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import os
from typing import Callable, Dict, List, Tuple, Union
import lightning as L
import torch
import typeguard
from functorch.dim import tree_map
from jaxtyping import Float, jaxtyped
from lightning.pytorch.utilities import grad_norm
from pdeinvbench.utils.pytorch_utils import compute_grad_norm
from lightning.pytorch.loggers import WandbLogger
from pdeinvbench.data.utils import unnormalize_params
from pdeinvbench.utils.logging_utils import get_best_model_weights
from copy import deepcopy
import time
from pdeinvbench.losses import (
get_param_metric,
get_pde_residual_function,
pde_residual_reduction,
)
from pdeinvbench.losses.metrics import (
classification_metrics_darcy_flow,
param_relative_loss,
)
from pdeinvbench.models.inverse_model import InverseModel
from pdeinvbench.utils import pytorch_utils as ptu
from pdeinvbench.utils.types import (
PDE,
DataMetrics,
ParamMetrics,
TypeBatch,
TypeBatch1D,
TypeBatch2D,
TypeLossDict,
TypeParam,
TypePredict1D,
TypePredict2D,
)
class InverseModule(L.LightningModule):
"""
Inverse Problem Module. Takes a set of conditioning frames from a PDE trajectory and predicts the value of the PDE parameter(s).
:param model: Model.
:param optimizer: Optimizer.
:param lr_scheduler: Learning rate scheduler.
:param pde: enum of the PDE to use for the residual calculation.
:param param_loss_metric: Metric to use for the parameter loss.
:param inverse_residual_loss_weight: Weight for the PDE residual loss obtained from the predicted parameters.
:param n_past: Number of past frames to condition on.
:param use_partials: whether to append the partial derivatives to the input.
:param params_to_predict: list of strings of parameters to predict.
:param residual_filter: Whether to use residual filtering.
:param batch_size: Batch size.
"""
def __init__(
self,
model: InverseModel,
optimizer: torch.optim.Optimizer,
lr_scheduler: torch.optim.lr_scheduler,
pde: PDE,
param_loss_metric: ParamMetrics,
inverse_residual_loss_weight: float,
inverse_param_loss_weight: float,
n_past: int,
use_partials: bool,
params_to_predict: List[str],
residual_filter: bool,
batch_size: int,
):
super().__init__()
self.save_hyperparameters(ignore=["model"])
self.model = model
self.old_inverse_model = None
self.new_inverse_model = None
self.pde_residual = get_pde_residual_function(pde)
self.pde = pde
self.param_loss_metric = get_param_metric(param_loss_metric)
self.inverse_residual_loss_weight = inverse_residual_loss_weight
self.inverse_param_loss_weight = inverse_param_loss_weight
self.validation_step_outputs = []
self.validation_step_targets = []
"""
Loss dicts for validation. Each element is a tuple of (losses, pde_params).
PDE_params comes directly from the dataloader.
Loss: Dict[str, torch.Tensor] with keys (shape)
'data_loss' (B), 'residual_loss' (B), 'loss' (), 'data_loss_per_batch_element' (B), 'residual_per_batch_element' (B).
"""
self.validation_step_loss_dicts = []
self.n_past = n_past
self.use_partials = use_partials
self.params_to_predict = params_to_predict
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.residual_filter = residual_filter
self.batch_size = batch_size
@jaxtyped(typechecker=typeguard.typechecked)
def predict_with_loss(
self,
batch: Union[TypeBatch1D, TypeBatch2D],
stage: str,
gumbel: bool = False,
) -> Tuple[TypeParam, TypeParam, Dict[str, Union[torch.tensor, TypeBatch]]]:
"""
Common method which computes a prediction of the PDE parameter from the conditioning frames and returns the loss.
:param batch: Batch of data.
:param stage: Which of "train", "validation", "test","tailoring" is the current stage.
"""
_, _, initial_frames, _, _, _, _ = batch
start_time = time.time()
