X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=5019aed3b0953a7037bc213296ca7371d1b3c279;hb=6045e9a7dd61f0dab60bd1c6ff71f6bd5c32778b;hp=fb85576a55168bc48ade65d90b4bddf6561bf33b;hpb=a2ffcd9b27aa0f3cc0b56090a32e88b73dfa0a54;p=picoclvr.git diff --git a/tasks.py b/tasks.py index fb85576..5019aed 100755 --- a/tasks.py +++ b/tasks.py @@ -1,5 +1,10 @@ #!/usr/bin/env python +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + import math, os, tqdm import torch, torchvision @@ -7,6 +12,13 @@ import torch, torchvision from torch import nn from torch.nn import functional as F +from mygpt import BracketedSequence + +try: + from graph import save_attention_image +except ImportError: + save_attention_image = None + ###################################################################### @@ -29,7 +41,7 @@ def masked_inplace_autoregression( batches, dynamic_ncols=True, desc=progress_bar_desc, - # total=input.size(0) // batch_size, + total=(input.size(0) + batch_size - 1) // batch_size, ) with torch.autograd.no_grad(): @@ -60,43 +72,9 @@ class Task: pass -###################################################################### - - -class Problem: - def generate_sequences(self, nb): - pass - - def log_performance(self, sequences, logger): - pass +#################### - -class ProblemByheart(Problem): - def __init__(self): - nb_seq, len_prompt, len_result = 100, 5, 5 - self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result)) - self.seq[:, len_prompt] = 10 - - def generate_sequences(self, nb): - sequences = self.seq[torch.randint(self.seq.size(0), (nb,))] - ar_mask = (sequences == 10).long() - ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) - return sequences, ar_mask - - # problems = [ProblemByheart()] - # nb_common_codes = 100 - - # def generate_sequences(nb_samples): - # problem_indexes = torch.randint(len(problems), (nb_samples,)) - # nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0) - # print(f"{nb_samples_per_problem}") - # all_seq = [] - # for nb, p in zip(nb_samples_per_problem, problems): - # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb])) - # return all_seq - - # for strain, stest in zip(train_seq, test_seq): - # s = torch.cat((strain, stest), 0) +import problems class SandBox(Task): @@ -114,11 +92,21 @@ class SandBox(Task): self.batch_size = batch_size self.device = device + self.problem = problem - self.train_input, self.train_ar_mask = problem.generate_sequences( + self.train_input, self.train_ar_mask = self.problem.generate_sequences( nb_train_samples ) - self.test_input, self.test_ar_mask = problem.generate_sequences(nb_test_samples) + self.test_input, self.test_ar_mask = self.problem.generate_sequences( + nb_test_samples + ) + + self.train_input, self.train_ar_mask = self.train_input.to( + device + ), self.train_ar_mask.to(device) + self.test_input, self.test_ar_mask = self.test_input.to( + device + ), self.test_ar_mask.to(device) self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 @@ -147,10 +135,12 @@ class SandBox(Task): return self.nb_codes def produce_results( - self, n_epoch, model, result_dir, logger, deterministic_synthesis + self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 ): - def compute_accuracy(input, ar_mask): + def compute_accuracy(input, ar_mask, logger=None): + input, ar_mask = input[:nmax], ar_mask[:nmax] result = input.clone() * (1 - ar_mask) + masked_inplace_autoregression( model, self.batch_size, @@ -161,6 +151,15 @@ class SandBox(Task): device=self.device, ) + if logger is not None: + for sp, st in zip(result[:10], input[:10]): + logger( + f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}" + ) + logger( + f" {n_epoch} ground truth {self.problem.seq2str(st)}" + ) + nb_total = ar_mask.sum().item() nb_correct = ((result == input).long() * ar_mask).sum().item() @@ -175,13 +174,50 @@ class SandBox(Task): ) test_nb_total, test_nb_correct = compute_accuracy( - self.test_input, self.test_ar_mask + self.test_input, self.test_ar_mask, logger ) logger( f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + + if save_attention_image is None: + logger("no save_attention_image (is pycairo installed?)") + else: + for k in range(10): + ns = torch.randint(self.test_input.size(0), (1,)).item() + input = self.test_input[ns : ns + 1].clone() + + with torch.autograd.no_grad(): + t = model.training + model.eval() + model.record_attention(True) + model(BracketedSequence(input)) + model.train(t) + ram = model.retrieve_attention() + model.record_attention(False) + + tokens_output = [c for c in self.problem.seq2str(input[0])] + tokens_input = ["n/a"] + tokens_output[:-1] + for n_head in range(ram[0].size(1)): + filename = os.path.join( + result_dir, f"sandbox_attention_{k}_h{n_head}.pdf" + ) + attention_matrices = [m[0, n_head] for m in ram] + save_attention_image( + filename, + tokens_input, + tokens_output, + attention_matrices, + k_top=10, + # min_total_attention=0.9, + token_gap=12, + layer_gap=50, + ) + logger(f"wrote {filename}") + ###################################################################### @@ -337,6 +373,10 @@ class PicoCLVR(Task): f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%" ) + logger( + f"main_test_accuracy {n_epoch} {1-nb_missing_properties/nb_requested_properties}" + ) + ###################################################################### def produce_results( @@ -607,6 +647,8 @@ class Maze(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + if count is not None: proportion_optimal = count.diagonal().sum().float() / count.sum() logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%") @@ -746,6 +788,8 @@ class Snake(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + ###################################################################### @@ -855,6 +899,8 @@ class Stack(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + ############################################################## # Log a few generated sequences input = self.test_input[:10, : 12 * (1 + self.nb_digits)] @@ -883,6 +929,297 @@ class Stack(Task): ############################################################## +###################################################################### + +import rpl + + +class RPL(Task): + def tensorize(self, sequences): + len_max = max([len(x) for x in sequences]) + return torch.cat( + [ + torch.tensor( + [ + [ + self.token2id[str(c)] + for c in s + [""] * (len_max - len(s)) + ] + for s in sequences + ] + ) + ], + 0, + ) + + def seq2str(self, seq): + return " ".join([self.id2token[i] for i in seq]) + + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + nb_starting_values=3, + max_input=9, + prog_len=6, + nb_runs=5, + no_prog=False, + logger=None, + device=torch.device("cpu"), + ): + super().__init__() + + self.batch_size = batch_size + self.device = device + self.no_prog = no_prog + + train_sequences = [ + rpl.generate( + nb_starting_values=nb_starting_values, + nb_result_values_max=4 * nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) + for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data") + ] + + test_sequences = [ + rpl.generate( + nb_starting_values=nb_starting_values, + nb_result_values_max=4 * nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) + for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data") + ] + + symbols = list( + set([""] + [x for l in train_sequences + test_sequences for x in l]) + ) + val_max = max([x if type(x) is int else 0 for x in symbols]) + symbols = list(filter(lambda x: type(x) is str, symbols)) + symbols.sort() + symbols += [str(n) for n in range(val_max + 1)] + self.token2id = dict([(c, n) for n, c in enumerate(symbols)]) + self.id2token = dict([(n, c) for c, n in self.token2id.items()]) + + self.t_nul = self.token2id[""] + self.t_input = self.token2id[""] + self.t_output = self.token2id[""] + self.t_prog = self.token2id[""] + self.t_end = self.token2id[""] + + self.train_input = self.tensorize(train_sequences) + self.test_input = self.tensorize(test_sequences) + + if no_prog: + # Excise the program from every train and test example + k = torch.arange(self.train_input.size(1), device=self.train_input.device)[ + None, : + ] + p = ( + ((self.train_input == self.t_prog).long() * k) + .max(1, keepdim=True) + .values + ) + self.train_input = ( + self.train_input * (k <= p).long() + + self.t_end * (k == p + 1).long() + + self.t_nul * (k > p + 1).long() + ) + k = torch.arange(self.test_input.size(1), device=self.test_input.device)[ + None, : + ] + p = ( + ((self.test_input == self.t_prog).long() * k) + .max(1, keepdim=True) + .values + ) + self.test_input = ( + self.test_input * (k <= p).long() + + self.t_end * (k == p + 1).long() + + self.t_nul * (k > p + 1).long() + ) + + if logger is not None: + logger(f"value_max {val_max}") + for x in self.train_input[:25]: + end = (x != self.t_nul).nonzero().max().item() + 1 + seq = [self.id2token[i.item()] for i in x[:end]] + s = " ".join(seq) + logger(f"example_seq {s}") + + self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + + 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 + ): + last = (batch != self.t_nul).max(0).values.nonzero().max() + 3 + batch = batch[:, :last].to(self.device) + yield batch + + def vocabulary_size(self): + return self.nb_codes + + def produce_results( + self, n_epoch, model, result_dir, logger, deterministic_synthesis + ): + # -------------------------------------------------------------------- + def compute_nb_errors_prog(input, nb_to_log=0): + result = input.clone() + s = (result == self.t_prog).long() + ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1) + result = (1 - ar_mask) * result + ar_mask * self.t_nul + + masked_inplace_autoregression( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) + + sum_nb_total, sum_nb_errors = 0, 0 + for one_input, one_result in zip(input, result): + seq = [self.id2token[i.item()] for i in one_result] + nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq) + sum_nb_total += 1 + sum_nb_errors += 0 if nb_errors == 0 else 1 + if nb_to_log > 0: + gt_seq = [self.id2token[i.item()] for i in one_input] + _, _, 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}]" + ) + nb_to_log -= 1 + + return sum_nb_total, sum_nb_errors + + # -------------------------------------------------------------------- + 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, 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( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) + + sum_nb_total, sum_nb_errors = 0, 0 + for one_input, one_result, i, j in zip( + input, result, last_output_idx, first_prog_idx + ): + seq = [self.id2token[i.item()] for i in one_result] + sum_nb_total += 1 + correct = (one_input - one_result).abs().max() == 0 + sum_nb_errors += 0 if correct else 1 + if nb_to_log > 0: + result_stack = [ + self.id2token[i.item()] for i in one_result[i : j + 1] + ] + target_stack = [ + self.id2token[i.item()] for i in one_input[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 + + # -------------------------------------------------------------------- + + if not self.no_prog: + test_nb_total, test_nb_errors = compute_nb_errors_prog( + self.test_input[:1000].to(self.device), nb_to_log=10 + ) + + logger( + 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}%" + ) + + logger(f"main_test_accuracy {n_epoch} {1-test_nb_errors/test_nb_total}") + + 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}%" + ) + + if save_attention_image is None: + logger("no save_attention_image (is pycairo installed?)") + else: + ns = torch.randint(self.test_input.size(0), (1,)).item() + input = self.test_input[ns : ns + 1].clone() + last = (input != self.t_nul).max(0).values.nonzero().max() + 3 + input = input[:, :last].to(self.device) + + with torch.autograd.no_grad(): + t = model.training + model.eval() + model.record_attention(True) + model(BracketedSequence(input)) + model.train(t) + ram = model.retrieve_attention() + model.record_attention(False) + + tokens_output = [self.id2token[i.item()] for i in input[0]] + tokens_input = ["n/a"] + tokens_output[:-1] + for n_head in range(ram[0].size(1)): + filename = os.path.join( + result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf" + ) + attention_matrices = [m[0, n_head] for m in ram] + save_attention_image( + filename, + tokens_input, + tokens_output, + attention_matrices, + k_top=10, + # min_total_attention=0.9, + token_gap=12, + layer_gap=50, + ) + logger(f"wrote {filename}") + + ###################################################################### @@ -1036,6 +1373,8 @@ class Expr(Task): f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}") + nb_total = test_nb_delta.sum() + test_nb_missed for d in range(test_nb_delta.size(0)): logger( @@ -1119,8 +1458,6 @@ class World(Task): device_storage=device_storage, ) - print(f"{train_action_seq.size()=}") - train_frame_seq = self.frame2seq(train_frames).to(device_storage) test_frame_seq = self.frame2seq(test_frames).to(device_storage) @@ -1132,7 +1469,7 @@ class World(Task): self.nb_codes = nb_frame_codes + nb_action_codes train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1) - print(f"{train_action_seq.device=} {nb_frame_codes.device=}") + train_action_seq += nb_frame_codes self.train_input = torch.cat( (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1 @@ -1191,7 +1528,6 @@ class World(Task): (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1 ) result = result.reshape(-1, result.size(-1)) - print(f"{result.size()=}") frames = self.seq2frame(result) image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")