnConvGradW(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 
 @register_opt('cudnn')
 @alpha_merge(GpuDnnConvGradI, alpha_in=4, beta_in=5)
 def local_dnn_convi_alpha_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [GpuDnnConvGradI(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 

theano/gpuarray/opt_util.py

@@ -185,6 +185,7 @@ def alpha_merge(cls, alpha_in, beta_in):
                 except NotScalarConstantError:
                     inputs[alpha_in] = lr * targ.inputs[alpha_in]
                     inputs[beta_in] = lr * targ.inputs[beta_in]
+                with inherit_stack_trace(node.outputs):
                     return maker(targ, *inputs)
         return opt
     return wrapper