# Any copyright is dedicated to the Public Domain.
# https://creativecommons.org/publicdomain/zero/1.0/
-# Written by Francois Fleuret <francois@fleuret.org>
+# > A > f(A) > B ; > f(B)
+# < f(B) ; < B < f(A) < A
-# torch.backends.cuda.matmul.allow_tf23
-# torch.autocast(torch.bfloat16)
+# Written by Francois Fleuret <francois@fleuret.org>
-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")
+from quiz_machine import one_batch_masked_inplace_autoregression
+
+import threading, subprocess
+
+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, expr",
-)
-
-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("--nb_epochs", type=int, default=None)
+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("--batch_size", type=int, default=None)
+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("--optim", type=str, default="adam")
+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=1e-4)
+parser.add_argument("--learning_rate", type=float, default=5e-4)
-parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
+parser.add_argument("--schedule_free", action="store_true", default=False)
-parser.add_argument("--dim_model", type=int, default=512)
+# ----------------------------------
+parser.add_argument("--model", type=str, default=None)
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--dim_model", type=int, default=None)
-parser.add_argument("--dim_hidden", type=int, default=2048)
+parser.add_argument("--dim_keys", type=int, default=None)
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--dim_hidden", type=int, default=None)
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--nb_heads", type=int, default=None)
+
+parser.add_argument("--nb_blocks", 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("--no_checkpoint", action="store_true", default=False)
-
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--problem", type=str, default="grids")
-parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
+parser.add_argument("--nb_threads", type=int, default=1)
-##############################
-# picoclvr options
+parser.add_argument("--gpus", type=str, default="all")
-parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
+# ----------------------------------
-parser.add_argument("--picoclvr_height", type=int, default=12)
+parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--picoclvr_width", type=int, default=16)
+parser.add_argument("--max_fail_to_validate", type=int, default=3)
-parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.95)
-##############################
-# Maze options
+parser.add_argument("--proba_understands", type=float, default=0.95)
-parser.add_argument("--maze_height", type=int, default=13)
+parser.add_argument("--proba_not_understands", type=float, default=0.1)
-parser.add_argument("--maze_width", type=int, default=21)
+parser.add_argument("--temperature_hot", type=float, default=1.5)
-parser.add_argument("--maze_nb_walls", type=int, default=15)
+parser.add_argument("--temperature_cold", type=float, default=1)
-##############################
-# Snake options
+parser.add_argument("--prompt_noise", type=float, default=0.05)
-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("--test", type=str, default=None)
-parser.add_argument("--snake_nb_colors", type=int, default=5)
+######################################################################
-parser.add_argument("--snake_length", type=int, default=200)
+grids_tasks = ", ".join(
+ [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
+)
-##############################
-# Snake options
+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("--stack_nb_steps", type=int, default=100)
+parser.add_argument(
+ "--grids_science_tasks",
+ type=str,
+ default=None,
+ help="A comma-separated subset of: " + grids_tasks + ", or None.",
+)
-parser.add_argument("--stack_nb_stacks", type=int, default=1)
+######################################################################
-parser.add_argument("--stack_nb_digits", type=int, default=3)
+parser.add_argument("--sky_height", type=int, default=6)
-parser.add_argument("--stack_fraction_values_for_train", type=float, default=None)
+parser.add_argument("--sky_width", type=int, default=8)
-##############################
-# Expr options
+parser.add_argument("--sky_nb_birds", type=int, default=3)
-parser.add_argument("--expr_nb_variables", type=int, default=5)
+parser.add_argument("--sky_nb_iterations", type=int, default=2)
-parser.add_argument("--expr_sequence_length", type=int, default=30)
+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_{args.task}"
+ args.result_dir = f"results_culture"
+
+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 = {
- "picoclvr": {
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
