From f918fc90b8d71d1287590b016d926268be573de0 Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Tue, 27 Jan 2026 15:43:42 -0600 Subject: Add model wrapper modules (embedding, reranker, llm, preference_extractor) Add Python wrappers for: - Qwen3/Nemotron embedding models - BGE/Qwen3 rerankers - vLLM/Llama/Qwen LLM backends - GPT-4o/LLM-based preference extractors Co-Authored-By: Claude Opus 4.5 --- src/personalization/models/llm/__init__.py | 4 + src/personalization/models/llm/base.py | 29 +++ src/personalization/models/llm/llama_instruct.py | 129 ++++++++++++++ src/personalization/models/llm/prompt_builder.py | 0 src/personalization/models/llm/qwen_instruct.py | 164 +++++++++++++++++ src/personalization/models/llm/vllm_chat.py | 217 +++++++++++++++++++++++ 6 files changed, 543 insertions(+) create mode 100644 src/personalization/models/llm/__init__.py create mode 100644 src/personalization/models/llm/base.py create mode 100644 src/personalization/models/llm/llama_instruct.py create mode 100644 src/personalization/models/llm/prompt_builder.py create mode 100644 src/personalization/models/llm/qwen_instruct.py create mode 100644 src/personalization/models/llm/vllm_chat.py (limited to 'src/personalization/models/llm') diff --git a/src/personalization/models/llm/__init__.py b/src/personalization/models/llm/__init__.py new file mode 100644 index 0000000..3f1af81 --- /dev/null +++ b/src/personalization/models/llm/__init__.py @@ -0,0 +1,4 @@ +from .qwen_instruct import QwenInstruct + +__all__ = ["QwenInstruct"] + diff --git a/src/personalization/models/llm/base.py b/src/personalization/models/llm/base.py new file mode 100644 index 0000000..72b6ca8 --- /dev/null +++ b/src/personalization/models/llm/base.py @@ -0,0 +1,29 @@ +from typing import List, Protocol, Optional +from personalization.types import ChatTurn + +class ChatModel(Protocol): + def answer( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + temperature: float = 0.7, + top_p: float = 0.9, + top_k: Optional[int] = None, + ) -> str: + """ + Generate an assistant response given conversation history and memory notes. + + Args: + history: The conversation history ending with the current user turn. + memory_notes: List of retrieved memory content strings. + max_new_tokens: Max tokens to generate. + temperature: Sampling temperature. + top_p: Top-p sampling. + top_k: Top-k sampling. + + Returns: + The generated assistant response text. + """ + ... + diff --git a/src/personalization/models/llm/llama_instruct.py b/src/personalization/models/llm/llama_instruct.py new file mode 100644 index 0000000..bdf0dff --- /dev/null +++ b/src/personalization/models/llm/llama_instruct.py @@ -0,0 +1,129 @@ +from typing import List, Optional +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from personalization.models.llm.base import ChatModel +from personalization.types import ChatTurn + +class LlamaChatModel(ChatModel): + def __init__( + self, + model_path: str, + device: str = "cuda", + dtype: str = "bfloat16", # Keep type hint as str for legacy, but handle torch.dtype + max_context_length: int = 8192, + ): + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + + # Handle dtype if it's already a torch.dtype object + if isinstance(dtype, str): + torch_dtype = getattr(torch, dtype) + else: + torch_dtype = dtype + + # Handle specific device assignment (e.g., "cuda:0", "cuda:1") + if device and device.startswith("cuda:"): + # Load to CPU first, then move to specific GPU + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch_dtype, + device_map=None, + low_cpu_mem_usage=True, + ) + self.model = self.model.to(device) + else: + # Use accelerate's device mapping + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=torch_dtype, + device_map=device, + ) + + self.max_context_length = max_context_length + if self.tokenizer.pad_token_id is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + def _build_prompt( + self, + history: List[ChatTurn], + memory_notes: List[str], + ) -> str: + memory_block = "" + if memory_notes: + bullet = "\n".join(f"- {n}" for n in memory_notes) + memory_block = ( + "Here are the user's preferences and memories:\n" + f"{bullet}\n\n" + ) + + # Build prompt manually or use chat template if available. + # Llama 3 use specific tags. + # <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n ... <|eot_id|> + # But we can try to use tokenizer.apply_chat_template if it exists. + + if hasattr(self.tokenizer, "apply_chat_template"): + messages = [{"role": "system", "content": "You are a helpful assistant.\n" + memory_block}] + for turn in history: + messages.append({"role": turn.role, "content": turn.text}) + return self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + + # Fallback manual construction (simplified Llama 2/3 style or generic) + # This is risky for Llama 3 specifically which needs exact tokens. + # Let's assume apply_chat_template works for Llama-3-Instruct models. + + # If fallback needed: + history_lines = [] + for turn in history[-8:]: + role_tag = "user" if turn.role == "user" else "assistant" + # Generic format + history_lines.append(f"{role_tag}: {turn.text}") + + prompt = ( + "System: You are a helpful assistant.