X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=5abe39b767c13299d8dcffcc3369682bfbfff69f;hb=HEAD;hp=5407859d169be5aac42ed7f7b0da3bdc0cc54901;hpb=917e01027ce0c289384dbb87e19d162c695c320b;p=beaver.git diff --git a/beaver.py b/beaver.py index 5407859..5abe39b 100755 --- a/beaver.py +++ b/beaver.py @@ -127,24 +127,14 @@ def log_string(s): sys.stdout.flush() +log_string(f"cmd {' '.join(sys.argv)}") + for n in vars(args): log_string(f"args.{n} {getattr(args, n)}") ###################################################################### -def generation_order(x, prompt_len=0): - if args.random_regression_order: - order = torch.rand(x.size(), device=x.device) - order[:, :prompt_len] = torch.arange(-prompt_len, 0, device=x.device) - order = order.sort(1).indices - else: - order = ( - torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1) - ) - return order - - def reorder(x, order, reverse=False): # x is NxTxD1x...xDk, order is NxT' u = x.reshape(x.size()[:2] + (-1,)) order = order.unsqueeze(-1).expand(-1, -1, u.size(-1)) @@ -157,7 +147,14 @@ def reorder(x, order, reverse=False): # x is NxTxD1x...xDk, order is NxT' def shuffle(x, prompt_len): - order = generation_order(x, prompt_len) + if args.random_regression_order: + order = torch.rand(x.size(), device=x.device) + order[:, :prompt_len] = torch.arange(-prompt_len, 0, device=x.device) + order = order.sort(1).indices + else: + order = ( + torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1) + ) return reorder(x, order), order @@ -238,7 +235,39 @@ def oneshot_trace_loss(mazes, output, policies, height, width): return (output - targets).abs().sum() / masks.sum() -def oneshot(gpt, task): +def oneshot(model, learning_rate_scheduler, task): + t = model.training + model.eval() + mazes = task.test_input[:48].clone() + mazes[:, task.height * task.width :] = 0 + policies = task.test_policies[:48] + targets = maze.stationary_densities( + mazes[:, : task.height * task.width].view(-1, task.height, task.width), + policies.view(-1, 4, task.height, task.width), + ).flatten(-2) + output = eval_mygpt(model, mazes, prompt_len=task.height * task.width) + output = F.softmax(output, dim=2) + print(f"{output.size()=}") + proba_path = output[:, task.height * task.width :, 4].reshape( + -1, task.height, task.width + ) + mazes = mazes[:, : task.height * task.width].reshape(-1, task.height, task.width) + targets = targets.reshape(-1, task.height, task.width) + paths = task.test_input[:48, task.height * task.width :].reshape( + -1, task.height, task.width + ) + filename = f"oneshot.png" + maze.save_image( + os.path.join(args.result_dir, filename), + mazes=mazes, + # target_paths=paths, + score_paths=proba_path, + score_truth=targets, + ) + log_string(f"wrote {filename}") + + +def oneshot_old(gpt, learning_rate_scheduler, task): t = gpt.training gpt.eval() @@ -266,8 +295,12 @@ def oneshot(gpt, task): nn.Linear(args.dim_model, dim_out), ).to(device) + learning_rate_scheduler.reset() + for n_epoch in range(args.nb_epochs): - learning_rate = learning_rate_schedule[n_epoch] + learning_rate = learning_rate_scheduler.get_learning_rate() + log_string(f"learning_rate {n_epoch} {learning_rate}") + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) acc_train_loss, nb_train_samples = 0, 0 @@ -285,6 +318,8 @@ def oneshot(gpt, task): loss.backward() optimizer.step() + learning_rate_scheduler.update(n_epoch + 1, acc_train_loss) + acc_test_loss, nb_test_samples = 0, 0 for mazes, policies in task.policy_batches(split="test"): output_gpt = eval_mygpt( @@ -300,8 +335,8 @@ def oneshot(gpt, task): ) # ------------------- - mazes = task.test_input[:32, : task.height * task.width] - policies = task.test_policies[:32] + mazes = task.test_input[:48, : task.height * task.width] + policies = task.test_policies[:48] output_gpt = eval_mygpt( gpt, mazes, mode=args.oneshot_input, prompt_len=task.height * task.width ) @@ -342,6 +377,75 @@ def oneshot(gpt, task): ###################################################################### +class LearningRateScheduler: + def get_learning_rate(self): + pass + + def update(self, nb_finished_epochs, loss): + pass + + def reset(self): + pass + + def get_state(self): + return vars(self) + + def set_state(self, state): + print(f"{state=}") + for k, v in state.items(): + setattr(self, k, v) + + +class StepWiseScheduler(LearningRateScheduler): + def __init__(self, schedule): + self.nb_finished_epochs = 0 + self.schedule = schedule + + def get_learning_rate(self): + return self.schedule[self.nb_finished_epochs] + + def update(self, nb_finished_epochs, loss): + self.nb_finished_epochs = nb_finished_epochs + + def reset(self): + self.nb_finished_epochs = 0 + + def get_state(self): + return {"nb_finished_epochs": self.nb_finished_epochs} + + +class AutoScheduler(LearningRateScheduler): + def __init__(self, learning_rate_init, growth=1.0, degrowth=0.2): + self.learning_rate_init = learning_rate_init + self.learning_rate = learning_rate_init + self.growth = growth + self.degrowth = degrowth + self.pred_loss = None + + def get_learning_rate(self): + return self.learning_rate + + def update(self, nb_finished_epochs, loss): + if self.pred_loss is not None: + if loss >= self.pred_loss: + self.learning_rate *= self.degrowth + else: + self.learning_rate *= self.growth + self.pred_loss = loss + + def reset(self): + self.learning_rate = self.learning_rate_init + + def get_state(self): + return { + "learning_rate_init": self.learning_rate_init, + "pred_loss": self.pred_loss, + } + + +###################################################################### + + class Task: def batches(self, split="train", nb_to_use=-1, desc=None): pass @@ -475,7 +579,7 @@ class TaskMaze(Task): f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) - input = self.test_input[:32] + input = self.test_input[:48] result = input.clone() ar_mask = result.new_zeros(result.size()) ar_mask[:, self.height * self.width :] = 1 @@ -528,14 +632,24 @@ def noncausal_prompt_amm_generator(d): q = torch.arange(d)[:, None] k = torch.arange(d)[None, :] s = args.maze_height * args.maze_width - # return torch.logical_and(q < k, torch.logical_or(q >= s, k >= s)) - return q < k + return torch.logical_and(q < k, torch.logical_or(q >= s, k >= s)) + # return q < k -amm_generator = None +def noncausal_prompt_oneshot_amm_generator(d): + q = torch.arange(d)[:, None] + k = torch.arange(d)[None, :] + s = args.maze_height * args.maze_width + return k >= s + # return q < k -if args.noncausal_prompt: + +if args.oneshot: + amm_generator = noncausal_prompt_oneshot_amm_generator +elif args.noncausal_prompt: amm_generator = noncausal_prompt_amm_generator +else: + amm_generator = None model = mygpt.MyGPT( vocabulary_size=vocabulary_size, @@ -556,6 +670,36 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### +if args.learning_rate_schedule == "auto": + learning_rate_scheduler = AutoScheduler(args.learning_rate) + +elif args.learning_rate_schedule == "cos": + schedule = {} + for n_epoch in range(args.nb_epochs): + u = n_epoch / args.nb_epochs * math.pi + schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u)) + learning_rate_scheduler = StepWiseScheduler(schedule) + log_string(f"learning_rate_schedule {schedule}") + +else: + u = { + int(k): float(v) + for k, v in [ + tuple(x.split(":")) for x in args.learning_rate_schedule.split(",") + ] + } + + schedule = {} + learning_rate = args.learning_rate + for n_epoch in range(args.nb_epochs): + if n_epoch in u: + learning_rate = u[n_epoch] + schedule[n_epoch] = learning_rate + learning_rate_scheduler = StepWiseScheduler(schedule) + log_string(f"learning_rate_schedule {schedule}") + +###################################################################### + nb_epochs_finished = 0 if args.no_checkpoint: @@ -567,6 +711,7 @@ else: checkpoint = torch.load(checkpoint_name) nb_epochs_finished = checkpoint["nb_epochs_finished"] model.load_state_dict(checkpoint["model_state"]) + learning_rate_scheduler.set_state(checkpoint["learning_rate_scheduler_state"]) torch.set_rng_state(checkpoint["rng_state"]) if torch.cuda.is_available(): torch.cuda.set_rng_state(checkpoint["cuda_rng_state"]) @@ -576,9 +721,15 @@ else: except FileNotFoundError: log_string("starting from scratch.") - except: - log_string("error when loading the checkpoint.") - exit(1) + # except: + # log_string("error when loading the checkpoint.") + # exit(1) + +###################################################################### + +if args.oneshot: + oneshot(model, learning_rate_scheduler, task) + exit(0) ###################################################################### @@ -591,30 +742,6 @@ train_set_perplexity = math.exp(entropy) ############################## -if args.learning_rate_schedule == "cos": - learning_rate_schedule = {} - for n_epoch in range(args.nb_epochs): - u = n_epoch / args.nb_epochs * math.pi - learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u)) -else: - u = { - int(k): float(v) - for k, v in [ - tuple(x.split(":")) for x in args.learning_rate_schedule.split(",") - ] - } - - learning_rate_schedule = {} - learning_rate = args.learning_rate - for n_epoch in range(args.nb_epochs): - if n_epoch in u: - learning_rate = u[n_epoch] - learning_rate_schedule[n_epoch] = learning_rate - -log_string(f"learning_rate_schedule {learning_rate_schedule}") - -############################## - if nb_epochs_finished >= args.nb_epochs: n_epoch = nb_epochs_finished train_perplexity = compute_perplexity( @@ -632,10 +759,11 @@ if nb_epochs_finished >= args.nb_epochs: ############################## -for n_epoch in range(nb_epochs_finished, args.nb_epochs): - learning_rate = learning_rate_schedule[n_epoch] +learning_rate_scheduler.reset() - log_string(f"learning_rate {learning_rate}") +for n_epoch in range(nb_epochs_finished, args.nb_epochs): + learning_rate = learning_rate_scheduler.get_learning_rate() + log_string(f"learning_rate {n_epoch} {learning_rate}") if args.optim == "sgd": optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) @@ -665,6 +793,8 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs): loss.backward() optimizer.step() + learning_rate_scheduler.update(n_epoch + 1, acc_train_loss) + train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples)) test_perplexity = compute_perplexity( model, task, prompt_len=task.height * task.width, split="test" @@ -679,6 +809,7 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs): checkpoint = { "nb_epochs_finished": n_epoch + 1, "model_state": model.state_dict(), + "learning_rate_scheduler_state": learning_rate_scheduler.get_state(), "rng_state": torch.get_rng_state(), } @@ -690,8 +821,3 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs): log_string(f"saved checkpoint {checkpoint_name}") ###################################################################### - -if args.oneshot: - oneshot(model, task) - -######################################################################