# 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 math, sys, argparse, time, tqdm, os, datetime, warnings
import torch, torchvision
from torch import nn
from torch.nn import functional as F
-import mygpt, tensorstack
+import ffutils
-######################################################################
+import mygpt
+import sky, grids, quiz_machine
-if torch.cuda.is_available():
- device = torch.device("cuda")
- torch.backends.cuda.matmul.allow_tf32 = True
-else:
- device = torch.device("cpu")
+import threading
+
+import torch.multiprocessing as mp
######################################################################
parser = argparse.ArgumentParser(
- description="An implementation of GPT with cache.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
-parser.add_argument(
- "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack"
-)
-
-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="results_default")
+parser.add_argument("--result_dir", type=str, default=None)
parser.add_argument("--seed", type=int, default=0)
-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--resume", action="store_true", default=False)
-parser.add_argument("--batch_size", type=int, default=None)
+parser.add_argument("--max_percents_of_test_in_train", type=int, default=-1)
-parser.add_argument("--nb_train_samples", type=int, default=250000)
+########################################
-parser.add_argument("--nb_test_samples", type=int, default=10000)
+parser.add_argument("--nb_epochs", type=int, default=10000)
-parser.add_argument("--optim", type=str, default="adam")
+parser.add_argument("--batch_size", type=int, default=None)
-parser.add_argument("--learning_rate", type=float, default=1e-4)
+parser.add_argument("--physical_batch_size", type=int, default=None)
-parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
+parser.add_argument("--nb_train_samples", type=int, default=None)
-parser.add_argument("--dim_model", type=int, default=512)
+parser.add_argument("--nb_test_samples", type=int, default=None)
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--learning_rate", type=float, default=5e-4)
-parser.add_argument("--dim_hidden", type=int, default=2048)
+########################################
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--model", type=str, default=None)
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--dim_model", type=int, default=None)
-parser.add_argument("--dropout", type=float, default=0.1)
+parser.add_argument("--dim_keys", type=int, default=None)
-parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--dim_hidden", type=int, default=None)
-parser.add_argument("--no_checkpoint", action="store_true", default=False)
+parser.add_argument("--nb_heads", type=int, default=None)
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--nb_blocks", type=int, default=None)
-parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
+parser.add_argument("--dropout", type=float, default=0.1)
-##############################
-# picoclvr options
+########################################
-parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
+parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
-parser.add_argument("--picoclvr_height", type=int, default=12)
+parser.add_argument("--problem", type=str, default="grids")
-parser.add_argument("--picoclvr_width", type=int, default=16)
+parser.add_argument("--nb_threads", type=int, default=1)
-parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
+parser.add_argument("--gpus", type=str, default="all")
-##############################
-# Maze options
+parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--maze_height", type=int, default=13)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
-parser.add_argument("--maze_width", type=int, default=21)
+parser.add_argument("--proba_understands", type=float, default=0.99)
-parser.add_argument("--maze_nb_walls", type=int, default=15)
+parser.add_argument("--proba_not_understands", type=float, default=0.5)
-##############################
-# Snake options
+parser.add_argument("--generation_temperature", type=float, default=2.0)
-parser.add_argument("--snake_height", type=int, default=6)
+parser.add_argument("--dirty_debug", action="store_true", default=False)
-parser.add_argument("--snake_width", type=int, default=8)
+######################################################################
-parser.add_argument("--snake_nb_colors", type=int, default=5)
+grids_tasks = ", ".join(
+ [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
+)
-parser.add_argument("--snake_length", type=int, default=200)
+parser.add_argument(
+ "--grids_tasks",
+ type=str,
+ default=None,
+ help="A comma-separated subset of: " + grids_tasks + ", or None for all.",
+)
-##############################
-# Snake options
