# Any copyright is dedicated to the Public Domain.
# https://creativecommons.org/publicdomain/zero/1.0/
+# > A > f(A) > B ; > f(B)
+# < f(B) ; < B < f(A) < A
+
# Written by Francois Fleuret <francois@fleuret.org>
import math, sys, argparse, time, tqdm, os, datetime, warnings
from torch.nn import functional as F
import ffutils
-import mygpt
-import sky, quizz_machine
-
-# world quizzes vs. culture quizzes
-######################################################################
+import mygpt
+import sky, grids, quiz_machine
-accuracy_to_make_c_quizzes = 0.975
-nb_new_c_quizzes_for_train = 1000
-nb_new_c_quizzes_for_test = 100
+from quiz_machine import one_batch_masked_inplace_autoregression
-######################################################################
+import threading, subprocess
-if torch.cuda.is_available():
- device = torch.device("cuda")
- torch.backends.cuda.matmul.allow_tf32 = True
-else:
- device = torch.device("cpu")
+import torch.multiprocessing as mp
######################################################################
parser = argparse.ArgumentParser(
- description="An implementation of GPT with cache.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
-parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
+parser.add_argument("--log_filename", type=str, default="train.log")
parser.add_argument("--result_dir", type=str, default=None)
parser.add_argument("--seed", type=int, default=0)
-parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
+parser.add_argument("--resume", action="store_true", default=False)
+
+parser.add_argument("--max_percents_of_test_in_train", type=int, default=-1)
-########################################
+parser.add_argument("--log_command", type=str, default=None)
+
+# ----------------------------------
parser.add_argument("--nb_epochs", type=int, default=10000)
parser.add_argument("--physical_batch_size", type=int, default=None)
+parser.add_argument("--inference_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)
-parser.add_argument("--learning_rate", type=float, default=1e-4)
+parser.add_argument("--nb_new_c_quizzes_for_train", type=int, default=None)
+
+parser.add_argument("--nb_new_c_quizzes_for_test", type=int, default=None)
-########################################
+parser.add_argument("--learning_rate", type=float, default=5e-4)
+parser.add_argument("--schedule_free", action="store_true", default=False)
+
+# ----------------------------------
parser.add_argument("--model", type=str, default=None)
parser.add_argument("--dim_model", type=int, default=None)
parser.add_argument("--dropout", type=float, default=0.1)
-########################################
-
+# ----------------------------------
parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--problem", type=str, default="grids")
+
+parser.add_argument("--nb_threads", type=int, default=1)
+
+parser.add_argument("--gpus", type=str, default="all")
+
+# ----------------------------------
+
parser.add_argument("--nb_gpts", type=int, default=5)
+parser.add_argument("--max_fail_to_validate", type=int, default=3)
+
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.95)
+
+parser.add_argument("--proba_understands", type=float, default=0.95)
+
+parser.add_argument("--proba_not_understands", type=float, default=0.1)
+
+parser.add_argument("--temperature_hot", type=float, default=1.5)
+
+parser.add_argument("--temperature_cold", type=float, default=1)
+
+parser.add_argument("--prompt_noise", type=float, default=0.05)
+
parser.add_argument("--dirty_debug", action="store_true", default=False)
+parser.add_argument("--test", type=str, default=None)
+
+######################################################################
+
+grids_tasks = ", ".join(
+ [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
+)
+
+parser.add_argument(
+ "--grids_world_tasks",
+ type=str,
+ default="replace_color,translate,grow,frame",
+ help="A comma-separated subset of: " + grids_tasks + ".",
+)
+
+parser.add_argument(
+ "--grids_science_tasks",
+ type=str,
+ default=None,
+ help="A comma-separated subset of: " + grids_tasks + ", or None.",
+)
+
+######################################################################
+
+parser.add_argument("--sky_height", type=int, default=6)
+
+parser.add_argument("--sky_width", type=int, default=8)
+
+parser.add_argument("--sky_nb_birds", type=int, default=3)
+
+parser.add_argument("--sky_nb_iterations", type=int, default=2)
+
+parser.add_argument("--sky_speed", type=int, default=3)
+
######################################################################
args = parser.parse_args()
if args.result_dir is None:
args.result_dir = f"results_culture"
-######################################################################
-
-if args.dirty_debug:
- accuracy_to_make_c_quizzes = 0.0
- nb_new_c_quizzes_for_train = 100
- nb_new_c_quizzes_for_test = 10
+assert not args.grids_science_tasks or (
+ len(
+ set(args.grids_world_tasks.split(","))
+ & set(args.grids_science_tasks.split(","))
+ )
+ == 0
+), "World and science tasks have to be disjoint"
######################################################################
default_args = {
"model": "37M",
- "batch_size": 100,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "batch_size": 25,