- },
- "mnist": {
- "nb_epochs": 25,
- "batch_size": 10,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "model": "37M",
+ "batch_size": 25,
+ "inference_batch_size": 50,
+ "nb_train_samples": 40000,
+ "nb_test_samples": 1000,
+}
+
+for k, v in default_args.items():
+ if getattr(args, k) is None:
+ setattr(args, k, v)
+
+######################################################################
+
+default_model_args = {
+ "17K": {
+ "dim_model": 32,
+ "dim_keys": 32,
+ "dim_hidden": 32,
+ "nb_heads": 2,
+ "nb_blocks": 2,
},
- "maze": {
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "4M": {
+ "dim_model": 256,
+ "dim_keys": 32,
+ "dim_hidden": 1024,
+ "nb_heads": 4,
+ "nb_blocks": 6,
},
- "snake": {
- "nb_epochs": 5,
- "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,
},
- "stack": {
- "nb_epochs": 5,
- "batch_size": 25,
- "nb_train_samples": 100000,
- "nb_test_samples": 1000,
+ "122M": {
+ "dim_model": 768,
+ "dim_keys": 64,
+ "dim_hidden": 2048,
+ "nb_heads": 8,
+ "nb_blocks": 24,
},
- "expr": {
- "nb_epochs": 50,
- "batch_size": 25,
- "nb_train_samples": 250000,
- "nb_test_samples": 10000,
+ "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}")
######################################################################
-try:
- os.mkdir(args.result_dir)
-except FileExistsError:
- if not args.overwrite_results:
+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)
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):
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(",")]
-# ra_mask is boolean, with 1s on the values to generate
+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")
-def masked_inplace_autoregression(
- model,
- batch_size,
- input,
- ar_mask,
- forbidden_tokens=None,
- progress_bar_desc="autoregression",
- device=torch.device("cpu"),
-):
- batches = zip(input.split(batch_size), ar_mask.split(batch_size))
+if args.dirty_debug:
+ args.nb_train_samples = 2500
+ args.nb_test_samples = 100
- if progress_bar_desc is not None:
- batches = tqdm.tqdm(
- batches,
- dynamic_ncols=True,
- desc=progress_bar_desc,
- total=input.size(0) // batch_size,
+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,
+ )
+
+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)
- 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]
+ 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=main_device,
+)
+
######################################################################
+log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
-class Task:
- def batches(self, split="train"):
- pass
+vocabulary_size = quiz_machine.vocabulary_size()
- def vocabulary_size(self):
- pass
+log_string(f"vocabulary_size {vocabulary_size}")
+
+######################################################################
- def produce_results(self, n_epoch, model):
- pass
+
+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)
######################################################################
-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))
+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()
- return input, loss_masks
+ 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)
+ )
- 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,
+ for input, mask_loss in tqdm.tqdm(
+ src,
+ dynamic_ncols=True,
+ desc="test",
+ total=full_input.size(0) // args.batch_size,
+ ):
+ input = input.to(local_device)
+ mask_loss = mask_loss.to(local_device)
+ targets = input
+
+ 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))
- 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"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
- 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
+ model.main_test_accuracy = quiz_machine.produce_results(
+ n_epoch=n_epoch,
+ model=model,
+ input=full_input[:2000],
+ result_dir=args.result_dir,
)
- 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 vocabulary_size(self):
- return len(self.token2id)
- 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 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)
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
+def one_epoch(model, quiz_machine, local_device=main_device):
+ model.to(local_device).train()
+ optimizer_to(model.optimizer, local_device)
- 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}%"
+ if args.schedule_free:
+ model.optimizer.train()
+
+ nb_train_samples, acc_train_loss = 0, 0.0
+
+ 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:
+ model.optimizer.zero_grad()
+
+ targets = input
+
+ 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() + model.loss
+ acc_train_loss += loss.item() * input.size(0)
- ######################################################################
+ loss_per_samples = loss_per_token.detach().flatten(1).mean(dim=1)