# To fix batching issue. Sometimes the input batch_size is less than the defined batch_size.
# This causes logging issues downstream during logging. This snippet repeats the final batch element
# to match the specified input batch_size.
num_repeat_elements = 0
if initial_frames.shape[0] < self.batch_size:
num_repeat_elements = self.batch_size - initial_frames.shape[0]
batch = tree_map(
lambda x: torch.cat(
[x] + [x[-1].unsqueeze(0) for _ in range(num_repeat_elements)]
),
batch,
)
spatial_grid, t, initial_frames, _, _, _, pde_params = batch
forward_pass_start_time = time.time()
predicted_pde_params, residual, true_residual = self.model(
initial_frames, pde_params, spatial_grid, t, gumbel=gumbel
)
forward_pass_end_time = time.time()
forward_pass_time = forward_pass_end_time - forward_pass_start_time
if self.residual_filter:
ids = [
true_residual.reshape(true_residual.shape[0], -1).max(dim=1)[0] < 100
]
residual = residual[ids]
true_residual = true_residual[ids]
for key in pde_params:
predicted_pde_params[key] = predicted_pde_params[key][ids]
pde_params[key] = pde_params[key][ids]
residual_loss = pde_residual_reduction(
residual
) # Convert residual tensor to float loss
true_residual_loss = pde_residual_reduction(true_residual)
param_losses = self.param_loss_metric(
predicted_pde_params,
pde_params,
reduction="mean",
)
relative_param_losses = param_relative_loss(
{param: prediction for param, prediction in predicted_pde_params.items()},
pde_params,
reduction="mean",
)
# To avoid scenario where the averaged loss is computed with zero errors corresponding to params
# that are not predicted for darcy flow.
backprop_losses: list[torch.Tensor]
if self.pde == PDE.DarcyFlow2D and stage != "tailoring":
backprop_losses = [v for v in param_losses.values() if v.item() != 0]
else:
backprop_losses = list(param_losses.values())
param_loss = torch.stack(backprop_losses).mean()
individual_param_losses = {
f"param_loss_{k}": v for k, v in param_losses.items()
}
individual_relative_param_losses = {
f"relative_param_loss_{k}": v for k, v in relative_param_losses.items()
}
weighted_param_loss = self.inverse_param_loss_weight * param_loss
weighted_residual_loss = self.inverse_residual_loss_weight * residual_loss
loss = weighted_param_loss + weighted_residual_loss
losses = {
"param_loss": param_loss,
"residual_loss": residual_loss,
"true_residual_loss": true_residual_loss,
"loss": loss,
**individual_param_losses,
**individual_relative_param_losses,
}
if self.pde == PDE.DarcyFlow2D:
# Additional metrics for darcy flow to be consistent with PINO
darcy_losses: dict[str, float] = classification_metrics_darcy_flow(
predicted_coeff=predicted_pde_params["coeff"],
true_coeff=pde_params["coeff"],
)
losses.update(darcy_losses)
# In the case of validation, we want to handle some additional metrics
if "validation" in stage or "test" in stage or "tailoring" in stage:
# we want to stratify the losses based on PDE parameter
# Stratification happens in the plotting callback - this performs bookkeeping.
param_loss_per_batch_element, residual_per_batch_element = (
self.stratify_losses(
predicted_pde_params,
pde_params,
residual,
)
)
losses["param_loss_per_batch_element"] = param_loss_per_batch_element
losses["residual_per_batch_element"] = residual_per_batch_element
# reset batch to original size by removing the repeated last element in orginal batch
if num_repeat_elements > 0:
predicted_pde_params = tree_map(
lambda x: x[: -1 * num_repeat_elements], predicted_pde_params
)
pde_params = tree_map(lambda x: x[: -1 * num_repeat_elements], pde_params)
losses = tree_map(
lambda x: x[: -1 * num_repeat_elements] if x.numel() > 1 else x, losses
)
end_time = time.time()
losses["timing_metrics"] = {
"predict_with_loss_time": end_time - start_time,
"forward_pass_time": forward_pass_time,
}
return predicted_pde_params, pde_params, losses
@jaxtyped(typechecker=typeguard.typechecked)
def training_step(
self,
batch: Union[TypeBatch1D, TypeBatch2D],
batch_idx: int,
) -> TypeLossDict:
stage = "train"
_, _, losses = self.predict_with_loss(batch, stage="train")
self.log_metrics(stage, losses)
return losses
@jaxtyped(typechecker=typeguard.typechecked)
def validation_step(
self,
batch: Union[TypeBatch1D, TypeBatch2D],
batch_idx: int,
dataloader_idx: int = 0,
stage: str = None,
) -> Union[None, TypeLossDict]:
stage = "validation" if stage == None else stage
prediction, target, losses = self.predict_with_loss(batch, stage=stage)
#### Bookkeeping for plotting. See logging_callbacks.py for plotting. #####
# NOTE: If we dont convert to numpy, there will be an OOM exception
if self.pde == PDE.DarcyFlow2D:
