######################################################################
-accuracy_to_make_c_quizzes = 0.975
nb_new_c_quizzes_for_train = 1000
nb_new_c_quizzes_for_test = 100
parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--nb_correct_to_validate", type=int, default=4)
+parser.add_argument("--nb_models_for_generation", type=int, default=1)
+
+parser.add_argument("--generation_mode", type=str, default="groupthink")
+
+parser.add_argument("--min_to_validate", type=int, default=4)
+
+parser.add_argument("--max_to_validate", type=int, default=4)
+
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
parser.add_argument("--dirty_debug", action="store_true", default=False)
######################################################################
if args.dirty_debug:
- accuracy_to_make_c_quizzes = 0.0
+ args.accuracy_to_make_c_quizzes = 0.0
nb_new_c_quizzes_for_train = 100
nb_new_c_quizzes_for_test = 10
return sum(
[
sum([x.size(0) for x in recorded[n]])
- for n in range(args.nb_correct_to_validate, len(models))
+ for n in range(args.min_to_validate, args.max_to_validate + 1)
]
)
- while nb_validated() < nb_for_train + nb_for_test:
- nb_to_validate = nb_for_train + nb_for_test
-
- if len(model_indexes) == 0:
- model_indexes = [i.item() for i in torch.randperm(len(models))]
-
- model = models[model_indexes.pop()]
-
- new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
- nb=nb_to_validate,
- model_for_generation=model,
- models_for_validation=models,
+ nb_to_create = nb_for_train + nb_for_test
+
+ while nb_validated() < nb_to_create:
+ (
+ new_c_quizzes,
+ nb_correct,
+ ave_seq_logproba,
+ ) = quizz_machine.gang_create_c_quizzes(
+ nb=nb_to_create,
+ nb_models_for_generation=args.nb_models_for_generation,
+ models=models,
+ mode=args.generation_mode,
min_ave_seq_logproba=min_ave_seq_logproba,
n_epoch=n_epoch,
result_dir=args.result_dir,
recorded[n].append(new_c_quizzes[nb_correct == n].clone())
log_string(
- f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_validate}"
+ f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}"
)
# concatenate and shuffle
del recorded[n]
new_c_quizzes = torch.cat(
- [recorded[n] for n in range(args.nb_correct_to_validate, len(models))], dim=0
+ [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)],
+ dim=0,
)
new_c_quizzes = new_c_quizzes[
quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
for n in recorded.keys():
- s = "_validated" if n >= args.nb_correct_to_validate and n < len(models) else ""
+ s = (
+ "_validated"
+ if n >= args.min_to_validate and n <= args.max_to_validate
+ else ""
+ )
quizz_machine.problem.save_quizzes(
recorded[n][:72],
args.result_dir,
f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
)
- if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
+ if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
ave_seq_logproba = create_c_quizzes(
models,
quizz_machine,
bs = self.readout(bs)
return bs
- # ar_mask is a tensor with 0s and 1s, of same shape as input, with
- # 1s where tokens should be generated. The others are kept
- # unchanged.
