jax_transformer_translation.py

# jax_transformer_translation.py

import jax
import jax.numpy as jnp
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Define the transformer model
def jax_transformer(inputs, targets, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate):
    def positional_encoding(pos, d_model):
        angles = jnp.arange(d_model)[:, np.newaxis] / jnp.power(10000, (2 * (jnp.arange(d_model) // 2)) / d_model)
        angles = pos * angles
        angles[:, 0::2] = jnp.sin(angles[:, 0::2])
        angles[:, 1::2] = jnp.cos(angles[:, 1::2])
        return angles

    def multi_head_attention(q, k, v, mask):
        attention_scores = jnp.matmul(q, jnp.transpose(k, (0, 2, 1))) / jnp.sqrt(d_model)
        if mask is not None:
            attention_scores += mask * -1e9
        attention_weights = jax.nn.softmax(attention_scores)
        output = jnp.matmul(attention_weights, v)
        return output

    def encoder_layer(inputs, mask):
        attention_output = multi_head_attention(inputs, inputs, inputs, mask)
        attention_output = jax.nn.dropout(attention_output, rate=dropout_rate)
        attention_output = jax.nn.layer_norm(inputs + attention_output)
        
        ffn_output = jax.nn.dense(attention_output, dff, activation=jax.nn.relu)
        ffn_output = jax.nn.dense(ffn_output, d_model)
        ffn_output = jax.nn.dropout(ffn_output, rate=dropout_rate)
        ffn_output = jax.nn.layer_norm(attention_output + ffn_output)
        
        return ffn_output

    def decoder_layer(inputs, enc_outputs, mask):
        attention_output = multi_head_attention(inputs, inputs, inputs, mask)
        attention_output = jax.nn.dropout(attention_output, rate=dropout_rate)
        attention_output = jax.nn.layer_norm(inputs + attention_output)
        
        enc_attention_output = multi_head_attention(attention_output, enc_outputs, enc_outputs, None)
        enc_attention_output = jax.nn.dropout(enc_attention_output, rate=dropout_rate)
        enc_attention_output = jax.nn.layer_norm(attention_output + enc_attention_output)
        
        ffn_output = jax.nn.dense(enc_attention_output, dff, activation=jax.nn.relu)
        ffn_output = jax.nn.dense(ffn_output, d_model)
        ffn_output = jax.nn.dropout(ffn_output, rate=dropout_rate)
        ffn_output = jax.nn.layer_norm(enc_attention_output + ffn_output)
        
        return ffn_output

    def encoder(inputs, mask):
        inputs = jax.nn.embedding(inputs, d_model, input_vocab_size)
        inputs *= jnp.sqrt(d_model)
        inputs += positional_encoding(jnp.arange(inputs.shape[1]), d_model)
        inputs = jax.nn.dropout(inputs, rate=dropout_rate)
        
        for _ in range(num_layers):
            inputs = encoder_layer(inputs, mask)
        
        return inputs

    def decoder(inputs, enc_outputs, mask):
        inputs = jax.nn.embedding(inputs, d_model, target_vocab_size)
        inputs *= jnp.sqrt(d_model)
        inputs += positional_encoding(jnp.arange(inputs.shape[1]), d_model)
        inputs = jax.nn.dropout(inputs, rate=dropout_rate)
        
        for _ in range(num_layers):
            inputs = decoder_layer(inputs, enc_outputs, mask)
        
        outputs = jax.nn.dense(inputs, target_vocab_size)
        return outputs

    enc_inputs = inputs
    dec_inputs = targets[:, :-1]
    dec_outputs_real = targets[:, 1:]
    
    enc_outputs = encoder(enc_inputs, None)
    dec_outputs = decoder(dec_inputs, enc_outputs, None)
    
    return dec_outputs, dec_outputs_real

# Define the loss function
def jax_loss_fn(params, inputs, targets, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate):
    dec_outputs, dec_outputs_real = jax_transformer(inputs, targets, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate)
    loss = jnp.mean(jax.nn.sparse_softmax_cross_entropy_with_logits(logits=dec_outputs, labels=dec_outputs_real))
    return loss

