Initial commit: Original Python LLMTester project

.core/.engine/.cpu/v1.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+import os
+import time
+from pathlib import Path
+
+try:
+    from llama_cpp import Llama
+except ImportError as exc:  # pragma: no cover - dependency validation
+    raise RuntimeError(
+        "llama-cpp-python is required for the CPU engine runtime. Install it with 'pip install llama-cpp-python'."
+    ) from exc
+
+from engine_loader import BaseRuntime
+
+
+class EngineRuntime(BaseRuntime):
+    name = "llama_cpp_cpu"
+
+    def __init__(self, *, context_window: int = 4096) -> None:
+        super().__init__()
+        self.context_window = context_window
+        self.threads = max(os.cpu_count() or 1, 1)
+        self._llm: Llama | None = None
+        self._model_path: Path | None = None
+
+    def load_model(self, model_path: Path) -> None:
+        if self._model_path == model_path and self._llm is not None:
+            return
+
+        self.unload()
+        self._llm = Llama(
+            model_path=str(model_path),
+            n_ctx=self.context_window,
+            n_threads=self.threads,
+            n_gpu_layers=0,
+            verbose=False,
+        )
+        self._model_path = model_path
+
+    def generate(self, prompt: str, *, temperature: float) -> dict:
+        if self._llm is None:
+            raise RuntimeError("Model must be loaded before calling generate().")
+
+        start_time = time.time()
+        response = self._llm.create_chat_completion(
+            messages=[{"role": "user", "content": prompt}],
+            temperature=temperature,
+        )
+        elapsed = max(time.time() - start_time, 1e-5)
+
+        content = response["choices"][0]["message"]["content"]
+        usage = response.get("usage", {})
+        completion_tokens = usage.get("completion_tokens", 0)
+        total_tokens = usage.get("total_tokens", completion_tokens)
+
+        return {
+            "response": content,
+            "metrics": {
+                "tokens_per_second": round(completion_tokens / elapsed, 2) if completion_tokens else 0,
+                "total_tokens": total_tokens,
+                "time_to_first_token": round(usage.get("prompt_eval_duration", 0) / 1_000_000_000, 2)
+                if "prompt_eval_duration" in usage
+                else 0,
+                "stop_reason": response["choices"][0].get("finish_reason", "unknown"),
+            },
+        }
+
+    def unload(self) -> None:
+        if self._llm is not None:
+            self._llm = None
+            self._model_path = None