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from typing import List, Dict, Any, Tuple, Union
from collections import namedtuple
import torch
from ding.rl_utils import get_train_sample
from ding.torch_utils import Adam, to_device
from ding.utils import POLICY_REGISTRY, split_data_generator
from ding.utils.data import default_collate, default_decollate
from .base_policy import Policy
from ..model import model_wrap
@POLICY_REGISTRY.register('prompt_pg')
class PromptPGPolicy(Policy):
r"""
Overview:
Policy class of Prompt Policy Gradient (PromptPG) algorithm.
Link of the original paper: https://arxiv.org/abs/2209.14610
"""
config = dict(
# (string) RL policy register name (refer to function "register_policy").
type='prompt_pg',
# (bool) whether to use cuda for network.
cuda=True,
# (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same)
on_policy=True, # for pg strictly on policy algorithm, this line should not be modified by users
# (bool) whether to use deterministic action for evaluation.
deterministic_eval=True,
learn=dict(
# (int) the number of samples for one update.
batch_size=64,
# (float) the step size of one gradient descend.
learning_rate=0.001,
# ==============================================================
# The following configs is algorithm-specific
# ==============================================================
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.01,
# (float) max grad norm value.
grad_norm=5,
# (bool) whether to ignore done signal for non-termination env.
ignore_done=False,
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
# n_episode=8,
# (int) trajectory unroll length
unroll_len=1,
# ==============================================================
# The following configs is algorithm-specific
# ==============================================================
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0,
collector=dict(get_train_sample=True),
),
eval=dict(),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'language_transformer', ['ding.model.template.language_transformer']
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Init the optimizer, algorithm config, main and target models.
"""
# Optimizer
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
self._entropy_weight = self._cfg.learn.entropy_weight
self._grad_norm = self._cfg.learn.grad_norm
self._learn_model = self._model # for compatibility
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs', 'action', 'reward']
Returns:
- info_dict (:obj:`Dict[str, Any]`): Including current lr and loss.
"""
self._model.train()
return_infos = []
for i in range(0, len(data), self._cfg.learn.batch_size):
batch = default_collate(data[i:i + self._cfg.learn.batch_size])
if self._cuda:
batch = to_device(batch, self._device)
# Prepare train_sample (the question to be answered) and the candidate_samples (the prompts to be selected)
train_samples, cand_samples = batch["obs"]["train_sample"], batch["obs"]["candidate_samples"]
for ii in range(len(cand_samples)):
cand_samples[ii] = cand_samples[ii][0]
output = self._learn_model.forward(train_samples, cand_samples)
return_ = batch['return']
# calculate PG loss
real_act = batch['action'] # shape: (B, shot_number)
# Calculate loss.
total_policy_loss, total_entropy_loss = 0, 0
for ii in range(self._cfg.shot_number):
log_prob = output['dist'].log_prob(real_act[:, ii])
policy_loss = -(log_prob * return_).mean()
total_policy_loss += policy_loss
total_entropy_loss += -self._cfg.learn.entropy_weight * output['dist'].entropy().mean()
total_loss = total_entropy_loss + total_policy_loss
# update
self._optimizer.zero_grad()
total_loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
list(self._learn_model.parameters()),
max_norm=self._grad_norm,
)
self._optimizer.step()
# only record last updates information in logger
return_info = {
'cur_lr': self._optimizer.param_groups[0]['lr'],
'total_loss': total_loss.item(),
'policy_loss': total_policy_loss.item(),
'entropy_loss': total_entropy_loss.item(),
'return_abs_max': return_.abs().max().item(),
'grad_norm': grad_norm,
}
return_infos.append(return_info)
return return_infos
def _init_collect(self) -> None:
self._unroll_len = self._cfg.collect.unroll_len
self._gamma = self._cfg.collect.discount_factor
self._collect_model = model_wrap(self._model, wrapper_name='combination_multinomial_sample')
def _forward_collect(self, data: dict) -> dict:
data_id = list(data.keys())
data = default_collate(list(data.values()))
self._model.eval()
with torch.no_grad():
# Prepare train_sample (the question to be answered) and the candidate_samples (the prompts to be selected)
for ii in range(len(data['candidate_samples'])):
data['candidate_samples'][ii] = data['candidate_samples'][ii][0]
output = self._collect_model.forward(self._cfg.shot_number, data['train_sample'], data['candidate_samples'])
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> dict:
r"""
Overview:
Generate dict type transition data from inputs.
Arguments:
- obs (:obj:`Any`): Env observation
- model_output (:obj:`dict`): Output of collect model, including at least ['action']
- timestep (:obj:`namedtuple`): Output after env step, including at least ['obs', 'reward', 'done'] \
(here 'obs' indicates obs after env step).
Returns:
- transition (:obj:`dict`): Dict type transition data.
"""
return {
'obs': obs,
'action': model_output['action'],
'reward': timestep.reward,
'done': timestep.done,
}
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
r"""
Overview:
Get the trajectory and the n step return data, then sample from the n_step return data
Arguments:
- data (:obj:`list`): The trajectory's buffer list
Returns:
- samples (:obj:`dict`): The training samples generated
"""
if self._cfg.learn.ignore_done:
raise NotImplementedError
R = 0.
for i in reversed(range(len(data))):
R = self._gamma * R + data[i]['reward']
data[i]['return'] = R
return get_train_sample(data, self._unroll_len)
def _init_eval(self) -> None:
self._eval_model = model_wrap(self._model, wrapper_name='combination_argmax_sample')
def _forward_eval(self, data: dict) -> dict:
data_id = list(data.keys())
data = default_collate(list(data.values()))
self._model.eval()
with torch.no_grad():
# Prepare train_sample (the question to be answered) and the candidate_samples (the prompts to be selected)
for ii in range(len(data['candidate_samples'])):
data['candidate_samples'][ii] = data['candidate_samples'][ii][0]
output = self._eval_model.forward(self._cfg.shot_number, data['train_sample'], data['candidate_samples'])
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _monitor_vars_learn(self) -> List[str]:
return super()._monitor_vars_learn() + ['policy_loss', 'entropy_loss', 'return_abs_max', 'grad_norm']
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