Fix local_optimizer() call sites in cuda and gpuarray backends.

theano/sandbox/cuda/GpuConv3D.py

@@ -284,7 +284,7 @@ conv_rows_stack( float* img, float* kern, float* bias, float* out,
 gpu_convd = GpuConv3D()
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([Conv3D])
 def local_gpu_conv3d(node):
     if isinstance(node.op, Conv3D):
         if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):

theano/sandbox/cuda/GpuConvGrad3D.py

@@ -341,7 +341,7 @@ convgrad_rows_stack( float* img, float* dCdH, float* dCdW,
 gpu_conv_grad3d = GpuConvGrad3D()
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([ConvGrad3D])
 def local_gpu_conv_gradd(node):
     if isinstance(node.op, ConvGrad3D):
         if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):

theano/sandbox/cuda/GpuConvTransp3D.py

@@ -348,7 +348,7 @@ conv_transp_rows_stack( float* H, float* kern, float* bias, float* R,
 gpu_conv_transpd = GpuConvTransp3D()
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([ConvTransp3D])
 def local_gpu_conv_transpd(node):
     if isinstance(node.op, ConvTransp3D):
         if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):

theano/sandbox/cuda/neighbours.py

@@ -405,7 +405,7 @@ def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
     return GpuImages2Neibs(mode)(ten4, neib_shape, neib_step)
 
 
-@local_optimizer()
+@local_optimizer([Images2Neibs])
 def use_gpu_images2neibs(node):
     if (type(node.op) is Images2Neibs and
         node.inputs[0].dtype == 'float32' and

theano/sandbox/cuda/opt.py

@@ -121,7 +121,7 @@ gpu_seqopt.register('InputToGpuOptimizer', InputToGpuOptimizer(),
                     'merge')  # TODO: how to make it mandatory for gpu_seqopt?
 
 
-@local_optimizer([])
+@local_optimizer([gpu_from_host, host_from_gpu])
 def local_cut_gpu_host_gpu(node):
     if tensor.opt.opt.check_chain(node, gpu_from_host, host_from_gpu):
         return [node.inputs[0].owner.inputs[0]]
@@ -170,7 +170,7 @@ def dtype_in_elemwise_supported(op):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.Elemwise])
 def local_gpu_elemwise_0(node):
     """elemwise(..., host_from_gpu, ...)
        -> host_from_gpu(elemwise(gpu_from_host, ..., gpu_from_host)
@@ -229,7 +229,7 @@ def local_gpu_elemwise_0(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host])
 def local_gpu_elemwise_1(node):
     """
     gpu_from_host(Elemwise)) -> GpuElemwise(gpu_from_host(...))
@@ -265,7 +265,7 @@ def local_gpu_elemwise_1(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.DimShuffle, gpu_from_host])
 def local_gpu_dimshuffle_0(node):
     """
     dimshuffle(host_from_gpu()) -> host_from_gpu(gpu_dimshuffle)
@@ -290,7 +290,7 @@ def local_gpu_dimshuffle_0(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.SpecifyShape, gpu_from_host])
 def local_gpu_specifyShape_0(node):
     """
     specify_shape(host_from_gpu()) -> host_from_gpu(specify_shape)
@@ -313,7 +313,7 @@ def local_gpu_specifyShape_0(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host]) # XXX: broken: tensor.basic.dot is not an op
 def local_gpu_dot_to_dot22(node):
     """
     gpu_from_host(dot) -> gpudot(gpu_from_host)
@@ -376,7 +376,7 @@ def local_gpu_dot_to_dot22(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([theano.ifelse.IfElse, gpu_from_host])
 def local_gpu_lazy_ifelse(node):
     """
     gpu_from_host(ifelse) -> gpu_ifelse(gpu_from_host)
@@ -434,7 +434,7 @@ def local_gpu_lazy_ifelse(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.blas._dot22])
 def local_gpu_dot22(node):
     """
     gpu_from_host(dot22) -> gpudot(gpu_from_host)
@@ -456,7 +456,7 @@ def local_gpu_dot22(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.blas._dot22scalar])
 def local_gpu_dot22scalar(node):
     """
     gpu_from_host(dot22scalar) -> gpudot(gpu_from_host)
@@ -482,7 +482,7 @@ def local_gpu_dot22scalar(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.blas_c.CGemv, tensor.blas.Gemv])
 def local_gpu_gemv(node):
     """
     gpu_from_host(gemv) -> gpu_gemv(gpu_from_host)
@@ -523,7 +523,8 @@ def local_gpu_gemv(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.blas_c.CGer, tensor.blas.Ger,
+                  tensor.blas_scipy.ScipyGer])
 def local_gpu_ger(node):
     """
     gpu_from_host(ger) -> gpu_ger(gpu_from_host)
@@ -566,7 +567,7 @@ def local_gpu_ger(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.blas.gemm_no_inplace, gpu_from_host])
 def local_gpu_gemm(node):
     """
     gpu_from_host(gemm) -> gpu_gemm(gpu_from_host)
@@ -601,7 +602,7 @@ def local_gpu_gemm(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.elemwise.CAReduce])
 def local_gpu_careduce(node):
     if isinstance(node.op, tensor.elemwise.CAReduce):
         scalar_op = node.op.scalar_op
@@ -671,7 +672,7 @@ def local_gpu_careduce(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.Reshape])
 def local_gpu_reshape(node):
     if node.op == gpu_from_host:
         host_input = node.inputs[0]
@@ -705,7 +706,7 @@ def local_gpu_reshape(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.Flatten])
 def local_gpu_flatten(node):
     if node.op == gpu_from_host:
         host_input = node.inputs[0]
@@ -724,7 +725,7 @@ def local_gpu_flatten(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.Subtensor])
 def local_gpu_subtensor(node):
     if node.op == gpu_from_host:
         host_input = node.inputs[0]
@@ -745,7 +746,7 @@ def local_gpu_subtensor(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.AdvancedSubtensor1])
 def local_gpu_advanced_subtensor1(node):
     if node.op == gpu_from_host:
         host_input = node.inputs[0]
@@ -763,8 +764,11 @@ def local_gpu_advanced_subtensor1(node):
     return False
 
