X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=42d912674641db0fec26a48ce5687b7040cba5cb;hb=291c38d093894d46fba6eb45f82e5b65a2a1cb8b;hp=706e1d913c20be0bf9449f94bf3346658a5a1bdc;hpb=8d2ebe29b48e3cf2f0a3937ab1e44d0e12a4924e;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 706e1d9..42d9126 100755
--- a/tasks.py
+++ b/tasks.py
@@ -1,5 +1,10 @@
 #!/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
@@ -7,6 +12,13 @@ import torch, torchvision
 from torch import nn
 from torch.nn import functional as F
 
+from mygpt import BracketedSequence
+
+try:
+    from graph import save_attention_image
+except ImportError:
+    save_attention_image = None
+
 ######################################################################
 
 
@@ -29,7 +41,7 @@ def masked_inplace_autoregression(
             batches,
             dynamic_ncols=True,
             desc=progress_bar_desc,
-            # total=input.size(0) // batch_size,
+            total=(input.size(0) + batch_size - 1) // batch_size,
         )
 
     with torch.autograd.no_grad():
@@ -76,7 +88,7 @@ class Problem:
 
 class ProblemLevel0(Problem):
     def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
-        self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
+        self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
         self.seq[:, len_prompt] = 10
 
     def generate_sequences(self, nb):
@@ -96,19 +108,19 @@ class ProblemLevel1(Problem):
             num_classes=len_source,
         )
 
-
-
     def generate_sequences(self, nb):
         nb_operators = torch.randint(self.operators.size(0), (nb,))
         operators = self.operators[nb_operators]
-        nb_operators = (nb_operators[:, None] // 10 ** torch.arange(self.len_nb_operator-1,-1,-1)) % 10
-        marker1 = torch.full((nb,1),10)
-        source = torch.randint(10, (nb, self.len_source))
-        marker2 = torch.full((nb,1),11)
+        nb_operators = (
+            nb_operators[:, None]
+            // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
+        ) % 10
+        marker1 = torch.full((nb, 1), 10)
+        # source = torch.randint(10, (nb, self.len_source))
+        source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+        marker2 = torch.full((nb, 1), 11)
         result = operators.bmm(source[:, :, None]).squeeze(-1)
-        print(f"{nb_operators.dtype=} {marker1.dtype=}")
-        sequences = torch.cat((nb_operators, marker1, source,marker2,result),1)
-        print(f"{sequences.size()=}")
+        sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
         ar_mask = (sequences == 11).long()
         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
         return sequences, ar_mask
@@ -117,6 +129,35 @@ class ProblemLevel1(Problem):
         return "".join("0123456789|>"[x.item()] for x in seq)
 
 
+class ProblemLevel2(Problem):
+    def __init__(self, len_source=5, len_result=8):
+        self.len_source = len_source
+        self.len_result = len_result
+
+    def generate_sequences(self, nb):
+        operators = F.one_hot(
+            torch.rand(nb, self.len_result, self.len_source).argmax(-1),
+            num_classes=self.len_source,
+        )
+        source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+        marker1 = torch.full((nb, 1), 10)
+        result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
+        marker2 = torch.full((nb, 1), 11)
+        source2 = torch.randint(10, (nb, self.len_source))
+        marker3 = torch.full((nb, 1), 12)
+        result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
+
+        sequences = torch.cat(
+            (source1, marker1, result1, marker2, source2, marker3, result2), 1
+        )
+        ar_mask = (sequences == 12).long()
+        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+        return sequences, ar_mask
+
+    def seq2str(self, seq):
+        return "".join("0123456789>|~"[x.item()] for x in seq)
+
+
 ####################
 
 
@@ -989,6 +1030,292 @@ 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,
+        )
+
+    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}%"
+            )
+
+        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 not None:
+            input = self.test_input[:1].clone()
+            last = (input != self.t_nul).max(0).values.nonzero().max() + 3
+            input = input[:, :last].to(self.device)
+
+            with torch.autograd.no_grad():
+                t = model.training
+                model.eval()
+                model.record_attention(True)
+                model(BracketedSequence(input))
+                model.train(t)
+                ram = model.retrieve_attention()
+                model.record_attention(False)
+
+            tokens_output = [self.id2token[i.item()] for i in input[0]]
+            tokens_input = ["n/a"] + tokens_output[:-1]
+            for n_head in range(ram[0].size(1)):
+                filename = os.path.join(
+                    result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf"
+                )
+                attention_matrices = [m[0, n_head] for m in ram]
+                save_attention_image(
+                    filename,
+                    tokens_input,
+                    tokens_output,
+                    attention_matrices,
+                    k_top=10,
+                    # min_total_attention=0.9,
+                    token_gap=12,
+                    layer_gap=50,
+                )
+                logger(f"wrote {filename}")
+
+
 ######################################################################