def __init__(self, opt: Opt, agents=None, shared=None):
super().__init__(opt, agents, shared)
self.batchsize = opt["batchsize"]
self.batch_agents = []
self.batch_acts = []
self.batch_goals = [] # for case when num_episodes < batchsize
self.batch_tod_world_metrics = []
for i in range(self.batchsize):
here_agents = []
for j, agent in enumerate(agents):
if (
j == SYSTEM_UTT_IDX
): # handle separately cause we expect it to be same as API_CALL agent
here_agents.append(here_agents[API_CALL_IDX])
continue
share = agent.share()
batch_opt = copy.deepcopy(share["opt"])
batch_opt["batchindex"] = i
here_agents.append(share["class"](batch_opt, share))
self.batch_agents.append(here_agents)
self.batch_acts.append([Message.padding_example()] * 4)
self.batch_tod_world_metrics.append(tod_metrics.TodMetrics())
self.end_episode = [False] * self.batchsize
self.max_turns = self.opt.get("max_turns", 30)
self.turns = 0
self.need_grounding = True
def _observe_and_act(
self, observe_idx, act_idx, info="for regular parley", override_act_idx=None
):
act_agent_idx = override_act_idx if override_act_idx else act_idx
act_agent = self.agents[act_agent_idx]
record_output_idx = act_idx
if hasattr(act_agent, "batch_act"):
batch_observations = []
for i in range(self.batchsize):
if not self.end_episode[i]:
observe = self.batch_acts[i][observe_idx]
observe = self.batch_agents[i][act_agent_idx].observe(observe)
batch_observations.append(Message(observe))
else:
# We're done with this episode, so just do a pad.
# NOTE: This could cause issues with RL down the line
batch_observations.append(Message.padding_example())
self.batch_acts[i][record_output_idx] = {"text": "", "id": ""}
batch_actions = act_agent.batch_act(batch_observations)
for i in range(self.batchsize):
if self.end_episode[i]:
continue
self.batch_acts[i][record_output_idx] = batch_actions[i]
self.batch_agents[i][record_output_idx].self_observe(batch_actions[i])
else: # Run on agents individually
for i in range(self.batchsize):
act_agent = (
self.batch_agents[i][override_act_idx]
if override_act_idx
else self.batch_agents[i][act_idx]
)
if hasattr(act_agent, "episode_done") and act_agent.episode_done():
self.end_episode[i] = True
if self.end_episode[i]:
# Following line exists because:
# 1. Code for writing converseations is not hapy if an "id" does not exists with a sample
# 2. Because of the `self.end_episode` code, no agent will see this example anyway.
self.batch_acts[i][record_output_idx] = {"text": "", "id": ""}
continue
act_agent.observe(self.batch_acts[i][observe_idx])
if isinstance(act_agent, LocalHumanAgent):
print(
f"Getting message for {SPEAKER_TO_NAME[record_output_idx]} for {info} in batch {i}"
)
try:
self.batch_acts[i][record_output_idx] = act_agent.act()
except StopIteration:
self.end_episode[i] = True
for i in range(self.batchsize):
if self.end_episode[i]:
continue
self.batch_tod_world_metrics[i].handle_message(
self.batch_acts[i][record_output_idx], SPEAKER_TO_NAME[act_agent_idx]
)
if tod.STANDARD_DONE in self.batch_acts[i][record_output_idx].get(
"text", ""
):
# User models trained to output a "DONE" on last turn; same with human agents.
self.end_episode[i] = True
