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utils.py
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# Copyright (c) 2022 Graphcore Ltd. All rights reserved.
import logging
import numpy as np
import tensorflow as tf
import wandb
from data_utils.packed_batch_generator import PackedBatchGenerator
from model.gnn.loss import get_loss_functions_gnn, MaskedMeanAbsoluteError
from model.gnn.losses_and_metrics import MaskedMeanAbsoluteError
from model.hybrid.model import create_hybrid
from model.hybrid.utils import enforce_GNN_param_defaults
def create_model_input_dict(micro_batch_size, input_spec):
return {
input_s["input_name"]: tf.keras.Input((input_s["shape"]),
name=input_s["input_name"],
dtype=input_s["model_dtype"],
batch_size=micro_batch_size)
for input_s in input_spec.values()
}
def create_model(batch_generator, dataset, options, input_spec=None):
inputs = create_model_input_dict(options.model.micro_batch_size, input_spec)
logging.info(f"Model inputs: {inputs}")
if options.dataset.input_masking_groups:
# Find the unique set of input features to be masked
masked_features = list(set([x for y in options.dataset.input_masking_groups for x in y]))
else:
masked_features = []
general_options = dict(
n_nodes_per_pack=batch_generator.n_nodes_per_pack,
n_edges_per_pack=batch_generator.n_edges_per_pack,
n_graphs_per_pack=batch_generator.n_graphs_per_pack,
micro_batch_size=options.model.micro_batch_size,
eigv_rand_sign_flip=options.model.eigv_rand_sign_flip,
dataset=dataset,
dataset_name=options.dataset.dataset_name,
weight_dtype=options.layer.weight_dtype,
pipeline_stages=options.ipu_opts.num_pipeline_stages,
num_gaussian_kernels=options.model.num_gaussian_kernels,
gaussian_kernel_init=(options.model.gaussian_kernel_init_low, options.model.gaussian_kernel_init_high),
gaussian_kernel_scale=options.model.gaussian_kernel_scale,
gaussian_kernel_epsilon=options.model.gaussian_kernel_epsilon,
use_distance_sum_feature=options.dataset.use_distance_sum_feature,
masked_features=masked_features,
inputs=inputs)
gnn_options = dict(
node_latent=options.model.node_latent,
node_exp_ratio=options.model.node_exp_ratio,
node_mlp_layers=options.model.node_mlp_layers,
node_dropout=options.model.node_dropout,
node_prenorm=options.model.node_prenorm,
use_edges=options.model.use_edges,
edge_latent=options.model.edge_latent,
edge_exp_ratio=options.model.edge_exp_ratio,
edge_mlp_layers=options.model.edge_mlp_layers,
edge_dropout=options.model.edge_dropout,
edge_prenorm=options.model.edge_prenorm,
edge_dropout_loc=options.model.edge_dropout_loc,
use_globals=options.model.use_globals,
global_latent=options.model.global_latent,
global_exp_ratio=options.model.global_exp_ratio,
global_mlp_layers=options.model.global_mlp_layers,
global_dropout=options.model.global_dropout,
global_prenorm=options.model.global_prenorm,
encoder_latent=options.model.encoder_latent,
encoder_exp_ratio=options.model.encoder_exp_ratio,
encoder_mlp_layers=options.model.encoder_mlp_layers,
encoder_dropout=options.model.encoder_dropout,
encoder_prenorm=options.model.encoder_prenorm,
encoder_norm_pos=options.model.encoder_norm_pos,
encoder_act_fn=options.model.encoder_act_fn,
encoder_atom_model=options.model.atom_encoder_model,
encoder_bond_model=options.model.bond_encoder_model,
encoder_concat_mode=options.model.encoder_concat_mode,
override_encoder_dropout=options.model.override_encoder_dropout,
n_embedding_channels=options.model.n_embedding_channels,
one_hot_embeddings=options.layer.one_hot_embeddings,
expand_first_hidden=options.model.expand_first_hidden,
mlp_norm=options.layer.mlp_norm,
activation_function=options.layer.activation_function,
gather_from=options.layer.gather_from,
scatter_to=options.layer.scatter_to,
concat_globals_to=options.layer.concat_globals_to,
node_combine_method=options.layer.node_combine_method,
aggregators=options.layer.aggregator,
direct_neighbour_aggregation=options.layer.direct_neighbour_aggregation,
rn_multiplier=options.layer.rn_multiplier,
gather_scatter=options.layer.gather_scatter,
decoder_mode=options.layer.decoder_mode,
)
output_options = dict(noisy_nodes=options.model.use_noisy_nodes,
noisy_edges=options.model.use_noisy_edges,
noisy_node_model=options.model.noisy_node_model)
hybrid_options = dict(
ffn_dim=options.model.ffn_dim,
n_attn_heads=options.model.n_attn_heads,
