new sparse opt. that remove dense_from_sparse(sparse_from_dense(x)) -> x

theano/sparse/opt.py

@@ -6,7 +6,7 @@ import scipy
 import theano
 from theano import gof, scalar, tensor
 from theano.tensor import blas
-from theano.tensor.opt import register_specialize
+from theano.tensor.opt import register_specialize, register_canonicalize
 from theano.sparse import (CSC, CSR, csm_properties,
                            csm_grad, usmm, csm_indices, csm_indptr,
                            csm_data)
@@ -177,6 +177,16 @@ theano.compile.optdb.register('local_inplace_addsd_ccode',
                               60, 'fast_run', 'inplace')
 
 
+@register_canonicalize("fast_compile")
+@register_specialize
+@gof.local_optimizer([sparse.DenseFromSparse])
+def local_dense_from_sparse_sparse_from_dense(node):
+    if isinstance(node.op, sparse.DenseFromSparse):
+        inp = node.inputs[0]
+        if inp.owner and isinstance(inp.owner.op, sparse.SparseFromDense):
+            return inp.owner.inputs
+
+
 @gof.local_optimizer([sparse.AddSD])
 def local_addsd_ccode(node):
     """

theano/sparse/tests/test_opt.py

@@ -139,3 +139,17 @@ def test_local_sampling_dot_csr():
             # be inserted
             assert not any(isinstance(node.op, sparse.opt.SamplingDotCSR) for node
                        in f.maker.fgraph.toposort())
+
+
+def test_local_dense_from_sparse_sparse_from_dense():
+    mode = theano.compile.mode.get_default_mode()
+    mode = mode.including("local_dense_from_sparse_sparse_from_dense")
+
+    m = theano.tensor.matrix()
+    for op in [theano.sparse.csr_from_dense, theano.sparse.csc_from_dense]:
+        s = op(m)
+        o = theano.sparse.dense_from_sparse(s)
+        f = theano.function([m], o, mode=mode)
+        # We should just have a deep copy.
+        assert len(f.maker.fgraph.apply_nodes) == 1
+        f([[1,2], [3, 4]])