#!/usr/bin/env python
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
import math, os, tqdm
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
+
######################################################################
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
+ assert input.size() == ar_mask.size()
+
batches = zip(input.split(batch_size), ar_mask.split(batch_size))
if progress_bar_desc is not None:
batches,
dynamic_ncols=True,
desc=progress_bar_desc,
- total=input.size(0) // batch_size,
+ total=(input.size(0) + batch_size - 1) // batch_size,
)
- for input, ar_mask in batches:
- model.masked_inplace_autoregression(
- input, ar_mask, forbidden_tokens, deterministic_synthesis
- )
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, ar_mask in batches:
+ model.masked_inplace_autoregression(
+ input, ar_mask, forbidden_tokens, deterministic_synthesis
+ )
+
+ model.train(t)
+
+
+######################################################################
class Task:
pass
+######################################################################
+
+
+class Problem:
+ def generate_sequences(self, nb):
+ pass
+
+ def seq2str(self, seq):
+ return "[NOT IMPLEMENTED]"
+
+
+####################
+
+
+class ProblemLevel0(Problem):
+ def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
+ self.seq = torch.randint(10, (nb_sentences, 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
+
+
+class ProblemLevel1(Problem):
+ def __init__(self, nb_operators=100, len_source=5, len_result=8):
+ self.len_source = len_source
+ self.len_result = len_result
+ self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
+ self.operators = F.one_hot(
+ torch.rand(nb_operators, len_result, len_source).argmax(-1),
+ num_classes=len_source,
+ )
+
+ def generate_sequences(self, nb):
+ nb_operators = torch.randint(self.operators.size(0), (nb,))
+ operators = self.operators[nb_operators]
+ nb_operators = (
+ nb_operators[:, None]
+ // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
+ ) % 10
+ marker1 = torch.full((nb, 1), 10)
+ # source = torch.randint(10, (nb, self.len_source))
+ source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+ marker2 = torch.full((nb, 1), 11)
+ result = operators.bmm(source[:, :, None]).squeeze(-1)
+ sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
+ ar_mask = (sequences == 11).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join("0123456789|>"[x.item()] for x in seq)
+
+
+class ProblemLevel2(Problem):
+ def __init__(self, len_source=5, len_result=8):
+ self.len_source = len_source
+ self.len_result = len_result
+
+ def generate_sequences(self, nb):
+ operators = F.one_hot(
+ torch.rand(nb, self.len_result, self.len_source).argmax(-1),
+ num_classes=self.len_source,
+ )
+ source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+ # source1 = torch.randint(10, (nb, self.len_source))
+ marker1 = torch.full((nb, 1), 10)
+ result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
+ marker2 = torch.full((nb, 1), 11)
+ source2 = torch.randint(10, (nb, self.len_source))
+ marker3 = torch.full((nb, 1), 12)
+ result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
+
+ sequences = torch.cat(
+ (source1, marker1, result1, marker2, source2, marker3, result2), 1
+ )
+ ar_mask = (sequences == 12).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join("0123456789>|~"[x.item()] for x in seq)
+
+
+####################
+
+
+class ProblemAddition(Problem):
+ def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False):
+ self.nb_digits = nb_digits
+ self.zero_padded = zero_padded
+ self.inverted_result = inverted_result
+ self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")])
+ self.id2char = dict([(n, c) for c, n in self.char2id.items()])
+
+ def tensorize(self, strings):
+ len_max = max([len(x) for x in strings])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [self.char2id[c] for c in s + "$" * (len_max - len(s))]
+ for s in strings
+ ]
+ )
+ ],
+ 0,
+ )
+
+ def generate_sequences(self, nb):
+ sequences = []
+ for k in range(nb):
+ a, b = torch.randint(10**self.nb_digits, (2,))
+ c = a + b
+ a, b, c = str(a.item()), str(b.item()), str(c.item())
+ if self.zero_padded:
+ a = "0" * (self.nb_digits - len(a)) + a
+ b = "0" * (self.nb_digits - len(b)) + b
+ c = "0" * (self.nb_digits + 1 - len(c)) + c
+ if self.inverted_result:
+ c = c[::-1]
+ sequences.append(f"{a}+{b}={c}$")
+
+ sequences = self.tensorize(sequences)
+ ar_mask = (sequences == self.char2id["="]).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join(self.id2char[x.item()] for x in seq)
+
+
+# class ProblemUnion(Problem):
+# 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)
+
+####################
+
+
+class SandBox(Task):
+ def __init__(
+ self,
+ problem,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ logger=None,
+ device=torch.device("cpu"),
+ max_nb_codes=1024,
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+ self.problem = problem
+
+ self.train_input, self.train_ar_mask = self.problem.generate_sequences(
+ nb_train_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
+
+ # A bit of paranoia never hurts
+ assert (
+ self.nb_codes <= max_nb_codes
+ and self.train_input.min() >= 0
+ and self.test_input.min() >= 0
+ and tuple(self.train_ar_mask.unique()) == (0, 1)
+ and tuple(self.test_ar_mask.unique()) == (0, 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
