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Diffstat (limited to 'src/personalization/models')
16 files changed, 1071 insertions, 0 deletions
diff --git a/src/personalization/models/__init__.py b/src/personalization/models/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/src/personalization/models/__init__.py diff --git a/src/personalization/models/embedding/__init__.py b/src/personalization/models/embedding/__init__.py new file mode 100644 index 0000000..05221aa --- /dev/null +++ b/src/personalization/models/embedding/__init__.py @@ -0,0 +1,11 @@ +from .base import EmbeddingModel +from .qwen3_8b import Qwen3Embedding8B +from .nemotron_8b import LlamaEmbedNemotron8B + +__all__ = [ + "EmbeddingModel", + "Qwen3Embedding8B", + "LlamaEmbedNemotron8B", +] + + diff --git a/src/personalization/models/embedding/base.py b/src/personalization/models/embedding/base.py new file mode 100644 index 0000000..9f9d4d1 --- /dev/null +++ b/src/personalization/models/embedding/base.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Iterable, List, Sequence + +import torch + + +class EmbeddingModel(ABC): + @abstractmethod + def encode( + self, + texts: Sequence[str], + batch_size: int = 8, + max_length: int = 512, + normalize: bool = True, + return_tensor: bool = False, + ) -> List[List[float]] | torch.Tensor: + """Encode a batch of texts into dense embeddings.""" + raise NotImplementedError + + +def _mean_pool(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + # last_hidden_state: [batch, seq_len, hidden] + # attention_mask: [batch, seq_len] + mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state) # [b, s, 1] + summed = (last_hidden_state * mask).sum(dim=1) + counts = mask.sum(dim=1).clamp_min(1e-6) + return summed / counts + + +def _maybe_normalize(x: torch.Tensor, normalize: bool) -> torch.Tensor: + if not normalize: + return x + return torch.nn.functional.normalize(x, p=2, dim=-1) + + diff --git a/src/personalization/models/embedding/qwen3_8b.py b/src/personalization/models/embedding/qwen3_8b.py new file mode 100644 index 0000000..fb02e67 --- /dev/null +++ b/src/personalization/models/embedding/qwen3_8b.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from typing import List, Sequence + +import torch +from transformers import AutoModel, AutoTokenizer + +from personalization.config.registry import choose_dtype, choose_device_map +from personalization.config.settings import LocalModelsConfig +from .base import EmbeddingModel, _mean_pool, _maybe_normalize + + +class Qwen3Embedding8B(EmbeddingModel): + def __init__( + self, + model_path: str, + dtype: torch.dtype, + device_map: str = "auto", + trust_remote_code: bool = True, + ) -> None: + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, use_fast=True, trust_remote_code=trust_remote_code + ) + + # Handle specific device assignment (e.g., "cuda:0", "cuda:1") + if device_map and device_map.startswith("cuda:"): + # Load to CPU first, then move to specific GPU + self.model = AutoModel.from_pretrained( + model_path, + torch_dtype=dtype, + device_map=None, # Don't use accelerate's device_map + trust_remote_code=trust_remote_code, + low_cpu_mem_usage=True, + ) + self.model = self.model.to(device_map) + else: + # Use accelerate's auto device mapping + self.model = AutoModel.from_pretrained( + model_path, + torch_dtype=dtype, + device_map=device_map, + trust_remote_code=trust_remote_code, + low_cpu_mem_usage=True, + ) + + @classmethod + def from_config(cls, cfg: LocalModelsConfig) -> "Qwen3Embedding8B": + if not cfg.embedding or not cfg.embedding.qwen3: + raise ValueError("Embedding config for qwen3 is missing") + spec = cfg.embedding.qwen3 + dtype = choose_dtype(spec.dtype) + device_map = choose_device_map(spec.device_map) + return cls( + spec.local_path, + dtype=dtype, + device_map=device_map, + trust_remote_code=True, + ) + + @torch.inference_mode() + def encode( + self, + texts: Sequence[str], + batch_size: int = 8, + max_length: int = 512, + normalize: bool = True, + return_tensor: bool = False, + ) -> List[List[float]] | torch.Tensor: + device = next(self.model.parameters()).device + outputs: List[torch.Tensor] = [] + for i in range(0, len(texts), batch_size): + batch = list(texts[i : i + batch_size]) + enc = self.tokenizer( + batch, + padding=True, + truncation=True, + max_length=max_length, + return_tensors="pt", + ).to(device) + model_out = self.model(**enc, output_hidden_states=False, return_dict=True) + pooled = _mean_pool(model_out.last_hidden_state, enc["attention_mask"]) # type: ignore[attr-defined] + pooled = _maybe_normalize(pooled, normalize) + outputs.append(pooled) + emb = torch.cat(outputs, dim=0) + if return_tensor: + return emb + return emb.cpu().to(torch.float32).tolist() + + 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/prompt_builder.py b/src/personalization/models/llm/prompt_builder.