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Hessian calculation fails using tf.jacobian

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Issue 类型
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技术栈
python, tensorflow
领域
machine-learning

调研方向

从 issue 中嵌套的 GradientTape 示例和 tfq.layers.Expectation 调用开始，然后按照 TensorFlow 的高级 autodiff 模式重现 TfqAdjointGradient 的 LookupError。跟踪所涉及的 differentiator 入口点，并将“在不发生缺少梯度的失败的情况下获得 Hessian，同时为报告的参数化电路案例提供覆盖”定义为完成标准。

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描述

kind/question

Python: 3.8
TFQ: 0.4.0
Hi,

I am trying to get some Hessians from the one of my parameterized quantum circuits. This trainstep works as intended:

def build_train_step(circuit: cirq.Circuit, symbols: List,
                     paulisum: List[cirq.PauliSum], learning_rate: float) -> \
        Tuple[Any, tf.Variable, tfq.layers.Expectation]:
    model_params = tf.Variable(tf.random.uniform([1, len(symbols)]) * 2,
                               constraint=lambda x: tf.clip_by_value(x, 0, 4))
    expectation_layer = tfq.layers.Expectation()
    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    @tf.function
    def train_step():
        with tf.GradientTape() as tape:
            expectation_batch = expectation_layer(circuit,
                                                  symbol_names=symbols,
                                                  symbol_values=model_params,
                                                  operators=paulisum)
            energy = tf.reduce_sum(expectation_batch)
        gradients = tape.gradient(energy, model_params)
        optimizer.apply_gradients(zip([gradients], [model_params]))
        return energy

    return train_step, model_params, expectation_layer

Following this example in the TensorFlow 2 docs I was hoping I could get the Hessian with the following code:

def build_train_step_hessians(circuit: cirq.Circuit, symbols: List,
                     paulisum: List[cirq.PauliSum], learning_rate: float) -> \
        Tuple[Any, tf.Variable, tfq.layers.Expectation]:
    model_params = tf.Variable(tf.random.uniform([1, len(symbols)]) * 2,
                               constraint=lambda x: tf.clip_by_value(x, 0, 4))
    expectation_layer = tfq.layers.Expectation()
    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    @tf.function
    def train_step():
        with tf.GradientTape() as t2:
            with tf.GradientTape() as t1:
                expectation_batch = expectation_layer(circuit,
                                                      symbol_names=symbols,
                                                      symbol_values=model_params,
                                                      operators=paulisum)
                energy = tf.reduce_sum(expectation_batch)
            gradients = t1.gradient(energy, model_params)
        hess = t2.jacobian(gradients, model_params)
        optimizer.apply_gradients(zip([gradients], [model_params]))

        return energy, hess

    return train_step, model_params, expectation_layer

But this throws the error:

...
    LookupError: No gradient defined for operation 'TfqAdjointGradient' (op type: TfqAdjointGradient)

From which I conclude that calculating gradients of gradients is not supported yet ( I tried the other differentiators as well). Am I out of luck here? Or is there a hack I can use to get the Hessians from the circuit? Thanks! If you need an example where I use this train step I can throw one together.

P.S.
I am in the process of rewriting all my research code to TFQ and so far everything has worked like a charm. No more super slow graph building times and worrying about how to extract stuff the graph with my own TF1 simulator. And the adjoint differentiator in TFQ is amazing as well; I ran a VQE optimization with like 500 parameters the other day without any issues. Great stuff!

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