`chemprop.models.model`#

Module Contents#

Classes#

MPNN

An MPNN is a sequence of message passing layers, an aggregation routine, and a

class chemprop.models.model.MPNN(message_passing, agg, predictor, batch_norm=True, metrics=None, warmup_epochs=2, init_lr=0.0001, max_lr=0.001, final_lr=0.0001, X_d_transform=None)[source]#

Bases: lightning.pytorch.LightningModule

An MPNN is a sequence of message passing layers, an aggregation routine, and a predictor routine.

The first two modules calculate learned fingerprints from an input molecule reaction graph, and the final module takes these learned fingerprints as input to calculate a final prediction. I.e., the following operation:

\[\mathtt{MPNN}(\mathcal{G}) = \mathtt{predictor}(\mathtt{agg}(\mathtt{message\_passing}(\mathcal{G})))\]

The full model is trained end-to-end.

Parameters:

message_passing (MessagePassing) – the message passing block to use to calculate learned fingerprints
agg (Aggregation) – the aggregation operation to use during molecule-level predictor
predictor (Predictor) – the function to use to calculate the final prediction
batch_norm (bool, default=True) – if True, apply batch normalization to the output of the aggregation operation
metrics (Iterable[Metric] | None, default=None) – the metrics to use to evaluate the model during training and evaluation
warmup_epochs (int, default=2) – the number of epochs to use for the learning rate warmup
init_lr (int, default=1e-4) – the initial learning rate
max_lr (float, default=1e-3) – the maximum learning rate
final_lr (float, default=1e-4) – the final learning rate
X_d_transform (chemprop.nn.transforms.ScaleTransform | None)

Raises:

ValueError – if the output dimension of the message passing block does not match the input dimension of the predictor function

property output_dim: int#

Return type:: int

property n_tasks: int#

Return type:: int

property n_targets: int#

Return type:: int

property criterion: chemprop.nn.LossFunction#

Return type:: chemprop.nn.LossFunction

fingerprint(bmg, V_d=None, X_d=None)[source]#

the learned fingerprints for the input molecules

Parameters:

bmg (chemprop.data.BatchMolGraph)
V_d (torch.Tensor | None)
X_d (torch.Tensor | None)

Return type:

torch.Tensor

encoding(bmg, V_d=None, X_d=None, i=-1)[source]#

Calculate the i-th hidden representation

Parameters:

bmg (chemprop.data.BatchMolGraph)
V_d (torch.Tensor | None)
X_d (torch.Tensor | None)
i (int)

Return type:

torch.Tensor

forward(bmg, V_d=None, X_d=None)[source]#

Generate predictions for the input molecules/reactions

Parameters:

bmg (chemprop.data.BatchMolGraph)
V_d (torch.Tensor | None)
X_d (torch.Tensor | None)

Return type:

torch.Tensor

training_step(batch, batch_idx)[source]#

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Parameters:

batch (chemprop.data.TrainingBatch) – The output of your data iterable, normally a DataLoader.
batch_idx – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Tensor - The loss tensor
dict - A dictionary which can include any keys, but must include the key 'loss' in the case of automatic optimization.
None - In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

def __init__(self):
    super().__init__()
    self.automatic_optimization = False


# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx):
    opt1, opt2 = self.optimizers()

    # do training_step with encoder
    ...
    opt1.step()
    # do training_step with decoder
    ...
    opt2.step()

Note

When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

on_validation_model_eval()[source]#

Called when the validation loop starts.

The validation loop by default calls .eval() on the LightningModule before it starts. Override this hook to change the behavior. See also on_validation_model_train().

Return type:: None

validation_step(batch, batch_idx=0)[source]#

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Parameters:

batch (chemprop.data.TrainingBatch) – The output of your data iterable, normally a DataLoader.
batch_idx (int) – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx): ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to validate you don’t need to implement this method.

Note

When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

test_step(batch, batch_idx=0)[source]#

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Parameters:

batch (chemprop.data.TrainingBatch) – The output of your data iterable, normally a DataLoader.
batch_idx (int) – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx): ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to test you don’t need to implement this method.

