jax2tf.ipynb

Kernel: Python 3

Copyright 2023 The TensorFlow Authors.

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Importe um modelo JAX usando JAX2TF

Ver em TensorFlow.org

Executar no Google Colab

Ver fonte no GitHub

Baixar notebook

Este notebook fornece um exemplo completo e executável de como criar um modelo usando JAX e trazê-lo para o TensorFlow para continuar o treinamento. Isso é possível graças ao JAX2TF, uma API leve que fornece um caminho do ecossistema JAX para o ecossistema TensorFlow.

JAX é uma biblioteca de computação de arrays de alto desempenho. Para criar o modelo, este notebook usa o Flax, uma biblioteca de rede neural para JAX. Para treiná-lo, utiliza o Optax, uma biblioteca de otimização para JAX.

Se você é um pesquisador que usa JAX, o JAX2TF oferece um caminho para a produção usando as ferramentas testadas do TensorFlow.

Há muitas maneiras pelas quais isto pode ser útil, eis aqui algumas:

• Inferência: pegar um modelo escrito para JAX e implantá-lo em servidor usando o TF Serving, em dispositivos usando TFLite ou na web usando TensorFlow.js.

• Ajuste fino: tendo um modelo que foi treinado usando JAX, você pode levar seus componentes para o TF usando o JAX2TF e continuar treinando-o no TensorFlow com seus dados de treinamento e configuração existentes.

• Fusão: combinando partes de modelos que foram treinados usando JAX com outros treinados usando TensorFlow, para máxima flexibilidade.

A chave para permitir esse tipo de interoperação entre JAX e TensorFlow é jax2tf.convert, que utiliza componentes de modelo criados sobre JAX (sua função de perda, função de previsão, etc.) e cria representações equivalentes delas como funções do TensorFlow, que podem então ser exportadas como um SavedModel do TensorFlow.

Configuração

import tensorflow as tf
import numpy as np
import jax
import jax.numpy as jnp
import flax
import optax
import os
from matplotlib import pyplot as plt
from jax.experimental import jax2tf
from threading import Lock # Only used in the visualization utility.
from functools import partial

# Needed for TensorFlow and JAX to coexist in GPU memory.
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = "false"
gpus = tf.config.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  except RuntimeError as e:
    # Memory growth must be set before GPUs have been initialized.
    print(e)

#@title Visualization utilities

plt.rcParams["figure.figsize"] = (20,8)

# The utility for displaying training and validation curves.
def display_train_curves(loss, avg_loss, eval_loss, eval_accuracy, epochs, steps_per_epochs, ignore_first_n=10):

  ignore_first_n_epochs = int(ignore_first_n/steps_per_epochs)

  # The losses.
  ax = plt.subplot(121)
  if loss is not None:
    x = np.arange(len(loss)) / steps_per_epochs #* epochs
    ax.plot(x, loss)
  ax.plot(range(1, epochs+1), avg_loss, "-o", linewidth=3)
  ax.plot(range(1, epochs+1), eval_loss, "-o", linewidth=3)
  ax.set_title('Loss')
  ax.set_ylabel('loss')
  ax.set_xlabel('epoch')
  if loss is not None:
    ax.set_ylim(0, np.max(loss[ignore_first_n:]))
    ax.legend(['train', 'avg train', 'eval'])
  else:
    ymin = np.min(avg_loss[ignore_first_n_epochs:])
    ymax = np.max(avg_loss[ignore_first_n_epochs:])
    ax.set_ylim(ymin-(ymax-ymin)/10, ymax+(ymax-ymin)/10)
    ax.legend(['avg train', 'eval'])

  # The accuracy.
  ax = plt.subplot(122)
  ax.set_title('Eval Accuracy')
  ax.set_ylabel('accuracy')
  ax.set_xlabel('epoch')
  ymin = np.min(eval_accuracy[ignore_first_n_epochs:])
  ymax = np.max(eval_accuracy[ignore_first_n_epochs:])
  ax.set_ylim(ymin-(ymax-ymin)/10, ymax+(ymax-ymin)/10)
  ax.plot(range(1, epochs+1), eval_accuracy, "-o", linewidth=3)

