# 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
"--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=" ")
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)
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
######################################################################
-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": {
"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,
},
}
-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:
######################################################################
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,
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,