+######################################################################
-parser.add_argument("--stack_nb_steps", type=int, default=25)
+parser.add_argument("--sky_height", type=int, default=6)
-parser.add_argument("--stack_nb_stacks", type=int, default=1)
+parser.add_argument("--sky_width", type=int, default=8)
-parser.add_argument("--stack_nb_values", type=int, default=10)
+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()
-assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
+if args.result_dir is None:
+ args.result_dir = f"results_culture"
-try:
- os.mkdir(args.result_dir)
-except FileExistsError:
- if not args.overwrite_results:
- print(f"result directory {args.result_dir} already exists")
- exit(1)
+######################################################################
-log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
+default_args = {
+ "model": "37M",
+ "batch_size": 25,
+ "nb_train_samples": 100000,
+ "nb_test_samples": 10000,
+}
-if args.seed >= 0:
- # torch.backends.cudnn.deterministic = True
- # torch.backends.cudnn.benchmark = False
- # torch.use_deterministic_algorithms(True)
- torch.manual_seed(args.seed)
- if torch.cuda.is_available():
- torch.cuda.manual_seed_all(args.seed)
+for k, v in default_args.items():
+ if getattr(args, k) is None:
+ setattr(args, k, v)
######################################################################
-default_args = {
- "picoclvr": {
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+default_model_args = {
+ "17K": {
+ "dim_model": 32,
+ "dim_keys": 32,
+ "dim_hidden": 32,
+ "nb_heads": 2,
+ "nb_blocks": 2,
},
- "mnist": {
- "nb_epochs": 25,
- "batch_size": 10,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "4M": {
+ "dim_model": 256,
+ "dim_keys": 32,
+ "dim_hidden": 1024,
+ "nb_heads": 4,
+ "nb_blocks": 6,
},
- "maze": {
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "37M": {
+ "dim_model": 512,
+ "dim_keys": 64,
+ "dim_hidden": 2048,
+ "nb_heads": 8,
+ "nb_blocks": 12,
},
- "snake": {
- "nb_epochs": 5,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "122M": {
+ "dim_model": 768,
+ "dim_keys": 64,
+ "dim_hidden": 2048,
+ "nb_heads": 8,
+ "nb_blocks": 24,
},
- "stack": {
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 10000,
- "nb_test_samples": 1000,
+ "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}")
+
+######################################################################
+
+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")
+
+if args.seed >= 0:
+ # torch.backends.cudnn.deterministic = True
+ # torch.backends.cudnn.benchmark = False
+ # torch.use_deterministic_algorithms(True)
+ torch.manual_seed(args.seed)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed_all(args.seed)
######################################################################
sys.stdout.flush()
-for n in vars(args):
- log_string(f"args.{n} {getattr(args, n)}")
+now = time.strftime("%Y%m%d-%H%M%S", time.localtime())
-######################################################################
+os.system(f"tar zcvf {args.result_dir}/src-{now}.tgz *.py")
+log_string(f"argv {' '.join(sys.argv)}")
-# ra_mask is boolean, with 1s on the values to generate
-
-
-def masked_inplace_autoregression(
- model,
- batch_size,
- input,
- ar_mask,
- forbidden_tokens=None,
- progress_bar_desc="autoregression",
- device=torch.device("cpu"),
-):
- # p = logits.softmax(1)
- # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2)
- batches = zip(input.split(batch_size), ar_mask.split(batch_size))
- if progress_bar_desc is not None:
- tqdm.tqdm(
- batches,
- dynamic_ncols=True,
- desc=progress_bar_desc,
- total=input.size(0) // batch_size,
- )
- for input, ar_mask in batches:
- i = (ar_mask.sum(0) > 0).nonzero()
- if i.min() > 0:
- model(
- mygpt.BracketedSequence(input, 0, i.min())
- ) # Needed to initialize the model's cache
- for s in range(i.min(), i.max() + 1):
- output = model(mygpt.BracketedSequence(input, s, 1)).x
- logits = output[:, s]
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
- if args.deterministic_synthesis:
- t_next = logits.argmax(1)
- else:
- dist = torch.distributions.categorical.Categorical(logits=logits)
- t_next = dist.sample()
- input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
+for n in vars(args):
+ log_string(f"args.{n} {getattr(args, n)}")
######################################################################
+if args.gpus == "all":
+ gpus_idx = range(torch.cuda.device_count())
+else:
+ gpus_idx = [int(k) for k in args.gpus.split(",")]
-class Task:
- def batches(self, split="train"):
- pass
+gpus = [torch.device(f"cuda:{n}") for n in gpus_idx]
- def vocabulary_size(self):
- pass
+if torch.cuda.is_available():
+ main_device = gpus[0]
+else:
+ assert len(gpus) == 0
+ main_device = torch.device("cpu")
- def produce_results(self, n_epoch, model):
