def _run_initial_turn(self) -> None:
"""
Show the image to the human and bot, and show the bot's response to the human.
"""
system_id = 'SYSTEM'
system_agent_idx = None
# Show the image to the human
image_act_for_human = {
'episode_done': False,
'id': system_id,
'text':
f"""Welcome! You'll now have a conversation with your partner.
<-- FIRST, YOUR PARTNER WILL SAY SOMETHING ABOUT THIS IMAGE TO YOUR LEFT.
Be sure to talk about this image a little bit before discussing other things!
""",
'task_data': {
'image_src': self.image_src
},
'agent_idx': system_agent_idx,
}
self.agent.observe(validate(image_act_for_human))
# Show the image to the bot
image_act = {
**self.image_act,
'episode_done': False,
'id': system_id,
'agent_idx': system_agent_idx,
}
self.bot.observe(validate(image_act))
del image_act['image']
# Don't save the image features to disk
# Have the bot respond
bot_first_act_raw = self.bot.act()
bot_first_act_raw = Message(
Compatibility.maybe_fix_act(
bot_first_act_raw)).json_safe_payload()
bot_first_act_raw['id'] = self.bot.agent_id
self.agent.observe(validate(bot_first_act_raw))
bot_first_act = {
'episode_done': False,
'id': bot_first_act_raw['id'],
'text': bot_first_act_raw['text'],
'agent_idx': 1,
}
# Record lines of dialogue
self.dialog.append(image_act)
self.dialog.append(bot_first_act)
def _run_initial_turn(self) -> None:
"""
Run the initial turn for both the human and the bot.
Optionally show the bot its persona. If we are in BST conversation mode, show 2
previous BST utterances to both the human and the bot; if we are in Meena-like
conversation mode, show "Hi!" to the human and the bot and let the bot respond
accordingly.
"""
control_msg = {"episode_done": False}
time.sleep(2)
coordinator_first_msg = {
'episode_done': False,
'id': 'Coordinator',
'text':
'Please chitchat with another worker for 6 turns as if you were catching up since last time you two spoke.',
'fake_start': True,
'agent_idx': 2,
'task_data': self.task_data,
}
self.agent.observe(validate(coordinator_first_msg))
time.sleep(2)
human_first_msg = {
'episode_done': False,
'id': self.agent.id,
'text': self.context_for_bot_prompt,
'fake_start': True,
'agent_idx': 0,
'task_data': self.task_data,
}
for k, v in control_msg.items():
human_first_msg[k] = v
# self.dialog.append(human_first_msg)
# self.agent.observe(validate(human_first_msg))
print(human_first_msg)
self.bot.observe(validate(human_first_msg))
first_bot_act = self.bot.act()
first_bot_act = Compatibility.maybe_fix_act(first_bot_act)
first_bot_act['id'] = 'THEY'
self.agent.observe(validate(first_bot_act))
bot_utterance_data = {
'agent_idx': 1,
'text': first_bot_act['text'],
'id': 'THEY',
}
self.dialog.append(bot_utterance_data)
def _run_initial_turn(self) -> None:
"""
Run the initial turn for both the human and the bot.
Optionally show the bot its persona. If we are in BST conversation mode, show 2
previous BST utterances to both the human and the bot; if we are in Meena-like
conversation mode, show "Hi!" to the human and the bot and let the bot respond
accordingly.
"""
control_msg = {"episode_done": False}
if self.opt['include_persona']:
# The Bot agent
# We add the personas and 1/3 of the time WoW topic as the
# first utterance in the history.
# Previously for BST task, we also had a big first utterance
# that gave instructions. Removing that for this task.
persona_strings = [s.strip() for s in self.personas[1]]
persona_utterance = self._get_persona_utterance(
persona_strings=persona_strings,
context_dataset=self.context_info['context_dataset'],
additional_context=self.context_info['additional_context'],
is_bot=True,
)
message = control_msg.copy()
message['text'] = persona_utterance
# The bot seeing its persona does not count as a "turn"
self.bot.observe(validate(message), increment_turn=False)
if self.opt['conversation_start_mode'] == 'bst':
print('[Displaying first utterances as per BST task.]')