Note

When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

predict_step(batch, batch_idx, dataloader_idx=0)[source]#

Return the predictions of the input batch

Parameters:

batch (TrainingBatch) – the input batch
batch_idx (int)
dataloader_idx (int)

Returns:

a tensor of varying shape depending on the task type:

regression/binary classification: n x (t * s), where n is the number of input

molecules/reactions, t is the number of tasks, and s is the number of targets per task. The final dimension is flattened, so that the targets for each task are grouped. I.e., the first t elements are the first target for each task, the second t elements the second target, etc. * multiclass classification: n x t x c, where c is the number of classes

Return type:

Tensor

configure_optimizers()[source]#

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.

Returns:

Any of these 6 options.

Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).
Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.
None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

# The ReduceLROnPlateau scheduler requires a monitor
def configure_optimizers(self):
    optimizer = Adam(...)
    return {
        "optimizer": optimizer,
        "lr_scheduler": {
            "scheduler": ReduceLROnPlateau(optimizer, ...),
            "monitor": "metric_to_track",
            "frequency": "indicates how often the metric is updated",
            # If "monitor" references validation metrics, then "frequency" should be set to a
            # multiple of "trainer.check_val_every_n_epoch".
        },
    }


# In the case of two optimizers, only one using the ReduceLROnPlateau scheduler
def configure_optimizers(self):
    optimizer1 = Adam(...)
    optimizer2 = SGD(...)
    scheduler1 = ReduceLROnPlateau(optimizer1, ...)
    scheduler2 = LambdaLR(optimizer2, ...)
    return (
        {
            "optimizer": optimizer1,
            "lr_scheduler": {
                "scheduler": scheduler1,
                "monitor": "metric_to_track",
            },
        },
        {"optimizer": optimizer2, "lr_scheduler": scheduler2},
    )

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note

Some things to know:

Lightning calls .backward() and .step() automatically in case of automatic optimization.
If a learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.
If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.
If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.
If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the optimizer_step() hook.

classmethod load_submodules(checkpoint_path, **kwargs)[source]#

classmethod load_from_checkpoint(checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs)[source]#

Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to __init__ in the checkpoint under "hyper_parameters".

Any arguments specified through **kwargs will override args stored in "hyper_parameters".

Parameters:

checkpoint_path – Path to checkpoint. This can also be a URL, or file-like object
map_location – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().
hparams_file –
Optional path to a .yaml or .csv file with hierarchical structure as in this example:
```
drop_prob: 0.2
dataloader:
    batch_size: 32
```
You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningModule for use.

If your model’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your model to treat hparams as dict.
strict – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict. Defaults to True unless LightningModule.strict_loading is set, in which case it defaults to the value of LightningModule.strict_loading.
**kwargs – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Returns:

LightningModule instance with loaded weights and hyperparameters (if available).

Return type:

MPNN

Note

load_from_checkpoint is a class method. You should use your LightningModule class to call it instead of the LightningModule instance, or a TypeError will be raised.

Note

To ensure all layers can be loaded from the checkpoint, this function will call configure_model() directly after instantiating the model if this hook is overridden in your LightningModule. However, note that load_from_checkpoint does not support loading sharded checkpoints, and you may run out of memory if the model is too large. In this case, consider loading through the Trainer via .fit(ckpt_path=...).

Example:

# load weights without mapping ...
model = MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')

# or load weights mapping all weights from GPU 1 to GPU 0 ...
map_location = {'cuda:1':'cuda:0'}
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    map_location=map_location
)

# or load weights and hyperparameters from separate files.
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    hparams_file='/path/to/hparams_file.yaml'
)

# override some of the params with new values
model = MyLightningModule.load_from_checkpoint(
    PATH,
    num_layers=128,
    pretrained_ckpt_path=NEW_PATH,
)

# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)

classmethod load_from_file(model_path, map_location=None, strict=True)[source]#

Return type:: MPNN

chemprop.models.model

Contents

chemprop.models.model#

Module Contents#

Classes#

`chemprop.models.model`#