attention_dropout_rate=options.model.attention_dropout_rate,
ffn_dropout_rate=options.model.ffn_dropout_rate,
gnn_output_dropout_rate=options.model.gnn_output_dropout_rate,
mhsa_output_dropout_rate=options.model.mhsa_output_dropout_rate,
ffn_output_dropout_rate=options.model.ffn_output_dropout_rate,
max_shortest_path_distance=options.model.max_shortest_path_distance,
graph_dropout_rate=options.model.graph_dropout_rate,
override_graph_dropout_rate=options.model.override_graph_dropout_rate,
hybrid_mpnn_extra_node_residual=options.model.hybrid_mpnn_extra_node_residual,
)
return create_hybrid(layer_specs=options.model.layer_specs,
layer_repeats=options.model.layer_repeats,
**general_options,
**enforce_GNN_param_defaults(**gnn_options),
**output_options,
**hybrid_options,
dtype=options.model.dtype)
def get_tf_dataset(preprocessed_dataset,
split_name,
shuffle,
options,
pad_remainder=False,
input_spec=None,
ensemble=False):
if split_name == 'train':
if shuffle:
prop_to_use = options.dataset.prop_to_use
else:
prop_to_use = options.dataset.clean_train.prop_to_use
elif split_name == 'valid':
prop_to_use = options.dataset.valid.prop_to_use
else:
prop_to_use = 1.0
batch_generator = PackedBatchGenerator(dataset=preprocessed_dataset,
n_packs_per_batch=options.model.micro_batch_size,
fold=split_name,
n_graphs_per_pack=options.model.n_graphs_per_pack,
n_edges_per_pack=options.model.n_edges_per_pack,
n_nodes_per_pack=options.model.n_nodes_per_pack,
n_epochs=options.model.epochs,
noisy_nodes=options.model.use_noisy_nodes,
noisy_edges=options.model.use_noisy_edges,
noisy_nodes_noise_prob=options.model.noisy_nodes_noise_prob,
noisy_edges_noise_prob=options.model.noisy_edges_noise_prob,
normalize_labels=options.dataset.normalize_labels,
ogbBL_norm=options.dataset.ogbBL_norm,
distance_norm=options.dataset.distance_norm,
packing_strategy=options.dataset.packing_strategy,
input_masking_groups=options.dataset.input_masking_groups,
input_masking_weights=options.dataset.input_masking_weights,
randomize=shuffle,
pad_remainder=pad_remainder,
prop_to_use=prop_to_use,
input_spec=input_spec)
batch_generator.get_averaged_global_batch_size(
options.model.micro_batch_size, options.ipu_opts.gradient_accumulation_factor, options.ipu_opts.replicas)
logging.info(f"Packing stats: {batch_generator.stats}")
ground_truth_and_masks = batch_generator.get_ground_truth_and_masks() if not shuffle else None
return batch_generator, ground_truth_and_masks
def get_loss_functions(dataset, options):
return get_loss_functions_gnn(dataset=dataset,
use_noisy_nodes=options.model.use_noisy_nodes,
use_noisy_edges=options.model.use_noisy_edges,
noisy_nodes_weight=options.model.noisy_nodes_weight,
noisy_edges_weight=options.model.noisy_edges_weight,
noisy_node_method=options.model.noisy_node_method)
def get_metrics(denormalize_fn, options):
if options.dataset.dataset_name in ("pcqm4mv2", "generated", "pcqm4mv2_28features", "pcqm4mv2_conformers",
"pcqm4mv2_conformers_28features"):
def MAE(y_true, y_pred):
if options.dataset.dataset_name in ("pcqm4mv2", "pcqm4mv2_28features", "pcqm4mv2_conformers",
"pcqm4mv2_conformers_28features") and options.dataset.normalize_labels:
return MaskedMeanAbsoluteError(y_true, y_pred, transform=denormalize_fn)
else:
return MaskedMeanAbsoluteError(y_true, y_pred)
metrics = {'Main': MAE}
return metrics
else:
# these need to be updated to masked versions
return [tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.AUC(dtype=tf.float32)]
def check_loaded_weights(model, all_initial_weights):
for layer, initial_weights in zip(model.layers, all_initial_weights):
weights = layer.get_weights()
logging.info(f"Layer name {layer.name}")
logging.info(f"No. of weights in layer: {len(weights)}")
for weight, initial_weight in zip(weights, initial_weights):
if np.array_equal(weight, initial_weight):
logging.warning(f'------Checkpoint does not contain weights for weight {weight.shape}------')
else:
logging.info(f'++++++Checkpoint contains weights for weight {weight.shape}++++++')
def load_checkpoint_into_model(model, ckpt_path, debug=False):
logging.info("Attempting to load checkpoint from" f" path {ckpt_path}.")
all_initial_weights = [layer.get_weights() for layer in model.layers]
model.load_weights(ckpt_path).expect_partial()
if debug:
check_loaded_weights(model, all_initial_weights)