# Display the previous two utterances
human_first_msg = {
'episode_done': False,
'id': self.agent.id,
'text': self.context_info['person1_seed_utterance'],
'fake_start': True,
'agent_idx': 0,
}
for k, v in control_msg.items():
human_first_msg[k] = v
bot_first_msg = {
'episode_done': False,
'id': self.bot.id,
'text': self.context_info['person2_seed_utterance'],
'fake_start': True,
'agent_idx': 1,
}
print(
f'human_first_msg: {human_first_msg}, bot_first_msg: {bot_first_msg}'
)
self.dialog.append(human_first_msg)
self.dialog.append(bot_first_msg)
for observer in [self.agent, self.bot]:
observer.observe(validate(human_first_msg))
observer.observe(validate(bot_first_msg))
elif self.opt['conversation_start_mode'] == 'hi':
print('[Displaying "Hi!" only as per Meena task.]')
human_first_msg = {
'episode_done': False,
'id': self.agent.id,
'text': 'Hi!',
'fake_start': True,
'agent_idx': 0,
}
for k, v in control_msg.items():
human_first_msg[k] = v
self.dialog.append(human_first_msg)
self.agent.observe(validate(human_first_msg))
self.bot.observe(validate(human_first_msg))
first_bot_act = self.bot.act()
first_bot_act = Compatibility.maybe_fix_act(first_bot_act)
self.agent.observe(validate(first_bot_act))
bot_utterance_data = {
'agent_idx': 1,
'text': first_bot_act['text'],
'id': first_bot_act['id'],
}
self.dialog.append(bot_utterance_data)
else:
raise ValueError(
f"Conversation start mode {self.opt['conversation_start_mode']} "
f"not recognized!")
def parley(self):
print(
f'{self.__class__.__name__}:{self.tag}: is at turn {self.task_turn_idx}, with {self.num_turns} pairs of turns needed...'
)
if self.task_turn_idx == 0:
self._run_initial_turn()
self.task_turn_idx += 1
return
"""Otherwise, we proceed accordingly"""
print(
f'{self.__class__.__name__}:{self.tag}: About to act with task turn idx: {self.task_turn_idx}'
)
acts = [None, None]
for idx, agent in enumerate([self.agent, self.bot]):
if not self.chat_done:
acts[idx] = agent.act(timeout=self.max_resp_time)
acts[idx] = Compatibility.maybe_fix_act(acts[idx])
if 'metrics' in acts[idx]:
del acts[idx]['metrics']
# Metrics can't be saved to JSON and are not needed here
print(
f'Got act for agent idx {idx}, act was: {acts[idx]} and self.task_turn_idx: {self.task_turn_idx}.'
)
if acts[idx].get('task_data', {}).get('final_rating') is not None:
self.chat_done = True
# agent ends chat after exceeding minimum number of turns
if self.task_turn_idx > self.num_turns:
# Human has just responded. Any problem data received now will be
# regarding the bot's prior utterance
p = acts[idx]['task_data'].get(
'problem_data_for_prior_message')
if p is not None:
turn_idx = -1
# Attach the problem data to the last utterance, since the human
# hasn't said anything since then
self.__add_problem_data_to_utterance(p,
turn_idx=turn_idx)
# Save the final chat data
time_string = time.strftime('%Y%m%d_%H%M%S')
chat_data_folder = self.opt['chat_data_folder']
os.makedirs(chat_data_folder, exist_ok=True)
chat_data_path = os.path.join(
chat_data_folder,
f'{time_string}_{np.random.randint(0, 1000)}_{self.task_type}.json',
)
final_chat_data = self.get_final_chat_data()
self.agent.mephisto_agent.state.messages.append(
{'final_chat_data': final_chat_data})
# Append the chat data directly to the agent state's message list in
# order to prevent the worker from seeing a new text response in the UI
with open(chat_data_path, 'w+') as f_json:
data_str = json.dumps(final_chat_data)
f_json.write(data_str)
print(f'{self.__class__.__name__}:{self.tag}: Data saved at '
f'{chat_data_path} for model: {self.bot.worker_id}.')