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/src/personalization/models/llm/prompt_builder.py diff --git a/src/personalization/models/llm/vllm_chat.py b/src/personalization/models/llm/vllm_chat.py new file mode 100644 index 0000000..d577a30 --- /dev/null +++ b/src/personalization/models/llm/vllm_chat.py @@ -0,0 +1,244 @@ +""" +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, + global_notes: List[str] = None, + ) -> 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. + + Args: + global_notes: If provided, these are always-applicable preferences + displayed in a separate section from task-specific retrieved notes. + """ + # Use CollaborativeAgents-style system prompt + has_any_notes = memory_notes or global_notes + if has_any_notes: + # Build preference sections + pref_sections = "" + if global_notes: + global_bullet = "\n".join(f"- {n}" for n in global_notes) + pref_sections += f"## General Preferences (always apply)\n{global_bullet}\n\n" + if memory_notes: + task_bullet = "\n".join(f"- {n}" for n in memory_notes) + if global_notes: + pref_sections += f"## Task-Specific Preferences\n{task_bullet}\n" + else: + pref_sections += f"{task_bullet}\n" + + 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\n" + "**IMPORTANT**: If the user explicitly requests something in THIS conversation (e.g., asks you to change " + "your format, style, or approach), that request takes PRIORITY over the remembered preferences below. " + "Always adapt to the user's direct feedback first.\n\n" + "Based on your past interactions with the user, you have maintained a set of notes about the user's preferences:\n" + f"{pref_sections}\n" + "# Before Responding\n" + "Before writing your response, briefly consider:\n" + "1. Which preferences above are relevant to this specific request?\n" + "2. How will you satisfy each relevant preference in your response?\n\n" + "# Conversation Guidelines:\n" + "- If the user asks you to adjust your response (e.g., 'be more concise', 'focus on intuition'), you MUST change your approach accordingly. Do NOT repeat the same response.\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" + "- **Verify**: Before finalizing, check that your response satisfies the relevant preferences listed above.\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, + global_notes: List[str] = None, + ) -> 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, global_notes=global_notes) + + 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") diff --git a/src/personalization/models/preference_extractor/__init__.py b/src/personalization/models/preference_extractor/__init__.py new file mode 100644 index 0000000..65e2595 --- /dev/null +++ b/src/personalization/models/preference_extractor/__init__.py @@ -0,0 +1,5 @@ +from .rule_extractor import QwenRuleExtractor +from .gpt4o_extractor import GPT4OExtractor +from .base import PreferenceExtractor + +__all__ = ["QwenRuleExtractor", "GPT4OExtractor", "PreferenceExtractor"] diff --git a/src/personalization/models/preference_extractor/base.py b/src/personalization/models/preference_extractor/base.py new file mode 100644 index 0000000..850292f --- /dev/null +++ b/src/personalization/models/preference_extractor/base.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any, Dict, List +from personalization.retrieval.preference_store.schemas import ChatTurn, PreferenceList + +class PreferenceExtractorBase(ABC): + @abstractmethod + def extract_turn(self, turns: List[ChatTurn]) -> PreferenceList: + """ + Extract preferences from a window of chat turns (history + current query). + """ + raise NotImplementedError + +# Alias for backward compatibility if needed, +# though specific extractors should inherit from PreferenceExtractorBase now. +PreferenceExtractor = PreferenceExtractorBase diff --git a/src/personalization/models/preference_extractor/gpt4o_extractor.py b/src/personalization/models/preference_extractor/gpt4o_extractor.py new file mode 100644 index 0000000..0f70522 --- /dev/null +++ b/src/personalization/models/preference_extractor/gpt4o_extractor.