From: François Fleuret <francois@fleuret.org>
Date: Wed, 19 Jul 2023 11:54:59 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=c9dbc3abf436df8af1379d04ab51159e821496f1;p=culture.git

Update.
---

diff --git a/main.py b/main.py
index efcc0dd..63e6668 100755
--- a/main.py
+++ b/main.py
@@ -36,7 +36,7 @@ parser.add_argument(
     "--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=" ")
@@ -206,6 +206,12 @@ default_task_args = {
         "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,
@@ -419,6 +425,14 @@ elif args.task == "expr":
         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,
diff --git a/rpl.py b/rpl.py
new file mode 100755
index 0000000..42db38c
--- /dev/null
+++ b/rpl.py
@@ -0,0 +1,125 @@
+#!/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))
diff --git a/tasks.py b/tasks.py
index c5418b4..a3d47f5 100755
--- a/tasks.py
+++ b/tasks.py
@@ -1021,6 +1021,124 @@ class Stack(Task):
         ##############################################################
 
 
+######################################################################
+
+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}%"
+        )
+
+
 ######################################################################