- def produce_results(self, n_epoch, model):
- self.compute_missing_properties(n_epoch, model)
+ nb_train_samples += input.size(0)
- if self.pruner_eval is not None:
- self.compute_missing_properties(n_epoch, model, self.pruner_eval)
+ loss.backward()
- nb_tokens_to_generate = self.height * self.width + 3
- result_descr = []
- nb_per_primer = 8
- primer = []
+ if nb_train_samples % args.batch_size == 0:
+ model.optimizer.step()
- 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
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- 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)
+ log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
- np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
+ run_tests(model, quiz_machine)
- acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
- acc_nb_results = len(result_descr)
+ # threshold = torch.cat([l for _, l in hard_w_quizzes], dim=0).sort().values
+ # threshold = threshold[threshold.size(0) // 2]
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
+ # model.hard_w_quizzes = torch.cat(
+ # [x[l >= threshold] for x, l in hard_w_quizzes], dim=0
+ # )
- 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}%"
- )
+ model.to(main_device)
+ optimizer_to(model.optimizer, main_device)
- 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,
- )
+######################################################################
+
+
+def model_transformer_hot(model):
+ model.temperature = args.temperature_hot
+ # model.set_noise_injection(1.0, ("ffw", args.nb_blocks // 2))
- 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}")
+def model_transformer_cold(model):
+ model.temperature = args.temperature_cold
+ # pass
+
+
+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),
+]
######################################################################
-class TaskMNIST(Task):
- def __init__(self, batch_size, device=torch.device("cpu")):
- self.device = device
- self.batch_size = batch_size
+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]))
- def batches(self, split="train"):
- assert split in {"train", "test"}
- data_set = torchvision.datasets.MNIST(
- root="./data", train=(split == "train"), download=True
+ ##
+
+ 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,
+ quizzes=science_w_quizzes.to(main_device),
+ struct=struct,
+ mask=mask,
)
- 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
- def vocabulary_size(self):
- return 256
+ 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()
- 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
+ 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}"
)
- 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,
+
+ i = correct == 1
+ j = correct != 1
+
+ result = torch.cat([result[i], result[j]], dim=0)
+ correct = torch.cat([correct[i], correct[j]], dim=0)
+ correct_parts = predicted_parts * correct[:, None]
+
+ 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,
)
- log_string(f"wrote {image_name}")
######################################################################
-import maze
+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
-class TaskMaze(Task):
- def map2seq(self, *m):
- return torch.cat([x.flatten(1) for x in m], 1)
+ recorded_validated = []
- 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)))
+ start_time = time.perf_counter()
- 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"),
+ nb_validated_per_model = torch.zeros(len(models), dtype=torch.int64)
+
+ 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,
+ procedure=c_quizzes_procedure,
)
- 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"),
+
+ # We discard the trivial ones, according to a criterion
+ # specific to the world quizzes (e.g. B=f(B))
+
+ to_keep = quiz_machine.problem.trivial(c_quizzes) == False
+
+ c_quizzes = c_quizzes[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)
)
- 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,
+
+ 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)
)
- 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
+ c_quizzes = c_quizzes[to_keep]
- if count.max() == 0:
- count = None
+ 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:
- count = count[
- : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
- ]
+ nb_validated = 0
- return nb_total, nb_correct, count
+ total_nb_validated = nb_validated_per_model.sum().item()
- def produce_results(self, n_epoch, model):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
+ duration = time.perf_counter() - start_time
- train_nb_total, train_nb_correct, count = self.compute_error(
- model, "train", nb_to_use=1000
- )
- log_string(
- f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- )
+ 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 = "???"
