--- /dev/null
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, sys, argparse, time, tqdm, itertools, os
+
+import torch, torchvision
+from torch import nn
+from torch.nn import functional as F
+
+import mygpt, tensorstack
+
+######################################################################
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+######################################################################
+
+parser = argparse.ArgumentParser(
+ description="An implementation of GPT with cache to solve a toy geometric reasonning task."
+)
+
+parser.add_argument("--log_filename", type=str, default="train.log")
+
+parser.add_argument("--result_dir", type=str, default="results_default")
+
+parser.add_argument("--seed", type=int, default=0)
+
+parser.add_argument("--nb_epochs", type=int, default=25)
+
+parser.add_argument("--batch_size", type=int, default=100)
+
+parser.add_argument("--data_size", type=int, default=-1)
+
+parser.add_argument("--optim", type=str, default="adam")
+
+parser.add_argument("--learning_rate", type=float, default=1e-3)
+
+parser.add_argument(
+ "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
+)
+
+parser.add_argument("--dim_model", type=int, default=512)
+
+parser.add_argument("--dim_keys", type=int, default=64)
+
+parser.add_argument("--dim_hidden", type=int, default=2048)
+
+parser.add_argument("--nb_heads", type=int, default=8)
+
+parser.add_argument("--nb_blocks", type=int, default=12)
+
+parser.add_argument("--dropout", type=float, default=0.1)
+
+parser.add_argument("--nb_oneshot_blocks", type=int, default=-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("--checkpoint_name", type=str, default="checkpoint.pth")
+
+##############################
+# picoclvr options
+
+parser.add_argument("--nb_colors", type=int, default=5)
+
+parser.add_argument("--height", type=int, default=12)
+
+parser.add_argument("--width", type=int, default=16)
+
+parser.add_argument("--prune_properties", type=str, default="none")
+
+######################################################################
+
+args = parser.parse_args()
+
+assert args.prune_properties in {"none", "train+eval", "eval"}
+
+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), "w")
+
+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)
+
+######################################################################
+
+
+def log_string(s):
+ t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
+
+ if log_file is not None:
+ log_file.write(t + s + "\n")
+ log_file.flush()
+
+ print(t + s)
+ sys.stdout.flush()
+
+
+for n in vars(args):
+ log_string(f"args.{n} {getattr(args, n)}")
+
+######################################################################
+
+
+def masked_inplace_autoregression(
+ model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu")
+):
+
+ for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
+ 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]
+
+
+######################################################################
+
+
+class Task:
+ def batches(self, split="train"):
+ pass
+
+ def vocabulary_size(self):
+ pass
+
+ def produce_results(self, n_epoch, model):
+ pass
+
+
+######################################################################
+
+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,
+ device=self.device,
+ )
+ model.train(t)
+
+ input, loss_masks = self.trim((input, loss_masks))
+
+ return input, loss_masks
+
+ ######################
+
+ def __init__(
+ self,
+ 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
