Create inference.py

inference.py

@@ -0,0 +1,231 @@
+"""
+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()