- test_nb_total, test_nb_correct, count = self.compute_error(
- model, "test", nb_to_use=1000
- )
- log_string(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ log_string(
+ 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)"
+ )
- 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
- )
+ validated_quizzes = torch.cat(recorded_validated, dim=0)
- 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}")
+ ######################################################################
+ # store the new c_quizzes which have been validated
- model.train(t)
+ 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]]
-import snake
+ 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()
-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,
+ 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
)
- self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
- nb_test_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
+
+
+######################################################################
+
+# 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,
)
- 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
+ generator.train(t)
+
+ generator.reset_transformations()
- # snake.solver(result,ar_mask)
+ prologued_c_quizzes = (
+ prologued_c_quizzes * (prologued_c_quizzes < vocabulary_size).long()
+ )
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
+ 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
)
- nb_total = ((prior_visits > 0) * ar_mask).sum()
+ # 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),
+ )
- nb_correct = (
- (result == input).long() * (prior_visits > 0) * ar_mask
- ).sum()
+ probas = seq_logproba.exp()
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+ u0 = probas <= args.proba_not_understands
+ u2 = probas >= args.proba_understands
+ u1 = (u0 | u2) == False
- return nb_total, nb_correct
+ prologs = (
+ (u0.long() * token_prolog_0)
+ + (u1.long() * token_prolog_1)
+ + (u2.long() * token_prolog_2)
+ )
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
+ prologued_c_quizzes = torch.cat([prologs, c_quizzes], dim=1)
- # 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}%"
- # )
+ # 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)]
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
+ if prologued_c_quizzes.size(0) > 0:
+ samples.append(prologued_c_quizzes)
- log_string(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ # Now we yield a batch
- model.train(t)
+ 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)
-import stack
+ 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,
+ )
-class TaskStack(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- nb_steps,
- nb_stacks,
- nb_digits,
- fraction_values_for_train=None,
- device=torch.device("cpu"),
+ for input in tqdm.tqdm(
+ src,
+ dynamic_ncols=True,
+ desc="training",
+ total=args.nb_train_samples // args.batch_size,
):
- self.batch_size = batch_size
- self.nb_steps = nb_steps
- self.nb_stacks = nb_stacks
- self.nb_digits = nb_digits
- self.device = device
-
- if fraction_values_for_train is None:
- values_for_train = None
- values_for_test = None
- else:
- all = torch.randperm(10**nb_digits)
- nb_for_train = int(all.size(0) * fraction_values_for_train)
- values_for_train = all[:nb_for_train]
- values_for_test = all[nb_for_train:]
-
- self.train_input, self.train_stack_counts = stack.generate_sequences(
- nb_train_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_train,
- self.device,
- )
+ input = input.to(local_device)
- self.test_input, self.test_stack_counts = stack.generate_sequences(
- nb_test_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_test,
- self.device,
- )
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
- i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
- counts = self.test_stack_counts.flatten()[i.flatten()]
- counts = F.one_hot(counts).sum(0)
- log_string(f"test_pop_stack_counts {counts}")
-
- 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):
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
+ targets = input
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
- )
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
+ 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)
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
+ loss.backward()
- return nb_total, nb_correct
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.step()
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- log_string(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ log_string(f"train_perplexity {n_epoch} generator - {train_perplexity}")
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- for n in range(result.size(0)):
- log_string(
- f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ 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,
)
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
+ 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),
)
- for n in range(result.size(0)):
- log_string(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+
+ comments = []
+
+ for l in seq_logproba:
+ comments.append(
+ f"proba {l[model_pred1.id].exp().item():.02f} {l[model_pred2.id].exp().item():.02f}"
)
- ##############################################################
- model.train(t)
+ 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 = []
-import expr
+def compute_causal_attzero(t_q, t_k):
+ return t_q < t_k
-class TaskExpr(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- nb_variables,
- sequence_length,
- batch_size,
- device=torch.device("cpu"),
- ):
- self.batch_size = batch_size
- self.device = device
-
- train_sequences = expr.generate_sequences(
- nb_train_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- # length=2 * sequence_length,
- # randomize_length=True,
- )
- test_sequences = expr.generate_sequences(
- nb_test_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- )
- self.char2id = dict(
- [
- (c, n)
- for n, c in enumerate(
- set("#" + "".join(train_sequences + test_sequences))
- )
- ]
+
+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_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)
+
+ model.id = k
+