+ nb = args.data_size if args.data_size > 0 else 250000
+ self.pruner_train = pruner_train
+ self.pruner_eval = pruner_eval
+
+ param = {
+ "nb": nb,
+ "height": height,
+ "width": width,
+ "nb_colors": nb_colors,
+ "batch_size": batch_size,
+ "rng_state": list(torch.get_rng_state()),
+ }
+
+ log_string(f"generating {nb} samples (can take some time)")
+ self.train_descr = generate_descr(
+ (nb * 4) // 5, "train", pruner=self.pruner_train
+ )
+ self.test_descr = generate_descr((nb * 1) // 5, "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)
+
+ 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}%"
+ )
+
+ ######################################################################
+
+ def produce_results(self, n_epoch, model):
+
+ self.compute_missing_properties(n_epoch, model)
+
+ if self.pruner_eval is not None:
+ self.compute_missing_properties(n_epoch, model, self.pruner_eval)
+
+ nb_tokens_to_generate = self.height * self.width + 3
+ result_descr = []
+ nb_per_primer = 8
+ primer = []
+
+ 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)
+
+ np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
+
+ acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
+ acc_nb_results = len(result_descr)
+
+ nb_requested_properties = sum(acc_nb_requested_properties)
+ nb_missing_properties = sum(acc_nb_missing_properties)
+
+ 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}%"
+ )
+
+ img = picoclvr.descr2img(
+ result_descr, [0], 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"result_{n_epoch:04d}.png")
+ torchvision.utils.save_image(
+ img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0
+ )
+ log_string(f"wrote {image_name}")
+
+
+######################################################################
+
+log_string(f"device {device}")
+
+
+def pruner_horizontal_green(p):
+ return not ("green" in p and ("left" in p or "right" in p))
+
+
+task = TaskPicoCLVR(
+ batch_size=args.batch_size,
+ height=args.height,
+ width=args.width,
+ nb_colors=args.nb_colors,
+ device=device,
+ pruner_train=pruner_horizontal_green
+ if args.prune_properties in {"train+eval"}
+ else None,
+ pruner_eval=(lambda p: not pruner_horizontal_green(p))
+ if args.prune_properties in {"train+eval", "eval"}
+ else None,
+)
+
+vocabulary_size = task.vocabulary_size()
+
+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,
+)
+
+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"])
+
+ log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
+
+ 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
+
+log_string(f"learning_rate_schedule {learning_rate_schedule}")
+
+##############################
+
+nb_samples_seen = 0
+
+if nb_epochs_finished >= nb_epochs:
+ task.produce_results(nb_epochs_finished, model)
+
+for n_epoch in range(nb_epochs_finished, nb_epochs):
+
+ learning_rate = learning_rate_schedule[n_epoch]
+
+ log_string(f"learning_rate {learning_rate}")
+
+ 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()
+
+ 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)
+
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+ with torch.autograd.no_grad():
+
+ model.eval()
+
+ nb_test_samples, acc_test_loss = 0, 0.0
+
+ for input in task.batches(split="test"):
+ input = input.to(device)
+
+ # input, loss_masks, true_images = task.excise_last_image(input)
+ # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
+
+ 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)
+
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
+ test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+
