From: François Fleuret Date: Tue, 26 Mar 2024 08:00:00 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=08b58304225e044a21419dd30302d985acc1824c;p=picoclvr.git Update. --- diff --git a/tasks.py b/tasks.py index 57a4c39..870ab95 100755 --- a/tasks.py +++ b/tasks.py @@ -1915,13 +1915,13 @@ class Escape(Task): for batch in tqdm.tqdm( input.split(self.batch_size), dynamic_ncols=True, desc=desc ): - t = torch.arange(input.size(1), device=input.device)[None, :] - u = torch.randint(input.size(1), (input.size(0), 1), device=input.device) + 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() - input = lr_mask * escape.lookahead_reward2code(2) + (1 - lr_mask) * input + batch = lr_mask * escape.lookahead_reward2code(2) + (1 - lr_mask) * batch yield batch def vocabulary_size(self): @@ -1930,11 +1930,6 @@ class Escape(Task): 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, :] - - result[:, self.it_len :] = -1 - snapshots = [] def ar(result, ar_mask, logit_biases=None): @@ -1955,38 +1950,37 @@ class Escape(Task): # 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() + 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(self.it_len, result.size(1) - self.it_len + 1, self.it_len), + range(0, result.size(1), self.it_len), desc="thinking", ): - # Generate the lookahead_reward and state - ar_mask = (t % self.it_len == self.index_lookahead_reward).long() * ( - t <= u + self.index_lookahead_reward - ).long() - ar(result, ar_mask) - - # Generate the lookahead_reward and state - ar_mask = (t >= u + self.index_states).long() * ( - t < u + self.index_states + self.state_len - ).long() - ar(result, ar_mask) - - # Re-generate the lookahead_reward - ar_mask = (t % self.it_len == self.index_lookahead_reward).long() * ( - t <= u + self.index_lookahead_reward - ).long() - ar(result, ar_mask, logit_biases=optimistic_bias) - - # Generate the action and 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) + # 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: for n in range(10):