Add tests.

theano/sandbox/gpuarray/tests/test_blocksparse.py

+from __future__ import absolute_import, print_function, division
+import numpy
+
+import theano
+from theano import tensor
+import theano.tests.unittest_tools as utt
+from theano.tensor.nnet.tests import test_blocksparse
+
+from .config import mode_with_gpu, test_ctx_name
+
+from ..type import gpuarray_shared_constructor
+from ..blocksparse import (GpuSparseBlockGemv,
+                           GpuSparseBlockOuter,
+                           gpu_sparse_block_gemv,
+                           gpu_sparse_block_outer)
+
+
+class BlockSparse_Gemv_and_Outer(test_blocksparse.BlockSparse_Gemv_and_Outer):
+    def setUp(self):
+        utt.seed_rng()
+        self.mode = mode_with_gpu.excluding('constant_folding')
+        self.gemv_op = gpu_sparse_block_gemv
+        self.outer_op = gpu_sparse_block_outer
+        self.gemv_class = GpuSparseBlockGemv
+        self.outer_class = GpuSparseBlockOuter
+
+    # This test is temporarily disabled since we disabled the output_merge
+    # and alpha_merge optimizations for blocksparse due to brokeness.
+    # Re-enable when those are re-added.
+    def Xtest_blocksparse_grad_merge(self):
+        b = tensor.fmatrix()
+        h = tensor.ftensor3()
+        iIdx = tensor.lmatrix()
+        oIdx = tensor.lmatrix()
+
+        W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
+        W = gpuarray_shared_constructor(W_val, context=test_ctx_name)
+
+        o = gpu_sparse_block_gemv(b.take(oIdx, axis=0), W, h, iIdx, oIdx)
+        gW = theano.grad(o.sum(), W)
+
+        lr = numpy.asarray(0.05, dtype='float32')
+
+        upd = W - lr * gW
+
+        f1 = theano.function([h, iIdx, b, oIdx], updates=[(W, upd)],
+                             mode=mode_with_gpu)
+
+        # Make sure the lr update was merged.
+        assert isinstance(f1.maker.fgraph.outputs[0].owner.op,
+                          GpuSparseBlockOuter)
+
+        # Exclude the merge optimizations.
+        mode = mode_with_gpu.excluding('local_merge_blocksparse_alpha')
+        mode = mode.excluding('local_merge_blocksparse_output')
+
+        f2 = theano.function([h, iIdx, b, oIdx], updates=[(W, upd)], mode=mode)
+
+        # Make sure the lr update is not merged.
+        assert not isinstance(f2.maker.fgraph.outputs[0].owner.op,
+                              GpuSparseBlockOuter)
+
+        f2(h_val, iIdx_val, b_val, oIdx_val)
+        W_ref = W.get_value()
+
+        # reset the var
+        W.set_value(W_val)
+        f1(h_val, iIdx_val, b_val, oIdx_val)
+        W_opt = W.get_value()
+
+        utt.assert_allclose(W_ref, W_opt)