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from enum import Enum
from typing import Union, Optional
import io
from PIL import Image
import numpy as np
import torch
import struct
from enum import auto
from typing import List, Dict, Tuple
import wave
from dataclasses import dataclass
import torch.nn as nn
import torch.nn.functional as F
from loguru import logger
from einops import rearrange
from torch import Tensor
@dataclass
class ModelConfig:
"""Configuration for the enhanced BytePredictor model."""
vocab_size: int = 256 # Standard byte range
hidden_size: int = 1024
num_layers: int = 12
num_key_value_heads: int = 8 # For multi-query attention
num_query_heads: int = 32 # More query heads than kv heads
dropout: float = 0.1
max_sequence_length: int = 8192
rope_theta: float = 10000.0
layer_norm_eps: float = 1e-5
vocab_parallel: bool = False
qk_norm: bool = True
qk_norm_scale: float = None
attention_bias: bool = False
class MultiQueryAttention(nn.Module):
"""Fixed Multi-Query Attention implementation."""
def __init__(self, config: ModelConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.num_query_heads = config.num_query_heads
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // config.num_query_heads
self.qk_scale = config.qk_norm_scale or (self.head_dim**-0.5)
self.q_proj = nn.Linear(
config.hidden_size, config.num_query_heads * self.head_dim
)
self.k_proj = nn.Linear(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
)
self.v_proj = nn.Linear(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
)
self.o_proj = nn.Linear(
config.num_query_heads * self.head_dim, config.hidden_size
)
self.qk_norm = config.qk_norm
if self.qk_norm:
self.q_norm = nn.LayerNorm(self.head_dim)
self.k_norm = nn.LayerNorm(self.head_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size, seq_length, _ = hidden_states.shape
# Project and reshape
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
# Reshape to [seq_len, batch, heads, head_dim]
q = q.view(
batch_size,
seq_length,
self.num_query_heads,
self.head_dim,
).permute(1, 0, 2, 3)
k = k.view(
batch_size,
seq_length,
self.num_key_value_heads,
self.head_dim,
).permute(1, 0, 2, 3)
v = v.view(
batch_size,
seq_length,
self.num_key_value_heads,
self.head_dim,
).permute(1, 0, 2, 3)
# Apply rotary embeddings
# q, k = self.rotary(q, k, seq_length)
# Apply QK normalization if enabled
if self.qk_norm:
q = self.q_norm(q)
k = self.k_norm(k)
# Handle MQA head expansion
if self.num_key_value_heads != self.num_query_heads:
k = k.repeat_interleave(
self.num_query_heads // self.num_key_value_heads,
dim=2,
)
v = v.repeat_interleave(
self.num_query_heads // self.num_key_value_heads,
dim=2,
)
# Compute attention
# Reshape for matmul: [batch, heads, seq_length, head_dim]
q = q.permute(1, 2, 0, 3)
k = k.permute(1, 2, 0, 3)
v = v.permute(1, 2, 0, 3)
attn_weights = (
torch.matmul(q, k.transpose(-2, -1)) * self.qk_scale
)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = F.softmax(attn_weights, dim=-1)
output = torch.matmul(attn_weights, v)
# Reshape back to [batch, seq_length, hidden_size]
output = (
output.transpose(1, 2)
.contiguous()
.view(batch_size, seq_length, -1)
)
output = self.o_proj(output)
return output
class EnhancedBytePredictor(nn.Module):
"""Enhanced byte prediction model with state-of-the-art features."""
def __init__(self, config: ModelConfig):
super().__init__()
self.config = config
# Token embeddings
self.tok_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size
)
# Transformer layers
self.layers = nn.ModuleList(
[
nn.ModuleDict(
{
"attention": MultiQueryAttention(config),
"attention_norm": nn.LayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
),
"feed_forward": nn.Sequential(
nn.Linear(
config.hidden_size,
4 * config.hidden_size,
),
nn.GELU(),
nn.Linear(
4 * config.hidden_size,
config.hidden_size,
),
),
"feed_forward_norm": nn.LayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
),
}
)
for _ in range(config.num_layers)
]
)
self.norm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
self.output = nn.Linear(
config.hidden_size, config.vocab_size, bias=False
)
# Initialize weights
self.apply(self._init_weights)
def _init_weights(self, module: nn.Module) -> None:
"""Initialize weights with scaled normal distribution."""