+ log_string(
+ f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
+ )
+
+ 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}")
+
+######################################################################
--- /dev/null
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math
+
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+
+######################################################################
+
+
+class WithResidual(nn.Module):
+ def __init__(self, *f):
+ super().__init__()
+ self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
+
+ def forward(self, bs):
+ bs.x = bs.x + self.f(bs).x
+ return bs
+
+
+######################################################################
+
+# A BracketedSequence is a BxTx... tensor with a first and a nb time
+# steps to compute.
+
+# Modules able to process it expect that they will have to process a
+# first bracket starting at t=0, followed by a succession of brackets
+# that move forward in time, do not overlap, and cover the axis T with
+# no holes.
+#
+# Although it is more general, for a classical prompt-conditioned
+# auto-regressive process it will be a first bracket starting at 0 and
+# of arbitrary length for the "prompt", followed by brackets of length
+# 1 for the successive tokens.
+#
+# Modules able to process brackets may implement a cache that is
+# resetted when the input bracket starts at t=0
+
+
+class BracketedSequence:
+ def __init__(self, x, first=None, nb=None):
+ self.x = x
+ self.first = 0 if first is None else first
+ self.nb = x.size(1) if nb is None else nb
+
+ def slice(self):
+ return self.x[:, self.first : self.first + self.nb]
+
+
+######################################################################
+
+
+class CacheWrapper(nn.Module):
+ def __init__(self, *f):
+ super().__init__()
+ self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
+
+ def forward(self, bs):
+ if bs.first == 0:
+ y = self.f(bs.slice())
+ self.cache_y = y.new(*((y.size(0), bs.x.size(1)) + y.size()[2:]))
+ self.cache_y[:, bs.first : bs.first + bs.nb] = y
+ else:
+ self.cache_y[:, bs.first : bs.first + bs.nb] = self.f(bs.slice())
+
+ bs.x = self.cache_y
+
+ return bs
+
+
+##############################
+
+
+class AddPositionalEncoding(nn.Module):
+ def __init__(self, len_max):
+ super().__init__()
+ self.len_max = len_max
+
+ # [Vaswani et al 2018] PE_{t,2i} = sin(t/(L^{2i/D})), PE_{t,2i+1} = cos(t/(L^{2i/D}))
+
+ def forward(self, bs):
+ if bs.first == 0:
+ t = torch.arange(bs.x.size(1), dtype=bs.x.dtype, device=bs.x.device)[
+ :, None
+ ]
+ j = torch.arange(bs.x.size(2), dtype=bs.x.dtype, device=bs.x.device)[
+ None, :
+ ]
+ k = j % 2
+ self.pe = torch.sin(
+ t / (self.len_max ** ((j - k) / bs.x.size(2))) + math.pi / 2 * k
+ )
+ self.cache_y = bs.x.new(bs.x.size())
+
+ self.cache_y[:, bs.first : bs.first + bs.nb] = (
+ bs.slice() + self.pe[bs.first : bs.first + bs.nb]
+ )
+
+ bs.x = self.cache_y
+
+ return bs
+
+
+##############################
+
+
+class QKVAttention(nn.Module):
+ def __init__(
+ self, dim_in, dim_qk, dim_v, nb_heads=1, causal=False, attention_dropout=0.0
+ ):
+ super().__init__()
+
+ def randw(*d):
+ return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
+
+ self.causal = causal
+ self.attention_dropout = attention_dropout
+
+ self.w_q = randw(nb_heads, dim_qk, dim_in)
+ self.w_k = randw(nb_heads, dim_qk, dim_in)
+ self.w_v = randw(nb_heads, dim_v, dim_in)
+ self.w_o = randw(dim_v * nb_heads, dim_in)
+
+ def forward(self, bs_q, x_kv=None):
+ x_q = bs_q.x
+ if x_kv is None:
+ x_kv = x_q
+