+ "inference_batch_size": 50,
+ "nb_train_samples": 40000,
+ "nb_test_samples": 1000,
}
for k, v in default_args.items():
######################################################################
-try:
- os.mkdir(args.result_dir)
-except FileExistsError:
- print(f"result directory {args.result_dir} already exists")
- exit(1)
+if args.resume:
+ assert os.path.isdir(args.result_dir)
+
+else:
+ try:
+ os.mkdir(args.result_dir)
+ except FileExistsError:
+ print(f"result directory {args.result_dir} already exists")
+ exit(1)
log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
sys.stdout.flush()
+######################################################################
+# Create a time-stamped archive of the source code
+
+with open("this_run.sh", "w") as f:
+ f.write(f"{' '.join(sys.argv)}\n")
+
+now = time.strftime("%Y%m%d-%H%M%S", time.localtime())
+
+os.system(f"tar zcvf {args.result_dir}/src-{now}.tgz *.py *.sh")
+
+######################################################################
+
log_string(f"argv {' '.join(sys.argv)}")
for n in vars(args):
######################################################################
+if args.gpus == "all":
+ gpus_idx = range(torch.cuda.device_count())
+else:
+ gpus_idx = [int(k) for k in args.gpus.split(",")]
+
+gpus = [torch.device(f"cuda:{n}") for n in gpus_idx]
+
+if torch.cuda.is_available():
+ main_device = gpus[0]
+else:
+ assert len(gpus) == 0
+ main_device = torch.device("cpu")
+
if args.dirty_debug:
args.nb_train_samples = 2500
args.nb_test_samples = 100
assert args.nb_train_samples % args.batch_size == 0
assert args.nb_test_samples % args.batch_size == 0
-quizz_machine = quizz_machine.QuizzMachine(
- problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.physical_batch_size,
+if args.problem == "sky":
+ problem = sky.Sky(
+ height=args.sky_height,
+ width=args.sky_width,
+ nb_birds=args.sky_nb_birds,
+ nb_iterations=args.sky_nb_iterations,
+ speed=args.sky_speed,
+ max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
+ chunk_size=100,
+ nb_threads=args.nb_threads,
+ )
+
+elif args.problem == "grids":
+ problem = grids.Grids(
+ max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
+ chunk_size=100,
+ nb_threads=args.nb_threads,
+ tasks=args.grids_world_tasks,
+ )
+
+ if args.grids_science_tasks is None:
+ science_w_quizzes = None
+ else:
+ science_problem = grids.Grids(
+ max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
+ chunk_size=100,
+ nb_threads=args.nb_threads,
+ tasks=args.grids_science_tasks,
+ )
+ science_w_quizzes = science_problem.generate_w_quizzes(100)
+
+ if not args.resume:
+ science_problem.save_some_examples(args.result_dir, "science_")
+
+
+else:
+ raise ValueError
+
+if not args.resume:
+ problem.save_some_examples(args.result_dir)
+
+quiz_machine = quiz_machine.QuizMachine(
+ problem=problem,
+ batch_size=args.inference_batch_size,
result_dir=args.result_dir,
+ prompt_noise=args.prompt_noise,
logger=log_string,
- device=device,
+ device=main_device,
)
######################################################################
-log_string(f"device {device}")
+log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
-vocabulary_size = quizz_machine.vocabulary_size()
+vocabulary_size = quiz_machine.vocabulary_size()
log_string(f"vocabulary_size {vocabulary_size}")
######################################################################
-# Compute the entropy of the training tokens
-token_count = 0
-for input in quizz_machine.batches(split="train", desc="train-entropy"):
- token_count += F.one_hot(input, num_classes=quizz_machine.vocabulary_size()).sum(
- (0, 1)
- )
-token_probas = token_count / token_count.sum()
-entropy = -torch.xlogy(token_probas, token_probas).sum()
-train_set_perplexity = math.exp(entropy)
+def optimizer_to(optim, device):
+ for param in optim.state.values():
+ # Not sure there are any global tensors in the state dict
+ if isinstance(param, torch.Tensor):
+ param.data = param.data.to(device)
+ if param._grad is not None:
+ param._grad.data = param._grad.data.to(device)
+ elif isinstance(param, dict):
+ for subparam in param.values():
+ if isinstance(subparam, torch.Tensor):
+ subparam.data = subparam.data.to(device)
+ if subparam._grad is not None:
+ subparam._grad.data = subparam._grad.data.to(device)
+
######################################################################
-# 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(
- quizz_machine.batches(split="test", desc="test-check"), 25000
- ):
- in_train = set()
- for train_subset in subsets_as_tuples(
- quizz_machine.batches(split="train", desc="train-check"), 25000
+
+
+def run_tests(model, quiz_machine, local_device=main_device):
+ with torch.autograd.no_grad():
+ model.to(local_device).eval()
+ if args.schedule_free:
+ model.optimizer.eval()
+
+ nb_test_samples, acc_test_loss = 0, 0.0
+ nb_samples_accumulated = 0
+
+ full_input, full_mask_loss = quiz_machine.data_input(model, split="test")
+ src = zip(
+ full_input.split(args.batch_size), full_mask_loss.split(args.batch_size)
+ )
+
+ for input, mask_loss in tqdm.tqdm(
+ src,
+ dynamic_ncols=True,
+ desc="test",
+ total=full_input.size(0) // args.batch_size,
):
- in_train.update(test_subset.intersection(train_subset))
