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Optimize TensorFlow & Keras models with L-BFGS from TensorFlow Probability

Summary: This post showcases a workaround to optimize a tf.keras.Model model with a TensorFlow-based L-BFGS optimizer from TensorFlow Probability. The complete code can be found at my GitHub Gist here.

While SGD, Adam, etc. optimizers nowadays dominate the training of deep neural networks, some, including me, may want to use second-order methods, such as L-BFGS. The problem is TensorFlow 2.0 does not have L-BFGS.

PyTorch provides L-BFGS, so I guess that using Keras with PyTorch backend may be a possible workaround. But I don’t use original Keras. I use TensorFlow 2.0 and build Keras models with the tf.keras module. That means I’m not able to switch the backend. And it’s difficult for me to migrate my code to original Keras because my code has many customized things that require TensorFlow 2.0.

A workaround is to use the L-BFGS solver from SciPy library to train a tf.keras.Model or its subclasses. We can find some example code of this workaround from Google search. The problem of this workaround, however, is that vanilla SciPy is not GPU-capable. And many people like me use TensorFlow because we need GPU computing. In addition, I personally don’t think SciPy is capable of doing serious and large-scale calculations. It is for prototyping, not for something supposed to run on HPC clusters. Though it’s just my personal opinion.

Fortunately, TensorFlow-based L-BFGS solver exists in a library called TensorFlow Probability. The API documentation of this solver is here. We can use it through something like import tensorflow_probability as tfp and then result = tfp.optimizer.lbfgs_minimize(...). The returned object, result, contains several data. And the final optimized parameters will be in result.position. If using a GPU version of TensorFlow, then this L-BFGS solver should also run on GPUs.

Apparently, the solver is not implemented as a subclass of tf.keras.optimizers.Optimizer. So we are not able to use this solver directly with model.compile(...) and model.fit(...). The solver is a function. We need some workaround or wrappers to use this solver. Let’s first see the arguments of this function:

tfp.optimizer.lbfgs_minimize(
    value_and_gradients_function,
    initial_position,
    num_correction_pairs=10,
    tolerance=1e-08,
    x_tolerance=0,
    f_relative_tolerance=0,
    initial_inverse_hessian_estimate=None,
    max_iterations=50,
    parallel_iterations=1,
    stopping_condition=None,
    name=None
)
Arguments of tfp.optimizer.lbfgs_minimize

The value_and_gradients_function is a function or a callable object that returns the loss and the gradients with respect to parameters. And this callable object should take in the parameters that we want to optimize, which are a model’s trainable parameters (i.e., the kernels & biases of trainable layers). See the first notable thing here? The callable object value_and_gradients_function takes in model parameters, not training data.

The second thing is that the model parameters fed into value_and_gradients_function should be a 1D tf.Tensor. But TensorFlow and Keras store trainable model parameters as a list of multidimensional tf.Variable. We can easily see this with print(model.trainable_variables) (assuming model is an instance of tf.keras.Model or its subclasses). This means we need to find a way to transform a list of multidimensional tf.Variable to a single 1D tensor. This can be done with tf.dynamic_stitch.

We also need a way to convert a 1D tf.Tensor back to a list of multidimensional tf.Tensor, tf.Variable or numpy.ndarray so that we can update the parameters of the model. (Basically, under most situations, we can treat tf.Variable like tf.Tensor, and vice versa.) There are two ways to update a model’s parameters

  1. We can use model.set_weights(params) to update the values of the parameters. In this case, params is a list of multidimensional numpy.ndarray. And we need to convert the aforementioned 1D tf.Tensor to the params. In TensorFlow 2.0, this is not difficult because of the default eager execution behavior. We first partition the 1D tf.Tensor with tf.dynamic_partition to a list of tensors, convert the list of tensors to a list of numpy.ndarray with tensor.numpy(), and then reshape each array to the corresponding shape. Finally, we can call model.set_weights(params).
  2. The other way is more efficient in computing and can work with the graph mode and without eager execution. Each element in model.trainable_variables is a tf.Variable, which provides a member method, assign, to update the values in it. So we first partition the 1D tf.Tensor to a list of tensors with tf.dynamic_partition. Next, we use a for loop to reshape each tensor and then assign it to the corresponding tf.Variable in model.trainable_variables.

The third thing is that when returning the gradients, the gradients should also be a 1D tf.Tensor to match the value_and_gradients_function. This is again can be done with tf.dynamic_stitch.

In addition, the argument initial_position of tfp.optimizer.lbfgs_minimize should be the initial parameter values of the model. And of course, we should use tf.dynamic_stitch to covert the initial model parameters to a 1D tf.Tensor before passing them to initial_position.

In a nutshell, when we create a function for the value_and_gradients_function argument, this function should store the following information inside it:

  1. the tf.keras.Model model we want to use,
  2. the loss function to use,
  3. the training data that we want to evaluate the loss,
  4. the information required by tf.dynamic_stitch to convert a list of multidimensional tf.Tensor to a 1D tf.Tensor, and
  5. the information required by tf.dynamic_partition to convert a 1D tf.Tensor to a list of multidimensional tf.Tensor or numpy.ndarray.

We can define a function factory to create such a function for us. Here’s an example:

def function_factory(model, loss, train_x, train_y):
    """A factory to create a function required by tfp.optimizer.lbfgs_minimize.