for k in prediction.keys():
prediction[k] = prediction[k].cpu().numpy()
target[k] = target[k].cpu().numpy()
self.validation_step_outputs.append(prediction)
self.validation_step_targets.append(target)
self.validation_step_loss_dicts.append(
(
ptu.torch_dict_to_numpy(losses),
ptu.torch_dict_to_numpy(batch[-1]),
)
)
else:
self.validation_step_outputs.append(prediction)
self.validation_step_targets.append(target)
self.validation_step_loss_dicts.append((losses, batch[-1]))
losses["predictions"] = prediction
losses["targets"] = target
self.log_metrics(stage, losses)
return losses
@jaxtyped(typechecker=typeguard.typechecked)
def test_step(
self,
batch: Union[TypeBatch1D, TypeBatch2D],
batch_idx: int,
dataloader_idx: int = 0,
) -> TypeLossDict:
stage = "test"
self.model.train()
test_losses = self.validation_step(batch, batch_idx, dataloader_idx, stage)
return test_losses
def log_metrics(self, stage, losses, prefix_dir=""):
if "validation" in stage or "test" in stage:
on_step = False
on_epoch = True
else:
on_step = True
on_epoch = False
main_loss_type = "param_loss"
self.log(
os.path.join(stage, prefix_dir, main_loss_type),
losses["param_loss"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
for param in self.params_to_predict:
self.log(
os.path.join(stage, prefix_dir, f"{main_loss_type}_{param}"),
losses[f"param_loss_{param}"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, f"relative_param_loss_{param}"),
losses[f"relative_param_loss_{param}"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, "residual_loss"),
losses["residual_loss"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, "true_residual_loss"),
losses["true_residual_loss"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
if isinstance(self.model, InverseModel):
# Check for darcy inverse model
if self.pde == PDE.DarcyFlow2D:
self.log(
os.path.join(
stage, prefix_dir, "darcy_flow_classification_accuracy"
),
losses["darcy_flow_classification_accuracy"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, "darcy_flow_classification_iou"),
losses["darcy_flow_classification_iou"],
prog_bar=True,
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, "loss"),
losses["loss"],
on_step=on_step,
on_epoch=on_epoch,
)
if "timing_metrics" in losses:
self.log(
os.path.join(stage, prefix_dir, "predict_with_loss_time"),
losses["timing_metrics"]["predict_with_loss_time"],
on_step=on_step,
on_epoch=on_epoch,
)
self.log(
os.path.join(stage, prefix_dir, "forward_pass_time"),
losses["timing_metrics"]["forward_pass_time"],
on_step=on_step,
on_epoch=on_epoch,
)
def configure_optimizers(self):
optimizer = self.optimizer
lr_scheduler = self.lr_scheduler
return [optimizer], [lr_scheduler]
def on_before_optimizer_step(self, optimizer):
# logging the sum of the l2 norms of both the forward and inverse model
self.old_inverse_model = torch.cat(
[p.flatten() for p in self.model.parameters()]
)
inverse_norms = grad_norm(self.model, 2)
inverse_total_norm = sum(inverse_norms.values())
self.log(
f"outer_loss_grad_norms/inverse_model",
inverse_total_norm,
on_step=True,
)
def optimizer_step(self, *args, **kwargs):
super().optimizer_step(*args, **kwargs)
self.new_inverse_model = torch.cat(
[p.flatten() for p in self.model.parameters()]
)
param_diff_norm = torch.norm(self.new_inverse_model - self.old_inverse_model)
original_param_norm = torch.norm(self.old_inverse_model)
rel_diff = param_diff_norm / original_param_norm
self.log(
"outer_loss_grad_norms/callback_inverse_param_rel_diff",
rel_diff,
on_step=True,
)
def stratify_losses(self, predicted_pde_params, pde_params, residual):
# Compute the per trajectory param loss & residual
param_loss_per_batch_element = self.param_loss_metric(
predicted_pde_params, pde_params, reduction="none"
)
# Edge case where we instead sum the error for darcy flow
if self.pde == PDE.DarcyFlow2D:
coeff = param_loss_per_batch_element["coeff"]
# In the case we use a relative error, we don't need to do this
if len(coeff.shape) == 4:
coeff = torch.flatten(coeff, start_dim=2).sum(axis=2)
param_loss_per_batch_element["coeff"] = coeff
param_loss_per_batch_element = (
torch.stack(list(param_loss_per_batch_element.values()))
.squeeze(-1)
.mean(dim=0)
)
residual_per_batch_element = pde_residual_reduction(
residual, dim=tuple(range(1, residual.dim()))
)
return param_loss_per_batch_element, residual_per_batch_element
def on_test_epoch_start(self):
super().on_test_epoch_start()
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