class Progress:
    """Text mode progress bar.
    Usage:
            p = Progress(30)
            p.step()
            p.step()
            p.step(reset=True) # to restart form 0%
    The progress bar displays a new header at each restart."""
    def __init__(self, maxi, size=100, msg=""):
        """
        :param maxi: the number of steps required to reach 100%
        :param size: the number of characters taken on the screen by the progress bar
        :param msg: the message displayed in the header of the progress bar
        """
        self.maxi = maxi
        self.p = self.__start_progress(maxi)()  # `()`: to get the iterator from the generator.
        self.header_printed = False
        self.msg = msg
        self.size = size
        self.lock = Lock()

    def step(self, reset=False):
        with self.lock:
            if reset:
                self.__init__(self.maxi, self.size, self.msg)
            if not self.header_printed:
                self.__print_header()
            next(self.p)

    def __print_header(self):
        print()
        format_string = "0%{: ^" + str(self.size - 6) + "}100%"
        print(format_string.format(self.msg))
        self.header_printed = True

    def __start_progress(self, maxi):
        def print_progress():
            # Bresenham's algorithm. Yields the number of dots printed.
            # This will always print 100 dots in max invocations.
            dx = maxi
            dy = self.size
            d = dy - dx
            for x in range(maxi):
                k = 0
                while d >= 0:
                    print('=', end="", flush=True)
                    k += 1
                    d -= dx
                d += dy
                yield k
            # Keep yielding the last result if there are too many steps.
            while True:
              yield k

        return print_progress

Baixe e prepare o dataset MNIST

(x_train, train_labels), (x_test, test_labels) = tf.keras.datasets.mnist.load_data()

train_data = tf.data.Dataset.from_tensor_slices((x_train, train_labels))
train_data = train_data.map(lambda x,y: (tf.expand_dims(tf.cast(x, tf.float32)/255.0, axis=-1),
                                         tf.one_hot(y, depth=10)))

BATCH_SIZE = 256
train_data = train_data.batch(BATCH_SIZE, drop_remainder=True)
train_data = train_data.cache()
train_data = train_data.shuffle(5000, reshuffle_each_iteration=True)

test_data = tf.data.Dataset.from_tensor_slices((x_test, test_labels))
test_data = test_data.map(lambda x,y: (tf.expand_dims(tf.cast(x, tf.float32)/255.0, axis=-1),
                                         tf.one_hot(y, depth=10)))
test_data = test_data.batch(10000)
test_data = test_data.cache()

(one_batch, one_batch_labels) = next(iter(train_data)) # just one batch
(all_test_data, all_test_labels) = next(iter(test_data)) # all in one batch since batch size is 10000

Configure o treinamento

Este notebook criará e treinará um modelo simples para fins de demonstração.

# Training hyperparameters.
JAX_EPOCHS = 3
TF_EPOCHS = 7
STEPS_PER_EPOCH = len(train_labels)//BATCH_SIZE
LEARNING_RATE = 0.01
LEARNING_RATE_EXP_DECAY = 0.6

# The learning rate schedule for JAX (with Optax).
jlr_decay = optax.exponential_decay(LEARNING_RATE, transition_steps=STEPS_PER_EPOCH, decay_rate=LEARNING_RATE_EXP_DECAY, staircase=True)

# THe learning rate schedule for TensorFlow.
tflr_decay = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=LEARNING_RATE, decay_steps=STEPS_PER_EPOCH, decay_rate=LEARNING_RATE_EXP_DECAY, staircase=True)

Crie o modelo usando Flax

class ConvModel(flax.linen.Module):

  @flax.linen.compact
  def __call__(self, x, train):
    x = flax.linen.Conv(features=12, kernel_size=(3,3), padding="SAME", use_bias=False)(x)
    x = flax.linen.BatchNorm(use_running_average=not train, use_scale=False, use_bias=True)(x)
    x = x.reshape((x.shape[0], -1))  # flatten
    x = flax.linen.Dense(features=200, use_bias=True)(x)
    x = flax.linen.BatchNorm(use_running_average=not train, use_scale=False, use_bias=True)(x)
    x = flax.linen.Dropout(rate=0.3, deterministic=not train)(x)
    x = flax.linen.relu(x)
    x = flax.linen.Dense(features=10)(x)
    #x = flax.linen.log_softmax(x)
    return x