 
+#tensor.opt.local_inplace_incsubtensor1.add_track(GpuAdvancedIncSubtensor1)
+#tensor.opt.local_inplace_incsubtensor1.add_track(GpuAdvancedIncSubtensor1_dev20)
+
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.AdvancedIncSubtensor1])
 def local_gpu_advanced_incsubtensor1(node):
     if node.op == gpu_from_host:
         host_input = node.inputs[0]
@@ -837,8 +841,10 @@ def local_gpu_advanced_incsubtensor1(node):
     return False
 
 
+#tensor.opt.local_inplace_setsubtensor.add_track(GpuIncSubtensor)
+
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.IncSubtensor])
 def local_gpu_incsubtensor(node):
     if node.op == gpu_from_host:
         host_output = node.inputs[0]
@@ -885,7 +891,7 @@ def local_gpu_incsubtensor(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.Shape])
 def local_gpu_shape(node):
     if isinstance(node.op, tensor.Shape):
         x, = node.inputs
@@ -896,7 +902,7 @@ def local_gpu_shape(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.Rebroadcast])
 def local_gpu_rebroadcast(node):
     '''rebroadcast(host_from_gpu(x)) -> host_from_gpu(rebroadcast(x))'''
     if isinstance(node.op, tensor.Rebroadcast):
@@ -911,7 +917,7 @@ def gpu_print_wrapper(op, cnda):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.printing.Print])
 def local_gpu_print_op(node):
     if isinstance(node.op, tensor.printing.Print):
         x, = node.inputs
@@ -932,7 +938,7 @@ import theano.tensor.nnet
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias])
 def local_gpu_crossentorpy_softmax_argmax_1hot_with_bias(node):
     if isinstance(node.op, tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias):
         x, b, y = node.inputs
@@ -962,7 +968,7 @@ def local_gpu_crossentorpy_softmax_argmax_1hot_with_bias(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.nnet.CrossentropySoftmax1HotWithBiasDx])
 def local_gpu_crossentorpy_softmax_1hot_with_bias_dx(node):
     if isinstance(node.op, tensor.nnet.CrossentropySoftmax1HotWithBiasDx):
         dnll, sm, yidx = node.inputs
@@ -977,7 +983,7 @@ def local_gpu_crossentorpy_softmax_1hot_with_bias_dx(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.nnet.Softmax])
 def local_gpu_softmax(node):
     if isinstance(node.op, tensor.nnet.Softmax):
         x, = node.inputs
@@ -989,7 +995,7 @@ def local_gpu_softmax(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.nnet.SoftmaxWithBias])
 def local_gpu_softmax_with_bias(node):
     if isinstance(node.op, tensor.nnet.SoftmaxWithBias):
         x, b = node.inputs
@@ -1005,7 +1011,7 @@ from theano.tensor.nnet import conv
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, conv.ConvOp])
 def local_gpu_conv(node):
     """
     gpu_from_host(conv) -> gpu_conv(gpu_from_host)
@@ -1085,7 +1091,7 @@ import theano.tensor.signal.downsample as downsample
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([downsample.DownsampleFactorMax])
 def local_gpu_downsample_factor_max(node):
     if isinstance(node.op, downsample.DownsampleFactorMax):
         x, = node.inputs
@@ -1095,7 +1101,7 @@ def local_gpu_downsample_factor_max(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([downsample.DownsampleFactorMaxGrad])
 def local_gpu_downsample_factor_max_grad(node):
     if isinstance(node.op, downsample.DownsampleFactorMaxGrad):
         x, z, gz = node.inputs
@@ -1111,7 +1117,7 @@ from theano.sandbox.cuda.basic_ops import gpu_join
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([tensor.Join])
 def local_gpu_join(node):
     """
     Inspired by the opt for convop.
@@ -1185,7 +1191,7 @@ def local_inplace_gemv(node):
         return [gpu_gemv_inplace(*node.inputs)]
 