# Soft-block the worker if there were acceptability violations
acceptability_violations = final_chat_data[
'acceptability_violations'][0]
if (acceptability_violations is not None
and acceptability_violations != ''):
print(
f'**NOTE** Acceptability violations detected: {acceptability_violations}'
)
# Grant the failed qualification
self.agent.mephisto_agent.get_worker().grant_qualification(
self.block_qualification, 1)
return
else:
utterance_data = {
'agent_idx': idx,
# Get rid of annotations HTML if it's the bot response
'text': acts[idx]['text'].split('<br>')[0],
'id': acts[idx]['id'] if 'id' in acts[idx] else
'NULL_ID', # Person1 or Polyencoder
}
self.dialog.append(utterance_data)
if idx == 0:
# Human has just responded. Any problem data received now will be
# regarding the bot's prior utterance
p = acts[idx]['task_data'].get(
'problem_data_for_prior_message')
if p is not None:
turn_idx = -2
# Attach the problem data to the second-to-last utterance, since
# the last utterance is what the human just said
self.__add_problem_data_to_utterance(p,
turn_idx=turn_idx)
self._postprocess_acts(acts=acts, agent_idx=idx)
for other_agent in [self.agent, self.bot]:
if other_agent != agent:
other_agent.observe(validate(acts[idx]))
print(
f'[agent {idx}] self.task_turn_idx: {self.task_turn_idx}, self.dialog is: {self.dialog}'
)
self.task_turn_idx += 1
def parley(self):
"""
The main function that controls the logic of the task. Uses self.task_turn_idx
to control the sequence of the conversation.
Specifically, when self.task_turn_idx is even, we know that the bots just gave
their potential responses, and that it is the human's turn to choose one of the
responses and give a justification value.
When self.task_turn_idx is odd, we know that the human just chose one of the
bots' responses, and now needs to respond to that response.
self.task_turn_idx is initially 0, and during _run_initial_turn() the UI is
redrawn to have the human select between the bots' responses. Then,
self.task_turn_idx is incremented to 1.
During self.agent.observe(), the UI is redrawn for the following human input,
and during self.agent.act(), the code awaits the human input.
"""
logging.info(
f'{self.__class__.__name__}:{self.tag}: is at task_turn_idx '
f'{self.task_turn_idx}, with {self.num_turns} pairs of turns needed...'
)
if self.task_turn_idx == 0:
self._run_initial_turn()
self.task_turn_idx += 1
return
logging.info(
f'{self.__class__.__name__}:{self.tag}: About to act with task turn idx: '
f'{self.task_turn_idx}')
# At this point, we know that the human now needs to respond to the bot's
# response that the human just chose
# We retrieve information regarding the human's choice and justification using
# self.agent.act()
human_choose_bot_response_act = self.agent.act(
timeout=self.max_resp_time)
human_choose_bot_response_act = Message(
Compatibility.maybe_fix_act(
human_choose_bot_response_act)).json_safe_payload()
logging.info(
f'Got act for human, act was: {human_choose_bot_response_act} and '
f'self.task_turn_idx: {self.task_turn_idx}.')
