X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=49cb1f672075795f84021e9e8a5d6573a6d6f56e;hb=10c1e2159ef57a55724fb1753381dc30e8aa77c2;hp=a3a56151f2ddb18994580979c5a4c568611108f5;hpb=15d0ad06d29c3e8042bdf64b78a01572e9b7ddc5;p=beaver.git

diff --git a/beaver.py b/beaver.py
index a3a5615..49cb1f6 100755
--- a/beaver.py
+++ b/beaver.py
@@ -64,6 +64,8 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
+parser.add_argument("--random_regression_order", action="store_true", default=False)
+
 parser.add_argument("--no_checkpoint", action="store_true", default=False)
 
 parser.add_argument("--overwrite_results", action="store_true", default=False)
@@ -79,11 +81,14 @@ parser.add_argument("--maze_width", type=int, default=21)
 
 parser.add_argument("--maze_nb_walls", type=int, default=15)
 
+##############################
+# one-shot prediction
+
 parser.add_argument("--oneshot", action="store_true", default=False)
 
 parser.add_argument("--oneshot_input", type=str, default="head")
 
-parser.add_argument("--oneshot_output", type=str, default="policy")
+parser.add_argument("--oneshot_output", type=str, default="trace")
 
 ######################################################################
 
@@ -126,18 +131,38 @@ for n in vars(args):
 ######################################################################
 
 
+def random_order(result, fixed_len):
+    if args.random_regression_order:
+        order = torch.rand(result.size(), device=result.device)
+        order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device)
+        return order.sort(1).indices
+    else:
+        return torch.arange(result.size(1)).unsqueeze(0).expand(result.size(0), -1)
+
+
+def shuffle(x, order, reorder=False):
+    if x.dim() == 3:
+        order = order.unsqueeze(-1).expand(-1, -1, x.size(-1))
+    if reorder:
+        y = x.new(x.size())
+        y.scatter_(1, order, x)
+        return y
+    else:
+        return x.gather(1, order)
+
+
 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
 # tokens that should be generated
 
 
-def masked_inplace_autoregression(model, batch_size, input, ar_mask):
+def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
     for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
         i = (ar_mask.sum(0) > 0).nonzero()
         if i.min() > 0:
             # Needed to initialize the model's cache
-            model(mygpt.BracketedSequence(input, 0, i.min()))
+            model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
         for s in range(i.min(), i.max() + 1):
-            output = model(mygpt.BracketedSequence(input, s, 1)).x
+            output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
             logits = output[:, s]
             if args.deterministic_synthesis:
                 t_next = logits.argmax(1)
@@ -159,8 +184,9 @@ def compute_perplexity(model, split="train"):
 
         for input in task.batches(split=split):
             input = input.to(device)
-
-            output = model(mygpt.BracketedSequence(input)).x
+            order = random_order(input, task.height * task.width)
+            input = shuffle(input, order)
+            output = model(mygpt.BracketedSequence(input), order=order).x
             loss = F.cross_entropy(output.transpose(1, 2), input)
             acc_loss += loss.item() * input.size(0)
             nb_samples += input.size(0)
@@ -224,12 +250,10 @@ def oneshot(gpt, task):
 
         acc_train_loss, nb_train_samples = 0, 0
         for mazes, policies in task.policy_batches(split="train"):
-            ####
-            # print(f'{mazes.size()=} {policies.size()=}')
-            # s = maze.stationary_densities(
-            # exit(0)
-            ####
-            output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+            order = random_order(mazes, task.height * task.width)
+            x = shuffle(mazes, order)
+            x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+            output_gpt = shuffle(x, order, reorder=True)
             output = model(output_gpt)
 
             loss = compute_loss(mazes, output, policies, task.height, task.width)
@@ -242,7 +266,10 @@ def oneshot(gpt, task):
 
