X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=591621590d8101cf7bc3676a3640c6d94738f190;hb=c1fd5d2d11dbd5ccd0efde47060bac879afed9ee;hp=2cc214019f48c47da2fc29edd8f84cdc8a10e7ba;hpb=41c7509dc3d2153da79ed09ecf4a3b592503f15e;p=beaver.git diff --git a/beaver.py b/beaver.py index 2cc2140..5916215 100755 --- a/beaver.py +++ b/beaver.py @@ -68,8 +68,6 @@ parser.add_argument("--no_checkpoint", action="store_true", default=False) parser.add_argument("--overwrite_results", action="store_true", default=False) -parser.add_argument("--one_shot", action="store_true", default=False) - parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") ############################## @@ -81,6 +79,12 @@ parser.add_argument("--maze_width", type=int, default=21) parser.add_argument("--maze_nb_walls", type=int, default=15) +parser.add_argument("--oneshot", action="store_true", default=False) + +parser.add_argument("--oneshot_input", type=str, default="head") + +parser.add_argument("--oneshot_output", type=str, default="policy") + ###################################################################### args = parser.parse_args() @@ -169,66 +173,115 @@ def compute_perplexity(model, split="train"): ###################################################################### -def one_shot(gpt, task): +def oneshot_policy_loss(mazes, output, policies, height, width): + masks = (mazes == maze.v_empty).unsqueeze(-1) + targets = policies.permute(0, 2, 1) * masks + output = output * masks + return -(output.log_softmax(-1) * targets).sum() / masks.sum() + + +def oneshot_trace_loss(mazes, output, policies, height, width): + masks = mazes == maze.v_empty + targets = maze.stationary_densities( + mazes.view(-1, height, width), policies.view(-1, 4, height, width) + ).flatten(-2) + targets = targets * masks + output = output.squeeze(-1) * masks + return (output - targets).abs().sum() / masks.sum() + + +def oneshot(gpt, task): t = gpt.training gpt.eval() + + if args.oneshot_input == "head": + dim_in = args.dim_model + elif args.oneshot_input == "deep": + dim_in = args.dim_model * args.nb_blocks * 2 + else: + raise ValueError(f"{args.oneshot_input=}") + + if args.oneshot_output == "policy": + dim_out = 4 + compute_loss = oneshot_policy_loss + elif args.oneshot_output == "trace": + dim_out = 1 + compute_loss = oneshot_trace_loss + else: + raise ValueError(f"{args.oneshot_output=}") + model = nn.Sequential( + nn.Linear(dim_in, args.dim_model), + nn.ReLU(), nn.Linear(args.dim_model, args.dim_model), nn.ReLU(), - nn.Linear(args.dim_model, 4), + nn.Linear(args.dim_model, dim_out), ).to(device) - print(f"{args.nb_epochs=}") - for n_epoch in range(args.nb_epochs): - print(f"{n_epoch=}") learning_rate = learning_rate_schedule[n_epoch] optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) acc_train_loss, nb_train_samples = 0, 0 - for input, targets in task.policy_batches(split="train"): - output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + for mazes, policies in task.policy_batches(split="train"): + #### + # print(f'{mazes.size()=} {policies.size()=}') + # s = maze.stationary_densities( + # exit(0) + #### + output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x output = model(output_gpt) - loss = ( - -(output.log_softmax(-1) * targets).sum(-1).mean() - + targets.xlogy(targets).sum(-1).mean() - ) - acc_train_loss += loss.item() * input.size(0) - nb_train_samples += input.size(0) + + loss = compute_loss(mazes, output, policies, task.height, task.width) + acc_train_loss += loss.item() * mazes.size(0) + nb_train_samples += mazes.size(0) optimizer.zero_grad() loss.backward() optimizer.step() acc_test_loss, nb_test_samples = 0, 0 - for input, targets in task.policy_batches(split="test"): - output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + for mazes, policies in task.policy_batches(split="test"): + output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x output = model(output_gpt) - loss = ( - -(output.log_softmax(-1) * targets).sum(-1).mean() - + targets.xlogy(targets).sum(-1).mean() - ) - acc_test_loss += loss.item() * input.size(0) - nb_test_samples += input.size(0) + loss = compute_loss(mazes, output, policies, task.height, task.width) + acc_test_loss += loss.item() * mazes.size(0) + nb_test_samples += mazes.size(0) log_string( f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}" ) # ------------------- - input, targets = next(task.policy_batches(split="test")) - output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + 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 output = model(output_gpt) - losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1) - losses = losses / losses.max() - print(f"{input.size()=} {losses.size()=} {losses.min()=} {losses.max()=}") - losses = losses * (input == 0) - losses = losses.reshape(-1, args.maze_height, args.maze_width) - input = input.reshape(-1, args.maze_height, args.maze_width) + if args.oneshot_output == "policy": + targets = policies.permute(0, 2, 1) + scores = ( + (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0 + ).float() + elif args.oneshot_output == "trace": + targets = maze.stationary_densities( + mazes.view(-1, task.height, task.width), + policies.view(-1, 4, task.height, task.width), + ).flatten(-2) + scores = output + else: + raise ValueError(f"{args.oneshot_output=}") + + scores = scores.reshape(-1, task.height, task.width) + mazes = mazes.reshape(-1, task.height, task.width) + targets = targets.reshape(-1, task.height, task.width) maze.save_image( - os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"), - mazes=input, - score_paths=losses, + os.path.join( + args.result_dir, + f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png", + ), + mazes=mazes, + score_paths=scores, + score_truth=targets, ) # ------------------- @@ -285,7 +338,7 @@ class TaskMaze(Task): progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"), ) self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device)) - self.train_policies = train_policies.flatten(-2).permute(0, 2, 1).to(device) + self.train_policies = train_policies.flatten(-2).to(device) test_mazes, test_paths, test_policies = maze.create_maze_data( nb_test_samples, @@ -295,7 +348,7 @@ class TaskMaze(Task): progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"), ) self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device)) - self.test_policies = test_policies.flatten(-2).permute(0, 2, 1).to(device) + self.test_policies = test_policies.flatten(-2).to(device) self.nb_codes = self.train_input.max() + 1 @@ -312,16 +365,16 @@ class TaskMaze(Task): def policy_batches(self, split="train", nb_to_use=-1): assert split in {"train", "test"} input = self.train_input if split == "train" else self.test_input - targets = self.train_policies if split == "train" else self.test_policies + policies = self.train_policies if split == "train" else self.test_policies input = input[:, : self.height * self.width] - targets = targets * (input != maze.v_wall)[:, :, None] + policies = policies * (input != maze.v_wall)[:, None] if nb_to_use > 0: input = input[:nb_to_use] - targets = targets[:nb_to_use] + policies = policies[:nb_to_use] for batch in tqdm.tqdm( - zip(input.split(self.batch_size), targets.split(self.batch_size)), + zip(input.split(self.batch_size), policies.split(self.batch_size)), dynamic_ncols=True, desc=f"epoch-{split}", ): @@ -482,8 +535,8 @@ log_string(f"learning_rate_schedule {learning_rate_schedule}") ############################## -if args.one_shot: - one_shot(model, task) +if args.oneshot: + oneshot(model, task) exit(0) ############################## @@ -515,7 +568,7 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs): elif args.optim == "adamw": optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) else: - raise ValueError(f"Unknown optimizer {args.optim}.") + raise ValueError(f"{args.optim=}") model.train()