# Written by Francois Fleuret <francois@fleuret.org>
-import math, os, tqdm, warnings
+import math, os, tqdm, warnings, sys
import torch, torchvision
import threading
+######################################################################
+# if output is log(P(X=y)) and target is Y, returns -log P(X=Y) + H(X
+# | X != Y)
+
+
+# output is NxCxT and target is NxT
+def confusion(output, target, reduction="mean"):
+ N, C, T = output.shape
+ output = output.permute(0, 2, 1).reshape(-1, C)
+ target = target.flatten()
+ all_t = torch.arange(N * T, device=output.device)
+ output = output.log_softmax(dim=-1)
+ result = -output[all_t, target]
+
+ output[all_t, target] = float("-inf")
+ output = output.log_softmax(dim=-1)
+ e = output.exp()
+ output[all_t, target] = 0
+ result = result - (output * e).sum(-1)
+
+ if reduction == "none":
+ return result.reshape(N, T)
+ elif reduction == "mean":
+ return result.reshape(N, T).mean()
+ elif reduction == "sum":
+ return result.reshape(N, T).sum()
+ else:
+ raise ValueError(f"unknown reduction '{reduction}'.")
+
+
######################################################################
# ar_mask is a tensor with 0s and 1s, of same shape as input, with
self.train_c_quizzes = []
self.test_c_quizzes = []
- def save_quizzes(
+ def save_quiz_illustrations(
self,
result_dir,
filename_prefix,
predicted_prompts *= 2
predicted_answers *= 2
- self.problem.save_quizzes(
+ self.problem.save_quiz_illustrations(
result_dir,
filename_prefix,
quizzes[:, 1 : 1 + self.prompt_len],
backward_nb_total = correct[n_backward].size(0)
self.logger(
- f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
- )
-
- self.logger(
- f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
+ f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}"
)
return result, correct
- compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
+ # compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
test_result, test_correct = compute_accuracy(
model.test_w_quizzes[:nmax], log_prefix="test"
##############################
- self.save_quizzes(
+ self.save_quiz_illustrations(
result_dir,
f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
quizzes=test_result[:72],
else:
self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
+ def save_c_quizzes(self, filename):
+ torch.save((self.train_c_quizzes, self.test_c_quizzes), filename)
+
+ def load_c_quizzes(self, filename):
+ self.train_c_quizzes, self.test_c_quizzes = torch.load(filename)
+
######################################################################
- def logproba_of_solutions(self, models, c_quizzes):
+ def solution_token_logprobas(self, models, c_quizzes):
logproba = c_quizzes.new_zeros(
- c_quizzes.size(0), len(models), device=self.device
+ c_quizzes.size(0),
+ len(models),
+ c_quizzes.size(1),
+ device=self.device,
+ dtype=torch.float32,
)
for model in models:
- for input, l in zip(
- c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
- ):
- input = input.to(self.device)
- ar_mask = self.make_ar_mask(input)
- output = model(mygpt.BracketedSequence(input)).x
- ce = (
- F.cross_entropy(output.transpose(1, 2), input, reduction="none")
- * ar_mask
- )
- l[:, model.id] = -ce.sum(dim=-1)
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, l in zip(
+ c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
+ ):
+ input = input.to(self.device)
+ ar_mask = self.make_ar_mask(input)
+ output = model(mygpt.BracketedSequence(input)).x
+ l[:, model.id] = (
+ -F.cross_entropy(
+ output.transpose(1, 2), input, reduction="none"
+ )
+ * ar_mask
+ )
+
+ model.train(t)
return logproba.to("cpu")