accepted_bot_response = human_choose_bot_response_act['task_data'][
'accepted_bot_response']
accepted_bot_id = human_choose_bot_response_act['task_data'][
'accepted_bot_id']
accepted_bot_justification_value = human_choose_bot_response_act[
'task_data']['justification_value']
not_accepted_bot_response = human_choose_bot_response_act['task_data'][
'not_accepted_bot_response']
not_accepted_bot_id = human_choose_bot_response_act['task_data'][
'not_accepted_bot_id']
# We have both bots observe the accepted bot's response so that the conversation
# history stays the same
self.bots[0].observe(accepted_bot_response)
self.bots[1].observe(accepted_bot_response)
task_data = {}
accepted_bot_utterance_data = {
'text': accepted_bot_response['text'].split('<br>')[0],
'id': accepted_bot_id,
}
not_accepted_bot_utterance_data = {
'text': not_accepted_bot_response['text'].split('<br>')[0],
'id': not_accepted_bot_id,
}
bot_utterance_data = {
'agent_idx': 1,
'accepted_bot_data': accepted_bot_utterance_data,
'not_accepted_bot_data': not_accepted_bot_utterance_data,
'human_choice': accepted_bot_id,
'human_justification': accepted_bot_justification_value,
}
self.dialog.append(bot_utterance_data)
self._postprocess_acts(acts=None, agent_idx=0)
# All logic and processing for this step has now been done, so we do
# self.agent.observe() to send the accepted response back to the frontend to
# display and update task turn index, as well as await for the next action,
# which is the human typing their response
task_data['task_turn_idx'] = self.task_turn_idx
# The UI will ask the human to respond to the chosen bot response
self.agent.observe({
'text': accepted_bot_response['text'],
'task_data': task_data
})
# Make self.task_turn_idx even now
self.task_turn_idx += 1
# Check for whether 6 pairs of turns has been done, since the last message of a
# conversation should always be the bot's response
if (human_choose_bot_response_act is not None
and human_choose_bot_response_act.get(
'task_data', {}).get('finished') is not None):
self.chat_done = True
# agent ends chat after exceeding minimum number of turns
# Bot has just responded. Any problem data received now will be
# regarding this bot's utterance
# Get the final chat data
self.final_chat_data = self.get_final_chat_data()
# Soft-block the worker if there were acceptability violations
acceptability_violations = self.final_chat_data[
'acceptability_violations'][0]
if acceptability_violations is not None and acceptability_violations != '':
logging.info(f'**NOTE** Acceptability violations detected: '
f'{acceptability_violations}')
# Grant the failed qualification
self.agent.mephisto_agent.get_worker().grant_qualification(
self.block_qualification, 1)
return
logging.info(
f'[human agent] self.task_turn_idx: {self.task_turn_idx}, self.dialog is: '
f'{self.dialog}')
logging.info(
f'Got act for human, act was: {human_choose_bot_response_act} and '
f'self.task_turn_idx: {self.task_turn_idx}.')
# At this point, we know that the human now needs to respond to the bot's
# response that the human just chose
# We retrieve information regarding the human's response using self.agent.act()
human_response_act = self.agent.act(timeout=self.max_resp_time)
# Again, we have both bots observe the human response so that the conversation
# history stays the same
self.bots[0].observe(validate(human_response_act))
self.bots[1].observe(validate(human_response_act))
# Check that the models' conversation histories are the same
bot_1_history = self.bots[0].model_agent.history.history_strings
bot_2_history = self.bots[1].model_agent.history.history_strings
assert (
bot_1_history == bot_2_history
), f"The two bots' conversation histories are different.\nBot 1 history: {bot_1_history}\nBot 2 history: {bot_2_history}"
# After the bots have observed the human response, it's time for them to produce
# their response to the human using self.bots.act()
bot_1_response = self.bots[0].act()
bot_1_response = Compatibility.maybe_fix_act(bot_1_response)
bot_2_response = self.bots[1].act()
bot_2_response = Compatibility.maybe_fix_act(bot_2_response)
# We display the result to the frontend randomly so there is no selection bias.
# Also, we attach our result to task_data to send arbitrary data to the frontend
if random.random() > 0.5:
task_data = {
'top_bot_data': {
'top_bot_id': self.bots[0].worker_id,
'top_bot_response': bot_1_response,
},
'bottom_bot_data': {
'bottom_bot_id': self.bots[1].worker_id,
'bottom_bot_response': bot_2_response,
},
'task_turn_idx': self.task_turn_idx,
}
else:
task_data = {
'top_bot_data': {
'top_bot_id': self.bots[1].worker_id,
'top_bot_response': bot_2_response,
},
'bottom_bot_data': {
'bottom_bot_id': self.bots[0].worker_id,
'bottom_bot_response': bot_1_response,
},
'task_turn_idx': self.task_turn_idx,
}
human_utterance_data = {
'agent_idx':
0,
# Get rid of annotations HTML if it's the bot response
'text':
human_response_act['text'].split('<br>')[0],
'id':
human_response_act['id'] if 'id' in human_response_act else
'NULL_ID', # Person1 or Polyencoder
}
self.dialog.append(human_utterance_data)
# Human has just responded. Any problem data received now will be regarding the
# bot's prior utterance
p = human_response_act['task_data'].get(
'problem_data_for_prior_message')
if p is not None:
turn_idx = -2
# Attach the problem data to the second-to-last utterance, since the last
# utterance is what the human just said
self.__add_problem_data_to_utterance(p, turn_idx=turn_idx)
self._postprocess_acts(acts=None, agent_idx=0)
task_data['task_turn_idx'] = self.task_turn_idx
# All logic and processing for this step has now been done, so we do
# self.agent.observe() to send the two bots' responses back to the frontend to
# display and update task turn index, as well as await for the next action,
# which is the human choosing from the two responses and providing a
# justification value
# The UI will ask the human to choose between two bot responses and give a
# justification
logging.info(f'*** self.task_turn_idx: {self.task_turn_idx} ***')
self.agent.observe({'text': '', 'task_data': task_data})
# Make self.task_turn_idx odd now
self.task_turn_idx += 1
logging.info(
f'[bot agent] self.task_turn_idx: {self.task_turn_idx}, self.dialog is: '
f'{self.dialog}')
def _run_initial_turn(self) -> None:
"""
Run the initial turn for both the human and the bot.
