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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| import time | ||
| from functools import partial | ||
| from typing import Callable, Tuple | ||
|
|
||
| import haiku as hk | ||
| import jax | ||
| from jax import jit | ||
| import jax.numpy as jnp | ||
| import jraph | ||
| import optax | ||
|
|
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| from segnn_jax import SteerableGraphsTuple | ||
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| @partial(jit, static_argnames=["model_fn", "criterion", "task", "do_mask", "eval_trn"]) | ||
| def loss_fn( | ||
| params: hk.Params, | ||
| state: hk.State, | ||
| st_graph: SteerableGraphsTuple, | ||
| target: jnp.ndarray, | ||
| model_fn: Callable, | ||
| criterion: Callable, | ||
| task: str = "node", | ||
| do_mask: bool = True, | ||
| eval_trn: Callable = None, | ||
| ) -> Tuple[float, hk.State]: | ||
| pred, state = model_fn(params, state, st_graph) | ||
| if eval_trn is not None: | ||
| pred = eval_trn(pred) | ||
| if task == "node": | ||
| mask = jraph.get_node_padding_mask(st_graph.graph) | ||
| if task == "graph": | ||
| mask = jraph.get_graph_padding_mask(st_graph.graph) | ||
| # broadcase mask for vector targets | ||
| if len(pred.shape) == 2: | ||
| mask = mask[:, jnp.newaxis] | ||
| if do_mask: | ||
| target = target * mask | ||
| pred = pred * mask | ||
| assert target.shape == pred.shape | ||
| return jnp.sum(criterion(pred, target)) / jnp.count_nonzero(mask), state | ||
|
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| @partial(jit, static_argnames=["loss_fn", "opt_update"]) | ||
| def update( | ||
| params: hk.Params, | ||
| state: hk.State, | ||
| graph: SteerableGraphsTuple, | ||
| target: jnp.ndarray, | ||
| opt_state: optax.OptState, | ||
| loss_fn: Callable, | ||
| opt_update: Callable, | ||
| ) -> Tuple[float, hk.Params, hk.State, optax.OptState]: | ||
| (loss, state), grads = jax.value_and_grad(loss_fn, has_aux=True)( | ||
| params, state, graph, target | ||
| ) | ||
| updates, opt_state = opt_update(grads, opt_state, params) | ||
| return loss, optax.apply_updates(params, updates), state, opt_state | ||
|
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||
|
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| def evaluate( | ||
| loader, | ||
| params: hk.Params, | ||
| state: hk.State, | ||
| loss_fn: Callable, | ||
| graph_transform: Callable, | ||
| ) -> Tuple[float, float]: | ||
| eval_loss = 0.0 | ||
| eval_times = 0.0 | ||
| for data in loader: | ||
| graph, target = graph_transform(data, training=False) | ||
| eval_start = time.perf_counter_ns() | ||
| loss, _ = jax.lax.stop_gradient(loss_fn(params, state, graph, target)) | ||
| eval_loss += jax.block_until_ready(loss) | ||
| eval_times += (time.perf_counter_ns() - eval_start) / 1e6 | ||
| return eval_times / len(loader), eval_loss / len(loader) | ||
|
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|
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||
| def train( | ||
| key, | ||
| segnn, | ||
| loader_train, | ||
| loader_val, | ||
| loader_test, | ||
| loss_fn, | ||
| eval_loss_fn, | ||
| graph_transform, | ||
| args, | ||
| ): | ||
| init_graph, _ = graph_transform(next(iter(loader_train))) | ||
| params, segnn_state = segnn.init(key, init_graph) | ||
|
|
||
| print( | ||
| f"Starting {args.epochs} epochs " | ||
| f"with {hk.data_structures.tree_size(params)} parameters.\n" | ||
| "Jitting..." | ||
| ) | ||
|
|
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| total_steps = args.epochs * len(loader_train) | ||
|
|
||
| # set up learning rate and optimizer | ||
| learning_rate = args.lr | ||
| if args.lr_scheduling: | ||
| learning_rate = optax.piecewise_constant_schedule( | ||
| learning_rate, | ||
| boundaries_and_scales={ | ||
| int(total_steps * 0.7): 0.1, | ||
| int(total_steps * 0.9): 0.1, | ||
| }, | ||
| ) | ||
| opt_init, opt_update = optax.adamw( | ||
| learning_rate=learning_rate, weight_decay=args.weight_decay | ||
| ) | ||
|
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| model_fn = segnn.apply | ||
|
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| loss_fn = partial(loss_fn, model_fn=model_fn) | ||
| eval_loss_fn = partial(eval_loss_fn, model_fn=model_fn) | ||
| update_fn = partial(update, loss_fn=loss_fn, opt_update=opt_update) | ||
| eval_fn = partial(evaluate, loss_fn=eval_loss_fn, graph_transform=graph_transform) | ||
|
|
||
| opt_state = opt_init(params) | ||
| avg_time = [] | ||
| best_val = 1e10 | ||
|
|
||
| for e in range(args.epochs): | ||
| train_loss = 0.0 | ||
| train_start = time.perf_counter_ns() | ||
| for data in loader_train: | ||
| graph, target = graph_transform(data) | ||
| loss, params, segnn_state, opt_state = update_fn( | ||
| params=params, | ||
| state=segnn_state, | ||
| graph=graph, | ||
| target=target, | ||
| opt_state=opt_state, | ||
| ) | ||
| train_loss += loss | ||
| train_time = (time.perf_counter_ns() - train_start) / 1e6 | ||
| train_loss /= len(loader_train) | ||
| print( | ||
| f"[Epoch {e+1:>4}] train loss {train_loss:.6f}, epoch {train_time:.2f}ms", | ||
| end="", | ||
| ) | ||
| if e % args.val_freq == 0: | ||
| eval_time, val_loss = eval_fn(loader_val, params, segnn_state) | ||
| avg_time.append(eval_time) | ||
| tag = "" | ||
| if val_loss < best_val: | ||
| best_val = val_loss | ||
| tag = " (best)" | ||
| _, test_loss_ckp = eval_fn(loader_test, params, segnn_state) | ||
| print(f" - val loss {val_loss:.6f}{tag}, infer {eval_time:.2f}ms", end="") | ||
|
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| print() | ||
|
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| test_loss = 0 | ||
| _, test_loss = eval_fn(loader_test, params, segnn_state) | ||
| # ignore compilation time | ||
| avg_time = avg_time[2:] | ||
| avg_time = sum(avg_time) / len(avg_time) | ||
| print( | ||
| "Training done.\n" | ||
| f"Final test loss {test_loss:.6f} - checkpoint test loss {test_loss_ckp:.6f}.\n" | ||
| f"Average (model) eval time {avg_time:.2f}ms" | ||
| ) |
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