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Forward pass of the model.
Args:
input_ids: Tensor of shape (batch_size, sequence_length)
attention_mask: Optional attention mask
Returns:
Tensor of logits with shape (batch_size, sequence_length, vocab_size)
"""
hidden_states = self.tok_embeddings(input_ids)
# Create causal mask if needed
if attention_mask is None:
attention_mask = torch.triu(
torch.ones(
(input_ids.size(1), input_ids.size(1)),
device=input_ids.device,
dtype=torch.bool,
),
diagonal=1,
)
attention_mask = attention_mask.masked_fill(
attention_mask == 1, float("-inf")
)
# Apply transformer layers
for layer in self.layers:
# Attention block
hidden_states = hidden_states + layer["attention"](
layer["attention_norm"](hidden_states), attention_mask
)
# Feed-forward block
hidden_states = hidden_states + layer["feed_forward"](
layer["feed_forward_norm"](hidden_states)
)
hidden_states = self.norm(hidden_states)
logits = self.output(hidden_states)
return logits
def compute_loss(
self,
input_ids: torch.Tensor,
target_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Compute cross entropy loss.
Args:
input_ids: Input token ids
target_ids: Target token ids
attention_mask: Optional attention mask
Returns:
Loss value
"""
logits = self(input_ids, attention_mask)
loss = F.cross_entropy(
rearrange(logits, "b s v -> (b s) v"),
rearrange(target_ids, "b s -> (b s)"),
)
return loss
@torch.no_grad()
def _generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 100,
temperature: float = 1.0,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
repetition_penalty: float = 1.0,
) -> torch.Tensor:
"""
Generate new tokens autoregressively.
Args:
input_ids: Starting sequence
max_new_tokens: Number of tokens to generate
temperature: Sampling temperature
top_k: K for top-k sampling
top_p: P for nucleus sampling
repetition_penalty: Penalty for repeating tokens
Returns:
Generated sequence
"""
batch_size, seq_length = input_ids.shape
generated = input_ids.clone()
for _ in range(max_new_tokens):
if generated.size(1) >= self.config.max_sequence_length:
break
# Forward pass
logits = self(generated)[:, -1, :]
# Apply temperature
logits = logits / temperature
# Apply repetition penalty
if repetition_penalty != 1.0:
for i in range(batch_size):
for token_id in set(generated[i].tolist()):
logits[i, token_id] /= repetition_penalty
# Apply top-k sampling
if top_k is not None:
indices_to_remove = (
logits
< torch.topk(logits, top_k)[0][..., -1, None]
)
logits[indices_to_remove] = float("-inf")
# Apply nucleus (top-p) sampling
if top_p is not None:
sorted_logits, sorted_indices = torch.sort(
logits, descending=True
)
cumulative_probs = torch.cumsum(
F.softmax(sorted_logits, dim=-1), dim=-1
)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = (
sorted_indices_to_remove[..., :-1].clone()
)
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = torch.zeros_like(
logits, dtype=torch.bool
)
indices_to_remove.scatter_(
1, sorted_indices, sorted_indices_to_remove
)
logits[indices_to_remove] = float("-inf")
# Sample next token
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append to sequence
generated = torch.cat([generated, next_token], dim=1)
return generated
def generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 100,
temperature: float = 1.0,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
repetition_penalty: float = 1.0,
):
tensor_data = self._generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
)
return tensor_to_data(tensor_data)
# import torch
# from typing import Optional
class DataType(Enum):
TEXT = "text"
IMAGE = "image"
AUDIO = "audio"
VIDEO = "video"
BINARY = "binary"
class ByteDetokenizer:
"""Utility class for converting model output bytes back to original data formats."""
@staticmethod
def tensor_to_bytes(tensor: torch.Tensor) -> bytes:
"""Convert model output tensor to bytes."""