# Tokenize and preprocess the data
def preprocess_data(english_sentences, french_sentences, max_length):
    english_tokenizer = Tokenizer(filters='')
    english_tokenizer.fit_on_texts(english_sentences)
    english_sequences = english_tokenizer.texts_to_sequences(english_sentences)
    english_sequences = pad_sequences(english_sequences, maxlen=max_length, padding='post')
    
    french_tokenizer = Tokenizer(filters='')
    french_tokenizer.fit_on_texts(french_sentences)
    french_sequences = french_tokenizer.texts_to_sequences(french_sentences)
    french_sequences = pad_sequences(french_sequences, maxlen=max_length+1, padding='post')
    
    return english_sequences, french_sequences, english_tokenizer, french_tokenizer

# Train the transformer model
def jax_train(params, optimizer, train_data, num_epochs, batch_size, num_layers, d_model, num_heads, dff, dropout_rate):
    for epoch in range(num_epochs):
        epoch_loss = 0
        
        for i in range(0, len(train_data), batch_size):
            batch_data = train_data[i:i+batch_size]
            english_sequences = np.array([data[0] for data in batch_data])
            french_sequences = np.array([data[1] for data in batch_data])
            
            loss_value, grads = jax.value_and_grad(jax_loss_fn)(params, english_sequences, french_sequences, num_layers, d_model, num_heads, dff, len(english_tokenizer.word_index)+1, len(french_tokenizer.word_index)+1, dropout_rate)
            params = optimizer.update(grads, params)
            epoch_loss += loss_value
        
        epoch_loss /= (len(train_data) // batch_size)
        print(f"Epoch {epoch+1}, Loss: {epoch_loss}")
    
    return params

# Translate a sentence using the trained model
def jax_translate(params, sentence, english_tokenizer, french_tokenizer, max_length, num_layers, d_model, num_heads, dff, dropout_rate):
    sentence = sentence.lower().strip()
    sentence = english_tokenizer.texts_to_sequences([sentence])
    sentence = pad_sequences(sentence, maxlen=max_length, padding='post')
    
    enc_inputs = sentence
    dec_inputs = np.zeros((1, max_length+1))
    dec_inputs[0, 0] = french_tokenizer.word_index['<start>']
    
    for i in range(1, max_length+1):
        dec_outputs, _ = jax_transformer(enc_inputs, dec_inputs, num_layers, d_model, num_heads, dff, len(english_tokenizer.word_index)+1, len(french_tokenizer.word_index)+1, dropout_rate)
        dec_outputs = dec_outputs[:, i-1, :]
        pred_idx = jnp.argmax(dec_outputs)
        
        if pred_idx == french_tokenizer.word_index['<end>']:
            break
        
        dec_inputs[0, i] = pred_idx
    
    decoded_sentence = [french_tokenizer.index_word[idx] for idx in dec_inputs[0] if idx > 0]
    return ' '.join(decoded_sentence)

# Example usage
english_sentences = [
    'I love to learn machine learning.',
    'JAX is a great library for numerical computing.',
    'Transformers have revolutionized natural language processing.'
]

french_sentences = [
    "J'adore apprendre le machine learning.",
    "JAX est une excellente bibliothèque pour le calcul numérique.",
    "Les transformateurs ont révolutionné le traitement du langage naturel."
]

max_length = 20
english_sequences, french_sequences, english_tokenizer, french_tokenizer = preprocess_data(english_sentences, french_sentences, max_length)

train_data = list(zip(english_sequences, french_sequences))

# Initialize transformer parameters
rng = jax.random.PRNGKey(0)
params = jax.random.normal(rng, (max_length, d_model))

# Initialize optimizer
optimizer = jax.optim.Adam(learning_rate=0.001)

# Train the transformer model
params = jax_train(params, optimizer, train_data, num_epochs=10, batch_size=2, num_layers=2, d_model=128, num_heads=8, dff=512, dropout_rate=0.1)

# Translate a sentence
sentence = "I enjoy coding with JAX."
translated_sentence = jax_translate(params, sentence, english_tokenizer, french_tokenizer, max_length, num_layers=2, d_model=128, num_heads=8, dff=512, dropout_rate=0.1)
print("Translated sentence:", translated_sentence)

# Possible Errors and Solutions:

# ValueError: operands could not be broadcast together with shapes (x, y) (a, b)
# Solution: Ensure that the shapes of the predictions and targets match exactly when calculating the loss.

# ImportError: No module named 'tensorflow.keras.preprocessing.text'
# Solution: Ensure TensorFlow is installed using `pip install tensorflow`.

# IndexError: list index out of range
# Solution: Verify that the indices used in operations are within the valid range of the data structures being accessed.

# RuntimeError: Invalid argument: Non-scalable parameters
# Solution: Ensure all operations in the model are scalable and support JAX's JIT compilation.