X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=870ab95e913e7597a07494ba40dd595200ae1f4c;hb=08b58304225e044a21419dd30302d985acc1824c;hp=56c2b0fd3f8f5b54eb80116ed96aa20581044279;hpb=62ad2378c60cdf322c0111279bd45fbef8365fc2;p=picoclvr.git diff --git a/tasks.py b/tasks.py index 56c2b0f..870ab95 100755 --- a/tasks.py +++ b/tasks.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import math, os, tqdm +import math, os, tqdm, warnings import torch, torchvision @@ -27,6 +27,7 @@ def masked_inplace_autoregression( ar_mask, deterministic_synthesis, forbidden_tokens=None, + logit_biases=None, progress_bar_desc="autoregression", device=torch.device("cpu"), ): @@ -48,7 +49,11 @@ def masked_inplace_autoregression( 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) @@ -1888,12 +1893,17 @@ class Escape(Task): states, actions, rewards = escape.generate_episodes( nb_train_samples + nb_test_samples, height, width, T, nb_walls ) - seq = escape.episodes2seq(states, actions, rewards, lookahead_delta=T) + 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) - self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + 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"} @@ -1905,21 +1915,24 @@ class Escape(Task): 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): - return self.nb_codes + 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 - it_len = state_len + 3 # state / action / reward / lookahead_reward + snapshots = [] - def ar(result, ar_mask): + def ar(result, ar_mask, logit_biases=None): ar_mask = ar_mask.expand_as(result) result *= 1 - ar_mask masked_inplace_autoregression( @@ -1927,50 +1940,64 @@ class Escape(Task): self.batch_size, result, ar_mask, - deterministic_synthesis, + deterministic_synthesis=deterministic_synthesis, + logit_biases=logit_biases, device=self.device, progress_bar_desc=None, ) + warnings.warn("keeping thinking snapshots", RuntimeWarning) + snapshots.append(result[:10].detach().clone()) # Generate iteration after iteration + result = self.test_input[:250].clone() + result[:, self.it_len :] = -1 + 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", ): - # Put the lookahead reward to either 0 or -1 for the - # current iteration, sample the next state - s = -1 # (torch.rand(result.size(0), device = result.device) < 0.2).long() - result[:, u - 1] = s + 1 + escape.first_lookahead_rewards_code - ar_mask = (t >= u).long() * (t < u + state_len).long() + # 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) - # Put the lookahead reward to +1 for the current - # iteration, sample the action and reward - s = 1 - result[:, u - 1] = s + 1 + escape.first_lookahead_rewards_code - ar_mask = (t >= u + state_len).long() * (t < u + state_len + 2).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) - # Fix the previous lookahead rewards in a consistant state - for v in range(0, u, it_len): - # Extract the rewards - r = result[:, range(v + state_len + 1 + it_len, u + it_len - 1, it_len)] - r = r - escape.first_rewards_code - 1 - a = r.min(dim=1).values - b = r.max(dim=1).values - s = (a < 0).long() * a + (a >= 0).long() * b - result[:, v + state_len + 2] = ( - s + 1 + escape.first_lookahead_rewards_code - ) + 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 - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True - ) + 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: @@ -1980,16 +2007,16 @@ class Escape(Task): def produce_results( self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 ): - result = self.test_input[:100].clone() + result = self.test_input[:250].clone() # Saving the ground truth - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True + 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: @@ -2016,12 +2043,12 @@ class Escape(Task): # Saving the generated sequences - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True + 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: