Update.

diff --git a/beaver.py b/beaver.py
index 4f694da..dfbb7b6 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -170,8 +170,11 @@ def compute_perplexity(model, split="train"):
 
 
 def one_shot(gpt, task):
-    pass
-
+    t = gpt.training
+    gpt.eval()
+    for input, targets in task.policy_batches():
+        output = gpt(mygpt.BracketedSequence(input), with_readout = False).x
+    gpt.train(t)
 
 ######################################################################
 
@@ -215,25 +218,25 @@ class TaskMaze(Task):
         self.width = width
         self.device = device
 
-        mazes_train, paths_train = maze.create_maze_data(
+        train_mazes, train_paths, train_policies = maze.create_maze_data(
             nb_train_samples,
             height=height,
             width=width,
             nb_walls=nb_walls,
             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
         )
-        mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
-        self.train_input = self.map2seq(mazes_train, paths_train)
+        self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
+        self.train_policies = train_policies.to(device)
 
-        mazes_test, paths_test = maze.create_maze_data(
+        test_mazes, test_paths, test_policies = maze.create_maze_data(
             nb_test_samples,
             height=height,
             width=width,
             nb_walls=nb_walls,
             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
         )
-        mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
-        self.test_input = self.map2seq(mazes_test, paths_test)
+        self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
+        self.test_policies = test_policies.to(device)
 
         self.nb_codes = self.train_input.max() + 1
 
@@ -247,6 +250,24 @@ class TaskMaze(Task):
         ):
             yield batch
 
+    def policy_batches(self, split="train", nb_to_use=-1):
+        assert split in {"train", "test"}
+        input = self.train_input if split == "train" else self.test_input
+        targets = self.train_policies if split == "train" else self.test_policies
+        input = input[:, : self.height * self.width]
+        targets = targets.flatten(-2) * (input != maze.v_wall)[:,None]
+
+        if nb_to_use > 0:
+            input = input[:nb_to_use]
+            targets = targets[:nb_to_use]
+
+        for batch in tqdm.tqdm(
+            zip(input.split(self.batch_size), targets.split(self.batch_size)),
+            dynamic_ncols=True,
+            desc=f"epoch-{split}",
+        ):
+            yield batch
+
     def vocabulary_size(self):
         return self.nb_codes
 

diff --git a/maze.py b/maze.py
index cfdede3..d11ab6e 100755 (executable)
--- a/maze.py
+++ b/maze.py
@@ -113,18 +113,16 @@ def compute_policy(walls, i, j):
 ######################################################################
 
 
-def mark_path(walls, i, j, goal_i, goal_j):
-    policy = compute_policy(walls, goal_i, goal_j)
+def mark_path(walls, i, j, goal_i, goal_j, policy):
     action = torch.distributions.categorical.Categorical(
         policy.permute(1, 2, 0)
     ).sample()
-    walls[i, j] = 4
     n, nmax = 0, walls.numel()
     while i != goal_i or j != goal_j:
         di, dj = [(0, -1), (0, 1), (-1, 0), (1, 0)][action[i, j]]
         i, j = i + di, j + dj
         assert walls[i, j] == 0
-        walls[i, j] = 4
+        walls[i, j] = v_path
         n += 1
         assert n < nmax
 
@@ -160,6 +158,7 @@ def create_maze_data(
 ):
     mazes = torch.empty(nb, height, width, dtype=torch.int64)
     paths = torch.empty(nb, height, width, dtype=torch.int64)
+    policies = torch.empty(nb, 4, height, width, dtype=torch.int64)
 
     for n in progress_bar(range(nb)):
         maze = create_maze(height, width, nb_walls)
@@ -168,18 +167,21 @@ def create_maze_data(
             start, goal = i[torch.randperm(i.size(0))[:2]]
             if (start - goal).abs().sum() >= dist_min:
                 break
+        start_i, start_j, goal_i, goal_j = start[0], start[1], goal[0], goal[1]
 
+        policy = compute_policy(maze, goal_i, goal_j)
         path = maze.clone()
-        mark_path(path, start[0], start[1], goal[0], goal[1])
-        maze[start[0], start[1]] = v_start
-        maze[goal[0], goal[1]] = v_goal
-        path[start[0], start[1]] = v_start
-        path[goal[0], goal[1]] = v_goal
+        mark_path(path, start_i, start_j, goal_i, goal_j, policy)
+        maze[start_i, start_j] = v_start
+        maze[goal_i, goal_j] = v_goal
+        path[start_i, start_j] = v_start
+        path[goal_i, goal_j] = v_goal
 
         mazes[n] = maze
         paths[n] = path
+        policies[n] = policy
 
-    return mazes, paths
+    return mazes, paths, policies
 
 
 ######################################################################

diff --git a/mygpt.py b/mygpt.py
index a0f3dbf..d424eef 100755 (executable)
--- a/mygpt.py
+++ b/mygpt.py
@@ -246,11 +246,11 @@ class MyGPT(nn.Module):
                     m.bias.zero_()
                     m.weight.fill_(1.0)
 
-    def forward(self, bs):
+    def forward(self, bs, with_readout = True):
         bs.x = F.pad(bs.x, (1, -1))
         bs = self.embedding(bs)
         bs = self.trunk(bs)
-        bs = self.readout(bs)
+        if with_readout: bs = self.readout(bs)
         return bs