merge changes from development

conda_requirements.txt

@@ -8,3 +8,4 @@ ase
 h5py
 tqdm
 python-graphviz
+lightning

docs/source/conf.py

@@ -19,7 +19,7 @@
 
 project = "hippynn"
 copyright = "2019, Los Alamos National Laboratory"
-author = "Nicholas Lubbers"
+author = "Nicholas Lubbers et al"
 
 # The full version, including alpha/beta/rc tags
 import hippynn
@@ -47,7 +47,7 @@
 }
 
 # The following are highly optional, so we mock them for doc purposes.
-autodoc_mock_imports = ["pyanitools", "seqm", "schnetpack", "cupy", "lammps", "numba"]
+autodoc_mock_imports = ["pyanitools", "seqm", "schnetpack", "cupy", "lammps", "numba", "triton", "pytorch_lightning"]
 
 
 # -- Options for HTML output -------------------------------------------------

docs/source/installation.rst

@@ -10,16 +10,18 @@ Requirements:
     * Python_ >= 3.9
     * pytorch_ >= 1.9
     * numpy_
+
 Optional Dependencies:
     * triton_ (recommended, for improved GPU performance)
     * numba_ (recommended for improved CPU performance)
-    * cupy_ (Alternative for accelerating GPU performance)
-    * ASE_ (for usage with ase)
+    * cupy_ (alternative for accelerating GPU performance)
+    * ASE_ (for usage with ase and other misc. features)
     * matplotlib_ (for plotting)
     * tqdm_ (for progress bars)
-    * graphviz_ (for viewing model graphs as figures)
+    * graphviz_ (for visualizing model graphs)
     * h5py_ (for loading ani-h5 datasets)
     * pyanitools_ (for loading ani-h5 datasets)
+    * pytorch-lightning_ (for distributed training)
 
 Interfacing codes:
     * ASE_
@@ -40,7 +42,7 @@ Interfacing codes:
 .. _ASE: https://wiki.fysik.dtu.dk/ase/
 .. _LAMMPS: https://www.lammps.org/
 .. _PYSEQM: https://github.com/lanl/PYSEQM
-
+.. _pytorch-lightning: https://github.com/Lightning-AI/pytorch-lightning
 
 Installation Instructions
 ^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -67,9 +69,6 @@ Clone the hippynn_ repository and navigate into it, e.g.::
 
 .. _hippynn: https://github.com/lanl/hippynn/
 
-.. note::
-  If you wish to do a cpu-only install, you may need to comment
-  out ``cupy`` from the conda_requirements.txt file.
 
 Dependencies using conda
 ........................
@@ -78,6 +77,10 @@ Install dependencies from conda using recommended channels::
 
     $ conda install -c pytorch -c conda-forge --file conda_requirements.txt
 
+.. note::
+  If you wish to do a cpu-only install, you may need to comment
+  out ``cupy`` from the conda_requirements.txt file.
+
 Dependencies using pip
 .......................
 

docs/source/user_guide/settings.rst

@@ -31,7 +31,7 @@ The following settings are available:
      - Dynamic
    * - PROGRESS
      - Progress bars function during training, evaluation, and prediction
-     - tqdm, none
+     - tqdm, none, or floating point string specifying default update rate in seconds (default 1).
      - tqdm
      - Yes, but assign this to a generator-wrapper such as ``tqdm.tqdm``, or with a python ``None`` to disable. The wrapper must accept ``tqdm`` arguments, although it technically doesn't have to do anything with them.
    * - DEFAULT_PLOT_FILETYPE

