From 0c6d29f73e35adbbaab1263de439f73efa98d99e Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Fri, 21 Jul 2023 00:14:16 +0200 Subject: [PATCH] Update. --- rpl.py | 14 ++++++++++---- tasks.py | 47 +++++++++++++++++++++++++++-------------------- 2 files changed, 37 insertions(+), 24 deletions(-) diff --git a/rpl.py b/rpl.py index f826fc4..b51edef 100755 --- a/rpl.py +++ b/rpl.py @@ -105,8 +105,10 @@ def decompose(seq): k = 0 while seq[k] == "": o = next_marker(seq, [""], start=k + 1) + if o is None: + raise ValueError("Missing output markers (should be correct in the prompt)") e = next_marker(seq, ["", ""], start=o) - if o is None or e is None: + if e is None: raise ValueError( "Missing input/output markers (should be correct in the prompt)" ) @@ -133,6 +135,12 @@ def decompose(seq): return prog, io +def stack_distance(target_stack, result_stack): + return abs(len(result_stack) - len(target_stack)) + sum( + [0 if x == y else 1 for x, y in zip(result_stack, target_stack)] + ) + + def compute_nb_errors(seq): prog, io = decompose(seq) @@ -152,9 +160,7 @@ def compute_nb_errors(seq): for start_stack, target_stack in io: result_stack = rpl_exec(prog, start_stack) nb_total += len(target_stack) - e = abs(len(result_stack) - len(target_stack)) + sum( - [0 if x == y else 1 for x, y in zip(result_stack, target_stack)] - ) + e = stack_distance(target_stack, result_stack) nb_errors += e stacks.append((start_stack, target_stack, result_stack, e == 0)) diff --git a/tasks.py b/tasks.py index 0827a44..da39a83 100755 --- a/tasks.py +++ b/tasks.py @@ -1182,9 +1182,13 @@ class RPL(Task): def compute_nb_errors_output(input, nb_to_log=0): result = input.clone() k = torch.arange(result.size(1), device=result.device)[None, :] - last_output_idx = ((result == self.t_output) * k).max(dim=1, keep_dim=True) - first_prog_idx = ((result == self.t_prog) * k).min(dim=1, keep_dim=True) - ar_mask = (k > last_output_idx).long() * (k < first_prog_idx) + last_output_idx = ( + ((result == self.t_output) * k).max(dim=1, keepdim=True).values + ) + first_prog_idx = ( + ((result == self.t_prog) * k).max(dim=1, keepdim=True).values + ) + ar_mask = (k > last_output_idx).long() * (k < first_prog_idx).long() result = (1 - ar_mask) * result + ar_mask * self.t_nul masked_inplace_autoregression( @@ -1197,25 +1201,20 @@ class RPL(Task): ) sum_nb_total, sum_nb_errors = 0, 0 - for x, y in zip(input, result): + for x, y, i, j in zip(input, result, last_output_idx, first_prog_idx): seq = [self.id2token[i.item()] for i in y] sum_nb_total += 1 - sum_nb_errors += 0 if (x - y).abs().max() == 0 else 1 + correct = (x - y).abs().max() == 0 + sum_nb_errors += 0 if correct else 1 if nb_to_log > 0: - gt_seq = [self.id2token[i.item()] for i in x] - _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq) - gt_prog = " ".join([str(x) for x in gt_prog]) - prog = " ".join([str(x) for x in prog]) - comment = "*" if nb_errors == 0 else "-" - logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]") - for start_stack, target_stack, result_stack, correct in stacks: - comment = "*" if correct else "-" - start_stack = " ".join([str(x) for x in start_stack]) - target_stack = " ".join([str(x) for x in target_stack]) - result_stack = " ".join([str(x) for x in result_stack]) - logger( - f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]" - ) + result_stack = [self.id2token[i.item()] for i in y[i : j + 1]] + target_stack = [self.id2token[i.item()] for i in x[i : j + 1]] + comment = "*" if correct else "-" + result_stack = " ".join([str(x) for x in result_stack]) + target_stack = " ".join([str(x) for x in target_stack]) + logger( + f"output_test {comment} [{target_stack}] PREDICTED [{result_stack}]" + ) nb_to_log -= 1 return sum_nb_total, sum_nb_errors @@ -1227,7 +1226,15 @@ class RPL(Task): ) logger( - f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" + f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" + ) + + test_nb_total, test_nb_errors = compute_nb_errors_output( + self.test_input[:1000].to(self.device), nb_to_log=10 + ) + + logger( + f"accuracy_output_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" ) -- 2.39.5