self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
# A bit of paranoia never hurts
- assert (
- self.nb_codes <= max_nb_codes
- and self.train_input.min() >= 0
- and self.test_input.min() >= 0
- and tuple(self.train_ar_mask.unique()) == (0, 1)
- and tuple(self.test_ar_mask.unique()) == (0, 1)
- )
+ assert self.nb_codes <= max_nb_codes
+ assert self.train_input.min() >= 0
+ assert self.test_input.min() >= 0
+ assert tuple(x.item() for x in self.train_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
+ assert tuple(x.item() for x in self.test_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
+
+ if logger is not None:
+ for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
+ logger(f"train_sequences {self.problem.seq2str(s)}")
+ a = "".join(["01"[x.item()] for x in a])
+ logger(f" {a}")
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
device=self.device,
)
+ log_ground_truth = ar_mask.min() == 0
+
if logger is not None:
for sp, st in zip(result[:10], input[:10]):
logger(
f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}"
)
- logger(
- f" {n_epoch} ground truth {self.problem.seq2str(st)}"
- )
+ if log_ground_truth:
+ logger(
+ f" {n_epoch} ground truth {self.problem.seq2str(st)}"
+ )
- nb_total = ar_mask.sum().item()
- nb_correct = ((result == input).long() * ar_mask).sum().item()
+ nb_total, nb_correct = self.problem.compute_nb_correct(
+ input, ar_mask, result
+ )
+
+ # nb_total = ar_mask.sum().item()
+ # nb_correct = ((result == input).long() * ar_mask).sum().item()
return nb_total, nb_correct
with torch.autograd.no_grad():
t = model.training
model.eval()
- model.record_attention(True)
+ # model.record_attention(True)
model(BracketedSequence(input))
model.train(t)
- ram = model.retrieve_attention()
- model.record_attention(False)
-
- tokens_output = [c for c in self.problem.seq2str(input[0])]
- tokens_input = ["n/a"] + tokens_output[:-1]
- for n_head in range(ram[0].size(1)):
- filename = os.path.join(
- result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
- )
- attention_matrices = [m[0, n_head] for m in ram]
- save_attention_image(
- filename,
- tokens_input,
- tokens_output,
- attention_matrices,
- k_top=10,
- # min_total_attention=0.9,
- token_gap=12,
- layer_gap=50,
- )
- logger(f"wrote {filename}")
+ # ram = model.retrieve_attention()
+ # model.record_attention(False)
+
+ # tokens_output = [c for c in self.problem.seq2str(input[0])]
+ # tokens_input = ["n/a"] + tokens_output[:-1]
+ # for n_head in range(ram[0].size(1)):
+ # filename = os.path.join(
+ # result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
+ # )
+ # attention_matrices = [m[0, n_head] for m in ram]
+ # save_attention_image(
+ # filename,
+ # tokens_input,
+ # tokens_output,
+ # attention_matrices,
+ # k_top=10,
+ ##min_total_attention=0.9,
+ # token_gap=12,
+ # layer_gap=50,
+ # )
+ # logger(f"wrote {filename}")
######################################################################
class QMLP(Task):
-
######################
def __init__(
nb_train_samples,
nb_test_samples,
batch_size,
+ result_dir,
logger=None,
device=torch.device("cpu"),
):
f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
)
- seq, q_test_set = generate_sequence_and_test_set(
- nb_mlps=nb_train_samples+nb_test_samples,
+ seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
+ nb_mlps=nb_train_samples + nb_test_samples,
nb_samples=self.nb_samples_per_mlp,
device=self.device,
batch_size=64,
nb_epochs=250,
- nb_mlps_per_batch=1024
+ nb_mlps_per_batch=1024,
)
self.train_input = seq[:nb_train_samples]
self.train_q_test_set = q_test_set[:nb_train_samples]
+ self.train_ref_test_errors = test_error[:nb_train_samples]
self.test_input = seq[nb_train_samples:]
self.test_q_test_set = q_test_set[nb_train_samples:]
+ self.test_ref_test_errors = test_error[nb_train_samples:]
+
+ filename = os.path.join(result_dir, f"train_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.train_ref_test_errors:
+ f.write(f"{e}\n")
+
+ filename = os.path.join(result_dir, f"test_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.test_ref_test_errors:
+ f.write(f"{e}\n")
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
):
- yield self.trim(batch)
+ yield batch
def vocabulary_size(self):
return self.nb_codes
):
correct = self.test_input[:1000]
result = correct.clone()
- ar_mask = torch.arange(result.size(1)) > self.nb_samples_per_mlp * 3 + 1
+ ar_mask = (
+ torch.arange(result.size(1), device=result.device)
+ > self.nb_samples_per_mlp * 3 + 1
+ ).long()[None, :]
+ ar_mask = ar_mask.expand_as(result)
result *= 1 - ar_mask # paraaaaanoiaaaaaaa
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"test_before {e}")
-
masked_inplace_autoregression(
model,
self.batch_size,
device=self.device,
)
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"test_after {e}")
-
- logger(f"----------------------------------------------------------")
-
- q_train_set = result[:, : nb_samples * 3]
- q_params = result[:, nb_samples * 3 + 1 :]
- error_test = evaluate_q_params(q_params, q_test_set, nb_mlps_per_batch=17)
+ q_train_set = result[:, : self.nb_samples_per_mlp * 3]
+ q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
+ error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
- logger(f"{error_test=}")
+ filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
+ with open(filename, "w") as f:
+ for e in error_test:
+ f.write(f"{e}\n")
######################################################################
projects / picoclvr.git / blobdiff