X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;fp=main.py;h=dace5f2e2276ff4ba2472b90677b253dea58a46e;hb=798d9526e726b644979cf1124e714f705fdd5966;hp=9437136ce1a45b066d6884e205540083bfb4d2d6;hpb=3528c66810984055a0e0f0cf7a4169c3340be0c8;p=picoclvr.git

diff --git a/main.py b/main.py
index 9437136..dace5f2 100755
--- a/main.py
+++ b/main.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, sys, argparse, time, tqdm, os, datetime
+import math, sys, argparse, time, tqdm, os, datetime, warnings
 
 import torch, torchvision
 from torch import nn
@@ -46,10 +46,12 @@ parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
 
 ########################################
 
-parser.add_argument("--nb_epochs", type=int, default=25)
+parser.add_argument("--nb_epochs", type=int, default=50)
 
 parser.add_argument("--batch_size", type=int, default=None)
 
+parser.add_argument("--physical_batch_size", type=int, default=None)
+
 parser.add_argument("--nb_train_samples", type=int, default=None)
 
 parser.add_argument("--nb_test_samples", type=int, default=None)
@@ -82,7 +84,7 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--no_checkpoint", action="store_true", default=False)
 
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--resume", action="store_true", default=False)
 
 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 
@@ -144,6 +146,11 @@ parser.add_argument("--snake_nb_colors", type=int, default=5)
 
 parser.add_argument("--snake_length", type=int, default=200)
 
+##############################
+# ByHeart options
+
+parser.add_argument("--byheart_separation", type=int, default=1)
+
 ##############################
 # Stack options
 
@@ -153,7 +160,7 @@ parser.add_argument("--stack_nb_stacks", type=int, default=3)
 
 parser.add_argument("--stack_nb_digits", type=int, default=3)
 
-parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
+parser.add_argument("--stack_fraction_values_for_train", type=float, default=None)
 
 ##############################
 # Expr options
@@ -367,7 +374,7 @@ else:
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
-    if not args.overwrite_results:
+    if not args.resume:
         print(f"result directory {args.result_dir} already exists")
         exit(1)
 
@@ -422,6 +429,14 @@ picoclvr_pruner_eval = (
 
 ######################################################################
 
+if args.physical_batch_size is None:
+    args.physical_batch_size = args.batch_size
+else:
+    assert args.batch_size % args.physical_batch_size == 0
+
+assert args.nb_train_samples % args.batch_size == 0
+assert args.nb_test_samples % args.batch_size == 0
+
 if args.task == "file":
     assert (
         args.filetask_train_file is not None and args.filetask_test_file is not None
@@ -431,7 +446,7 @@ if args.task == "file":
         args.filetask_test_file,
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         shuffle=True,
         device=device,
     )
@@ -439,10 +454,10 @@ if args.task == "file":
 
 elif args.task == "byheart":
     task = tasks.SandBox(
-        problem=problems.ProblemByHeart(),
+        problem=problems.ProblemByHeart(separation=args.byheart_separation),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -453,7 +468,7 @@ elif args.task == "learnop":
         problem=problems.ProblemLearnOperator(),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -464,7 +479,7 @@ elif args.task == "guessop":
         problem=problems.ProblemGuessOperator(),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -475,7 +490,7 @@ elif args.task == "twotargets":
         problem=problems.ProblemTwoTargets(),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -485,7 +500,7 @@ elif args.task == "memory":
         problem=problems.ProblemMemory(),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -497,7 +512,7 @@ elif args.task == "mixing":
         ),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -507,7 +522,7 @@ elif args.task == "addition":
         problem=problems.ProblemAddition(),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         device=device,
     )
@@ -516,7 +531,7 @@ elif args.task == "picoclvr":
     task = tasks.PicoCLVR(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.picoclvr_height,
         width=args.picoclvr_width,
         nb_colors=args.picoclvr_nb_colors,
@@ -530,7 +545,7 @@ elif args.task == "mnist":
     task = tasks.MNIST(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         device=device,
     )
 
@@ -538,18 +553,18 @@ elif args.task == "maze":
     task = tasks.Maze(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.maze_height,
         width=args.maze_width,
         nb_walls=args.maze_nb_walls,
-        device=device,
+        device="cpu",
     )
 
 elif args.task == "snake":
     task = tasks.Snake(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.snake_height,
         width=args.snake_width,
         nb_colors=args.snake_nb_colors,
@@ -562,7 +577,7 @@ elif args.task == "stack":
     task = tasks.Stack(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         nb_steps=args.stack_nb_steps,
         nb_stacks=args.stack_nb_stacks,
@@ -579,7 +594,7 @@ elif args.task == "expr":
         sequence_length=args.expr_sequence_length,
         operand_max=args.expr_operand_max,
         result_max=args.expr_result_max,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         device=device,
     )
 
