curobo.opt.opt_base module
Base module for Optimization.
- class OptimizerConfig(
- d_action: int,
- action_lows: List[float],
- action_highs: List[float],
- action_horizon: int,
- horizon: int,
- n_iters: int,
- cold_start_n_iters: int | None,
- rollout_fn: RolloutBase,
- tensor_args: TensorDeviceType,
- use_cuda_graph: bool,
- store_debug: bool,
- debug_info: Any,
- n_problems: int,
- num_particles: int | None,
- sync_cuda_time: bool,
- use_coo_sparse: bool,
Bases:
objectConfiguration for an
Optimizer.- cold_start_n_iters: int | None
Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).
- rollout_fn: RolloutBase
Rollout function to use for computing cost, given optimization variables.
- tensor_args: TensorDeviceType
Tensor device to use for optimization.
- use_cuda_graph: bool
Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.
- store_debug: bool
Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.
- num_particles: int | None
Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.
- use_coo_sparse: bool
Matmul with a Sparse tensor is used to create particles for each problem index to save memory and compute. Some versions of pytorch do not support coo sparse, specifically during torch profile runs. Set this to False to use a standard tensor.
- static create_data_dict(
- data_dict: Dict,
- rollout_fn: RolloutBase,
- tensor_args: TensorDeviceType = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32),
- child_dict: Dict | None = None,
Helper function to create dictionary from optimizer parameters and rollout class.
- Parameters:
data_dict – optimizer parameters dictionary.
rollout_fn – rollout function.
tensor_args – tensor cuda device.
child_dict – new dictionary where parameters will be stored.
- Returns:
Dictionary with parameters to create a
OptimizerConfig
- class Optimizer(
- config: OptimizerConfig | None = None,
Bases:
OptimizerConfigBase optimization solver class
- Parameters:
config – Initialized with parameters from a dataclass.
- optimize(
- opt_tensor: Tensor,
- shift_steps=0,
- n_iters=None,
Find a solution through optimization given the initial values for variables.
- Parameters:
opt_tensor – Initial value of optimization variables. Shape: [n_problems, action_horizon, d_action]
shift_steps – Shift variables along action_horizon. Useful in mpc warm-start setting.
n_iters – Override number of iterations to run optimization.
- Returns:
Optimized values returned as a tensor of shape [n_problems, action_horizon, d_action].
- update_params(
- goal: Goal,
Update parameters in the
curobo.rollout.rollout_base.RolloutBaseinstance.- Parameters:
goal – parameters to update rollout instance.
- reset()
Reset optimizer.
- update_nproblems(
- n_problems: int,
Update the number of problems that need to be optimized.
- Parameters:
n_problems – number of problems.
- get_nproblem_tensor(x)
This function takes an input tensor of shape (n_problem,….) and converts it into (n_particles,…).
- reset_seed()
Reset seeds.
- reset_cuda_graph()
Reset CUDA Graphs. This does not work, workaround is to create a new instance.
- reset_shape()
Reset any flags in rollout class. Useful to reinitialize tensors for a new shape.
- get_all_rollout_instances() List[RolloutBase]
Get all instances of Rollout class in the optimizer.
- abstract _optimize(
- opt_tensor: Tensor,
- shift_steps=0,
- n_iters=None,
Implement this function in a derived class containing the solver.
- Parameters:
opt_tensor – Initial value of optimization variables. Shape: [n_problems, action_horizon, d_action]
shift_steps – Shift variables along action_horizon. Useful in mpc warm-start setting.
n_iters – Override number of iterations to run optimization.
- Returns:
Optimized variables in tensor shape [action_horizon, d_action].
- abstract _shift(shift_steps=0)
Shift the variables in the solver to hotstart the next timestep.
- Parameters:
shift_steps – Number of timesteps to shift.
- _update_problem_kernel( )
Update matrix used to map problem index to number of particles.
- Parameters:
n_problems – Number of optimization problems.
num_particles – Number of particles per problem.
- static create_data_dict(
- data_dict: Dict,
- rollout_fn: RolloutBase,
- tensor_args: TensorDeviceType = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32),
- child_dict: Dict | None = None,
Helper function to create dictionary from optimizer parameters and rollout class.
- Parameters:
data_dict – optimizer parameters dictionary.
rollout_fn – rollout function.
tensor_args – tensor cuda device.
child_dict – new dictionary where parameters will be stored.
- Returns:
Dictionary with parameters to create a
OptimizerConfig
- cold_start_n_iters: int | None
Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).
- rollout_fn: RolloutBase
Rollout function to use for computing cost, given optimization variables.
- tensor_args: TensorDeviceType
Tensor device to use for optimization.
- use_cuda_graph: bool
Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.
- store_debug: bool
Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.
- num_particles: int | None
Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.