    Args:
        model [in]: an instance of `tf.keras.Model` or its subclasses.
        loss [in]: a function with signature loss_value = loss(pred_y, true_y).
        train_x [in]: the input part of training data.
        train_y [in]: the output part of training data.

    Returns:
        A function that has a signature of:
            loss_value, gradients = f(model_parameters).
    """

    # obtain the shapes of all trainable parameters in the model
    shapes = tf.shape_n(model.trainable_variables)
    n_tensors = len(shapes)

    # we'll use tf.dynamic_stitch and tf.dynamic_partition later, so we need to
    # prepare required information first
    count = 0
    idx = [] # stitch indices
    part = [] # partition indices

    for i, shape in enumerate(shapes):
        n = numpy.product(shape)
        idx.append(tf.reshape(tf.range(count, count+n, dtype=tf.int32), shape))
        part.extend([i]*n)
        count += n

    part = tf.constant(part)

    @tf.function
    def assign_new_model_parameters(params_1d):
        """A function updating the model's parameters with a 1D tf.Tensor.

        Args:
            params_1d [in]: a 1D tf.Tensor representing the model's trainable parameters.
        """

        params = tf.dynamic_partition(params_1d, part, n_tensors)
        for i, (shape, param) in enumerate(zip(shapes, params)):
            model.trainable_variables[i].assign(tf.reshape(param, shape))

    # now create a function that will be returned by this factory
    @tf.function
    def f(params_1d):
        """A function that can be used by tfp.optimizer.lbfgs_minimize.

        This function is created by function_factory.

        Args:
           params_1d [in]: a 1D tf.Tensor.

        Returns:
            A scalar loss and the gradients w.r.t. the `params_1d`.
        """

        # use GradientTape so that we can calculate the gradient of loss w.r.t. parameters
        with tf.GradientTape() as tape:
            # update the parameters in the model
            assign_new_model_parameters(params_1d)
            # calculate the loss
            loss_value = loss(model(train_x, training=True), train_y)

        # calculate gradients and convert to 1D tf.Tensor
        grads = tape.gradient(loss_value, model.trainable_variables)
        grads = tf.dynamic_stitch(idx, grads)

        # print out iteration & loss
        f.iter.assign_add(1)
        tf.print("Iter:", f.iter, "loss:", loss_value)

        return loss_value, grads

    # store these information as members so we can use them outside the scope
    f.iter = tf.Variable(0)
    f.idx = idx
    f.part = part
    f.shapes = shapes
    f.assign_new_model_parameters = assign_new_model_parameters

    return f

And here’s an example how to use this function factory together with tfp.optimizer.lbfgs_minimize to train a tf.keras.Model model.

    pred_model = tf.keras.Sequential(
        [tf.keras.Input(shape=[2,]),
         tf.keras.layers.Dense(64, "tanh"),
         tf.keras.layers.Dense(64, "tanh"),
         tf.keras.layers.Dense(1, None)])

    loss_fun = tf.keras.losses.MeanSquaredError()
    func = function_factory(pred_model, loss_fun, inps, outs)

    # convert initial model parameters to a 1D tf.Tensor
    init_params = tf.dynamic_stitch(func.idx, pred_model.trainable_variables)

    # train the model with L-BFGS solver
    results = tfp.optimizer.lbfgs_minimize(
        value_and_gradients_function=func, initial_position=init_params, max_iterations=500)

    # after training, the final optimized parameters are still in results.position
    # so we have to manually put them back to the model
    func.assign_new_model_parameters(results.position)

    # do some prediction
    pred_outs = pred_model.predict(inps)

The complete example code can be found at my GitHub Gist here.

Finally, the example code is just to show a sense of how to use the L-BFGS solver from TensorFlow Probability. Using a function factory is not the only option. value_and_gradients_function can be a callable object. So we can also wrap the model with a Python class and implement the __call__ method. It’s all up to us.

7 Comments

  1. Anonymous Anonymous

    Thanks for sharing this code – was incredibly useful!!

  2. Anonymous Anonymous

    Nice buddy, but what about early stopping?

    • The early stopping mechanism in TensorFlow may not work with lbfgs, because the lbfgs function is not a TensorFlow optimizer object. I usually just allow the lbfgs to run up to the max iterations. But I can think of two workarounds:

      1. Check the loss in, for example, the “func” in the blog post. Whenever the true loss reaches a satisfying value, we force “func” to return zero for the loss. And the lbfgs will stop because it thinks the loss is zero already.

      2. Use the “tolerance”, “x_tolerance”, “f_relative_tolerance” arguments of the lbfgs function to stop the optimization early.

      Just some thoughts. I didn’t test these ideas.

  3. Alex Alex

    appreciated for your great work!!!

  4. Anonymous Anonymous

    Thanks a lot for sharing this code. Do you know if it is possible to create a list of all the loss values?

    • During each epoch/iteration, L-BFGS optimizer calls the function that returns loss and gradients several times. So it depends on what kind of loss history you want to put into the list — the loss each time the loss-gradient function is called, or the loss at the end of each epoch/iteration.

      To save the loss each time the function “func” is called, we can add “f.history = []” after line 105 and add “f.history.append(loss_value)” before line 98. Finally, after optimization, retrieve the loss history with “func.history”

      But to save only the loss at the end of each epoch, I haven’t had a good solution to achieve it.

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