X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=b9b52d6e18162292f09394de9d9150edf281a9f3;hb=6c8bed86221baae24a7c2aaaa41c009444efb5c9;hp=69ee58f8d050ec021082f30b5190cceea5090587;hpb=a92a5ca00f4277f7a133fa6cfaada2bc1981f524;p=picoclvr.git

diff --git a/main.py b/main.py
index 69ee58f..b9b52d6 100755
--- a/main.py
+++ b/main.py
@@ -5,16 +5,14 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-# torch.backends.cuda.matmul.allow_tf23
-# torch.autocast(torch.bfloat16)
-
 import math, sys, argparse, time, tqdm, os
 
 import torch, torchvision
 from torch import nn
 from torch.nn import functional as F
 
-import mygpt, tasks
+import ffutils
+import mygpt, tasks, problems
 
 ######################################################################
 
@@ -35,7 +33,7 @@ parser.add_argument(
     "--task",
     type=str,
     default="sandbox",
-    help="sandbox, picoclvr, mnist, maze, snake, stack, expr, world",
+    help="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, world",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
@@ -58,15 +56,17 @@ 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_model", type=int, default=None)
 
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--dim_keys", type=int, default=None)
 
-parser.add_argument("--dim_hidden", type=int, default=2048)
+parser.add_argument("--dim_hidden", type=int, default=None)
 
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--nb_heads", type=int, default=None)
 
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--nb_blocks", type=int, default=None)
 
 parser.add_argument("--dropout", type=float, default=0.1)
 
@@ -78,6 +78,30 @@ parser.add_argument("--overwrite_results", action="store_true", default=False)
 
 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 
+##############################
+# rpl options
+
+parser.add_argument("--rpl_nb_starting_values", type=int, default=5)
+
+parser.add_argument("--rpl_max_input", type=int, default=9)
+
+parser.add_argument("--rpl_prog_len", type=int, default=10)
+
+parser.add_argument("--rpl_nb_runs", type=int, default=8)
+
+parser.add_argument("--rpl_no_prog", action="store_true", default=False)
+
+##############################
+# sandbox 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
 
@@ -149,11 +173,11 @@ if args.result_dir is None:
 
 ######################################################################
 
-default_args = {
+default_task_args = {
     "sandbox": {
-        "nb_epochs": 10,
+        "nb_epochs": 50,
         "batch_size": 25,
-        "nb_train_samples": 25000,
+        "nb_train_samples": 100000,
         "nb_test_samples": 10000,
     },
     "picoclvr": {
@@ -192,6 +216,12 @@ default_args = {
         "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,
@@ -200,13 +230,53 @@ default_args = {
     },
 }
 
-if args.task in default_args:
-    for k, v in default_args[args.task].items():
+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.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}")
+
+######################################################################
+
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
@@ -264,7 +334,30 @@ picoclvr_pruner_eval = (
 ######################################################################
 
 if args.task == "sandbox":
+    if args.sandbox_level == 0:
+        problem = problems.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 = problems.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 = problems.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,
+        # problems.ProblemAddition(zero_padded=False, inverted_result=False),
+        problems.ProblemLenId(len_max=args.sandbox_levels_len_source),
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
@@ -343,6 +436,20 @@ elif args.task == "expr":
         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,
+        nb_starting_values=args.rpl_nb_starting_values,
+        max_input=args.rpl_max_input,
+        prog_len=args.rpl_prog_len,
+        nb_runs=args.rpl_nb_runs,
+        no_prog=args.rpl_no_prog,
+        logger=log_string,
+        device=device,
+    )
+
 elif args.task == "world":
     task = tasks.World(
         nb_train_samples=args.nb_train_samples,
@@ -412,12 +519,12 @@ 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,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
+        input_file=args.expr_input_file,
     )
 
     exit(0)
@@ -493,11 +600,11 @@ nb_samples_seen = 0
 
 if nb_epochs_finished >= nb_epochs:
     task.produce_results(
-        nb_epochs_finished,
-        model,
-        args.result_dir,
-        log_string,
-        args.deterministic_synthesis,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
     )
 
 for n_epoch in range(nb_epochs_finished, nb_epochs):
@@ -551,7 +658,11 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         )
 
         task.produce_results(
-            n_epoch, model, args.result_dir, log_string, args.deterministic_synthesis
+            n_epoch=n_epoch,
+            model=model,
+            result_dir=args.result_dir,
+            logger=log_string,
+            deterministic_synthesis=args.deterministic_synthesis,
         )
 
     checkpoint = {