Merge remote-tracking branch 'refs/remotes/fork_lenz/develop' into qu…

…ickfix_acsd

docs/source/advanced_usage/predictions.rst

@@ -81,11 +81,13 @@ Gaussian representation of atomic positions. In this algorithm, most of the
 computational overhead of the total energy calculation is offloaded to the
 computation of this Gaussian representation. This calculation is realized via
 LAMMPS and can therefore be GPU accelerated (parallelized) in the same fashion
-as the bispectrum descriptor calculation. Simply activate this option via
+as the bispectrum descriptor calculation. If a GPU is activated (and LAMMPS
+is available), this option will be used by default. It can also manually be
+activated via
 
     .. code-block:: python
 
-        parameters.descriptors.use_atomic_density_energy_formula = True
+        parameters.use_atomic_density_formula = True
 
 The Gaussian representation algorithm is describe in
 the publication `Predicting electronic structures at any length scale with machine learning <doi.org/10.1038/s41524-023-01070-z>`_.

docs/source/advanced_usage/trainingmodel.rst

@@ -194,22 +194,64 @@ keyword, you can fine-tune the number of new snapshots being created.
 By default, the same number of snapshots as had been provided will be created
 (if possible).
 
-Using tensorboard
-******************
+Logging metrics during training
+*******************************
+
+Training progress in MALA can be visualized via tensorboard or wandb, as also shown
+in the file ``advanced/ex03_tensor_board``. Simply select a logger prior to training as
+
+      .. code-block:: python
+
+            parameters.running.logger = "tensorboard"
+            parameters.running.logging_dir = "mala_vis"
 
-Training routines in MALA can be visualized via tensorboard, as also shown
-in the file ``advanced/ex03_tensor_board``. Simply enable tensorboard
-visualization prior to training via
+or
 
       .. code-block:: python
 
-            # 0: No visualizatuon, 1: loss and learning rate, 2: like 1,
-            # but additionally weights and biases are saved
-            parameters.running.logging = 1
+            import wandb
+            wandb.init(
+                  project="mala_training",
+                  entity="your_wandb_entity"
+            )
+            parameters.running.logger = "wandb"
             parameters.running.logging_dir = "mala_vis"
 
 where ``logging_dir`` specifies some directory in which to save the
-MALA logging data. Afterwards, you can run the training without any
+MALA logging data. You can also select which metrics to record via
+
+      .. code-block:: python
+
+            parameters.validation_metrics = ["ldos", "dos", "density", "total_energy"]
+
+Full list of available metrics:
+      - "ldos": MSE of the LDOS.
+      - "band_energy": Band energy.
+      - "band_energy_actual_fe": Band energy computed with ground truth Fermi energy.
+      - "total_energy": Total energy.
+      - "total_energy_actual_fe": Total energy computed with ground truth Fermi energy.
+      - "fermi_energy": Fermi energy.
+      - "density": Electron density.
+      - "density_relative": Rlectron density (Mean Absolute Percentage Error).
+      - "dos": Density of states.
+      - "dos_relative": Density of states (Mean Absolute Percentage Error).
+
+To save time and resources you can specify the logging interval via
+
+      .. code-block:: python
+
+            parameters.running.validate_every_n_epochs = 10
+
+If you want to monitor the degree to which the model overfits to the training data,
+you can use the option
+
+      .. code-block:: python
+            
+            parameters.running.validate_on_training_data = True
+
+MALA will evaluate the validation metrics on the training set as well as the validation set.
+
+Afterwards, you can run the training without any
 other modifications. Once training is finished (or during training, in case
 you want to use tensorboard to monitor progress), you can launch tensorboard
 via
@@ -221,6 +263,7 @@ via
 The full path for ``path_to_log_directory`` can be accessed via
 ``trainer.full_logging_path``.
 
+If you're using wandb, you can monitor the training progress on the wandb website.
 
 Training in parallel
 ********************

docs/source/basic_usage/trainingmodel.rst

@@ -28,7 +28,7 @@ options to train a simple network with example data, namely
             parameters = mala.Parameters()
 
             parameters.data.input_rescaling_type = "feature-wise-standard"
-            parameters.data.output_rescaling_type = "normal"
+            parameters.data.output_rescaling_type = "minmax"
 
             parameters.network.layer_activations = ["ReLU"]
 
@@ -43,15 +43,18 @@ sub-objects dealing with the individual aspects of the workflow. In the first
 two lines, which data scaling MALA should employ. Scaling data greatly
 improves the performance of NN based ML models. Options are
 
-* ``None``: No normalization is applied.
+* ``None``: No scaling is applied.
 
-* ``standard``: Standardization (Scale to mean 0, standard deviation 1)
+* ``standard``: Standardization (Scale to mean 0, standard deviation 1) is
+  applied to the entire array.
 
-* ``normal``: Min-Max scaling (Scale to be in range 0...1)
+* ``minmax``: Min-Max scaling (Scale to be in range 0...1) is applied to the entire array.
 
