X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=901b1d0529bb525b1cbca2b5c3bc91af7b12bf36;hb=439c597d409c344283f8996f042daf79d3f24de2;hp=5b49468227503e3b1d3957229cec7334367bc3c7;hpb=68aa86a6645dfef3f919aad5732a1a09db77bfae;p=picoclvr.git

diff --git a/main.py b/main.py
index 5b49468..901b1d0 100755
--- a/main.py
+++ b/main.py
@@ -14,7 +14,8 @@ import torch, torchvision
 from torch import nn
 from torch.nn import functional as F
 
-import mygpt, tasks, tensorstack
+import ffutils
+import mygpt, tasks
 
 ######################################################################
 
@@ -34,8 +35,8 @@ parser = argparse.ArgumentParser(
 parser.add_argument(
     "--task",
     type=str,
-    default="picoclvr",
-    help="picoclvr, mnist, maze, snake, stack, expr",
+    default="sandbox",
+    help="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, world",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
@@ -58,15 +59,17 @@ parser.add_argument("--learning_rate", type=float, default=1e-4)
 
 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
 
-parser.add_argument("--dim_model", type=int, default=512)
+parser.add_argument("--model", type=str, default="37M")
 
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--dim_model", type=int, default=None)
 
-parser.add_argument("--dim_hidden", type=int, default=2048)
+parser.add_argument("--dim_keys", type=int, default=None)
 
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--dim_hidden", type=int, default=None)
 
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--nb_heads", type=int, default=None)
+
+parser.add_argument("--nb_blocks", type=int, default=None)
 
 parser.add_argument("--dropout", type=float, default=0.1)
 
@@ -81,6 +84,17 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 ##############################
 # picoclvr options
 
+parser.add_argument("--sandbox_level", type=int, default=0)
+
+parser.add_argument("--sandbox_levels_nb_items", type=int, default=25)
+
+parser.add_argument("--sandbox_levels_len_source", type=int, default=6)
+
+parser.add_argument("--sandbox_levels_len_result", type=int, default=8)
+
+##############################
+# picoclvr options
+
 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
 
 parser.add_argument("--picoclvr_height", type=int, default=12)
@@ -110,7 +124,7 @@ parser.add_argument("--snake_nb_colors", type=int, default=5)
 parser.add_argument("--snake_length", type=int, default=200)
 
 ##############################
-# Snake options
+# Stack options
 
 parser.add_argument("--stack_nb_steps", type=int, default=100)
 
@@ -125,7 +139,18 @@ parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.7
 
 parser.add_argument("--expr_nb_variables", type=int, default=5)
 
-parser.add_argument("--expr_sequence_length", type=int, default=30)
+parser.add_argument("--expr_sequence_length", type=int, default=40)
+
+parser.add_argument("--expr_operand_max", type=int, default=9)
+
+parser.add_argument("--expr_result_max", type=int, default=99)
+
+parser.add_argument("--expr_input_file", type=str, default=None)
+
+##############################
+# World options
+
+parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
 
 ######################################################################
 
@@ -138,7 +163,13 @@ if args.result_dir is None:
 
 ######################################################################
 
-default_args = {
+default_task_args = {
+    "sandbox": {
+        "nb_epochs": 50,
+        "batch_size": 25,
+        "nb_train_samples": 100000,
+        "nb_test_samples": 10000,
+    },
     "picoclvr": {
         "nb_epochs": 25,
         "batch_size": 25,
@@ -170,17 +201,69 @@ default_args = {
         "nb_test_samples": 1000,
     },
     "expr": {
-        "nb_epochs": 50,
+        "nb_epochs": 40,
         "batch_size": 25,
-        "nb_train_samples": 250000,
+        "nb_train_samples": 1000000,
+        "nb_test_samples": 10000,
+    },
+    "rpl": {
+        "nb_epochs": 40,
+        "batch_size": 25,
+        "nb_train_samples": 100000,
         "nb_test_samples": 10000,
     },
+    "world": {
+        "nb_epochs": 10,
+        "batch_size": 25,
+        "nb_train_samples": 25000,
+        "nb_test_samples": 1000,
+    },
+}
+
+if args.task in default_task_args:
+    for k, v in default_task_args[args.task].items():
+        if getattr(args, k) is None:
+            setattr(args, k, v)
+
+######################################################################
+
+default_model_args = {
+    "17K": {
+        "dim_model": 32,
+        "dim_keys": 32,
+        "dim_hidden": 32,
+        "nb_heads": 2,
+        "nb_blocks": 2,
+    },
+    "37M": {
+        "dim_model": 512,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 12,
+    },
+    "122M": {
+        "dim_model": 768,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 24,
+    },
+    "352M": {
+        "dim_model": 1024,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 48,
+    },
 }
 