- nb_in_train += len(in_train)
- nb_test += len(test_subset)
+ input = input.to(local_device)
+ mask_loss = mask_loss.to(local_device)
+ targets = input
- 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"
- )
+ output = model(mygpt.BracketedSequence(input)).x
+ loss_per_token = F.cross_entropy(
+ output.transpose(1, 2), targets, reduction="none"
+ )
+ loss = (loss_per_token * mask_loss).mean()
+ acc_test_loss += loss.item() * input.size(0)
+ nb_test_samples += input.size(0)
+
+ test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+
+ log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
+
+ model.main_test_accuracy = quiz_machine.produce_results(
+ n_epoch=n_epoch,
+ model=model,
+ input=full_input[:2000],
+ result_dir=args.result_dir,
+ )
- 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"
-##############################
+######################################################################
-def one_epoch(model, quizz_machine):
- optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+def one_epoch(model, quiz_machine, local_device=main_device):
+ model.to(local_device).train()
+ optimizer_to(model.optimizer, local_device)
- model.train()
+ if args.schedule_free:
+ model.optimizer.train()
nb_train_samples, acc_train_loss = 0, 0.0
- for input in quizz_machine.batches(split="train"):
- input = input.to(device)
+ hard_w_quizzes = []
+
+ full_input, full_mask_loss = quiz_machine.data_input(model, split="train")
+ src = zip(full_input.split(args.batch_size), full_mask_loss.split(args.batch_size))
+
+ for input, mask_loss in tqdm.tqdm(
+ src,
+ dynamic_ncols=True,
+ desc="training",
+ total=full_input.size(0) // args.batch_size,
+ ):
+ input = input.to(local_device)
+ mask_loss = mask_loss.to(local_device)
if nb_train_samples % args.batch_size == 0:
- optimizer.zero_grad()
+ model.optimizer.zero_grad()
+
+ targets = input
output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
+ loss_per_token = F.cross_entropy(
+ output.transpose(1, 2), targets, reduction="none"
+ )
+ loss = (loss_per_token * mask_loss).mean() + model.loss
acc_train_loss += loss.item() * input.size(0)
+ loss_per_samples = loss_per_token.detach().flatten(1).mean(dim=1)
+
nb_train_samples += input.size(0)
loss.backward()
if nb_train_samples % args.batch_size == 0:
- optimizer.step()
+ model.optimizer.step()
train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- log_string(f"train_perplexity {n_epoch} {train_perplexity}")
+ log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
+
+ run_tests(model, quiz_machine)
+
+ # threshold = torch.cat([l for _, l in hard_w_quizzes], dim=0).sort().values
+ # threshold = threshold[threshold.size(0) // 2]
+
+ # model.hard_w_quizzes = torch.cat(
+ # [x[l >= threshold] for x, l in hard_w_quizzes], dim=0
+ # )
+
+ model.to(main_device)
+ optimizer_to(model.optimizer, main_device)
######################################################################
-def run_tests(model, quizz_machine, deterministic_synthesis):
- with torch.autograd.no_grad():
- model.eval()
+def model_transformer_hot(model):
+ model.temperature = args.temperature_hot
+ # model.set_noise_injection(1.0, ("ffw", args.nb_blocks // 2))
- nb_test_samples, acc_test_loss = 0, 0.0
- nb_samples_accumulated = 0
- for input in quizz_machine.batches(split="test"):
- input = input.to(device)
+def model_transformer_cold(model):
+ model.temperature = args.temperature_cold
+ # pass
- bs = model(mygpt.BracketedSequence(input))
- output = bs.x
- loss = F.cross_entropy(output.transpose(1, 2), input)
+c_quizzes_procedure = [
+ (("f_B", "f_A", "A", "B"), (1, 0, 0, 0), model_transformer_hot),
+ (("f_B", "f_A", "A", "B"), (0, 1, 1, 1), model_transformer_cold),
+ (("A", "f_A", "B", "f_B"), (0, 0, 0, 1), model_transformer_cold),
+ (("f_A", "A", "f_B", "B"), (0, 0, 0, 1), model_transformer_cold),
+]
- acc_test_loss += loss.item() * input.size(0)
+######################################################################
- nb_test_samples += input.size(0)
- main_test_accuracy = quizz_machine.produce_results(
- n_epoch=n_epoch,
+def save_additional_results(model, models, science_w_quizzes):
+ # Save generated quizzes with the successive steps
+
+ recorder = []
+
+ c_quizzes = quiz_machine.generate_c_quizzes(
+ 64,
+ model_for_generation=model,
+ procedure=c_quizzes_procedure,
+ recorder=recorder,
+ )
+
+ # This is nb_quizzes x nb_models
+
+ seq_logproba = quiz_machine.models_logprobas(
+ models, c_quizzes, ("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ ) + quiz_machine.models_logprobas(
+ models, c_quizzes, ("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ )
+
+ probas = seq_logproba.exp()
+
+ comments = []
+
+ for l in seq_logproba:
+ comments.append("proba " + " ".join([f"{x.exp().item():.02f}" for x in l]))
+
+ ##
+
+ c_quizzes = torch.cat([c[:, None, :] for c, _, in recorder], dim=1)
+ predicted_parts = torch.cat([t[:, None, :] for _, t in recorder], dim=1)
+ nb_steps = c_quizzes.size(1)
+ c_quizzes = c_quizzes.reshape(-1, c_quizzes.size(-1))
+ predicted_parts = predicted_parts.reshape(-1, predicted_parts.size(-1))
+
+ # We have comments only for the final quiz, not the successive
+ # steps, so we have to add nb_steps-1 empty comments