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ 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,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ progress_bar_desc=None,
+ 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()
+
+ return nb_total, nb_correct
+
+ train_nb_total, train_nb_correct = compute_accuracy(
+ self.train_input, self.train_ar_mask
+ )
+
+ logger(
+ f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+ )
+
+ test_nb_total, test_nb_correct = compute_accuracy(
+ 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}%"
+ )
+
+
######################################################################
import picoclvr
pruner_train=None,
pruner_eval=None,
):
+ super().__init__()
+
def generate_descr(nb, cache_suffix, pruner):
return picoclvr.generate(
nb,
def __init__(
self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
):
+ super().__init__()
+
self.nb_train_samples = (nb_train_samples,)
self.nb_test_samples = (nb_test_samples,)
self.batch_size = batch_size
nb_walls,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.height = height
self.width = width
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- train_nb_total, train_nb_correct, count = self.compute_error(
- model,
- "train",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- logger(
- f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- )
-
- test_nb_total, test_nb_correct, count = self.compute_error(
- model,
- "test",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- 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}%"
- )
+ train_nb_total, train_nb_correct, count = self.compute_error(
+ model,
+ "train",
+ nb_to_use=1000,
+ deterministic_synthesis=deterministic_synthesis,
+ )
+ logger(
+ f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+ )
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
- ) as f:
- for i in range(count.size(0)):
- for j in range(count.size(1)):
- eol = " " if j < count.size(1) - 1 else "\n"
- f.write(f"{count[i,j]}{eol}")
-
- input = self.test_input[:48]
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ test_nb_total, test_nb_correct, count = self.compute_error(
+ model,
+ "test",
+ nb_to_use=1000,
+ deterministic_synthesis=deterministic_synthesis,
+ )
+ 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}%"
+ )
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
- maze.save_image(
- filename,
- mazes=mazes,
- target_paths=paths,
- predicted_paths=predicted_paths,
- path_correct=maze.path_correctness(mazes, predicted_paths),
- path_optimal=maze.path_optimality(paths, predicted_paths),
- )
- logger(f"wrote {filename}")
+ if count is not None:
+ proportion_optimal = count.diagonal().sum().float() / count.sum()
+ logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
+ with open(
+ os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
+ ) as f:
+ for i in range(count.size(0)):
+ for j in range(count.size(1)):
+ eol = " " if j < count.size(1) - 1 else "\n"
+ f.write(f"{count[i,j]}{eol}")
+
+ input = self.test_input[:48]
+ result = input.clone()
+ ar_mask = result.new_zeros(result.size())
+ ar_mask[:, self.height * self.width :] = 1
+ result *= 1 - ar_mask
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- model.train(t)
+ mazes, paths = self.seq2map(input)
+ _, predicted_paths = self.seq2map(result)
+
+ filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
+ maze.save_image(
+ filename,
+ mazes=mazes,
+ target_paths=paths,
+ predicted_paths=predicted_paths,
+ path_correct=maze.path_correctness(mazes, predicted_paths),
+ path_optimal=maze.path_optimality(paths, predicted_paths),
+ )
+ logger(f"wrote {filename}")
######################################################################
prompt_length,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.height = height
self.width = width
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input, prior_visits):
- result = input.clone()
- i = torch.arange(result.size(1), device=result.device)[None, :]
- ar_mask = (
- torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
- .long()
- .expand_as(result)
- )
- result *= 1 - ar_mask
-
- # snake.solver(result,ar_mask)
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- nb_total = ((prior_visits > 0) * ar_mask).sum()
-
- nb_correct = (
- (result == input).long() * (prior_visits > 0) * ar_mask
- ).sum()
-
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+ def compute_nb_correct(input, prior_visits):
+ result = input.clone()
+ i = torch.arange(result.size(1), device=result.device)[None, :]
+ ar_mask = (
+ torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
+ .long()
+ .expand_as(result)
+ )
+ result *= 1 - ar_mask
- return nb_total, nb_correct
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
+ nb_total = ((prior_visits > 0) * ar_mask).sum()
- # logger(
- # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- # )
+ nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
+ return nb_total, nb_correct
- 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}%"
- )
+ test_nb_total, test_nb_correct = compute_nb_correct(
+ self.test_input[:1000], self.test_prior_visits[:1000]
+ )
- model.train(t)
+ 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}%"
+ )
######################################################################
fraction_values_for_train=None,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.nb_steps = nb_steps