- pass
+if args.dirty_debug:
+ args.nb_train_samples = 2500
+ args.nb_test_samples = 100
+if args.physical_batch_size is None:
+ args.physical_batch_size = args.batch_size
+else:
+ assert args.batch_size % args.physical_batch_size == 0
+
+assert args.nb_train_samples % args.batch_size == 0
+assert args.nb_test_samples % args.batch_size == 0
+
+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,
+ )
+ back_accuracy = False
+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_tasks,
+ )
+ back_accuracy = True
+else:
+ raise ValueError
+
+problem.save_some_examples(args.result_dir)
+
+quiz_machine = quiz_machine.QuizMachine(
+ problem=problem,
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ back_accuracy=back_accuracy,
+ batch_size=args.physical_batch_size,
+ result_dir=args.result_dir,
+ logger=log_string,
+ device=main_device,
+)
######################################################################
-import picoclvr
-
-
-class TaskPicoCLVR(Task):
- # Make a tensor from a list of strings
- def tensorize(self, descr):
- token_descr = [s.strip().split(" ") for s in descr]
- l = max([len(s) for s in token_descr])
- token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
- id_descr = [[self.token2id[u] for u in s] for s in token_descr]
- return torch.tensor(id_descr, device=self.device)
-
- # Make a list of strings from a tensor
- def detensorize(self, x):
- return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="<nul>"):
- n = self.token2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
-
- ######################
- # Not the cleanest part of the code
-
- # Extract the last image of each sequence, from the last <img>
- # included, and set to <nul> all the tokens from the beginning of
- # that image to the end
- def excise_last_image(self, input):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = input.clone()
- t = (input == t_img).long()
- tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
- i = (t * tail_masks).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- images = self.trim(input[j])
- input[j] = t_nul
- loss_masks = 1 - tail_masks
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks, images
-
- def add_true_image(self, input, images, loss_masks):
- t_nul = self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- input[j] = images
- loss_masks[j] = 1
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks
-
- def add_generated_image(self, input, loss_masks, model):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- input[i] = t_img
-
- j = (
- i[0][:, None],
- i[1][:, None]
- + 1
- + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
- )
- ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
- ar_masks[j] = 1
- forbidden_tokens = (
- torch.arange(self.vocabulary_size(), device=input.device) == t_nul
- )
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- input,
- ar_masks,
- forbidden_tokens,
- progress_bar_desc=None,
- device=self.device,
- )
- model.train(t)
-
- input, loss_masks = self.trim((input, loss_masks))
+log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
- return input, loss_masks
+vocabulary_size = quiz_machine.vocabulary_size()
- ######################
+log_string(f"vocabulary_size {vocabulary_size}")
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors=5,
- device=torch.device("cpu"),
- pruner_train=None,
- pruner_eval=None,
- ):
- def generate_descr(nb, cache_suffix, pruner):
- return picoclvr.generate(
- nb,
- height=self.height,
- width=self.width,
- nb_colors=nb_colors,
- pruner=pruner,
- )
+######################################################################
- self.height = height
- self.width = width
- self.batch_size = batch_size
- self.device = device
- self.pruner_train = pruner_train
- self.pruner_eval = pruner_eval
-
- param = {
- "nb_train_samples": nb_train_samples,
- "nb_test_samples": nb_test_samples,
- "height": height,
- "width": width,
- "nb_colors": nb_colors,
- "batch_size": batch_size,
- "rng_state": list(torch.get_rng_state()),
- }
- log_string(
- f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
- )
- self.train_descr = generate_descr(
- nb_train_samples, "train", pruner=self.pruner_train
- )
- self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
-
- # Build the tokenizer
- tokens = {"<nul>", "<img>"}
- for d in [self.train_descr, self.test_descr]:
- for s in d:
- for t in s.strip().split(" "):
- tokens.add(t)
- # make this set a sorted list to get the same tensors given
- # the same descr
- tokens = list(tokens)
- tokens.sort()
- self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
- self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
-
- # Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
-
- def batches(self, split="train"):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
- ):
