diff --git a/.gitignore b/.gitignore index 1b6a7ac..4964d98 100644 --- a/.gitignore +++ b/.gitignore @@ -232,3 +232,5 @@ cache/ .Trashes ehthumbs.db Thumbs.db + +/_submitit_logs/ diff --git a/download_model.py b/download_model.py new file mode 100644 index 0000000..eb01f62 --- /dev/null +++ b/download_model.py @@ -0,0 +1,32 @@ +""" +Download model from Hub to a local path. + +Usage: +uv run download_model.py \ +--repo_name Qwen/Qwen3-0.6B \ +--local_path /model-weights/Qwen3-0.6B + +uv run download_model.py \ +--repo_name Qwen/Qwen3-4B-Thinking-2507 \ +--local_path /model-weights/Qwen3-4B-Thinking-2507 + +uv run download_model.py \ +--repo_name Qwen/Qwen3-1.7B \ +--local_path /model-weights/Qwen3-1.7B +""" + +import argparse + +from transformers import AutoModelForCausalLM, AutoTokenizer + + +parser = argparse.ArgumentParser() +parser.add_argument("--repo_name", required=True) +parser.add_argument("--local_path", required=True) + +if __name__ == "__main__": + args = parser.parse_args() + AutoTokenizer.from_pretrained(args.repo_name).save_pretrained(args.local_path) + AutoModelForCausalLM.from_pretrained(args.repo_name).save_pretrained( + args.local_path + ) diff --git a/pyproject.toml b/pyproject.toml index 36d8be9..660ea68 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,17 +13,32 @@ description = "Starter templates that launch on Slurm via Hydra + Submitit" authors = [{name = "Farnaz Kohankhaki", email = "farnaz.kohankhaki@vectorinstitute.ai"}] requires-python = "==3.12.*" dependencies = [ - "hydra-core>=1.3.2,<1.4", - "hydra-submitit-launcher>=1.2.0,<1.5", - "submitit>=1.5.0,<2.0", - "torch>=2.0.0,<2.6.0", - "transformers>=4.36.0,<4.52.0", - "datasets>=2.16.0,<3.6.0", - "accelerate>=0.26.0,<1.0.0", + "hydra-core>=1.3.2", + 'hydra-submitit-launcher @ git+https://github.com/facebookresearch/hydra.git@8018b394532bae4cc78638159a848414072f4c6d#subdirectory=plugins/hydra_submitit_launcher', + "submitit>=1.5.0", + "torch>=2.0.0", + "transformers>=4.36.0", + "datasets>=2.16.0", + "accelerate>=0.26.0", "pyarrow==16.1.0", - "pillow>=10.0.0,<11.0.0", + "pillow>=10.0.0", "ruff>=0.1.0,<1.0.0", "pre-commit>=3.0.0,<4.0.0", + "pydantic>=2.11.7", + "rich>=14.1.0", + "openai-agents>=0.2.11", + "basedpyright>=1.31.4", + "langfuse>=3.3.4", + "nest-asyncio>=1.6.0", + "pydantic-ai[logfire]>=1.0.6", + "vllm>=0.11.0", +] + +[dependency-groups] +dev = [ + "basedpyright>=1.31.4", + "pytest-asyncio>=1.3.0", + "pytest>=9.0.1", ] [tool.ruff] @@ -38,7 +53,7 @@ docstring-code-format = true [tool.ruff.lint] select = ["A","B","COM","C4","RET","SIM","ICN","Q","RSE","D","E","F","I","W","N","ERA","PL"] fixable = ["A","B","COM","C4","RET","SIM","ICN","Q","RSE","D","E","F","I","W","N","ERA","PL"] -ignore = ["B905","E501","D203","D213","PLR2004","PLR0913","COM812"] +ignore = ["B905","E501","D203","D213","PLC0415","PLR2004","PLR0913","COM812", "ERA001"] [tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402","F401","F403","F811"] diff --git a/starters/llm_fine_tuning/README.md b/starters/llm_fine_tuning/README.md new file mode 100644 index 0000000..adaa2b3 --- /dev/null +++ b/starters/llm_fine_tuning/README.md @@ -0,0 +1,6 @@ +### LLM fine-tuning starters + +This directory includes minimal examples for LLM fine-tuning with vec-tool: + +- [text_classification](text_classification/): Fine-tunes a small Transformer on AG News using Hugging Face Trainer, with Submitit-compatible checkpointing and resume. +- [rlvr](rlvr/): RL fine-tuning, where reward is extrinsic and verifiable. diff --git a/templates/configs/compute/vaughan/run_in_container.sh b/templates/configs/compute/vaughan/run_in_container.sh new file mode 100755 index 0000000..95d7177 --- /dev/null +++ b/templates/configs/compute/vaughan/run_in_container.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# Script for running vLLM on Bon Echo +# Example: +# bash run_in_container.sh uv run vllm serve /model-weights/Qwen3-8B +source ~/.bashrc +source /opt/lmod/lmod/init/bash +export MODULEPATH=/opt/modulefiles:/pkgs/modulefiles:/pkgs/environment-modules + +module load singularity-ce +export SINGULARITYENV_SLURM_CONF=/opt/slurm/etc/slurm.conf +export SINGULARITYENV_PATH="/opt/slurm/bin:$PATH" +export SINGULARITYENV_LD_LIBRARY_PATH="/opt/slurm/lib:/opt/slurm/lib64:/opt/munge/lib:/opt/munge/lib64:${LD_LIBRARY_PATH:-}" + +unset VIRTUAL_ENV +unset VIRTUAL_ENV_PROMPT +if [[ -n "$UV_PROJECT_ENVIRONMENT" ]]; then + source ${UV_PROJECT_ENVIRONMENT}/bin/activate +fi + +singularity exec \ +--nv \ +--bind /model-weights:/model-weights \ +--bind /projects/llm:/projects/llm \ +--bind $HOME:$HOME \ +--bind $SCRATCH:$SCRATCH \ +/projects/llm/unsloth-vllm-trl-latest.sif \ +$@ diff --git a/templates/configs/compute/vaughan/run_in_venv.sh b/templates/configs/compute/vaughan/run_in_venv.sh new file mode 100755 index 0000000..589943c --- /dev/null +++ b/templates/configs/compute/vaughan/run_in_venv.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +# Script for running vLLM on Bon Echo +# Example: +# bash run_in_container.sh uv run vllm serve /model-weights/Qwen3-8B +source ~/.bashrc + +unset VIRTUAL_ENV +unset VIRTUAL_ENV_PROMPT +if [[ -n "$UV_PROJECT_ENVIRONMENT" ]]; then + source ${UV_PROJECT_ENVIRONMENT}/bin/activate +fi + +nvidia-smi + +$@ \ No newline at end of file diff --git a/templates/configs/user.yaml b/templates/configs/user.yaml index 756bb42..c58aa7e 100644 --- a/templates/configs/user.yaml +++ b/templates/configs/user.yaml @@ -3,3 +3,9 @@ user: account: vector additional_parameters: qos: m2 + exclude: gpu027,gpu028 + +# Required for Vaughan, as that cluster runs a dated version of Linux. +hydra: + launcher: + python: "templates/configs/compute/vaughan/run_in_container.sh uv run python" diff --git a/templates/src/rlvr/README.md b/templates/src/rlvr/README.md new file mode 100644 index 0000000..9245633 --- /dev/null +++ b/templates/src/rlvr/README.md @@ -0,0 +1,83 @@ +# RL with Verifiable Reward (RLVR) Reference Implementations + +This folder contains scripts for running RLVR algorithms on LLMs on the Vector cluster. + +Supported algorithms: + +- GRPO + +Features: + +- Compatibility with Chat Completion models. +- LLM-as-a-judge for more involved reward verifications. +- Optimized for heterogenous compute environments- run backpropagation on H100/A100, and use L40S/A40/RTX8000 for rollout and LLM judge via dedicated SLURM jobs (see [submitit_vllm.py](submitit_vllm.py) for details.) + +Current limitations and TODO items: + +- Single-GPU finetuning only. +- Backprop GPU does not participate in rollouts. +- Rollout GPUs might sit idle when all rollouts are done and only eval is pending. +- Verify support for function calling via Chat Completion. +- Integrate LangFuse datasets to track eval traces across steps. + +## Setup + +Basic: you will need uv and a working PyTorch installation. Running from within a container is possible, but you need to make sure SLURM commands are available from within the container. + +### Option A- running vLLM in uv venv + +Make sure vLLM runs on your environment. + +- Create vLLM uv venv following [instructions from vllm](https://docs.vllm.ai/en/stable/getting_started/installation/gpu.html#set-up-using-python) +- Make a copy of [run_in_venv.sh](/run_in_venv.sh) and point the uv venv to your newly-created vllm venv. +- Remember to `chmod a+x ` +- Make sure that in a new GPU job, ` uv run vllm serve ` launches the vLLM server. + +Example: clusters running modern Linux distros for which pre-built vLLM wheels are available + +- Vector Institute Killarney +- Mila TamIA + +### Option B- running vLLM via Singularity + +It might be difficult to install vLLM on some clusters- e.g., unusual Linux distribution. As long as vLLM runs through Singularity on these environments, these reference implementations would work there as well. Steps: + +- Make sure you can manually spin up singularity and run `vllm serve` from within the GPU container. +- Make a copy of [run_in_container.sh](/run_in_container.sh) and point to your singularity image. Remember, all scripts will be added to `$@` and sent to singularity. +- Remember to `chmod a+x ` +- Make sure that in a new GPU job, ` uv run vllm serve ` launches the vLLM server. + +Examples: + +- Vector Institute Bon Echo +- Compute Canada Narval + +## Run Trainer + +```bash +uv run python \ +-m rlvr.grpo.launch \ +--multirun \ +compute=vaughan/a40_1x \ +requeue=off \ ++trainer.num_epochs=10 \ ++trainer.data.train_split="train\[:100\]" +``` + +## Adapting to your workflow + +Configurable options: + +- GRPO hyperparameters +- dataset (the example uses `openai/gsm8k`) +- evaluation scheme and LLM judge setup + +## Optional- Observability Integration + +Set up LangFuse to track the output of your models as training proceeds. + +```bash +export LANGFUSE_SECRET_KEY="sk-lf-..." +export LANGFUSE_PUBLIC_KEY="pk-lf-..." +export LANGFUSE_HOST="https://us.cloud.langfuse.com" +``` diff --git a/templates/src/rlvr/agents_integration/examples.py b/templates/src/rlvr/agents_integration/examples.py new file mode 100644 index 0000000..46cae31 --- /dev/null +++ b/templates/src/rlvr/agents_integration/examples.py @@ -0,0 +1,178 @@ +""" +OpenAI Agents SDK demo: function tool `get_weather` + minimal agent. + +Setup (with astral-uv): + uv venv && uv pip install -U pip + uv add openai-agents pydantic + # Or, if you prefer pip: `pip install openai-agents pydantic` + +Environment: + export OPENAI_API_KEY=... # Required for the default OpenAI client + +Run: + uv run python agents_sdk_get_weather_demo.py +""" + +from __future__ import annotations + +from datetime import date, datetime, timedelta, timezone +from typing import Literal + +from agents import Agent, Runner, function_tool +from pydantic import BaseModel, Field + + +class WeatherReport(BaseModel): + """Structured output for a weather report. + + Attributes + ---------- + city: + Echo of the requested city (canonicalized). + unit: + "c" for Celsius or "f" for Fahrenheit. + temperature: + Air temperature in the requested unit. + feels_like: + Apparent temperature in the requested unit. + condition: + One of: "clear", "partly cloudy", "cloudy", "rain", "snow", "windy". + humidity: + Relative humidity percentage (0–100). + wind_kph: + Wind speed in kilometers per hour. + observation_time: + UTC timestamp when the reading was generated. + """ + + city: str + unit: Literal["c", "f"] + temperature: float + feels_like: float + condition: Literal["clear", "partly cloudy", "cloudy", "rain", "snow", "windy"] + humidity: int = Field(ge=0, le=100) + wind_kph: float = Field(ge=0) + observation_time: datetime + + +@function_tool +def get_weather( + city: str, + unit: Literal["c", "f"] = "c", + when: Literal["now", "today", "tomorrow"] = "now", +) -> str: + """Return a deterministic, mock weather report for demos. + + The function is *offline* and *stable across runs* for a given `(city, date)` + so it's ideal for showcasing **function-tool** calls without network flakiness. + + Args: + city: + Human-readable city name (e.g., "Vancouver"). + unit: + Temperature unit: "c" for Celsius, "f" for Fahrenheit. Defaults to "c". + when: + Time window for the report: "now", "today", or "tomorrow". + Defaults to "now". + + Returns + ------- + JSON string representing a `WeatherReport`. + """ + canonical = city.strip() + + # City baselines (°C). Extend this mapping to taste. + baselines: dict[str, float] = { + "vancouver": 14.0, + "new york": 12.0, + "london": 11.0, + "singapore": 28.0, + "shanghai": 28.0, + "auckland": 20.0, + "tokyo": 17.0, + "paris": 13.0, + "san francisco": 16.0, + "berlin": 12.0, + "mexico city": 19.0, + } + + key = canonical.lower() + base_c = baselines.get(key, 15.0) + + # Reference date for deterministic seeding + today = date.today() + ref_date = today if when in ("now", "today") else today + timedelta(days=1) + + # Seeded pseudo-randoms derived from (city, date) + seed = abs(hash(f"{key}|{ref_date.isoformat()}")) + + def prand(a: float, b: float, salt: int) -> float: + # Deterministic pseudo-random in [a, b] + return a + (seed ^ salt) % 10 / 9.0 * (b - a) + + temp_c = base_c + prand(-4.0, 4.0, 0xA5A5) - 0.5 + humidity = int(round(prand(40, 90, 0xB6B6))) + wind_kph = round(prand(0.0, 30.0, 0xC7C7), 1) + + band = seed % 100 + if band < 20: + condition = "clear" + elif band < 45: + condition = "partly cloudy" + elif band < 65: + condition = "cloudy" + elif band < 85: + condition = "rain" + elif band < 95: + condition = "windy" + else: + condition = "snow" + + feels_c = temp_c - 0.1 * wind_kph + 0.02 * (humidity - 50) + + def to_unit(tc: float, u: Literal["c", "f"]) -> float: + return round(tc if u == "c" else (tc * 9 / 5 + 32), 1) + + report = WeatherReport( + city=canonical, + unit=unit, + temperature=to_unit(temp_c, unit), + feels_like=to_unit(feels_c, unit), + condition=condition, # type: ignore[arg-type] + humidity=humidity, + wind_kph=wind_kph, + observation_time=datetime.now(timezone.utc), + ) + + # Agents SDK tools should return a string (or something that stringifies cleanly). + return report.model_dump_json() + + +# --- Minimal agent wiring ---------------------------------------------------- +weather_agent = Agent( + name="Weather Helper", + instructions=( + "You answer weather questions. When the user asks about weather, " + "call the `get_weather` tool. If it returns JSON, parse it and reply " + "concisely with temperature, feels-like, condition, and units." + ), + tools=[get_weather], # register the function tool +) + + +def main() -> None: + """Run a single demo turn with the agent and print the final output.""" + # Example inputs that strongly encourage tool use + user_inputs: list[str] = [ + "What's the weather in Vancouver today in celsius?", + "NYC now, in Fahrenheit — include feels-like and wind, please.", + ] + + for i, prompt in enumerate(user_inputs, start=1): + print(f"\n=== Demo turn {i} ===") + result = Runner.run_sync(weather_agent, prompt) + print(result.final_output) + + +if __name__ == "__main__": + main() diff --git a/templates/src/rlvr/agents_integration/notes.md b/templates/src/rlvr/agents_integration/notes.md new file mode 100644 index 0000000..ad631a6 --- /dev/null +++ b/templates/src/rlvr/agents_integration/notes.md @@ -0,0 +1,99 @@ +# Agent SDK output to HuggingFace + +HuggingFace transformer requires the following format: `messages` and `tools`. + +Messages in the usual OpenAI Chat Completion format, including a special "tool" role for tool outputs. + +```json +[ + { "role": "user", "content": "Turn on the living room lights." }, + { + "role": "assistant", + "tool_calls": [ + { + "type": "function", + "function": { + "name": "control_light", + "arguments": { "room": "living room", "state": "on" } + } + } + ] + }, + { + "role": "tool", + "name": "control_light", + "content": "The lights in the living room are now on." + }, + { "role": "assistant", "content": "Done!" } +] +``` + +Tools as a list of dict of the following format: + +```json +{ + "type": "function", + "function": { + "name": "control_light", + "description": "Controls the lights in a room.", + "parameters": { + "type": "object", + "properties": { + "room": { "type": "string", "description": "The name of the room." }, + "state": { + "type": "string", + "description": "The desired state of the light (\"on\" or \"off\")." + } + }, + "required": ["room", "state"] + }, + "return": { + "type": "string", + "description": "str: A message indicating the new state of the lights." + } + } +} +``` + +Test the format using `tokenizer.apply_chat_template`: + +```python +formatted = tokenizer.apply_chat_template( + messages, + tools=(tools or None), # type: ignore[arg-type] + tokenize=False, + add_generation_prompt=False, +) +``` + + +Example output from Qwen3 format: + +``` +<|im_start|>system +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{"type": "function", "function": {"name": "get_weather", "parameters": {"properties": {"city": {"description": "\nHuman-readable city name (e.g., \"Vancouver\").", "title": "City", "type": "string"}, "unit": {"default": "c", "description": "\nTemperature unit: \"c\" for Celsius, \"f\" for Fahrenheit. Defaults to \"c\".", "enum": ["c", "f"], "title": "Unit", "type": "string"}, "when": {"default": "now", "description": "\nTime window for the report: \"now\", \"today\", or \"tomorrow\". Defaults to \"now\".", "enum": ["now", "today", "tomorrow"], "title": "When", "type": "string"}}, "required": ["city", "unit", "when"], "title": "get_weather_args", "type": "object", "additionalProperties": false}, "description": "Return a deterministic, mock weather report for demos.\n\nThe function is *offline* and *stable across runs* for a given `(city, date)`\nso it's ideal for showcasing **function-tool** calls without network flakiness."}} + + +For each function call, return a json object with function name and arguments within XML tags: + +{"name": , "arguments": } +<|im_end|> +<|im_start|>user +Weather in Auckland<|im_end|> +<|im_start|>user + +{"city":"Auckland","unit":"c","temperature":16.4,"feels_like":16.5,"condition":"cloudy","humidity":73,"wind_kph":3.3,"observation_time":"2025-09-12T18:01:56.767763Z"} +<|im_end|> +<|im_start|>assistant + +Okay, let me process the tool response. The user asked for the weather in Auckland, so I need to present the data concisely. The temperature is 16.4°C, feels like 16.5°C. The condition is cloudy. Humidity is 73%, wind speed 3.3 kph. I should mention the time as well. Let me check the units again to make sure they're correct. Celsius for temperature and feels-like, and the condition. No need for extra info unless specified. Alright, putting it all together in a clear sentence. + + +The weather in Auckland is currently 16.4°C with a feels-like temperature of 16.5°C, with a cloudy condition and 73% humidity. Wind is blowing at 3.3 kph.<|im_end|> +``` diff --git a/templates/src/rlvr/agents_integration/rollout_translation.py b/templates/src/rlvr/agents_integration/rollout_translation.py new file mode 100644 index 0000000..0691f33 --- /dev/null +++ b/templates/src/rlvr/agents_integration/rollout_translation.py @@ -0,0 +1,258 @@ +"""Entrypoint for testing agents SDK integration. + +Usage: +PYTHONPATH="." uv run starters/llm_fine_tuning/rlvr/agents/main.py +""" + +from typing import Any, cast + +import agents +import pydantic +from openai.types.chat import ( + ChatCompletionAssistantMessageParam, + ChatCompletionMessageFunctionToolCallParam, + ChatCompletionToolMessageParam, + ChatCompletionUserMessageParam, +) + +from templates.src.rlvr.shared_types import ChatMessage + + +class Rollout(pydantic.BaseModel): + """Rollout in ChatCompletion / HF-compatible format.""" + + messages: list[ChatMessage] + tools: list[dict[str, Any]] + + +def _introspect_tools(agent_obj: agents.AgentBase) -> list[dict[str, Any]]: + """Convert registered agent tools to OpenAI-compatible tools schema. + + Best-effort introspection; silently falls back to no tools on failure. + """ + tools_out: list[dict[str, Any]] = [] + try: + agent_tools = getattr(agent_obj, "tools", []) + for t in agent_tools or []: + name = getattr(t, "name", None) + params = getattr(t, "params_json_schema", None) or getattr( + t, "parameters", None + ) + desc = getattr(t, "description", None) + if name and params: + fn = {"name": name, "parameters": params} + if isinstance(desc, str) and desc: + fn["description"] = desc + tools_out.append({"type": "function", "function": fn}) + except Exception: + tools_out = [] + return tools_out + + +def _derive_final_assistant_text(resp_obj: agents.RunResult) -> str: + """Extract a plain assistant text as a fallback.""" + for attr in ("final_output", "output", "text"): + if hasattr(resp_obj, attr): + val = getattr(resp_obj, attr) + if isinstance(val, str) and val.strip(): + return val.strip() + return str(resp_obj) + + +def _get_items_list(resp_obj: agents.RunResult) -> list[object] | None: + """Return a list of new items from a run, if available.""" + for attr in ("new_items", "items"): + if hasattr(resp_obj, attr): + value = getattr(resp_obj, attr) + if isinstance(value, list): + return value + return None + + +def _collect_call_id_to_name(items: list[object]) -> dict[str, str]: + """Build a mapping from tool call id to function name.""" + call_id_to_name: dict[str, str] = {} + for item in items: + item_type = getattr(item, "type", None) + if item_type != "tool_call_item": + continue + + raw = getattr(item, "raw_item", None) + call_id = getattr(raw, "call_id", None) if raw is not None else None + # name is not needed for tool message shape + + if call_id is None and isinstance(raw, dict): + call_id = raw.get("call_id") + name = raw.get("name") + + if isinstance(call_id, str) and isinstance(name, str): + call_id_to_name[call_id] = name + + return call_id_to_name + + +def _append_tool_output_messages( + messages_out: list[ChatMessage], + items: list[object], + call_id_to_name: dict[str, str], +) -> None: + """Append tool output messages to the chat messages list. + + Produces OpenAI-compatible tool messages including optional `name` and + `tool_call_id` fields when available. + """ + for item in items: + if getattr(item, "type", None) != "tool_call_output_item": + continue + + raw = getattr(item, "raw_item", None) + call_id = getattr(raw, "call_id", None) if raw is not None else None + output_text = getattr(raw, "output", None) if raw is not None else None + # No need to extract name for tool messages + + if call_id is None and isinstance(raw, dict): + call_id = raw.get("call_id") + output_text = raw.get("output") + + if output_text is None: + v = getattr(item, "output", None) + if isinstance(v, str) and v.strip(): + output_text = v.strip() + + if isinstance(output_text, str) and output_text: + # ChatCompletionToolMessageParam requires role, content, and tool_call_id + tool_msg_typed: ChatCompletionToolMessageParam = { + "role": "tool", + "content": output_text, + "tool_call_id": call_id or "", + } + messages_out.append(cast(ChatMessage, tool_msg_typed)) + + +def _find_first_tool_output_index(items: list[object]) -> int | None: + """Return the index of the first tool output item, if any.""" + for i, it in enumerate(items): + if getattr(it, "type", None) == "tool_call_output_item": + return i + return None + + +def _collect_reason_text_before(items: list[object], end: int) -> list[str]: + """Collect assistant "reason" texts before the first tool output.""" + parts: list[str] = [] + for it in items[:end]: + if getattr(it, "type", None) != "message_output_item": + continue + try: + from agents.items import ItemHelpers + + it_any: Any = it + text = ItemHelpers.text_message_output(it_any) + except Exception: + text = None + if isinstance(text, str) and text.strip(): + parts.append(text.strip()) + return parts + + +def _collect_function_tool_calls_before( + items: list[object], end: int +) -> list[dict[str, Any]]: + """Collect function tool calls before the first tool output as `tool_calls`.""" + out: list[dict[str, Any]] = [] + for it in items[:end]: + if getattr(it, "type", None) != "tool_call_item": + continue + raw = getattr(it, "raw_item", None) + name = getattr(raw, "name", None) if raw is not None else None + arguments = getattr(raw, "arguments", None) if raw is not None else None + _type = getattr(raw, "type", None) if raw is not None else None + call_id = getattr(raw, "call_id", None) if raw is not None else None + + if isinstance(raw, dict): + name = raw.get("name") + arguments = raw.get("arguments") + _type = raw.get("type") + call_id = raw.get("call_id") + + if ( + _type == "function_call" + and isinstance(name, str) + and isinstance(arguments, str) + ): + entry: dict[str, Any] = { + "type": "function", + "function": {"name": name, "arguments": arguments}, + } + if isinstance(call_id, str) and call_id: + entry["id"] = call_id + out.append(entry) + return out + + +def _build_assistant_tool_invocation_message(items: list[object]) -> ChatMessage | None: + """Build an assistant message that invokes tools via `tool_calls`. + + - Aggregates function tool calls from run items. + - Includes preceding assistant text ("reason") before the first tool output, if any. + - Note: HF chat templates (e.g., Qwen3) ignore `function_call` and expect + `tool_calls`. + """ + first_tool_output_idx = _find_first_tool_output_index(items) + scan_upto = ( + first_tool_output_idx if first_tool_output_idx is not None else len(items) + ) + + reason_text_parts = _collect_reason_text_before(items, scan_upto) + tool_calls = _collect_function_tool_calls_before(items, scan_upto) + + if not tool_calls: + return None + + content_str = "\n".join(reason_text_parts).strip() + asst_typed: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": content_str or None, + "tool_calls": cast( + "list[ChatCompletionMessageFunctionToolCallParam]", tool_calls + ), + } + return cast(ChatMessage, asst_typed) + + +def translate_rollout( + resp: agents.RunResult, query: str, agent: agents.AgentBase +) -> Rollout: + """Build messages and tools suitable for HF chat templates. + + - Includes tool outputs and subsequent assistant messages when present. + - Falls back to a simple user/assistant exchange otherwise. + """ + messages_out: list[ChatMessage] = [ + cast(ChatMessage, ChatCompletionUserMessageParam(role="user", content=query)) + ] + + tools_out = _introspect_tools(agent) + + items = _get_items_list(resp) + if items: + # Add assistant message that invokes tools (if any) + assistant_invocation = _build_assistant_tool_invocation_message(items) + if assistant_invocation is not None: + messages_out.append(assistant_invocation) + + # Add tool output messages + call_id_to_name = _collect_call_id_to_name(items) + _append_tool_output_messages(messages_out, items, call_id_to_name) + + # Append final assistant response derived from run + last_asst: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": _derive_final_assistant_text(resp), + } + messages_out.append(cast(ChatMessage, last_asst)) + + return Rollout( + messages=messages_out, + tools=tools_out, + ) diff --git a/templates/src/rlvr/async_utils.py b/templates/src/rlvr/async_utils.py new file mode 100644 index 0000000..d39f190 --- /dev/null +++ b/templates/src/rlvr/async_utils.py @@ -0,0 +1,63 @@ +"""Utils for async workflows.""" + +import asyncio +import types +from typing import Any, Coroutine, Sequence, TypeVar + +from rich.progress import ( + BarColumn, + Progress, + TextColumn, + TimeElapsedColumn, + TimeRemainingColumn, +) + + +T = TypeVar("T") + + +async def indexed(index: int, coro: Coroutine[None, None, T]) -> tuple[int, T]: + """Return (index, await coro).""" + return index, (await coro) + + +async def gather_with_progress( + coros: "list[types.CoroutineType[Any, Any, T]]", + description: str = "Running tasks", +) -> Sequence[T]: + """ + Run a list of coroutines concurrently, display a rich.Progress bar as each finishes. + + Returns the results in the same order as the input list. + + :param coros: List of coroutines to run. + :return: List of results, ordered to match the input coroutines. + """ + # Wrap each coroutine in a Task and remember its original index + tasks = [ + asyncio.create_task(indexed(index=index, coro=coro)) + for index, coro in enumerate(coros) + ] + + # Pre‐allocate a results list; we'll fill in each slot as its Task completes + results: list[T | None] = [None] * len(tasks) + + # Create and start a Progress bar with a total equal to the number of tasks + with Progress( + TextColumn("[bold blue]{task.description}"), + BarColumn(), + TextColumn("{task.completed}/{task.total}"), + TimeRemainingColumn(), + TimeElapsedColumn(), + ) as progress: + progress_task = progress.add_task(description, total=len(tasks)) + + # as_completed yields each Task as soon as it finishes + for finished in asyncio.as_completed(tasks): + index, result = await finished + results[index] = result + progress.update(progress_task, advance=1) + + # At this point, every slot in `results` is guaranteed to be non‐None + # so we can safely cast it back to List[T] + return results # type: ignore diff --git a/templates/src/rlvr/data_collation/batch.py b/templates/src/rlvr/data_collation/batch.py new file mode 100644 index 0000000..f6956eb --- /dev/null +++ b/templates/src/rlvr/data_collation/batch.py @@ -0,0 +1,196 @@ +"""Utils for generating strongly-typed batches.""" + +from typing import Annotated, Generic, Iterator, Type, TypeVar + +import pydantic +import torch +from pydantic.json_schema import SkipJsonSchema + +from templates.src.rlvr.data_collation.data_types import TypedBatch + + +T = TypeVar("T", bound=TypedBatch) + + +class FieldConfig(pydantic.BaseModel): + """Field configs. + + Extra dimension(s) will be added after the sequence dimension. + To add extra dimensions, specify the shape along these dimensions. + For (batch, size, vocab), set extra_dimensions to (vocab,) + """ + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + padding_value: float | int | bool + dtype: Annotated[type, SkipJsonSchema[None]] + extra_dimensions: tuple[int, ...] = () + + +class TypedBatcher(Generic[T]): + """Util for generating strongly typed batches.""" + + def __init__( + self, + field_data: dict[str, list[list] | torch.Tensor], + batch_model: Type[T], + field_configs: dict[str, FieldConfig], + batch_size: int, + pad_to_length: int, + ): + self.field_data = field_data + self.batch_model = batch_model + self.batch_size = batch_size + self.fields = field_configs + + self.pad_to_length = pad_to_length + + # Validate all fields have same number of sequences + seq_lens = {k: len(seqs) for k, seqs in field_data.items()} + assert len(set(seq_lens.values())) == 1, f"Inconsistent seq count: {seq_lens}" + + # Check for additional dimensions. + token_data_shapes = {k: _infer_token_shape(v) for k, v in field_data.items()} + extra_dimensions = { + k: _cfg.extra_dimensions for k, _cfg in field_configs.items() + } + for k, _token_data_shape in token_data_shapes.items(): + if _token_data_shape != extra_dimensions[k]: + msg = ( + "Extra dimension mismatch. Did you mean to specify extra dimensions? " + f"token_data_shapes: {token_data_shapes} vs " + f"extra_dimensions from field_configs: {extra_dimensions}, " + f"mismatch at field '{k}'" + ) + raise ValueError(msg) + + def __len__(self) -> int: + """Estimate number of batches to return.""" + num_sequences = len(next(iter(self.field_data.values()))) + return (num_sequences + self.batch_size - 1) // self.batch_size + + def __iter__(self) -> Iterator[T]: + """Yield strongly-typed batches.""" + field_devices = { + name: _infer_device(self.field_data[name]) for name in self.fields + } + + # Initialize batch tensors on a per-field device + batch_arrays = {} + for name, cfg in self.fields.items(): + torch_dtype = _torch_dtype(cfg.dtype) + batch_arrays[name] = torch.full( + (self.batch_size, self.pad_to_length, *cfg.extra_dimensions), + cfg.padding_value, + dtype=torch_dtype, + device=field_devices[name], + ) + + mask_device = field_devices.get( + "input_ids", next(iter(field_devices.values()), torch.device("cpu")) + ) + attention_mask = torch.zeros( + (self.batch_size, self.pad_to_length), dtype=torch.bool, device=mask_device + ) + + pos_in_batch = 0 + num_sequences = len(next(iter(self.field_data.values()))) + + for i in range(num_sequences): + # Copy data for all fields + for name, arrays in batch_arrays.items(): + torch_dtype = arrays.dtype + seq_source = self.field_data[name] + seq = seq_source[i] + # actual sequence length, capped at pad_to_length + seq_len = min(len(seq), self.pad_to_length) + seq_tensor = _coerce_to_tensor( + seq, dtype=torch_dtype, device=arrays.device + ) + arrays[pos_in_batch, :seq_len] = seq_tensor[:seq_len] + attention_mask[pos_in_batch, :seq_len] = True + + pos_in_batch += 1 + + if pos_in_batch == self.batch_size: + yield self.batch_model( + **{k: v.clone() for k, v in batch_arrays.items()}, + attention_mask=attention_mask.clone(), + num_valid=pos_in_batch, + ) + + # Reset batch arrays + attention_mask.fill_(False) + for k, array in batch_arrays.items(): + array.fill_(self.fields[k].padding_value) + + pos_in_batch = 0 + + if pos_in_batch > 0: + yield self.batch_model( + **{k: v.clone() for k, v in batch_arrays.items()}, + attention_mask=attention_mask.clone(), + num_valid=pos_in_batch, + ) + + +def _torch_dtype(python_dtype: type | torch.dtype) -> torch.dtype: + if isinstance(python_dtype, torch.dtype): + return python_dtype + + mapping: dict[type, torch.dtype] = { + bool: torch.bool, + int: torch.long, + float: torch.float32, + } + + if python_dtype in mapping: + return mapping[python_dtype] + + msg = f"Unsupported dtype for torch conversion: {python_dtype}" + raise TypeError(msg) + + +def _infer_device(field_value: object) -> torch.device: + if isinstance(field_value, torch.Tensor): + return field_value.device + + if isinstance(field_value, (list, tuple)) and field_value: + first = field_value[0] + if isinstance(first, torch.Tensor): + return first.device + + return torch.device("cpu") + + +def _infer_token_shape(field_value: object) -> tuple[int, ...]: + if isinstance(field_value, torch.Tensor): + return tuple(field_value.shape[2:]) + + if isinstance(field_value, (list, tuple)) and field_value: + first_sequence = field_value[0] + if isinstance(first_sequence, torch.Tensor): + return tuple(first_sequence.shape[1:]) + + if isinstance(first_sequence, (list, tuple)) and first_sequence: + first_token = first_sequence[0] + if isinstance(first_token, torch.Tensor): + return tuple(first_token.shape) + + if hasattr(first_token, "__len__") and not isinstance( + first_token, (str, bytes) + ): + try: + return (len(first_token),) + except TypeError: + return () + + return () + + +def _coerce_to_tensor( + sequence: object, *, dtype: torch.dtype, device: torch.device +) -> torch.Tensor: + if isinstance(sequence, torch.Tensor): + return sequence.to(device=device, dtype=dtype) + + return torch.tensor(sequence, dtype=dtype, device=device) diff --git a/templates/src/rlvr/data_collation/data_types.py b/templates/src/rlvr/data_collation/data_types.py new file mode 100644 index 0000000..45afe90 --- /dev/null +++ b/templates/src/rlvr/data_collation/data_types.py @@ -0,0 +1,55 @@ +"""Types for RLVR.""" + +from typing import Annotated, Any, Type, TypeVar + +import numpy as np +import pydantic +import torch +from pydantic.json_schema import SkipJsonSchema + + +T = TypeVar("T", bound=pydantic.BaseModel) + + +class TypedBatch(pydantic.BaseModel): + """A batch of data with dynamic fields.""" + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + attention_mask: Annotated[torch.Tensor, SkipJsonSchema[None]] + num_valid: int + + def __init__(self, **kwargs): + super().__init__(**kwargs) + # Validate all arrays have same batch dimension + batch_dims: dict[str, int] = {} + for k, v in kwargs.items(): + if k == "num_valid": + continue + if isinstance(v, torch.Tensor): + batch_dims[k] = v.shape[0] + else: + batch_dims[k] = len(v) + assert len(set(batch_dims.values())) == 1, ( + f"Inconsistent batch dim: {batch_dims}" + ) + + def to_torch(self, device: torch.device, target_class: Type[T]) -> T: + """Transfer tensors to torch. + + Params: + device: target torch device. + target_class: should be a Pydantic class + where the fields are named the same but + of type torch.Tensor in place of np.ndarray. + """ + output_dict: dict[str, Any] = {} + for field_name in self.__pydantic_fields__: + value = getattr(self, field_name) + if isinstance(value, torch.Tensor): + output_dict[field_name] = value.to(device) + elif isinstance(value, np.ndarray): + output_dict[field_name] = torch.as_tensor(value, device=device) + else: + output_dict[field_name] = value + + return target_class(**output_dict) diff --git a/templates/src/rlvr/grpo/config.py b/templates/src/rlvr/grpo/config.py new file mode 100644 index 0000000..0bdd456 --- /dev/null +++ b/templates/src/rlvr/grpo/config.py @@ -0,0 +1,110 @@ +"""Typing and validator for GRPO config.""" + +import datetime +from pathlib import Path + +import pydantic + + +class SubmititArgs(pydantic.BaseModel): + """Submitit args.""" + + job_name: str | None = None + partition: str | None = None + account: str | None = None + qos: str | None = None + mem: str | None = "72GB" + cpus_per_task: str | None = "16" + gres: str | None = "gpu:1" + time: str = "1:00:00" + use_srun: bool = False + + def to_submitit_parameters(self) -> dict[str, int | str]: + """Produce submit-compatible dict consisting of non-None values.""" + return {k: v for k, v in self.model_dump().items() if v is not None} + + @property + def time_in_minutes(self) -> float: + """Return self.time as a float.""" + d = 0 + time = self.time + if "-" in time: + _days, time = time.split("-") + d = int(_days) + + h, m, s = map(int, time.split(":")) + return ( + datetime.timedelta(days=d, hours=h, minutes=m, seconds=s).total_seconds() + / 60 + ) + + +class GRPOHyperparameters(pydantic.BaseModel): + """Hyperparameters for GRPO.""" + + max_model_len: int + + batch_size_forward: int + batch_size_backprop: int + + grad_acc_steps: int + + learning_rate: float = 1e-5 + adam_betas: tuple[float, float] = (0.9, 0.999) + adam_weight_decay: float = 0.0 + + +class VLLMConfigs(pydantic.BaseModel): + """Configs for vLLM and SLURM rollout for one model. + + E.g., one config for policy, and one for evaluator. + """ + + max_model_len: int + num_replicas: int + concurrency_per_replica: int + + cache_dir: Path + + uv_venv: Path + submitit_args: SubmititArgs + submitit_python: str | None = None + + +class LLMJudgeConfig(VLLMConfigs): + """Configs for evaluator model for LLM as a judge.""" + + model_name: str + + +class DataConfig(pydantic.BaseModel): + """Configs for GRPO RLVR data.""" + + dataset_name: str + subset: str | None = None + train_split: str = "train" + test_split: str = "test" + + query_column: str = "question" + target_column: str = "answer" + target_regexp: str | None = None + + +class GRPOConfig(pydantic.BaseModel): + """Typing for GRPO config.""" + + base_model: str + tokenizer_name: str + checkpoint_folder: Path + keep_all_checkpoints: bool + + submitit_logs_folder: Path + + rollout_vllm: VLLMConfigs + llm_judge_vllm: LLMJudgeConfig + + data: DataConfig + hyperparameters: GRPOHyperparameters + + optimizer_folder: Path | None = None + num_epochs: int diff --git a/templates/src/rlvr/grpo/config.yaml b/templates/src/rlvr/grpo/config.yaml new file mode 100644 index 0000000..3dc808f --- /dev/null +++ b/templates/src/rlvr/grpo/config.yaml @@ -0,0 +1,55 @@ +defaults: + - vllm_base + +vllm_cache_dir_base: "${oc.env:SCRATCH}/.cache/vllm_compiled_graphs/" + +base_vllm_config: &base_vllm_config + uv_venv: "${oc.env:SCRATCH}/uv-venvs/vllm-serving/" # optional; useful if you installed vllm in a separate venv. + submitit_python: "templates/configs/compute/vaughan/run_in_container.sh uv run python" # required for vaughan + submitit_args: + partition: "a40" + qos: "m5" + account: "${user.slurm.account}" + +trainer: + base_model: "/model-weights/Qwen2.5-1.5B-Instruct" + tokenizer_name: "${base_model}" + checkpoint_folder: "${oc.env:SCRATCH}/checkpoints/20251123-Qwen2.5-1.5B-Instruct" + keep_all_checkpoints: false + submitit_logs_folder: ${work_dir} + num_epochs: 100 + + rollout_vllm: + <<: *base_vllm_config + + max_model_len: 2048 + num_replicas: 5 # 5 runners via SLURM, plus local GPU + concurrency_per_replica: 128 + + cache_dir: "${vllm_cache_dir_base}/${trainer.base_model}" + + llm_judge_vllm: + <<: *base_vllm_config + + max_model_len: 2048 + num_replicas: 0 # Use local GPU only. + concurrency_per_replica: 128 + + model_name: "/model-weights/Qwen3-8B" + cache_dir: "${vllm_cache_dir_base}/${llm_judge_vllm.model_name}" + + data: + dataset_name: "openai/gsm8k" + subset: "main" + train_split: "train[:1000]" # subsample + test_split: "test[:100]" # subsample + query_column: "question" + target_column: "answer" + target_regexp: "#### (\\d+)" + + hyperparameters: + max_model_len: 2048 + + batch_size_forward: 2 + batch_size_backprop: 2 + grad_acc_steps: 8 diff --git a/templates/src/rlvr/grpo/data_types.py b/templates/src/rlvr/grpo/data_types.py new file mode 100644 index 0000000..9591a28 --- /dev/null +++ b/templates/src/rlvr/grpo/data_types.py @@ -0,0 +1,294 @@ +"""Data types for GRPO.""" + +from typing import Annotated, Any, Sequence + +import numpy as np +import pydantic +import torch +from pydantic.json_schema import SkipJsonSchema +from transformers import PreTrainedTokenizerFast + +from templates.src.rlvr.data_collation.batch import ( + FieldConfig, + TypedBatcher, +) +from templates.src.rlvr.data_collation.data_types import TypedBatch +from templates.src.rlvr.shared_types import ChatMessage + + +def _assistant_char_spans( + messages: Sequence[ChatMessage], formatted: str +) -> list[tuple[int, int]]: + """Find [start, end) character spans of assistant contents inside formatted string. + + We search sequentially to disambiguate duplicate substrings across turns. + If a piece isn't found (e.g., template alters content), it is skipped. + """ + spans: list[tuple[int, int]] = [] + cursor = 0 + for message in messages: + if message.get("role") != "assistant": + continue + content: str = message.get("content", "") # type: ignore + if not content: + continue + idx = formatted.find(content, cursor) + if idx != -1: + spans.append((idx, idx + len(content))) + cursor = idx + len(content) + return spans + + +class RewardDetailTokenized(pydantic.BaseModel): + """Reward details for one rollout, outcome-supervised only. + + Attention mask is not included. + Rather, input_ids should be truncated on both sides. + """ + + input_ids: list[int] + loss_mask: list[bool] + reward: float + + def __init__(self, input_ids: list[int], loss_mask: list[bool], **kwargs): + """Verify input_ids and loss_mask shape.""" + input_ids_np = np.asarray(input_ids) + loss_mask_np = np.asarray(loss_mask) + + _shape = input_ids_np.shape + assert len(_shape) == 1, f"Flat list of indices required; got {_shape}" + assert loss_mask_np.shape == _shape, "Loss mask shape must match input_ids." + + super().__init__(input_ids=input_ids, loss_mask=loss_mask, **kwargs) + + @staticmethod + def from_messages( + messages: list[ChatMessage], + reward: float, + tokenizer: PreTrainedTokenizerFast, + pad_to: int, + ) -> "RewardDetailTokenized": + """Tokenize a chat and create a per-token boolean loss mask.""" + chat_template = getattr(tokenizer, "chat_template", None) + if chat_template is None: + raise ValueError(f"Template is required but not available in: {tokenizer}") + formatted = tokenizer.apply_chat_template( + messages, # type: ignore[arg-type] + tokenize=False, + add_generation_prompt=False, + ) + assert isinstance(formatted, str) + add_special_tokens = False # per HF docs when tokenizing the formatted string + + assistant_spans = _assistant_char_spans(messages, formatted) + + # TODO: Verify HF typing + enc: Any = tokenizer( + formatted, + add_special_tokens=add_special_tokens, + return_offsets_mapping=True, # requires FastTokenizer + truncation=True, + padding="max_length", + max_length=pad_to, + ) + input_ids: list[int] = enc["input_ids"] + offsets: list[tuple[int, int]] = enc["offset_mapping"] + + # One bool per token. + # Mark as True if the token span is completely in any assistant span + loss_mask: list[bool] = [ + any(span_a <= _a < _b <= span_b for span_a, span_b in assistant_spans) + for _a, _b in offsets + ] + + return RewardDetailTokenized( + input_ids=input_ids, loss_mask=loss_mask, reward=reward + ) + + +class _AdvantageDetail(RewardDetailTokenized): + """Reward details plus group-relative advantage info. + + Important: + advantages is a per-token list for extensibility. + However, in outcome supervision formulations (e.g., DeepSeek Math) + advantages is the same number repeated for all tokens. + + """ + + advantages: list[float] # per-token, same length as input_ids. + + +class PerTokenProbs(pydantic.BaseModel): + """Per-token probability output stored as dense tensors. + + Attributes + ---------- + full: + Tensor of shape (batch, max_len, vocab_size) with next-token distributions. + selected: + Tensor of shape (batch, max_len) containing probabilities for taken tokens. + attention_mask: + Boolean tensor of shape (batch, max_len) indicating valid token positions. + """ + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + + full: torch.Tensor + selected: torch.Tensor + attention_mask: torch.Tensor + + def __add__(self, other: "PerTokenProbs") -> "PerTokenProbs": + """Concatenate along the batch axis (first dimension).""" + if not isinstance(other, PerTokenProbs): + return NotImplemented + + if self.attention_mask.shape[1:] != other.attention_mask.shape[1:]: + msg = ( + "Cannot concatenate PerTokenProbs with mismatched token dimensions. " + f"left={tuple(self.attention_mask.shape)} " + f"right={tuple(other.attention_mask.shape)}" + ) + raise ValueError(msg) + + return PerTokenProbs( + full=torch.cat((self.full, other.full), dim=0), + selected=torch.cat((self.selected, other.selected), dim=0), + attention_mask=torch.cat( + (self.attention_mask, other.attention_mask), dim=0 + ), + ) + + def to(self, device: torch.device) -> "PerTokenProbs": + """Return copy where tensors are on the specified device.""" + return PerTokenProbs( + full=self.full.to(device), + selected=self.selected.to(device), + attention_mask=self.attention_mask.to(device), + ) + + def __radd__(self, other: object) -> "PerTokenProbs": + """Support sum(..., other=0).""" + if isinstance(other, int) and other == 0: + return self + + if isinstance(other, PerTokenProbs): + return other + self + + return NotImplemented + + +class BatchForInference(TypedBatch): + """Typed batch for getting probs.""" + + # (batch, input_tokens) + input_ids: Annotated[torch.Tensor, SkipJsonSchema[None]] + + +class BatchForGRPO(TypedBatch): + """Typed batch for GRPO training.""" + + # (batch, input_tokens) + input_ids: Annotated[torch.Tensor, SkipJsonSchema[None]] + loss_masks: Annotated[torch.Tensor, SkipJsonSchema[None]] + per_token_advantage: Annotated[torch.Tensor, SkipJsonSchema[None]] + + +class AdvantageData(pydantic.BaseModel): + """Batch of outputs for group-relative advantage estimate.""" + + advantage_details: list[_AdvantageDetail] + + def _group_rewards(self) -> list[float]: + """All rewards in this group.""" + return [_detail.reward for _detail in self.advantage_details] + + @staticmethod + def from_list_of_rewards( + reward_details: list[RewardDetailTokenized], + ) -> "AdvantageData": + """Compute advantage for a given batch of rewards.""" + if len(reward_details) == 0: + msg = "reward_details must be a non-empty list, but an empty list is given." + raise ValueError(msg) + + # per-trace only, not token-level + rewards = np.asarray([_detail.reward for _detail in reward_details]) + advantages_np: np.ndarray = np.asarray( + (rewards - rewards.mean()) / rewards.std() + ) + advantages: list[float] = advantages_np.flatten().tolist() + + advantage_details: list[_AdvantageDetail] = [] + for _reward_detail, _advantage in zip(reward_details, advantages): + # Same (sequence-level) advantage for each token. + _per_token_advantages = [_advantage] * len(_reward_detail.input_ids) + _advantage_detail = _AdvantageDetail( + **_reward_detail.model_dump(), advantages=_per_token_advantages + ) + advantage_details.append(_advantage_detail) + + return AdvantageData(advantage_details=advantage_details) + + def get_iterator_for_training( + self, batch_size: int, pad_to_length: int + ) -> TypedBatcher[BatchForGRPO]: + """Obtain batched iterator for training.""" + return TypedBatcher( + field_data={ + "input_ids": [ + _advantage.input_ids for _advantage in self.advantage_details + ], + "loss_masks": [ + _advantage.loss_mask for _advantage in self.advantage_details + ], + "per_token_advantage": [ + _advantage.advantages for _advantage in self.advantage_details + ], + }, + batch_model=BatchForGRPO, + field_configs={ + "input_ids": FieldConfig(padding_value=0, dtype=int), + "loss_masks": FieldConfig(padding_value=False, dtype=bool), + "per_token_advantage": FieldConfig(padding_value=0, dtype=float), + }, + batch_size=batch_size, + pad_to_length=pad_to_length, + ) + + @property + def avg_reward(self) -> float | None: + """Return average reward of rollouts in advantage_details.""" + rewards = [_advantage.reward for _advantage in self.advantage_details] + if len(rewards) > 0: + return sum(rewards) / len(rewards) + + return None + + +GRPOBatcher = TypedBatcher[BatchForGRPO] + + +class GRPOMetrics(pydantic.BaseModel): + """Metrics for GRPO.""" + + avg_loss: float | None = None + grad_norm: float | None = None + + +class GRPOHyperparameters(pydantic.BaseModel): + """GRPO Hyperparameters. + + TODO: divide into performance-related parameters and + parameters related to numerical values. + """ + + max_model_len: int + train_batch_size: int + + # for forward pass only, and not for vLLM rollout. + inference_batch_size: int + + learning_rate: float = 1e-5 + adam_betas: tuple[float, float] = (0.9, 0.999) + adam_weight_decay: float = 0.0 diff --git a/templates/src/rlvr/grpo/grpo_backend.py b/templates/src/rlvr/grpo/grpo_backend.py new file mode 100644 index 0000000..de234fa --- /dev/null +++ b/templates/src/rlvr/grpo/grpo_backend.py @@ -0,0 +1,247 @@ +"""Numerical logic related to GRPO.""" + +import logging +import math + +import torch +from rich.progress import Progress +from torch.nn.functional import log_softmax, softmax +from transformers import PreTrainedModel + +from templates.src.rlvr.grpo.data_types import ( + BatchForGRPO, + BatchForInference, + GRPOBatcher, + GRPOMetrics, + PerTokenProbs, +) + + +def get_per_token_probs( + batch: BatchForInference | BatchForGRPO, model: "PreTrainedModel" +) -> PerTokenProbs: + """Obtain per-token probs for a given batch and a given model. + + Params: + batch: Tokenized _Batch (batch, length). + model: Pretrained Causal LM. + + Returns + ------- + PerTokenProbs storing dense torch tensors on the model device. + (batch, length - 1) since no label is available for the last token. + """ + device = model.device + input_ids = torch.as_tensor(batch.input_ids, dtype=torch.long, device=device) + attention_mask = torch.as_tensor( + batch.attention_mask, dtype=torch.long, device=device + ) + + with torch.inference_mode(): + output = model(input_ids=input_ids, attention_mask=attention_mask) + # logits: (batch, length, vocab) where + # (j, k, :) contains distribution for tokens k+1 of batch j. + logits = output.logits + + # (batch, length - 1, vocab) skipping last token which has no label. + # for all possible vocabs. + probabilities_all = softmax(logits, dim=-1)[:, :-1, :] + + # (batch, length - 1, 1) skipping first token which is always given. + target_token_ids = input_ids[:, 1:].unsqueeze(-1) + + # (batch, length - 1) for input_ids only. + probabilities_selected = torch.gather( + probabilities_all, dim=-1, index=target_token_ids + ).squeeze(-1) + + return PerTokenProbs( + attention_mask=attention_mask[: batch.num_valid], + full=probabilities_all.detach()[: batch.num_valid], + selected=probabilities_selected.detach()[: batch.num_valid], + ) + + +def compute_grpo_loss( + *, + logits_next: torch.Tensor, + target_token_ids: torch.Tensor, + valid_token_mask: torch.Tensor, + taken_prob_old: torch.Tensor, + taken_prob_ref: torch.Tensor, + advantage_per_token: torch.Tensor, + kl_coefficient: float, + numerical_stability_eps: float = 1e-12, +) -> torch.Tensor: + """ + Compute the GRPO loss for a mini-batch. + + The per-token objective (to MAXIMIZE) is: + J_token = r * A - β * (x - log x - 1), + + where: + • r = π_current(a|s) / π_old(a|s) + • x = π_ref(a|s) / π_current(a|s) + • A is the rollout-level normalized advantage + (broadcast across tokens in that rollout) + • β is the KL coefficient. + + This function returns the NEGATIVE batch objective (i.e., a loss to MINIMIZE). + Reduction matches Eq. (19) of the GRPO formulation: + 1) mean over valid tokens for each rollout, + 2) mean over rollouts in the mini-batch. + + Args: + logits_next: Logits for next-token prediction at positions [:, :-1], (B, T, V). + target_token_ids: Gold next-token ids, (B, T). + valid_token_mask: Boolean mask selecting generated tokens to train on, (B, T). + taken_prob_old: Probability under the pi_frozen for the taken token, (B, T). + taken_prob_ref: Probability under the pi_kl_ref for the taken token, (B, T). + advantage_per_token: Token-level normalized advantages, (B, T). + kl_coefficient: Strength of the KL penalty to the reference policy. + numerical_stability_eps: Small epsilon to avoid log(0). + + Returns + ------- + A scalar tensor: the loss to minimize (negative batch objective). + """ + # Log-probabilities of all tokens under current policy + log_prob_all = log_softmax(logits_next, dim=-1) # (B, T, V) + + # Log-probability of the *taken* tokens under current policy + log_prob_taken_current = torch.gather( + log_prob_all, dim=-1, index=target_token_ids.unsqueeze(-1) + ).squeeze(-1) # (B, T) + + # Clamp old/ref probabilities for stability and take logs + taken_prob_old = taken_prob_old.clamp(min=numerical_stability_eps) + taken_prob_ref = taken_prob_ref.clamp(min=numerical_stability_eps) + log_prob_taken_old = torch.log(taken_prob_old) # (B, T) + log_prob_taken_ref = torch.log(taken_prob_ref) # (B, T) + + # Importance ratio: r = π_current / π_old (for the taken token) + log_ratio_current_over_old = log_prob_taken_current - log_prob_taken_old # (B, T) + ratio_current_over_old = torch.exp(log_ratio_current_over_old) # (B, T) + + # KL penalty term (per taken token): x - log x - 1, where x = pi_ref / pi_current + log_ratio_ref_over_current = log_prob_taken_ref - log_prob_taken_current # (B, T) + ratio_ref_over_current = torch.exp(log_ratio_ref_over_current) # (B, T) + kl_scalar_taken = ( + ratio_ref_over_current - log_ratio_ref_over_current - 1.0 + ) # (B, T) + + # Per-token objective contribution + token_objective = ( + ratio_current_over_old * advantage_per_token - kl_coefficient * kl_scalar_taken + ) # (B, T) + + # Mask to only include valid positions + token_objective = token_objective * valid_token_mask.float() # (B, T) + + # Mean over valid tokens per rollout (avoid division by zero) + tokens_per_rollout = valid_token_mask.sum(dim=1) # (B,) + per_rollout_mean = token_objective.sum(dim=1) / torch.clamp( + tokens_per_rollout, min=1 + ) # (B,) + + # Batch mean; return negative to obtain a loss to minimize + batch_objective_mean = per_rollout_mean.mean() + return -batch_objective_mean + + +def optimize_grpo_one_epoch( + batcher: GRPOBatcher, + model: PreTrainedModel, + model_pi_ref: PreTrainedModel, + optimizer: torch.optim.Optimizer, + gradient_accumulation_steps: int, + kl_coefficient: float = 0.05, + max_grad_norm: float = 1.0, + use_mixed_precision: bool = True, + numerical_stability_eps: float = 1e-12, +) -> tuple[PreTrainedModel, torch.optim.Optimizer, GRPOMetrics]: + """ + Perform one optimization epoch of GRPO on a HuggingFace causal language model. + + This revision reduces line count by inlining the mini-batch slicing directly at + the call sites (model forward and loss computation). See `compute_grpo_loss` + for the objective definition and reduction details. + + Scaler is not used for bfloat16. + """ + logger = logging.getLogger(__name__) + model.train() + device = next(model.parameters()).device + loss_metrics: list[float] = [] + + num_steps = len(batcher) + expected_updates = math.ceil(num_steps / max(1, gradient_accumulation_steps)) + + accumulated_microbatches = 0 + optimizer.zero_grad(set_to_none=True) + + progress = Progress().__enter__() + step_task_id = progress.add_task( + "[cyan]GRPO optimization[/cyan]", total=expected_updates + ) + + for batch_raw in batcher: + batch = batch_raw.to_torch(device=device, target_class=BatchForGRPO) + taken_prob_base = get_per_token_probs(batch, model=model) # (B, T - 1) + taken_prob_ref = get_per_token_probs(batch, model=model_pi_ref) # (B, T - 1) + + with torch.amp.autocast( + "cuda", enabled=(use_mixed_precision and torch.cuda.is_available()) + ): + # outputs.logits: (B, T - 1, V) + outputs = model( + input_ids=batch.input_ids.to(torch.long), + attention_mask=batch.attention_mask.to(torch.long), + ) + logger.debug( + {k: getattr(v, "shape", None) for k, v in batch.model_dump().items()} + ) + + # Note that the first input token is skipped. + loss_to_minimize = compute_grpo_loss( + # After slicing: (B, T - 1, V) + logits_next=outputs.logits[:, :-1, :], + # After slicing: (B, T - 1) + target_token_ids=batch.input_ids[:, 1:].to(torch.long), + valid_token_mask=batch.loss_masks[:, 1:], + advantage_per_token=batch.per_token_advantage[:, 1:].to(torch.float32), + # (B, T - 1) + taken_prob_old=taken_prob_base.selected.to(torch.float32), + taken_prob_ref=taken_prob_ref.selected.to(torch.float32), + # Hyperparameters + kl_coefficient=kl_coefficient, + numerical_stability_eps=numerical_stability_eps, + ) / max(1, gradient_accumulation_steps) + + loss_to_minimize.backward() + + loss_metrics.append(loss_to_minimize.detach().cpu().item()) + accumulated_microbatches += 1 + + if accumulated_microbatches % max(1, gradient_accumulation_steps) == 0: + if max_grad_norm and max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + + optimizer.step() + optimizer.zero_grad(set_to_none=True) + progress.update(step_task_id, advance=1) + + # Flush leftover microbatches + if accumulated_microbatches % max(1, gradient_accumulation_steps) != 0: + if max_grad_norm and max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + + optimizer.zero_grad(set_to_none=True) + progress.update(step_task_id, advance=1) + + progress.__exit__(None, None, None) + + if not loss_metrics: + raise RuntimeError("No batch was proceed!") + + return model, optimizer, GRPOMetrics(avg_loss=sum(loss_metrics) / len(loss_metrics)) diff --git a/templates/src/rlvr/grpo/grpo_frontend.py b/templates/src/rlvr/grpo/grpo_frontend.py new file mode 100644 index 0000000..5039aad --- /dev/null +++ b/templates/src/rlvr/grpo/grpo_frontend.py @@ -0,0 +1,75 @@ +"""Non-ML data logic related to GRPO.""" + +import logging +import pathlib + +import torch +from rlvr.grpo.grpo_backend import optimize_grpo_one_epoch +from transformers import AutoModelForCausalLM + +from templates.src.rlvr.grpo.config import GRPOHyperparameters +from templates.src.rlvr.grpo.data_types import ( + AdvantageData, + GRPOMetrics, +) + + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +def grpo_optimization_step( + advantage_data: AdvantageData, + current_policy_path: pathlib.Path, + kl_ref_path: pathlib.Path, + checkpoint_output_path: pathlib.Path, + hyperparameters: GRPOHyperparameters, + optimizer_path: pathlib.Path | None, +) -> GRPOMetrics: + """Run one GRPO optimization step given advantages.""" + device = torch.device("cuda:0") + + logger.info(f"Loading kl_ref weights to CUDA: {kl_ref_path}") + kl_ref_model = AutoModelForCausalLM.from_pretrained(kl_ref_path) + kl_ref_model = kl_ref_model.to(device).bfloat16() # type: ignore[argument] + + logger.info(f"Loading weights to CUDA {current_policy_path}") + policy_model = AutoModelForCausalLM.from_pretrained(current_policy_path) + policy_model = policy_model.to(device).bfloat16() # type: ignore[argument] + + optimizer = torch.optim.AdamW( + policy_model.parameters(), + lr=hyperparameters.learning_rate, + betas=hyperparameters.adam_betas, + weight_decay=hyperparameters.adam_weight_decay, + ) + if optimizer_path and optimizer_path.exists(): + logger.info(f"Loading optimizer state: {optimizer_path}") + optimizer.load_state_dict(torch.load(optimizer_path)) + else: + logger.info( + "Re-initializing optimizer state since optimizer_path " + "is None or does not exist" + ) + + policy_model, optimizer, metrics = optimize_grpo_one_epoch( + batcher=advantage_data.get_iterator_for_training( + batch_size=hyperparameters.batch_size_backprop, + pad_to_length=hyperparameters.max_model_len, + ), + model_pi_ref=kl_ref_model, + model=policy_model, + optimizer=optimizer, + gradient_accumulation_steps=1, + ) + logger.info(f"metrics: {metrics.model_dump_json(indent=2)}") + logger.info(f"Writing model to: {checkpoint_output_path}") + policy_model.save_pretrained(checkpoint_output_path) + + if optimizer_path: + logger.info(f"Writing optimizer to: {optimizer_path}") + torch.save(optimizer.state_dict(), optimizer_path) + else: + logger.info("Not saving optimizer since optimizer_path is None.") + + return metrics diff --git a/templates/src/rlvr/grpo/launch.py b/templates/src/rlvr/grpo/launch.py new file mode 100644 index 0000000..f1ee564 --- /dev/null +++ b/templates/src/rlvr/grpo/launch.py @@ -0,0 +1,38 @@ +"""Launch script for GRPO, Hydra + Submitit.""" + +import logging +import os + +import hydra +from omegaconf import DictConfig, OmegaConf + +from templates.src.rlvr.grpo.config import GRPOConfig +from templates.src.rlvr.grpo.train import GRPOTrainer + + +_CONFIG_PATH = os.path.normpath( + os.path.join(os.path.dirname(__file__), "../../../configs") +) +log = logging.getLogger(__name__) + + +@hydra.main(config_path=_CONFIG_PATH, config_name="_global", version_base=None) +def main(cfg: DictConfig): + """Run entrypoint that merges local config and runs the Trainer.""" + local_cfg = OmegaConf.load(os.path.join(os.path.dirname(__file__), "config.yaml")) + cfg = OmegaConf.merge(local_cfg, cfg) # type: ignore + OmegaConf.set_struct(cfg, False) + + if "trainer" in cfg: + trainer_cfg = cfg.trainer + cfg = OmegaConf.merge(cfg, trainer_cfg) # type: ignore + + grpo_config = GRPOConfig.model_validate(cfg.