self.train_input = seq[:nb_train_samples].to(self.device)
self.test_input = seq[nb_train_samples:].to(self.device)
+ self.state_len = self.height * self.width
+ self.index_lookahead_reward = 0
+ self.index_states = 1
+ self.index_action = self.state_len + 1
+ self.index_reward = self.state_len + 2
+ self.it_len = self.state_len + 3 # lookahead_reward / state / action / reward
+
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
+ t = torch.arange(batch.size(1), device=batch.device)[None, :]
+ u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
+ lr_mask = (t <= u).long() * (
+ t % self.it_len == self.index_lookahead_reward
+ ).long()
+
+ batch = lr_mask * escape.lookahead_reward2code(2) + (1 - lr_mask) * batch
yield batch
def vocabulary_size(self):
def thinking_autoregression(
self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- result = self.test_input[:250].clone()
- t = torch.arange(result.size(1), device=result.device)[None, :]
-
- state_len = self.height * self.width
- index_lookahead_reward = 0
- index_states = 1
- index_action = state_len + 1
- index_reward = state_len + 2
- it_len = state_len + 3 # lookahead_reward / state / action / reward
-
- result[:, it_len:] = -1
-
snapshots = []
def ar(result, ar_mask, logit_biases=None):
# Generate iteration after iteration
- optimistic_bias = result.new_zeros(escape.nb_codes, device=result.device)
- optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1)
- optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1)
+ result = self.test_input[:250].clone()
+ # Erase all the content but that of the first iteration
+ result[:, self.it_len :] = -1
+ # Set the lookahead_reward of the firs to UNKNOWN
+ result[:, self.index_lookahead_reward] = escape.lookahead_reward2code(2)
+
+ t = torch.arange(result.size(1), device=result.device)[None, :]
for u in tqdm.tqdm(
- range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking"
+ range(0, result.size(1), self.it_len),
+ desc="thinking",
):
- lr, _, _, _ = escape.seq2episodes(result[:, :u], self.height, self.width)
-
- # Generate the lookahead_reward and state
- ar_mask = (t % it_len == index_lookahead_reward).long() * (
- t <= u + index_lookahead_reward
+ # Generate the next state but keep the initial one, the
+ # lookahead_reward of previous iterations are set to
+ # UNKNOWN
+ if u > 0:
+ result[
+ :, u + self.index_lookahead_reward
+ ] = escape.lookahead_reward2code(2)
+ ar_mask = (t >= u + self.index_states).long() * (
+ t < u + self.index_states + self.state_len
+ ).long()
+ ar(result, ar_mask)
+
+ # Generate the action and reward with lookahead_reward to +1
+ result[:, u + self.index_lookahead_reward] = escape.lookahead_reward2code(1)
+ ar_mask = (t >= u + self.index_action).long() * (
+ t <= u + self.index_reward
).long()
ar(result, ar_mask)
- # Generate the lookahead_reward and state
- ar_mask = (t >= u + index_states).long() * (
- t < u + index_states + state_len
- ).long()
- ar(result, ar_mask)
-
- # Re-generate the lookahead_reward
- ar_mask = (t % it_len == index_lookahead_reward).long() * (
- t <= u + index_lookahead_reward
- ).long()
- ar(result, ar_mask, logit_biases=optimistic_bias)
-
- # Generate the action and reward
- ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long()
- ar(result, ar_mask)
+ # Set the lookahead_reward to UNKNOWN for the next iterations
+ result[:, u + self.index_lookahead_reward] = escape.lookahead_reward2code(2)
filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
with open(filename, "w") as f: