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From GitBox <...@apache.org>
Subject [GitHub] [singa] joddiy commented on issue #565: fixed broadcast div pow
Date Fri, 29 Nov 2019 02:23:36 GMT
joddiy commented on issue #565: fixed broadcast div pow
URL: https://github.com/apache/singa/pull/565#issuecomment-559638167
 
 
   For div based cpu
   ```
           dev = cpu_dev
           cases = [
               ([3, 4, 5], [5]),  # 3d vs 1d
               ([3, 4, 5], [4, 5]),  # 3d vs 2d
               ([3, 4, 5, 6], [5, 6]),  # 4d vs 2d
               ([3, 4, 5, 6], [4, 5, 6]),  # 4d vs 3d
               ([1, 4, 1, 6], [3, 1, 5, 6])  # 4d vs 4d
           ]
           for in1, in2 in cases:
               x = np.random.randn(*in1).astype(np.float32)
               x1 = np.random.randn(*in2).astype(np.float32) + 1.0
               y = x / x1
   
               dy = np.random.randn(*y.shape).astype(np.float32)
               grad0 = np.sum(np.power(x1, -1) * dy, axis=axis_helper(y.shape, x.shape)).reshape(x.shape)
               grad1 = np.sum(x * - np.power(x1, -2) * dy, axis=axis_helper(y.shape, x1.shape)).reshape(x1.shape)
               
               x = tensor.from_numpy(x)
               x1 = tensor.from_numpy(x1)
               dy = tensor.from_numpy(dy)
               x.to_device(dev)
               x1.to_device(dev)
               dy.to_device(dev)
   
               result = autograd.div(x,x1)
               dx0,dx1 = result.creator.backward(dy.data)
               np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
               np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)),
grad0, decimal=5)
               np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)),
grad1, decimal=5)
   ```
   
   For pow based gpu
   ```
           dev = gpu_dev
           cases = [
               ([3, 4, 5], [5]),  # 3d vs 1d
               ([3, 4, 5], [4, 5]),  # 3d vs 2d
               ([3, 4, 5, 6], [5, 6]),  # 4d vs 2d
               ([3, 4, 5, 6], [4, 5, 6]),  # 4d vs 3d
               ([1, 4, 1, 6], [3, 1, 5, 6])  # 4d vs 4d
           ]
           for in1, in2 in cases:
               x = np.random.randint(1, 10, size=in1).astype(np.float32)
               x1 = np.random.randint(1, 5, size=in2).astype(np.float32)
               y = np.power(x, x1).astype(np.float32)
   
               dy = np.random.randn(*y.shape).astype(np.float32)
               grad0 = np.sum(x1 * np.power(x, x1-1) * dy, axis=axis_helper(y.shape, x.shape)).reshape(x.shape)
               grad1 = np.sum(np.power(x, x1) * np.log(x) * dy, axis=axis_helper(y.shape,
x1.shape)).reshape(x1.shape)
               
               x = tensor.from_numpy(x)
               x1 = tensor.from_numpy(x1)
               dy = tensor.from_numpy(dy)
               x.to_device(dev)
               x1.to_device(dev)
               dy.to_device(dev)
   
               result = autograd.pow(x,x1)
               dx0,dx1 = result.creator.backward(dy.data)
               np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
               np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)),
grad0, decimal=5)
               np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)),
grad1, decimal=5)
   ```

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