+
+ steps_comments = []
+ for c in comments:
+ steps_comments += [""] * (nb_steps - 1) + [c]
+
+ filename = f"non_validated_{n_epoch:04d}_{model.id:02d}.png"
+
+ quiz_machine.problem.save_quizzes_as_image(
+ args.result_dir,
+ filename,
+ quizzes=c_quizzes,
+ predicted_parts=predicted_parts,
+ comments=steps_comments,
+ nrow=nb_steps * 2, # two quiz per row
+ )
+
+ log_string(f"wrote {filename}")
+
+ ######################################################################
+
+ if science_w_quizzes is not None:
+ struct = ("A", "f_A", "B", "f_B")
+ mask = (0, 0, 0, 1)
+ result, correct = quiz_machine.predict(
model=model,
- result_dir=args.result_dir,
- logger=log_string,
- deterministic_synthesis=deterministic_synthesis,
+ quizzes=science_w_quizzes.to(main_device),
+ struct=struct,
+ mask=mask,
)
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+ predicted_parts = torch.tensor(mask, device=correct.device)[None, :].expand(
+ correct.size(0), -1
+ )
+ correct = (2 * correct - 1) * (predicted_parts.sum(dim=-1) == 1).long()
+
+ nb_correct = (correct == 1).long().sum()
+ nb_total = (correct != 0).long().sum()
+
+ log_string(
+ f"science_accuracy {n_epoch} model {model.id} val {nb_correct} / {nb_total}"
+ )
+
+ i = correct == 1
+ j = correct != 1
- log_string(f"test_perplexity {n_epoch} {test_perplexity}")
+ result = torch.cat([result[i], result[j]], dim=0)
+ correct = torch.cat([correct[i], correct[j]], dim=0)
+ correct_parts = predicted_parts * correct[:, None]
- model.main_test_accuracy = main_test_accuracy
+ result = result[:128]
+ predicted_parts = predicted_parts[:128]
+ correct_parts = correct_parts[:128]
+
+ quiz_machine.problem.save_quizzes_as_image(
+ args.result_dir,
+ f"culture_science_{n_epoch:04d}_{model.id:02d}.png",
+ quizzes=result,
+ predicted_parts=predicted_parts,
+ correct_parts=correct_parts,
+ )
######################################################################
-def create_c_quizzes(
- models,
- quizz_machine,
- nb_for_train=1000,
- nb_for_test=100,
- min_ave_seq_logproba=None,
-):
- kept = []
- model_indexes = []
- sum_logits, sum_nb_c_quizzes = 0, 0
+def record_new_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100):
+ nb_to_validate = nb_for_train + nb_for_test
+ nb_to_generate_per_iteration = max(args.physical_batch_size, nb_to_validate)
+ nb_validated = 0
- while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
- nb_to_generate = nb_for_train + nb_for_test
+ recorded_validated = []
- if len(model_indexes) == 0:
- model_indexes = [i.item() for i in torch.randperm(len(models))]
+ start_time = time.perf_counter()
- model = models[model_indexes.pop()]
+ nb_validated_per_model = torch.zeros(len(models), dtype=torch.int64)
- new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
- nb=nb_to_generate,
+ while nb_validated_per_model.sum() < nb_to_validate:
+ # We use the model that has generated the fewest quizzes to
+ # balance the number of quizzes per model overall
+
+ # model_for_generation = sorted(
+ # models, key=lambda m: nb_validated_per_model[m.id]
+ # )[0]
+
+ model_for_generation = models[torch.randint(len(models), (1,)).item()]
+
+ # We generate quizzes with a procedure that injects some
+ # structured noise
+
+ c_quizzes = quiz_machine.generate_c_quizzes(
+ nb_to_generate_per_iteration,
model_for_generation=model,
- models_for_validation=models,
- min_ave_seq_logproba=min_ave_seq_logproba,
- n_epoch=n_epoch,
- result_dir=args.result_dir,
- logger=log_string,
+ procedure=c_quizzes_procedure,
)
- sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
- sum_nb_c_quizzes += new_c_quizzes.size(0)
+ # We discard the trivial ones, according to a criterion
+ # specific to the world quizzes (e.g. B=f(B))
- to_keep = new_c_quizzes[nb_correct == len(models) - 1]
+ to_keep = quiz_machine.problem.trivial(c_quizzes) == False
- if args.dirty_debug:
- to_keep = new_c_quizzes[
- torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device)
- == 0
- ]
+ c_quizzes = c_quizzes[to_keep]
- kept.append(to_keep)
+ # This is nb_quizzes x nb_models
+
+ seq_logproba = quiz_machine.models_logprobas(
+ models, c_quizzes, ("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ ) + quiz_machine.models_logprobas(
+ models, c_quizzes, ("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ )
+
+ probas = seq_logproba.exp()
+
+ nb_succeed = (probas >= args.proba_understands).long().sum(dim=1)
+ nb_fail = (probas <= args.proba_not_understands).long().sum(dim=1)
+
+ to_keep = (
+ (nb_succeed + nb_fail == probas.size(1))
+ & (nb_fail >= 1)
+ & (nb_fail <= args.max_fail_to_validate)
+ )
+
+ c_quizzes = c_quizzes[to_keep]
+
+ if c_quizzes.size(0) > 0:
+ nb_validated_per_model[model_for_generation.id] += c_quizzes.size(0)
+ recorded_validated.append(c_quizzes)
+ nb_validated = c_quizzes.size(0)
+ else:
+ nb_validated = 0
+
+ total_nb_validated = nb_validated_per_model.sum().item()
+
+ duration = time.perf_counter() - start_time
+
+ if total_nb_validated > 0:
+ if total_nb_validated < nb_to_validate:
+ d = (
+ (nb_to_validate - total_nb_validated)
+ * duration
+ / total_nb_validated
+ )
+ e = (datetime.datetime.now() + datetime.timedelta(seconds=d)).strftime(
+ "%a %H:%M"
+ )
+ else:
+ e = "now!"