-
- def masked_inplace_autoregression(
- self,
- input,
- ar_mask,
- seq_logproba,
- temperature=1.0,
- deterministic_synthesis=False,
- forbidden_tokens=None,
- forced_biases=None,
- ):
- to_generate = (ar_mask.sum(0) > 0).nonzero()
-
- if to_generate.min() > 0:
- self(
- BracketedSequence(input, 0, to_generate.min())
- ) # Needed to initialize the model's cache
- for s in range(to_generate.min(), to_generate.max() + 1):
- output = self(BracketedSequence(input, s, 1)).x
-
- logits = output[:, s]
-
- logits = (logits / temperature).log_softmax(dim=-1)
-
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
-
- if forced_biases is not None:
- logits = logits + forced_biases[None, :]
-
- if deterministic_synthesis:
- t_next = logits.argmax(-1)
- else:
- dist = torch.distributions.categorical.Categorical(logits=logits)
- t_next = dist.sample()
-
- all_n = torch.arange(t_next.size(0))
- seq_logproba += logits[all_n, t_next].sum(dim=-1)
-
- input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-
def record_attention(self, v=True):
for m in self.modules():
if isinstance(m, QKVAttention):
######################################################################
+class Gang(nn.Module):
+ def __init__(self, models, nb_models_for_generation, mode="groupthink"):
+ super().__init__()
+ self.models = models
+ self.nb_models_for_generation = nb_models_for_generation
+ self.mode = mode
+
+ def forward(self, bs):
+ # If first = 0, we are re-starting an auto-regressive process,
+ # that's the right moment to randomize who gonna do it
+ if bs.first == 0:
+ self.models_to_use = [
+ self.models[k]
+ for k in torch.randperm(len(self.models))[
+ : self.nb_models_for_generation
+ ]
+ ]
+
+ all_the_logits = torch.cat(
+ [model(bs).x[None] for model in self.models_to_use], dim=0
+ )
+
+ if self.mode == "groupthink":
+ y = all_the_logits.mean(dim=0)
+ elif self.mode == "groupwork":
+ m = torch.rand(all_the_logits.size(), device=all_the_logits.device)
+ m = (m.sort(dim=0).indices == 0).long()
+ y = (y * m).sum(dim=0)
+ else:
+ raise ValueError(f"Invalid mode {self.mode}")
+
+ return BracketedSequence(y, bs.first, bs.nb)
+
+
+######################################################################
+
+# ar_mask is a tensor with 0s and 1s, of same shape as input, with
+# 1s where tokens should be generated. The others are kept
+# unchanged.
+
+
+def one_batch_masked_inplace_autoregression(
+ model,
+ input,
+ ar_mask,
+ seq_logproba,
+ temperature=1.0,
+ deterministic_synthesis=False,
+ forbidden_tokens=None,
+ forced_biases=None,
+):
+ to_generate = (ar_mask.sum(0) > 0).nonzero()
+
+ if to_generate.min() > 0:
+ model(
+ BracketedSequence(input, 0, to_generate.min())
+ ) # Needed to initialize the model's cache
+ for s in range(to_generate.min(), to_generate.max() + 1):
+ output = model(BracketedSequence(input, s, 1)).x
+
+ logits = output[:, s]
+
+ logits = (logits / temperature).log_softmax(dim=-1)
+
+ if forbidden_tokens is not None:
+ logits = logits.masked_fill(forbidden_tokens, float("-inf"))
+
+ if forced_biases is not None:
+ logits = logits + forced_biases[None, :]
+
+ if deterministic_synthesis:
+ t_next = logits.argmax(-1)
+ else:
+ dist = torch.distributions.categorical.Categorical(logits=logits)
+ t_next = dist.sample()
+
+ all_n = torch.arange(t_next.size(0))
+ seq_logproba += logits[all_n, t_next].sum(dim=-1)
+
+ input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
+
+
def masked_inplace_autoregression(
model,
batch_size,
model.eval()
for input, ar_mask, seq_logproba in batches:
- model.masked_inplace_autoregression(
+ one_batch_masked_inplace_autoregression(
+ model=model,
input=input,
ar_mask=ar_mask,
seq_logproba=seq_logproba,
self.batch_size = batch_size
self.device = device
- self.train_w_quizzes = self.problem.generate_seq(nb_train_samples).to(device)
- self.test_w_quizzes = self.problem.generate_seq(nb_test_samples).to(device)
+ self.train_w_quizzes = self.problem.generate_token_sequences(
+ nb_train_samples
+ ).to(device)
+ self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to(
+ device
+ )
self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
input = self.train_w_quizzes if for_train else self.test_w_quizzes
