ar_mask,
deterministic_synthesis,
forbidden_tokens=None,
+ logit_biases=None,
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
for input, ar_mask in batches:
model.masked_inplace_autoregression(
- input, ar_mask, forbidden_tokens, deterministic_synthesis
+ input,
+ ar_mask,
+ deterministic_synthesis,
+ forbidden_tokens,
+ logit_biases,
)
model.train(t)
class TaskFromFile(Task):
- def tensorize(self, pairs):
+ def tensorize(self, pairs, shuffle):
len_max = max([len(x[0]) for x in pairs])
input = torch.cat(
0,
).to("cpu")
+ if shuffle:
+ i = torch.randperm(input.size(0))
+ input = input[i].contiguous()
+ pred_mask = pred_mask[i].contiguous()
+
return input, pred_mask
# trim all the tensors in the tuple z to remove as much token from
def __init__(
self,
- filename,
+ train_filename,
+ test_filename,
nb_train_samples,
nb_test_samples,
batch_size,
+ shuffle=False,
device=torch.device("cpu"),
):
self.batch_size = batch_size
self.device = device
- pairs = []
- with open(filename, "r") as f:
- for _ in range(nb_train_samples + nb_test_samples):
- sequence = f.readline().strip()
- pred_mask = f.readline().strip()
- assert len(sequence) == len(pred_mask)
- assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
- pairs.append((sequence, pred_mask))
-
- symbols = ["#"] + list(set("".join([x[0] for x in pairs])) - set(["#"]))
+ def read_file(filename, nb=-1):
+ pairs = []
+ with open(filename, "r") as f:
+ while True:
+ sequence = f.readline().strip()
+ if not sequence:
+ break
+ pred_mask = f.readline().strip()
+ assert len(sequence) == len(pred_mask)
+ assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
+ pairs.append((sequence, pred_mask))
+ if len(pairs) == nb:
+ break
+
+ if nb > 0:
+ pairs = pairs[:nb]
+ assert len(pairs) == nb
+
+ return pairs
+
+ train_pairs = read_file(train_filename, nb_train_samples)
+ test_pairs = read_file(test_filename, nb_test_samples)
+
+ symbols = ["#"] + list(
+ set("".join([x[0] for x in train_pairs + test_pairs])) - set(["#"])
+ )
self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
self.id2char = dict([(n, c) for c, n in self.char2id.items()])
self.train_input, self.train_pred_masks = self.tensorize(
- pairs[:nb_train_samples]
+ train_pairs, shuffle=shuffle
+ )
+ self.test_input, self.test_pred_masks = self.tensorize(
+ test_pairs, shuffle=shuffle
)
- self.test_input, self.test_pred_masks = self.tensorize(pairs[nb_train_samples:])
-
- assert self.train_input.size(0) == nb_train_samples
- assert self.test_input.size(0) == nb_test_samples
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
logger(f"----------------------------------------------------------")
- for e in self.tensor2str(result[:10]):
+ for e in self.tensor2str(result[:50]):
logger(f"test_before {e}")
masked_inplace_autoregression(
logger(f"----------------------------------------------------------")
- for e, c in zip(self.tensor2str(result[:10]), self.tensor2str(correct[:10])):
+ for e, c in zip(self.tensor2str(result[:50]), self.tensor2str(correct[:50])):
logger(f"test_after {e}")
logger(f"correct {c}")
######################################################################
+
+import escape
+
+
+class Escape(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ T,
+ nb_walls,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+ self.height = height
+ self.width = width
+
+ states, actions, rewards = escape.generate_episodes(
+ nb_train_samples + nb_test_samples, height, width, T, nb_walls
+ )
+ seq = escape.episodes2seq(states, actions, rewards)
+ # seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3]
+ self.train_input = seq[:nb_train_samples].to(self.device)
+ self.test_input = seq[nb_train_samples:].to(self.device)
+
+ 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
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return escape.nb_codes
+
+ 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
+
+ def ar(result, ar_mask, logit_biases=None):
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis=deterministic_synthesis,
+ logit_biases=logit_biases,
+ device=self.device,
+ progress_bar_desc=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)
+
+ snapshots = []
+
+ for u in tqdm.tqdm(
+ range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking"
+ ):
+ # Generate the lookahead_reward and state
+ ar_mask = (t >= u + index_lookahead_reward).long() * (
+ t < u + index_states + state_len
+ ).long()
+ ar(result, ar_mask)
+ snapshots.append(result[:10].detach().clone())
+ backup_lookahead_reward = result[:, u + index_lookahead_reward]
+
+ # Re-generate the lookahead_reward
+ ar_mask = (t == u + index_lookahead_reward).long()
+ ar(result, ar_mask, logit_biases=optimistic_bias)
+ snapshots.append(result[:10].detach().clone())
+
+ # Generate the action and reward
+ ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long()
+ ar(result, ar_mask)
+ snapshots.append(result[:10].detach().clone())
+
+ result[:, u + index_lookahead_reward] = backup_lookahead_reward
+
+ filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ for n in range(10):
+ for s in snapshots:
+ lr, s, a, r = escape.seq2episodes(
+ s[n : n + 1], self.height, self.width
+ )
+ str = escape.episodes2str(
+ lr, s, a, r, unicode=True, ansi_colors=True
+ )
+ f.write(str)
+ f.write("\n\n")
+
+ # Saving the generated sequences
+
+ lr, s, a, r = escape.seq2episodes(result, self.height, self.width)
+ str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ result = self.test_input[:250].clone()
+
+ # Saving the ground truth
+
+ lr, s, a, r = escape.seq2episodes(
+ result,
+ self.height,
+ self.width,
+ )
+ str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ # Re-generating from the first frame
+
+ ar_mask = (
+ torch.arange(result.size(1), device=result.device)
+ >= self.height * self.width + 3
+ ).long()[None, :]
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ # Saving the generated sequences
+
+ lr, s, a, r = escape.seq2episodes(
+ result,
+ self.height,
+ self.width,
+ )
+ str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ self.thinking_autoregression(
+ n_epoch, model, result_dir, logger, deterministic_synthesis, nmax
+ )
+
+
+######################################################################
projects / picoclvr.git / blobdiff