Added outputs argument

theano/gpuarray/dnn.py

@@ -1414,7 +1414,7 @@ class GpuDnnSoftmaxGrad(GpuDnnSoftmaxBase):
                   AbstractConv2d_gradInputs])
 @register_opt2([AbstractConv2d, AbstractConv2d_gradWeights,
                 AbstractConv2d_gradInputs], 'fast_compile')
-def local_abstractconv_cudnn_graph(op, context_name, inputs):
+def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
     if (not isinstance(op, (AbstractConv2d,
                             AbstractConv2d_gradWeights,
                             AbstractConv2d_gradInputs))):
@@ -1536,7 +1536,7 @@ def local_dnn_convi_output_merge(node, *inputs):
 @register_opt('cudnn', 'fast_compile')
 @op_lifter([Pool])
 @register_opt2([Pool], 'fast_compile')
-def local_pool_dnn_alternative(op, ctx_name, inputs):
+def local_pool_dnn_alternative(op, ctx_name, inputs, outputs):
     if not dnn_available(ctx_name):
         raise_no_cudnn()
     if not op.ignore_border:
@@ -1553,7 +1553,7 @@ def local_pool_dnn_alternative(op, ctx_name, inputs):
 @register_opt('cudnn', 'fast_compile')
 @op_lifter([MaxPoolGrad])
 @register_opt2([MaxPoolGrad], 'fast_compile')
-def local_pool_dnn_grad_stride(op, ctx_name, inputs):
+def local_pool_dnn_grad_stride(op, ctx_name, inputs, outputs):
     if not dnn_available(ctx_name):
         raise_no_cudnn()
     if not op.ignore_border:
@@ -1578,7 +1578,7 @@ def local_pool_dnn_grad_stride(op, ctx_name, inputs):
 @register_opt('cudnn', 'fast_compile')
 @op_lifter([AveragePoolGrad])
 @register_opt2([AveragePoolGrad], 'fast_compile')
-def local_avg_pool_dnn_grad_stride(op, ctx_name, inputs):
+def local_avg_pool_dnn_grad_stride(op, ctx_name, inputs, outputs):
     if not dnn_available(ctx_name):
         raise_no_cudnn()
     if not op.ignore_border:
@@ -1632,7 +1632,7 @@ def local_log_softmax_dnn(node):
 @register_opt('cudnn', 'fast_compile')
 @op_lifter([LogSoftmax])
 @register_opt2([LogSoftmax], 'fast_compile')
-def local_logsoftmax_to_dnn(op, ctx_name, inputs):
+def local_logsoftmax_to_dnn(op, ctx_name, inputs, outputs):
     # Transform the input in the format expected by GpuDnnSoftmax
     inp = inputs[0]
     if inp.ndim != 2:
@@ -1671,7 +1671,7 @@ gpu_seqopt.register("NoCuDNNRaise", NoCuDNNRaise(), 0, 'cudnn')
 @register_opt('cudnn', 'fast_compile')
 @op_lifter([SoftmaxGrad])
 @register_opt2([SoftmaxGrad], 'cudnn', 'fast_compile')
-def local_softmax_dnn_grad(op, ctx_name, inputs):
+def local_softmax_dnn_grad(op, ctx_name, inputs, outputs):
     if not dnn_available(ctx_name):
         raise_no_cudnn("cuDNN needed for SoftmaxGrad")
     ins = []

theano/gpuarray/extra_ops.py

@@ -454,7 +454,7 @@ class GpuCumsum(GpuKernelBase, Op):
 @register_opt('fast_compile')
 @op_lifter([CumsumOp])
 @register_opt2([CumsumOp], 'fast_compile')
-def use_gpu_cumsumop(op, ctx_name, inputs):
+def use_gpu_cumsumop(op, ctx_name, inputs, outputs):
     if inputs[0].dtype == 'float32':
         axis = op.axis
         x = inputs[0]

theano/gpuarray/multinomial.py

@@ -230,7 +230,7 @@ KERNEL void k_multi_warp_multinomial(
 @register_opt('fast_compile')
 @op_lifter([theano.sandbox.multinomial.MultinomialFromUniform])
 @register_opt2([theano.sandbox.multinomial.MultinomialFromUniform], 'fast_compile')
-def local_gpua_multinomial(op, context_name, inputs):
+def local_gpua_multinomial(op, context_name, inputs, outputs):
     # TODO : need description for function
 
     if len(inputs) == 2:

theano/gpuarray/nerv.py

@@ -150,7 +150,7 @@ if (GpuKernel_init(&k_%(name)s, c->ctx, 1, &bcode, &sz,
 @opt.register_opt('fast_compile')
 @opt.op_lifter([tensor.Dot])
 @opt.register_opt2([tensor.Dot], 'fast_compile')
-def local_dot_to_gemm16(op, ctx_name, inputs):
+def local_dot_to_gemm16(op, ctx_name, inputs, outputs):
     if nerv is None:
         return
     A = inputs[0]