-* ``feature-wise-standard``: Row Standardization (Scale to mean 0, standard deviation 1)
+* ``feature-wise-standard``: Standardization (Scale to mean 0, standard
+  deviation 1) is applied to each feature dimension individually.
 
-* ``feature-wise-normal``: Row Min-Max scaling (Scale to be in range 0...1)
+* ``feature-wise-minmax``: Min-Max scaling (Scale to be in range 0...1) is
+  applied to each feature dimension individually.
 
 Here, we specify that MALA should standardize the input (=descriptors)
 by feature (i.e., each entry of the vector separately on the grid) and

docs/source/conf.py

@@ -72,7 +72,6 @@
     "scipy",
     "oapackage",
     "matplotlib",
-    "horovod",
     "lammps",
     "total_energy",
     "pqkmeans",

examples/advanced/ex01_checkpoint_training.py

@@ -21,7 +21,7 @@ def initial_setup():
     parameters = mala.Parameters()
     parameters.data.data_splitting_type = "by_snapshot"
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.network.layer_activations = ["ReLU"]
     parameters.running.max_number_epochs = 9
     parameters.running.mini_batch_size = 8

examples/advanced/ex03_tensor_board.py

@@ -13,7 +13,7 @@
 
 parameters = mala.Parameters()
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 parameters.targets.ldos_gridsize = 11
 parameters.targets.ldos_gridspacing_ev = 2.5
 parameters.targets.ldos_gridoffset_ev = -5
@@ -32,11 +32,19 @@
 
 data_handler = mala.DataHandler(parameters)
 data_handler.add_snapshot(
-    "Be_snapshot0.in.npy", data_path, "Be_snapshot0.out.npy", data_path, "tr",
+    "Be_snapshot0.in.npy",
+    data_path,
+    "Be_snapshot0.out.npy",
+    data_path,
+    "tr",
     calculation_output_file=os.path.join(data_path, "Be_snapshot0.out"),
 )
 data_handler.add_snapshot(
-    "Be_snapshot1.in.npy", data_path, "Be_snapshot1.out.npy", data_path, "va",
+    "Be_snapshot1.in.npy",
+    data_path,
+    "Be_snapshot1.out.npy",
+    data_path,
+    "va",
     calculation_output_file=os.path.join(data_path, "Be_snapshot1.out"),
 )
 data_handler.prepare_data()

examples/advanced/ex05_checkpoint_hyperparameter_optimization.py

@@ -17,7 +17,7 @@
 def initial_setup():
     parameters = mala.Parameters()
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.running.max_number_epochs = 10
     parameters.running.mini_batch_size = 40
     parameters.running.learning_rate = 0.00001

examples/advanced/ex06_distributed_hyperparameter_optimization.py

@@ -24,7 +24,7 @@
 parameters = mala.Parameters()
 # Specify the data scaling.
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 parameters.running.max_number_epochs = 5
 parameters.running.mini_batch_size = 40
 parameters.running.learning_rate = 0.00001

examples/advanced/ex07_advanced_hyperparameter_optimization.py

@@ -17,7 +17,7 @@ def optimize_hyperparameters(hyper_optimizer):
 
     parameters = mala.Parameters()
     parameters.data.input_rescaling_type = "feature-wise-standard"
-    parameters.data.output_rescaling_type = "normal"
+    parameters.data.output_rescaling_type = "minmax"
     parameters.running.max_number_epochs = 10
     parameters.running.mini_batch_size = 40
     parameters.running.learning_rate = 0.00001