+ if bs_q.first == 0:
+ self.cache_k = x_q.new_zeros(
+ x_q.size(0), self.w_k.size(0), x_kv.size(1), self.w_k.size(1)
+ )
+ self.cache_v = x_q.new_zeros(
+ x_q.size(0), self.w_v.size(0), x_kv.size(1), self.w_v.size(1)
+ )
+ self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1))
+
+ q = torch.einsum(
+ "ntc,hdc->nhtd", x_q[:, bs_q.first : bs_q.first + bs_q.nb], self.w_q
+ )
+ self.cache_k[:, :, bs_q.first : bs_q.first + bs_q.nb] = torch.einsum(
+ "ntc,hdc->nhtd", x_kv[:, bs_q.first : bs_q.first + bs_q.nb], self.w_k
+ )
+ self.cache_v[:, :, bs_q.first : bs_q.first + bs_q.nb] = torch.einsum(
+ "ntc,hdc->nhtd", x_kv[:, bs_q.first : bs_q.first + bs_q.nb], self.w_v
+ )
+
+ a = torch.einsum(
+ "nhtd,nhsd->nhts", q, self.cache_k[:, :, : bs_q.first + bs_q.nb]
+ ) / math.sqrt(self.w_q.size(1))
+
+ if self.causal:
+ if bs_q.first == 0:
+ self.cache_attzero = (
+ torch.arange(x_q.size(1), device=q.device)[None, None, :, None]
+ < torch.arange(x_kv.size(1), device=q.device)[None, None, None, :]
+ )
+ a = a.masked_fill(
+ self.cache_attzero[
+ :, :, bs_q.first : bs_q.first + bs_q.nb, : bs_q.first + bs_q.nb
+ ],
+ float("-inf"),
+ )
+
+ a = a.softmax(dim=3)
+ a = F.dropout(a, self.attention_dropout, self.training)
+
+ y = torch.einsum(
+ "nhts,nhsd->nthd", a, self.cache_v[:, :, : bs_q.first + bs_q.nb]
+ ).flatten(2)
+
+ self.cache_y[:, bs_q.first : bs_q.first + bs_q.nb] = y @ self.w_o
+
+ bs_q.x = self.cache_y
+
+ return bs_q
+
+
+##############################
+
+
+class MyGPT(nn.Module):
+ def __init__(
+ self,
+ vocabulary_size,
+ dim_model,
+ dim_keys,
+ dim_hidden,
+ nb_heads,
+ nb_blocks,
+ causal=False,
+ dropout=0.0,
+ len_max=1e5,
+ ):
+
+ super().__init__()
+
+ assert dim_model % nb_heads == 0
+
+ self.embedding = nn.Sequential(
+ CacheWrapper(nn.Embedding(vocabulary_size, dim_model), nn.Dropout(dropout)),
+ AddPositionalEncoding(len_max),
+ )
+
+ trunk_blocks = []
+
+ for b in range(nb_blocks):
+ trunk_blocks += [
+ WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ QKVAttention(
+ dim_in=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ causal=causal,
+ attention_dropout=dropout,
+ ),
+ ),
+ WithResidual(
+ CacheWrapper(
+ nn.LayerNorm((dim_model,)),
+ nn.Linear(in_features=dim_model, out_features=dim_hidden),
+ nn.ReLU(),
+ nn.Linear(in_features=dim_hidden, out_features=dim_model),
+ nn.Dropout(dropout),
+ ),
+ ),
+ ]
+
+ self.trunk = nn.Sequential(*trunk_blocks)
+
+ self.readout = CacheWrapper(
+ nn.Linear(in_features=dim_model, out_features=vocabulary_size)
+ )
+
+ with torch.no_grad():
+ for m in self.modules():
+ if isinstance(m, nn.Embedding):
+ m.weight.normal_(mean=0, std=2e-2)
+ elif isinstance(m, nn.LayerNorm):
+ m.bias.zero_()
+ m.weight.fill_(1.0)
+
+ def forward(self, bs):
+ bs.x = F.pad(bs.x, (1, -1))
+ bs = self.embedding(bs)
+ bs = self.trunk(bs)
+ bs = self.readout(bs)
+ return bs
+
+
+######################################################################
+
+if __name__ == "__main__":
+
+ print("Basic check.")
+
+ vocabulary_size = 10
+ x = torch.randint(vocabulary_size, (9, 7))
+
+ model = MyGPT(
+ vocabulary_size=vocabulary_size,
+ dim_model=18,
+ dim_keys=50,
+ dim_hidden=100,
+ nb_heads=2,
+ nb_blocks=1,
+ dropout=0.1,
+ )
+
+ model.eval()
+
+ y1 = model(BracketedSequence(x)).x
+
+ y2 = torch.randn_like(y1)
+ for s in range(x.size(1)):
+ z = model(BracketedSequence(x, s, 1))
+ y2[:, s] = z.x[:, s]
+
+ # print(y1.max(dim = 2).values)
+ # print(y2.max(dim = 2).values)
+ print(f"error={((y1 - y2).norm() / (y1.norm() + y2.norm())).item()}")
+
