X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=443419eb340704273b64152edadb1286aae50cbf;hb=refs%2Fheads%2Fmaster;hp=78910a06d7a24977b462af0836a9219073381507;hpb=b35745d09b33aed20670ecb96726f89206487a24;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 78910a0..443419e 100755
--- a/tasks.py
+++ b/tasks.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, os, tqdm
+import math, os, tqdm, warnings
 
 import torch, torchvision
 
@@ -27,6 +27,7 @@ def masked_inplace_autoregression(
     ar_mask,
     deterministic_synthesis,
     forbidden_tokens=None,
+    logit_biases=None,
     progress_bar_desc="autoregression",
     device=torch.device("cpu"),
 ):
@@ -48,7 +49,11 @@ def masked_inplace_autoregression(
 
         for input, ar_mask in batches:
             model.masked_inplace_autoregression(
-                input, ar_mask, forbidden_tokens, deterministic_synthesis
+                input,
+                ar_mask,
+                deterministic_synthesis,
+                forbidden_tokens,
+                logit_biases,
             )
 
         model.train(t)
@@ -58,7 +63,7 @@ def masked_inplace_autoregression(
 
 
 class Task:
-    def batches(self, split="train"):
+    def batches(self, split="train", nb_to_use=-1, desc=None):
         pass
 
     def vocabulary_size(self):
@@ -71,7 +76,7 @@ class Task:
 
 
 class TaskFromFile(Task):
-    def tensorize(self, pairs):
+    def tensorize(self, pairs, shuffle):
         len_max = max([len(x[0]) for x in pairs])
 
         input = torch.cat(
@@ -98,6 +103,11 @@ class TaskFromFile(Task):
             0,
         ).to("cpu")
 
+        if shuffle:
+            i = torch.randperm(input.size(0))
+            input = input[i].contiguous()
+            pred_mask = pred_mask[i].contiguous()
+
         return input, pred_mask
 
     # trim all the tensors in the tuple z to remove as much token from
@@ -117,32 +127,52 @@ class TaskFromFile(Task):
 
     def __init__(
         self,
-        filename,
+        train_filename,
+        test_filename,
         nb_train_samples,
         nb_test_samples,
         batch_size,
+        shuffle=False,
         device=torch.device("cpu"),
     ):
         self.batch_size = batch_size
         self.device = device
 
-        pairs = []
-        with open(filename, "r") as f:
-            for _ in range(nb_train_samples + nb_test_samples):
-                sequence = f.readline().strip()
-                pred_mask = f.readline().strip()
-                assert len(sequence) == len(pred_mask)
-                assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
-                pairs.append((sequence, pred_mask))
-
-        symbols = ["#"] + list(set("".join([x[0] for x in pairs])) - set(["#"]))
+        def read_file(filename, nb=-1):
+            pairs = []
+            with open(filename, "r") as f:
+                while True:
+                    sequence = f.readline().strip()
+                    if not sequence:
+                        break
+                    pred_mask = f.readline().strip()
+                    assert len(sequence) == len(pred_mask)
+                    assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
+                    pairs.append((sequence, pred_mask))
+                    if len(pairs) == nb:
+                        break
+
+            if nb > 0:
+                pairs = pairs[:nb]
+                assert len(pairs) == nb
+
+            return pairs
+
+        train_pairs = read_file(train_filename, nb_train_samples)
+        test_pairs = read_file(test_filename, nb_test_samples)
+
+        symbols = ["#"] + list(
+            set("".join([x[0] for x in train_pairs + test_pairs])) - set(["#"])
+        )
         self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 
         self.train_input, self.train_pred_masks = self.tensorize(
-            pairs[:nb_train_samples]
+            train_pairs, shuffle=shuffle
+        )
+        self.test_input, self.test_pred_masks = self.tensorize(
+            test_pairs, shuffle=shuffle
         )
-        self.test_input, self.test_pred_masks = self.tensorize(pairs[nb_train_samples:])
 
     def batches(self, split="train", nb_to_use=-1, desc=None):
         assert split in {"train", "test"}
@@ -173,7 +203,7 @@ class TaskFromFile(Task):
 
         logger(f"----------------------------------------------------------")
 