+ if args.schedule_free:
+ model.optimizer = schedulefree.AdamWScheduleFree(
+ model.parameters(), lr=args.learning_rate
)
- self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
- self.filler, self.space = self.char2id["#"], self.char2id[" "]
-
- len_max = max([len(x) for x in train_sequences])
- self.train_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in train_sequences
- ]
- )
- ],
- 0,
- ).to(device)
-
- len_max = max([len(x) for x in test_sequences])
- self.test_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in test_sequences
- ]
- )
- ],
- 0,
- ).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
- ):
- if split == "train":
- last = (batch != self.filler).max(0).values.nonzero().max() + 1
- batch = batch[:, :last]
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def seq2str(self, s):
- return "".join([self.id2char[k.item()] for k in s])
-
- def produce_results(self, n_epoch, model):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input):
- result = input.clone()
- ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
+ else:
+ model.optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+
+ model.main_test_accuracy = 0.0
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
+ model.train_w_quizzes = quiz_machine.problem.generate_w_quizzes(
+ args.nb_train_samples
+ )
- #######################################################################
- # Comput predicted vs. true variable values
+ model.test_w_quizzes = quiz_machine.problem.generate_w_quizzes(args.nb_test_samples)
- values_input = expr.extract_results([self.seq2str(s) for s in input])
- max_input = max([max(x.values()) for x in values_input])
- values_result = expr.extract_results([self.seq2str(s) for s in result])
- max_result = max(
- [-1 if len(x) == 0 else max(x.values()) for x in values_result]
+ 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,
)
+ ),
+ )
- nb_missing = torch.zeros(max_input + 1)
- nb_predicted = torch.zeros(max_input + 1, max_result + 1)
+ print(model)
+ exit(0)
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- if vr is None or vr < 0:
- nb_missing[vi] += 1
- else:
- nb_predicted[vi, vr] += 1
- ######################################################################
- return nb_total, nb_correct
+######################################################################
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+current_epoch = 0
- log_string(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+if args.resume:
+ for model in models:
+ filename = f"gpt_{model.id:03d}.pth"
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10]
- result = input.clone()
- ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- for n in range(result.size(0)):
- log_string(f"test_before {self.seq2str(result[n])}")
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- log_string(f"test_after {self.seq2str(result[n])} {comment}")
- log_string(f"correct {self.seq2str(correct[n])}")
- ##############################################################
+ 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
- model.train(t)
+ 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)")
-def picoclvr_pruner_horizontal_green(p):
- return not ("green" in p and ("left" in p or "right" in p))
+######################################################################
+if args.nb_new_c_quizzes_for_train is None:
+ args.nb_new_c_quizzes_for_train = args.nb_train_samples // 100
-picoclvr_pruner_train = (
- picoclvr_pruner_horizontal_green
- if args.picocvlr_prune_properties in {"train+eval"}
- else None
-)
+if args.nb_new_c_quizzes_for_test is None:
+ args.nb_new_c_quizzes_for_test = args.nb_test_samples // 100
-picoclvr_pruner_eval = (
- (lambda p: not picoclvr_pruner_horizontal_green(p))
- if args.picocvlr_prune_properties in {"train+eval", "eval"}
- else None
+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}"
)
######################################################################
-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,
- )
+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
-elif args.task == "mnist":
- task = TaskMNIST(
- batch_size=args.batch_size,
- device=device,
- )
+######################################################################
-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,
- )
+if args.test == "tsne":
+ model = models[0]
-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,
- )
+ quizzes = []
+ labels = []
+ nb_samples_per_task = 1000
-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_digits=args.stack_nb_digits,
- fraction_values_for_train=args.stack_fraction_values_for_train,
- device=device,
- )
+ 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))
-elif args.task == "expr":
- task = TaskExpr(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- nb_variables=args.expr_nb_variables,
- sequence_length=args.expr_sequence_length,
- batch_size=args.batch_size,
- device=device,
- )
+ quizzes = torch.cat(quizzes, dim=0)
+ labels = torch.cat(labels, dim=0)
-else:
- raise ValueError(f"Unknown task {args.task}")
+ 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))
-######################################################################
+ 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"device {device}")
+ print(f"{x.size()=} {y.size()=}")
+ # torch.save((x,y), "/tmp/embed.pth")
+ # exit(0)
-vocabulary_size = task.vocabulary_size()
+ from sklearn.manifold import TSNE
-log_string(f"vocabulary_size {vocabulary_size}")
+ x_np = x.numpy()
+ z_np = TSNE(n_components=2, perplexity=50).fit_transform(x_np)
+ z = torch.from_numpy(z_np)
-##############################
-
-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,
-)
+ print(f"{z.size()=}")
-model.to(device)
+ 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")
-nb_parameters = sum(p.numel() for p in model.parameters())
-log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
+ exit(0)
######################################################################
-nb_epochs_finished = 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
-if args.no_checkpoint:
- log_string(f"not trying to load checkpoint.")
