def teacherforcing_batch(self, batch: DictList, batch_lengths,
sketch_lengths,
recurrence) -> (DictList, DictList):
"""
:param batch: DictList object [bsz, seqlen]
:param batch_lengths: [bsz]
:param sketch_lengths: [bsz]
:param recurrence: an int
:return:
stats: A DictList of bsz, mem_size
extra_info: A DictList of extra info
"""
bsz, seqlen = batch.actions.shape[0], batch.actions.shape[1]
sketchs = batch.tasks
final_outputs = DictList({})
extra_info = DictList({})
mems = None
if self.is_recurrent:
mems = self.init_memory(sketchs, sketch_lengths)
for t in range(seqlen):
final_output = DictList({})
model_output = self.forward(batch.states[:, t], sketchs,
sketch_lengths, mems)
logprobs = model_output.dist.log_prob(batch.actions[:, t].float())
if 'log_end' in model_output:
# p_end + (1 - pend) action_prob
log_no_end_term = model_output.log_no_end + logprobs
logprobs = torch.logsumexp(torch.stack(
[model_output.log_end, log_no_end_term], dim=-1),
dim=-1)
final_output.log_end = model_output.log_end
final_output.logprobs = logprobs
if 'p' in model_output:
extra_info.append({'p': model_output.p})
final_outputs.append(final_output)
# Update memory
next_mems = None
if self.is_recurrent:
next_mems = model_output.mems
if (t + 1) % recurrence == 0:
next_mems = next_mems.detach()
mems = next_mems
# Stack on time dim
final_outputs.apply(lambda _tensors: torch.stack(_tensors, dim=1))
extra_info.apply(lambda _tensors: torch.stack(_tensors, dim=1))
sequence_mask = torch.arange(
batch_lengths.max().item(),
device=batch_lengths.device)[None, :] < batch_lengths[:, None]
final_outputs.loss = -final_outputs.logprobs
if 'log_end' in final_outputs:
batch_ids = torch.arange(bsz, device=batch.states.device)
final_outputs.loss[batch_ids, batch_lengths -
1] = final_outputs.log_end[batch_ids,
batch_lengths - 1]
final_outputs.apply(lambda _t: _t.masked_fill(~sequence_mask, 0.))
return final_outputs, extra_info
def run_batch(batch: DictList,
batch_lengths,
sketch_lengths,
bot: ModelBot,
mode='train') \
-> (DictList, torch.Tensor):
"""
:param batch: DictList object [bsz, seqlen]
:param bot: A model Bot
:param mode: 'train' or 'eval'
:return:
stats: A DictList of bsz, mem_size
"""
bsz, seqlen = batch.actions.shape[0], batch.actions.shape[1]
sketchs = batch.tasks
final_outputs = DictList({})
mems = None
if bot.is_recurrent:
mems = bot.init_memory(sketchs, sketch_lengths)
for t in range(seqlen):
final_output = DictList({})
model_output = bot.forward(batch.states[:, t], sketchs, sketch_lengths,
mems)
logprobs = model_output.dist.log_prob(batch.actions[:, t].float())
if 'log_end' in model_output:
# p_end + (1 - pend) action_prob
log_no_end_term = model_output.log_no_end + logprobs
logprobs = torch.logsumexp(torch.stack(
[model_output.log_end, log_no_end_term], dim=-1),
dim=-1)
final_output.log_end = model_output.log_end
final_output.logprobs = logprobs
final_outputs.append(final_output)
# Update memory
next_mems = None
if bot.is_recurrent:
next_mems = model_output.mems
if (t + 1) % FLAGS.il_recurrence == 0 and mode == 'train':
next_mems = next_mems.detach()
mems = next_mems
# Stack on time dim
final_outputs.apply(lambda _tensors: torch.stack(_tensors, dim=1))
sequence_mask = torch.arange(
batch_lengths.max().item(),
device=batch_lengths.device)[None, :] < batch_lengths[:, None]
final_outputs.loss = -final_outputs.logprobs
if 'log_end' in final_outputs:
batch_ids = torch.arange(bsz, device=batch.states.device)
final_outputs.loss[batch_ids, batch_lengths -
1] = final_outputs.log_end[batch_ids,
batch_lengths - 1]
final_outputs.apply(lambda _t: _t.masked_fill(~sequence_mask, 0.))
return final_outputs
