调整ignore的文件
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qihuanye
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"""Lagrangian solver for stable world model."""
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import time
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from typing import Any
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import gymnasium as gym
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import numpy as np
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import torch
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import torch.nn.functional as F
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from gymnasium.spaces import Box
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from loguru import logger as logging
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from .solver import Costable
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class LagrangianSolver(torch.nn.Module):
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"""Lagrangian solver for stable world model.
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get_cost returns the cost tensor (B, S). If the model also implements get_constraints,
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it should return the constraint violations (B, S, C), where C is the number of constraints.
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The constraint_cost should represent the cost of violating the constraints, where the constraint
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is satisfied when constraint_cost <= 0. The Lagrangian solver will optimize the following objective:
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L = cost + sum_{i=1}^C lambda_i * constraint_cost_i + sum_{i=1}^C rho_i * max(0, constraint_cost_i)^2
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If you want to use equality constraint, you can convert it to two inequality constraints. For example, if you want to enforce constraint_cost_i == 0, you can add two constraints: constraint_cost_i <= 0 and -constraint_cost_i <= 0.
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Args:
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model: World model implementing the Costable protocol. Its get_cost() returns
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a plain cost tensor (B, S). If it also has get_constraints(), that method
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returns constraints of shape (B, S, C).
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n_steps: Number of gradient descent steps per outer iteration.
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n_outer_steps: Number of dual ascent (outer) iterations.
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batch_size: Number of environments to process in parallel.
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num_samples: Number of action samples to optimize in parallel.
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var_scale: Initial variance scale for action perturbations.
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action_noise: Noise added to actions during optimization.
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rho_init: Initial penalty coefficient for the quadratic constraint term.
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rho_max: Maximum value of the penalty coefficient.
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rho_scale: Multiplicative growth factor for rho after each outer step.
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persist_multipliers: Whether to warm-start Lagrange multipliers across solve() calls.
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device: Device for tensor computations.
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seed: Random seed for reproducibility.
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optimizer_cls: PyTorch optimizer class to use.
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optimizer_kwargs: Keyword arguments for the optimizer.
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"""
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def __init__(
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self,
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model: Costable,
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n_steps: int,
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n_outer_steps: int = 5,
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batch_size: int | None = None,
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num_samples: int = 1,
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var_scale: float = 1.0,
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action_noise: float = 0.0,
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rho_init: float = 1.0,
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rho_max: float = 1e4,
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rho_scale: float = 2.0,
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persist_multipliers: bool = True,
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device: str | torch.device = 'cpu',
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seed: int = 1234,
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optimizer_cls: type[torch.optim.Optimizer] = torch.optim.Adam,
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optimizer_kwargs: dict | None = None,
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) -> None:
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super().__init__()
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self.model = model
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self.n_steps = n_steps
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self.n_outer_steps = n_outer_steps
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self.batch_size = batch_size
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self.num_samples = num_samples
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self.var_scale = var_scale
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self.action_noise = action_noise
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self.rho_init = rho_init
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self.rho_max = rho_max
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self.rho_scale = rho_scale
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self.persist_multipliers = persist_multipliers
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self.device = device
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self.torch_gen = torch.Generator(device=device).manual_seed(seed)
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self.optimizer_cls = optimizer_cls
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self.optimizer_kwargs = (
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optimizer_kwargs if optimizer_kwargs is not None else {'lr': 1.0}
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)
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self._configured = False
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self._n_envs = None
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self._action_dim = None
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self._config = None
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self._lambdas: torch.Tensor | None = None # (n_envs, C)
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def configure(
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self, *, action_space: gym.Space, n_envs: int, config: Any
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) -> None:
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"""Configure the solver with environment specifications."""
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self._action_space = action_space
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self._n_envs = n_envs
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self._config = config
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self._action_dim = int(np.prod(action_space.shape[1:]))
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self._configured = True
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if not isinstance(action_space, Box):
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logging.warning(
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f'Action space is discrete, got {type(action_space)}. LagrangianSolver may not work as expected.'
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)
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@property
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def n_envs(self) -> int:
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"""Number of parallel environments."""
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return self._n_envs
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@property
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def action_dim(self) -> int:
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"""Flattened action dimension including action_block grouping."""
