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
+
######################################################################
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)
-
-####################
+import problems
class SandBox(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 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}")
+
######################################################################
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(
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}%")
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}")
+
######################################################################
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)]
self.id2token = dict([(n, c) for c, n in self.token2id.items()])
self.t_nul = self.token2id["<nul>"]
- self.t_input = self.token2id["<input>"]
- self.t_output = self.token2id["<output>"]
- self.t_prog = self.token2id["<prog>"]
+ self.t_input = self.token2id["<in>"]
+ self.t_output = self.token2id["<out>"]
+ self.t_prog = self.token2id["<prg>"]
self.t_end = self.token2id["<end>"]
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, :
]
)
sum_nb_total, sum_nb_errors = 0, 0
- for x, y in zip(input, result):
- seq = [self.id2token[i.item()] for i in y]
+ 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 x]
+ 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])
)
sum_nb_total, sum_nb_errors = 0, 0
- for x, y, i, j in zip(input, result, last_output_idx, first_prog_idx):
- seq = [self.id2token[i.item()] for i in y]
+ 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 = (x - y).abs().max() == 0
+ 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 y[i : j + 1]]
- target_stack = [self.id2token[i.item()] for i in x[i : j + 1]]
+ 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])
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
)
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}")
+
######################################################################
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(
##############################################################
+######################################################################
+
+import grid
+
+
+class Grid(Task):
+ # Make a tensor from a list of strings
+ def tensorize(self, descr):
+ token_descr = [s.strip().split(" ") for s in descr]
+ l = max([len(s) for s in token_descr])
+ token_descr = [s + ["#"] * (l - len(s)) for s in token_descr]
+ id_descr = [[self.token2id[u] for u in s] for s in token_descr]
+ return torch.tensor(id_descr, device=self.device)
+
+ # Make a list of strings from a tensor
+ def detensorize(self, x):
+ return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
+
+ # trim all the tensors in the tuple z to remove as much token from
+ # left and right in the first tensor. If z is a tuple, all its
+ # elements are trimed according to the triming for the first
+ def trim(self, z, token="#"):
+ n = self.token2id[token]
+ if type(z) == tuple:
+ x = z[0]
+ i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
+ a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
+ return tuple([t[:, a:b] for t in z])
+ else:
+ i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
+ a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
+ return z[:, a:b]
+
+ ######################
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ size,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.device = device
+ self.batch_size = batch_size
+ self.grid_factory = grid.GridFactory(size=size)
+
+ if logger is not None:
+ logger(
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+ )
+
+ self.train_descr = self.grid_factory.generate_samples(
+ nb_train_samples, lambda r: tqdm.tqdm(r)
+ )
+ self.test_descr = self.grid_factory.generate_samples(
+ nb_test_samples, lambda r: tqdm.tqdm(r)
+ )
+
+ # Build the tokenizer
+ tokens = set()
+ for d in [self.train_descr, self.test_descr]:
+ for s in d:
+ for t in s.strip().split(" "):
+ tokens.add(t)
+ # make this set a sorted list to get the same tensors given
+ # the same descr
+ tokens = list(tokens)
+ tokens.sort()
+ tokens = ["#"] + tokens
+ self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
+ self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
+ self.t_nul = self.token2id["#"]
+ self.t_true = self.token2id["<true>"]
+ self.t_false = self.token2id["<false>"]
+
+ # Tokenize the train and test sets
+ self.train_input = self.tensorize(self.train_descr)
+ self.test_input = self.tensorize(self.test_descr)
+
+ def batches(self, split="train"):
+ assert split in {"train", "test"}
+ input = self.train_input if split == "train" else self.test_input
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
+ ):
+ yield self.trim(batch)
+
+ def vocabulary_size(self):
+ return len(self.token2id)
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ correct = self.test_input[:1000]
+ result = correct.clone()
+ ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
+ result *= 1 - ar_mask
+
+ for e in self.detensorize(result[:10]):
+ logger(f"test_before {e}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ for e in self.detensorize(result[:10]):
+ logger(f"test_after {e}")
+
+ nb_total = ar_mask.sum().item()
+ nb_correct = ((correct == result).long() * ar_mask).sum().item()
+
+ logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+ logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
+
+
######################################################################
import world
projects / picoclvr.git / blobdiff