nb = min(nb, input.size(0))
input[:-nb] = input[nb:].clone()
- input[-nb:] = self.problem.generate_seq(nb).to(self.device)
+ input[-nb:] = self.problem.generate_token_sequences(nb).to(self.device)
def store_c_quizzes(self, new_c_quizzes, for_train=True):
if for_train:
else:
self.test_c_quizzes.append(new_c_quizzes)
- def create_c_quizzes(
- self,
- nb,
- model_for_generation,
- models_for_validation,
- min_ave_seq_logproba,
- n_epoch,
- result_dir,
- logger,
- ):
- ###############################################################
- # Generate quizzes with model
-
- c_quizzes = torch.empty(
- nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
- )
-
- ar_mask = torch.full(c_quizzes.size(), 1, device=self.device)
- seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
-
- temperature = 1
- d_temperature = 1 / 3
-
- while True:
- seq_logproba[...] = 0
-
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=False,
- # progress_bar_desc="sampling c_quizzes",
- device=self.device,
- )
-
- ave_seq_logproba = seq_logproba.mean()
-
- if min_ave_seq_logproba is None:
- break
-
- # Oh man that's ugly
- if ave_seq_logproba < min_ave_seq_logproba:
- if d_temperature > 0:
- d_temperature *= -1 / 3
- temperature += d_temperature
- elif ave_seq_logproba > min_ave_seq_logproba * 0.99:
- if d_temperature < 0:
- d_temperature *= -1 / 3
- temperature += d_temperature
- else:
- break
-
- logger(f"changing temperature to {temperature}")
-
+ def comput_correctness(self, c_quizzes, models_for_validation):
###############################################################
# Create the reverse quizzes
nb_correct.append((correct * reverse_correct)[None, :])
- nb_correct = torch.cat(nb_correct, dim=0).sum(dim=0)
+ return torch.cat(nb_correct, dim=0).sum(dim=0)
+
+ def generate_quizzes(self, nb, model_for_generation, min_ave_seq_logproba):
+ ###############################################################
+ # Generate quizzes with model
- return c_quizzes, nb_correct, seq_logproba.mean()
+ c_quizzes = torch.empty(
+ nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
+ )
+
+ ar_mask = torch.full(c_quizzes.size(), 1, device=self.device)
+ seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
+
+ # bracketing of the temperature to get the target logproba
+
+ temperature = 1
+ d_temperature = 1 / 3
+
+ while True:
+ seq_logproba[...] = 0
+
+ masked_inplace_autoregression(
+ model=model_for_generation,
+ batch_size=self.batch_size,
+ input=c_quizzes,
+ ar_mask=ar_mask,
+ seq_logproba=seq_logproba,
+ temperature=temperature,
+ deterministic_synthesis=False,
+ # progress_bar_desc="sampling c_quizzes",
+ device=self.device,
+ )
+
+ ave_seq_logproba = seq_logproba.mean()
+
+ # If we do not have target logprobs, get out now
+ if min_ave_seq_logproba is None:
+ break
+
+ # Oh man that's ugly
+ if ave_seq_logproba < min_ave_seq_logproba:
+ if d_temperature > 0:
+ d_temperature *= -1 / 3
+ temperature += d_temperature
+ elif ave_seq_logproba > min_ave_seq_logproba * 0.99:
+ if d_temperature < 0:
+ d_temperature *= -1 / 3
+ temperature += d_temperature
+ else:
+ break
+
+ logger(f"changing temperature to {temperature}")
+
+ return c_quizzes, seq_logproba.mean()
+
+ ######################################################################
+
+ def create_c_quizzes(
+ self,
+ nb,
+ model_for_generation,
+ models_for_validation,
+ min_ave_seq_logproba,
+ n_epoch,
+ result_dir,
+ logger,
+ ):
+ c_quizzes, ave_seq_logproba = self.generate_quizzes(
+ nb, model_for_generation, min_ave_seq_logproba
+ )
+
+ nb_correct = self.comput_correctness(c_quizzes, models_for_validation)
+
+ return c_quizzes, nb_correct, ave_seq_logproba
+
+ ######################################################################
+
+ def gang_create_c_quizzes(
+ self,
+ nb,
+ nb_models_for_generation,
+ models,
+ mode,
+ min_ave_seq_logproba,
+ n_epoch,
+ result_dir,
+ logger,
+ ):
+ model_for_generation = Gang(models, nb_models_for_generation, mode)
+ models_for_validation = models
+ return self.create_c_quizzes(
+ nb,
+ model_for_generation,
+ models_for_validation,
+ min_ave_seq_logproba,
+ n_epoch,
+ result_dir,
+ logger,
+ )
projects / culture.git / commitdiff