minor changes

theano/gpuarray/opt.py

@@ -265,10 +265,13 @@ def op_lifter(OP, cuda_only=False):
                             return x.transfer('cpu')
                     # copy stack traces onto gpu outputs
                     # also copy the stack traces onto HostFromGpu outputs
+                    on_cpu = []
                     for old_output, new_output in zip(node.outputs, new_outputs):
                         copy_stack_trace(old_output, new_output)
-                        copy_stack_trace(old_output, to_cpu_fn(new_output))
-                    return new_outputs
+                        cpu = to_cpu_fn(new_output)
+                        on_cpu.append(cpu)
+                        copy_stack_trace(old_output, cpu)
+                    return on_cpu
             return False
         local_opt.__name__ = maker.__name__
         return local_optimizer(OP)(local_opt)
@@ -1355,28 +1358,24 @@ def local_gpua_gemmbatch(op, context_name, inputs, outputs):
 @register_opt()
 @alpha_merge(GpuGemm, alpha_in=1, beta_in=4)
 def local_gpua_gemm_alpha_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [gpugemm_no_inplace(*inputs)]
 
 
 @register_opt()
 @output_merge(GpuGemm, alpha_in=1, beta_in=4, out_in=0)
 def local_gpua_gemm_output_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [gpugemm_no_inplace(*inputs)]
 
 
 @register_opt()
 @alpha_merge(GpuGemmBatch, alpha_in=1, beta_in=4)
 def local_gpua_gemmbatch_alpha_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [gpugemmbatch_no_inplace(*inputs)]
 
 
 @register_opt()
 @output_merge(GpuGemmBatch, alpha_in=1, beta_in=4, out_in=0)
 def local_gpua_gemmbatch_output_merge(node, *inputs):
-    with inherit_stack_trace(node.outputs):
     return [gpugemmbatch_no_inplace(*inputs)]