# Written by Francois Fleuret <francois@fleuret.org>
-import math, sys, argparse, time, tqdm, os
+import math, sys, argparse, time, tqdm, os, datetime
import torch, torchvision
from torch import nn
parser.add_argument(
"--task",
type=str,
- default="sandbox",
- help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl",
+ default="twotargets",
+ help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed",
)
parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
########################################
-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--nb_epochs", type=int, default=25)
parser.add_argument("--batch_size", type=int, default=None)
########################################
-parser.add_argument("--model", type=str, default="37M")
+parser.add_argument("--model", type=str, default=None)
parser.add_argument("--dim_model", type=int, default=None)
parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
+##############################
+# filetask
+
+parser.add_argument("--filetask_train_file", type=str, default=None)
+
+parser.add_argument("--filetask_test_file", type=str, default=None)
+
##############################
# rpl options
-parser.add_argument("--rpl_nb_starting_values", type=int, default=5)
+parser.add_argument("--rpl_nb_starting_values", type=int, default=3)
parser.add_argument("--rpl_max_input", type=int, default=9)
-parser.add_argument("--rpl_prog_len", type=int, default=10)
+parser.add_argument("--rpl_prog_len", type=int, default=8)
-parser.add_argument("--rpl_nb_runs", type=int, default=8)
+parser.add_argument("--rpl_nb_runs", type=int, default=5)
parser.add_argument("--rpl_no_prog", action="store_true", default=False)
+##############################
+# grid options
+
+parser.add_argument("--grid_size", type=int, default=6)
+
+parser.add_argument("--grid_fraction_play", type=float, default=0)
+
##############################
# picoclvr options
##############################
# Maze options
-parser.add_argument("--maze_height", type=int, default=23)
+parser.add_argument("--maze_height", type=int, default=13)
-parser.add_argument("--maze_width", type=int, default=39)
+parser.add_argument("--maze_width", type=int, default=21)
-parser.add_argument("--maze_nb_walls", type=int, default=45)
+parser.add_argument("--maze_nb_walls", type=int, default=15)
##############################
# Snake options
-parser.add_argument("--snake_height", type=int, default=6)
+parser.add_argument("--snake_height", type=int, default=9)
-parser.add_argument("--snake_width", type=int, default=8)
+parser.add_argument("--snake_width", type=int, default=12)
parser.add_argument("--snake_nb_colors", type=int, default=5)
parser.add_argument("--expr_input_file", type=str, default=None)
##############################
-# World options
+# Mixing
+
+parser.add_argument("--mixing_hard", action="store_true", default=False)
+
+parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
+
+##############################
+# greed options
+
+parser.add_argument("--greed_height", type=int, default=5)
+
+parser.add_argument("--greed_width", type=int, default=7)
+
+parser.add_argument("--greed_T", type=int, default=25)
-parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
+parser.add_argument("--greed_nb_walls", type=int, default=5)
+
+parser.add_argument("--greed_nb_coins", type=int, default=2)
######################################################################
######################################################################
default_task_args = {
- "sandbox": {
- "nb_epochs": 50,
+ "file": {
+ "model": "37M",
"batch_size": 25,
- "nb_train_samples": 100000,
+ "nb_train_samples": 250000,
"nb_test_samples": 10000,
},
- "picoclvr": {
- "nb_epochs": 25,
+ "addition": {
+ "model": "352M",
"batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
- "mnist": {
- "nb_epochs": 25,
- "batch_size": 10,
+ "byheart": {
+ "model": "37M",
+ "batch_size": 25,
+ "nb_train_samples": 50000,
+ "nb_test_samples": 10000,
+ },
+ "expr": {
+ "model": "352M",
+ "batch_size": 25,
+ "nb_train_samples": 2500000,
+ "nb_test_samples": 10000,
+ },
+ "grid": {
+ "model": "37M",
+ "batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
+ "qmlp": {
+ "model": "37M",
+ "batch_size": 10,
+ "nb_train_samples": 100000,
+ "nb_test_samples": 1000,
+ },
+ "guessop": {
+ "model": "352M",
+ "batch_size": 25,
+ "nb_train_samples": 1000000,
+ "nb_test_samples": 10000,
+ },
+ "learnop": {
+ "model": "37M",
+ "batch_size": 25,
+ "nb_train_samples": 50000,
+ "nb_test_samples": 10000,
+ },
"maze": {
- "nb_epochs": 25,
+ "model": "37M",
"batch_size": 5,
+ "nb_train_samples": 100000,
+ "nb_test_samples": 10000,
+ },
+ "picoclvr": {
+ "model": "37M",
+ "batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
+ "rpl": {
+ "model": "352M",
+ "batch_size": 5,
+ "nb_train_samples": 2500000,
+ "nb_test_samples": 10000,
+ },
"snake": {
- "nb_epochs": 5,
+ "model": "37M",
"batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
"stack": {
- "nb_epochs": 5,
+ "model": "37M",
"batch_size": 25,
"nb_train_samples": 100000,
"nb_test_samples": 1000,
},
- "expr": {
- "nb_epochs": 40,
+ "twotargets": {
+ "model": "37M",
"batch_size": 25,
- "nb_train_samples": 1000000,
+ "nb_train_samples": 50000,
"nb_test_samples": 10000,
},
- "rpl": {
- "nb_epochs": 40,
+ "memory": {
+ "model": "37M",
+ "batch_size": 100,
+ "nb_train_samples": 25000,
+ "nb_test_samples": 1000,
+ },
+ "mixing": {
+ "model": "37M",
