Clean up and cached GpuCAReduceCuda

theano/gpuarray/dnn.py

@@ -1427,8 +1427,6 @@ class GpuDnnSoftmaxGrad(GpuDnnSoftmaxBase):
         return Apply(self, [dy, sm], [sm.type()])
 
 
-@op_lifter([AbstractConv2d, AbstractConv2d_gradWeights,
-            AbstractConv2d_gradInputs])
 @register_opt2([AbstractConv2d, AbstractConv2d_gradWeights,
                 AbstractConv2d_gradInputs], 'conv_dnn', 'cudnn', 'gpuarray', 'fast_compile')
 def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
@@ -1481,10 +1479,10 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
                   AbstractConv2d_gradInputs])
 def local_abstractconv_cudnn(node):
     ctx = infer_context_name(*node.inputs)
-    return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs)
+    return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
 
 conv_groupopt.register('local_abstractconv_cudnn_graph',
-                       local_abstractconv_cudnn_graph, 20,
+                       local_abstractconv_cudnn, 20,
                        'fast_compile', 'fast_run',
                        'gpuarray', 'conv_dnn', 'cudnn')
 

theano/gpuarray/elemwise.py

@@ -2587,6 +2587,18 @@ class GpuCAReduceCuda(GpuKernelBase, HideC, CAReduceDtype):
         return kernels
 
 
+# Caching GpuCAReduceCuda
+def gpu_ca_reduce_cuda(scalar_op, axis=None, reduce_mask=None, dtype=None, acc_dtype=None,
+                       pre_scalar_op=None):
+    key = (scalar_op, axis, reduce_mask, dtype, acc_dtype,
+           pre_scalar_op)
+    if key not in gpu_ca_reduce_cuda.cache:
+        gpu_ca_reduce_cuda.cache[key] = GpuCAReduceCuda(scalar_op, axis, reduce_mask, dtype,
+                                                        acc_dtype, pre_scalar_op)
+    return gpu_ca_reduce_cuda.cache[key]
+gpu_ca_reduce_cuda.cache = {}
+
+
 class GpuCAReduceCPY(GpuKernelBase, HideC, CAReduceDtype):
     """
     CAReduce that reuse the python code from gpuarray.

theano/gpuarray/opt.py

@@ -30,7 +30,7 @@ from theano.tensor.nnet.abstract_conv import (AbstractConv2d,
 from theano.tests.breakpoint import PdbBreakpoint
 
 from .type import (GpuArrayType, GpuArrayConstant, get_context,
-                   ContextNotDefined)
+                   ContextNotDefined, GpuArrayVariable, GpuArraySharedVariable)
 from .basic_ops import (as_gpuarray_variable, infer_context_name,
                         host_from_gpu, GpuToGpu,
                         HostFromGpu, GpuFromHost,
@@ -48,7 +48,7 @@ from .nnet import (gpu_crossentropy_softmax_1hot_with_bias_dx,
                    gpu_softmax_with_bias, gpu_softmax)
 
 from .elemwise import (GpuElemwise, GpuDimShuffle, GpuCAReduceCuda,
-                       GpuCAReduceCPY)
+                       GpuCAReduceCPY, gpu_ca_reduce_cuda)
 from .subtensor import (GpuIncSubtensor, GpuSubtensor,
                         GpuAdvancedSubtensor1,
                         GpuAdvancedIncSubtensor1,
@@ -310,7 +310,6 @@ class GraphToGPU(NavigatorOptimizer):
 
             # Move only if any of the inputs are on the GPU.
             move_to_GPU = False
-            from .type import GpuArrayVariable, GpuArraySharedVariable
             if any([isinstance(i, GpuArrayVariable) or
                    isinstance(i, GpuArraySharedVariable)
                    for i in [mapping[v] for v in node.inputs] +
@@ -883,12 +882,11 @@ def local_gpua_join(op, context_name, inputs, outputs):
 
 @register_opt('fast_compile')
 @local_optimizer([GpuJoin])
-@register_opt2([GpuJoin], 'fast_compile')
-def local_gpuajoin_1(op, context_name, inputs, outputs):
+def local_gpuajoin_1(node):
     # join of a single element
-    if (isinstance(op, GpuJoin) and
-            len(inputs) == 2):
-        return [inputs[1]]
+    if (isinstance(node.op, GpuJoin) and
+            len(node.inputs) == 2):
+        return [node.inputs[1]]
 
 
 @register_opt('fast_compile')
@@ -1306,7 +1304,7 @@ def local_lift_abstractconv2d(op, context_name, inputs, outputs):
 register_opt('fast_compile')(conv_groupopt)
 
 
-@register_opt("low_memory", 'fast_compile')
+@register_opt("low_memory")
 @local_optimizer([GpuCAReduceCuda])
 def local_gpu_elemwise_careduce(node):
     """
@@ -1324,7 +1322,7 @@ def local_gpu_elemwise_careduce(node):
             isinstance(node.inputs[0].owner.op.scalar_op, scalar.basic.Sqr)):
         op = node.op
         inp = node.inputs[0].owner.inputs[0]
-        return [GpuCAReduceCuda(scalar_op=op.scalar_op,
+        return [gpu_ca_reduce_cuda(scalar_op=op.scalar_op,
                                    axis=op.axis,
                                    reduce_mask=op.reduce_mask,
                                    pre_scalar_op=scalar.basic.sqr)(inp)]