X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=5019aed3b0953a7037bc213296ca7371d1b3c279;hb=6045e9a7dd61f0dab60bd1c6ff71f6bd5c32778b;hp=af71b85ed7de9d0639b5fc4e95351693608be030;hpb=00b2d5ed01fb523fbc4e699f0419329efbee0ea8;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index af71b85..5019aed 100755
--- a/tasks.py
+++ b/tasks.py
@@ -12,6 +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
+
 ######################################################################
 
 
@@ -65,158 +72,9 @@ class Task:
         pass
 
 
-######################################################################
-
-
-class Problem:
-    def generate_sequences(self, nb):
-        pass
-
-    def seq2str(self, seq):
-        return "[NOT IMPLEMENTED]"
-
-
-####################
-
-
-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))
-        self.seq[:, len_prompt] = 10
-
-    def generate_sequences(self, nb):
-        sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
-        ar_mask = (sequences == 10).long()
-        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
-        return sequences, ar_mask
-
-
-class ProblemLevel1(Problem):
-    def __init__(self, nb_operators=100, len_source=5, len_result=8):
-        self.len_source = len_source
-        self.len_result = len_result
-        self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
-        self.operators = F.one_hot(
-            torch.rand(nb_operators, len_result, len_source).argmax(-1),
-            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))
-        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)
-        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
-
-    def seq2str(self, seq):
-        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]
-        # source1 = torch.randint(10, (nb, 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)
-
-
 ####################
 
-
-class ProblemAddition(Problem):
-    def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False):
-        self.nb_digits = nb_digits
-        self.zero_padded = zero_padded
-        self.inverted_result = inverted_result
-        self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")])
-        self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
-    def tensorize(self, strings):
-        len_max = max([len(x) for x in strings])
-        return torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "$" * (len_max - len(s))]
-                        for s in strings
-                    ]
-                )
-            ],
-            0,
-        )
-
-    def generate_sequences(self, nb):
-        sequences = []
-        for k in range(nb):
-            a, b = torch.randint(10**self.nb_digits, (2,))
-            c = a + b
-            a, b, c = str(a.item()), str(b.item()), str(c.item())
-            if self.zero_padded:
-                a = "0" * (self.nb_digits - len(a)) + a
-                b = "0" * (self.nb_digits - len(b)) + b
-                c = "0" * (self.nb_digits + 1 - len(c)) + c
-            if self.inverted_result:
-                c = c[::-1]
-            sequences.append(f"{a}+{b}={c}$")
-
-        sequences = self.tensorize(sequences)
-        ar_mask = (sequences == self.char2id["="]).long()
-        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
-        return sequences, ar_mask
-
-    def seq2str(self, seq):
-        return "".join(self.id2char[x.item()] for x in seq)
-
-
-# class ProblemUnion(Problem):
-# problems = [ProblemByheart()]
-# nb_common_codes = 100
-
-# def generate_sequences(nb_samples):
-# problem_indexes = torch.randint(len(problems), (nb_samples,))
-# nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
-# print(f"{nb_samples_per_problem}")
-# all_seq = []
-# for nb, p in zip(nb_samples_per_problem, problems):
-# all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
-# return all_seq
-
-# for strain, stest in zip(train_seq, test_seq):
-# s = torch.cat((strain, stest), 0)
-
-####################
+import problems
 
 
 class SandBox(Task):
@@ -323,6 +181,43 @@ 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}%"
         )
 
+        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
+        if save_attention_image is None:
+            logger("no save_attention_image (is pycairo installed?)")
+        else:
+            for k in range(10):
+                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"sandbox_attention_{k}_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}")
+
 
 ######################################################################
 
@@ -478,6 +373,10 @@ class PicoCLVR(Task):
             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
         )
 
+        logger(
+            f"main_test_accuracy {n_epoch} {1-nb_missing_properties/nb_requested_properties}"
+        )
+
     ######################################################################
 
     def produce_results(
@@ -748,6 +647,8 @@ class Maze(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}%"
         )
 
+        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
         if count is not None:
             proportion_optimal = count.diagonal().sum().float() / count.sum()
             logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
@@ -887,6 +788,8 @@ class Snake(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}%"
         )
 
+        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
 
 ######################################################################
 
@@ -996,6 +899,8 @@ class Stack(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}%"
         )
 
+        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
         ##############################################################
         # Log a few generated sequences
         input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
@@ -1102,9 +1007,9 @@ class RPL(Task):
         self.id2token = dict([(n, c) for c, n in self.token2id.items()])
 
         self.t_nul = self.token2id["<nul>"]
-        self.t_input = self.token2id["<input>"]
-        self.t_output = self.token2id["<output>"]
-        self.t_prog = self.token2id["<prog>"]
+        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)
@@ -1268,6 +1173,8 @@ class RPL(Task):
                 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}%"
             )
 
+            logger(f"main_test_accuracy {n_epoch} {1-test_nb_errors/test_nb_total}")
+
         test_nb_total, test_nb_errors = compute_nb_errors_output(
             self.test_input[:1000].to(self.device), nb_to_log=10
         )
@@ -1276,6 +1183,42 @@ class RPL(Task):
             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 None:
+            logger("no save_attention_image (is pycairo installed?)")
+        else:
+            ns = torch.randint(self.test_input.size(0), (1,)).item()
+            input = self.test_input[ns : ns + 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}")
+
 
 ######################################################################
 
@@ -1430,6 +1373,8 @@ class Expr(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}%"
         )
 
+        logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
         nb_total = test_nb_delta.sum() + test_nb_missed
         for d in range(test_nb_delta.size(0)):
             logger(