\n" + + memory_block + + "\n".join(history_lines) + + "\nassistant:" + ) + return prompt + + def answer( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + temperature: float = 0.7, + top_p: float = 0.9, + top_k: Optional[int] = None, + ) -> str: + prompt = self._build_prompt(history, memory_notes) + inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, + max_length=self.max_context_length).to(self.model.device) + + gen_kwargs = { + "max_new_tokens": max_new_tokens, + "do_sample": temperature > 0, + "temperature": temperature, + "top_p": top_p, + } + if top_k is not None: + gen_kwargs["top_k"] = top_k + + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + eos_token_id=self.tokenizer.eos_token_id, + **gen_kwargs, + ) + full = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + # naive stripping + # With chat template, 'full' usually contains the whole conversation. + # We need to extract just the new part. + # But 'prompt' string might not match decoded output exactly due to special tokens skipping. + # Better: slice output ids. + + input_len = inputs["input_ids"].shape[1] + gen_ids = outputs[0][input_len:] + answer_text = self.tokenizer.decode(gen_ids, skip_special_tokens=True).strip() + + return answer_text + diff --git a/src/personalization/models/llm/prompt_builder.py b/src/personalization/models/llm/prompt_builder.py new file mode 100644 index 0000000..e69de29 diff --git a/src/personalization/models/llm/qwen_instruct.py b/src/personalization/models/llm/qwen_instruct.py new file mode 100644 index 0000000..cf2047d --- /dev/null +++ b/src/personalization/models/llm/qwen_instruct.py @@ -0,0 +1,164 @@ +from typing import List, Optional, Dict, Any +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +from personalization.models.llm.base import ChatModel +from personalization.types import ChatTurn +from personalization.config.settings import LocalModelsConfig +from personalization.config.registry import choose_dtype, choose_device_map + +class QwenInstruct(ChatModel): + def __init__( + self, + model_path: str, + device: str = "cuda", + dtype: torch.dtype = torch.bfloat16, + max_context_length: int = 4096, + ): + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, + use_fast=True, + trust_remote_code=True, + ) + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=dtype, # dtype is already torch.dtype, no getattr needed + device_map=device, + trust_remote_code=True, + ) + self.max_context_length = max_context_length + if self.tokenizer.pad_token_id is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + # Legacy helper for manual generation without template + @torch.inference_mode() + def generate( + self, + prompt: str, + max_new_tokens: int = 256, + temperature: float = 0.7, + top_p: float = 0.9, + stop: Optional[List[str]] = None, + top_k: Optional[int] = None, + ) -> str: + inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device) + + gen_kwargs = { + "max_new_tokens": max_new_tokens, + "do_sample": temperature > 0, + "temperature": temperature, + "top_p": top_p, + "pad_token_id": self.tokenizer.pad_token_id, + "eos_token_id": self.tokenizer.eos_token_id, + } + if top_k is not None: + gen_kwargs["top_k"] = top_k + + outputs = self.model.generate( + **inputs, + **gen_kwargs + ) + # Return only the newly generated portion, not the echoed prompt + input_len = inputs["input_ids"].shape[1] + gen_ids = outputs[0][input_len:] + text = self.tokenizer.decode(gen_ids, skip_special_tokens=True) + if stop: + for s in stop: + if s in text: + text = text.split(s)[0] + break + return text + + def _build_prompt( + self, + history: List[ChatTurn], + memory_notes: List[str], + ) -> str: + """ + Construct prompt using ChatML-like structure via apply_chat_template if available, + or manual construction. Qwen usually supports apply_chat_template. + We will map ChatTurn to messages list. + """ + memory_block = "" + if memory_notes: + bullet = "\n".join(f"- {n}" for n in memory_notes) + memory_block = ( + "Here are the user's preferences and memories:\n" + f"{bullet}\n\n" + ) + + messages = [{"role": "system", "content": "You are a helpful assistant.