- yield self.trim(batch)
+def run_tests(model, quiz_machine, deterministic_synthesis, local_device=main_device):
+ with torch.autograd.no_grad():
+ model.eval().to(local_device)
- def vocabulary_size(self):
- return len(self.token2id)
+ nb_test_samples, acc_test_loss = 0, 0.0
+ nb_samples_accumulated = 0
- def compute_missing_properties(self, n_epoch, model, pruner=None):
- acc_nb_requested_properties = []
- acc_nb_missing_properties = []
- acc_nb_results = 0
+ for input in quiz_machine.batches(model, split="test"):
+ input = input.to(local_device)
- for input in tqdm.tqdm(
- self.test_input.split(self.batch_size),
- dynamic_ncols=True,
- desc=f"test-properties",
- ):
- tape, loss_masks, _ = self.excise_last_image(input)
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
- np = picoclvr.nb_properties(
- result_descr,
- height=self.height,
- width=self.width,
- pruner=pruner,
- )
- nb_requested_properties, _, nb_missing_properties = zip(*np)
- acc_nb_requested_properties += nb_requested_properties
- acc_nb_missing_properties += nb_missing_properties
- acc_nb_results += len(result_descr)
+ bs = model(mygpt.BracketedSequence(input))
+ output = bs.x
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
+ loss = F.cross_entropy(output.transpose(1, 2), input)
- prefix = "" if pruner is None else "pruned_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- log_string(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
+ acc_test_loss += loss.item() * input.size(0)
- ######################################################################
+ nb_test_samples += input.size(0)
- def produce_results(self, n_epoch, model):
- self.compute_missing_properties(n_epoch, model)
+ test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
- if self.pruner_eval is not None:
- self.compute_missing_properties(n_epoch, model, self.pruner_eval)
+ log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
- nb_tokens_to_generate = self.height * self.width + 3
- result_descr = []
- nb_per_primer = 8
- primer = []
+ model.main_test_accuracy = quiz_machine.produce_results(
+ n_epoch=n_epoch,
+ model=model,
+ result_dir=args.result_dir,
+ deterministic_synthesis=deterministic_synthesis,
+ )
- for primer_descr in [
- "red above green <sep> green top <sep> blue right of red",
- "there is red <sep> there is yellow <sep> there is blue",
- "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
- "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
- ]:
- primer += [primer_descr] * nb_per_primer
- tape = self.tensorize(primer)
- loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
+def one_epoch(model, quiz_machine, local_device=main_device):
+ model.to(local_device).train()
- np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
- acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
- acc_nb_results = len(result_descr)
+ nb_train_samples, acc_train_loss = 0, 0.0
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
+ for input in quiz_machine.batches(model, split="train"):
+ input = input.to(local_device)
- prefix = "demo_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- log_string(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
- img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
-
- if img.dim() == 5:
- if img.size(1) == 1:
- img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
- else:
- img = torch.cat(
- [
- torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
- for x in img
- ],
- 0,
- )
-
- image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
- )
- log_string(f"wrote {image_name}")
+ output = model(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()
-class TaskMNIST(Task):
- def __init__(self, batch_size, device=torch.device("cpu")):
- self.device = device
- self.batch_size = batch_size
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- def batches(self, split="train"):
- assert split in {"train", "test"}
- data_set = torchvision.datasets.MNIST(
- root="./data", train=(split == "train"), download=True
- )
- data_input = data_set.data.view(-1, 28 * 28).long()
- if args.nb_train_samples is not None:
- data_input = data_input[: args.nb_train_samples]
- for batch in tqdm.tqdm(
- data_input.split(self.batch_size), desc=f"epoch-{split}"
- ):
- yield batch
+ log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
- def vocabulary_size(self):
- return 256
+ run_tests(model, quiz_machine, deterministic_synthesis=False)
- def produce_results(self, n_epoch, model):
- results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
- ar_mask = torch.full_like(results, 1)
- masked_inplace_autoregression(
- model, self.batch_size, results, ar_mask, device=self.device
- )
- image_name = os.path.join(args.result_dir, f"mnist_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- 1 - results.reshape(-1, 1, 28, 28) / 255.0,
- image_name,
- nrow=16,
- pad_value=0.8,
- )
- log_string(f"wrote {image_name}")
+ model.to(main_device)
######################################################################
-import maze
+def standard_validity(logproba):
+ l = logproba.sort(dim=-1).values
+ return (l[:, 0] < math.log(args.proba_not_understands)) & (
+ l[:, 1] > math.log(args.proba_understands)
+ )
-class TaskMaze(Task):
- def map2seq(self, *m):
- return torch.cat([x.flatten(1) for x in m], 1)
- def seq2map(self, s):
- s = s.reshape(s.size(0), -1, self.height, self.width)
- return (s[:, k] for k in range(s.size(1)))
+def valid_quizzes_and_logprobas(recorded, criteria):
+ validated_quizzes, validated_logprobas = [], []
+ for q, lp in recorded:
+ validated_indices = criteria(lp)
+ validated_quizzes.append(q[validated_indices])
+ validated_logprobas.append(lp[validated_indices])
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_walls,
- device=torch.device("cpu"),
- ):
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
-
- train_mazes, train_paths, _ = maze.create_maze_data(
- nb_train_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
- )
- self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
-
- test_mazes, test_paths, _ = maze.create_maze_data(
- nb_test_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
- )
- self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def compute_error(self, model, split="train", nb_to_use=-1):
- nb_total, nb_correct = 0, 0
- count = torch.zeros(
- self.width * self.height,
- self.width * self.height,
- device=self.device,
- dtype=torch.int64,
+ if len(validated_quizzes) > 0:
+ return torch.cat(validated_quizzes, dim=0), torch.cat(
+ validated_logprobas, dim=0
)
- for input in tqdm.tqdm(
- task.batches(split, nb_to_use),
- dynamic_ncols=True,
- desc=f"test-mazes",
- ):
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- progress_bar_desc=None,
- device=self.device,
- )
- mazes, paths = self.seq2map(result)
- path_correctness = maze.path_correctness(mazes, paths)
- nb_correct += path_correctness.long().sum()
- nb_total += mazes.size(0)
-
- optimal_path_lengths = (
- (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
- )
- predicted_path_lengths = (
- (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
- )
- optimal_path_lengths = optimal_path_lengths[path_correctness]
- predicted_path_lengths = predicted_path_lengths[path_correctness]
- count[optimal_path_lengths, predicted_path_lengths] += 1
-
- if count.max() == 0:
- count = None
- else:
- count = count[
- : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
- ]
-
- return nb_total, nb_correct, count
-
- def produce_results(self, n_epoch, model):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- train_nb_total, train_nb_correct, count = self.compute_error(
- model, "train", nb_to_use=1000
- )
- log_string(
- f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- )
-
- test_nb_total, test_nb_correct, count = self.compute_error(
- model, "test", nb_to_use=1000
- )
- log_string(
- f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
-
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
- ) as f:
- for i in range(count.size(0)):
- for j in range(count.size(1)):
- eol = " " if j < count.size(1) - 1 else "\n"
- f.write(f"{count[i,j]}{eol}")
-
- input = self.test_input[:48]
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
-
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.png")
- maze.save_image(
- filename,
- mazes=mazes,
- target_paths=paths,
- predicted_paths=predicted_paths,
- path_correct=maze.path_correctness(mazes, predicted_paths),
- path_optimal=maze.path_optimality(paths, predicted_paths),
- )
- log_string(f"wrote {filename}")
-
- model.train(t)
+ else:
+ return None, None
######################################################################
-import snake
-
-
-class TaskSnake(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- device=torch.device("cpu"),
- ):
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
- self.prompt_length = prompt_length
-
- self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
- nb_train_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
- self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
- nb_test_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def produce_results(self, n_epoch, model):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input, prior_visits):
- result = input.clone()
- i = torch.arange(result.size(1), device=result.device)[None, :]
- ar_mask = (
- torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
- .long()
- .expand_as(result)
- )
- result *= 1 - ar_mask
-
- # snake.solver(result,ar_mask)
-
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
+def create_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100):