# Convert logits/probabilities to byte values
if tensor.dim() > 1:
# If we have logits, convert to byte indices
byte_indices = tensor.argmax(dim=-1)
else:
byte_indices = tensor
# Convert to Python bytes
return bytes(
byte_indices.cpu().numpy().astype(np.uint8).tolist()
)
@staticmethod
def decode_text(byte_sequence: bytes) -> str:
"""Convert bytes to text."""
try:
return byte_sequence.decode("utf-8")
except UnicodeDecodeError:
# Try with error handling
return byte_sequence.decode("utf-8", errors="replace")
@staticmethod
def decode_image(
byte_sequence: bytes,
mode: str = "RGB",
size: Optional[tuple] = None,
) -> Image.Image:
"""Convert bytes to image.
Args:
byte_sequence: Raw image bytes
mode: Image mode (RGB, RGBA, L, etc.)
size: Optional tuple of (width, height)
"""
try:
# Try to load as-is first (for standard image formats)
img = Image.open(io.BytesIO(byte_sequence))
if size:
img = img.resize(size)
return img
except:
# If failed, assume raw pixel data
if not size:
# Try to determine size from byte count
pixel_count = len(byte_sequence) // len(mode)
size = (
int(np.sqrt(pixel_count)),
int(np.sqrt(pixel_count)),
)
# Convert raw bytes to pixel array
pixels = np.frombuffer(byte_sequence, dtype=np.uint8)
pixels = pixels.reshape((*size, len(mode)))
return Image.fromarray(pixels, mode=mode)
@staticmethod
def decode_audio(
byte_sequence: bytes,
sample_rate: int = 44100,
channels: int = 2,
sample_width: int = 2,
) -> np.ndarray:
"""Convert bytes to audio samples.
Args:
byte_sequence: Raw audio bytes
sample_rate: Audio sample rate in Hz
channels: Number of audio channels
sample_width: Bytes per sample (1, 2, or 4)
"""
# Determine format string based on sample width
format_str = {
1: "b", # signed char
2: "h", # short
4: "i", # int
}[sample_width]
# Unpack bytes to samples
sample_count = len(byte_sequence) // (channels * sample_width)
samples = struct.unpack(
f"<{sample_count * channels}{format_str}", byte_sequence
)
# Reshape to [samples, channels]
return np.array(samples).reshape(-1, channels)
def decode_data(
self,
model_output: Union[torch.Tensor, bytes],
data_type: DataType,
**kwargs,
) -> Union[str, Image.Image, np.ndarray, bytes]:
"""Main method to decode model output to desired format.
Args:
model_output: Either tensor from model or raw bytes
data_type: Type of data to decode to
**kwargs: Additional parameters for specific decoders
Returns:
Decoded data in specified format
"""
# Convert tensor to bytes if needed
if isinstance(model_output, torch.Tensor):
byte_sequence = self.tensor_to_bytes(model_output)
else:
byte_sequence = model_output
# Decode based on type
if data_type == DataType.TEXT:
return self.decode_text(byte_sequence)
elif data_type == DataType.IMAGE:
return self.decode_image(byte_sequence, **kwargs)
elif data_type == DataType.AUDIO:
return self.decode_audio(byte_sequence, **kwargs)
elif data_type == DataType.VIDEO:
raise NotImplementedError(
"Video decoding not yet implemented"
)
else: # BINARY
return byte_sequence
# Usage example
class Modality(Enum):
TEXT = auto()
IMAGE = auto()
AUDIO = auto()
VIDEO = auto()
BINARY = auto()
MULTIMODAL = auto()
@dataclass
class ModalityInfo:
"""Information about detected modality."""
modality: Modality
confidence: float
metadata: Dict[str, any]
sub_modalities: Optional[List["ModalityInfo"]] = None
class ModalityDetector:
"""Detects data modalities from byte sequences."""
# Common file signatures (magic numbers)
SIGNATURES = {
# Images
b"\xFF\xD8\xFF": "JPEG",
b"\x89PNG\r\n\x1a\n": "PNG",
b"GIF87a": "GIF",
b"GIF89a": "GIF",
b"RIFF": "WEBP",
# Audio
b"RIFF....WAVE": "WAV",
b"ID3": "MP3",
b"\xFF\xFB": "MP3",
b"OggS": "OGG",
# Video
b"\x00\x00\x00\x18ftypmp42": "MP4",
b"\x00\x00\x00\x1Cftypav01": "MP4",
b"\x1A\x45\xDF\xA3": "WEBM",
}
def __init__(self):
self.magic = magic.Magic(mime=True)
def _check_text_probability(self, data: bytes) -> float:
"""Estimate probability that data is text."""