+######################################################################
--- /dev/null
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import torch, torchvision
+import torch.nn.functional as F
+
+colors = [
+ [255, 255, 255],
+ [255, 0, 0],
+ [0, 128, 0],
+ [0, 0, 255],
+ [255, 255, 0],
+ [0, 0, 0],
+ [128, 0, 0],
+ [139, 0, 0],
+ [165, 42, 42],
+ [178, 34, 34],
+ [220, 20, 60],
+ [255, 99, 71],
+ [255, 127, 80],
+ [205, 92, 92],
+ [240, 128, 128],
+ [233, 150, 122],
+ [250, 128, 114],
+ [255, 160, 122],
+ [255, 69, 0],
+ [255, 140, 0],
+ [255, 165, 0],
+ [255, 215, 0],
+ [184, 134, 11],
+ [218, 165, 32],
+ [238, 232, 170],
+ [189, 183, 107],
+ [240, 230, 140],
+ [128, 128, 0],
+ [154, 205, 50],
+ [85, 107, 47],
+ [107, 142, 35],
+ [124, 252, 0],
+ [127, 255, 0],
+ [173, 255, 47],
+ [0, 100, 0],
+ [34, 139, 34],
+ [0, 255, 0],
+ [50, 205, 50],
+ [144, 238, 144],
+ [152, 251, 152],
+ [143, 188, 143],
+ [0, 250, 154],
+ [0, 255, 127],
+ [46, 139, 87],
+ [102, 205, 170],
+ [60, 179, 113],
+ [32, 178, 170],
+ [47, 79, 79],
+ [0, 128, 128],
+ [0, 139, 139],
+ [0, 255, 255],
+ [0, 255, 255],
+ [224, 255, 255],
+ [0, 206, 209],
+ [64, 224, 208],
+ [72, 209, 204],
+ [175, 238, 238],
+ [127, 255, 212],
+ [176, 224, 230],
+ [95, 158, 160],
+ [70, 130, 180],
+ [100, 149, 237],
+ [0, 191, 255],
+ [30, 144, 255],
+ [173, 216, 230],
+ [135, 206, 235],
+ [135, 206, 250],
+ [25, 25, 112],
+ [0, 0, 128],
+ [0, 0, 139],
+ [0, 0, 205],
+ [65, 105, 225],
+ [138, 43, 226],
+ [75, 0, 130],
+ [72, 61, 139],
+ [106, 90, 205],
+ [123, 104, 238],
+ [147, 112, 219],
+ [139, 0, 139],
+ [148, 0, 211],
+ [153, 50, 204],
+ [186, 85, 211],
+ [128, 0, 128],
+ [216, 191, 216],
+ [221, 160, 221],
+ [238, 130, 238],
+ [255, 0, 255],
+ [218, 112, 214],
+ [199, 21, 133],
+ [219, 112, 147],
+ [255, 20, 147],
+ [255, 105, 180],
+ [255, 182, 193],
+ [255, 192, 203],
+ [250, 235, 215],
+ [245, 245, 220],
+ [255, 228, 196],
+ [255, 235, 205],
+ [245, 222, 179],
+ [255, 248, 220],
+ [255, 250, 205],
+ [250, 250, 210],
+ [255, 255, 224],
+ [139, 69, 19],
+ [160, 82, 45],
+ [210, 105, 30],
+ [205, 133, 63],
+ [244, 164, 96],
+ [222, 184, 135],
+ [210, 180, 140],
+ [188, 143, 143],
+ [255, 228, 181],
+ [255, 222, 173],
+ [255, 218, 185],
+ [255, 228, 225],
+ [255, 240, 245],
+ [250, 240, 230],
+ [253, 245, 230],
+ [255, 239, 213],
+ [255, 245, 238],
+ [245, 255, 250],
+ [112, 128, 144],
+ [119, 136, 153],
+ [176, 196, 222],
+ [230, 230, 250],
+ [255, 250, 240],
+ [240, 248, 255],
+ [248, 248, 255],
+ [240, 255, 240],
+ [255, 255, 240],
+ [240, 255, 255],
+ [255, 250, 250],
+ [192, 192, 192],
+ [220, 220, 220],
+ [245, 245, 245],
+]
+
+color_names = [
+ "white",
+ "red",
+ "green",
+ "blue",
+ "yellow",
+ "black",
+ "maroon",
+ "dark_red",
+ "brown",
+ "firebrick",
+ "crimson",
+ "tomato",
+ "coral",
+ "indian_red",
+ "light_coral",
+ "dark_salmon",
+ "salmon",
+ "light_salmon",
+ "orange_red",
+ "dark_orange",
+ "orange",
+ "gold",
+ "dark_golden_rod",
+ "golden_rod",
+ "pale_golden_rod",
+ "dark_khaki",
+ "khaki",
+ "olive",
+ "yellow_green",
+ "dark_olive_green",
+ "olive_drab",
+ "lawn_green",
+ "chartreuse",
+ "green_yellow",
+ "dark_green",
+ "forest_green",
+ "lime",
+ "lime_green",
+ "light_green",
+ "pale_green",
+ "dark_sea_green",
+ "medium_spring_green",
+ "spring_green",
+ "sea_green",
+ "medium_aqua_marine",
+ "medium_sea_green",
+ "light_sea_green",
+ "dark_slate_gray",
+ "teal",
+ "dark_cyan",
+ "aqua",
+ "cyan",
+ "light_cyan",
+ "dark_turquoise",
+ "turquoise",
+ "medium_turquoise",
+ "pale_turquoise",