@@ -587,7 +602,7 @@ elif args.task == "rpl":
     task = tasks.RPL(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         nb_starting_values=args.rpl_nb_starting_values,
         max_input=args.rpl_max_input,
         prog_len=args.rpl_prog_len,
@@ -601,7 +616,7 @@ elif args.task == "grid":
     task = tasks.Grid(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         size=args.grid_size,
         fraction_play=args.grid_fraction_play,
         logger=log_string,
@@ -612,7 +627,7 @@ elif args.task == "qmlp":
     task = tasks.QMLP(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         result_dir=args.result_dir,
         logger=log_string,
         device=device,
@@ -622,7 +637,7 @@ elif args.task == "greed":
     task = tasks.Greed(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.greed_height,
         width=args.greed_width,
         T=args.greed_T,
@@ -773,8 +788,6 @@ log_string(f"learning_rate_schedule {learning_rate_schedule}")
 
 ##############################
 
-nb_samples_seen = 0
-
 if nb_epochs_finished >= args.nb_epochs:
     task.produce_results(
         n_epoch=nb_epochs_finished,
@@ -802,40 +815,77 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 
     model.train()
 
-    nb_train_samples, acc_train_loss = 0, 0.0
+    nb_train_samples, acc_train_loss_ar, acc_train_loss_ae = 0, 0.0, 0.0
 
     for input in task.batches(split="train"):
         input = input.to(device)
-        output = model(mygpt.BracketedSequence(input)).x
-        loss = F.cross_entropy(output.transpose(1, 2), input)
-        acc_train_loss += loss.item() * input.size(0)
+
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.zero_grad()
+
+        if args.autoencoder_weight > 0:
+            bs_ar, bs_ae = model(mygpt.BracketedSequence(input), autoencoder=True)
+            output_ar, output_ae = bs_ar.x, bs_ae.x
+            loss_ar = F.cross_entropy(output_ar.transpose(1, 2), input)
+            loss_ae = F.cross_entropy(output_ae[:, 1:].transpose(1, 2), input[:, :-1])
+        else:
+            output = model(mygpt.BracketedSequence(input)).x
+            loss_ar = F.cross_entropy(output.transpose(1, 2), input)
+            loss_ae = loss_ar.new_full((1,), 0.0)
+
+        acc_train_loss_ar += loss_ar.item() * input.size(0)
+        acc_train_loss_ae += loss_ae.item() * input.size(0)
+
         nb_train_samples += input.size(0)
-        nb_samples_seen += input.size(0)
 
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
+        (loss_ar + args.autoencoder_weight * loss_ae).backward()
+
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.step()
 
     with torch.autograd.no_grad():
         model.eval()
 
-        nb_test_samples, acc_test_loss = 0, 0.0
+        nb_test_samples, acc_test_loss_ar, acc_test_loss_ae = 0, 0.0, 0.0
+        nb_samples_accumulated = 0
 
         for input in task.batches(split="test"):
             input = input.to(device)
 
-            output = model(mygpt.BracketedSequence(input)).x
-            loss = F.cross_entropy(output.transpose(1, 2), input)
-            acc_test_loss += loss.item() * input.size(0)
+            if args.autoencoder_weight > 0:
+                bs_ar, bs_ae = model(mygpt.BracketedSequence(input), autoencoder=True)
+                output_ar, output_ae = bs_ar.x, bs_ae.x
+                loss_ae = F.cross_entropy(
+                    output_ae[:, 1:].transpose(1, 2), input[:, :-1]
+                )
+                acc_test_loss_ae += loss_ae.item() * input.size(0)
+            else:
+                bs_ar = model(mygpt.BracketedSequence(input))
+                output_ar = bs_ar.x
+
+            loss_ar = F.cross_entropy(output_ar.transpose(1, 2), input)
+
+            acc_test_loss_ar += loss_ar.item() * input.size(0)
+
             nb_test_samples += input.size(0)
 
-        train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
-        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+        train_ar_perplexity = math.exp(min(100, acc_train_loss_ar / nb_train_samples))
+        test_ar_perplexity = math.exp(min(100, acc_test_loss_ar / nb_test_samples))
 
         log_string(
-            f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
+            f"perplexity_ar {n_epoch} train_set {train_set_perplexity} train_prediction {train_ar_perplexity} test_prediction {test_ar_perplexity}"
         )
 
+        if args.autoencoder_weight > 0:
+            train_ae_perplexity = math.exp(
+                min(100, acc_train_loss_ae / nb_train_samples)
+            )
+            test_ae_perplexity = math.exp(min(100, acc_test_loss_ae / nb_test_samples))
+
+            log_string(
+                f"perplexity_ae {n_epoch} train_set {train_set_perplexity} train_prediction {train_ae_perplexity} test_prediction {test_ae_perplexity}"
+            )
+
         task.produce_results(
             n_epoch=n_epoch,
             model=model,