 
-@local_optimizer([gpu_gemm_no_inplace])
+@local_optimizer([gpu_ger_no_inplace])
 def local_inplace_ger(node):
     if node.op == gpu_ger_no_inplace:
         return [gpu_ger_inplace(*node.inputs)]
@@ -1316,7 +1322,7 @@ optdb.register('gpu_inplace_elemwise_opt', gpu_inplace_elemwise_optimizer, 75,
 
 
 @register_opt()
-@local_optimizer([tensor.Alloc])
+@local_optimizer([tensor.alloc])
 def local_gpualloc(node):
     replace = False
     if node.op == tensor.alloc:
@@ -1363,7 +1369,7 @@ def local_gpualloc(node):
 
 
 @register_opt()
-@local_optimizer([tensor.Alloc])
+@local_optimizer([GpuAlloc])
 def local_gpualloc_memset_0(node):
     if isinstance(node.op, GpuAlloc) and not node.op.memset_0:
         inp = node.inputs[0]
@@ -1375,7 +1381,7 @@ def local_gpualloc_memset_0(node):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer([gpu_from_host, tensor.Eye])
 def local_gpu_eye(node):
     """
     gpu_from_host(eye) -> gpueye(gpu_from_host)
@@ -1459,7 +1465,7 @@ def tensor_to_cuda(x):
 
 
 @register_opt()
-@local_optimizer([])
+@local_optimizer(None) # XXX: linalg is in sandbox, so don't import it globally
 def local_gpu_extract_diagonal(node):
     """
     extract_diagonal(host_from_gpu()) -> host_from_gpu(extract_diagonal)
@@ -1485,7 +1491,7 @@ def local_gpu_extract_diagonal(node):
 
 
 @register_opt('scan')
-@local_optimizer([])
+@local_optimizer([gpu_from_host, scan_op.Scan])
 def gpuScanOptimization(node):
     """
     scan(host_from_gpu) -> host_from_gpu(GPUscan)

theano/sandbox/cuda/rng_curand.py

@@ -346,7 +346,7 @@ class CURAND_RandomStreams(object):
         return  rval
 
 
-@local_optimizer([None])
+@local_optimizer([CURAND_Base])
 def local_destructive(node):
     op = node.op
     if isinstance(op, CURAND_Base) and not op.destructive:

theano/sandbox/gpuarray/opt.py

@@ -107,7 +107,7 @@ gpu_seqopt.register('InputToGpuArrayOptimizer', InputToGpuOptimizer(),
                     0, 'fast_run', 'fast_compile', 'merge')
 
 
-@local_optimizer([])
+@local_optimizer([gpu_from_host, host_from_gpu])
 def local_cut_gpu_host_gpu(node):
     if tensor.opt.opt.check_chain(node, gpu_from_host, host_from_gpu):
         return [node.inputs[0].owner.inputs[0]]