+ "aqua_marine",
+ "powder_blue",
+ "cadet_blue",
+ "steel_blue",
+ "corn_flower_blue",
+ "deep_sky_blue",
+ "dodger_blue",
+ "light_blue",
+ "sky_blue",
+ "light_sky_blue",
+ "midnight_blue",
+ "navy",
+ "dark_blue",
+ "medium_blue",
+ "royal_blue",
+ "blue_violet",
+ "indigo",
+ "dark_slate_blue",
+ "slate_blue",
+ "medium_slate_blue",
+ "medium_purple",
+ "dark_magenta",
+ "dark_violet",
+ "dark_orchid",
+ "medium_orchid",
+ "purple",
+ "thistle",
+ "plum",
+ "violet",
+ "magenta",
+ "orchid",
+ "medium_violet_red",
+ "pale_violet_red",
+ "deep_pink",
+ "hot_pink",
+ "light_pink",
+ "pink",
+ "antique_white",
+ "beige",
+ "bisque",
+ "blanched_almond",
+ "wheat",
+ "corn_silk",
+ "lemon_chiffon",
+ "light_golden_rod_yellow",
+ "light_yellow",
+ "saddle_brown",
+ "sienna",
+ "chocolate",
+ "peru",
+ "sandy_brown",
+ "burly_wood",
+ "tan",
+ "rosy_brown",
+ "moccasin",
+ "navajo_white",
+ "peach_puff",
+ "misty_rose",
+ "lavender_blush",
+ "linen",
+ "old_lace",
+ "papaya_whip",
+ "sea_shell",
+ "mint_cream",
+ "slate_gray",
+ "light_slate_gray",
+ "light_steel_blue",
+ "lavender",
+ "floral_white",
+ "alice_blue",
+ "ghost_white",
+ "honeydew",
+ "ivory",
+ "azure",
+ "snow",
+ "silver",
+ "gainsboro",
+ "white_smoke",
+]
+
+color_id = dict([(n, k) for k, n in enumerate(color_names)])
+color_tokens = dict([(n, c) for n, c in zip(color_names, colors)])
+
+######################################################################
+
+
+def all_properties(height, width, nb_squares, square_i, square_j, square_c):
+ s = []
+
+ for r, c_r in [(k, color_names[square_c[k]]) for k in range(nb_squares)]:
+ s += [f"there is {c_r}"]
+
+ if square_i[r] >= height - height // 3:
+ s += [f"{c_r} bottom"]
+ if square_i[r] < height // 3:
+ s += [f"{c_r} top"]
+ if square_j[r] >= width - width // 3:
+ s += [f"{c_r} right"]
+ if square_j[r] < width // 3:
+ s += [f"{c_r} left"]
+
+ for t, c_t in [(k, color_names[square_c[k]]) for k in range(nb_squares)]:
+ if square_i[r] > square_i[t]:
+ s += [f"{c_r} below {c_t}"]
+ if square_i[r] < square_i[t]:
+ s += [f"{c_r} above {c_t}"]
+ if square_j[r] > square_j[t]:
+ s += [f"{c_r} right of {c_t}"]
+ if square_j[r] < square_j[t]:
+ s += [f"{c_r} left of {c_t}"]
+
+ return s
+
+
+######################################################################
+
+# Generates sequences
+
+
+def generate(
+ nb,
+ height,
+ width,
+ max_nb_squares=5,
+ max_nb_properties=10,
+ nb_colors=5,
+ pruner=None,
+):
+
+ assert nb_colors >= max_nb_squares and nb_colors <= len(color_tokens) - 1
+
+ descr = []
+
+ for n in range(nb):
+
+ nb_squares = torch.randint(max_nb_squares, (1,)) + 1
+ square_position = torch.randperm(height * width)[:nb_squares]
+
+ # color 0 is white and reserved for the background
+ square_c = torch.randperm(nb_colors)[:nb_squares] + 1
+ square_i = square_position.div(width, rounding_mode="floor")
+ square_j = square_position % width
+
+ img = [0] * height * width
+ for k in range(nb_squares):
+ img[square_position[k]] = square_c[k]
+
+ # generates all the true properties
+
+ s = all_properties(height, width, nb_squares, square_i, square_j, square_c)
+
+ if pruner is not None:
+ s = list(filter(pruner, s))
+
+ # picks at most max_nb_properties at random
+
+ nb_properties = torch.randint(max_nb_properties, (1,)) + 1
+ s = (
+ " <sep> ".join([s[k] for k in torch.randperm(len(s))[:nb_properties]])
+ + " <img> "
+ + " ".join([f"{color_names[n]}" for n in img])
+ )
+
+ descr += [s]
+
+ return descr
+
+
+######################################################################
