dcslin commented on a change in pull request #662:
URL: https://github.com/apache/singa/pull/662#discussion_r412652946
##########
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):
+ """ `RNN_direct` class implements with c++ backend and run the operation
+ directly on cuDNN
+
+ While `RNN` class implements with high level singa API
+ """
+ def __init__(self, input_size, hidden_size, rnn_mode="lstm"):
+ """
+ Args:
+ input_size: input feature dim
+ hidden_size: hidden feature dim
+ rnn_mode: accepted value: "vanilla", "tanh", "relu", "lstm",
"gru"
+ """
+ assert singa.USE_CUDA is True, "Not able to run without CUDA"
+
+ self.rnn_mode = rnn_mode
+ self.input_size = input_size
+ self.hidden_size = hidden_size
+
+ # TODO: CPU parameter
+
+ # GPU parameter
+ # cudnn_rnn_mode: 0 - RNN RELU, 1 - RNN TANH, 2 - LSTM, 3 - GRU
+ if self.rnn_mode == "lstm":
+ self.cudnn_rnn_mode = 2
+ elif self.rnn_mode == "vanilla" or self.rnn_mode == "tanh":
+ self.cudnn_rnn_mode = 1
+ elif self.rnn_mode == "relu":
+ self.cudnn_rnn_mode = 0
+ elif self.rnn_mode == "gru":
+ self.cudnn_rnn_mode = 3
+
+ def __call__(self, x):
+ if not hasattr(self, "handle"):
+ cpp_x = singa.VecTensor()
+ [cpp_x.append(i.data) for i in x]
+
+ # TODO: CPU handle
+
+ # GPU handle
+ self.handle = singa.CudnnRNNHandle(cpp_x, self.input_size,
self.hidden_size, self.cudnn_rnn_mode)
+
+ self.W = Tensor(shape=(self.handle.weights_size,),
+ requires_grad=True,
+ stores_grad=True)
+ self.W.gaussian(0.0, 1.0)
+
+ return _RNN(self.handle)(x, self.W)[0]
Review comment:
this is rectified
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