examples/advanced/ex10_convert_numpy_openpmd.py

@@ -0,0 +1,98 @@
+import mala
+
+from mala.datahandling.data_repo import data_path
+import os
+
+parameters = mala.Parameters()
+parameters.descriptors.descriptors_contain_xyz = False
+
+# First, convert from Numpy files to openPMD.
+
+data_converter = mala.DataConverter(parameters)
+
+for snapshot in range(2):
+    data_converter.add_snapshot(
+        descriptor_input_type="numpy",
+        descriptor_input_path=os.path.join(
+            data_path, "Be_snapshot{}.in.npy".format(snapshot)
+        ),
+        target_input_type="numpy",
+        target_input_path=os.path.join(
+            data_path, "Be_snapshot{}.out.npy".format(snapshot)
+        ),
+        additional_info_input_type=None,
+        additional_info_input_path=None,
+        target_units=None,
+    )
+
+data_converter.convert_snapshots(
+    descriptor_save_path="./",
+    target_save_path="./",
+    additional_info_save_path="./",
+    naming_scheme="converted_from_numpy_*.h5",
+    descriptor_calculation_kwargs={"working_directory": "./"},
+)
+
+# Convert those files back to Numpy to verify the data stays the same.
+
+data_converter = mala.DataConverter(parameters)
+
+for snapshot in range(2):
+    data_converter.add_snapshot(
+        descriptor_input_type="openpmd",
+        descriptor_input_path="converted_from_numpy_{}.in.h5".format(snapshot),
+        target_input_type="openpmd",
+        target_input_path="converted_from_numpy_{}.out.h5".format(snapshot),
+        additional_info_input_type=None,
+        additional_info_input_path=None,
+        target_units=None,
+    )
+
+data_converter.convert_snapshots(
+    descriptor_save_path="./",
+    target_save_path="./",
+    additional_info_save_path="./",
+    naming_scheme="verify_against_original_numpy_data_*.npy",
+    descriptor_calculation_kwargs={"working_directory": "./"},
+)
+
+for snapshot in range(2):
+    for i_o in ["in", "out"]:
+        original = os.path.join(
+            data_path, "Be_snapshot{}.{}.npy".format(snapshot, i_o)
+        )
+        roundtrip = "verify_against_original_numpy_data_{}.{}.npy".format(
+            snapshot, i_o
+        )
+        import numpy as np
+
+        original_a = np.load(original)
+        roundtrip_a = np.load(roundtrip)
+        np.testing.assert_allclose(original_a, roundtrip_a)
+
+# Now, convert some openPMD data back to Numpy.
+
+data_converter = mala.DataConverter(parameters)
+
+for snapshot in range(2):
+    data_converter.add_snapshot(
+        descriptor_input_type="openpmd",
+        descriptor_input_path=os.path.join(
+            data_path, "Be_snapshot{}.in.h5".format(snapshot)
+        ),
+        target_input_type="openpmd",
+        target_input_path=os.path.join(
+            data_path, "Be_snapshot{}.out.h5".format(snapshot)
+        ),
+        additional_info_input_type=None,
+        additional_info_input_path=None,
+        target_units=None,
+    )
+
+data_converter.convert_snapshots(
+    descriptor_save_path="./",
+    target_save_path="./",
+    additional_info_save_path="./",
+    naming_scheme="converted_from_openpmd_*.npy",
+    descriptor_calculation_kwargs={"working_directory": "./"},
+)

examples/basic/ex01_train_network.py

@@ -20,7 +20,7 @@
 # Specify the data scaling. For regular bispectrum and LDOS data,
 # these have proven successful.
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 # Specify the used activation function.
 parameters.network.layer_activations = ["ReLU"]
 # Specify the training parameters.

examples/basic/ex04_hyperparameter_optimization.py

@@ -19,7 +19,7 @@
 ####################
 parameters = mala.Parameters()
 parameters.data.input_rescaling_type = "feature-wise-standard"
-parameters.data.output_rescaling_type = "normal"
+parameters.data.output_rescaling_type = "minmax"
 parameters.running.max_number_epochs = 20
 parameters.running.mini_batch_size = 40
 parameters.running.optimizer = "Adam"

external_modules/total_energy_module/total_energy.f90

@@ -11,7 +11,8 @@ SUBROUTINE initialize(file_name, y_planes_in, calculate_eigts_in)
   USE mp_global,         ONLY : mp_startup
   USE mp,                ONLY : mp_size
   USE read_input,        ONLY : read_input_file
-  USE command_line_options, ONLY: input_file_, command_line, ndiag_, nyfft_
+  USE command_line_options, ONLY: input_file_, command_line, ndiag_, nyfft_, &
+          pencil_decomposition_
   !
   IMPLICIT NONE
   CHARACTER(len=256) :: srvaddress
@@ -37,9 +38,9 @@ SUBROUTINE initialize(file_name, y_planes_in, calculate_eigts_in)
   IF (PRESENT(y_planes_in)) THEN
     IF (y_planes_in > 1) THEN
       nyfft_ = y_planes_in
+      pencil_decomposition_ = .true.
     ENDIF
   ENDIF
-
   !! checks if first string is contained in the second
   !
   CALL mp_startup ( start_images=.true., images_only=.true.)

mala/common/parallelizer.py

@@ -5,7 +5,6 @@
 import os
 import warnings
 
-import torch
 import torch.distributed as dist
 
 use_ddp = False
@@ -154,6 +153,11 @@ def get_local_rank():
     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
     FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
     DEALINGS IN THE SOFTWARE.
+
+    Returns
+    -------
+    local_rank : int
+        The local rank of the current thread.
     """
     if use_ddp:
         return int(os.environ.get("LOCAL_RANK"))
@@ -189,15 +193,14 @@ def get_size():
         return comm.Get_size()
 
 
-# TODO: This is hacky, improve it.
 def get_comm():
     """
     Return the MPI communicator, if MPI is being used.
 
     Returns
     -------
     comm : MPI.COMM_WORLD
-        A MPI communicator.
+        An MPI communicator.
 
     """
     return comm
@@ -221,7 +224,7 @@ def printout(*values, sep=" ", min_verbosity=0):
 
     Parameters
     ----------
-    values
+    values : object
         Values to be printed.
 
     sep : string
@@ -245,7 +248,7 @@ def parallel_warn(warning, min_verbosity=0, category=UserWarning):
 
     Parameters
     ----------
-    warning
+    warning : str
         Warning to be printed.
     min_verbosity : int
         Minimum number of verbosity for this output to still be printed.