+ nb_to_create = nb_for_train + nb_for_test
- nb_total = ((prior_visits > 0) * ar_mask).sum()
+ recorded_quizzes_logprobas = []
- nb_correct = (
- (result == input).long() * (prior_visits > 0) * ar_mask
- ).sum()
+ nb_validated = 0
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+ while nb_validated < nb_to_create:
+ model_for_generation = models[torch.randint(len(models), (1,))]
- return nb_total, nb_correct
-
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
+ c_quizzes = quiz_machine.generate_quizzes(
+ nb_to_create,
+ model_for_generation=model_for_generation,
+ temperature=args.generation_temperature,
+ )
- # log_string(
- # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- # )
+ c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
+ if c_quizzes.size(0) > 0:
+ logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
+ recorded_quizzes_logprobas.append((c_quizzes, logproba))
- log_string(
- f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ validated_quizzes, validated_logprobas = valid_quizzes_and_logprobas(
+ recorded_quizzes_logprobas, standard_validity
)
- model.train(t)
+ if validated_quizzes is not None:
+ nb_validated = validated_quizzes.size(0)
+ log_string(
+ f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
+ )
-######################################################################
-
+ # store the new c_quizzes which have been validated
-import stack
+ quiz_machine.reverse_random_half_in_place(validated_quizzes)
+ quiz_machine.store_c_quizzes(validated_quizzes[:nb_for_train], for_train=True)
+ quiz_machine.store_c_quizzes(
+ validated_quizzes[nb_for_train:nb_to_create], for_train=False
+ )
+ ######################################################################
+ # save the log probas
-class TaskStack(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- nb_steps,
- nb_stacks,
- nb_values,
- device=torch.device("cpu"),
- ):
- self.batch_size = batch_size
- self.nb_steps = nb_steps
- self.nb_stacks = nb_stacks
- self.nb_values = nb_values
- self.device = device
-
- self.train_input, self.train_stack_counts = stack.generate_sequences(
- nb_train_samples, nb_steps, nb_stacks, nb_values, self.device
- )
+ file_name = os.path.join(
+ args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat"
+ )
- self.test_input, self.test_stack_counts = stack.generate_sequences(
- nb_test_samples, nb_steps, nb_stacks, nb_values, self.device
- )
+ with open(file_name, "w") as logp_file:
+ for _, ll in recorded_quizzes_logprobas:
+ for l in ll:
+ s = " ".join([str(x.item()) for x in l])
+ logp_file.write(s + "\n")
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
+ ######################################################################
+ # save images with their logprobas
- def vocabulary_size(self):
- return self.nb_codes
+ vq = validated_quizzes[:72]
+ vl = validated_logprobas[:72]
- def produce_results(self, n_epoch, model):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
+ if vq.size(0) > 0:
+ prefix = f"culture_c_quiz_{n_epoch:04d}"
- def compute_nb_correct(input):
- result = input.clone()
- stack.remove_poped_values(result,self.nb_stacks)
- ar_mask = (result != input).long()
- result *= 1 - ar_mask
+ file_name = os.path.join(args.result_dir, prefix + "_logp.dat")
+ with open(file_name, "w") as logp_file:
+ for l in vl:
+ s = " ".join([str(x.item()) for x in l])
+ logp_file.write(s + "\n")
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
+ quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
- nb_total = ar_mask.sum()
- nb_correct = (
- (result == input).long() * ar_mask
- ).sum()
+######################################################################
- return nb_total, nb_correct
+models = []
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+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_keys=args.dim_keys,
+ dim_hidden=args.dim_hidden,
+ nb_heads=args.nb_heads,
+ nb_blocks=args.nb_blocks,
+ causal=True,
+ dropout=args.dropout,
+ ).to(main_device)
- log_string(
- f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ model.main_test_accuracy = 0.0
+ model.id = k
- model.train(t)
+ model.train_w_quizzes = quiz_machine.generate_token_sequences(args.nb_train_samples)
+ quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
+ model.test_w_quizzes = quiz_machine.generate_token_sequences(args.nb_test_samples)
+ quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
+ models.append(model)
######################################################################
+if args.resume:
+ try:
+ 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[0])
+ model.main_test_accuracy = d[1]
+ 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
-def picoclvr_pruner_horizontal_green(p):
- return not ("green" in p and ("left" in p or "right" in p))
-
+ except:
+ log_string(f"error when loading {filename}.")