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return self._action_dim * self._config.action_block
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@property
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def horizon(self) -> int:
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"""Planning horizon in timesteps."""
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return self._config.horizon
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def __call__(self, *args: Any, **kwargs: Any) -> dict:
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"""Make solver callable, forwarding to solve()."""
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return self.solve(*args, **kwargs)
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def init_action(self, actions: torch.Tensor | None = None) -> None:
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"""Initialize the action tensor for optimization."""
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if actions is None:
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actions = torch.zeros((self._n_envs, 0, self.action_dim))
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remaining = self.horizon - actions.shape[1]
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if remaining > 0:
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new_actions = torch.zeros(self._n_envs, remaining, self.action_dim)
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actions = torch.cat([actions, new_actions], dim=1).to(self.device)
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actions = actions.unsqueeze(1).repeat_interleave(
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self.num_samples, dim=1
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)
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actions[:, 1:] += (
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torch.randn(
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actions[:, 1:].shape,
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generator=self.torch_gen,
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device=self.device,
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)
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* self.var_scale
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)
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if hasattr(self, 'init'):
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self.init.copy_(actions)
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else:
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self.register_parameter('init', torch.nn.Parameter(actions))
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def _init_multipliers(self, num_constraints: int) -> None:
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"""Lazily initialize Lagrange multipliers to zeros."""
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self._lambdas = torch.zeros(
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self._n_envs, num_constraints, device=self.device
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)
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def _augmented_lagrangian_loss(
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self,
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costs: torch.Tensor, # (B, S)
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constraints: torch.Tensor, # (B, S, C)
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lambdas_batch: torch.Tensor, # (B, C)
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rho: float,
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) -> torch.Tensor:
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"""Compute the augmented Lagrangian loss.
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L = cost + Σ_i lambda_i * g_i + Σ_i rho * max(0, g_i)^2
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"""
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# lambdas_batch: (B, C) -> (B, 1, C) for broadcasting with constraints (B, S, C)
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linear_penalty = (lambdas_batch.unsqueeze(1) * constraints).sum(
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dim=-1
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) # (B, S)
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quadratic_penalty = rho * F.relu(constraints).pow(2).sum(
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dim=-1
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) # (B, S)
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return (costs + linear_penalty + quadratic_penalty).sum()
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def _update_multipliers(
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self,
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constraints: torch.Tensor, # (B, S, C) — detached, no grad
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lambdas_batch: torch.Tensor, # (B, C)
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rho: float,
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) -> torch.Tensor:
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"""Dual ascent: lambda_i <- max(0, lambda_i + rho * mean_samples(g_i))."""
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mean_g = constraints.mean(dim=1) # (B, C)
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return torch.clamp(lambdas_batch + rho * mean_g, min=0.0)
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def solve(
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self, info_dict: dict, init_action: torch.Tensor | None = None
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) -> dict:
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"""Solve the planning problem using augmented Lagrangian gradient descent."""
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start_time = time.time()
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outputs: dict = {
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'cost': [],
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'constraint_violation': [],
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'actions': None,
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'lambdas': None,
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}
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with torch.no_grad():
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self.init_action(init_action)
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if not self.persist_multipliers:
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self._lambdas = None
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batch_size = (
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self.batch_size if self.batch_size is not None else self.n_envs
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)
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total_envs = self.n_envs
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batch_top_actions_list = []
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for start_idx in range(0, total_envs, batch_size):
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end_idx = min(start_idx + batch_size, total_envs)
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current_bs = end_idx - start_idx
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batch_init = self.init[start_idx:end_idx].clone().detach()
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batch_init.requires_grad = True
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# Expand info_dict for current batch — same pattern as GradientSolver
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expanded_infos = {}
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for k, v in info_dict.items():
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if torch.is_tensor(v):
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batch_v = v[start_idx:end_idx]
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batch_v = batch_v.unsqueeze(1)
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batch_v = batch_v.expand(
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current_bs, self.num_samples, *batch_v.shape[2:]
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)
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elif isinstance(v, np.ndarray):
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batch_v = v[start_idx:end_idx]
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batch_v = np.repeat(
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batch_v[:, None, ...], self.num_samples, axis=1
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)
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else:
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batch_v = v
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expanded_infos[k] = batch_v
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rho = self.rho_init
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batch_cost_history = []
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costs = None
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final_constraints = None
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for _outer in range(self.n_outer_steps):
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# Fresh optimizer each outer step — avoids stale momentum after dual ascent
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optim = self.optimizer_cls(
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[batch_init], **self.optimizer_kwargs
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)
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for _step in range(self.n_steps):
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current_info = expanded_infos.copy()
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costs = self.model.get_cost(current_info, batch_init)
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constraints = (
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self.model.get_constraints(
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expanded_infos.copy(), batch_init
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)
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if hasattr(self.model, 'get_constraints')
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else None
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)
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assert isinstance(costs, torch.Tensor), (
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f'Got {type(costs)} cost, expect torch.Tensor'
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)
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assert costs.ndim == 2 and costs.shape == (
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current_bs,
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self.num_samples,
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), (
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f'Cost should be of shape ({current_bs}, {self.num_samples}), got {costs.shape}'
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)
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assert costs.requires_grad, (
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'Cost must requires_grad for LagrangianSolver.'
