def backward(self, x, gy):
xp = cuda.get_array_module(*x)
if (xp != numpy and cuda.cudnn_enabled and self.use_cudnn and
_cudnn_version >= 3000):
oz_dtype = 'd' if x[0].dtype == 'd' else 'f'
one = numpy.array(1, dtype=oz_dtype).ctypes
zero = numpy.array(0, dtype=oz_dtype).ctypes
handle = cudnn.get_handle()
gx = xp.empty_like(x[0])
gx_cube = gx.reshape(gx.shape[:2] + (-1, 1))
desc = cudnn.create_tensor_descriptor(gx_cube)
libcudnn.softmaxBackward(
handle, _algorithm, _mode, one.data, desc.value,
self.y.data.ptr, desc.value, gy[0].data.ptr, zero.data,
desc.value, gx.data.ptr)
else:
gx = gy[0] - xp.exp(self.y) * gy[0].sum(axis=1, keepdims=True)
return gx,
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