""" OmniCoreX Real-Time Inference Pipeline This module implements a super advanced, ultra high-tech, real-time inference pipeline for OmniCoreX, supporting streaming inputs, adaptive response generation, and dynamic decision making. Features: - Streaming input handling with buffer and timeout control. - Adaptive context management with sliding window history. - Efficient batching and asynchronous execution for low latency. - Integration with model's decision-making modules. - Support for multi-modal inputs and outputs. - Highly configurable inference parameters. """ import time import threading import queue from typing import Dict, Optional, List, Any, Callable import torch import torch.nn.functional as F class StreamingInference: def __init__(self, model: torch.nn.Module, tokenizer: Optional[Callable[[str], List[int]]] = None, device: Optional[torch.device] = None, max_context_length: int = 512, max_response_length: int = 128, streaming_timeout: float = 2.0, batch_size: int = 1): """ Initialize the real-time streaming inference pipeline. Args: model: OmniCoreX model instance. tokenizer: Optional tokenizer for input preprocessing. device: Device to run inference on. max_context_length: Max tokens in context window. max_response_length: Max tokens in generated response. streaming_timeout: Max seconds to wait for input buffering. batch_size: Batch size for inference. """ self.model = model self.tokenizer = tokenizer self.device = device or (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")) self.max_context_length = max_context_length self.max_response_length = max_response_length self.streaming_timeout = streaming_timeout self.batch_size = batch_size self.model.to(self.device) self.model.eval() self.input_queue = queue.Queue() self.output_queue = queue.Queue() self.context_history: List[str] = [] self.lock = threading.Lock() self._stop_event = threading.Event() self._thread = threading.Thread(target=self._inference_loop, daemon=True) def start(self): """Start the background inference processing thread.""" self._stop_event.clear() if not self._thread.is_alive(): self._thread = threading.Thread(target=self._inference_loop, daemon=True) self._thread.start() def stop(self): """Stop the inference processing thread.""" self._stop_event.set() self._thread.join(timeout=5) def submit_input(self, input_text: str): """ Submit streaming input text for inference. Args: input_text: Incoming user or sensor input string. """ self.input_queue.put(input_text) def get_response(self, timeout: Optional[float] = None) -> Optional[str]: """ Retrieve the next generated response from the output queue. Args: timeout: Seconds to wait for response. Returns: Generated string response or None if timeout. """ try: response = self.output_queue.get(timeout=timeout) return response except queue.Empty: return None def _encode_context(self, context_texts: List[str]) -> torch.Tensor: """ Converts list of context sentences into token tensor for model input. Args: context_texts: List of text strings. Returns: Tensor of shape (1, seq_len) on device. """ if self.tokenizer is None: raise RuntimeError("Tokenizer must be provided for text encoding.") full_text = " ".join(context_texts) token_ids = self.tokenizer(full_text) token_ids = token_ids[-self.max_context_length:] input_tensor = torch.tensor([token_ids], dtype=torch.long, device=self.device) return input_tensor @torch.no_grad() def _generate_response(self, input_tensor: torch.Tensor) -> str: """ Generates text response from model given input tokens. Args: input_tensor: Tensor of token ids shape (1, seq_len). Returns: Generated string response. """ outputs = self.model(input_tensor) # Expected output shape (1, seq_len, vocab_size) logits = outputs[0, -self.max_response_length:, :] # Take last tokens logits probabilities = F.softmax(logits, dim=-1) token_ids = torch.multinomial(probabilities, num_samples=1).squeeze(-1).cpu().tolist() if self.tokenizer and hasattr(self.tokenizer, "decode"): response = self.tokenizer.decode(token_ids) else: # Fallback: Map token ids to chars mod 256 (dummy) response = "".join([chr(t % 256) for t in token_ids]) return response def _inference_loop(self): """ Background thread to process inputs, maintain context, and generate outputs. """ buffer = [] last_input_time = time.time() while not self._stop_event.is_set(): try: # Wait for input or timeout timed_out = False while True: try: inp = self.input_queue.get(timeout=0.1) buffer.append(inp) last_input_time = time.time() except queue.Empty: if time.time() - last_input_time > self.streaming_timeout: timed_out = True break if len(buffer) == 0 and not timed_out: continue if timed_out or len(buffer) >= self.batch_size: with self.lock: # Update running context history with new buffer inputs self.context_history.extend(buffer) # Restrict context history length (simple sliding window) if len(self.context_history) > 20: self.context_history = self.context_history[-20:] cur_context = self.context_history.copy() buffer.clear() # Encode context and generate response input_tensor = self._encode_context(cur_context) response = self._generate_response(input_tensor) # Append response to context history with self.lock: self.context_history.append(response) self.output_queue.put(response) except Exception as e: print(f"[Inference] Exception in inference loop: {e}") print("[Inference] Stopped inference loop.") if __name__ == "__main__": # Minimal example using dummy tokenizer and dummy model for demonstration. class DummyTokenizer: def __call__(self, text): # Simple char to token id mapping (mod 100 + 1) return [ord(c) % 100 + 1 for c in text] def decode(self, token_ids): return "".join(chr((tid - 1) % 100 + 32) for tid in token_ids) class DummyModel(torch.nn.Module): def __init__(self): super().__init__() self.vocab_size = 128 def forward(self, x): batch_size, seq_len = x.shape # Return random logits tensor: (batch, seq_len, vocab_size) logits = torch.randn(batch_size, seq_len, self.vocab_size) return logits tokenizer = DummyTokenizer() model = DummyModel() inference_engine = StreamingInference(model=model, tokenizer=tokenizer, max_context_length=50) inference_engine.start() test_inputs = [ "Hello, OmniCoreX! ", "How are you today? ", "Generate a super intelligent response." ] for inp in test_inputs: print(f">> Input: {inp.strip()}") inference_engine.submit_input(inp) time.sleep(0.5) output = inference_engine.get_response(timeout=5.0) if output: print(f"<< Response: {output}") inference_engine.stop()