+ exit(1)
-picoclvr_pruner_train = (
- picoclvr_pruner_horizontal_green
- if args.picocvlr_prune_properties in {"train+eval"}
- else None
-)
+######################################################################
-picoclvr_pruner_eval = (
- (lambda p: not picoclvr_pruner_horizontal_green(p))
- if args.picocvlr_prune_properties in {"train+eval", "eval"}
- else None
-)
+nb_parameters = sum(p.numel() for p in models[0].parameters())
+log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
######################################################################
-if args.task == "picoclvr":
- task = TaskPicoCLVR(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
- height=args.picoclvr_height,
- width=args.picoclvr_width,
- nb_colors=args.picoclvr_nb_colors,
- device=device,
- pruner_train=picoclvr_pruner_train,
- pruner_eval=picoclvr_pruner_eval,
- )
-
-elif args.task == "mnist":
- task = TaskMNIST(
- batch_size=args.batch_size,
- device=device,
- )
+# Compute the entropy of the training tokens
-elif args.task == "maze":
- task = TaskMaze(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
- height=args.maze_height,
- width=args.maze_width,
- nb_walls=args.maze_nb_walls,
- device=device,
+token_count = 0
+for input in quiz_machine.batches(models[0], split="train", desc="train-entropy"):
+ token_count += F.one_hot(input, num_classes=quiz_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)
-elif args.task == "snake":
- task = TaskSnake(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
- height=args.snake_height,
- width=args.snake_width,
- nb_colors=args.snake_nb_colors,
- length=args.snake_length,
- prompt_length=args.snake_length // 2,
- device=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(
+ quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
+ ):
+ in_train = set()
+ for train_subset in subsets_as_tuples(
+ quiz_machine.batches(models[0], 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)
-elif args.task == "stack":
- task = TaskStack(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
- nb_steps = args.stack_nb_steps,
- nb_stacks = args.stack_nb_stacks,
- nb_values = args.stack_nb_values,
- device=device,
+ 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"
)
-else:
- raise ValueError(f"Unknown task {args.task}")
+ 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"
######################################################################
-log_string(f"device {device}")
-
-vocabulary_size = task.vocabulary_size()
+nb_new_c_quizzes_for_train = args.nb_train_samples // 50
+nb_new_c_quizzes_for_test = args.nb_test_samples // 50
-log_string(f"vocabulary_size {vocabulary_size}")
-
-##############################
-
-model = mygpt.MyGPT(
- vocabulary_size=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,
- causal=True,
- dropout=args.dropout,
+log_string(
+ f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}"
)
-model.to(device)
-
-nb_parameters = sum(p.numel() for p in model.parameters())
-log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
-
######################################################################
-nb_epochs_finished = 0
-
-if args.no_checkpoint:
- log_string(f"not trying to load checkpoint.")
-
-else:
- try:
- checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
- checkpoint = torch.load(checkpoint_name)
- nb_epochs_finished = checkpoint["nb_epochs_finished"]
- model.load_state_dict(checkpoint["model_state"])
- torch.set_rng_state(checkpoint["rng_state"])
- if torch.cuda.is_available():
- torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
+if args.dirty_debug:
+ args.accuracy_to_make_c_quizzes = 0.0
+ args.nb_gpts = 2
+ nb_new_c_quizzes_for_train = 100
+ nb_new_c_quizzes_for_test = 10
- log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
+ def standard_validity(logproba):
+ l = logproba.sort(dim=-1).values
+ return l[:, 0] < math.log(0.5)
- except FileNotFoundError:
- log_string("starting from scratch.")
