#!/usr/bin/env python
-import math, os, tqdm
+# 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, warnings
import torch, torchvision
from torch import nn
from torch.nn import functional as F
+from mygpt import BracketedSequence
+
+# from graph import save_attention_image
+save_attention_image = None
+
######################################################################
ar_mask,
deterministic_synthesis,
forbidden_tokens=None,
+ logit_biases=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,
)
with torch.autograd.no_grad():
for input, ar_mask in batches:
model.masked_inplace_autoregression(
- input, ar_mask, forbidden_tokens, deterministic_synthesis
+ input,
+ ar_mask,
+ deterministic_synthesis,
+ forbidden_tokens,
+ logit_biases,
)
model.train(t)
class Task:
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
pass
def vocabulary_size(self):
pass
+class TaskFromFile(Task):
+ def tensorize(self, pairs, shuffle):
+ len_max = max([len(x[0]) for x in pairs])
+
+ input = torch.cat(
+ [
+ torch.tensor(
+ [
+ [self.char2id[c] for c in s[0] + "#" * (len_max - len(s[0]))]
+ for s in pairs
+ ]
+ )
+ ],
+ 0,
+ ).to("cpu")
+
+ pred_mask = torch.cat(
+ [
+ torch.tensor(
+ [
+ [int(c) for c in s[1] + "0" * (len_max - len(s[1]))]
+ for s in pairs
+ ]
+ )
+ ],
+ 0,
+ ).to("cpu")
+
+ if shuffle:
+ i = torch.randperm(input.size(0))
+ input = input[i].contiguous()
+ pred_mask = pred_mask[i].contiguous()
+
+ return input, pred_mask
+
+ # 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.char2id[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,
+ train_filename,
+ test_filename,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ shuffle=False,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ def read_file(filename, nb=-1):
+ pairs = []
+ with open(filename, "r") as f:
+ while True:
+ sequence = f.readline().strip()
+ if not sequence:
+ break
+ pred_mask = f.readline().strip()
+ assert len(sequence) == len(pred_mask)
+ assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
+ pairs.append((sequence, pred_mask))
+ if len(pairs) == nb:
+ break
+
+ if nb > 0:
+ pairs = pairs[:nb]
+ assert len(pairs) == nb
+
+ return pairs
+
+ train_pairs = read_file(train_filename, nb_train_samples)
+ test_pairs = read_file(test_filename, nb_test_samples)
+
+ symbols = ["#"] + list(
+ set("".join([x[0] for x in train_pairs + test_pairs])) - set(["#"])
+ )
+ self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
+ self.id2char = dict([(n, c) for c, n in self.char2id.items()])
+
+ self.train_input, self.train_pred_masks = self.tensorize(
+ train_pairs, shuffle=shuffle
+ )
+ self.test_input, self.test_pred_masks = self.tensorize(
+ test_pairs, shuffle=shuffle
+ )
+
+ 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 self.trim(batch).to(self.device)
+
+ def vocabulary_size(self):
+ return len(self.char2id)
+
+ def tensor2str(self, t):
+ return ["".join([self.id2char[x.item()] for x in s]) for s in t]
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ correct = self.trim(self.test_input[:1000]).to(self.device)
+ result = correct.clone()
+ pred_mask = self.test_pred_masks[:1000, : result.size(1)].to(self.device)
+ ar_mask = (pred_mask > 0).long()
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:50]):
+ logger(f"test_before {e}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ logger(f"----------------------------------------------------------")
+
+ for e, c in zip(self.tensor2str(result[:50]), self.tensor2str(correct[:50])):
+ logger(f"test_after {e}")
+ logger(f"correct {c}")
+
+ logger(f"----------------------------------------------------------")
+
+ err_mask = (pred_mask == 2).long()
+ nb_total = err_mask.sum().item()
+ nb_correct = ((correct == result).long() * err_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 problems
+
+
+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
+ assert self.train_input.min() >= 0
+ assert self.test_input.min() >= 0
+ assert tuple(x.item() for x in self.train_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
+ assert tuple(x.item() for x in self.test_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
+
+ if logger is not None:
+ for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
+ logger(f"train_sequences {self.problem.seq2str(s)}")
+ a = "".join(["01"[x.item()] for x in a])
+ logger(f" {a}")
+
+ 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,
+ )
+
+ log_ground_truth = ar_mask.min() == 0
+
+ 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)}"
+ )
+ if log_ground_truth:
+ logger(
+ f" {n_epoch} ground truth {self.problem.seq2str(st)}"
+ )
+
+ nb_total, nb_correct = self.problem.compute_nb_correct(