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import json +import os +from typing import Any, Dict, List + +from openai import OpenAI +from personalization.config.settings import LocalModelsConfig +from personalization.models.preference_extractor.base import PreferenceExtractorBase as PreferenceExtractor +from personalization.retrieval.preference_store.schemas import ( + ChatTurn, + PreferenceList, + preference_list_json_schema, +) + + +class GPT4OExtractor(PreferenceExtractor): + def __init__(self, api_key: str, model: str = "gpt-4o") -> None: + self.client = OpenAI(api_key=api_key) + self.model = model + + # Load system prompt template + template_path = "fine_tuning_prompt_template.txt" + if os.path.exists(template_path): + with open(template_path, "r", encoding="utf-8") as f: + self.system_prompt = f.read() + else: + # Structured prompt that enforces the PreferenceList schema + self.system_prompt = ( + "You are a preference extraction assistant. " + "Given a user message, extract any user preferences as condition-action rules.\n\n" + "Return a JSON object with exactly this structure:\n" + '{"preferences": [{"condition": "<when this applies>", "action": "<what to do>", "confidence": <0.0-1.0>}]}\n\n' + "Examples of preferences:\n" + '- {"condition": "general", "action": "respond in Chinese", "confidence": 0.9}\n' + '- {"condition": "when writing code", "action": "use Python with type hints", "confidence": 0.8}\n' + '- {"condition": "when explaining math", "action": "show step-by-step derivation", "confidence": 0.7}\n\n' + "If no preferences are found, return {\"preferences\": []}.\n" + "IMPORTANT: The output MUST be a JSON object with a \"preferences\" key containing a list." + ) + + @classmethod + def from_config(cls, cfg: LocalModelsConfig) -> "GPT4OExtractor": + # We rely on env var for API key, config for other potential settings if needed + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY environment variable not set") + return cls(api_key=api_key) + + def build_preference_prompt(self, query: str) -> str: + # GPT4OExtractor uses the system prompt loaded in __init__ + return self.system_prompt + + def _call_kwargs(self, messages): + """Build kwargs for chat completion, skipping temperature for models that don't support it.""" + kwargs = { + "model": self.model, + "messages": messages, + "response_format": {"type": "json_object"}, + } + # GPT-5 series doesn't support temperature=0 + if not self.model.startswith("gpt-5"): + kwargs["temperature"] = 0.0 + return kwargs + + def extract_preferences(self, query: str) -> Dict[str, Any]: + # Reuse logic but return raw dict + try: + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": query}, + ] + response = self.client.chat.completions.create(**self._call_kwargs(messages)) + content = response.choices[0].message.content + if content: + return json.loads(content) + except Exception as e: + print(f"Error calling GPT-4o: {e}") + return {"preferences": []} + + def extract_turn(self, turns) -> PreferenceList: + # Accept both a single ChatTurn and a list of ChatTurns (history) + if isinstance(turns, list): + # Find the last user message in history + last_user_msg = None + for t in reversed(turns): + if hasattr(t, 'role') and t.role == "user": + last_user_msg = t.text + break + if not last_user_msg: + return PreferenceList(preferences=[]) + else: + # Single ChatTurn + if turns.role != "user": + return PreferenceList(preferences=[]) + last_user_msg = turns.text + + try: + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": last_user_msg}, + ] + response = self.client.chat.completions.create(**self._call_kwargs(messages)) + + content = response.choices[0].message.content + if not content: + return PreferenceList(preferences=[]) + + data = json.loads(content) + return self._parse_to_preference_list(data) + + except Exception as e: + print(f"Error calling GPT-4o: {e}") + return PreferenceList(preferences=[]) + + @staticmethod + def _parse_to_preference_list(data: dict) -> PreferenceList: + """Robustly convert GPT output to PreferenceList, handling non-standard formats.""" + # Best case: already matches schema + if "preferences" in data and isinstance(data["preferences"], list): + prefs = [] + for item in data["preferences"]: + if isinstance(item, dict) and "condition" in item and "action" in item: + prefs.append({ + "condition": str(item["condition"])[:128], + "action": str(item["action"])[:256], + "confidence": float(item.get("confidence", 