+
+# Extracts the image after <img> in descr as a 1x3xHxW tensor
+
+
+def descr2img(descr, n, height, width):
+
+ if type(descr) == list:
+ return torch.cat([descr2img(d, n, height, width) for d in descr], 0)
+
+ if type(n) == list:
+ return torch.cat([descr2img(descr, k, height, width) for k in n], 0).unsqueeze(
+ 0
+ )
+
+ def token2color(t):
+ try:
+ return color_tokens[t]
+ except KeyError:
+ return [128, 128, 128]
+
+ d = descr.split("<img>")
+ d = d[n + 1] if len(d) > n + 1 else ""
+ d = d.strip().split(" ")[: height * width]
+ d = d + ["<unk>"] * (height * width - len(d))
+ d = [token2color(t) for t in d]
+ img = torch.tensor(d).permute(1, 0)
+ img = img.reshape(1, 3, height, width)
+
+ return img
+
+
+######################################################################
+
+# Returns all the properties of the image after <img> in descr
+
+
+def descr2properties(descr, height, width):
+
+ if type(descr) == list:
+ return [descr2properties(d, height, width) for d in descr]
+
+ d = descr.split("<img>")
+ d = d[-1] if len(d) > 1 else ""
+ d = d.strip().split(" ")[: height * width]
+ if len(d) != height * width:
+ return []
+
+ seen = {}
+ for k, x in enumerate(d):
+ if x != color_names[0]:
+ if x in color_tokens:
+ if x in seen:
+ return []
+ else:
+ return []
+ seen[x] = (color_id[x], k // width, k % width)
+
+ square_infos = tuple(zip(*seen.values()))
+
+ if square_infos:
+ square_c = torch.tensor(square_infos[0])
+ square_i = torch.tensor(square_infos[1])
+ square_j = torch.tensor(square_infos[2])
+ else:
+ square_c = torch.tensor([])
+ square_i = torch.tensor([])
+ square_j = torch.tensor([])
+
+ s = all_properties(height, width, len(seen), square_i, square_j, square_c)
+
+ return s
+
+
+######################################################################
+
+# Returns a triplet composed of (1) the total number of properties
+# before <img> in descr, (2) the total number of properties the image
+# after <img> verifies, and (3) the number of properties in (1) not in
+# (2)
+
+
+def nb_properties(descr, height, width, pruner=None):
+
+ if type(descr) == list:
+ return [nb_properties(d, height, width, pruner) for d in descr]
+
+ d = descr.split("<img>", 1)
+ if len(d) == 0:
+ return 0
+ d = d[0].strip().split("<sep>")
+ d = [x.strip() for x in d]
+
+ all_properties = set(descr2properties(descr, height, width))
+
+ if pruner is None:
+ requested_properties = set(d)
+ else:
+ requested_properties = set(filter(pruner, d))
+
+ missing_properties = requested_properties - all_properties
+
+ return (len(requested_properties), len(all_properties), len(missing_properties))
+
+
+######################################################################
+
+if __name__ == "__main__":
+ for n in range(16):
+ descr = generate(nb=1, height=12, width=16)
+
+ print(nb_properties(descr, height=12, width=16))
+
+ with open(f"picoclvr_example_{n:02d}.txt", "w") as f:
+ for d in descr:
+ f.write(f"{d}\n\n")
+
+ img = descr2img(descr, n=0, height=12, width=16)
+ if img.size(0) == 1:
+ img = F.pad(img, (1, 1, 1, 1), value=64)
+
+ torchvision.utils.save_image(
+ img / 255.0,
+ f"picoclvr_example_{n:02d}.png",
+ padding=1,
+ nrow=4,
+ pad_value=0.8,
+ )
+
+ import time
+
+ start_time = time.perf_counter()
+ descr = generate(nb=1000, height=12, width=16)
+ end_time = time.perf_counter()
+ print(f"{len(descr) / (end_time - start_time):.02f} samples per second")
+
+######################################################################
projects / picoclvr.git / commitdiff