  # JAX differentiation requires a function `f(params, other_state, data, labels)` -> `loss` (as a single number).
  # `jax.grad` will differentiate it against the fist argument.
  # The user must split trainable and non-trainable variables into `params` and `other_state`.
  # Must pass a different RNG key each time for the dropout mask to be different.
  def loss(self, params, other_state, rng, data, labels, train):
    logits, batch_stats = self.apply({'params': params, **other_state},
                                     data,
                                     mutable=['batch_stats'],
                                     rngs={'dropout': rng},
                                     train=train)
    # The loss averaged across the batch dimension.
    loss = optax.softmax_cross_entropy(logits, labels).mean()
    return loss, batch_stats

  def predict(self, state, data):
    logits = self.apply(state, data, train=False) # predict and accuracy disable dropout and use accumulated batch norm stats (train=False)
    probabilities = flax.linen.log_softmax(logits)
    return probabilities

  def accuracy(self, state, data, labels):
    probabilities = self.predict(state, data)
    predictions = jnp.argmax(probabilities, axis=-1)
    dense_labels = jnp.argmax(labels, axis=-1)
    accuracy = jnp.equal(predictions, dense_labels).mean()
    return accuracy

Escreva a função de passo de treinamento

# The training step.
@partial(jax.jit, static_argnums=[0]) # this forces jax.jit to recompile for every new model
def train_step(model, state, optimizer_state, rng, data, labels):

  other_state, params = state.pop('params') # differentiate only against 'params' which represents trainable variables
  (loss, batch_stats), grads = jax.value_and_grad(model.loss, has_aux=True)(params, other_state, rng, data, labels, train=True)

  updates, optimizer_state = optimizer.update(grads, optimizer_state)
  params = optax.apply_updates(params, updates)
  new_state = state.copy(add_or_replace={**batch_stats, 'params': params})

  rng, _ = jax.random.split(rng)

  return new_state, optimizer_state, rng, loss

Escreva o loop de treinamento

def train(model, state, optimizer_state, train_data, epochs, losses, avg_losses, eval_losses, eval_accuracies):
  p = Progress(STEPS_PER_EPOCH)
  rng = jax.random.PRNGKey(0)
  for epoch in range(epochs):

    # This is where the learning rate schedule state is stored in the optimizer state.
    optimizer_step = optimizer_state[1].count

    # Run an epoch of training.
    for step, (data, labels) in enumerate(train_data):
      p.step(reset=(step==0))
      state, optimizer_state, rng, loss = train_step(model, state, optimizer_state, rng, data.numpy(), labels.numpy())
      losses.append(loss)
    avg_loss = np.mean(losses[-step:])
    avg_losses.append(avg_loss)

    # Run one epoch of evals (10,000 test images in a single batch).
    other_state, params = state.pop('params')
    # Gotcha: must discard modified batch_stats here
    eval_loss, _ = model.loss(params, other_state, rng, all_test_data.numpy(), all_test_labels.numpy(), train=False)
    eval_losses.append(eval_loss)
    eval_accuracy = model.accuracy(state, all_test_data.numpy(), all_test_labels.numpy())
    eval_accuracies.append(eval_accuracy)

    print("\nEpoch", epoch, "train loss:", avg_loss, "eval loss:", eval_loss, "eval accuracy", eval_accuracy, "lr:", jlr_decay(optimizer_step))

  return state, optimizer_state

Crie o modelo e o otimizador (com Optax)

# The model.
model = ConvModel()
state = model.init({'params':jax.random.PRNGKey(0), 'dropout':jax.random.PRNGKey(0)}, one_batch, train=True) # Flax allows a separate RNG for "dropout"

# The optimizer.
optimizer = optax.adam(learning_rate=jlr_decay) # Gotcha: it does not seem to be possible to pass just a callable as LR, must be an Optax Schedule
optimizer_state = optimizer.init(state['params'])

losses=[]
avg_losses=[]
eval_losses=[]
eval_accuracies=[]

Treine o modelo

new_state, new_optimizer_state = train(model, state, optimizer_state, train_data, JAX_EPOCHS+TF_EPOCHS, losses, avg_losses, eval_losses, eval_accuracies)

display_train_curves(losses, avg_losses, eval_losses, eval_accuracies, len(eval_losses), STEPS_PER_EPOCH, ignore_first_n=1*STEPS_PER_EPOCH)

Treine parcialmente o modelo

Você continuará treinando o modelo no TensorFlow em breve.

model = ConvModel()
state = model.init({'params':jax.random.PRNGKey(0), 'dropout':jax.random.PRNGKey(0)}, one_batch, train=True) # Flax allows a separate RNG for "dropout"

# The optimizer.
optimizer = optax.adam(learning_rate=jlr_decay) # LR must be an Optax LR Schedule
optimizer_state = optimizer.init(state['params'])

losses, avg_losses, eval_losses, eval_accuracies = [], [], [], []

state, optimizer_state = train(model, state, optimizer_state, train_data, JAX_EPOCHS, losses, avg_losses, eval_losses, eval_accuracies)

display_train_curves(losses, avg_losses, eval_losses, eval_accuracies, len(eval_losses), STEPS_PER_EPOCH, ignore_first_n=1*STEPS_PER_EPOCH)

Salve apenas o suficiente para inferência

Se seu objetivo é implantar seu modelo JAX (para que você possa executar inferência usando model.predict()), simplesmente exportá-lo para SavedModel é suficiente. Esta seção demonstra como fazer isso.

