[GitHub] [singa] joddiy commented on a change in pull request #736: Add expand operator

[GitBox <gi...@apache.org>]

joddiy commented on a change in pull request #736:
URL: https://github.com/apache/singa/pull/736#discussion_r440633751



##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
     return CosSim()(a, b)[0]
 
 
+class Expand(Operator):
+    """
+    Init a expand operator
+    """
+
+    def __init__(self, shape):
+        """
+        Args:
+            shape (list[int]: indicates the shape you want to expand to, 
+                following the broadcast rule
+        """
+        super(Expand, self).__init__()
+        self.shape = shape
+
+    def forward(self, x):
+        """
+        forward of Expand
+        Args:
+            x (CTensor): input tensor.
+        Returns:
+            the output CTensor.
+        """
+        if isinstance(self.shape, np.ndarray):
+            self.shape = self.shape.tolist()
+        else:
+            self.shape = list(self.shape)
+        self.dim_changed = True
+        self.x_shape = list(x.shape())
+        x_shape = self.x_shape.copy()
+        for s_1, s_2 in zip(self.shape[::-1], x_shape[::-1]):
+            if s_1 != 1 and s_2 !=1:
+                if len(self.shape)!=len(x_shape):
+                    assert False, ('not support dim_unchanged mode')
+                self.dim_changed = False
+                break
+        if self.dim_changed:
+            tmp_tensor = singa.Tensor(self.shape, x.device())
+            tmp_tensor.SetFloatValue(1.)
+            x = singa.__mul__(x, tmp_tensor)
+        else:
+            for axis, s_1, s_2 in zip(range(len(self.shape)), self.shape, x_shape):
+                if s_1 == s_2:
+                    continue
+                xs = [x] * (s_1//s_2)
+                x = singa.VecTensor(xs)
+                x = singa.ConcatOn(x, axis)
+        return x
+
+    def backward(self, dy):
+        """
+        backward of Expand
+        Args:f
+            dy (CTensor), gradient tensor.
+        Return:
+            the gradient tensor over input tensor.
+        """
+        x_shape = self.x_shape
+        if self.dim_changed:
+            dy = tensor.from_raw_tensor(dy)
+            if len(self.shape) > len(x_shape):
+                x_shape = [1] * (len(self.shape) - len(x_shape))+ x_shape 
+            for axis, s in zip(range(len(self.shape))[::-1], x_shape[::1]):
+                if s == 1:
+                    dy = tensor.sum(dy, axis)
+            dy = dy.data
+        else:
+            for axis, s_1, s_2 in zip(range(len(self.shape))[::-1], self.shape[::-1], x_shape[::-1]):
+                if s_1 > s_2:
+                    duplic = s_1//s_2
+                    dxs = []
+                    for i in range(s_2):
+                        tmp_tensor = None
+                        for j in range(duplic):
+                            if not tmp_tensor:
+                                tmp_tensor = singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+                            else:
+                                tmp_tensor += singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+                        dxs.append(tmp_tensor)
+                    dxs = singa.VecTensor(dxs)
+                    dy = singa.ConcatOn(dxs, axis)
+        dy = singa.Reshape(dy, self.x_shape)
+        return dy
+
+
+def expand(x, shape):
+    """
+    Produces a cos similarity operator

Review comment:
       just a typo, fixed




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