auto_differentiation.py

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from mxnet import np, npx, autograd
npx.set_np()

lhs_shape = (2048, 1024)
rhs_shape = (1024, 4096)

lhs = np.random.uniform(-1.0, 1.0, size=lhs_shape)
rhs = np.random.uniform(-1.0, 1.0, size=rhs_shape)

lhs.attach_grad()  # attach a gradient buffer to lhs
rhs.attach_grad()  # attach a gradient buffer to rhs

with autograd.record():  # autograd.record() gives a scope captures code
                         # that needs gradient computation
    out = np.dot(lhs, rhs)  # a normal NumPy opration

out.backward()  # compute the gradients with respect to its variables
                # in this case out = dot(lhs, rhs) so both lhs and rhs
                # are out's variables

# lhs's gradient is now in lhs.grad
# rhs's gradient is now in rhs.grad