Optionally show the bot its persona. If we are in Meena-like conversation mode
show "Hi!" to the human and the bot and let the bot respond accordingly.
Check parley() function for more information on the main logic.
"""
control_msg = {"episode_done": False}
if self.opt['include_persona']:
# The Bot agent
# We add the personas and 1/3 of the time WoW topic as the
# first utterance in the history.
# Previously for BST task, we also had a big first utterance
# that gave instructions. Removing that for this task.
persona_strings = [s.strip() for s in self.personas[1]]
persona_utterance = self._get_persona_utterance(
persona_strings=persona_strings,
context_dataset=self.context_info['context_dataset'],
additional_context=self.context_info['additional_context'],
is_bot=True,
)
message = control_msg.copy()
message['text'] = persona_utterance
# The bot seeing its persona does not count as a "turn"
self.bots[0].observe(validate(message), increment_turn=False)
self.bots[1].observe(validate(message), increment_turn=False)
if self.opt['conversation_start_mode'] == 'hi':
logging.info('[Displaying "Hi!" only as per Meena task.]')
if self.personas is not None:
human_persona_strings = [s.strip() for s in self.personas[0]]
else:
human_persona_strings = ['', '']
human_first_msg = {
'episode_done': False,
'id': self.agent.id,
'text': 'Hi!',
'fake_start': True,
'agent_idx': 0,
'task_data': {
'human_persona_string_1': human_persona_strings[0],
'human_persona_string_2': human_persona_strings[1],
'prompt_instruction': self.opt['task_question'],
},
}
for k, v in control_msg.items():
human_first_msg[k] = v
# The first message is always "Hi", so we have both bots observe the message
self.dialog.append(human_first_msg)
self.agent.observe(validate(human_first_msg))
self.bots[0].observe(validate(human_first_msg))
self.bots[1].observe(validate(human_first_msg))
bot_1_response = self.bots[0].act()
bot_1_response = Compatibility.maybe_fix_act(bot_1_response)
bot_2_response = self.bots[1].act()
bot_2_response = Compatibility.maybe_fix_act(bot_2_response)
if random.random() > 0.5:
task_data = {
'top_bot_data': {
'top_bot_id': self.bots[0].worker_id,
'top_bot_response': bot_1_response,
},
'bottom_bot_data': {
'bottom_bot_id': self.bots[1].worker_id,
'bottom_bot_response': bot_2_response,
},
'task_turn_idx': self.task_turn_idx,
}
else:
task_data = {
'top_bot_data': {
'top_bot_id': self.bots[1].worker_id,
'top_bot_response': bot_2_response,
},
'bottom_bot_data': {
'bottom_bot_id': self.bots[0].worker_id,
'bottom_bot_response': bot_1_response,
},
'task_turn_idx': self.task_turn_idx,
}
# Need an initial human's observe to observe the two choices from the bot
self.agent.observe({'text': '', 'task_data': task_data})
else:
raise ValueError(
f"Conversation start mode {self.opt['conversation_start_mode']} "
f"not recognized!")
def parley(self):
print(
f'{self.__class__.__name__}:{self.tag}: is at turn {self.task_turn_idx}, with {self.num_turns} pairs of turns needed...'
