+#!/usr/bin/env python
+
+import math, os, tqdm
+
+import torch, torchvision
+
+from torch import nn
+from torch.nn import functional as F
+
+######################################################################
+
+
+def masked_inplace_autoregression(
+ model,
+ batch_size,
+ input,
+ ar_mask,
+ deterministic_synthesis,
+ forbidden_tokens=None,
+ progress_bar_desc="autoregression",
+ device=torch.device("cpu"),
+):
+ batches = zip(input.split(batch_size), ar_mask.split(batch_size))
+
+ if progress_bar_desc is not None:
+ batches = tqdm.tqdm(
+ batches,
+ dynamic_ncols=True,
+ desc=progress_bar_desc,
+ total=input.size(0) // batch_size,
+ )
+
+ for input, ar_mask in batches:
+ model.masked_inplace_autoregression(
+ input, ar_mask, forbidden_tokens, deterministic_synthesis
+ )
+
+
+class Task:
+ def batches(self, split="train"):
+ pass
+
+ def vocabulary_size(self):
+ pass
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ pass
+
+
+######################################################################
+
+import picoclvr
+
+
+class PicoCLVR(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 + ["<nul>"] * (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="<nul>"):
+ 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]
+
+ ######################
+ # 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__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ nb_colors=5,
+ logger=None,
+ device=torch.device("cpu"),
+ pruner_train=None,
+ pruner_eval=None,
+ ):
+ def generate_descr(nb, cache_suffix, pruner):
+ return picoclvr.generate(
+ nb,
+ height=self.height,
+ width=self.width,
+ nb_colors=nb_colors,
+ pruner=pruner,
+ )
+
+ self.height = height
+ self.width = width
+ self.batch_size = batch_size
+ self.device = device
+ 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)"
+ )
+
+ self.train_descr = generate_descr(
+ nb_train_samples, "train", pruner=self.pruner_train
+ )
+ self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
+
+ # Build the tokenizer
+ tokens = {"<nul>", "<img>"}
+ 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()
+ self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
+ self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
+
+ # 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 compute_missing_properties(
+ self, n_epoch, model, logger, deterministic_synthesis, pruner=None
+ ):
+ acc_nb_requested_properties = []
+ acc_nb_missing_properties = []
+ acc_nb_results = 0
+
+ for input in tqdm.tqdm(
+ self.test_input.split(self.batch_size),
+ 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_descr = self.detensorize(tape)
+ np = picoclvr.nb_properties(
+ result_descr,
+ height=self.height,
+ width=self.width,
+ pruner=pruner,
+ )
+ nb_requested_properties, _, nb_missing_properties = zip(*np)
+ acc_nb_requested_properties += nb_requested_properties
+ acc_nb_missing_properties += nb_missing_properties
+ acc_nb_results += len(result_descr)
+
+ nb_requested_properties = sum(acc_nb_requested_properties)
+ nb_missing_properties = sum(acc_nb_missing_properties)
+
+ prefix = "" if pruner is None else "pruned_"
+ logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
+ logger(
+ f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
+ )
+ logger(
+ f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
+ )
+
+ ######################################################################
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
+
+ if self.pruner_eval is not None:
+ self.compute_missing_properties(n_epoch, model, self.pruner_eval)
+
+ nb_tokens_to_generate = self.height * self.width + 3
+ result_descr = []
+ nb_per_primer = 8
+ primer = []
+
+ for primer_descr in [
+ "red above green <sep> green top <sep> blue right of red",
+ "there is red <sep> there is yellow <sep> there is blue",
+ "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
+
+ 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_descr = self.detensorize(tape)
+
+ np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
+
+ acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
+ acc_nb_results = len(result_descr)
+
+ nb_requested_properties = sum(acc_nb_requested_properties)
+ nb_missing_properties = sum(acc_nb_missing_properties)
+
+ prefix = "demo_"
+ logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
+ logger(
+ f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
+ )
+ logger(
+ f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
+ )
+
+ img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
+
+ if img.dim() == 5:
+ if img.size(1) == 1:
+ img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
+ else:
+ img = torch.cat(
+ [
+ torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
+ for x in img
+ ],
+ 0,
+ )
+
+ image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
+ torchvision.utils.save_image(
+ img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
+ )
+ logger(f"wrote {image_name}")
+
+
+######################################################################
+
+
+class MNIST(Task):
+ def __init__(
+ self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
+ ):
+ self.nb_train_samples = (nb_train_samples,)
+ self.nb_test_samples = (nb_test_samples,)
+ self.batch_size = batch_size
+ self.device = device
+ data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
+ self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
+ data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
+ self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
+
+ 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 256
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
+ ar_mask = torch.full_like(results, 1)
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ results,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+ image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
+ torchvision.utils.save_image(
+ 1 - results.reshape(-1, 1, 28, 28) / 255.0,
+ image_name,
+ nrow=16,
+ pad_value=0.8,
+ )
+ logger(f"wrote {image_name}")
+
+
+######################################################################
+
+import maze
+
+
+class Maze(Task):
+ def map2seq(self, *m):
+ return torch.cat([x.flatten(1) for x in m], 1)
+
+ def seq2map(self, s):
+ s = s.reshape(s.size(0), -1, self.height, self.width)
+ return (s[:, k] for k in range(s.size(1)))
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ nb_walls,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.height = height
+ self.width = width
+ self.device = device
+
+ train_mazes, train_paths, _ = maze.create_maze_data(
+ nb_train_samples,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
+ )
+ self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
+
+ test_mazes, test_paths, _ = maze.create_maze_data(
+ nb_test_samples,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
+ )
+ self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
+
+ 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
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def compute_error(
+ self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
+ ):
+ nb_total, nb_correct = 0, 0
+ count = torch.zeros(
+ self.width * self.height,
+ self.width * self.height,
+ device=self.device,
+ dtype=torch.int64,
+ )
+
+ for input in self.batches(split, nb_to_use):
+ 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,
+ progress_bar_desc=None,
+ device=self.device,
+ )
+ mazes, paths = self.seq2map(result)
+ path_correctness = maze.path_correctness(mazes, paths)
+ nb_correct += path_correctness.long().sum()
+ nb_total += mazes.size(0)
+
+ optimal_path_lengths = (
+ (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
+ )
+ predicted_path_lengths = (
+ (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
+ )
+ optimal_path_lengths = optimal_path_lengths[path_correctness]
+ predicted_path_lengths = predicted_path_lengths[path_correctness]
+ count[optimal_path_lengths, predicted_path_lengths] += 1
+
+ if count.max() == 0:
+ count = None
+ else:
+ count = count[
+ : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
+ ]
+
+ return nb_total, nb_correct, count
+
+ 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}%"
+ )
+
+ 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,
+ )
+
+ 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}")
+
+ model.train(t)
+
+
+######################################################################
+
+
+import snake
+
+
+class Snake(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ nb_colors,
+ length,
+ prompt_length,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.height = height
+ self.width = width
+ self.device = device
+ self.prompt_length = prompt_length
+
+ self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
+ nb_train_samples,
+ height,
+ width,
+ nb_colors,
+ length,
+ prompt_length,
+ self.device,
+ )
+ self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
+ nb_test_samples,
+ height,
+ width,
+ nb_colors,
+ length,
+ prompt_length,
+ self.device,
+ )
+
+ 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
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ def compute_nb_correct(input, prior_visits):
+ result = input.clone()
+ i = torch.arange(result.size(1), device=result.device)[None, :]
+ ar_mask = (
+ torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
+ .long()
+ .expand_as(result)
+ )
+ result *= 1 - ar_mask
+
+ # snake.solver(result,ar_mask)
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ nb_total = ((prior_visits > 0) * ar_mask).sum()
+
+ nb_correct = (
+ (result == input).long() * (prior_visits > 0) * ar_mask
+ ).sum()
+
+ # nb_total = result.size(0)
+ # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+
+ 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]
+ )
+
+ 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)
+
+
+######################################################################
+
+
+import stack
+
+
+class Stack(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ logger,
+ nb_steps,
+ nb_stacks,
+ nb_digits,
+ fraction_values_for_train=None,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.nb_steps = nb_steps
+ self.nb_stacks = nb_stacks
+ self.nb_digits = nb_digits
+ self.device = device
+
+ if fraction_values_for_train is None:
+ values_for_train = None
+ values_for_test = None
+ else:
+ all = torch.randperm(10**nb_digits)
+ nb_for_train = int(all.size(0) * fraction_values_for_train)
+ values_for_train = all[:nb_for_train]
+ values_for_test = all[nb_for_train:]
+
+ self.train_input, self.train_stack_counts = stack.generate_sequences(
+ nb_train_samples,
+ nb_steps,
+ nb_stacks,
+ nb_digits,
+ values_for_train,
+ self.device,
+ )
+
+ self.test_input, self.test_stack_counts = stack.generate_sequences(
+ nb_test_samples,
+ nb_steps,
+ nb_stacks,
+ nb_digits,
+ values_for_test,
+ self.device,
+ )
+
+ i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
+ counts = self.test_stack_counts.flatten()[i.flatten()]
+ counts = F.one_hot(counts).sum(0)
+ logger(f"test_pop_stack_counts {counts}")
+
+ 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
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ 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,
+ )
+
+ 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}%"
+ )
+
+ ##############################################################
+ # Log a few generated sequences
+ input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+ for n in range(result.size(0)):
+ logger(
+ f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ )
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+ for n in range(result.size(0)):
+ logger(
+ f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ )
+ ##############################################################
+
+ model.train(t)
+
+
+######################################################################
+
+
+import expr
+
+
+class Expr(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ nb_variables,
+ sequence_length,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ train_sequences = expr.generate_sequences(
+ nb_train_samples,
+ nb_variables=nb_variables,
+ length=sequence_length,
+ # length=2 * sequence_length,
+ # randomize_length=True,
+ )
+ test_sequences = expr.generate_sequences(
+ nb_test_samples,
+ nb_variables=nb_variables,
+ length=sequence_length,
+ )
+ self.char2id = dict(
+ [
+ (c, n)
+ for n, c in enumerate(
+ set("#" + "".join(train_sequences + test_sequences))
+ )
+ ]
+ )
+ 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.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
+ ):
+ if split == "train":
+ 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
+ ):
+ 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,
+ )
+
+ 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):
+ nb_missed += 1
+ 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[: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}%"
+ )
+
+ 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}%"
+ )
+ logger(
+ f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ )
+
+ ##############################################################
+ # 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)
+
+
+######################################################################