-if args.task in default_args:
-    for k, v in default_args[args.task].items():
+if args.model in default_model_args:
+    for k, v in default_model_args[args.model].items():
         if getattr(args, k) is None:
             setattr(args, k, v)
+else:
+    raise ValueError(f"Unknown model {args.model}")
 
 ######################################################################
 
@@ -240,7 +323,38 @@ picoclvr_pruner_eval = (
 
 ######################################################################
 
-if args.task == "picoclvr":
+if args.task == "sandbox":
+    if args.sandbox_level == 0:
+        problem = tasks.ProblemLevel0(
+            nb_sentences=args.sandbox_levels_nb_items,
+            len_prompt=args.sandbox_levels_len_source,
+            len_result=args.sandbox_levels_len_result,
+        )
+    elif args.sandbox_level == 1:
+        problem = tasks.ProblemLevel1(
+            nb_operators=args.sandbox_levels_nb_items,
+            len_source=args.sandbox_levels_len_source,
+            len_result=args.sandbox_levels_len_result,
+        )
+    elif args.sandbox_level == 2:
+        problem = tasks.ProblemLevel2(
+            len_source=args.sandbox_levels_len_source,
+            len_result=args.sandbox_levels_len_result,
+        )
+    else:
+        raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
+
+    task = tasks.SandBox(
+        problem,
+        # tasks.ProblemAddition(zero_padded=False, inverted_result=False),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "picoclvr":
     task = tasks.PicoCLVR(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
@@ -305,7 +419,28 @@ elif args.task == "expr":
         nb_test_samples=args.nb_test_samples,
         nb_variables=args.expr_nb_variables,
         sequence_length=args.expr_sequence_length,
+        operand_max=args.expr_operand_max,
+        result_max=args.expr_result_max,
+        batch_size=args.batch_size,
+        device=device,
+    )
+
+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,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "world":
+    task = tasks.World(
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
+        vqae_nb_epochs=args.world_vqae_nb_epochs,
+        logger=log_string,
         device=device,
     )
 
@@ -366,6 +501,20 @@ else:
 
 ######################################################################
 
+if args.task == "expr" and args.expr_input_file is not None:
+    task.produce_results(
+        nb_epochs_finished,
+        model,
+        args.result_dir,
+        log_string,
+        args.deterministic_synthesis,
+        args.expr_input_file,
+    )
+
+    exit(0)
+
+######################################################################
+
 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
 
 # Compute the entropy of the training tokens
@@ -383,20 +532,28 @@ train_set_perplexity = math.exp(entropy)
 
 train_examples = {}
 
+
 for input in task.batches(split="train"):
-    assert input.dim()==2 and input.dtype==torch.int64
+    assert input.dim() == 2 and input.dtype == torch.int64
     for x in input:
-        train_examples[x.sum().item()]=x
+        train_examples[x.sum().item()] = x
 
+nb_total, nb_collisions = 0, 0
 for input in task.batches(split="test"):
-    assert input.dim()==2 and input.dtype==torch.int64
+    assert input.dim() == 2 and input.dtype == torch.int64
     for x in input:
+        nb_total += 1
         y = train_examples.get(x.sum().item())
         if y is not None:
-            assert x.size() != y.size() or (x-y).abs().sum() > 0
+            if x.size() == y.size() and (x - y).abs().sum() == 0:
+                nb_collisions += 1
 
 del train_examples
 
+log_string(
+    f"data_check {nb_collisions*100/nb_total:.02f}% ({nb_collisions}/{nb_total}) of test samples are in the train set"
+)
+
 ##############################
 
 if args.learning_rate_schedule == "cos":