# Test data with a different batch size to test polymorphic shapes.
x, y = next(iter(train_data.unbatch().batch(13)))

m = tf.Module()
# Wrap the JAX state in `tf.Variable` (needed when calling the converted JAX function.
state_vars = tf.nest.map_structure(tf.Variable, state)
# Keep the wrapped state as flat list (needed in TensorFlow fine-tuning).
m.vars = tf.nest.flatten(state_vars)
# Convert the desired JAX function (`model.predict`).
predict_fn = jax2tf.convert(model.predict, polymorphic_shapes=["...", "(b, 28, 28, 1)"])
# Wrap the converted function in `tf.function` with the correct `tf.TensorSpec` (necessary for dynamic shapes to work).
@tf.function(autograph=False, input_signature=[tf.TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32)])
def predict(data):
    return predict_fn(state_vars, data)
m.predict = predict
tf.saved_model.save(m, "./")

# Test the converted function.
print("Converted function predictions:", np.argmax(m.predict(x).numpy(), axis=-1))
# Reload the model.
reloaded_model = tf.saved_model.load("./")
# Test the reloaded converted function (the result should be the same).
print("Reloaded  function predictions:", np.argmax(reloaded_model.predict(x).numpy(), axis=-1))

Salve tudo

Se seu objetivo é uma exportação abrangente (útil se você planeja colocar o modelo no TensorFlow para ajuste fino, fusão etc.), esta seção demonstra como salvar o modelo para que você possa acessar métodos, incluindo:

• model.predict

• model.accuracy

• model.loss (incluindo train=True/False bool, RNG para dropout e atualizações de estado BatchNorm)

from collections import abc

def _fix_frozen(d):
  """Changes any mappings (e.g. frozendict) back to dict."""
  if isinstance(d, list):
    return [_fix_frozen(v) for v in d]
  elif isinstance(d, tuple):
    return tuple(_fix_frozen(v) for v in d)
  elif not isinstance(d, abc.Mapping):
    return d
  d = dict(d)
  for k, v in d.items():
    d[k] = _fix_frozen(v)
  return d

class TFModel(tf.Module):
  def __init__(self, state, model):
    super().__init__()

    # Special care needed for the train=True/False parameter in the loss
    @jax.jit
    def loss_with_train_bool(state, rng, data, labels, train):
      other_state, params = state.pop('params')
      loss, batch_stats = jax.lax.cond(train,
                                       lambda state, data, labels: model.loss(params, other_state, rng, data, labels, train=True),
                                       lambda state, data, labels: model.loss(params, other_state, rng, data, labels, train=False),
                                       state, data, labels)
      # must use JAX to split the RNG, therefore, must do it in a @jax.jit function
      new_rng, _ = jax.random.split(rng)
      return loss, batch_stats, new_rng

    self.state_vars = tf.nest.map_structure(tf.Variable, state)
    self.vars = tf.nest.flatten(self.state_vars)
    self.jax_rng = tf.Variable(jax.random.PRNGKey(0))

    self.loss_fn = jax2tf.convert(loss_with_train_bool, polymorphic_shapes=["...", "...", "(b, 28, 28, 1)", "(b, 10)", "..."])
    self.accuracy_fn = jax2tf.convert(model.accuracy, polymorphic_shapes=["...", "(b, 28, 28, 1)", "(b, 10)"])
    self.predict_fn = jax2tf.convert(model.predict, polymorphic_shapes=["...", "(b, 28, 28, 1)"])

  # Must specify TensorSpec manually for variable batch size to work
  @tf.function(autograph=False, input_signature=[tf.TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32)])
  def predict(self, data):
    # Make sure the TfModel.predict function implicitly use self.state_vars and not the JAX state directly
    # otherwise, all model weights would be embedded in the TF graph as constants.
    return self.predict_fn(self.state_vars, data)

