Merge pull request #1789 from nouiz/faster_opt

theano/sandbox/cuda/basic_ops.py

@@ -296,38 +296,15 @@ class GpuDimShuffle(GpuOp):
     def __init__(self, input_broadcastable, new_order):
         input_broadcastable = tuple(input_broadcastable)
         self.input_broadcastable = input_broadcastable
-        new_order = tuple(new_order)
         self.new_order = new_order
 
-        # list of dimensions of the input to drop
-        self.drop = []
-        # this maps i before dropping dimensions to j after dropping
-        # dimensions so self.shuffle can be set properly later on
-        i2j = {}
-        j = 0
         for i, b in enumerate(input_broadcastable):
             if i not in new_order:
-                # we want to drop this dimension because it's not a
-                # value in new_order
-                if b == 1:  # 1 aka True
-                    self.drop.append(i)
-                else:
+                if not b:
                     # we cannot drop non-broadcastable dimensions
                     raise ValueError("You cannot drop a non-broadcastable"
                                      " dimension.",
                                      (input_broadcastable, new_order))
-            else:
-                i2j[i] = j
-                j += 1
-
-        # transposition of non-broadcastable dimensions This is how
-        # the dimensions will be permuted, without accounting for the
-        # extra 'x' broadcastable dimensions to insert.
-        self.shuffle = [i2j[x] for x in new_order if x != 'x']
-
-        # list of dimensions of the output that are broadcastable and
-        # were not in the original input
-        self.augment = [i for i, x in enumerate(new_order) if x == 'x']
 
         self.view_map = {0: [0]}
 
@@ -481,8 +458,6 @@ class GpuDimShuffle(GpuOp):
             print self
             print "IN BROAD", self.input_broadcastable
             print "NEW ORDER", self.new_order
-            print "SHUFFLE", self.shuffle
-            print "AUGMENT", self.augment
             print '------------'
             print ''
             print sio.getvalue()

theano/sandbox/cuda/opt.py

@@ -284,7 +284,7 @@ def local_gpu_dimshuffle_0(node):
         if input.owner and isinstance(input.owner.op, HostFromGpu):
             # move the add to a GpuAdd
             new_op = GpuDimShuffle(node.op.input_broadcastable,
-                    node.op.new_order)
+                                   node.op.new_order)
             return [host_from_gpu(new_op(gpu_from_host(input)))]
     if isinstance(node.op, GpuFromHost):
         host_input = node.inputs[0]
@@ -292,7 +292,7 @@ def local_gpu_dimshuffle_0(node):
                                            tensor.DimShuffle):
             dimshuffle_node = host_input.owner
             new_op = GpuDimShuffle(dimshuffle_node.op.input_broadcastable,
-                    dimshuffle_node.op.new_order)
+                                   dimshuffle_node.op.new_order)
             return [new_op(gpu_from_host(dimshuffle_node.inputs[0]))]
     return False
 

theano/tensor/opt.py

@@ -2611,8 +2611,10 @@ register_canonicalize(gof.OpRemove(T.tensor_copy), name='remove_tensor_copy')
 def local_fill_sink(node):
     """
     f(fill(a, b), fill(c, d), e) -> fill(a, fill(c, f(b, d, e)))
+
+    f need to be an elemwise
     """
-    if not (node.op and isinstance(node.op, T.Elemwise) and node.op != T.fill):
+    if not isinstance(node.op, T.Elemwise) or node.op == T.fill:
         return False
     models = []
     inputs = []
@@ -2622,7 +2624,7 @@ def local_fill_sink(node):
             inputs.append(input.owner.inputs[1])
         else:
             inputs.append(input)
-    if inputs == node.inputs:
+    if not models:
         return False
     c = node.op(*inputs)
     for model in models: