diff --git a/executors/PPO/.python-version b/executors/PPO/.python-version new file mode 100644 index 00000000..24ee5b1b --- /dev/null +++ b/executors/PPO/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/executors/PPO/pyproject.toml b/executors/PPO/pyproject.toml new file mode 100644 index 00000000..7fde1b17 --- /dev/null +++ b/executors/PPO/pyproject.toml @@ -0,0 +1,90 @@ +[project] +name = "hypha-ppo-executor" +version = "0.0.0" +license = "Apache-2.0" +requires-python = ">=3.12" +dependencies = [ + "httpx>=0.28.1", + "safetensors>=0.7.0", +] + +[project.optional-dependencies] +mps_cu128 = [ + "torch>=2.9.0", +] +cpu = [ + "torch>=2.9.0", +] +cu128 = [ + "torch>=2.9.0", +] +cu130 = [ + "torch>=2.9.0", +] +rocm64 = [ + "torch>=2.9.0", + "pytorch-triton-rocm", +] + +[tool.uv] +conflicts = [ + [ + { extra = "mps_cu128" }, + { extra = "cpu" }, + { extra = "cu128" }, + { extra = "cu130" }, + { extra = "rocm64" }, + ], +] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu", extra = "cpu" }, + { index = "pytorch-cu128", extra = "cu128" }, + { index = "pytorch-cu130", extra = "cu130" }, + { index = "pytorch-rocm64", extra = "rocm64"}, +] +pytorch-triton-rocm = [ + { index = "pytorch-rocm64", extra = "rocm64"}, +] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true + +[[tool.uv.index]] +name = "pytorch-rocm64" +url = "https://download.pytorch.org/whl/rocm6.4" +explicit = true + +[dependency-groups] +dev = [ + "ruff>=0.14.9", +] + +[build-system] +requires = ["uv_build>=0.9.8,<0.10.0"] +build-backend = "uv_build" + +[tool.uv.build-backend] +module-name = "hypha.gym_executor" + +[tool.ruff] +line-length = 120 + +[tool.ruff.format] +docstring-code-format = true + +[tool.ruff.lint] +select = ["E", "F", "W", "I", "N", "UP", "SIM", "ARG", "PL"] diff --git a/executors/PPO/src/hypha/ppo_executor/__init__.py b/executors/PPO/src/hypha/ppo_executor/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/executors/PPO/src/hypha/ppo_executor/api.py b/executors/PPO/src/hypha/ppo_executor/api.py new file mode 100644 index 00000000..0f322327 --- /dev/null +++ b/executors/PPO/src/hypha/ppo_executor/api.py @@ -0,0 +1,72 @@ +import json +from collections.abc import Iterator +from contextlib import AbstractContextManager, contextmanager +from types import TracebackType +from typing import Any, override + +import httpx + + +class Session(AbstractContextManager["Session", None]): + def __init__(self, socket_path: str) -> None: + transport = httpx.HTTPTransport(uds=socket_path) + self._client: httpx.Client = httpx.Client(transport=transport) + + @override + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + self._client.close() + + def send_resource(self, resource: Any, path: str, timeout: float | None = None) -> None: + timeout_ms = int(timeout * 1000) if timeout is not None else None + req = {"resource": resource, "path": path, "timeout_ms": timeout_ms} + # We must allow the client to wait at least as long as the requested timeout. + # If timeout is None, wait forever. + _ = self._client.post("http://hypha/resources/send", json=req, timeout=timeout).raise_for_status() + + def send_action(self, payload: Any) -> Any: + resp = self._client.post("http://hypha/action/update", json=payload, timeout=None).raise_for_status() + return resp.json() + + def fetch(self, resource: Any) -> Any: + resp = self._client.post("http://hypha/resources/fetch", json=resource, timeout=None).raise_for_status() + return resp.json() + + @contextmanager + def receive(self, resource: Any, path: str, timeout: float | None = None) -> Iterator["EventSource"]: + req = {"resource": resource, "path": path} + # Use a short connect timeout to fail fast if the local side is unresponsive, + # but respect the provided timeout for the total duration/read. + # If timeout is None, we still enforce a connect timeout. + timeout_config = httpx.Timeout(timeout, connect=5.0) + with self._client.stream( + "POST", + "http://hypha/resources/receive", + json=req, + headers={"Accept": "text/event-stream"}, + timeout=timeout_config, + ) as resp: + yield EventSource(resp) + + +class EventSource: + def __init__(self, response: httpx.Response) -> None: + self._response: httpx.Response = response + + @property + def response(self) -> httpx.Response: + return