self.nb_stacks = nb_stacks
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input):
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ def compute_nb_correct(input):
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ errors = ((result != input).long() * ar_mask).reshape(
+ -1, 1 + self.nb_digits
+ )
+ ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
+
+ nb_total = ar_mask.max(1).values.sum()
+ nb_correct = nb_total - errors.max(1).values.sum()
+
+ return nb_total, nb_correct
+
+ test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+
+ 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}%"
+ )
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
+ ##############################################################
+ # Log a few generated sequences
+ input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+
+ # for n in range(result.size(0)):
+ # logger(
+ # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ # )
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ for n in range(result.size(0)):
+ logger(
+ f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ )
+ ##############################################################
+
+
+######################################################################
+
+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 + ["<nul>"] * (len_max - len(s))
+ ]
+ for s in sequences
+ ]
)
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
+ ],
+ 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")
+ ]
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
+ symbols = list(
+ set(["<nul>"] + [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()])
- return nb_total, nb_correct
+ self.t_nul = self.token2id["<nul>"]
+ self.t_input = self.token2id["<in>"]
+ self.t_output = self.token2id["<out>"]
+ self.t_prog = self.token2id["<prg>"]
+ self.t_end = self.token2id["<end>"]
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+ 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_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/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}%"
)
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ 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 not None:
+ input = self.test_input[:1]
result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- for n in range(result.size(0)):
- logger(
- f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- for n in range(result.size(0)):
- logger(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
- ##############################################################
+ 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
- model.train(t)
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+ model.record_attention(True)
+ model(BracketedSequence(result))
+ model.train(t)
+ ram = model.retrieve_attention()
+ model.record_attention(False)
+
+ tokens_output = [self.id2token[i.item()] for i in result[0]]
+ tokens_input = ["n/a"] + tokens_output[:-1]
+ for n_head in range(ram[0].size(1)):
+ filename = 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,
+ token_gap=12,
+ layer_gap=50,
+ k_top=10,
+ # min_total_attention=0.9,
+ )
+ logger(f"wrote {filename}")
######################################################################
nb_test_samples,
nb_variables,
sequence_length,
+ operand_max,
+ result_max,
batch_size,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.device = device
nb_train_samples,
nb_variables=nb_variables,
length=sequence_length,
- # length=2 * sequence_length,
- # randomize_length=True,
+ operand_max=operand_max,
+ result_max=result_max,
)
+
test_sequences = expr.generate_sequences(
nb_test_samples,
nb_variables=nb_variables,
length=sequence_length,
+ operand_max=operand_max,
+ result_max=result_max,
)
symbols = list(set("#" + "".join(train_sequences + test_sequences)))
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
- if split == "train":
- last = (batch != self.filler).max(0).values.nonzero().max() + 3
- batch = batch[:, :last]
+ last = (batch != self.filler).max(0).values.nonzero().max() + 3
+ batch = batch[:, :last]
yield batch
def vocabulary_size(self):
deterministic_synthesis,
input_file=None,
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input):
- result = input.clone()
- ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ def compute_nb_correct(input):
+ result = input.clone()
+ s = (result == self.space).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.filler
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
+ nb_total = input.size(0)
+ nb_correct = (input == result).long().min(1).values.sum()
- #######################################################################
- # Comput predicted vs. true variable values
+ #######################################################################
+ # Comput predicted vs. true variable values
- nb_delta = torch.zeros(5, dtype=torch.int64)
- nb_missed = 0
+ nb_delta = torch.zeros(5, dtype=torch.int64)
+ nb_missed = 0
- values_input = expr.extract_results([self.seq2str(s) for s in input])
- values_result = expr.extract_results([self.seq2str(s) for s in result])
+ values_input = expr.extract_results([self.seq2str(s) for s in input])
+ values_result = expr.extract_results([self.seq2str(s) for s in result])
+ filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
+
+ with open(filename, "w") as f:
for i, r in zip(values_input, values_result):