+ else:
+ e = "???"
log_string(
- f"keep c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}"
+ f"keep c_quizzes model {model_for_generation.id} validated {nb_validated} / {nb_to_generate_per_iteration} ({100*nb_validated/nb_to_generate_per_iteration:.02f}%) nb_accumulated {total_nb_validated} / {nb_to_validate} (finishes {e} -- {int((total_nb_validated * 3600)/duration)}/h)"
)
- new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+ validated_quizzes = torch.cat(recorded_validated, dim=0)
- quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
- quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
+ ######################################################################
+ # store the new c_quizzes which have been validated
- quizz_machine.problem.save_quizzes(
- new_c_quizzes[:72],
- args.result_dir,
- f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
+ v_train = validated_quizzes[:nb_for_train]
+ quiz_machine.store_c_quizzes(v_train, for_train=True)
+
+ v_test = validated_quizzes[nb_for_train:nb_to_validate]
+ quiz_machine.store_c_quizzes(v_test, for_train=False)
+
+ ######################################################################
+ # save images
+
+ vq = validated_quizzes[torch.randperm(validated_quizzes.size(0))[:128]]
+
+ if vq.size(0) > 0:
+ seq_logproba = quiz_machine.models_logprobas(
+ models, vq, ("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ ) + quiz_machine.models_logprobas(
+ models, vq, ("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ )
+
+ probas = seq_logproba.exp()
+
+ comments = []
+
+ for l in seq_logproba:
+ comments.append("proba " + " ".join([f"{x.exp().item():.02f}" for x in l]))
+
+ filename = f"culture_c_quiz_{n_epoch:04d}.png"
+ quiz_machine.problem.save_quizzes_as_image(
+ args.result_dir, filename, vq, comments=comments
+ )
+
+
+######################################################################
+
+# The generator is very similar to a "solving GPT" except that it
+# deals with quizzes prologued with one token per solving GPT that
+# indicates if the said model solves it or not.
+#
+# There are three levels of solving 0->proba<=proba_not_understands,
+# 2->proba>=proba_understands and 1 otherwise.
+
+
+def generate_c_quizzes_with_generator(generator, quiz_machine, nb):
+ generator.to(main_device)
+
+ struct = ("A", "f_A", "B", "f_B")
+
+ c_quizzes = quiz_machine.problem.create_empty_quizzes(nb, struct=struct)
+ ar_mask = quiz_machine.make_quiz_mask(c_quizzes, struct, (1, 1, 1, 1))
+
+ i = F.one_hot(
+ torch.randint(args.nb_gpts, (c_quizzes.size(0),)),
+ num_classes=args.nb_gpts,
+ )
+
+ prologs_c_quizzes = token_prolog_0 * i + token_prolog_2 * (1 - i)
+ prologs_ar_mask = ar_mask.new_zeros(ar_mask.size(0), prologs_c_quizzes.size(1))
+
+ prologued_c_quizzes = torch.cat([prologs_c_quizzes, c_quizzes], dim=1).to(
+ main_device
)
+ prologued_ar_mask = torch.cat([prologs_ar_mask, ar_mask], dim=1).to(main_device)
+
+ seq_logproba = torch.zeros(
+ prologued_c_quizzes.size(0), device=prologued_c_quizzes.device
+ )
+
+ generator.temperature = args.temperature_hot
+
+ with torch.autograd.no_grad():
+ t = generator.training
+ generator.eval()
+
+ one_batch_masked_inplace_autoregression(
+ generator,
+ prologued_c_quizzes,
+ prologued_ar_mask,
+ seq_logproba,
+ deterministic_synthesis=False,
+ )
+
+ generator.train(t)
+
+ generator.reset_transformations()
+
+ prologued_c_quizzes = (
+ prologued_c_quizzes * (prologued_c_quizzes < vocabulary_size).long()
+ )
+
+ c_quizzes = prologued_c_quizzes[:, prologs_c_quizzes.size(1) :]
+
+ return c_quizzes.to("cpu"), prologs_c_quizzes.to("cpu")
+
+
+def batches_for_generator(generator, quiz_machine, models, fraction_w_quizzes=1.0):
+ samples = []
+
+ for _ in range(args.nb_train_samples // args.batch_size):
+ while sum([x.size(0) for x in samples]) < args.batch_size:
+ # Generate a bunch of quizzes
+
+ if torch.rand(1).item() <= fraction_w_quizzes:
+ # Either we start with the world quizzes
+ c_quizzes = quiz_machine.problem.generate_w_quizzes(
+ args.batch_size, progress_bar=False
+ )
+ else:
+ # Or we use the generator itself to generate them
+ c_quizzes, _ = generate_c_quizzes_with_generator(
+ generator, quiz_machine, args.batch_size
+ )
+
+ # We remove the trivial ones
+ to_keep = quiz_machine.problem.trivial(c_quizzes) == False
+ c_quizzes = c_quizzes[to_keep]
+
+ # If there are remaining ones, we compute the true prolog
+ # that indicates how the GPTs solve it
+
+ if c_quizzes.size(0) > 0:
+ seq_logproba = quiz_machine.models_logprobas(
+ models,
+ c_quizzes,
+ ("A", "f_A", "B", "f_B"),
+ (0, 0, 0, 1),
+ (0, 0, 1, 0),
+ ) + quiz_machine.models_logprobas(
+ models,
+ c_quizzes,
+ ("f_A", "A", "f_B", "B"),
+ (0, 0, 0, 1),
+ (0, 0, 1, 0),
+ )
+
+ probas = seq_logproba.exp()
+
+ u0 = probas <= args.proba_not_understands
+ u2 = probas >= args.proba_understands
+ u1 = (u0 | u2) == False
+
+ prologs = (
+ (u0.long() * token_prolog_0)
+ + (u1.long() * token_prolog_1)
+ + (u2.long() * token_prolog_2)
+ )
+