  @tf.function(input_signature=[tf.TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32),
                                tf.TensorSpec(shape=(None, 10), dtype=tf.float32)],
               autograph=False)
  def train_loss(self, data, labels):
      loss, batch_stats, new_rng = self.loss_fn(self.state_vars, self.jax_rng, data, labels, True)
      # update batch norm stats
      flat_vars = tf.nest.flatten(self.state_vars['batch_stats'])
      flat_values = tf.nest.flatten(batch_stats['batch_stats'])
      for var, val in zip(flat_vars, flat_values):
        var.assign(val)
      # update RNG
      self.jax_rng.assign(new_rng)
      return loss

  @tf.function(input_signature=[tf.TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32),
                                tf.TensorSpec(shape=(None, 10), dtype=tf.float32)],
               autograph=False)
  def eval_loss(self, data, labels):
      loss, batch_stats, new_rng = self.loss_fn(self.state_vars, self.jax_rng, data, labels, False)
      return loss

  @tf.function(input_signature=[tf.TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32),
                                tf.TensorSpec(shape=(None, 10), dtype=tf.float32)],
               autograph=False)
  def accuracy(self, data, labels):
    return self.accuracy_fn(self.state_vars, data, labels)

# Instantiate the model.
tf_model = TFModel(state, model)

# Save the model.
tf.saved_model.save(tf_model, "./")

Recarregue o modelo

reloaded_model = tf.saved_model.load("./")

# Test if it works and that the batch size is indeed variable.
x,y = next(iter(train_data.unbatch().batch(13)))
print(np.argmax(reloaded_model.predict(x).numpy(), axis=-1))
x,y = next(iter(train_data.unbatch().batch(20)))
print(np.argmax(reloaded_model.predict(x).numpy(), axis=-1))

print(reloaded_model.accuracy(one_batch, one_batch_labels))
print(reloaded_model.accuracy(all_test_data, all_test_labels))

Continue treinando o modelo JAX convertido no TensorFlow

optimizer = tf.keras.optimizers.Adam(learning_rate=tflr_decay)

# Set the iteration step for the learning rate to resume from where it left off in JAX.
optimizer.iterations.assign(len(eval_losses)*STEPS_PER_EPOCH)

p = Progress(STEPS_PER_EPOCH)

for epoch in range(JAX_EPOCHS, JAX_EPOCHS+TF_EPOCHS):

  # This is where the learning rate schedule state is stored in the optimizer state.
  optimizer_step = optimizer.iterations

  for step, (data, labels) in enumerate(train_data):
    p.step(reset=(step==0))
    with tf.GradientTape() as tape:
      #loss = reloaded_model.loss(data, labels, True)
      loss = reloaded_model.train_loss(data, labels)
      grads = tape.gradient(loss, reloaded_model.vars)
      optimizer.apply_gradients(zip(grads, reloaded_model.vars))
      losses.append(loss)
  avg_loss = np.mean(losses[-step:])
  avg_losses.append(avg_loss)

  eval_loss = reloaded_model.eval_loss(all_test_data.numpy(), all_test_labels.numpy()).numpy()
  eval_losses.append(eval_loss)
  eval_accuracy = reloaded_model.accuracy(all_test_data.numpy(), all_test_labels.numpy()).numpy()
  eval_accuracies.append(eval_accuracy)

  print("\nEpoch", epoch, "train loss:", avg_loss, "eval loss:", eval_loss, "eval accuracy", eval_accuracy, "lr:", tflr_decay(optimizer.iterations).numpy())

display_train_curves(losses, avg_losses, eval_losses, eval_accuracies, len(eval_losses), STEPS_PER_EPOCH, ignore_first_n=2*STEPS_PER_EPOCH)

# The loss takes a hit when the training restarts, but does not go back to random levels.
# This is likely caused by the optimizer momentum being reinitialized.

Próximos passos

Você pode aprender mais sobre JAX e Flax em seus sites de documentação que contêm guias detalhados e exemplos. Se o JAX é novo para você, explore os tutoriais do JAX 101 e veja o início rápido do Flax. Para saber mais sobre como converter modelos JAX para o formato TensorFlow, veja o utilitário jax2tf no GitHub. Se você tiver interesse em converter modelos JAX para execução no navegador com TensorFlow.js, veja JAX na Web com TensorFlow.js. Se quiser preparar modelos JAX para execução no TensorFLow Lite, leia o guia Conversão de modelos JAX para o TFLite.