self._response + + def __iter__(self) -> Iterator[Any]: + for line in self._response.iter_lines(): + fieldname, _, value = line.rstrip("\n").partition(":") + + if fieldname == "data": + result = json.loads(value) + + yield result + # Ignore other SSE fields (e.g., event:, id:, retry:) diff --git a/executors/PPO/src/hypha/ppo_executor/main.py b/executors/PPO/src/hypha/ppo_executor/main.py new file mode 100644 index 00000000..64a046f2 --- /dev/null +++ b/executors/PPO/src/hypha/ppo_executor/main.py @@ -0,0 +1,253 @@ +import argparse +import json +import logging +import time +from collections import deque +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from torch.distributions.normal import Normal +import numpy as np +from safetensors.numpy import save_file + +from .api import Session + +MIN_LOOP_TIME_MS = 100 + +logger = logging.getLogger(__name__) + + +def system_time_to_epoch_ms(timeout: object) -> int | None: + if isinstance(timeout, dict): + secs = timeout.get("secs_since_epoch") + nanos = timeout.get("nanos_since_epoch", 0) + if secs is not None: + return int(secs * 1000 + int(nanos / 1_000_000)) + if isinstance(timeout, (int, float)): + # Fallback for numeric nanos representation. + return int(timeout / 1_000_000) + return None + + +def sleep_until_epoch_ms(target_ms: int) -> None: + now_ms = time.time() * 1000.0 + if target_ms > now_ms: + time.sleep((target_ms - now_ms) / 1000.0) + + +def gymnasium(socket_path: str, work_dir: str, job_id: str, config) -> None: + with Session(socket_path) as session: + envs = gym.make_vec(config["environment"], num_envs=4) + + replay_buffer = deque(maxlen=100) + episode_start = np.zeros(envs.num_envs, dtype=bool) + + num_local_rounds = 1 + observations, infos = envs.reset() + + all_observations = [] + + current_status = { + "executor": "gymnasium", + "details": {"state": "joined"}, + } + + ######### Parameters ######### + lr = 3e-4 + total_timesteps = 2000000 + capture_video = False + num_envs = 1 + num_steps = 2048 + gamma = .99 + gae_lambda = .95 + num_minibatches = 32 + update_epochs = 10 + clip_coef = .2 + ent_coef = .0 + vf_coef = 0.5 + max_grad_norm = .5 + target_kl = None + batch_size = int(num_envs * num_steps) + minibatch_size = int(batch_size // num_minibatches) + cuda = False + ######### Parameters ######### + + while True: + loop_start_ms = time.time() * 1000.0 + action_resp = session.send_action({"job_id": job_id, "status": current_status}) + next_action = action_resp.get("next", {}) + + if next_action.get("executor") != "gymnasium": + raise RuntimeError(f"Unexpected executor action: {next_action}") + + action = next_action.get("action", {}) + kind = action.get("kind") + + logger.info("Action: %s", kind) + + match kind: + case "terminate": + logger.info("Data generation finished") + break + + case "idle": + timeout_ms = system_time_to_epoch_ms(action.get("timeout")) + if timeout_ms is not None: + sleep_until_epoch_ms(timeout_ms) + + current_status = {"executor": "gymnasium", "details": {"state": "idle"}} + + case "train": + + # ALGO Logic: Storage setup + # Expected Data + # obs = torch.zeros((num_steps, num_envs) + envs.single_observation_space.shape).to(device) + # actions = torch.zeros((num_steps, num_envs) + envs.single_action_space.shape).to(device) + # logprobs = torch.zeros((num_steps, num_envs)).to(device) + # rewards = torch.zeros((num_steps, num_envs)).to(device) + # dones = torch.zeros((num_steps, num_envs)).to(device) + # values = torch.zeros((num_steps, num_envs)).to(device) + + # Annealing the rate if instructed to do so. + frac = 1.0 - (update - 1.0) / num_updates + lrnow = frac * lr + optimizer.param_groups[0]["lr"] = lrnow + + # bootstrap value if not done + with torch.no_grad(): + next_value = agent.get_value(next_obs).reshape(1, -1) + + advantages = torch.zeros_like(rewards).to(device) + lastgaelam = 0 + for t in reversed(range(num_steps)): + if t == num_steps - 1: + nextnonterminal = 1.0 - next_done + nextvalues = next_value + else: + nextnonterminal = 1.0 - dones[t + 1] + nextvalues = values[t + 1] + delta = rewards[t] + gamma * nextvalues * nextnonterminal - values[t] + advantages[t] = lastgaelam = delta + gamma * gae_lambda * nextnonterminal * lastgaelam + returns = advantages + values + + # flatten the batch + b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) + b_logprobs = logprobs.reshape(-1) + b_actions = actions.reshape((-1,) + envs.single_action_space.shape) + b_advantages = advantages.reshape(-1) + b_returns = returns.reshape(-1) + b_values = values.reshape(-1) + + # Optimizing the policy and value network + b_inds = np.arange(batch_size) + clipfracs = [] + for epoch in range(update_epochs): + np.random.shuffle(b_inds) + for start in range(0, batch_size, minibatch_size): + end = start + minibatch_size + mb_inds = b_inds[start:end] + + _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions[mb_inds]) + logratio = newlogprob - b_logprobs[mb_inds] + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfracs += [((ratio - 1.0).abs() > clip_coef).float().mean().item()] + + mb_advantages = b_advantages[mb_inds] + mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) + + # Policy loss + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 + v_clipped = b_values[mb_inds] + torch.clamp( + newvalue - b_values[mb_inds], + -clip_coef, + clip_coef, + ) + v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + + entropy_loss = entropy.mean() + loss = pg_loss - ent_coef * entropy_loss + v_loss * vf_coef + + optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(agent.parameters(), max_grad_norm) + optimizer.step() + + + y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() + var_y = np.var(y_true) + explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + + current_status = {"executor": "gymnasium", "details": {"state": "train", ""}} + + case "send": + target = action.get("target") + if target is None: + current_status = { + "executor": "gymnasium", + "details": {"state": "error", "type": "other", "message": "Send missing target reference"}, + } + continue + + observations_path = Path(work_dir) / "observations.safetensors" + save_file({"observations": all_observations}, observations_path) + + try: + session.send_resource(target, str(observations_path)) + current_status = {"executor": "gymnasium", "details": {"state": "send"}} + except Exception as exc: + current_status = { + "executor": "gymnasium", + "details": {"state": "error", "type": "connection", "message": str(exc)}, + } + continue + + case "update": + # TODO: load current model state and setup agent + # model_state = session.fetch(config["model"]["artifact"]) + # model_state = os.path.join(work_dir, model_state[0]["path"]) + # model = load_from_safetensor(model_state) + + # agent = PPOAgent(env, model) + # TODO: set agent state + current_status = {"executor": "gymnasium", "details": {"state": "update"}} + + elapsed = time.time() * 1000.0 - loop_start_ms + if elapsed < MIN_LOOP_TIME_MS: + time.sleep((MIN_LOOP_TIME_MS - elapsed) / 1000.0) + + envs.close() + logger.info("Environment closed") + + +def main(socket_path: str, work_dir: str, job_json: str) -> None: + job_spec = json.loads(job_json) + + executor = job_spec["executor"] + assert executor["class"] == "gymnasium" + + gymnasium(socket_path, work_dir, executor["job_id"], executor["config"]) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--socket", required=True) + parser.add_argument("--work-dir", required=True) + parser.add_argument("--job", required=True) + args = parser.parse_args() + main(args.socket, args.work_dir, args.job) diff --git a/executors/PPO/uv.lock b/executors/PPO/uv.lock new file mode 100644 index 00000000..372e0466 --- /dev/null +++ b/executors/PPO/uv.lock @@ -0,0 +1,1204 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "extra != 'extra-18-hypha-gym-executor-cpu' and extra != 'extra-18-hypha-gym-executor-cu128' and extra != 'extra-18-hypha-gym-executor-cu130' and extra != 'extra-18-hypha-gym-executor-mps-cu128' and extra == 'extra-18-hypha-gym-executor-rocm64'", + "extra != 'extra-18-hypha-gym-executor-cpu' and extra != 'extra-18-hypha-gym-executor-cu128' and extra != 'extra-18-hypha-gym-executor-cu130' and extra == 'extra-18-hypha-gym-executor-mps-cu128' and extra != 'extra-18-hypha-gym-executor-rocm64'", + "(python_full_version >= '3.15' and extra != 'extra-18-hypha-gym-executor-cpu' and extra != 'extra-18-hypha-gym-executor-cu128' and extra == 'extra-18-hypha-gym-executor-cu130' and extra != 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