)
if self.task_turn_idx == 0:
self._run_initial_turn()
self.task_turn_idx += 1
return
"""Otherwise, we proceed accordingly"""
print(
f'{self.__class__.__name__}:{self.tag}: About to act with task turn idx: {self.task_turn_idx}'
)
acts = [None, None]
for idx, agent in enumerate([self.agent, self.bot]):
if not self.chat_done:
acts[idx] = agent.act(timeout=self.max_resp_time)
if (agent == self.bot and hasattr(self.bot, 'agent_id')
and self.bot.agent_id):
# Set speaker name as self.bot_agent_id otherwise, at frontend bot name such as "TransformerGenerator" would appear
Compatibility.backward_compatible_force_set(
acts[idx], 'id', self.bot.agent_id)
acts[idx] = Message(Compatibility.maybe_fix_act(
acts[idx])).json_safe_payload()
print(
f'Got act for agent idx {idx}, act was: {acts[idx]} and self.task_turn_idx: {self.task_turn_idx}.'
)
if acts[idx].get('task_data', {}).get('final_rating') is not None:
self.chat_done = True
# agent ends chat after exceeding minimum number of turns
# Human has just responded. Any problem data received now will be
# regarding the bot's prior utterance
turn_idx = -1
# Attach the problem data and final rating to the last utterance, since
# the human hasn't said anything since then
p = acts[idx]['task_data'].get(
'problem_data_for_prior_message')
if p is not None:
self.__add_problem_data_to_utterance(p, turn_idx=turn_idx)
self.dialog[turn_idx]['final_rating'] = acts[idx]['task_data'][
'final_rating']
# Save the final chat data
date_folder = time.strftime('%Y_%m_%d')
time_string = time.strftime('%Y%m%d_%H%M%S')
chat_data_subfolder = os.path.join(
self.opt['chat_data_folder'], date_folder)
os.makedirs(chat_data_subfolder, exist_ok=True)
chat_data_path = os.path.join(
chat_data_subfolder,
f'{time_string}_{np.random.randint(0, 1000)}_{self.task_type}.json',
)
self.final_chat_data = self.get_final_chat_data()
self.agent.mephisto_agent.state.messages.append({
'final_chat_data':
self.final_chat_data,
'data': {},
'packet_type':
None,
'timestamp':
None,
})
# Append the chat data directly to the agent state's message list in
# order to prevent the worker from seeing a new text response in the UI.
# Add some dummy keys for compatibility with all agent state messages
# TODO: remove this when no longer saving data to disk manually
with open(chat_data_path, 'w+') as f_json:
data_str = json.dumps(self.final_chat_data)
f_json.write(data_str)
print(f'{self.__class__.__name__}:{self.tag}: Data saved at '
f'{chat_data_path} for model: {self.bot.worker_id}.')
# Soft-block the worker if there were acceptability violations
acceptability_violations = self.final_chat_data[
'acceptability_violations'][0]
if (acceptability_violations is not None
and acceptability_violations != ''):
print(
f'**NOTE** Acceptability violations detected: {acceptability_violations}'
)
# Grant the failed qualification
self.agent.mephisto_agent.get_worker().grant_qualification(
self.block_qualification, 1)
return
else:
utterance_data = {
'agent_idx': idx,
# Get rid of annotations HTML if it's the bot response
'text': acts[idx]['text'].split('<br>')[0],
'id':
acts[idx].get('id',
'NULL_ID'), # In case model doesn't set id
}
self.dialog.append(utterance_data)
if idx == 0:
# Human has just responded. Any problem data received now will be
# regarding the bot's prior utterance
p = acts[idx]['task_data'].get(
'problem_data_for_prior_message')
if p is not None:
turn_idx = -2
# Attach the problem data to the second-to-last utterance, since
# the last utterance is what the human just said
self.__add_problem_data_to_utterance(p,
turn_idx=turn_idx)
self._postprocess_acts(acts=acts, agent_idx=idx)
for other_agent in [self.agent, self.bot]:
if other_agent != agent:
other_agent.observe(validate(acts[idx]))
print(
f'[agent {idx}] self.task_turn_idx: {self.task_turn_idx}, self.dialog is: {self.dialog}'
)
self.task_turn_idx += 1