\n" + memory_block}] + + for turn in history: + messages.append({"role": turn.role, "content": turn.text}) + + return self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + def answer( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + temperature: float = 0.7, + top_p: float = 0.9, + top_k: Optional[int] = None, + ) -> str: + # Compatibility check: if history is dict list (legacy), convert to ChatTurn + # This allows old code to work if not fully updated, though we should update callers. + # But ChatTurn is required by Protocol. We assume callers are updated. + if history and isinstance(history[0], dict): + # Auto-convert for safety during migration + history = [ChatTurn( + user_id="unknown", session_id="unknown", turn_id=i, + role=h["role"], text=h["content"] + ) for i, h in enumerate(history)] + + prompt = self._build_prompt(history, memory_notes) + inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, + max_length=self.max_context_length).to(self.model.device) + + gen_kwargs = { + "max_new_tokens": max_new_tokens, + "do_sample": temperature > 0, + "temperature": temperature, + "top_p": top_p, + "pad_token_id": self.tokenizer.pad_token_id, + "eos_token_id": self.tokenizer.eos_token_id, + } + if top_k is not None: + gen_kwargs["top_k"] = top_k + + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + **gen_kwargs, + ) + + full = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + # remove prompt part manually since we didn't use self.generate helper here to keep full control + # input_ids length is inputs['input_ids'].shape[1] + input_len = inputs["input_ids"].shape[1] + gen_ids = outputs[0][input_len:] + answer_text = self.tokenizer.decode(gen_ids, skip_special_tokens=True).strip() + + return answer_text + + # Factory method for legacy config loading + @classmethod + def from_config(cls, cfg: LocalModelsConfig) -> "QwenInstruct": + spec = cfg.llm + dtype = choose_dtype(spec.dtype) + device_map = choose_device_map(spec.device_map) + # device_map usually handled by transformers if passed as device_map argument + # Here we pass it as 'device' arg to constructor if it is a string like "cuda:0" + # If it is "auto", constructor might need adjustment or we trust transformers. + # Our constructor takes 'device' string. + device = spec.device_map if isinstance(spec.device_map, str) else "cuda" + + return cls( + model_path=spec.local_path, + device=device, # Pass string + dtype=spec.dtype # Pass string name, constructor converts + ) diff --git a/src/personalization/models/llm/vllm_chat.py b/src/personalization/models/llm/vllm_chat.py new file mode 100644 index 0000000..b5c3a05 --- /dev/null +++ b/src/personalization/models/llm/vllm_chat.py @@ -0,0 +1,217 @@ +""" +vLLM-based ChatModel implementation for high-throughput inference. + +This provides the same interface as LlamaChatModel but uses vLLM HTTP API +for much faster inference (3000+ sessions/hr vs 20 sessions/hr). +""" + +from typing import List, Optional +import time +import requests + +from personalization.models.llm.base import ChatModel +from personalization.types import ChatTurn + + +class VLLMChatModel(ChatModel): + """ + ChatModel implementation using vLLM HTTP API. + + This is a drop-in replacement for LlamaChatModel that uses vLLM + for much faster inference. + """ + + def __init__( + self, + vllm_url: str = "http://localhost:8003/v1", + model_name: str = None, + max_context_length: int = 8192, + timeout: int = 120, + ): + self.vllm_url = vllm_url.rstrip('/') + self.model_name = model_name + self.max_context_length = max_context_length + self.timeout = timeout + + # Discover model name if not provided + if self.model_name is None: + self._discover_model() + + def _discover_model(self): + """Discover the model name from the vLLM server.""" + max_retries = 30 + for attempt in range(max_retries): + try: + response = requests.get(f"{self.vllm_url}/models", timeout=10) + response.raise_for_status() + models = response.json() + if models.get("data") and len(models["data"]) > 0: + self.model_name = models["data"][0]["id"] + return + except Exception as e: + if attempt < max_retries - 1: + wait_time = min(2 ** attempt * 0.5, 10) + time.sleep(wait_time) + + # Fallback + self.model_name = "default" + print(f"[VLLMChatModel] Warning: Could not discover model, using '{self.model_name}'") + + def health_check(self) -> bool: + """Check if the vLLM server is healthy.""" + try: + response = requests.get(f"{self.vllm_url.replace('/v1', '')}/health", timeout=5) + return response.status_code == 200 + except: + return False + + def _estimate_tokens(self, text: str) -> int: + """Estimate token count using character-based heuristic. + + For Llama models, ~4 characters per token is a reasonable estimate. + We use 3.5 to be conservative (slightly overestimate tokens). + """ + return int(len(text) / 3.5) + + def _build_messages( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + ) -> List[dict]: + """Build messages list for chat completion API with auto-truncation. + + If the context exceeds max_context_length, older conversation turns + are removed to keep only the most recent context that fits. + """ + # Use CollaborativeAgents-style system prompt + if memory_notes: + bullet = "\n".join(f"- {n}" for n in memory_notes) + system_content = ( + "You are a collaborative AI agent helping users solve writing, question answering, math, and coding problems.