-
- except:
- log_string("error when loading the checkpoint.")
- exit(1)
######################################################################
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
-token_count = 0
-for input in task.batches(split="train"):
- 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()
-train_set_perplexity = math.exp(entropy)
-
-##############################
-
-if args.learning_rate_schedule == "cos":
- learning_rate_schedule = {}
- for n_epoch in range(args.nb_epochs):
- u = n_epoch / args.nb_epochs * math.pi
- learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
-else:
- u = {
- int(k): float(v)
- for k, v in [
- tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
- ]
- }
-
- learning_rate_schedule = {}
- learning_rate = args.learning_rate
- for n_epoch in range(args.nb_epochs):
- if n_epoch in u:
- learning_rate = u[n_epoch]
- learning_rate_schedule[n_epoch] = learning_rate
+for n_epoch in range(args.nb_epochs):
+ log_string(f"--- epoch {n_epoch} ----------------------------------------")
-log_string(f"learning_rate_schedule {learning_rate_schedule}")
+ cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+ log_string(f"current_test_accuracies {cta}")
-##############################
+ ##################################################
+ # Select, improve, and eval the worst model
-nb_samples_seen = 0
+ ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
-if nb_epochs_finished >= nb_epochs:
- task.produce_results(nb_epochs_finished, model)
+ weakest_models = ranked_models[: len(gpus)]
-for n_epoch in range(nb_epochs_finished, nb_epochs):
- learning_rate = learning_rate_schedule[n_epoch]
+ threads = []
- log_string(f"learning_rate {learning_rate}")
+ for gpu, model in zip(gpus, weakest_models):
+ log_string(f"training model {model.id}")
- if args.optim == "sgd":
- optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
- elif args.optim == "adam":
- optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
- elif args.optim == "adamw":
- optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
- else:
- raise ValueError(f"Unknown optimizer {args.optim}.")
-
- model.train()
+ t = threading.Thread(
+ target=one_epoch, daemon=True, args=(model, quiz_machine, gpu)
+ )
- nb_train_samples, acc_train_loss = 0, 0.0
+ threads.append(t)
- for input in task.batches(split="train"):
- input = input.to(device)
- output = model(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)
- nb_samples_seen += input.size(0)
+ t.start()
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
+ for t in threads:
+ t.join()
- with torch.autograd.no_grad():
- model.eval()
+ # Save the models to disk
- nb_test_samples, acc_test_loss = 0, 0.0
+ for model in weakest_models:
+ filename = f"gpt_{model.id:03d}.pth"
+ torch.save(
+ (model.state_dict(), model.main_test_accuracy),
+ os.path.join(args.result_dir, filename),
+ )
+ log_string(f"wrote {filename}")
- for input in task.batches(split="test"):
- input = input.to(device)
+ # Renew the training samples
- output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
- acc_test_loss += loss.item() * input.size(0)
- nb_test_samples += input.size(0)
+ for model in weakest_models:
+ quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
- train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+ ##################################################
+ # If all the models are good enough, generate new quizzes and
+ # re-compute the test errors
- log_string(
- f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
+ if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
+ create_c_quizzes(
+ models,
+ quiz_machine,
+ nb_for_train=nb_new_c_quizzes_for_train,
+ nb_for_test=nb_new_c_quizzes_for_test,
)
- task.produce_results(n_epoch, model)
-
- checkpoint = {
- "nb_epochs_finished": n_epoch + 1,
- "model_state": model.state_dict(),
- "rng_state": torch.get_rng_state(),
- }
-
- if torch.cuda.is_available():
- checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
-
- checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
- torch.save(checkpoint, checkpoint_name)
- log_string(f"saved checkpoint {checkpoint_name}")
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
######################################################################