#!/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
+
+# from graph import save_attention_image
+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 TaskFromFile(Task):
+ def tensorize(self, pairs):
+ 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")
+
+ 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,
+ filename,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ pairs = []
+ with open(filename, "r") as f:
+ for _ in range(nb_train_samples + nb_test_samples):
+ sequence = f.readline().strip()
+ 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))
+
+ symbols = ["#"] + list(set("".join([x[0] for x in 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(
+ pairs[:nb_train_samples]
+ )
+ self.test_input, self.test_pred_masks = self.tensorize(pairs[nb_train_samples:])
+
+ assert self.train_input.size(0) == nb_train_samples
+ assert self.test_input.size(0) == nb_test_samples
+
+ 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[: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, c in zip(self.tensor2str(result[:10]), self.tensor2str(correct[:10])):
+ 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
a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
return z[:, a:b]
- ######################
- # Not the cleanest part of the code
-
- # Extract the last image of each sequence, from the last <img>
- # included, and set to <nul> all the tokens from the beginning of
- # that image to the end
- def excise_last_image(self, input):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = input.clone()
- t = (input == t_img).long()
- tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
- i = (t * tail_masks).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- images = self.trim(input[j])
- input[j] = t_nul
- loss_masks = 1 - tail_masks
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks, images
-
- def add_true_image(self, input, images, loss_masks):
- t_nul = self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- input[j] = images
- loss_masks[j] = 1
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks
-
- def add_generated_image(self, input, loss_masks, model, deterministic_synthesis):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- input[i] = t_img
-
- j = (
- i[0][:, None],
- i[1][:, None]
- + 1
- + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
- )
- ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
- ar_masks[j] = 1
- forbidden_tokens = (
- torch.arange(self.vocabulary_size(), device=input.device) == t_nul
- )
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- input,
- ar_masks,
- deterministic_synthesis,
- forbidden_tokens,
- progress_bar_desc=None,
- device=self.device,
- )
- model.train(t)
-
- input, loss_masks = self.trim((input, loss_masks))
-
- return input, loss_masks
-
######################
def __init__(
pruner_train=None,
pruner_eval=None,
):
+ super().__init__()
+
def generate_descr(nb, cache_suffix, pruner):
return picoclvr.generate(
nb,
self.pruner_train = pruner_train
self.pruner_eval = pruner_eval
- param = {
- "nb_train_samples": nb_train_samples,
- "nb_test_samples": nb_test_samples,
- "height": height,
- "width": width,
- "nb_colors": nb_colors,
- "batch_size": batch_size,
- "rng_state": list(torch.get_rng_state()),
- }
-
if logger is not None:
logger(
f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
tokens.sort()
self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
+ self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
# Tokenize the train and test sets
self.train_input = self.tensorize(self.train_descr)
dynamic_ncols=True,
desc=f"test-properties",
):
- tape, loss_masks, _ = self.excise_last_image(input)
- tape, loss_masks = self.add_generated_image(
- tape, loss_masks, model, deterministic_synthesis
+ result = input.clone()
+ ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.t_nul
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ progress_bar_desc=None,
+ device=self.device,
)
- result_descr = self.detensorize(tape)
+
+ result_descr = self.detensorize(result)
np = picoclvr.nb_properties(
result_descr,
height=self.height,
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(
"red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
"green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
]:
- primer += [primer_descr] * nb_per_primer
+ primer += [primer_descr + " <img>"] * nb_per_primer
- tape = self.tensorize(primer)
- loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
- tape, loss_masks = self.add_generated_image(
- tape, loss_masks, model, deterministic_synthesis
+ result = self.tensorize(primer)
+ fill = result.new_full(
+ result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
+ )
+ result = torch.cat((result, fill), 1)
+ ar_mask = (result == self.t_nul).long()
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
)
- result_descr = self.detensorize(tape)
+ result_descr = self.detensorize(result)
np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
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}")
+ 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}%")
+ 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
+ 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,
+ )
- 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 = ((prior_visits > 0) * ar_mask).sum()
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+ nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
- return nb_total, nb_correct
+ 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]
- )
+ test_nb_total, test_nb_correct = compute_nb_correct(
+ self.test_input[:1000], self.test_prior_visits[: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}%"
- )
+ 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}%"
+ )
- model.train(t)
+ 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
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)
+ 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}%"
+ )
+
+ logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
+ ##############################################################
+ # 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()
- return nb_total, nb_correct
+ # 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)}"
+ # )
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+ for n in range(result.size(0)):
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"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=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)}"
+
+######################################################################
+
+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
+ ]
)
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
+ ],
+ 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(["<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()])
+
+ 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):
+ 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"
)
- 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)}"
+ 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,
)
- ##############################################################
-
- model.train(t)
+ logger(f"wrote {filename}")
######################################################################
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
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,
)
- self.char2id = dict(
- [
- (c, n)
- for n, c in enumerate(
- set("#" + "".join(train_sequences + test_sequences))
- )
- ]
- )
+
+ 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[" "]
- len_max = max([len(x) for x in train_sequences])
- self.train_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in train_sequences
- ]
- )
- ],
- 0,
- ).to(device)
-
- len_max = max([len(x) for x in test_sequences])
- self.test_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in test_sequences
- ]
- )
- ],
- 0,
- ).to(device)
+ 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
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):
return "".join([self.id2char[k.item()] for k in s])
def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ self,
+ n_epoch,
+ model,
+ result_dir,
+ logger,
+ 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
+
+ (
+ test_nb_total,
+ test_nb_correct,
+ test_nb_delta,
+ test_nb_missed,
+ ) = compute_nb_correct(self.test_input[:10000])
- return nb_total, nb_correct, nb_delta, nb_missed
+ 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,
- test_nb_delta,
- test_nb_missed,
- ) = compute_nb_correct(self.test_input[:1000])
+ 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(
- 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"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}%"
+ )
- 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}%"
- )
+ ##############################################################
+ # 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()
+ 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 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"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
)
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10]
- 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)
+ 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"):
+ 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"):
+ 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")
######################################################################