\n\n" + "# User Preferences\n" + "The user has a set of preferences for how you should behave. If you do not follow these preferences, " + "the user will be unable to learn from your response and you will need to adjust your response to adhere " + "to these preferences (so it is best to follow them initially).\n" + "Based on your past interactions with the user, you have maintained a set of notes about the user's preferences:\n" + f"{bullet}\n\n" + "# Conversation Guidelines:\n" + "- If the user's message is unclear, lacks details, or is ambiguous (e.g. length of an essay, format requirements, " + "specific constraints), do not make assumptions. Ask for clarification and ensure you have enough information before providing an answer.\n" + "- Your goal is to help the user solve their problem. Adhere to their preferences and do your best to help them solve their problem.\n" + ) + else: + # Vanilla mode - no preferences + system_content = ( + "You are a collaborative AI agent helping users solve writing, question answering, math, and coding problems.\n\n" + "# Conversation Guidelines:\n" + "- If the user's message is unclear, lacks details, or is ambiguous (e.g. length of an essay, format requirements, " + "specific constraints), do not make assumptions. Ask for clarification and ensure you have enough information before providing an answer.\n" + "- Your goal is to help the user solve their problem. Do your best to help them.\n" + ) + system_message = {"role": "system", "content": system_content} + + # Calculate available tokens for conversation history + # Reserve space for: system prompt + max_new_tokens + safety margin + system_tokens = self._estimate_tokens(system_content) + available_tokens = self.max_context_length - system_tokens - max_new_tokens - 100 # 100 token safety margin + + # Build conversation messages from history + conversation_messages = [] + for turn in history: + conversation_messages.append({"role": turn.role, "content": turn.text}) + + # Check if truncation is needed + total_conv_tokens = sum(self._estimate_tokens(m["content"]) for m in conversation_messages) + + if total_conv_tokens > available_tokens: + # Truncate from the beginning (keep recent messages) + truncated_messages = [] + current_tokens = 0 + + # Iterate from most recent to oldest + for msg in reversed(conversation_messages): + msg_tokens = self._estimate_tokens(msg["content"]) + if current_tokens + msg_tokens <= available_tokens: + truncated_messages.insert(0, msg) + current_tokens += msg_tokens + else: + # Stop adding older messages + break + + conversation_messages = truncated_messages + if len(truncated_messages) < len(history): + print(f"[VLLMChatModel] Truncated context: kept {len(truncated_messages)}/{len(history)} turns " + f"({current_tokens}/{total_conv_tokens} estimated tokens)") + + messages = [system_message] + conversation_messages + return messages + + def build_messages( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + ) -> List[dict]: + """Public method to build messages without calling the API. + + Used for batch processing where messages are collected first, + then sent in batch to vLLM for concurrent processing. + """ + return self._build_messages(history, memory_notes, max_new_tokens) + + def answer( + self, + history: List[ChatTurn], + memory_notes: List[str], + max_new_tokens: int = 512, + temperature: float = 0.7, + top_p: float = 0.9, + top_k: Optional[int] = None, + ) -> str: + """Generate a response using vLLM HTTP API.""" + messages = self._build_messages(history, memory_notes, max_new_tokens) + + payload = { + "model": self.model_name, + "messages": messages, + "max_tokens": max_new_tokens, + "temperature": temperature, + "top_p": top_p, + } + + # Retry with exponential backoff + max_retries = 5 + for attempt in range(max_retries): + try: + response = requests.post( + f"{self.vllm_url}/chat/completions", + json=payload, + timeout=self.timeout + ) + + if response.status_code == 200: + result = response.json() + return result["choices"][0]["message"]["content"] + elif response.status_code == 400: + error_text = response.text + # Handle context length error + if "max_tokens" in error_text and max_new_tokens > 64: + payload["max_tokens"] = max(64, max_new_tokens // 2) + continue + raise RuntimeError(f"vLLM error: {error_text[:200]}") + else: + raise RuntimeError(f"vLLM HTTP {response.status_code}: {response.text[:200]}") + + except requests.exceptions.Timeout: + if attempt < max_retries - 1: + time.sleep(2 ** attempt) + continue + raise RuntimeError("vLLM request timeout") + except requests.exceptions.ConnectionError as e: + if attempt < max_retries - 1: + time.sleep(2 ** attempt) + continue + raise RuntimeError(f"vLLM connection error: {e}") + + raise RuntimeError("Max retries exceeded for vLLM request") -- cgit v1.2.3