+ prologued_c_quizzes = torch.cat([prologs, c_quizzes], dim=1)
+
+ # nb_u2 = u2.long().sum(dim=1)
+ # nb_u0 = u0.long().sum(dim=1)
+ # prologued_c_quizzes = prologued_c_quizzes[(nb_u2 >= 1) & (nb_u0 >= 1)]
+
+ if prologued_c_quizzes.size(0) > 0:
+ samples.append(prologued_c_quizzes)
+
+ # Now we yield a batch
+
+ x = torch.cat(samples, dim=0)
+ samples = [x[args.batch_size :]]
+
+ yield x[: args.batch_size]
+
+
+def one_generator_epoch(
+ generator, quiz_machine, models, fraction_w_quizzes, local_device=main_device
+):
+ model.to(local_device).train()
+
+ optimizer = torch.optim.Adam(generator.parameters(), lr=args.learning_rate)
+
+ nb_train_samples, acc_train_loss = 0, 0.0
+
+ src = batches_for_generator(
+ generator=generator,
+ quiz_machine=quiz_machine,
+ models=models,
+ fraction_w_quizzes=fraction_w_quizzes,
+ )
+
+ for input in tqdm.tqdm(
+ src,
+ dynamic_ncols=True,
+ desc="training",
+ total=args.nb_train_samples // args.batch_size,
+ ):
+ input = input.to(local_device)
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
+
+ targets = input
+
+ output = generator(mygpt.BracketedSequence(input)).x
+ loss = F.cross_entropy(output.transpose(1, 2), targets)
+ acc_train_loss += loss.item() * input.size(0)
+ nb_train_samples += input.size(0)
- return sum_logits / sum_nb_c_quizzes
+ loss.backward()
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.step()
+
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
+
+ log_string(f"train_perplexity {n_epoch} generator - {train_perplexity}")
+
+ generator.to(main_device)
+
+
+######################################################################
+
+
+def train_complexifier(model_gen, model_pred1, model_pred2):
+ samples = []
+ perf = []
+
+ optimizer = torch.optim.Adam(model_gen.parameters(), lr=args.learning_rate)
+
+ nb_train_samples, acc_train_loss = 0, 0.0
+
+ for n_epoch in range(args.nb_epochs):
+ for b in range(args.nb_train_samples // args.batch_size):
+ while sum([x.size(0) for x in samples]) < args.batch_size:
+ c_quizzes = quiz_machine.generate_c_quizzes(
+ args.inference_batch_size,
+ model_for_generation=model_gen,
+ procedure=c_quizzes_procedure,
+ )
+ to_keep = quiz_machine.problem.trivial(c_quizzes) == False
+ c_quizzes = c_quizzes[to_keep]
+ if c_quizzes.size(0) > 0:
+ seq_logproba = quiz_machine.models_logprobas(
+ [model_pred1, model_pred2],
+ c_quizzes,
+ ("A", "f_A", "B", "f_B"),
+ (0, 0, 0, 1),
+ ) + quiz_machine.models_logprobas(
+ [model_pred1, model_pred2],
+ c_quizzes,
+ ("f_A", "A", "f_B", "B"),
+ (0, 0, 0, 1),
+ )
+ probas = seq_logproba.exp()
+ to_keep = (probas[:, model_pred1.id] >= args.proba_understands) & (
+ probas[:, model_pred2.id] <= args.proba_not_understands
+ )
+ log_string(
+ f"generating {to_keep.long().sum()} / {c_quizzes.size(0)}"
+ )
+ c_quizzes = c_quizzes[to_keep]
+ if c_quizzes.size(0):
+ samples.append(c_quizzes)
+
+ log_string(f"full batch {sum([x.size(0) for x in samples])}")
+
+ x = torch.cat(samples, dim=0)
+
+ input = x[: args.batch_size]
+ samples = [x[args.batch_size :]]
+
+ # -------------------
+
+ seq_logproba = quiz_machine.models_logprobas(
+ [model_pred1, model_pred2],
+ input,
+ ("A", "f_A", "B", "f_B"),
+ (0, 0, 0, 1),
+ ) + quiz_machine.models_logprobas(
+ [model_pred1, model_pred2],
+ input,
+ ("f_A", "A", "f_B", "B"),
+ (0, 0, 0, 1),
+ )
+
+ comments = []
+
+ for l in seq_logproba:
+ comments.append(
+ f"proba {l[model_pred1.id].exp().item():.02f} {l[model_pred2.id].exp().item():.02f}"
+ )
+
+ filename = f"batch_{n_epoch:04d}_{b:04d}.png"
+ quiz_machine.problem.save_quizzes_as_image(
+ args.result_dir, filename, input, comments=comments
+ )
+ log_string(f"wrote {filename}")
+
+ # ------------------------
+
+ input = input.to(main_device)
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
+
+ output = model_gen(mygpt.BracketedSequence(input)).x
+ loss = F.cross_entropy(output.transpose(1, 2), input)
+ acc_train_loss += loss.item() * input.size(0)
+ nb_train_samples += input.size(0)
+
+ loss.backward()
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.step()
+
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
+
+ log_string(f"train_perplexity {n_epoch} model ae {train_perplexity}")
######################################################################
models = []
+
+def compute_causal_attzero(t_q, t_k):
+ return t_q < t_k
+
+
+if args.schedule_free:
+ import schedulefree
+
for k in range(args.nb_gpts):
+ log_string(f"creating model {k} and its w_quizzes")
+
model = mygpt.MyGPT(
vocabulary_size=vocabulary_size,
dim_model=args.dim_model,
dim_hidden=args.dim_hidden,
nb_heads=args.nb_heads,
nb_blocks=args.nb_blocks,
- causal=True,
+ compute_attzero=compute_causal_attzero,
dropout=args.dropout,
- ).to(device)
+ ).to(main_device)
- model.main_test_accuracy = 0.0
model.id = k
+ if args.schedule_free:
+ model.optimizer = schedulefree.AdamWScheduleFree(
+ model.parameters(), lr=args.learning_rate
+ )
+ else:
+ model.optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+
+ model.main_test_accuracy = 0.0
+
