From: François Fleuret Date: Mon, 20 Mar 2023 14:52:16 +0000 (+0100) Subject: Update X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=126857a5ef0f205a1d77f62aaf1ee283061396d8;p=beaver.git Update --- diff --git a/beaver.py b/beaver.py index 33d174d..b505156 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") ############################## @@ -81,7 +79,11 @@ parser.add_argument("--maze_width", type=int, default=21) parser.add_argument("--maze_nb_walls", type=int, default=15) -parser.add_argument("--oneshot_mode", type=str, default="head") +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") ###################################################################### @@ -171,10 +173,34 @@ def compute_perplexity(model, split="train"): ###################################################################### -def one_shot(gpt, task): +def oneshot_policy_loss(output, policies, mask): + targets = policies.permute(0, 2, 1) * mask.unsqueeze(-1) + output = output * mask.unsqueeze(-1) + return -(output.log_softmax(-1) * targets).sum() / mask.sum() + + +# loss = (output.softmax(-1) - targets).abs().max(-1).values.mean() + + +def oneshot(gpt, task): t = gpt.training gpt.eval() - dim_in = args.dim_model * (args.nb_blocks * 2 if args.oneshot_mode == "deep" else 1) + + 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 + else: + raise ValueError(f"{args.oneshot_output=}") + model = nn.Sequential( nn.Linear(dim_in, args.dim_model), nn.ReLU(), @@ -194,13 +220,11 @@ def one_shot(gpt, task): # s = maze.stationary_densities( # exit(0) #### - mask = input.unsqueeze(-1) == maze.v_empty - output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x + mask = input == maze.v_empty + output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x output = model(output_gpt) - targets = policies.permute(0, 2, 1) * mask - output = output * mask - # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean() - loss = -(output.log_softmax(-1) * targets).sum() / mask.sum() + + loss = compute_loss(output, policies, mask) acc_train_loss += loss.item() * input.size(0) nb_train_samples += input.size(0) @@ -210,13 +234,10 @@ def one_shot(gpt, task): acc_test_loss, nb_test_samples = 0, 0 for input, policies in task.policy_batches(split="test"): - mask = input.unsqueeze(-1) == maze.v_empty - output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x + mask = input == maze.v_empty + output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x output = model(output_gpt) - targets = policies.permute(0, 2, 1) * mask - output = output * mask - # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean() - loss = -(output.log_softmax(-1) * targets).sum() / mask.sum() + loss = compute_loss(output, policies, mask) acc_test_loss += loss.item() * input.size(0) nb_test_samples += input.size(0) @@ -227,24 +248,20 @@ def one_shot(gpt, task): # ------------------- input = task.test_input[:32, : task.height * task.width] targets = task.test_policies[:32].permute(0, 2, 1) - output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x + output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x output = model(output_gpt) - # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1) - # losses = losses * mask - # losses = losses / losses.max() - # losses = (output.softmax(-1) - targets).abs().max(-1).values - # losses = (losses >= 0.05).float() - losses = ( + scores = ( (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0 ).float() - losses = losses.reshape(-1, args.maze_height, args.maze_width) - input = input.reshape(-1, args.maze_height, args.maze_width) + scores = scores.reshape(-1, task.height, task.width) + input = input.reshape(-1, task.height, task.width) maze.save_image( os.path.join( - args.result_dir, f"oneshot_{args.oneshot_mode}_{n_epoch:04d}.png" + args.result_dir, + f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png", ), mazes=input, - score_paths=losses, + score_paths=scores, ) # ------------------- @@ -498,8 +515,8 @@ 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) ############################## @@ -531,7 +548,7 @@ 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()