-        for e in self.tensor2str(result[:10]):
+        for e in self.tensor2str(result[:50]):
             logger(f"test_before {e}")
 
         masked_inplace_autoregression(
@@ -187,7 +217,7 @@ class TaskFromFile(Task):
 
         logger(f"----------------------------------------------------------")
 
-        for e, c in zip(self.tensor2str(result[:10]), self.tensor2str(correct[:10])):
+        for e, c in zip(self.tensor2str(result[:50]), self.tensor2str(correct[:50])):
             logger(f"test_after  {e}")
             logger(f"correct     {c}")
 
@@ -459,7 +489,7 @@ class PicoCLVR(Task):
         self.train_input = self.tensorize(self.train_descr)
         self.test_input = self.tensorize(self.test_descr)
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -724,15 +754,17 @@ class Maze(Task):
     def compute_error(
         self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
     ):
+        model_device = next(model.parameters()).device
         nb_total, nb_correct = 0, 0
         count = torch.zeros(
             self.width * self.height,
             self.width * self.height,
-            device=self.device,
+            device=model_device,
             dtype=torch.int64,
         )
 
         for input in self.batches(split, nb_to_use):
+            input = input.to(model_device)
             result = input.clone()
             ar_mask = result.new_zeros(result.size())
             ar_mask[:, self.height * self.width :] = 1
@@ -806,7 +838,7 @@ class Maze(Task):
                         eol = " " if j < count.size(1) - 1 else "\n"
                         f.write(f"{count[i,j]}{eol}")
 
-        input = self.test_input[:48]
+        input = self.test_input[:48].to(next(model.parameters()).device)
         result = input.clone()
         ar_mask = result.new_zeros(result.size())
         ar_mask[:, self.height * self.width :] = 1
@@ -1068,6 +1100,34 @@ class Stack(Task):
             device=self.device,
         )
 
+        #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+        for label, input in [
+            ("train", self.train_input[:32]),
+            ("test", self.test_input[:32]),
+        ]:
+            output = model(BracketedSequence(input)).x
+            output = output.log_softmax(dim=-1)
+            filename = os.path.join(
+                result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
+            )
+            with open(filename, "w") as f:
+                for n in range(input.size(0)):
+                    s = stack.seq_to_str(
+                        input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
+                    )
+                    for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
+                        u = (
+                            " " * (10 - len(w))
+                            + w
+                            + " "
+                            + str(output[n][t][k].exp().item())
+                            + "\n"
+                        )
+                        f.write(u)
+                    f.write("\n")
+            logger(f"wrote {filename}")
+        #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
         for n in range(result.size(0)):
             logger(
                 f"test_after  {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
@@ -1655,7 +1715,7 @@ class Grid(Task):
         self.t_nul = self.token2id["#"]
         self.t_true = self.token2id["true"]
         self.t_false = self.token2id["false"]
-        self.t_pipe = self.token2id["|"]
+        # self.t_pipe = self.token2id["|"]
 
         # Tokenize the train and test sets
         self.train_input = self.str2tensor(self.train_descr)
@@ -1664,7 +1724,7 @@ class Grid(Task):
             None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
         )
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -1793,7 +1853,7 @@ class QMLP(Task):
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -1836,3 +1896,200 @@ class QMLP(Task):
 
