dcslin commented on a change in pull request #662:
URL: https://github.com/apache/singa/pull/662#discussion_r412652862
##########
File path: python/singa/autograd.py
##########
@@ -3330,6 +3330,94 @@ def step_forward(self, x, h, c, Wx, Wh, Bx, Bh):
return hout, cout
+class _RNN(Operation):
+ """ RNN operation with c++ backend
+ """
+ def __init__(self, handle):
+ assert singa.USE_CUDA is True, "Not able to run without CUDA"
+ super(_RNN, self).__init__()
+ self.handle = handle
+
+ def forward(self, x, W):
+ # TODO: CPU forward
+
+ # GPU forward
+ if training:
+ y = singa.GpuRNNForwardTraining(x, W, self.handle)
+ self.inputs = (x, W, y)
+ else:
+ y = singa.GpuRNNForwardInference(x, W, self.handle)
+
+ return y
+
+ def backward(self, dy):
+ assert training is True and hasattr(
+ self, "inputs"), "Please set training as True before do BP. "
+
+ # TODO: CPU backward
+
+ # GPU backward
+ dx = singa.GpuRNNBackwardx(self.inputs[2], dy, self.inputs[1],
self.handle)
+ dW = singa.GpuRNNBackwardW(self.inputs[0], self.inputs[2], self.handle)
+ return dx, dW
+
+class RNN_direct(Layer):
Review comment:
this is rectified
##########
File path: src/api/model_operation.i
##########
@@ -188,6 +189,50 @@ Tensor GpuPoolingForward(const CudnnPoolingHandle &cph,
const Tensor &x);
Tensor GpuPoolingBackward(const CudnnPoolingHandle &cph, const Tensor &dy,
const Tensor& x, const Tensor& y);
+
+class CudnnRNNHandle {
+ public:
+ CudnnRNNHandle(const std::vector<Tensor> &x,
+ const int feature_size,
+ const int hidden_size,
+ const int mode = 0,
+ const int num_layers = 1,
+ const int bias = 1,
+ const float dropout = 0.0f,
+ const int bidirectional = 0);
Review comment:
this is rectified
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