X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=6ec0fb290e2109077b6aefe1a2ae63d032e755b2;hb=2cd3f15987d2bf9050f737cd13506740ad3e90cb;hp=bdc12aa405624a866de0f9f2eb320f5aedf53210;hpb=2d5ddcda7171ef926cff50e20ca28be4bc8084aa;p=beaver.git diff --git a/beaver.py b/beaver.py index bdc12aa..6ec0fb2 100755 --- a/beaver.py +++ b/beaver.py @@ -129,18 +129,35 @@ for n in vars(args): ###################################################################### +def random_order(result, fixed_len): + order = torch.rand(result.size(), device=result.device) + order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device) + return order.sort(1).indices + + +def shuffle(x, order, reorder=False): + if x.dim() == 3: + order = order.unsqueeze(-1).expand(-1, -1, x.size(-1)) + if reorder: + y = x.new(x.size()) + y.scatter_(1, order, x) + return y + else: + return x.gather(1, order) + + # ar_mask is a Boolean matrix of same shape as input, with 1s on the # tokens that should be generated -def masked_inplace_autoregression(model, batch_size, input, ar_mask): +def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None): 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: # Needed to initialize the model's cache - model(mygpt.BracketedSequence(input, 0, i.min())) + model(mygpt.BracketedSequence(input, 0, i.min()), order=order) for s in range(i.min(), i.max() + 1): - output = model(mygpt.BracketedSequence(input, s, 1)).x + output = model(mygpt.BracketedSequence(input, s, 1), order=order).x logits = output[:, s] if args.deterministic_synthesis: t_next = logits.argmax(1) @@ -162,8 +179,9 @@ def compute_perplexity(model, split="train"): for input in task.batches(split=split): input = input.to(device) - - output = model(mygpt.BracketedSequence(input)).x + order = random_order(input, task.height * task.width) + input = shuffle(input, order) + output = model(mygpt.BracketedSequence(input), order=order).x loss = F.cross_entropy(output.transpose(1, 2), input) acc_loss += loss.item() * input.size(0) nb_samples += input.size(0) @@ -227,7 +245,10 @@ def oneshot(gpt, task): acc_train_loss, nb_train_samples = 0, 0 for mazes, policies in task.policy_batches(split="train"): - output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x + order = random_order(input, task.height * task.width) + x = shuffle(mazes, order) + x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x + output_gpt = shuffle(x, order, reorder=True) output = model(output_gpt) loss = compute_loss(mazes, output, policies, task.height, task.width) @@ -240,7 +261,10 @@ def oneshot(gpt, task): acc_test_loss, nb_test_samples = 0, 0 for mazes, policies in task.policy_batches(split="test"): - output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x + order = random_order(input, task.height * task.width) + x = shuffle(mazes, order) + x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x + output_gpt = shuffle(x, order, reorder=True) output = model(output_gpt) loss = compute_loss(mazes, output, policies, task.height, task.width) acc_test_loss += loss.item() * mazes.size(0) @@ -253,7 +277,10 @@ def oneshot(gpt, task): # ------------------- mazes = task.test_input[:32, : task.height * task.width] policies = task.test_policies[:32] - output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x + order = random_order(input, task.height * task.width) + x = shuffle(mazes, order) + x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x + output_gpt = shuffle(x, order, reorder=True) output = model(output_gpt) if args.oneshot_output == "policy": targets = policies.permute(0, 2, 1) @@ -290,7 +317,7 @@ def oneshot(gpt, task): class Task: - def batches(self, split="train"): + def batches(self, split="train", nb_to_use=-1, desc=None): pass def vocabulary_size(self): @@ -350,17 +377,19 @@ class TaskMaze(Task): self.nb_codes = self.train_input.max() + 1 - def batches(self, split="train", nb_to_use=-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=f"epoch-{split}" + input.split(self.batch_size), dynamic_ncols=True, desc=desc ): yield batch - def policy_batches(self, split="train", nb_to_use=-1): + def policy_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 policies = self.train_policies if split == "train" else self.test_policies @@ -371,10 +400,12 @@ class TaskMaze(Task): input = input[:nb_to_use] policies = policies[:nb_to_use] + if desc is None: + desc = f"epoch-{split}" for batch in tqdm.tqdm( zip(input.split(self.batch_size), policies.split(self.batch_size)), dynamic_ncols=True, - desc=f"epoch-{split}", + desc=desc, ): yield batch @@ -388,7 +419,11 @@ class TaskMaze(Task): 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) + order = random_order(result, self.height * self.width) + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, order=order + ) + result = shuffle(result, order, reorder=True) mazes, paths = self.seq2map(result) nb_correct += maze.path_correctness(mazes, paths).long().sum() nb_total += mazes.size(0) @@ -568,7 +603,9 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs): for input in task.batches(split="train"): input = input.to(device) - output = model(mygpt.BracketedSequence(input)).x + order = random_order(input, task.height * task.width) + input = shuffle(input, order) + output = model(mygpt.BracketedSequence(input), order=order).x loss = F.cross_entropy(output.transpose(1, 2), input) acc_train_loss += loss.item() * input.size(0) nb_train_samples += input.size(0)