Merge pull request #3362 from Thrandis/gpu_reshape

theano/sandbox/cuda/opt.py

@@ -120,6 +120,9 @@ gpu_optimizer.register('local_remove_all_assert',
                        theano.tensor.opt.local_remove_all_assert,
                        'unsafe')
 
+# Register local_reshape_chain
+register_opt(name='local_gpu_reshape_chain')(
+    theano.tensor.opt.local_reshape_chain(GpuReshape))
 
 # This is a partial list of CPU ops that can be in some circonstance
 # moved to the GPU. This list is used by an optimization.
@@ -944,35 +947,6 @@ def local_gpu_reshape(node):
     return False
 
 
-@local_optimizer([GpuReshape])
-def local_gpu_reshape_chain(node):
-    """
-    GuReshape(GpuReshape(shape1),shape2) -> GpuReshape(shape2)
-
-    """
-    if not tensor.opt.opt.check_chain(node, GpuReshape, GpuReshape):
-        return False
-
-    # TODO: this can permit a failing program to run by eliminating
-    #       the lower reshape
-    rval = node.op(node.inputs[0].owner.inputs[0], node.inputs[1])
-    # It might happen that the desired output of this node has a broadcastable
-    # pattern that does not match that of 'rval'. This is when originally, we
-    # were able to figure out that one of the dimensions of the reshape is one,
-    # but some other transformation replaced the shape by one for which this
-    # cannot be guessed.
-    # We should try to figure out why we lost the information about this
-    # constant value... but in the meantime, better not apply this
-    # optimization.
-    if rval.broadcastable == node.outputs[0].broadcastable:
-        return [rval]
-    else:
-        return False
-gpu_cut_copies.register('cut_local_gpu_reshape_chain',
-                        local_gpu_reshape_chain,
-                        'fast_run', 'gpu')
-
-
 @register_opt()
 @local_optimizer([gpu_from_host, tensor.Flatten])
 def local_gpu_flatten(node):

theano/sandbox/cuda/tests/test_opt.py

@@ -32,6 +32,7 @@ from theano.scalar.basic_scipy import erfinv
 from theano.sandbox.blocksparse import sparse_block_dot
 from theano.sandbox.cuda.blocksparse import GpuSparseBlockGemv, GpuSparseBlockOuter
 
+
 if theano.config.mode == 'FAST_COMPILE':
     mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
     mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
@@ -819,14 +820,10 @@ class test_diag(theano.tensor.tests.test_nlinalg.test_diag):
               self).__init__(name)
 
 
-def test_local_gpu_reshape():
-    mode = mode_with_gpu
-    a = tensor.fmatrix()
-    b = basic_ops.GpuReshape(3)(a, [2, 3, 4])
-    c = basic_ops.GpuReshape(1)(b, [24])
-    f = theano.function([a], c, mode=mode)
-    topo = f.maker.fgraph.toposort()
-    assert sum(isinstance(node.op, basic_ops.GpuReshape) for node in topo) == 1
+class Test_GpuReshape(test_opt.Test_Reshape):
+    def setUp(self):
+        self.mode = mode_with_gpu
+        self.op = basic_ops.GpuReshape
 
 
 if __name__ == '__main__':

theano/tensor/opt.py

@@ -3413,31 +3413,35 @@ def local_flatten_lift(node):
 ##################
 
 
-@gof.local_optimizer([T.Reshape])
-def local_reshape_chain(node):
-    """
-    Reshape(Reshape(shape1),shape2) -> Reshape(shape2)
+def local_reshape_chain(op):
+    @gof.local_optimizer([op])
+    def f(node):
+        """
+        Reshape(Reshape(shape1),shape2) -> Reshape(shape2)
 
-    """
-    if not opt.check_chain(node, T.Reshape, T.Reshape):
-        return False
+        """
+        if not opt.check_chain(node, op, op):
+            return False
 
-    # TODO: this can permit a failing program to run by eliminating
-    #       the lower reshape
-    rval = node.op(node.inputs[0].owner.inputs[0], node.inputs[1])
-    # It might happen that the desired output of this node has a broadcastable
-    # pattern that does not match that of 'rval'. This is when originally, we
-    # were able to figure out that one of the dimensions of the reshape is one,
-    # but some other transformation replaced the shape by one for which this
-    # cannot be guessed.
-    # We should try to figure out why we lost the information about this
-    # constant value... but in the meantime, better not apply this
-    # optimization.
-    if rval.broadcastable == node.outputs[0].broadcastable:
-        return [rval]
-    else:
-        return False
-register_canonicalize(local_reshape_chain)
+        # TODO: this can permit a failing program to run by eliminating
+        #       the lower reshape
+        rval = node.op(node.inputs[0].owner.inputs[0], node.inputs[1])
+        # It might happen that the desired output of this node has a
+        # broadcastable pattern that does not match that of 'rval'. This is
+        # when originally, we were able to figure out that one of the
+        # dimensions of the reshape is one, but some other transformation
+        # replaced the shape by one for which this cannot be guessed.
+        # We should try to figure out why we lost the information about this
+        # constant value... but in the meantime, better not apply this
+        # optimization.
+        if rval.broadcastable == node.outputs[0].broadcastable:
+            return [rval]
+        else:
+            return False
+
+    return f
+register_canonicalize(local_reshape_chain(T.Reshape),
+                      name='local_reshape_chain')
 
 
 @register_canonicalize

theano/tensor/tests/test_opt.py

@@ -5433,6 +5433,20 @@ def test_local_flatten_lift():
         assert isinstance(topo[1].op, tensor.Elemwise)
 
 
+class Test_Reshape(unittest.TestCase):
+    def setUp(self):
+        self.mode = mode_opt 
+        self.op = tensor.Reshape
+
+    def test_local_reshape(self):
+        a = tensor.fmatrix()
+        b = self.op(3)(a, [2, 3, 4])
+        c = self.op(1)(b, [24])
+        f = theano.function([a], c, mode=self.mode)
+        topo = f.maker.fgraph.toposort()
+        assert sum(isinstance(node.op, self.op) for node in topo) == 1
+
+
 def test_local_reshape_lift():
     x = tensor.tensor4()
     out = T.exp(x).reshape([x.size])