for n, vi in i.items():
vr = r.get(n)
+ f.write(f"{vi} {-1 if vr is None else vr}\n")
+
if vr is None or vr < 0:
nb_missed += 1
else:
else:
nb_delta[d] += 1
- ######################################################################
+ ######################################################################
- return nb_total, nb_correct, nb_delta, nb_missed
+ return nb_total, nb_correct, nb_delta, nb_missed
- (
- test_nb_total,
- test_nb_correct,
- test_nb_delta,
- test_nb_missed,
- ) = compute_nb_correct(self.test_input[:1000])
+ (
+ test_nb_total,
+ test_nb_correct,
+ test_nb_delta,
+ test_nb_missed,
+ ) = compute_nb_correct(self.test_input[:10000])
- 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"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
- nb_total = test_nb_delta.sum() + test_nb_missed
- for d in range(test_nb_delta.size(0)):
- logger(
- f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
- )
+ nb_total = test_nb_delta.sum() + test_nb_missed
+ for d in range(test_nb_delta.size(0)):
logger(
- f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
)
+ logger(
+ f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ )
- ##############################################################
- # Log a few generated sequences
- if input_file is None:
- input = self.test_input[:10]
- else:
- with open(input_file, "r") as f:
- sequences = [e.strip() for e in f.readlines()]
- sequences = [s + " " + "#" * 50 for s in sequences]
- input = self.tensorize(sequences)
+ ##############################################################
+ # Log a few generated sequences
+ if input_file is None:
+ input = self.test_input[:10]
+ else:
+ with open(input_file, "r") as f:
+ sequences = [e.strip() for e in f.readlines()]
+ sequences = [s + " " + "#" * 50 for s in sequences]
+ input = self.tensorize(sequences)
- result = input.clone()
- ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- for n in range(result.size(0)):
- logger(f"test_before {self.seq2str(result[n])}")
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- logger(f"test_after {self.seq2str(result[n])} {comment}")
- logger(f"correct {self.seq2str(correct[n])}")
- ##############################################################
-
- model.train(t)
+ result = input.clone()
+ s = (result == self.space).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.filler
+
+ for n in range(result.size(0)):
+ logger(f"test_before {self.seq2str(result[n])}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ correct = (1 - ar_mask) * self.space + ar_mask * input
+ for n in range(result.size(0)):
+ comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
+ logger(f"test_after {self.seq2str(result[n])} {comment}")
+ logger(f"truth {self.seq2str(correct[n])}")
+ ##############################################################
+
+
+######################################################################
+
+import world
+
+
+class World(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ vqae_nb_epochs,
+ logger=None,
+ device=torch.device("cpu"),
+ device_storage=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+
+ (
+ train_frames,
+ train_action_seq,
+ test_frames,
+ test_action_seq,
+ self.frame2seq,
+ self.seq2frame,
+ ) = world.create_data_and_processors(
+ nb_train_samples,
+ nb_test_samples,
+ mode="first_last",
+ nb_steps=30,
+ nb_epochs=vqae_nb_epochs,
+ logger=logger,
+ device=device,
+ device_storage=device_storage,
+ )
+
+ train_frame_seq = self.frame2seq(train_frames).to(device_storage)
+ test_frame_seq = self.frame2seq(test_frames).to(device_storage)
+
+ nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
+ nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
+
+ self.len_frame_seq = train_frame_seq.size(1)
+ self.len_action_seq = train_action_seq.size(1)
+ self.nb_codes = nb_frame_codes + nb_action_codes
+
+ train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
+
+ train_action_seq += nb_frame_codes
+ self.train_input = torch.cat(
+ (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
+ )
+
+ test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
+ test_action_seq += nb_frame_codes
+ self.test_input = torch.cat(
+ (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 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
+ ):
+ yield batch.to(self.device)
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ k = torch.arange(
+ 2 * self.len_frame_seq + self.len_action_seq, device=self.device
+ )[None, :]
+
+ input = self.test_input[:64].to(self.device)
+ result = input.clone()
+
+ ar_mask = (
+ (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
+ )
+ result *= 1 - ar_mask
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ seq_start = input[:, : self.len_frame_seq]
+ seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
+ seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
+
+ result = torch.cat(
+ (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
+ )
+ result = result.reshape(-1, result.size(-1))
+
+ frames = self.seq2frame(result)
+ image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
+ torchvision.utils.save_image(
+ frames.float() / (world.Box.nb_rgb_levels - 1),
+ image_name,
+ nrow=12,
+ padding=1,
+ pad_value=0.0,
+ )
+ logger(f"wrote {image_name}")
######################################################################
projects / picoclvr.git / blobdiff