         acc_test_loss, nb_test_samples = 0, 0
         for mazes, policies in task.policy_batches(split="test"):
-            output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+            order = random_order(mazes, task.height * task.width)
+            x = shuffle(mazes, order)
+            x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+            output_gpt = shuffle(x, order, reorder=True)
             output = model(output_gpt)
             loss = compute_loss(mazes, output, policies, task.height, task.width)
             acc_test_loss += loss.item() * mazes.size(0)
@@ -255,7 +282,10 @@ def oneshot(gpt, task):
         # -------------------
         mazes = task.test_input[:32, : task.height * task.width]
         policies = task.test_policies[:32]
-        output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+        order = random_order(mazes, task.height * task.width)
+        x = shuffle(mazes, order)
+        x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+        output_gpt = shuffle(x, order, reorder=True)
         output = model(output_gpt)
         if args.oneshot_output == "policy":
             targets = policies.permute(0, 2, 1)
@@ -268,7 +298,6 @@ def oneshot(gpt, task):
                 policies.view(-1, 4, task.height, task.width),
             ).flatten(-2)
             scores = output
-            print(scores)
         else:
             raise ValueError(f"{args.oneshot_output=}")
 
@@ -293,7 +322,7 @@ def oneshot(gpt, task):
 
 
 class Task:
-    def batches(self, split="train"):
+    def batches(self, split="train", nb_to_use=-1, desc=None):
         pass
 
     def vocabulary_size(self):
@@ -353,17 +382,19 @@ class TaskMaze(Task):
 
         self.nb_codes = self.train_input.max() + 1
 
-    def batches(self, split="train", nb_to_use=-1):
+    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=f"epoch-{split}"
+            input.split(self.batch_size), dynamic_ncols=True, desc=desc
         ):
             yield batch
 
-    def policy_batches(self, split="train", nb_to_use=-1):
+    def policy_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
         policies = self.train_policies if split == "train" else self.test_policies
@@ -374,10 +405,12 @@ class TaskMaze(Task):
             input = input[:nb_to_use]
             policies = policies[:nb_to_use]
 
+        if desc is None:
+            desc = f"epoch-{split}"
         for batch in tqdm.tqdm(
             zip(input.split(self.batch_size), policies.split(self.batch_size)),
             dynamic_ncols=True,
-            desc=f"epoch-{split}",
+            desc=desc,
         ):
             yield batch
 
@@ -391,7 +424,11 @@ class TaskMaze(Task):
             ar_mask = result.new_zeros(result.size())
             ar_mask[:, self.height * self.width :] = 1
             result *= 1 - ar_mask
-            masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
+            order = random_order(result, self.height * self.width)
+            masked_inplace_autoregression(
+                model, self.batch_size, result, ar_mask, order=order
+            )
+            result = shuffle(result, order, reorder=True)
             mazes, paths = self.seq2map(result)
             nb_correct += maze.path_correctness(mazes, paths).long().sum()
             nb_total += mazes.size(0)
@@ -536,12 +573,6 @@ log_string(f"learning_rate_schedule {learning_rate_schedule}")
 
 ##############################
 
-if args.oneshot:
-    oneshot(model, task)
-    exit(0)
-
-##############################
-
 if nb_epochs_finished >= args.nb_epochs:
     n_epoch = nb_epochs_finished
     train_perplexity = compute_perplexity(model, split="train")
@@ -553,8 +584,6 @@ if nb_epochs_finished >= args.nb_epochs:
 
     task.produce_results(n_epoch, model)
 
-    exit(0)
-
 ##############################
 
 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
@@ -577,7 +606,9 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 
     for input in task.batches(split="train"):
         input = input.to(device)
-        output = model(mygpt.BracketedSequence(input)).x
+        order = random_order(input, task.height * task.width)
+        input = shuffle(input, order)
+        output = model(mygpt.BracketedSequence(input), order=order).x
         loss = F.cross_entropy(output.transpose(1, 2), input)
         acc_train_loss += loss.item() * input.size(0)
         nb_train_samples += input.size(0)
@@ -609,3 +640,8 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs):
     log_string(f"saved checkpoint {checkpoint_name}")
 
 ######################################################################
+
+if args.oneshot:
+    oneshot(model, task)
+
+######################################################################