__dict__["_content"]["trainer"]) + trainer = GRPOTrainer(grpo_config) + metrics = trainer(grpo_config) + for _epoch, (_item, _reward) in metrics: + print(f"epoch: {_epoch}, reward: {_reward:.03f}, metrics: {_item}") + + +if __name__ == "__main__": + main() diff --git a/templates/src/rlvr/grpo/rollout_generation.py b/templates/src/rlvr/grpo/rollout_generation.py new file mode 100644 index 0000000..29098b8 --- /dev/null +++ b/templates/src/rlvr/grpo/rollout_generation.py @@ -0,0 +1,125 @@ +"""Rollout Generator for RL use cases. + +Usage: +PYTHONPATH="." uv run starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py +""" + +import contextlib +import os +from typing import Any, Callable + +import agents +import backoff +import openai +import pydantic + +from templates.src.rlvr.agents_integration.rollout_translation import ( + Rollout, +) +from templates.src.rlvr.submitit_vllm import SubmititVLLM + + +# Optional: Instrument OpenAI Agents SDK with Langfuse via Logfire (OTel) +def _setup_langfuse_instrumentation(service_name: str = "rlvr-grpo") -> None: + """Configure Logfire to instrument OpenAI Agents and initialize Langfuse. + + Follows guidance from Langfuse docs on OpenAI Agents SDK instrumentation. + This is a no-op if required packages or credentials are missing. + """ + # Only attempt setup if Langfuse credentials seem present + if not (os.getenv("LANGFUSE_PUBLIC_KEY") and os.getenv("LANGFUSE_SECRET_KEY")): + return + + try: + import logfire # type: ignore + except Exception: + return + + try: + # Configure logfire to export spans via OTLP (Langfuse client handles export) + logfire.configure( + service_name=os.getenv("LANGFUSE_SERVICE_NAME", service_name), + send_to_logfire=False, + ) + # Patch OpenAI Agents SDK to emit spans + logfire.instrument_openai_agents() + + # Initialize Langfuse client (reads env vars for auth and host) + from langfuse import get_client # type: ignore + + _lf = get_client() + # Optionally validate credentials; do not raise to avoid breaking flows + with contextlib.suppress(Exception): + _ = _lf.auth_check() + except Exception: + # Swallow all errors to keep core functionality intact + return + + +# Perform best-effort setup at import time. This is safe and no-ops if disabled. +_setup_langfuse_instrumentation() + + +class RLVRDataItem(pydantic.BaseModel): + """One row in the RLVR training dataset.""" + + query: str + target: str | float + metadata: dict[str, Any] = pydantic.Field(default_factory=dict) + + +class EvalResult(pydantic.BaseModel): + """Evaluation score, including explanation.""" + + explanation: str + score: float + + +class RewardDetail(pydantic.BaseModel): + """_EvalResult plus full ChatCompletion/HF-compatible rollout info.""" + + source_item: RLVRDataItem + rollout: Rollout + result: EvalResult + + +def split_reasoning(response: str) -> tuple[str, str]: + """Extract reasoning tokens from response if matched.""" + reasoning = "" + for _token in ("",): + if _token in response: + reasoning, _, response = response.partition(_token) + + return reasoning, response + + +class RLVREvaluator: + """Base class for stateful evaluator of verifiable rewards.""" + + def __init__( + self, + evaluator_agent: "agents.Agent", + split_reasoning: Callable[[str], tuple[str, str]] | None = split_reasoning, + ): + self.agent = evaluator_agent + self.split_reasoning = split_reasoning + + @backoff.on_exception(backoff.expo, openai.APIConnectionError) + async def __call__( + self, + item: RLVRDataItem, + proposed: str, + submitit_vllm: SubmititVLLM, + ) -> EvalResult: + """Evaluate one proposed response to one data item.""" + if self.split_reasoning: + _, proposed = self.split_reasoning(proposed) + + query = f"Ground Truth: {item.model_dump_json(indent=2)} \nProposed: {proposed}" + try: + response = await submitit_vllm.run_agent(self.agent, query=query) + except (openai.BadRequestError, agents.exceptions.ModelBehaviorError) as e: + # Input is too long- judge as False. + return EvalResult(explanation=str(e), score=0) + + return response.final_output_as(EvalResult) diff --git a/templates/src/rlvr/grpo/tests/submitit_local_executor.py b/templates/src/rlvr/grpo/tests/submitit_local_executor.py new file mode 100644 index 0000000..fc2ceb3 --- /dev/null +++ b/templates/src/rlvr/grpo/tests/submitit_local_executor.py @@ -0,0 +1,112 @@ +"""Test submitit local executor.""" + +import io +import os +import pathlib +import signal +import subprocess +import sys +import time + +import submitit +import submitit.core.utils + + +class _CommandFunction(submitit.helpers.CommandFunction): + """Submitit _CommandFunction, but handles Ctrl-C properly for LocalExecutor.""" + + def __call__(self, *args, **kwargs) -> str: + full_command = ( + self.command + + [str(x) for x in args] + + [f"--{x}={y}" for x, y in kwargs.items()] + ) + if self.verbose: + print(f'The following command is sent: "{" ".join(full_command)}"') + with subprocess.Popen( + full_command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=False, + cwd=self.cwd, + env=self.env, + ) as process: + try: + stdout_buffer = io.StringIO() + stderr_buffer = io.StringIO() + + submitit.core.utils.copy_process_streams( + process, stdout_buffer, stderr_buffer, self.verbose + ) + return stdout_buffer.getvalue().strip() + except KeyboardInterrupt: + process.send_signal(signal.SIGKILL) + raise + + +# Use a local executor +log_folder = "submitit_local_logs" +os.makedirs(log_folder, exist_ok=True) + +executor = submitit.LocalExecutor(folder=log_folder) +# Long timeout so we don't hit submitit's own timeout +executor.update_parameters(timeout_min=60) + +# This command: +# 1. Writes its own PID to child_pid.txt +# 2. Sleeps for 600 seconds +# +# Importantly, this process is a *child of the submitit job process*, +# not the controller. +cmd = _CommandFunction( + # cmd = submitit.helpers.CommandFunction( + [ + sys.executable, + "-u", + "-c", + ( + "import os, time, pathlib; " + "path = pathlib.Path('child_pid.txt'); " + "path.write_text(str(os.getpid())); " + "time.sleep(600)" + ), + ] +) + +print("Submitting CommandFunction job...") +job = executor.submit(cmd) +print(f"Submitted job with submitit job_id={job.job_id}") + +# Give the child command time to start and write child_pid.txt +time.sleep(3) + +pid_file = pathlib.Path("child_pid.txt") +if pid_file.exists(): + child_pid = pid_file.read_text().strip() + print(f"child_pid.txt found. Child PID recorded as: {child_pid}") +else: + print("child_pid.txt not found yet (command may not have started).") + +print("Cancelling job via job.cancel(check=True)...") +job.cancel(check=True) + +# job.cancel() is asynchronous for LocalExecutor; give it a moment +time.sleep(3) + +print(f"After cancel: job.done() -> {job.done()}, job.state -> {job.state}") + +if pid_file.exists(): + child_pid = pid_file.read_text().strip() + print() + print("===================================================") + print("Repro instructions (run these in a *separate* shell)") + print("===================================================") + print("1) Inspect the child process:") + print(f" ps -p {child_pid} -o pid,ppid,cmd") + print() + print("You should see the python -c '...time.sleep(600)...' process") + print("still running, even though submitit thinks the job is done and") + print("we already called job.cancel().") + print("===================================================") +else: + print("Could not read child_pid.txt; the child may have exited early.") diff --git a/templates/src/rlvr/grpo/tests/test_submitit_vllm_local.py b/templates/src/rlvr/grpo/tests/test_submitit_vllm_local.py new file mode 100644 index 0000000..81e5f0c --- /dev/null +++ b/templates/src/rlvr/grpo/tests/test_submitit_vllm_local.py @@ -0,0 +1,68 @@ +"""Test Local Submitit vLLM executor.""" + +import logging +import os + +import pytest +import submitit +from vllm.config import CompilationConfig +from vllm.engine.arg_utils import EngineArgs + +from templates.src.rlvr.submitit_vllm import ExecutorConfig, SubmititVLLM + + +MODEL = os.environ.get("MODEL_NAME", "/model-weights/Qwen3-8B") + + +@pytest.fixture +def executor_configs(): + """Get config for one local executor.""" + local_executor = submitit.LocalExecutor("/tmp/.submitit/") + visible_gpus = list( + map(int, os.environ.get("CUDA_VISIBLE_DEVICES", "0").split(",")) + ) + logging.info(f"visible_gpus: {visible_gpus}") + local_executor.update_parameters( + visible_gpus=visible_gpus, gpus_per_node=len(visible_gpus), timeout_min=60 + ) + return [ + ExecutorConfig( + name="local", executor=local_executor, num_replicas=1, concurrency=128 + ) + ] + + +@pytest.mark.asyncio +async def test_submitit_vllm_local(executor_configs: list[ExecutorConfig]): + """Run integration test.""" + cache_dir = f"/tmp/.cache/vllm_compiled_graphs/{MODEL}" + logging.basicConfig(level=logging.DEBUG) + with SubmititVLLM( + logger_name_prefix="submitit_vllm.policy", + executor_configs=executor_configs, + engine_args=EngineArgs( + model=MODEL, + compilation_config=CompilationConfig(cache_dir=cache_dir), + max_model_len=1024, + ), + ) as submitit_vllm: + async with submitit_vllm.get_client() as client: + response = await client.responses.create( + model=MODEL, input="Why is the sky blue?", max_output_tokens=128 + ) + print(response.model_dump_json(indent=2)) + + with SubmititVLLM( + logger_name_prefix="submitit_vllm.policy", + executor_configs=executor_configs, + engine_args=EngineArgs( + model="/model-weights/Qwen3-8B", + compilation_config=CompilationConfig(cache_dir=cache_dir), + max_model_len=1024, + ), + ) as submitit_vllm: + async with submitit_vllm.get_client() as client: + response = await client.responses.create( + model=MODEL, input="Why is the sky blue?", max_output_tokens=128 + ) + print(response.model_dump_json(indent=2)) diff --git a/templates/src/rlvr/grpo/train.py b/templates/src/rlvr/grpo/train.py new file mode 100644 index 0000000..1ae4f74 --- /dev/null +++ b/templates/src/rlvr/grpo/train.py @@ -0,0 +1,283 @@ +"""GRPO training using Hydra + Submitit.""" + +import asyncio +import logging +import pathlib +import re +from os import environ, makedirs +from shutil import rmtree +from typing import Sequence + +import agents +import datasets +import pydantic +import submitit +from transformers import AutoTokenizer +from vllm import EngineArgs +from vllm.config import CompilationConfig + +from templates.src.rlvr.agents_integration.rollout_translation import translate_rollout +from templates.src.rlvr.async_utils import gather_with_progress +from templates.src.rlvr.grpo.config import DataConfig, GRPOConfig +from templates.src.rlvr.grpo.data_types import AdvantageData, RewardDetailTokenized +from templates.src.rlvr.grpo.grpo_frontend import grpo_optimization_step +from templates.src.rlvr.grpo.rollout_generation import ( + EvalResult, + RLVRDataItem, + RLVREvaluator, +) +from templates.src.rlvr.submitit_vllm import ExecutorConfig, SubmititVLLM + + +policy_agent = agents.Agent( + "Math Problem Solver", instructions="Solve the math problem." +) + +# Eval agent must support structured output and use output_type EvalResult +eval_agent = agents.Agent( + "Evaluator", + instructions=( + "Evaluate if the `proposed` response matches the ground-truth `target`." + "Give a score of 0.0 if incorrect and 1.0 if correct." + ), + output_type=EvalResult, +) + + +class _CheckpointPaths(pydantic.BaseModel): + """Checkpoint paths.""" + + current: pathlib.Path + output: pathlib.Path + kl_ref: pathlib.Path + + +def load_data(data_cfg: DataConfig): + """Extract question-answer pairs from the given HuggingFace data source.""" + output: dict[str, list[RLVRDataItem]] = {} + for _split, _split_cfg in [ + ("train", data_cfg.train_split), + ("test", data_cfg.test_split), + ]: + _dataset = datasets.load_dataset( + data_cfg.dataset_name, data_cfg.subset, split=_split_cfg + ) + assert isinstance(_dataset, datasets.Dataset) + output[_split] = [] + + for _row in _dataset: + assert isinstance(_row, dict) + _query = _row[data_cfg.query_column] + _target = _row[data_cfg.target_column] + + if data_cfg.target_regexp is not None: + _match = re.search(data_cfg.target_regexp, _target, re.DOTALL) + if _match: + _target = _match.group(1) + + output[_split].append(RLVRDataItem(query=_query, target=_target)) + + return output + + +class GRPOTrainer(submitit.helpers.Checkpointable): + """GRPO using on-policy SLURM rollout and one-GPU backprop.""" + + def __init__(self, cfg: GRPOConfig): + self.ckpt_dir = None + self.logger = logging.getLogger(__name__) + self.logger.setLevel(logging.INFO) + self.cfg = cfg + self.optimizer_path = ( + cfg.optimizer_folder + if cfg.optimizer_folder + else cfg.checkpoint_folder / "optimizer_state" + ) + + # Keep history of checkpoints for KL calculations + # these are automatically recycled. + self.checkpoints: list[pathlib.Path] = [] + + # Initialize policy and KL ref to base model. + self.evaluator = RLVREvaluator(eval_agent) + + # Run one replica locally while running other replicas on SLURM. + self.local_executor = submitit.LocalExecutor( + folder=cfg.submitit_logs_folder, python="uv run python" + ) + visible_gpus = list( + map(int, environ.get("CUDA_VISIBLE_DEVICES", "0").split(",")) + ) + self.local_executor.update_parameters( + visible_gpus=visible_gpus, + gpus_per_node=len(visible_gpus), + timeout_min=cfg.rollout_vllm.submitit_args.time_in_minutes, + ) + local_executor_config = ExecutorConfig( + name="local", + executor=self.local_executor, + num_replicas=1, + concurrency=cfg.rollout_vllm.concurrency_per_replica, + ) + + slurm_executor = submitit.SlurmExecutor( + folder=cfg.submitit_logs_folder, python=cfg.rollout_vllm.submitit_python + ) + slurm_executor.update_parameters( + **cfg.rollout_vllm.submitit_args.to_submitit_parameters() + ) + + self.rollout_executor_configs = [ + local_executor_config, + ExecutorConfig( + name="slurm", + executor=slurm_executor, + num_replicas=cfg.rollout_vllm.num_replicas, + concurrency=cfg.rollout_vllm.concurrency_per_replica, + ), + ] + self.llm_judge_executor_configs = [ + local_executor_config, + ExecutorConfig( + name="slurm", + executor=slurm_executor, + num_replicas=cfg.llm_judge_vllm.num_replicas, + concurrency=cfg.llm_judge_vllm.concurrency_per_replica, + ), + ] + + def get_checkpoint_paths(self, step_index: int) -> _CheckpointPaths: + """Return checkpoint paths and recycle previous checkpoints if enabled. + + - At startup, use base_model as base policy and KL ref. + - At the next step, use new output as base model, and base model as KL ref. + - Subsequently, at the Nth step, use N-1 as base policy and N-2 as KL ref. + """ + new_checkpoint = self.cfg.checkpoint_folder / f"step_{step_index:03d}" + self.logger.info(f"Next checkpoint path: {new_checkpoint}") + makedirs(new_checkpoint, exist_ok=True) + + checkpoints = [ + self.cfg.base_model, # Initial KL + self.cfg.base_model, # Initial base policy + *self.checkpoints, + new_checkpoint, + ] + + *_, _kl_ref, _current, _output = checkpoints + + # Delete all but the two most recent checkpoints (kl, current) + if len(self.checkpoints) > 2: + *_to_delete, _kl_ref, _current = self.checkpoints + for _folder in _to_delete: + rmtree(_folder) + + return _CheckpointPaths(current=_current, output=_output, kl_ref=_kl_ref) + + def generate_rollout( + self, data: Sequence[RLVRDataItem], model_name: str | pathlib.Path + ) -> tuple[Sequence[agents.RunResult], Sequence[str]]: + """Generate rollouts using policy model/agent.""" + if isinstance(model_name, pathlib.Path): + model_name = model_name.as_posix() + + with SubmititVLLM( + logger_name_prefix="submitit_vllm.policy", + executor_configs=self.rollout_executor_configs, + engine_args=EngineArgs( + model=model_name, + compilation_config=CompilationConfig( + cache_dir=self.cfg.rollout_vllm.cache_dir.as_posix() + ), + max_model_len=self.cfg.rollout_vllm.max_model_len, + ), + ) as policy_vllm: + coros = [policy_vllm.run_agent(policy_agent, _item.query) for _item in data] + coro = gather_with_progress(coros, "Rollout...") + rollouts = asyncio.get_event_loop().run_until_complete(coro) + + return rollouts, [_result.final_output for _result in rollouts] + + def score_rollouts( + self, data: Sequence[RLVRDataItem], answers: Sequence[str] + ) -> Sequence[EvalResult]: + """Score a list of answers, using LLM judge.""" + with SubmititVLLM( + logger_name_prefix="submitit_vllm.evaluator", + executor_configs=self.llm_judge_executor_configs, + engine_args=EngineArgs( + model=self.cfg.llm_judge_vllm.model_name, + compilation_config=CompilationConfig( + cache_dir=self.cfg.llm_judge_vllm.cache_dir.as_posix() + ), + max_model_len=self.cfg.llm_judge_vllm.max_model_len, + ), + ) as evaluator_vllm: + coros = [ + self.evaluator(_item, proposed=_answer, submitit_vllm=evaluator_vllm) + for _item, _answer in zip(data, answers) + ] + coro = gather_with_progress(coros, "LLM-Judge...") + return asyncio.get_event_loop().run_until_complete(coro) + + def calculate_advantage( + self, + data: Sequence[RLVRDataItem], + run_results: "Sequence[agents.RunResult]", + evals: Sequence[EvalResult], + ): + """Calculate advantage given rollouts and eval results.""" + tokenizer = AutoTokenizer.from_pretrained(self.cfg.base_model) + tokenized_reward_details = [ + RewardDetailTokenized.from_messages( + translate_rollout(_run_result, _item.query, policy_agent).messages, + reward=_eval.score, + tokenizer=tokenizer, + pad_to=self.cfg.hyperparameters.max_model_len, + ) + for _item, _run_result, _eval in zip(data, run_results, evals) + ] + + return AdvantageData.from_list_of_rewards(tokenized_reward_details) + + def run_step(self, index: int): + """Run one GRPO step.""" + _paths = self.get_checkpoint_paths(index) + + dataset = load_data(self.cfg.data) + num_train = len(dataset["train"]) + data_all = dataset["train"] + dataset["test"] # process both splits in one pass + + run_results, answers = self.generate_rollout(data_all, _paths.current) + evals = self.score_rollouts(data=data_all, answers=answers) + advantages_train = self.calculate_advantage( + data_all[:num_train], run_results[:num_train], evals=evals[:num_train] + ) + advantages_test = self.calculate_advantage( + data_all[num_train:], run_results[num_train:], evals=evals[num_train:] + ) + + self.logger.info("Running GRPO backprop...") + metrics = self.local_executor.submit( + lambda: grpo_optimization_step( + advantages_train, + current_policy_path=_paths.current, + kl_ref_path=_paths.kl_ref, + checkpoint_output_path=_paths.output, + optimizer_path=self.cfg.optimizer_folder, + hyperparameters=self.cfg.hyperparameters, + ) + ).result() + return metrics, advantages_test.avg_reward + + def __call__(self, cfg: GRPOConfig): + """Run full GRPO loop.""" + metrics = [] + for _epoch in range(cfg.num_epochs): + metrics.append(self.run_step(_epoch)) + + return metrics + + def checkpoint(self, *args, **kwargs) -> submitit.helpers.DelayedSubmission: + """Save state and launch the same callable with the same arguments.""" + return super().checkpoint(*args, **kwargs) diff --git a/templates/src/rlvr/shared_types.py b/templates/src/rlvr/shared_types.py new file mode 100644 index 0000000..b239ab7 --- /dev/null +++ b/templates/src/rlvr/shared_types.py @@ -0,0 +1,20 @@ +""" +Shared types. + +With reference to github.com/willccbb/verifiers +""" + +from typing import TYPE_CHECKING, Annotated + +from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam +from pydantic import SkipValidation + + +# typing aliases +if TYPE_CHECKING: + ChatMessage = ChatCompletionMessageParam +else: + # Use SkipValidation to preserve arbitrary keys like `tool_calls` as raw dicts + # without Pydantic coercion that can break downstream consumption by + # transformers' chat templates. + ChatMessage = Annotated[ChatCompletionMessageParam, SkipValidation] diff --git a/templates/src/rlvr/submitit_vllm.py b/templates/src/rlvr/submitit_vllm.py new file mode 100644 index 0000000..6e92b66 --- /dev/null +++ b/templates/src/rlvr/submitit_vllm.py @@ -0,0 +1,625 @@ +"""Launch `vLLM serve` using submitit.""" + +import asyncio +import dataclasses +import io +import json +import logging +import os +import re +import signal +import subprocess +import uuid +from contextlib import asynccontextmanager +from pathlib import Path +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Coroutine, + Mapping, + Sequence, + TypeVar, +) + +import httpx +import openai +import pydantic +import submitit +import submitit.core.utils +from vllm.engine.arg_utils import EngineArgs + + +if TYPE_CHECKING: + import agents + +T = TypeVar("T") +VLLM_CLI_PREFIX = "uv run vllm serve" +SERVED_MODEL_NAME = "model" +VLLM_URL_PATTERN = re.compile(r"http:\/\/0\.0\.0\.0[:\.](\d{1,5})", re.DOTALL) +VLLM_READY_PATTERN = re.compile(r"startup complete", re.DOTALL) + + +def _flatten_dict(nested: Mapping[str, Any]) -> dict[tuple[str, ...], Any]: + """ + Flatten a nested dictionary. + + Paths use original mapping keys for sequences. + """ + out: dict[tuple[str, ...], Any] = {} + stack: list[tuple[tuple[str, ...], Any]] = [((), nested)] + while stack: + path, value = stack.pop() + if isinstance(value, Mapping): + for k, v in value.items(): + stack.append((path + (k,), v)) + else: + out[path] = value + return out + + +def _serialize_config_element(element): + """Serialize various objects in vllm_config. + + Handles of a mix of Pydantic dataclass and built-in dataclass. + """ + if isinstance(element, set): + return [_serialize_config_element(_item) for _item in element] + + return getattr(element, "__dict__", element) + + +def _serialize_vllm_config(engine_args: EngineArgs) -> dict[tuple[str, ...], Any]: + """Serialize vllm engine args.""" + return _flatten_dict( + json.loads( + json.dumps( + dataclasses.asdict(engine_args), default=_serialize_config_element + ) + ) + ) + + +def get_vllm_cli_args( + engine_args: EngineArgs, + vllm_cli_prefix: str | list[str] = VLLM_CLI_PREFIX, + port: int | None = None, +) -> list[str]: + """Obtain vLLM serve command given engine args. + + Port is randomly generated. + """ + if isinstance(vllm_cli_prefix, str): + vllm_cli_prefix = vllm_cli_prefix.split(" ") + + engine_args_dict = _serialize_vllm_config(engine_args) + engine_args_dict_ref = _serialize_vllm_config(EngineArgs()) + + engine_args_patch = { + k: v for k, v in engine_args_dict.items() if v != engine_args_dict_ref.get(k) + } + + # Add only non-default values to cli. + # "model" flag must be specified as a position arg. + output = [*vllm_cli_prefix, engine_args_patch.pop(("model",))] + for k, v in engine_args_patch.items(): + if isinstance(v, bool): + if v: + output.append(f"--{'.'.join(k)}") + else: + output.extend((f"--{'.'.join(k)}", str(v))) + + if port is None: + port = uuid.uuid4().int % (65536 - 10000) + 10000 + + output.extend(("--port", str(port))) + + return output + + +async def _follow( + path: Path, callback: Callable[[str], Coroutine[None, None, None]], poll: float +) -> None: + """ + Tail a file and invoke `callback` for every new line until cancelled. + + Args: + path: Path to the log file. + callback: Callback invoked per line (without trailing newline). + poll: Sleep interval when no new data is available. + """ + f = path.open("r", encoding="utf-8", errors="ignore") + pending_tasks: list[asyncio.Task] = [] + try: + while True: + line = f.readline() + if line: + pending_tasks.append(asyncio.create_task(callback(line.rstrip("\n")))) + else: + await asyncio.sleep(poll) + except asyncio.CancelledError: + # Final quick drain before exiting. + for line in f.readlines(): + pending_tasks.append(asyncio.create_task(callback(line.rstrip("\n")))) + + await asyncio.gather(*pending_tasks) + raise + + finally: + await asyncio.gather(*pending_tasks) + f.close() + + +async def async_tail_job_logs( + job: submitit.Job, + on_stdout: Callable[[str], Coroutine[None, None, None]], + on_stderr: Callable[[str], Coroutine[None, None, None]], + on_interrupt: Callable[[], Coroutine[None, None, None]], + poll: float = 0.25, +) -> None: + """ + Asynchronously follow stdout/stderr and invoke callbacks for each new line. + + Args: + job: Submitit job returned by executor.submit(...). + on_stdout: Callback for stdout lines. + on_stderr: Callback for stderr lines. + on_interrupt: Callback when job exits (not when this task is cancelled.) + poll: Poll interval for file growth and completion checks. + """ + out_p, err_p = job.paths.stdout, job.paths.stderr + + # Wait for any log file to appear or the job to finish early. + while not ((out_p.exists() and err_p.exists()) or job.done()): + await asyncio.sleep(poll) + + # Both files will exist (or at least one); create tasks concisely. + tasks = [ + asyncio.create_task(_follow(_path, _callback, poll)) + for _path, _callback in [(out_p, on_stdout), (err_p, on_stderr)] + ] + + try: + while not job.done(): + await asyncio.sleep(poll) + await asyncio.sleep(poll * 2) # allow final flush + await on_interrupt() + finally: + for t in tasks: + t.cancel() + await asyncio.gather(*tasks, return_exceptions=False) + + +def _stop_subprocesses(process: subprocess.Popen): + """Stop subprocesses. + + Requires start_new_session=True when creating Popen. + """ + _job_pgid = os.getpgid(process.pid) + os.killpg(_job_pgid, signal.SIGTERM) + + # required to avoid defunct processes + process.terminate() + process.wait() + + +class _CommandFunction(submitit.helpers.CommandFunction): + """Submitit _CommandFunction, but handles SIGTERM properly for LocalExecutor.""" + + def __call__(self, *args: Any, **kwargs: Any) -> str: + full_command = ( + self.command + + [str(x) for x in args] + + [f"--{x}={y}" for x, y in kwargs.items()] + ) + if self.verbose: + print(f'The following command is sent: "{" ".join(full_command)}"') + + with subprocess.Popen( + full_command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=False, + cwd=self.cwd, + env=self.env, + start_new_session=True, + ) as process: + stdout_buffer = io.StringIO() + stderr_buffer = io.StringIO() + + # Handles SIGTERM + signal.signal(signal.SIGTERM, lambda *_: _stop_subprocesses(process)) + + try: + submitit.core.utils.copy_process_streams( + process, stdout_buffer, stderr_buffer, self.verbose + ) + return stdout_buffer.getvalue().strip() + + # Handles SIGINT + except (Exception, KeyboardInterrupt): + _stop_subprocesses(process) + raise + + +class SubmititVLLMWorker: + """Watcher for one replica of vLLM launched via submitit.""" + + def __init__( + self, + engine_args: "EngineArgs", + submitit_executor: submitit.Executor, + max_num_timeouts: int, + vllm_cli_prefix: str | list[str] = VLLM_CLI_PREFIX, + worker_name: str = __name__, + ping_sticky_seconds: float = 1.0, + ): + """ + Create Submitit watcher. + + Note that the job is started automatically. + If the job is interrupted/preempted, the watcher will launch a new one. + + To launch multiple workers, create one instance of this class + for each worker instance. + + Invoke the `stop` function to actually stop the watcher + and any related jobs. + + Params: + - engine_args: vLLM engine args. Must be CLI serializable. + - submitit_args: configs for submitit + - vllm_cli_prefix: shell command for launching `vllm serve`. + - worker_name: for logging. + - max_num_timeouts: restart worker if it times out after this many checks. + - ping_sticky_seconds: each successful ping is valid for this many seconds. + """ + self.logger = logging.getLogger(worker_name) + self.metrics_logger = logging.getLogger(f"{worker_name}.metrics") + + # remote backend: url is None when backend is not ready. + self.remote_status_signal = asyncio.Event() + self.remote_url_signal = asyncio.Event() + self.base_url: str | None = None + self.executor = submitit_executor + self.engine_args = engine_args + self.vllm_cli_prefix = vllm_cli_prefix + + self.watcher_task: asyncio.Task[None] + self.submitit_job, self.watcher_task = self._launch_worker() + + self.ping_lock = asyncio.Lock() + self.ping_is_ready: bool = False + self.ping_sticky_seconds = ping_sticky_seconds + self.max_num_timeouts = max_num_timeouts + self.num_timeouts = 0 + + def _launch_worker(self): + """Launch vLLM worker and return job handle as well as watcher task.""" + args = get_vllm_cli_args(self.engine_args, vllm_cli_prefix=self.vllm_cli_prefix) + submitit_job = self.executor.submit(_CommandFunction(args)) + self.logger.debug(f"Launch command: {' '.join(args)}") + self.logger.info(f"Launched: {submitit_job}") + self.num_timeouts = 0 + + watcher_task = asyncio.get_event_loop().create_task( + async_tail_job_logs( + submitit_job, + on_stdout=self._handle_job_output, + on_stderr=self._handle_job_output, + on_interrupt=self._handle_job_interrupted, + ) + ) + return submitit_job, watcher_task + + def stop(self): + """Stop worker and raise watcher exceptions (if any).""" + if not self.watcher_task.cancel(): + self.watcher_task.result() + + self.logger.info(f"Stopping job {self.submitit_job}") + # Clean up local jobs via SIGTERM. + # Note the BUG in submitit local executor- + # job.cancel creates SIGINT, which isn't forwarded to the job. + # Only SIGTERM (sent manually) is forwarded. + if ( + isinstance(self.submitit_job, submitit.LocalJob) + and self.submitit_job._process + ): + _job_pid = int(self.submitit_job.job_id) + self.logger.info(f"Sending SIGTERM to local job {_job_pid}") + os.kill(_job_pid, signal.SIGTERM) + os.waitpid(_job_pid, 0) + + # Clean up SLURM/Remote jobs + else: + self.submitit_job.cancel(check=True) + + self.remote_status_signal.set() + + async def _handle_job_interrupted(self): + """Handle job interruption by launching again.""" + self.base_url = None + self.remote_url_signal.clear() + self.remote_status_signal.clear() + self.submitit_job.cancel(check=True) + + # This callback is running within the previous self.watcher_task + # stop the previous watcher_task only after launching a new one. + previous_watcher_task = self.watcher_task + previous_job = self.submitit_job + self.submitit_job, self.watcher_task = self._launch_worker() + self.logger.info( + f"Job interrupted: {previous_job}. Relaunched: {self.submitit_job}" + ) + + # Raise exceptions (if any) from the previous watcher task. + if not previous_watcher_task.cancel(): + previous_watcher_task.result() + + async def _handle_job_output(self, log_line: str): + """Mark self as ready and update remote url when job is ready. + + Handles job output one line at a time. + """ + self.logger.debug(log_line) + if "throughput" in log_line: + self.metrics_logger.info(log_line) + + url_port_match = VLLM_URL_PATTERN.search(log_line) + ready_match = VLLM_READY_PATTERN.search(log_line) + + # Note the possible race condition- base_url might appear before or after + # the "ready" status is seen. + if url_port_match is not None: + # local executor would be missing the "NodeList info." + _job_host = self.submitit_job.get_info().get("NodeList", "localhost") + self.base_url = f"http://{_job_host}:{url_port_match.group(1)}" + self.remote_url_signal.set() + self.logger.debug(f"Base URL {self.base_url}") + + if ready_match is not None: + # Unblock remote_status_signal only after url is ready. + await self.remote_url_signal.wait() + self.remote_status_signal.set() + self.logger.info(f"Ready: {self.base_url}") + + async def _get_url(self) -> str: + """Return base url when ready, blocking (async) until ready.""" + await self.remote_status_signal.wait() + assert self.base_url is not None + return self.base_url + + async def _test_if_ready(self) -> bool: + """Check if the server is ready and reachable.""" + await self.remote_status_signal.wait() + + # Max one ping at a time + async with self.ping_lock: + # Each successful ping is good for ping_sticky_seconds. + if self.ping_is_ready: + return True + + assert self.base_url is not None + try: + response = await httpx.AsyncClient().get(f"{self.base_url}/ping") + response.raise_for_status() + except (httpx.RequestError, httpx.TimeoutException) as e: + self.logger.warning(e) + self.num_timeouts += 1 + return False + + self.ping_is_ready = True + asyncio.create_task(self._reset_ping_status()) + return True + + async def _reset_ping_status(self): + """Reset ping status to False after timer.""" + await asyncio.sleep(self.ping_sticky_seconds) + self.ping_is_ready = False + + async def get_client(self) -> openai.AsyncOpenAI: + """Wait until backend is ready and return client. + + The method updates the cumulative server timeout count. + """ + base_url = await self._get_url() + server_ready = False + while not server_ready: + server_ready = await self._test_if_ready() + + if self.num_timeouts >= self.max_num_timeouts: + self.logger.warning(f"Timeouts: {self.num_timeouts}; restarting") + await self._handle_job_interrupted() + + self.num_timeouts = 0 + return openai.AsyncOpenAI( + base_url=f"{base_url}/v1", api_key="EMPTY", max_retries=0 + ) + + +async def _rate_limited( + fn: Callable[[], Coroutine[None, None, T]], semaphore: asyncio.Semaphore +) -> tuple[T, asyncio.Semaphore]: + """Wait for fn, subject to semaphore. + + When this function returns normally, the semaphore isn't released automatically + but kept active. + + While this function cleans up the semaphore automatically if fn did not go through, + the caller is responsible for releasing the semaphore of all tasks that + *did* go through. + """ + await semaphore.acquire() + try: + result = await fn() + return result, semaphore + + # Clean up if the fn wait did not go through. + except asyncio.CancelledError as e: + semaphore.release() + raise e + + +class ExecutorConfig(pydantic.BaseModel): + """Configs for an executor.""" + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + + name: str + executor: submitit.Executor = pydantic.Field(exclude=True) + num_replicas: int + + # max per-worker concurrency + concurrency: int + + +class SubmititVLLM: + """Handles multiple replicas of submitit vLLM workers. + + For example, to launch three instances, including one local, set executors + to [(submitit.SlurmExecutor(...), 2), (submitit.LocalExecutor(...), 1)] + """ + + def __init__( + self, + engine_args: "EngineArgs", + executor_configs: Sequence[ExecutorConfig], + vllm_cli_prefix: str | list[str] = VLLM_CLI_PREFIX, + logger_name_prefix: str = "submitit_vllm", + ): + self.logger = logging.getLogger(f"{logger_name_prefix}.controller") + + if engine_args.served_model_name and ( + engine_args.served_model_name != engine_args.model + ): + self.logger.warning( + "engine_args.served_model_name != engine_args.model. " + "Overriding `served_model_name` with `model`" + ) + + # Use model_name as served_model_name + self.served_model_name = engine_args.model + engine_args.served_model_name = self.served_model_name + + self.workers: list[SubmititVLLMWorker] = [] + self.worker_semaphores: list[asyncio.Semaphore] = [] + + for _cfg in executor_configs: + with _cfg.executor.batch(): + _workers = [ + SubmititVLLMWorker( + engine_args=engine_args, + submitit_executor=_cfg.executor, + vllm_cli_prefix=vllm_cli_prefix, + max_num_timeouts=2 * _cfg.concurrency, + worker_name=f"{logger_name_prefix}.{_cfg.name}.{_index:03d}", + ) + for _index in range(_cfg.num_replicas) + ] + self.workers.extend(_workers) + + _semaphores = [ + asyncio.Semaphore(_cfg.concurrency) for _ in range(_cfg.num_replicas) + ] + self.worker_semaphores.extend(_semaphores) + + def stop(self): + """Spin down workers and raise any worker exceptions.""" + worker_exceptions: list[Exception] = [] + self.logger.info("Stopping all workers.") + for worker in self.workers: + try: + worker.stop() + except Exception as e: + # Ignore no-such-process errors + # from cleaning up local workers that exits on SIGINT + if "No such process" in str(e): + continue + + worker_exceptions.append(e) + + if worker_exceptions: + raise RuntimeError(*worker_exceptions) + + def __enter__(self): + """Use SubmititVLLM in context manager mode for auto-clean-up.""" + return self + + def __exit__(self, *_): + """Clean up.""" + self.stop() + return False + + @asynccontextmanager + async def get_client(self): + """Obtain rate-limited load-balanced OpenAI Async client. + + Blocks asynchronously if no backend is available yet. + """ + # Return first available worker, subject to per-worker rate limit. + _tasks = [ + asyncio.create_task( + _rate_limited(lambda _worker=_worker: _worker.get_client(), _semaphore) + ) + for _worker, _semaphore in zip(self.workers, self.worker_semaphores) + ] + + try: + # Avoid await in the following block to avoid race conditions + + # take the first finished result and release the rest. + (first, *others), pending = await asyncio.wait( + _tasks, return_when=asyncio.FIRST_COMPLETED + ) + # this block is synchronous, + # so no "pending" task would become ready in the meantime! + for _task in others: + _, _semaphore = _task.result() + _semaphore.release() + + for _task in pending: + _task.cancel() + + # only one task is left- yield this one and + # remember to release the sempahore when completed. + client, semaphore = first.result() + self.logger.debug(f"Yielding: {client.base_url}") + yield client + + semaphore.release() + + # Release semaphore + finally: + for _task in _tasks: + _task.cancel() + + @asynccontextmanager + async def get_oai_agents_config( + self, run_config_base: "agents.RunConfig | None" = None + ): + """Wrap around get_client.""" + import agents + + base_config = run_config_base.__dict__ if run_config_base else {} + base_config.pop("model", None) + + async with self.get_client() as client: + yield agents.RunConfig( + model=agents.OpenAIChatCompletionsModel( + model=self.served_model_name, openai_client=client + ), + **base_config, + ) + + async def run_agent( + self, + agent: "agents.Agent", + query: str, + run_config: "agents.RunConfig | None" = None, + ): + """Get client and invoke agent.""" + import agents + + async with self.get_oai_agents_config(run_config) as _config: + return await agents.Runner.run(agent, input=query, run_config=_config) diff --git a/uv.lock b/uv.lock index 0ff9ef8..4d57d10 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,11 @@ version = 1 -revision = 1 +revision = 2 requires-python = "==3.12.*" +resolution-markers = [ + "sys_platform == 'darwin'", + "platform_machine == 'aarch64' and sys_platform == 'linux'", + "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')", +] [[package]] name = "accelerate" @@ -15,18 +20,30 @@ dependencies = [ { name = "safetensors" }, { name = "torch" }, ] 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