removed solve op lifter from sandbox/cuda as it is now in gpuarray

theano/gpuarray/opt.py

@@ -69,7 +69,7 @@ from .subtensor import (GpuIncSubtensor, GpuSubtensor,
                         GpuAdvancedIncSubtensor1_dev20)
 from .opt_util import alpha_merge, output_merge, pad_dims, unpad_dims
 from .reduction import GpuMaxAndArgmax
-from .linalg import GpuCusolverSolve
+from .linalg import (GpuCusolverSolve, cusolver_available)
 
 _logger = logging.getLogger("theano.gpuarray.opt")
 
@@ -1889,9 +1889,11 @@ def local_gpu_maxandargmax(op, context_name, inputs, outputs):
 
 # solve
 @register_opt('fast_compile')
-@op_lifter([theano.tensor.slinalg.Solve])
+@op_lifter([slinalg.Solve])
 @register_opt2([theano.tensor.slinalg.Solve], 'fast_compile')
 def local_gpu_solve(op, context_name, inputs, outputs):
+    if not cusolver_available:
+        return
     return GpuCusolverSolve()
 
 # Do not register in fast_run or fast_compile.

theano/sandbox/cuda/opt.py

@@ -49,7 +49,6 @@ from theano.sandbox.cuda.blas import (
     GpuCorr3dMM, GpuCorr3dMM_gradInputs, GpuCorr3dMM_gradWeights)
 
 from theano.sandbox.cuda.blas import gpu_gemv_inplace
-from theano.sandbox.cuda.cusolver import gpu_solve
 
 from theano.sandbox.cuda.blas import gpu_gemv_no_inplace
 from theano.sandbox.cuda.blas import gpu_ger_inplace
@@ -700,36 +699,6 @@ def local_gpu_dot22scalar(node):
     return False
 
 
-@register_opt()
-@local_optimizer([gpu_from_host, slinalg.Solve])
-def local_gpu_solve(node):
-    """
-    gpu_from_host(CpuSolve) -> GpuSolve(gpu_from_host)
-
-    CpuSolve(host_from_gpu) -> host_from_gpu(GpuSolve)
-
-    """
-    if node.outputs[0].dtype != 'float32':
-        return
-    if isinstance(node.op, GpuFromHost):
-        host_input = node.inputs[0]
-        if (host_input.owner and
-            isinstance(host_input.owner.op,
-                       slinalg.Solve)):
-            x, y = host_input.owner.inputs
-            return [gpu_solve(as_cuda_ndarray_variable(x),
-                              as_cuda_ndarray_variable(y))]
-
-    if isinstance(node.op, slinalg.Solve):
-        if any([i.owner and isinstance(i.owner.op, HostFromGpu)
-                for i in node.inputs]):
-            x, y = node.inputs
-            return [host_from_gpu(
-                    gpu_solve(as_cuda_ndarray_variable(x),
-                              as_cuda_ndarray_variable(y)))]
-    return False
-
-
 @register_opt()
 @local_optimizer([gpu_from_host, tensor.blas_c.CGemv, tensor.blas.Gemv])
 def local_gpu_gemv(node):