# Check if data is valid UTF-8
try:
data.decode("utf-8")
# Count printable ASCII characters
printable = sum(1 for b in data if 32 <= b <= 126)
return printable / len(data)
except UnicodeDecodeError:
return 0.0
def _check_image_validity(self, data: bytes) -> Tuple[bool, Dict]:
"""Check if data is a valid image and extract metadata."""
try:
with io.BytesIO(data) as bio:
img = Image.open(bio)
return True, {
"format": img.format,
"size": img.size,
"mode": img.mode,
}
except:
return False, {}
def _check_audio_validity(self, data: bytes) -> Tuple[bool, Dict]:
"""Check if data is valid audio and extract metadata."""
try:
with io.BytesIO(data) as bio:
# Try to parse as WAV
with wave.open(bio) as wav:
return True, {
"channels": wav.getnchannels(),
"sample_width": wav.getsampwidth(),
"framerate": wav.getframerate(),
"frames": wav.getnframes(),
}
except:
# Check for other audio signatures
for sig in [b"ID3", b"\xFF\xFB", b"OggS"]:
if data.startswith(sig):
return True, {"format": "compressed_audio"}
return False, {}
def _detect_boundaries(
self, data: bytes
) -> List[Tuple[int, int, Modality]]:
"""Detect boundaries between different modalities in the data."""
boundaries = []
current_pos = 0
while current_pos < len(data):
# Look for known signatures
for sig, format_type in self.SIGNATURES.items():
if data[current_pos:].startswith(sig):
# Found a signature, determine its length
if format_type in ["JPEG", "PNG", "GIF"]:
# Find image end
try:
with io.BytesIO(
data[current_pos:]
) as bio:
img = Image.open(bio)
img.verify()
size = bio.tell()
boundaries.append(
(
current_pos,
current_pos + size,
Modality.IMAGE,
)
)
current_pos += size
continue
except:
pass
# Check for text sections
text_prob = self._check_text_probability(
data[current_pos : current_pos + 1024]
)
if text_prob > 0.8:
# Look for end of text section
end_pos = current_pos + 1
while end_pos < len(data):
if (
self._check_text_probability(
data[end_pos : end_pos + 32]
)
< 0.5
):
break
end_pos += 1
boundaries.append(
(current_pos, end_pos, Modality.TEXT)
)
current_pos = end_pos
continue
current_pos += 1
return boundaries
def detect_modality(self, data: bytes) -> ModalityInfo:
"""Detect modality of byte sequence."""
# First check for single modality
mime_type = self.magic.from_buffer(data)
# Check text
text_prob = self._check_text_probability(data)
if text_prob > 0.9:
return ModalityInfo(
modality=Modality.TEXT,
confidence=text_prob,
metadata={"mime_type": mime_type},
)
# Check image
is_image, image_meta = self._check_image_validity(data)
if is_image:
return ModalityInfo(
modality=Modality.IMAGE,
confidence=1.0,
metadata={**image_meta, "mime_type": mime_type},
)
# Check audio
is_audio, audio_meta = self._check_audio_validity(data)
if is_audio:
return ModalityInfo(
modality=Modality.AUDIO,
confidence=1.0,
metadata={**audio_meta, "mime_type": mime_type},
)
# Check for multimodal content
boundaries = self._detect_boundaries(data)
if len(boundaries) > 1:
sub_modalities = []
for start, end, modality in boundaries:
chunk_data = data[start:end]
sub_info = self.detect_modality(chunk_data)
if sub_info.modality != Modality.BINARY:
sub_modalities.append(sub_info)
if sub_modalities:
return ModalityInfo(
modality=Modality.MULTIMODAL,
confidence=0.8,
metadata={"mime_type": "multipart/mixed"},
sub_modalities=sub_modalities,
)
# Default to binary
return ModalityInfo(
modality=Modality.BINARY,
confidence=0.5,
metadata={"mime_type": mime_type},
)
def split_modalities(
self, data: bytes
) -> List[Tuple[Modality, bytes, Dict]]:
"""Split multimodal data into separate modalities."""