+ input, ar_mask, result
+ )
+
+ # 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}%"
+ )
+
+ logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
+ if save_attention_image is not None:
+ 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}")
+
+
######################################################################
import picoclvr
pruner_train=None,
pruner_eval=None,
):
+ super().__init__()
+
def generate_descr(nb, cache_suffix, pruner):
return picoclvr.generate(
nb,
self.train_input = self.tensorize(self.train_descr)
self.test_input = self.tensorize(self.test_descr)
- def batches(self, split="train"):
+ 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
for batch in tqdm.tqdm(
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(
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
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}%")
prompt_length,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.height = height
self.width = width
)
result *= 1 - ar_mask
- # snake.solver(result,ar_mask)
-
masked_inplace_autoregression(
model,
self.batch_size,
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()
-
return nb_total, nb_correct
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
-
- # logger(
- # f"accuracy_train 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_nb_correct(
self.test_input[:1000], self.test_prior_visits[:1000]
)
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}")
+
######################################################################
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
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)]
######################################################################
-
-import expr
+import rpl
-class Expr(Task):
+class RPL(Task):
def tensorize(self, sequences):
len_max = max([len(x) for x in sequences])
return torch.cat(
[
torch.tensor(
[
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+ [
+ self.token2id[str(c)]
+ for c in s + ["<nul>"] * (len_max - len(s))
+ ]
for s in sequences
]
)
],
0,
- ).to(self.device)
+ )
+
+ def seq2str(self, seq):
+ return " ".join([self.id2token[i] for i in seq])
def __init__(
self,
nb_train_samples,
nb_test_samples,
- nb_variables,
- sequence_length,
- operand_max,
- result_max,
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")
+ ]
- train_sequences = expr.generate_sequences(
- nb_train_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- 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(["<nul>"] + [x for l in train_sequences + test_sequences for x in l])
)
-
- symbols = list(set("#" + "".join(train_sequences + test_sequences)))
+ 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.char2id = dict([(c, n) for n, c in enumerate(symbols)])
- self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
- self.filler, self.space = self.char2id["#"], self.char2id[" "]
+ 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>"]
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):
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
- last = (batch != self.filler).max(0).values.nonzero().max() + 3
- batch = batch[:, :last]
+ 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 seq2str(self, s):
- return "".join([self.id2char[k.item()] for k in s])
-
def produce_results(
- self,
- n_epoch,
- model,
- result_dir,
- logger,
- deterministic_synthesis,
- input_file=None,
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- def compute_nb_correct(input):
+ # --------------------------------------------------------------------
+ def compute_nb_errors_prog(input, nb_to_log=0):
result = input.clone()
- s = (result == self.space).long()
+ 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.filler
+ result = (1 - ar_mask) * result + ar_mask * self.t_nul
+
masked_inplace_autoregression(
model,
self.batch_size,
device=self.device,
)
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
-
- #######################################################################
- # Comput predicted vs. true variable values
+ 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
- nb_delta = torch.zeros(5, dtype=torch.int64)
+ 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}")
+
+
+######################################################################
+
+
+import expr
+
+
+class Expr(Task):
+ def tensorize(self, sequences):
+ len_max = max([len(x) for x in sequences])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+ for s in sequences
+ ]
+ )
+ ],
+ 0,
+ ).to(self.device)
+
+ def __init__(
+ self,
+ nb_train_samples,
+ 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
+
+ train_sequences = expr.generate_sequences(
+ nb_train_samples,
+ nb_variables=nb_variables,