0.7)), + }) + return PreferenceList.model_validate({"preferences": prefs}) + + # GPT returned a flat dict of preferences - convert to condition/action pairs + prefs = [] + for key, value in data.items(): + if isinstance(value, str) and len(value) > 2: + prefs.append({ + "condition": str(key)[:128] if len(str(key)) > 1 else "general", + "action": str(value)[:256], + "confidence": 0.7, + }) + elif isinstance(value, dict): + # Nested dict: try to extract meaningful pairs + for sub_key, sub_val in value.items(): + if isinstance(sub_val, str) and len(sub_val) > 2: + prefs.append({ + "condition": str(sub_key)[:128], + "action": str(sub_val)[:256], + "confidence": 0.7, + }) + elif isinstance(value, list): + for item in value: + if isinstance(item, str) and len(item) > 2: + prefs.append({ + "condition": str(key)[:128], + "action": str(item)[:256], + "confidence": 0.7, + }) + + return PreferenceList.model_validate({"preferences": prefs[:20]}) + + def extract_session(self, turns: List[ChatTurn]) -> List[PreferenceList]: + results = [] + for turn in turns: + results.append(self.extract_turn(turn)) + return results + diff --git a/src/personalization/models/preference_extractor/llm_extractor.py b/src/personalization/models/preference_extractor/llm_extractor.py new file mode 100644 index 0000000..8f7a6cb --- /dev/null +++ b/src/personalization/models/preference_extractor/llm_extractor.py @@ -0,0 +1,153 @@ +from typing import List, Dict, Any +import torch +import json +import os +from transformers import AutoModelForCausalLM, AutoTokenizer + +from personalization.models.preference_extractor.base import PreferenceExtractorBase +from personalization.retrieval.preference_store.schemas import ChatTurn, PreferenceList +from personalization.config.settings import LocalModelsConfig +from personalization.config.registry import choose_dtype, choose_device_map + +class PreferenceExtractorLLM(PreferenceExtractorBase): + def __init__( + self, + model_path: str, + prompt_template_path: str = "fine_tuning_prompt_template.txt", + device_map: str = "auto", + dtype: torch.dtype = torch.bfloat16, + max_new_tokens: int = 512, + ) -> None: + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=dtype, + device_map=device_map, + trust_remote_code=True, + ) + self.max_new_tokens = max_new_tokens + + if os.path.exists(prompt_template_path): + with open(prompt_template_path, "r", encoding="utf-8") as f: + self.prompt_template = f.read() + else: + print(f"Warning: Prompt template not found at {prompt_template_path}. Using fallback.") + self.prompt_template = "Extract user preferences from the following conversation." + + @classmethod + def from_config(cls, cfg: LocalModelsConfig, name: str = "qwen3_0_6b_sft") -> "PreferenceExtractorLLM": + # We need to access the specific extractor config by name + # Assuming cfg has a way to access extra configs or we update LocalModelsConfig to support multiple extractors + # For now, let's look for it in the 'preference_extractor' dict if it was a Dict, but it is a ModelSpec. + # We need to update LocalModelsConfig to support a dictionary of extractors or a specific one. + # Based on user design doc: + # preference_extractor: + # qwen3_0_6b_sft: ... + + # We might need to manually parse the raw config or update settings.py + # Let's assume settings.py will be updated to hold a map or specific fields. + # For now, if we use the existing ModelSpec for preference_extractor in cfg, we assume it points to this model. + + # BUT the design doc says "preference_extractor" in local_models.yaml will have "qwen3_0_6b_sft" key. + # The current settings.py defines preference_extractor as a single ModelSpec. + # We will need to update settings.py first to support multiple extractors or a dict. + # I will proceed implementing this class assuming arguments are passed, and update settings/registry later. + + # This from_config might change depending on how settings.py is refactored. + # For now I will implement it assuming a direct ModelSpec is passed, or we handle it in registry. + pass + return None + + def _build_prompt(self, turns: List[ChatTurn]) -> str: + # Construct messages list for chat template + messages = [{"role": "system", "content": self.prompt_template}] + + # Window size 6 + window = turns[-6:] + + # Add conversation history + # We need to format the conversation as input context. + # Since the task is to extract preferences from the *whole* context (or latest