X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=066f1bbec05fcc0d65365823ea60931010e118cb;hb=26ef53ee3769c3b6b92b85d15b5a43cbd18ede07;hp=234e78060a3274026f50ab400e434af8e3528350;hpb=40d0010b6b76304e340ae734cb9814e714b691cc;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 234e780..066f1bb 100755
--- a/tasks.py
+++ b/tasks.py
@@ -72,157 +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]
-        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):
@@ -329,6 +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}")
+
 
 ######################################################################
 
@@ -484,6 +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(
@@ -754,6 +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}%")
@@ -893,6 +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}")
+
 
 ######################################################################
 
@@ -1002,6 +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)]
@@ -1274,6 +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
         )
@@ -1282,10 +1183,13 @@ 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 not None:
-            input = self.test_input[:1].clone()
+        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]
+            input = input[:, :last].to(self.device)
 
             with torch.autograd.no_grad():
                 t = model.training
@@ -1469,6 +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(
@@ -1515,77 +1421,185 @@ class Expr(Task):
 
 ######################################################################
 
-import world
+import grid
+
 
+class Grid(Task):
+    # Make a tensor from a list of strings
+    def str2tensor(self, descr):
+        token_descr = [s.strip().split(" ") for s in descr]
+        l = max([len(s) for s in token_descr])
+        token_descr = [s + ["#"] * (l - len(s)) for s in token_descr]
+        id_descr = [[self.token2id[u] for u in s] for s in token_descr]
+        return torch.tensor(id_descr, device=self.device)
+
+    # Make a list of strings from a tensor
+    def tensor2str(self, x):
+        return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
+
+    # trim all the tensors in the tuple z to remove as much token from
+    # left and right in the first tensor. If z is a tuple, all its
+    # elements are trimed according to the triming for the first
+    def trim(self, z, token="#"):
+        n = self.token2id[token]
+        if type(z) == tuple:
+            x = z[0]
+            i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
+            a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
+            return tuple([t[:, a:b] for t in z])
+        else:
+            i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
+            a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
+            return z[:, a:b]
+
+    ######################
 
-class World(Task):
     def __init__(
         self,
         nb_train_samples,
         nb_test_samples,
         batch_size,
-        vqae_nb_epochs,
+        size,
         logger=None,
         device=torch.device("cpu"),
-        device_storage=torch.device("cpu"),
     ):
         super().__init__()
 
-        self.batch_size = batch_size
         self.device = device
+        self.batch_size = batch_size
+        self.grid_factory = grid.GridFactory(size=size)
 
-        (
-            train_frames,
-            train_action_seq,
-            test_frames,
-            test_action_seq,
-            self.frame2seq,
-            self.seq2frame,
-        ) = world.create_data_and_processors(
-            nb_train_samples,
-            nb_test_samples,
-            mode="first_last",
-            nb_steps=30,
-            nb_epochs=vqae_nb_epochs,
-            logger=logger,
-            device=device,
-            device_storage=device_storage,
+        if logger is not None:
+            logger(
+                f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+            )
+
+        self.train_descr = self.grid_factory.generate_samples(
+            nb_train_samples, lambda r: tqdm.tqdm(r)
+        )
+        self.test_descr = self.grid_factory.generate_samples(
+            nb_test_samples, lambda r: tqdm.tqdm(r)
         )
 
-        train_frame_seq = self.frame2seq(train_frames).to(device_storage)
-        test_frame_seq = self.frame2seq(test_frames).to(device_storage)
+        # Build the tokenizer
+        tokens = set()
+        for d in [self.train_descr, self.test_descr]:
+            for s in d:
+                for t in s.strip().split(" "):
+                    tokens.add(t)
+        # make this set a sorted list to get the same tensors given
+        # the same descr
+        tokens = list(tokens)
+        tokens.sort()
+        tokens = ["#"] + tokens
+        self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
+        self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
+        self.t_nul = self.token2id["#"]
+        self.t_true = self.token2id["true"]
+        self.t_false = self.token2id["false"]
 
-        nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
-        nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
+        # Tokenize the train and test sets
+        self.train_input = self.str2tensor(self.train_descr)
+        self.test_input = self.str2tensor(self.test_descr)
 