 
 ######################################################################
+
+import greed
+
+
+class Greed(Task):
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        height,
+        width,
+        T,
+        nb_walls,
+        nb_coins,
+        logger=None,
+        device=torch.device("cpu"),
+    ):
+        super().__init__()
+
+        self.batch_size = batch_size
+        self.device = device
+
+        self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins)
+
+        states, actions, rewards = self.world.generate_episodes(
+            nb_train_samples + nb_test_samples
+        )
+        seq = self.world.episodes2seq(states, actions, rewards)
+        self.train_input = seq[:nb_train_samples].to(self.device)
+        self.test_input = seq[nb_train_samples:].to(self.device)
+
+    def wipe_lookahead_rewards(self, batch):
+        t = torch.arange(batch.size(1), device=batch.device)[None, :]
+        u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
+        lr_mask = (t <= u).long() * (
+            t % self.world.it_len == self.world.index_lookahead_reward
+        ).long()
+
+        return (
+            lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+            + (1 - lr_mask) * batch
+        )
+
+    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
+        ):
+            yield self.wipe_lookahead_rewards(batch)
+
+    def vocabulary_size(self):
+        return self.world.nb_codes
+
+    def thinking_autoregression(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+    ):
+        snapshots = []
+
+        def ar(result, ar_mask, logit_biases=None):
+            ar_mask = ar_mask.expand_as(result)
+            result *= 1 - ar_mask
+            masked_inplace_autoregression(
+                model,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis=deterministic_synthesis,
+                logit_biases=logit_biases,
+                device=self.device,
+                progress_bar_desc=None,
+            )
+            warnings.warn("keeping thinking snapshots", RuntimeWarning)
+            snapshots.append(result[:100].detach().clone())
+
+        # Generate iteration after iteration
+
+        result = self.test_input[:250].clone()
+        # Erase all the content but that of the first iteration
+        result[:, self.world.it_len :] = -1
+        # Set the lookahead_reward of the firs to UNKNOWN
+        result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code(
+            greed.REWARD_UNKNOWN
+        )
+
+        t = torch.arange(result.size(1), device=result.device)[None, :]
+
+        for u in tqdm.tqdm(
+            range(0, result.size(1), self.world.it_len),
+            desc="thinking",
+        ):
+            # Generate the next state but keep the initial one, the
+            # lookahead_reward of previous iterations are set to
+            # UNKNOWN
+            if u > 0:
+                result[
+                    :, u + self.world.index_lookahead_reward
+                ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+                ar_mask = (t >= u + self.world.index_states).long() * (
+                    t < u + self.world.index_states + self.world.state_len
+                ).long()
+                ar(result, ar_mask)
+
+            # Generate the action and reward with lookahead_reward to +1
+            result[
+                :, u + self.world.index_lookahead_reward
+            ] = self.world.lookahead_reward2code(greed.REWARD_PLUS)
+            ar_mask = (t >= u + self.world.index_reward).long() * (
+                t <= u + self.world.index_action
+            ).long()
+            ar(result, ar_mask)
+
+            # Set the lookahead_reward to UNKNOWN for the next iterations
+            result[
+                :, u + self.world.index_lookahead_reward
+            ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+
+        filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            for n in range(snapshots[0].size(0)):
+                for s in snapshots:
+                    lr, s, a, r = self.world.seq2episodes(
+                        s[n : n + 1],
+                    )
+                    str = self.world.episodes2str(
+                        lr, s, a, r, unicode=True, ansi_colors=True
+                    )
+                    f.write(str)
+                f.write("\n\n")
+
+        # Saving the generated sequences
+
+        lr, s, a, r = self.world.seq2episodes(result)
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+    ):
+        result = self.wipe_lookahead_rewards(self.test_input[:250].clone())
+
+        # Saving the ground truth
+
+        lr, s, a, r = self.world.seq2episodes(
+            result,
+        )
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+        # Re-generating from the first frame
+
+        ar_mask = (
+            torch.arange(result.size(1), device=result.device) >= self.world.it_len
+        ).long()[None, :]
+        ar_mask = ar_mask.expand_as(result)
+        result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
+
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis,
+            device=self.device,
+        )
+
+        # Saving the generated sequences
+
+        lr, s, a, r = self.world.seq2episodes(
+            result,
+        )
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+        self.thinking_autoregression(
+            n_epoch, model, result_dir, logger, deterministic_synthesis, nmax
+        )
+
+
+######################################################################