turn?), + # usually we provide the conversation and ask for extraction. + # But LLaMA-Factory SFT usually expects: + # System: <template> + # User: <input> + # Assistant: <output> + + # We should pack the conversation history into the User message? + # Or if we trained with multi-turn chat format? + # Assuming "Input" column in dataset was the conversation history. + + history_texts = [] + for t in window: + role = "User" if t.role == "user" else "Assistant" + history_texts.append(f"{role}: {t.text}") + + conversation_text = "\n".join(history_texts) + + # Construct the User input + # We append a trigger instruction if it wasn't part of the training input implicitly. + # But based on your template, the User Input Example was just the query "I am a Python developer..." + # So likely we should just feed the conversation text as the user message. + + messages.append({"role": "user", "content": conversation_text}) + + # Apply chat template + prompt = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + + return prompt + + def _generate(self, prompt: str) -> str: + inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device) + with torch.no_grad(): + outputs = self.model.generate( + **inputs, + max_new_tokens=self.max_new_tokens, + do_sample=False, + temperature=0.0, + eos_token_id=self.tokenizer.eos_token_id, + pad_token_id=self.tokenizer.pad_token_id, + ) + full_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) + return full_text[len(prompt):] + + def _parse_preferences(self, raw_output: str) -> PreferenceList: + start = raw_output.find("{") + end = raw_output.rfind("}") + + if start == -1 or end == -1 or end <= start: + return PreferenceList(preferences=[]) + + json_str = raw_output[start:end+1] + try: + data = json.loads(json_str) + return PreferenceList.model_validate(data) + except Exception: + return PreferenceList(preferences=[]) + + def extract_turn(self, turns: List[ChatTurn]) -> PreferenceList: + prompt = self._build_prompt(turns) + raw_output = self._generate(prompt) + return self._parse_preferences(raw_output) + + # Legacy support + def build_preference_prompt(self, query: str) -> str: + # Wrap query in a dummy turn + turn = ChatTurn( + user_id="dummy", session_id="dummy", turn_id=0, + role="user", text=query + ) + return self._build_prompt([turn]) + + def extract_preferences(self, query: str) -> Dict[str, Any]: + turn = ChatTurn( + user_id="dummy", session_id="dummy", turn_id=0, + role="user", text=query + ) + prefs = self.extract_turn([turn]) + return prefs.model_dump() + diff --git a/src/personalization/models/preference_extractor/rule_extractor.py b/src/personalization/models/preference_extractor/rule_extractor.py new file mode 100644 index 0000000..42f43ed --- /dev/null +++ b/src/personalization/models/preference_extractor/rule_extractor.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import json +import re +import os +from typing import Any, Dict, List + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from personalization.config.registry import choose_dtype, choose_device_map +from personalization.config.settings import LocalModelsConfig +from .base import PreferenceExtractor +from personalization.retrieval.preference_store.schemas import ( + PreferenceList, + preference_list_json_schema, + ChatTurn, +) + +# Hardcoded System Prompt to match SFT training +# This MUST match what was used in training (scripts/split_train_test.py) +SFT_SYSTEM_PROMPT = ( + "Extract user preferences from the query into JSON format based on the PreferenceList schema. " + "If no preferences are found, return {\"preferences\": []}." +) + +class QwenRuleExtractor(PreferenceExtractor): + """ + Extractor using a Fine-Tuned (SFT) Qwen model. + Despite the name 'RuleExtractor' (legacy), this now performs direct End-to-End extraction. + """ + def __init__(self, model_path: str, dtype: torch.dtype, device_map: str = "auto") -> None: + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, use_fast=True, trust_remote_code=True + ) + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + dtype=dtype, + device_map=device_map, + trust_remote_code=True, + ) + if self.tokenizer.pad_token_id is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + @classmethod + def from_config(cls, cfg: LocalModelsConfig) -> "QwenRuleExtractor": + spec = cfg.preference_extractor + dtype = choose_dtype(spec.dtype) + device_map = choose_device_map(spec.device_map) + return cls(spec.local_path, dtype=dtype, device_map=device_map) + + def