+ model.train_w_quizzes = quiz_machine.problem.generate_w_quizzes(
+ args.nb_train_samples
+ )
+
+ model.test_w_quizzes = quiz_machine.problem.generate_w_quizzes(args.nb_test_samples)
+
models.append(model)
+######################################################################
+
+if args.test == "quant":
+ nb_bits = 8
+ for model in models:
+ model.trunk.insert(
+ 12,
+ mygpt.CacheWrapper(
+ mygpt.RandomBypass(
+ nn.Sequential(
+ nn.Linear(args.dim_model, nb_bits),
+ mygpt.BSQ(nb_bits),
+ nn.Linear(nb_bits, args.dim_model),
+ ),
+ 0.1,
+ )
+ ),
+ )
+
+ print(model)
+ exit(0)
+
+
+######################################################################
+
+current_epoch = 0
+
+if args.resume:
+ for model in models:
+ filename = f"gpt_{model.id:03d}.pth"
+
+ try:
+ d = torch.load(os.path.join(args.result_dir, filename))
+ model.load_state_dict(d["state_dict"])
+ model.optimizer.load_state_dict(d["optimizer_state_dict"])
+ model.main_test_accuracy = d["main_test_accuracy"]
+ log_string(f"successfully loaded {filename}")
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+ try:
+ filename = "c_quizzes.pth"
+ quiz_machine.load_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"successfully loaded {filename}")
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+ try:
+ filename = "state.pth"
+ state = torch.load(os.path.join(args.result_dir, filename))
+ log_string(f"successfully loaded {filename}")
+ current_epoch = state["current_epoch"]
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+######################################################################
nb_parameters = sum(p.numel() for p in models[0].parameters())
log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
######################################################################
-min_ave_seq_logproba = None
+if args.nb_new_c_quizzes_for_train is None:
+ args.nb_new_c_quizzes_for_train = args.nb_train_samples // 100
-for n_epoch in range(args.nb_epochs):
- log_string(f"--- epoch {n_epoch} ----------------------------------------")
+if args.nb_new_c_quizzes_for_test is None:
+ args.nb_new_c_quizzes_for_test = args.nb_test_samples // 100
+
+log_string(
+ f"nb_new_c_quizzes_for_train {args.nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {args.nb_new_c_quizzes_for_test}"
+)
- a = [(model.id, float(model.main_test_accuracy)) for model in models]
- a.sort(key=lambda p: p[0])
- log_string(f"current accuracies {a}")
+######################################################################
+
+if args.dirty_debug:
+ args.accuracy_to_make_c_quizzes = 0.0
+ args.nb_gpts = 2
+ args.nb_new_c_quizzes_for_train = 100
+ args.nb_new_c_quizzes_for_test = 10
+
+######################################################################
- # select the model with lowest accuracy
- models.sort(key=lambda model: model.main_test_accuracy)
+if args.test == "tsne":
model = models[0]
- log_string(
- f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
- )
+ quizzes = []
+ labels = []
+ nb_samples_per_task = 1000
- # improve it
- one_epoch(model, quizz_machine)
+ for n, t in enumerate(args.grids_world_tasks.split(",")):
+ quizzes.append(
+ quiz_machine.problem.generate_w_quizzes(nb_samples_per_task, [t])
+ )
+ labels.append(torch.full((quizzes[-1].size(0),), n))
- quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+ quizzes = torch.cat(quizzes, dim=0)
+ labels = torch.cat(labels, dim=0)
- log_string(
- f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
- )
+ with torch.autograd.no_grad():
+ model.eval().to(main_device)
+ record = []
+ for input, targets in zip(
+ quizzes.split(args.batch_size), labels.split(args.batch_size)
+ ):
+ input = input.to(main_device)
+ bs = mygpt.BracketedSequence(input)
+ bs = mygpt.BracketedSequence(F.pad(bs.x, (1, -1)), bs.first, bs.nb)
+ bs = model.embedding(bs)
+ bs = model.trunk[args.nb_blocks // 2](bs)
+ record.append((bs.x.to("cpu"), targets))
- # test it
- run_tests(model, quizz_machine, deterministic_synthesis=False)
+ x = torch.cat([x for x, y in record], dim=0).flatten(1)
+ y = torch.cat([y for x, y in record], dim=0)
- log_string(
- f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
- )
+ print(f"{x.size()=} {y.size()=}")
+ # torch.save((x,y), "/tmp/embed.pth")
+ # exit(0)
- if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
- ave_seq_logproba = create_c_quizzes(
- models,
- quizz_machine,
- nb_for_train=nb_new_c_quizzes_for_train,
- nb_for_test=nb_new_c_quizzes_for_test,
- min_ave_seq_logproba=min_ave_seq_logproba,
+ from sklearn.manifold import TSNE
+
+ x_np = x.numpy()
+ z_np = TSNE(n_components=2, perplexity=50).fit_transform(x_np)
+ z = torch.from_numpy(z_np)
+
+ print(f"{z.size()=}")
+
+ with open("/tmp/result.dat", "w") as f:
+ for k in range(z.size(0)):
+ f.write(f"{y[k]} {z[k,0]} {z[k,1]}\n")
+
+ exit(0)
+
+######################################################################
+
+if args.test == "generator":
+ token_prolog_0 = vocabulary_size + 0
+ token_prolog_1 = vocabulary_size + 1