+ 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)
-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"])
+ generator.main_test_accuracy = 0.0
- log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
+ 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("starting from scratch.")
+ log_string(f"cannot find {filename}")
+ pass
- except:
- log_string("error when loading the checkpoint.")
- exit(1)
+ 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,
+ )
-######################################################################
+ 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}")
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
+ c_quizzes, prologs = generate_c_quizzes_with_generator(
+ generator, quiz_machine, args.batch_size
+ )
-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)
+ 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()
-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
+ u0 = probas <= args.proba_not_understands
+ u2 = probas >= args.proba_understands
+ u1 = (u0 | u2) == False
-log_string(f"learning_rate_schedule {learning_rate_schedule}")
+ predicted_prologs = (
+ (u0.long() * token_prolog_0)
+ + (u1.long() * token_prolog_1)
+ + (u2.long() * token_prolog_2)
+ )
-##############################
+ comments = []
-nb_samples_seen = 0
+ nb_errors = (predicted_prologs != prologs).long().sum()
+ nb_total = prologs.numel()
-if nb_epochs_finished >= nb_epochs:
- task.produce_results(nb_epochs_finished, model)
+ log_string(f"generator_error {nb_errors} / {nb_total}")
-for n_epoch in range(nb_epochs_finished, nb_epochs):
- learning_rate = learning_rate_schedule[n_epoch]
+ def readable(prologs):
+ return (prologs == token_prolog_1) + 2 * (prologs == token_prolog_2)
- log_string(f"learning_rate {learning_rate}")
+ 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)
- 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}.")
+ 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}")
- model.train()
+ exit(0)
- nb_train_samples, acc_train_loss = 0, 0.0
+######################################################################
- 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)
+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}")
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
+ log_string(f"--- epoch {n_epoch} ----------------------------------------")
- with torch.autograd.no_grad():
- model.eval()
+ cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+ log_string(f"current_test_accuracies {cta}")
- nb_test_samples, acc_test_loss = 0, 0.0
+ ##################################################
+ # If all the models are good enough, generate new quizzes and
+ # re-compute the test errors
- for input in task.batches(split="test"):
- input = input.to(device)
+ 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,
+ )
- 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)
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
- train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+ # Force one epoch of training
+ for model in models:
+ model.main_test_accuracy = 0.0
- log_string(
- f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
+ ##################################################
+ # 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)
)
- task.produce_results(n_epoch, model)
+ threads.append(t)
- checkpoint = {
- "nb_epochs_finished": n_epoch + 1,
- "model_state": model.state_dict(),
- "rng_state": torch.get_rng_state(),
- }
+ t.start()
- if torch.cuda.is_available():
- checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
+ 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)
- checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
- torch.save(checkpoint, checkpoint_name)
- log_string(f"saved checkpoint {checkpoint_name}")
+ if args.log_command is not None:
+ s = args.log_command.split()
+ s.insert(1, args.result_dir)
+ subprocess.run(s)
######################################################################