From 6c8bed86221baae24a7c2aaaa41c009444efb5c9 Mon Sep 17 00:00:00 2001
From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= <francois@fleuret.org>
Date: Sun, 23 Jul 2023 09:37:33 -1000
Subject: [PATCH] Update.

---
 main.py     |  3 ++-
 problems.py | 40 ++++++++++++++++++++++++++++++++++++++++
 tasks.py    | 32 ++++++++++++++++++++++++++++++++
 3 files changed, 74 insertions(+), 1 deletion(-)

diff --git a/main.py b/main.py
index 0f1fbb5..b9b52d6 100755
--- a/main.py
+++ b/main.py
@@ -355,8 +355,9 @@ if args.task == "sandbox":
         raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
 
     task = tasks.SandBox(
-        problem,
+        # problem,
         # problems.ProblemAddition(zero_padded=False, inverted_result=False),
+        problems.ProblemLenId(len_max=args.sandbox_levels_len_source),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
diff --git a/problems.py b/problems.py
index 5161587..1f4098a 100755
--- a/problems.py
+++ b/problems.py
@@ -21,6 +21,37 @@ class Problem:
 ####################
 
 
+class ProblemLenId(Problem):
+    def __init__(self, nb_sentences=100, len_max=5):
+        self.len_max = len_max
+
+    def generate_sequences(self, nb):
+        k = torch.arange(self.len_max * 3 + 3)[None, :]
+        l = torch.randint(self.len_max, (2, nb))[:, :, None] + 1
+        i = torch.randint(10, (2, nb))[:, :, None]
+        a = l[0]
+        b = l[0] + 1 + l[1]
+        c = l[0] + 1 + l[1] + 1 + l[0]
+        sequences = (
+            (k < a) * i[0]
+            + (k == a) * 10
+            + (k > a) * (k < b) * i[1]
+            + (k == b) * 11
+            + (k > b) * (k < c) * i[1]
+            + (k == c) * 12
+            + (k > c) * 13
+        )
+        ar_mask = (sequences == 11).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)
+
+
+####################
+
+
 class ProblemLevel0(Problem):
     def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
         self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
@@ -32,6 +63,12 @@ class ProblemLevel0(Problem):
         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)
+
+
+####################
+
 
 class ProblemLevel1(Problem):
     def __init__(self, nb_operators=100, len_source=5, len_result=8):
@@ -64,6 +101,9 @@ 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
diff --git a/tasks.py b/tasks.py
index b2f7d7d..038a8ac 100755
--- a/tasks.py
+++ b/tasks.py
@@ -181,6 +181,38 @@ class SandBox(Task):
             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 save_attention_image is not None:
+            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"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}")
+
 
 ######################################################################
 
-- 
2.39.5