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import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
from typing import Optional
class PositionalEncoding(layers.Layer):
"""Positional encoding layer for transformer"""
def __init__(self, max_length: int, d_model: int, **kwargs):
super().__init__(**kwargs)
self.max_length = max_length
self.d_model = d_model
# Create positional encoding matrix
position = np.arange(max_length)[:, np.newaxis]
div_term = np.exp(np.arange(0, d_model, 2) * -(np.log(10000.0) / d_model))
pe = np.zeros((max_length, d_model))
pe[:, 0::2] = np.sin(position * div_term)
pe[:, 1::2] = np.cos(position * div_term)
self.positional_encoding = tf.constant(pe, dtype=tf.float32)
def call(self, x):
seq_length = tf.shape(x)[1]
return x + self.positional_encoding[:seq_length, :]
def get_config(self):
config = super().get_config()
config.update({
'max_length': self.max_length,
'd_model': self.d_model
})
return config
class TransformerBlock(layers.Layer):
"""Transformer decoder block"""
def __init__(self, d_model: int, num_heads: int, ff_dim: int,
dropout_rate: float = 0.1, **kwargs):
super().__init__(**kwargs)
self.d_model = d_model
self.num_heads = num_heads
self.ff_dim = ff_dim
self.dropout_rate = dropout_rate
self.attention = layers.MultiHeadAttention(
num_heads=num_heads,
key_dim=d_model // num_heads,
dropout=dropout_rate
)
self.ffn = keras.Sequential([
layers.Dense(ff_dim, activation='gelu'),
layers.Dropout(dropout_rate),
layers.Dense(d_model),
layers.Dropout(dropout_rate)
])
self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
self.dropout = layers.Dropout(dropout_rate)
def call(self, x, training=False, mask=None):
# Causal self-attention
attn_output = self.attention(
query=x,
value=x,
key=x,
attention_mask=mask,
training=training
)
attn_output = self.dropout(attn_output, training=training)
out1 = self.layernorm1(x + attn_output)
# Feed forward network
ffn_output = self.ffn(out1, training=training)
return self.layernorm2(out1 + ffn_output)
def get_config(self):
config = super().get_config()
config.update({
'd_model': self.d_model,
'num_heads': self.num_heads,
'ff_dim': self.ff_dim,
'dropout_rate': self.dropout_rate
})
return config
class VedaProgrammingLLM(keras.Model):
"""Veda Programming Language Model"""
def __init__(
self,
vocab_size: int,
max_length: int = 512,
d_model: int = 256,
num_heads: int = 8,
num_layers: int = 6,
ff_dim: int = 1024,
dropout_rate: float = 0.1,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.max_length = max_length
self.d_model = d_model
self.num_heads = num_heads
self.num_layers = num_layers
self.ff_dim = ff_dim
self.dropout_rate = dropout_rate
# Embedding layers
self.token_embedding = layers.Embedding(
input_dim=vocab_size,
output_dim=d_model
)
self.positional_encoding = PositionalEncoding(max_length, d_model)
self.dropout = layers.Dropout(dropout_rate)
# Transformer blocks
self.transformer_blocks = [
TransformerBlock(d_model, num_heads, ff_dim, dropout_rate)
for _ in range(num_layers)
]
# Output layer
self.output_layer = layers.Dense(vocab_size)
def _create_causal_mask(self, seq_length):
"""Create causal attention mask"""
mask = tf.linalg.band_part(
tf.ones((seq_length, seq_length)), -1, 0
)
return mask
def call(self, inputs, training=False):
seq_length = tf.shape(inputs)[1]
# Create causal mask
mask = self._create_causal_mask(seq_length)
# Embeddings
x = self.token_embedding(inputs)
x = x * tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = self.positional_encoding(x)
x = self.dropout(x, training=training)
# Transformer blocks
for transformer_block in self.transformer_blocks:
x = transformer_block(x, training=training, mask=mask)
# Output projection
logits = self.output_layer(x)
return logits
def generate(
self,
prompt_tokens: list,
max_new_tokens: int = 100,
temperature: float = 0.7,
top_k: int = 50,
top_p: float = 0.9
):
"""Generate code given a prompt"""
generated = list(prompt_tokens)
for _ in range(max_new_tokens):
# Truncate if too long
context = generated[-self.max_length:]
# Get predictions
input_tensor = tf.expand_dims(context, 0)
logits = self(input_tensor, training=False)
next_token_logits = logits[0, -1, :] / temperature
# Apply top-k filtering
if top_k > 0:
top_k_logits, top_k_indices = tf.math.top_k(
next_token_logits, k=min(top_k, self.vocab_size)
)
# Create mask for non-top-k tokens
indices_to_remove = tf.less(
next_token_logits,
top_k_logits[-1]
)
next_token_logits = tf.where(
indices_to_remove,
tf.ones_like(next_token_logits) * float('-inf'),
next_token_logits
)
# Apply top-p (nucleus) filtering
if top_p < 1.0:
sorted_logits = tf.sort(next_token_logits, direction='DESCENDING')
sorted_probs = tf.nn.softmax(sorted_logits)
cumulative_probs = tf.cumsum(sorted_probs)
# Find cutoff
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove = tf.concat([
[False],
sorted_indices_to_remove[:-1]
], axis=0)
sorted_logits = tf.where(
sorted_indices_to_remove,
tf.ones_like(sorted_logits) * float('-inf'),
sorted_logits
)
# Sample from distribution
probs = tf.nn.softmax(next_token_logits)
next_token = tf.random.categorical(
tf.expand_dims(next_token_logits, 0),
num_samples=1
)[0, 0]
generated.append(int(next_token.numpy()))
# Stop if end token
if next_token == 3: # END token
break
return generated
def get_config(self):
return {
'vocab_size': self.vocab_size,
'max_length': self.max_length,
'd_model': self.d_model,
'num_heads': self.num_heads,
'num_layers': self.num_layers,
'ff_dim': self.ff_dim,
'dropout_rate': self.dropout_rate
}
@classmethod
def from_config(cls, config):
return cls(**config)
def create_veda_model(
vocab_size: int,
max_length: int = 512,
model_size: str = "small"
) -> VedaProgrammingLLM:
"""Factory function to create Veda Programming model"""
configs = {
"small": {
"d_model": 256,
"num_heads": 4,
"num_layers": 4,
"ff_dim": 512
},
"medium": {
"d_model": 512,
"num_heads": 8,
"num_layers": 6,
"ff_dim": 1024
},
"large": {
"d_model": 768,
"num_heads": 12,
"num_layers": 12,
"ff_dim": 2048
}
}
config = configs.get(model_size, configs["small"])
model = VedaProgrammingLLM(
vocab_size=vocab_size,
max_length=max_length,
**config
)
return model |