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)
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if constraints is not None:
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assert constraints.ndim == 3 and constraints.shape[
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:2
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] == (current_bs, self.num_samples), (
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f'Constraints should be of shape ({current_bs}, {self.num_samples}, C), got {constraints.shape}'
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)
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if self._lambdas is None:
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self._init_multipliers(constraints.shape[-1])
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lambdas_batch = self._lambdas[start_idx:end_idx]
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loss = self._augmented_lagrangian_loss(
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costs, constraints, lambdas_batch, rho
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)
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else:
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loss = costs.sum()
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loss.backward()
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optim.step()
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optim.zero_grad(set_to_none=True)
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if self.action_noise > 0:
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batch_init.data += (
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torch.randn(
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batch_init.shape, generator=self.torch_gen
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)
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* self.action_noise
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)
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batch_cost_history.append(loss.item())
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# Dual ascent after inner loop converges
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if constraints is not None:
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with torch.no_grad():
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final_constraints = self.model.get_constraints(
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expanded_infos.copy(), batch_init
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)
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lambdas_batch = self._update_multipliers(
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final_constraints, lambdas_batch, rho
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)
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self._lambdas[start_idx:end_idx] = lambdas_batch
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rho = min(self.rho_max, rho * self.rho_scale)
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with torch.no_grad():
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mean_cost = costs.mean().item()
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if constraints is not None:
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viol = F.relu(final_constraints).mean(dim=(0, 1)) # (C,)
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lam = lambdas_batch.mean(dim=0) # (C,)
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viol_str = ', '.join(f'{v:.4f}' for v in viol.tolist())
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lam_str = ', '.join(f'{l:.4f}' for l in lam.tolist())
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print(
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f' [outer {_outer+1}/{self.n_outer_steps}] '
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f'cost={mean_cost:.4f} | '
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f'constraint_viol=[{viol_str}] | '
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f'lambdas=[{lam_str}] | '
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f'rho={rho:.4f}'
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)
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else:
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print(
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f' [outer {_outer+1}/{self.n_outer_steps}] '
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f'cost={mean_cost:.4f}'
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)
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outputs['cost'].append(batch_cost_history)
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if final_constraints is not None:
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outputs['constraint_violation'].append(
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F.relu(final_constraints).mean().item()
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)
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with torch.no_grad():
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self.init[start_idx:end_idx] = batch_init
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top_idx = torch.argsort(costs, dim=1)[:, 0]
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batch_indices = torch.arange(current_bs)
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top_actions_batch = batch_init[batch_indices, top_idx]
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batch_top_actions_list.append(top_actions_batch.detach().cpu())
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outputs['actions'] = torch.cat(batch_top_actions_list, dim=0)
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outputs['lambdas'] = (
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self._lambdas.cpu() if self._lambdas is not None else None
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)
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constraint_info = ''
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if outputs['constraint_violation']:
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mean_viol = np.mean(outputs['constraint_violation'])
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constraint_info = f' | constraint_violation={mean_viol:.4f}'
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print(
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f'LagrangianSolver.solve completed in {time.time() - start_time:.4f} seconds{constraint_info}.'
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)
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return outputs
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