Fix shape error in tests (which also means that we had wrong behavior).

theano/sandbox/cuda/blocksparse.py

@@ -39,26 +39,26 @@ def gemm_batched(Al, Bl, Cl, m, n, k, lda, ldb, ldc,
 
 
 def gemv(alpha, A, x, beta, y):
-    assert A.shape[1] == x.shape[0]
-    assert A.shape[0] == y.shape[0]
+    assert A.shape[0] == x.shape[0]
+    assert A.shape[1] == y.shape[0]
 
     handle = scikits.cuda.misc._global_cublas_handle
 
-    cublas.cublasSgemv(handle, 't', A.shape[1], A.shape[0], alpha,
-                       A.gpudata, max(A.strides[0], A.strides[1]),
+    cublas.cublasSgemv(handle, 'n', A.shape[1], A.shape[0], alpha,
+                       A.gpudata, A.strides[0],
                        x.gpudata, x.strides[0],
                        beta, y.gpudata, y.strides[0])
 
 
 def ger(alpha, x, y, A):
-    assert A.shape[0] == x.shape[0]
-    assert A.shape[1] == y.shape[0]
+    assert A.shape[1] == x.shape[0]
+    assert A.shape[0] == y.shape[0]
 
     handle = scikits.cuda.misc._global_cublas_handle
 
     cublas.cublasSger(handle, A.shape[1], A.shape[0], alpha,
-                      y.gpudata, y.strides[0],
                       x.gpudata, x.strides[0],
+                      y.gpudata, y.strides[0],
                       A.gpudata, A.strides[0])
 
 

theano/sandbox/cuda/tests/test_blocksparse.py

@@ -36,7 +36,7 @@ def blocksparse_data():
     input = randn(inputWindowSize, inputSize).astype('float32')
     inputIndice = numpy.random.permutation(nInputBlock)[:inputWindowSize]
     outputIndice = numpy.random.permutation(nOutputBlock)[:outputWindowSize]
-    weight = randn(nInputBlock, nOutputBlock, outputSize, inputSize).astype('float32')
+    weight = randn(nInputBlock, nOutputBlock, inputSize, outputSize).astype('float32')
     bias = randn(nOutputBlock, outputSize).astype('float32')
 
     return weight, input, inputIndice, bias, outputIndice
@@ -51,7 +51,7 @@ def blocksparse(W, h, iIdx, b, oIdx):
             inputIdx = iIdx[i]
             w = W[inputIdx, outputIdx]
             # this below is a gemv I think
-            o[j, :] += numpy.dot(w, h[i])
+            o[j, :] += numpy.dot(h[i], w)
 
     return o
 
@@ -78,7 +78,7 @@ def test_blocksparse_grad():
     h_val = randn(2, 3).astype('float32')
     iIdx_val = numpy.random.permutation(3)[:2]
     oIdx_val = numpy.random.permutation(3)[:2]
-    W_val = randn(3, 3, 4, 3).astype('float32')
+    W_val = randn(3, 3, 3, 4).astype('float32')
     b_val = randn(3, 4).astype('float32')
 
     iIdx = theano.tensor.constant(iIdx_val)