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From GitBox <...@apache.org>
Subject [GitHub] [singa] nudles commented on a change in pull request #662: CUDNN LSTM
Date Thu, 09 Apr 2020 14:16:03 GMT
nudles commented on a change in pull request #662: CUDNN LSTM
URL: https://github.com/apache/singa/pull/662#discussion_r406223532
 
 

 ##########
 File path: python/singa/autograd.py
 ##########
 @@ -3239,95 +3239,176 @@ def __init__(
             bidirectional (bool): If True, becomes a bidirectional RNN. 
                 Default: False
         """
-        self.nonlinearity = nonlinearity
-
-        Wx_shape = (input_size, hidden_size)
-        self.Wx = []
-        for i in range(4):
-            w = Tensor(shape=Wx_shape, requires_grad=True, stores_grad=True)
-            w.gaussian(0.0, 1.0)
-            self.Wx.append(w)
-
-        Wh_shape = (hidden_size, hidden_size)
-        self.Wh = []
-        for i in range(4):
-            w = Tensor(shape=Wh_shape, requires_grad=True, stores_grad=True)
-            w.gaussian(0.0, 1.0)
-            self.Wh.append(w)
-
-        Bx_shape = (hidden_size,)
-        self.Bx = []
-        for i in range(4):
-            b = Tensor(shape=Bx_shape, requires_grad=True, stores_grad=True)
-            b.set_value(0.0)
-            self.Bx.append(b)
-
-        self.Bh = []
-        for i in range(4):
-            b = Tensor(shape=Bx_shape, requires_grad=True, stores_grad=True)
-            b.set_value(0.0)
-            self.Bh.append(b)
-
-        self.params = self.Wx + self.Wh + self.Bx + self.Bh
+        self.backend = backend
+        if backend == "singa":
+            self.nonlinearity = nonlinearity
+
+            Wx_shape = (input_size, hidden_size)
+            self.Wx = []
+            for i in range(4):
+                w = Tensor(shape=Wx_shape, requires_grad=True, stores_grad=True)
+                w.gaussian(0.0, 1.0)
+                self.Wx.append(w)
+
+            Wh_shape = (hidden_size, hidden_size)
+            self.Wh = []
+            for i in range(4):
+                w = Tensor(shape=Wh_shape, requires_grad=True, stores_grad=True)
+                w.gaussian(0.0, 1.0)
+                self.Wh.append(w)
+
+            Bx_shape = (hidden_size,)
+            self.Bx = []
+            for i in range(4):
+                b = Tensor(shape=Bx_shape, requires_grad=True, stores_grad=True)
+                b.set_value(0.0)
+                self.Bx.append(b)
+
+            self.Bh = []
+            for i in range(4):
+                b = Tensor(shape=Bx_shape, requires_grad=True, stores_grad=True)
+                b.set_value(0.0)
+                self.Bh.append(b)
+
+            self.params = self.Wx + self.Wh + self.Bx + self.Bh
+        elif backend == "cudnn":
+            if not singa.USE_CUDA:
+                raise Exception("Could not use cudnn without cuda compiled.\n")
+            if not inputs:
+                raise Exception("Input is required for init cudnn LSTM.\n")
 
 Review comment:
   do you  need the inputs data or just input shape?

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