+ length=sequence_length,
+ 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)))
+ symbols.sort()
+
+ self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
+ self.id2char = dict([(n, c) for c, n in self.char2id.items()])
+
+ self.filler, self.space = self.char2id["#"], self.char2id[" "]
+
+ self.train_input = self.tensorize(train_sequences)
+ self.test_input = self.tensorize(test_sequences)
+
+ 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.filler).max(0).values.nonzero().max() + 3
+ batch = batch[:, :last]
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def seq2str(self, s):
+ return "".join([self.id2char[k.item()] for k in s])
+
+ def produce_results(
+ self,
+ n_epoch,
+ model,
+ result_dir,
+ logger,
+ deterministic_synthesis,
+ input_file=None,
+ ):
+ 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()
+
+ #######################################################################
+ # Comput predicted vs. true variable values
+
+ 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])
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- if vr is None or vr < 0:
- nb_missed += 1
- else:
- d = abs(vr - vi)
- if d >= nb_delta.size(0):
+ 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:
- nb_delta[d] += 1
+ d = abs(vr - vi)
+ if d >= nb_delta.size(0):
+ nb_missed += 1
+ else:
+ nb_delta[d] += 1
######################################################################
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 str2tensor(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 tensor2str(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,
+ fraction_play=0.0,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.device = device
+ self.batch_size = batch_size
+ self.grid_factory = grid.GridFactory(size=size)
+ self.fraction_play = fraction_play
+
+ 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=nb_train_samples,
+ fraction_play=fraction_play,
+ progress_bar=lambda r: tqdm.tqdm(r),
+ )
+
+ self.test_descr = self.grid_factory.generate_samples(
+ nb=nb_test_samples, fraction_play=0.0, progress_bar=lambda r: tqdm.tqdm(r)
+ )
+
+ if fraction_play > 0:
+ self.play_descr = self.grid_factory.generate_samples(
+ nb=25, fraction_play=1.0, progress_bar=lambda r: tqdm.tqdm(r)
+ )
+ else:
+ self.play_descr = []
+
+ # Build the tokenizer
+ tokens = set()
+ for d in [self.train_descr, self.test_descr, self.play_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"]
+ # self.t_pipe = self.token2id["|"]
+
+ # Tokenize the train and test sets
+ self.train_input = self.str2tensor(self.train_descr)
+ self.test_input = self.str2tensor(self.test_descr)
+ self.play_input = (
+ None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
+ )
+
+ 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
+ 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 # paraaaaanoiaaaaaaa
+
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_before {e}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_after {e}")
+
+ logger(f"----------------------------------------------------------")
+
+ 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}")
+
+ if self.play_input is not None:
+ result = self.play_input.clone()
+ ar_mask = (result == self.t_pipe).long().cumsum(dim=1).clamp(max=1)
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"play_before {e}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"play_after {e}")
+
+ logger(f"----------------------------------------------------------")
+
+
+######################################################################
+
+import qmlp
+
+
+class QMLP(Task):
+ ######################
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ result_dir,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.device = device
+ self.batch_size = batch_size
+ self.nb_samples_per_mlp = 256
+
+ if logger is not None:
+ logger(
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+ )
+
+ seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
+ nb_mlps=nb_train_samples + nb_test_samples,
+ nb_samples=self.nb_samples_per_mlp,
+ device=self.device,
+ batch_size=64,
+ nb_epochs=250,
+ nb_mlps_per_batch=1024,
+ )
+
+ self.train_input = seq[:nb_train_samples]
+ self.train_q_test_set = q_test_set[:nb_train_samples]
+ self.train_ref_test_errors = test_error[:nb_train_samples]
+ self.test_input = seq[nb_train_samples:]
+ self.test_q_test_set = q_test_set[nb_train_samples:]
+ self.test_ref_test_errors = test_error[nb_train_samples:]
+
+ filename = os.path.join(result_dir, f"train_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.train_ref_test_errors:
+ f.write(f"{e}\n")
+
+ filename = os.path.join(result_dir, f"test_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.test_ref_test_errors:
+ f.write(f"{e}\n")
+
+ 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
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ correct = self.test_input[:1000]
+ result = correct.clone()
+ ar_mask = (
+ torch.arange(result.size(1), device=result.device)
+ > self.nb_samples_per_mlp * 3 + 1
+ ).long()[None, :]
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ q_train_set = result[:, : self.nb_samples_per_mlp * 3]
+ q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
+ error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
+
+ filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
+ with open(filename, "w") as f:
+ for e in error_test:
+ f.write(f"{e}\n")
+
+
+######################################################################
+
+import greed
+
+
+class Greed(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ T,
+ nb_walls,
+ nb_coins,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+
+ self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins)
+
+ states, actions, rewards = self.world.generate_episodes(
+ nb_train_samples + nb_test_samples
+ )
+ seq = self.world.episodes2seq(states, actions, rewards)
+ self.train_input = seq[:nb_train_samples].to(self.device)
+ self.test_input = seq[nb_train_samples:].to(self.device)
+
+ def wipe_lookahead_rewards(self, batch):
+ 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.world.it_len == self.world.index_lookahead_reward
+ ).long()
+
+ return (
+ lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+ + (1 - lr_mask) * batch
+ )
+
+ 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 self.wipe_lookahead_rewards(batch)
+
+ def vocabulary_size(self):
+ return self.world.nb_codes
+
+ def thinking_autoregression(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ snapshots = []
+
+ def ar(result, ar_mask, logit_biases=None):
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis=deterministic_synthesis,
+ logit_biases=logit_biases,
+ device=self.device,
+ progress_bar_desc=None,
+ )
+ warnings.warn("keeping thinking snapshots", RuntimeWarning)
+ snapshots.append(result[:100].detach().clone())
+
+ # Generate iteration after iteration
+
+ result = self.test_input[:250].clone()
+ # Erase all the content but that of the first iteration
+ result[:, self.world.it_len :] = -1
+ # Set the lookahead_reward of the firs to UNKNOWN
+ result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code(
+ greed.REWARD_UNKNOWN
+ )
+
+ t = torch.arange(result.size(1), device=result.device)[None, :]
+
+ for u in tqdm.tqdm(
+ range(0, result.size(1), self.world.it_len),
+ desc="thinking",
+ ):
+ # 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.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+ ar_mask = (t >= u + self.world.index_states).long() * (
+ t < u + self.world.index_states + self.world.state_len
+ ).long()
+ ar(result, ar_mask)
+
+ # Generate the action and reward with lookahead_reward to +1
+ result[
+ :, u + self.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_PLUS)
+ ar_mask = (t >= u + self.world.index_reward).long() * (
+ t <= u + self.world.index_action
+ ).long()
+ ar(result, ar_mask)
+
+ # Set the lookahead_reward to UNKNOWN for the next iterations
+ result[
+ :, u + self.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+
+ filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ for n in range(snapshots[0].size(0)):
+ for s in snapshots:
+ lr, s, a, r = self.world.seq2episodes(
+ s[n : n + 1],
+ )
+ str = self.world.episodes2str(
+ lr, s, a, r, unicode=True, ansi_colors=True
+ )
+ f.write(str)
+ f.write("\n\n")
+
+ # Saving the generated sequences
+
+ lr, s, a, r = self.world.seq2episodes(result)
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ result = self.wipe_lookahead_rewards(self.test_input[:250].clone())
+
+ # Saving the ground truth
+
+ lr, s, a, r = self.world.seq2episodes(
+ result,
+ )
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ # Re-generating from the first frame
+
+ ar_mask = (
+ torch.arange(result.size(1), device=result.device) >= self.world.it_len
+ ).long()[None, :]
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ # Saving the generated sequences
+
+ lr, s, a, r = self.world.seq2episodes(
+ result,
+ )
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+ filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
+ with open(filename, "w") as f:
+ f.write(str)
+ logger(f"wrote {filename}")
+
+ self.thinking_autoregression(
+ n_epoch, model, result_dir, logger, deterministic_synthesis, nmax
+ )
+
+
+######################################################################
projects / picoclvr.git / blobdiff