build_preference_prompt(self, query: str) -> str: + """ + Construct the prompt string using the tokenizer's chat template. + Matches the format seen during SFT training. + """ + messages = [ + {"role": "system", "content": SFT_SYSTEM_PROMPT}, + {"role": "user", "content": query} + ] + prompt = self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + return prompt + + @torch.inference_mode() + def extract_preferences(self, query: str) -> Dict[str, Any]: + """ + Directly extract preferences from query using the SFT model. + Returns a dict compatible with PreferenceList model (key: 'preferences'). + """ + prompt = self.build_preference_prompt(query) + inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device) + + outputs = self.model.generate( + **inputs, + do_sample=False, # Deterministic greedy decoding + max_new_tokens=512, # Allow enough space for JSON + pad_token_id=self.tokenizer.pad_token_id, + eos_token_id=self.tokenizer.eos_token_id, + ) + + input_len = inputs["input_ids"].shape[1] + gen_ids = outputs[0][input_len:] + text = self.tokenizer.decode(gen_ids, skip_special_tokens=True) + + if os.getenv("PREF_DEBUG") == "1": + print(f"[debug][extractor] Raw output: {text}") + + # Try parsing JSON + try: + # 1. Direct parse + data = json.loads(text) + + # 2. Validate against schema structure + validated = PreferenceList.model_validate(data) + return validated.model_dump() + + except Exception: + # Fallback: Try to find JSON blob if model outputted extra text (rare for SFT but possible) + extracted_json = self._extract_json_substring(text) + if extracted_json: + try: + data = json.loads(extracted_json) + validated = PreferenceList.model_validate(data) + return validated.model_dump() + except: + pass + + # If all fails, return empty + return {"preferences": []} + + def _extract_json_substring(self, text: str) -> str | None: + """Helper to find { ... } block in text.""" + # Find first '{' and last '}' + start = text.find('{') + end = text.rfind('}') + if start != -1 and end != -1 and end > start: + return text[start : end + 1] + return None + + @torch.inference_mode() + def batch_extract_preferences(self, queries: List[str], batch_size: int = 64) -> List[Dict[str, Any]]: + """ + Batch extract preferences from multiple queries using left-padded batching. + """ + if not queries: + return [] + + # Save and set padding side for decoder-only batched generation + orig_padding_side = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + + all_results = [] + prompts = [self.build_preference_prompt(q) for q in queries] + + for start in range(0, len(prompts), batch_size): + batch_prompts = prompts[start:start + batch_size] + inputs = self.tokenizer( + batch_prompts, return_tensors="pt", padding=True, truncation=True + ).to(self.model.device) + + outputs = self.model.generate( + **inputs, + do_sample=False, + max_new_tokens=512, + pad_token_id=self.tokenizer.pad_token_id, + eos_token_id=self.tokenizer.eos_token_id, + ) + + for i in range(len(batch_prompts)): + input_len = (inputs["attention_mask"][i] == 1).sum().item() + gen_ids = outputs[i][input_len:] + text = self.tokenizer.decode(gen_ids, skip_special_tokens=True) + + try: + data = json.loads(text) + validated = PreferenceList.model_validate(data) + all_results.append(validated.model_dump()) + except Exception: + extracted_json = self._extract_json_substring(text) + if extracted_json: + try: + data = json.loads(extracted_json) + validated = PreferenceList.model_validate(data) + all_results.append(validated.model_dump()) + continue + except Exception: + pass + all_results.append({"preferences": []}) + + self.tokenizer.padding_side = orig_padding_side + return all_results + + def extract_turn(self, turns: List[ChatTurn]) -> PreferenceList: + """ + Extract preferences from the LAST user turn in the history. + We don't concat history because our SFT model was trained on single-turn extraction. + Using context might confuse it unless we trained it that way. + """ + # Find the last user message + last_user_msg = None + for t in reversed(turns): + if t.role == "user": + last_user_msg = t.text + break + + if not last_user_msg: + return PreferenceList(preferences=[]) + + result_dict = self.extract_preferences(last_user_msg) + return PreferenceList.model_validate(result_dict) + + def extract_session(self, turns: List[ChatTurn]) -> List[PreferenceList]: + """ + Extract preferences from ALL user turns individually. + """ + results = [] + for turn in turns: + if turn.role == "user": + res = self.extract_preferences(turn.text) + results.append(PreferenceList.model_validate(res)) + else: + results.append(PreferenceList(preferences=[])) + return results diff --git a/src/personalization/models/reranker/__init__.py b/src/personalization/models/reranker/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/src/personalization/models/reranker/__init__.py diff --git a/src/personalization/models/reranker/base.py b/src/personalization/models/reranker/base.py new file mode 100644 index 0000000..34cf6ce --- /dev/null +++ b/src/personalization/models/reranker/base.py @@ -0,0 +1,16 @@ +from typing import List, Protocol + +class Reranker(Protocol): + def score( + self, + query: str, + docs: List[str], + **kwargs, + ) -> List[float]: + """ + Score multiple candidate documents for the same query. + Higher score indicates higher relevance. + Returns a list of floats with length equal to len(docs). + """ + ... + diff --git a/src/personalization/models/reranker/qwen3_reranker.py b/src/personalization/models/reranker/qwen3_reranker.py new file mode 100644 index 0000000..b648421 --- /dev/null +++ b/src/personalization/models/reranker/qwen3_reranker.py @@ -0,0 +1,96 @@ +from typing import List +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from .base import Reranker +from personalization.config.settings import LocalModelsConfig +from personalization.config.registry import choose_dtype, choose_device_map + +class Qwen3Reranker(Reranker): + def __init__(self, model_path: str, device_map: str = "auto", dtype: torch.dtype = torch.bfloat16): + # Ensure we pass trust_remote_code=True for Qwen models + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + + # Handle specific device assignment (e.g., "cuda:0", "cuda:1") + if device_map and device_map.startswith("cuda:"): + # Load to CPU first, then move to specific GPU + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=dtype, + device_map=None, + trust_remote_code=True, + low_cpu_mem_usage=True, + ) + self.model = self.model.to(device_map) + else: + # Use accelerate's auto device mapping + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=dtype, + device_map=device_map, + trust_remote_code=True, + ) + + self.yes_token_id = self.tokenizer("yes", add_special_tokens=False).input_ids[0] + + @classmethod + def from_config(cls, cfg: LocalModelsConfig) -> "Qwen3Reranker": + if not cfg.reranker or not cfg.reranker.qwen3_8b: + raise ValueError("Reranker config for qwen3_8b is missing") + spec = cfg.reranker.qwen3_8b + dtype = choose_dtype(spec.dtype) + device_map = choose_device_map(spec.device_map) + return cls(spec.local_path, device_map=device_map, dtype=dtype) + + def _build_prompt(self, query: str, doc: str) -> str: + return ( + "You are a reranker. " + "Given a user query and a memory note, answer 'yes' if the note is helpful " + "for answering the query, otherwise answer 'no'.\n\n" + f"Query: {query}\n" + f"Note: {doc}\n" + "Answer with a single token: yes or no." + ) + + @torch.inference_mode() + def score(self, query: str, docs: List[str], batch_size: int = 8, **kwargs) -> List[float]: + scores = [] + for i in range(0, len(docs), batch_size): + batch_docs = docs[i : i + batch_size] + prompts = [self._build_prompt(query, d) for d in batch_docs] + + inputs = self.tokenizer( + prompts, + return_tensors="pt", + padding=True, + truncation=True, + max_length=512 + ).to(self.model.device) + + outputs = self.model(**inputs) + # Take logits of the last token + # shape: [batch, seq_len, vocab_size] + logits = outputs.logits + + # We want the logits for the token position immediately after the prompt ends. + # But since we generated inputs directly from tokenizer(prompts), + # we look at the last position of the input. + # For causal LM, we usually look at the logits of the last token + # to predict the *next* token (which we hope is 'yes' or 'no'). + + # Get logits for the next token prediction (last position) + # For each sequence in batch, select the last token's logits + # inputs['input_ids'] shape: [B, L] + # logits shape: [B, L, V] + # We want logits[:, -1, :] + + last_token_logits = logits[:, -1, :] + + # Calculate log prob of 'yes' + # We can use log_softmax over the vocab dimension + log_probs = torch.log_softmax(last_token_logits, dim=-1) + yes_log_probs = log_probs[:, self.yes_token_id] + + scores.extend(yes_log_probs.float().cpu().numpy().tolist()) + + return scores + |