boundaries = self._detect_boundaries(data)
result = []
for start, end, modality in boundaries:
chunk = data[start:end]
info = self.detect_modality(chunk)
result.append((modality, chunk, info.metadata))
return result
class AutoDetectBytesDecoder:
"""Decoder that automatically detects and decodes different modalities."""
def __init__(self):
self.detector = ModalityDetector()
self.text_decoder = ByteDetokenizer() # From previous example
def decode(
self, data: bytes
) -> Union[str, Image.Image, np.ndarray, List[any]]:
"""Automatically detect and decode byte sequence."""
info = self.detector.detect_modality(data)
if info.modality == Modality.MULTIMODAL:
# Handle multimodal content
parts = self.detector.split_modalities(data)
return [
self.decode(chunk) for modality, chunk, _ in parts
]
if info.modality == Modality.TEXT:
return self.text_decoder.decode_text(data)
elif info.modality == Modality.IMAGE:
return self.text_decoder.decode_image(data)
elif info.modality == Modality.AUDIO:
return self.text_decoder.decode_audio(data)
else:
return data
# # Example usage
# def demo_auto_detection():
# """Demonstrate auto modality detection."""
# # Create mixed content
# text = "Hello, World!".encode('utf-8')
# # Create a small test image
# img = Image.new('RGB', (100, 100), color='red')
# img_bytes = io.BytesIO()
# img.save(img_bytes, format='PNG')
# # Combine into multimodal content
# mixed_content = text + img_bytes.getvalue()
# # Initialize decoder
# decoder = AutoDetectBytesDecoder()
# # Decode
# result = decoder.decode(mixed_content)
# if isinstance(result, list):
# print("Detected multimodal content:")
# for i, part in enumerate(result):
# print(f"Part {i+1}: {type(part)}")
# if __name__ == "__main__":
# demo_auto_detection()
def tensor_to_data(tensor: Tensor):
byte_sequence = ByteDetokenizer.tensor_to_bytes(tensor)
# Initialize auto-detector
decoder = AutoDetectBytesDecoder()
# Decode with automatic detection
result = decoder.decode(byte_sequence)
return result
def demo_byte_predictor():
"""Demo with smaller dimensions to test."""
# Initialize model configuration with adjusted dimensions
config = ModelConfig(
vocab_size=256,
hidden_size=128, # Smaller for testing
num_layers=2, # Fewer layers for testing
num_key_value_heads=2,
num_query_heads=4,
dropout=0.1,
max_sequence_length=1024,
)
# Initialize model
model = EnhancedBytePredictor(config)
logger.info("Model initialized")
# Move to GPU if available
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
model = model.to(device)
logger.info(f"Using device: {device}")
# Create sample input data
batch_size = 2
seq_length = 16 # Shorter sequence for testing
input_ids = torch.randint(
0, config.vocab_size, (batch_size, seq_length), device=device
)
logger.info(f"Created input tensor of shape: {input_ids.shape}")
# Test forward pass
try:
logits = model(input_ids)
logger.info(
f"Forward pass successful! Output shape: {logits.shape}"
)
# Test loss computation
target_ids = torch.randint(
0,
config.vocab_size,
(batch_size, seq_length),
device=device,
)
loss = model.compute_loss(input_ids, target_ids)
logger.info(
f"Loss computation successful! Loss value: {loss.item():.4f}"
)
# Test generation
prompt = torch.randint(
0,
config.vocab_size,
(1, 4), # Very short prompt for testing
device=device,
)
generated = model.generate(
prompt, max_new_tokens=8, temperature=0.8, top_k=50
)
logger.info(
f"Generation successful! Generated shape: {generated.shape}"
)
except Exception as e:
logger.error(f"Error during execution: {str(e)}")
raise
if __name__ == "__main__":
# Set up logging
# logger.remove() # Remove default handler
# logger.add(sys.stderr, format="<green>{time:HH:mm:ss}</green> | {level} | {message}")
demo_byte_predictor()
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