+ token_prolog_2 = vocabulary_size + 2
+ generator_vocabulary_size = vocabulary_size + 3
+
+ generator = mygpt.MyGPT(
+ vocabulary_size=generator_vocabulary_size,
+ dim_model=args.dim_model,
+ dim_keys=args.dim_keys,
+ dim_hidden=args.dim_hidden,
+ nb_heads=args.nb_heads,
+ nb_blocks=args.nb_blocks,
+ compute_attzero=compute_causal_attzero,
+ dropout=args.dropout,
+ ).to(main_device)
+
+ generator.main_test_accuracy = 0.0
+
+ filename = f"generator.pth"
+
+ try:
+ d = torch.load(os.path.join(args.result_dir, filename))
+ generator.load_state_dict(d[0])
+ generator.main_test_accuracy = d[1]
+ log_string(f"successfully loaded {filename}")
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+ for n_epoch in range(args.nb_epochs):
+ one_generator_epoch(
+ generator,
+ quiz_machine=quiz_machine,
+ models=models,
+ fraction_w_quizzes=1 if n_epoch < 25 else 0.5,
+ local_device=main_device,
)
- # We keep the first average logits as a reference
- if min_ave_seq_logproba is None:
- min_ave_seq_logproba = ave_seq_logproba
- else:
- log_string(
- f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
+ filename = f"generator.pth"
+ torch.save(
+ (generator.state_dict(), generator.main_test_accuracy),
+ os.path.join(args.result_dir, filename),
+ )
+ log_string(f"wrote {filename}")
+
+ c_quizzes, prologs = generate_c_quizzes_with_generator(
+ generator, quiz_machine, args.batch_size
+ )
+
+ seq_logproba = quiz_machine.models_logprobas(
+ models, c_quizzes, ("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ ) + quiz_machine.models_logprobas(
+ models, c_quizzes, ("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0)
+ )
+
+ probas = seq_logproba.exp()
+
+ u0 = probas <= args.proba_not_understands
+ u2 = probas >= args.proba_understands
+ u1 = (u0 | u2) == False
+
+ predicted_prologs = (
+ (u0.long() * token_prolog_0)
+ + (u1.long() * token_prolog_1)
+ + (u2.long() * token_prolog_2)
+ )
+
+ comments = []
+
+ nb_errors = (predicted_prologs != prologs).long().sum()
+ nb_total = prologs.numel()
+
+ log_string(f"generator_error {nb_errors} / {nb_total}")
+
+ def readable(prologs):
+ return (prologs == token_prolog_1) + 2 * (prologs == token_prolog_2)
+
+ for aa, ee, ff in zip(probas, readable(predicted_prologs), readable(prologs)):
+ sa = "prolog " + " ".join(
+ [f"{e.item()}/{f.item()}" for e, f in zip(ee, ff)]
)
+ sp = "proba " + " ".join([f"{p.item():.02f}" for p in aa])
+ comments.append(sa + "\n" + sp)
+
+ filename = f"generator_batch_{n_epoch:04d}.png"
+ quiz_machine.problem.save_quizzes_as_image(
+ args.result_dir, filename, c_quizzes, comments=comments
+ )
+ log_string(f"wrote {filename}")
+
+ exit(0)
+
+######################################################################
+
+for n_epoch in range(current_epoch, args.nb_epochs):
+ state = {"current_epoch": n_epoch}
+ filename = "state.pth"
+ torch.save(state, os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
+
+ log_string(f"--- epoch {n_epoch} ----------------------------------------")
+
+ cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+ log_string(f"current_test_accuracies {cta}")
- # We update everyone
+ ##################################################
+ # If all the models are good enough, generate new quizzes and
+ # re-compute the test errors
+
+ if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
+ record_new_c_quizzes(
+ models,
+ quiz_machine,
+ nb_for_train=args.nb_new_c_quizzes_for_train,
+ nb_for_test=args.nb_new_c_quizzes_for_test,
+ )
+
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
+
+ # Force one epoch of training
for model in models:
- run_tests(model, quizz_machine, deterministic_synthesis=False)
+ model.main_test_accuracy = 0.0
+
+ ##################################################
+ # Select, improve, and eval the worst model(s)
+
+ ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
+
+ weakest_models = ranked_models[: len(gpus)]
+
+ threads = []
+
+ for gpu, model in zip(gpus, weakest_models):
+ log_string(f"training model {model.id}")
+
+ t = threading.Thread(
+ target=one_epoch, daemon=True, args=(model, quiz_machine, gpu)
+ )
+
+ threads.append(t)
+
+ t.start()
+
+ for t in threads:
+ t.join()
+
+ # Save the models to disk
+
+ for model in weakest_models:
+ filename = f"gpt_{model.id:03d}.pth"
+ torch.save(
+ {
+ "state_dict": model.state_dict(),
+ "optimizer_state_dict": model.optimizer.state_dict(),
+ "main_test_accuracy": model.main_test_accuracy,
+ },
+ os.path.join(args.result_dir, filename),
+ )
+ log_string(f"wrote {filename}")
+
+ for model in weakest_models:
+ save_additional_results(model, models, science_w_quizzes)
+
+ ######################################################################
+
+ # Renew the training samples
+
+ for model in weakest_models:
+ quiz_machine.renew_train_w_quizzes(model=model)
+ if args.log_command is not None:
+ s = args.log_command.split()
+ s.insert(1, args.result_dir)
+ subprocess.run(s)
######################################################################
projects / culture.git / blobdiff