"--task",
type=str,
default="sandbox",
- help="sandbox, picoclvr, mnist, maze, snake, stack, expr, world",
+ help="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, world",
)
parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
"nb_train_samples": 1000000,
"nb_test_samples": 10000,
},
+ "rpl": {
+ "nb_epochs": 40,
+ "batch_size": 25,
+ "nb_train_samples": 1000000,
+ "nb_test_samples": 10000,
+ },
"world": {
"nb_epochs": 10,
"batch_size": 25,
device=device,
)
+elif args.task == "rpl":
+ task = tasks.RPL(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ device=device,
+ )
+
elif args.task == "world":
task = tasks.World(
nb_train_samples=args.nb_train_samples,
--- /dev/null
+#!/usr/bin/env python
+
+import math
+
+import torch, torchvision
+
+from torch import nn
+from torch.nn import functional as F
+
+######################################################################
+
+
+def rpl_exec(program, stack):
+ for op in program:
+ if op == "add":
+ if len(stack) > 1:
+ a, b = stack.pop(), stack.pop()
+ stack.append(a + b)
+ elif op == "min":
+ if len(stack) > 1:
+ a, b = stack.pop(), stack.pop()
+ stack.append(min(a, b))
+ elif op == "max":
+ if len(stack) > 1:
+ a, b = stack.pop(), stack.pop()
+ stack.append(max(a, b))
+ elif op == "swp":
+ if len(stack) > 1:
+ a, b = stack.pop(), stack.pop()
+ stack.append(a)
+ stack.append(b)
+ elif op == "rep":
+ if len(stack) > 1:
+ a, b = stack.pop(), stack.pop()
+ stack += [b] * a
+ elif op == "dup":
+ if len(stack) > 0:
+ a = stack.pop()
+ stack.append(a)
+ stack.append(a)
+ elif op == "del":
+ if len(stack) > 0:
+ a = stack.pop()
+ else:
+ raise ValueError(f"Unknown instruction {op}")
+
+
+rpl_ops = ["add", "min", "max", "swp", "rep", "dup", "del"]
+
+######################################################################
+
+
+def generate(nb_values=3, max_input=9, prog_len=6, nb_runs=5):
+ prog_len = 1 + torch.randint(prog_len - 1, (1,)).item()
+ prog = [rpl_ops[k] for k in torch.randint(len(rpl_ops), (prog_len,))]
+
+ result = []
+ for _ in range(nb_runs):
+ stack = [x.item() for x in torch.randint(max_input + 1, (nb_values,))]
+ result = result + ["<input>"] + stack
+ rpl_exec(prog, stack)
+ result = result + ["<output>"] + stack
+
+ result = result + ["<prog>"] + prog
+ result = result + ["<end>"]
+ return result
+
+
+def next_marker(seq, tokens, start=0):
+ pos = None
+ for t in tokens:
+ try:
+ i = seq.index(t, start)
+ if pos is None or i < pos:
+ pos = i
+ except ValueError:
+ pass
+ return pos
+
+
+def check(seq):
+ io = []
+ k = 0
+ while seq[k] == "<input>":
+ o = next_marker(seq, ["<output>"], start=k + 1)
+ e = next_marker(seq, ["<input>", "<prog>"], start=o)
+ if o is None or e is None:
+ raise ValueError("Invalid input/output")
+ io.append((seq[k + 1 : o], seq[o + 1 : e]))
+ k = e
+
+ if seq[k] == "<prog>":
+ e = next_marker(seq, ["<end>"], start=k)
+ if e is None:
+ prog = []
+ else:
+ prog = seq[k + 1 : e]
+
+ nb_total, nb_errors = 0, 0
+
+ if len(set(prog) - set(rpl_ops)) > 0:
+ for stack, target_stack in io:
+ nb_total += len(target_stack)
+ nb_errors += len(target_stack)
+
+ else:
+ for stack, target_stack in io:
+ # print(f"INIT {stack} PROG {prog}")
+ rpl_exec(prog, stack)
+ # print(f"CHECK {stack} REF {target_stack} NB_ERROR {abs(len(stack) - len(target_stack))+sum([0 if x == y else 1 for x, y in zip(stack, target_stack)])}")
+ nb_total += len(target_stack)
+ nb_errors += abs(len(stack) - len(target_stack))
+ nb_errors += sum([0 if x == y else 1 for x, y in zip(stack, target_stack)])
+
+ return nb_total, nb_errors
+
+
+######################################################################
+
+if __name__ == "__main__":
+ seq = generate()
+ print(seq)
+ seq[3] = 7
+ print(seq)
+ print(check(seq))
##############################################################
+######################################################################
+
+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
+ ]
+ )
+ ],
+ 0,
+ ).to(self.device)
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+
+ train_sequences = [
+ rpl.generate()
+ for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
+ ]
+ test_sequences = [
+ rpl.generate() 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)]
+ print(f"{val_max=}")
+ 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.t_prog = self.token2id["<nul>"], self.token2id["<prog>"]
+
+ 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.t_nul).max(0).values.nonzero().max() + 3
+ batch = batch[:, :last]
+ 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(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,
+ )
+
+ if nb_to_log > 0:
+ for x in result[:nb_to_log]:
+ s = " ".join([self.id2token[i.item()] for i in x])
+ logger(f"check {n_epoch} {s}")
+ nb_to_log -= min(nb_to_log, result.size(0))
+
+ sum_nb_total, sum_nb_errors = 0, 0
+ for x in result:
+ seq = [self.id2token[i.item()] for i in x]
+ nb_total, nb_errors = rpl.check(seq)
+ sum_nb_total += nb_total
+ sum_nb_errors += nb_errors
+
+ return sum_nb_total, sum_nb_errors
+
+ test_nb_total, test_nb_errors = compute_nb_errors(self.test_input, nb_to_log=10)
+
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
+ )
+
+
######################################################################
projects / picoclvr.git / commitdiff