"batch_size": 25,
- "nb_train_samples": 100000,
+ "nb_train_samples": 250000,
"nb_test_samples": 10000,
},
- "world": {
- "nb_epochs": 10,
+ "mnist": {
+ "model": "37M",
+ "batch_size": 10,
+ "nb_train_samples": 60000,
+ "nb_test_samples": 10000,
+ },
+ "greed": {
+ "model": "37M",
"batch_size": 25,
"nb_train_samples": 25000,
- "nb_test_samples": 1000,
+ "nb_test_samples": 10000,
},
}
"nb_heads": 2,
"nb_blocks": 2,
},
+ "4M": {
+ "dim_model": 256,
+ "dim_keys": 32,
+ "dim_hidden": 1024,
+ "nb_heads": 4,
+ "nb_blocks": 6,
+ },
"37M": {
"dim_model": 512,
"dim_keys": 64,
sys.stdout.flush()
+log_string(f"argv {' '.join(sys.argv)}")
+
for n in vars(args):
log_string(f"args.{n} {getattr(args, n)}")
######################################################################
-if args.task == "byheart":
+if args.task == "file":
+ assert (
+ args.filetask_train_file is not None and args.filetask_test_file is not None
+ ), "You have to specify the task train and test files"
+ task = tasks.TaskFromFile(
+ args.filetask_train_file,
+ args.filetask_test_file,
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ shuffle=True,
+ device=device,
+ )
+ args.max_percents_of_test_in_train = 0
+
+elif args.task == "byheart":
task = tasks.SandBox(
problem=problems.ProblemByHeart(),
nb_train_samples=args.nb_train_samples,
logger=log_string,
device=device,
)
-
+ args.max_percents_of_test_in_train = -1
elif args.task == "learnop":
task = tasks.SandBox(
device=device,
)
+elif args.task == "memory":
+ task = tasks.SandBox(
+ problem=problems.ProblemMemory(),
+ 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 == "mixing":
+ task = tasks.SandBox(
+ problem=problems.ProblemMixing(
+ hard=args.mixing_hard, random_start=not args.mixing_deterministic_start
+ ),
+ 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 == "addition":
task = tasks.SandBox(
problem=problems.ProblemAddition(),
device=device,
)
-elif args.task == "world":
- task = tasks.World(
+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,
+ size=args.grid_size,
+ fraction_play=args.grid_fraction_play,
+ logger=log_string,
+ device=device,
+ )
+
+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,
+ result_dir=args.result_dir,
+ logger=log_string,
+ device=device,
+ )
+
+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,
- vqae_nb_epochs=args.world_vqae_nb_epochs,
+ height=args.greed_height,
+ width=args.greed_width,
+ T=args.greed_T,
+ nb_walls=args.greed_nb_walls,
+ nb_coins=args.greed_nb_coins,
logger=log_string,
device=device,
)
######################################################################
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
# Compute the entropy of the training tokens
token_count = 0
-for input in task.batches(split="train"):
+for input in task.batches(split="train", desc="train-entropy"):
token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
token_probas = token_count / token_count.sum()
entropy = -torch.xlogy(token_probas, token_probas).sum()
######################################################################
# A bit of paranoia never hurts
+if args.max_percents_of_test_in_train >= 0:
+
+ def subsets_as_tuples(batches, cs):
+ s = set()
+ for batch in batches:
+ for x in batch:
+ s.add(tuple([v.item() for v in x]))
+ if len(s) == cs:
+ yield s
+ s = set()
+ yield s
+
+ nb_test, nb_in_train = 0, 0
+ for test_subset in subsets_as_tuples(
+ task.batches(split="test", desc="test-check"), 25000
+ ):
+ in_train = set()
+ for train_subset in subsets_as_tuples(
+ task.batches(split="train", desc="train-check"), 25000
+ ):
+ in_train.update(test_subset.intersection(train_subset))
+ nb_in_train += len(in_train)
+ nb_test += len(test_subset)
+
+ log_string(
+ f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
+ )
-def subsets_as_tuples(batches, cs):
- s = set()
- for batch in batches:
- for x in batch:
- s.add(tuple([v.item() for v in x]))
- if len(s) == cs:
- yield s
- s = set()
- yield s
-
-
-nb_test, nb_in_train = 0, 0
-for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
- in_train = set()
- for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
- in_train.update(test_subset.intersection(train_subset))
- nb_in_train += len(in_train)
- nb_test += len(test_subset)
-
-log_string(
- f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
-)
-
-assert (
- nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
-), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
+ assert (
+ nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
+ ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
##############################
nb_samples_seen = 0
-if nb_epochs_finished >= nb_epochs:
+if nb_epochs_finished >= args.nb_epochs:
task.produce_results(
n_epoch=nb_epochs_finished,
model=model,
deterministic_synthesis=args.deterministic_synthesis,
)
-for n_epoch in range(nb_epochs_finished, nb_epochs):
+time_pred_result = None
+
+for n_epoch in range(nb_epochs_finished, args.nb_epochs):
learning_rate = learning_rate_schedule[n_epoch]
log_string(f"learning_rate {learning_rate}")
deterministic_synthesis=args.deterministic_synthesis,
)
+ time_current_result = datetime.datetime.now()
+ if time_pred_result is not None:
+ log_string(
+ f"next_result {time_current_result + (time_current_result - time_pred_result)}"
+ )
+ time_pred_result = time_current_result
+
checkpoint = {
"nb_epochs_finished": n_epoch + 1,
"model_state": model.state_dict(),
projects / picoclvr.git / blobdiff