-        self.len_frame_seq = train_frame_seq.size(1)
-        self.len_action_seq = train_action_seq.size(1)
-        self.nb_codes = nb_frame_codes + nb_action_codes
+    def batches(self, split="train"):
+        assert split in {"train", "test"}
+        input = self.train_input if split == "train" else self.test_input
+        for batch in tqdm.tqdm(
+            input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
+        ):
+            yield self.trim(batch)
 
-        train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
+    def vocabulary_size(self):
+        return len(self.token2id)
+
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis
+    ):
+        correct = self.test_input[:1000]
+        result = correct.clone()
+        ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
+        result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
 
-        train_action_seq += nb_frame_codes
-        self.train_input = torch.cat(
-            (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
+        logger(f"----------------------------------------------------------")
+
+        for e in self.tensor2str(result[:10]):
+            logger(f"test_before {e}")
+
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis,
+            device=self.device,
         )
 
-        test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
-        test_action_seq += nb_frame_codes
-        self.test_input = torch.cat(
-            (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
+        logger(f"----------------------------------------------------------")
+
+        for e in self.tensor2str(result[:10]):
+            logger(f"test_after  {e}")
+
+        logger(f"----------------------------------------------------------")
+
+        nb_total = ar_mask.sum().item()
+        nb_correct = ((correct == result).long() * ar_mask).sum().item()
+
+        logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+        logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
+
+
+######################################################################
+
+import qmlp
+
+
+class QMLP(Task):
+
+    ######################
+
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        logger=None,
+        device=torch.device("cpu"),
+    ):
+        super().__init__()
+
+        self.device = device
+        self.batch_size = batch_size
+        self.nb_samples_per_mlp = 256
+
+        if logger is not None:
+            logger(
+                f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+            )
+
+        seq, q_test_set = generate_sequence_and_test_set(
+            nb_mlps=nb_train_samples+nb_test_samples,
+            nb_samples=self.nb_samples_per_mlp,
+            device=self.device,
+            batch_size=64,
+            nb_epochs=250,
+            nb_mlps_per_batch=1024
         )
 
-    def batches(self, split="train", nb_to_use=-1, desc=None):
+        self.train_input = seq[:nb_train_samples]
+        self.train_q_test_set = q_test_set[:nb_train_samples]
+        self.test_input = seq[nb_train_samples:]
+        self.test_q_test_set = q_test_set[nb_train_samples:]
+
+        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+    def batches(self, split="train"):
         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
+            input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
         ):
-            yield batch.to(self.device)
+            yield self.trim(batch)
 
     def vocabulary_size(self):
         return self.nb_codes
@@ -1593,17 +1607,15 @@ class World(Task):
     def produce_results(
         self, n_epoch, model, result_dir, logger, deterministic_synthesis
     ):
-        k = torch.arange(
-            2 * self.len_frame_seq + self.len_action_seq, device=self.device
-        )[None, :]
+        correct = self.test_input[:1000]
+        result = correct.clone()
+        ar_mask = torch.arange(result.size(1)) > self.nb_samples_per_mlp * 3 + 1
+        result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
 
-        input = self.test_input[:64].to(self.device)
-        result = input.clone()
+        logger(f"----------------------------------------------------------")
 
-        ar_mask = (
-            (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
-        )
-        result *= 1 - ar_mask
+        for e in self.tensor2str(result[:10]):
+            logger(f"test_before {e}")
 
         masked_inplace_autoregression(
             model,
@@ -1614,25 +1626,18 @@ class World(Task):
             device=self.device,
         )
 
-        seq_start = input[:, : self.len_frame_seq]
-        seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
-        seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
+        logger(f"----------------------------------------------------------")
 
-        result = torch.cat(
-            (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
-        )
-        result = result.reshape(-1, result.size(-1))
+        for e in self.tensor2str(result[:10]):
+            logger(f"test_after  {e}")
 
-        frames = self.seq2frame(result)
-        image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(
-            frames.float() / (world.Box.nb_rgb_levels - 1),
-            image_name,
-            nrow=12,
-            padding=1,
-            pad_value=0.0,
-        )
-        logger(f"wrote {image_name}")
+        logger(f"----------------------------------------------------------")
+
+        q_train_set = result[:, : nb_samples * 3]
+        q_params = result[:, nb_samples * 3 + 1 :]
+        error_test = evaluate_q_params(q_params, q_test_set, nb_mlps_per_batch=17)
+
+        logger(f"{error_test=}")
 
 
 ######################################################################