examples/barebones_lightning.py

@@ -0,0 +1,102 @@
+'''
+To obtain the data files needed for this example, use the script process_QM7_data.py,
+also located in this folder. The script contains further instructions for use.
+'''
+
+import torch
+
+# Setup pytorch things
+torch.set_default_dtype(torch.float32)
+
+import hippynn
+
+netname = "TEST_BAREBONES_LIGHTNING_SCRIPT"
+
+# Hyperparameters for the network
+# These are set deliberately small so that you can easily run the example on a laptop or similar.
+network_params = {
+    "possible_species": [0, 1, 6, 7, 8, 16],  # Z values of the elements in QM7
+    "n_features": 20,  # Number of neurons at each layer
+    "n_sensitivities": 20,  # Number of sensitivity functions in an interaction layer
+    "dist_soft_min": 1.6,  # qm7 is in Bohr!
+    "dist_soft_max": 10.0,
+    "dist_hard_max": 12.5,
+    "n_interaction_layers": 2,  # Number of interaction blocks
+    "n_atom_layers": 3,  # Number of atom layers in an interaction block
+}
+
+# Define a model
+from hippynn.graphs import inputs, networks, targets, physics
+
+species = inputs.SpeciesNode(db_name="Z")
+positions = inputs.PositionsNode(db_name="R")
+
+network = networks.Hipnn("hipnn_model", (species, positions), module_kwargs=network_params)
+henergy = targets.HEnergyNode("HEnergy", network, db_name="T")
+# hierarchicality = henergy.hierarchicality
+
+# define loss quantities
+from hippynn.graphs import loss
+
+mse_energy = loss.MSELoss.of_node(henergy)
+mae_energy = loss.MAELoss.of_node(henergy)
+rmse_energy = mse_energy ** (1 / 2)
+
+# Validation losses are what we check on the data between epochs -- we can only train to
+# a single loss, but we can check other metrics too to better understand how the model is training.
+# There will also be plots of these things over time when training completes.
+validation_losses = {
+    "RMSE": rmse_energy,
+    "MAE": mae_energy,
+    "MSE": mse_energy,
+}
+
+# This piece of code glues the stuff together as a pytorch model,
+# dropping things that are irrelevant for the losses defined.
+training_modules, db_info = hippynn.experiment.assemble_for_training(mse_energy, validation_losses)
+
+# Go to a directory for the model.
+# hippynn will save training files in the current working directory.
+with hippynn.tools.active_directory(netname):
+    # Log the output of python to `training_log.txt`
+    with hippynn.tools.log_terminal("training_log.txt", "wt"):
+        database = hippynn.databases.DirectoryDatabase(
+            name="data-qm7",  # Prefix for arrays in the directory
+            directory="../../../datasets/qm7_processed",
+            test_size=0.1,  # Fraction or number of samples to test on
+            valid_size=0.1,  # Fraction or number of samples to validate on
+            seed=2001,  # Random seed for splitting data
+            **db_info,  # Adds the inputs and targets db_names from the model as things to load
+            dataloader_kwargs=dict(persistent_workers=True,multiprocessing_context='fork'),
+            num_workers=2,
+        )
+
+        # Now that we have a database and a model, we can
+        # Fit the non-interacting energies by examining the database.
+        # This tends to stabilize training a lot.
+        from hippynn.pretraining import hierarchical_energy_initialization
+
+        hierarchical_energy_initialization(henergy, database, trainable_after=False)
+
+        # Parameters describing the training procedure.
+        from hippynn.experiment import setup_and_train
+
+        experiment_params = hippynn.experiment.SetupParams(
+            stopping_key="MSE",  # The name in the validation_losses dictionary.
+            batch_size=12,
+            optimizer=torch.optim.Adam,
+            max_epochs=100,
+            learning_rate=0.001,
+        )
+        # setup_and_train(
+        #     training_modules=training_modules,
+        #     database=database,
+        #     setup_params=experiment_params,
+        # )
+        from hippynn.experiment import HippynnLightningModule
+
+# lightning needs to run exactly where the script is located in distributed modes.
+lightmod, datamodule = HippynnLightningModule.from_experiment_setup(training_modules, database, experiment_params)
+import pytorch_lightning as pl
+trainer = pl.Trainer(accelerator='cpu') #'auto' detects MPS which doesn't work.
+trainer.fit(model=lightmod, datamodule=datamodule)

hippynn/_settings_setup.py

@@ -29,16 +29,22 @@
         TQDM_PROGRESS = None
 
 if TQDM_PROGRESS is not None:
-    TQDM_PROGRESS = partial(TQDM_PROGRESS, mininterval=1.0, leave=False)
-
+    DEFAULT_PROGRESS = partial(TQDM_PROGRESS, mininterval=1.0, leave=False)
+else:
+    DEFAULT_PROGRESS = None
 ### Progress handlers
 
-
 def progress_handler(prog_str):
     if prog_str == "tqdm":
-        return TQDM_PROGRESS
-    if prog_str.lower() == "none":
+        return DEFAULT_PROGRESS
+    elif prog_str.lower() == "none":
         return None
+    else:
+        try:
+            prog_float = float(prog_str)
+            return partial(TQDM_PROGRESS, mininterval=prog_float, leave=False)
+        except:
+            pass
     warnings.warn(f"Unrecognized progress setting: '{prog_str}'. Setting to none.")
 
 
@@ -63,7 +69,7 @@ def kernel_handler(kernel_string):
 
 # keys: defaults, types, and handlers
 default_settings = {
-    "PROGRESS": (TQDM_PROGRESS, progress_handler),
+    "PROGRESS": (DEFAULT_PROGRESS, progress_handler),
     "DEFAULT_PLOT_FILETYPE": (".pdf", str),
     "TRANSPARENT_PLOT": (False, strtobool),
     "DEBUG_LOSS_BROADCAST": (False, strtobool),
@@ -85,11 +91,16 @@ def kernel_handler(kernel_string):
 config_sources = {}  # Dictionary of configuration variable sources mapping to dictionary of configuration.
 # We add to this dictionary in order of application
 
+SECTION_NAME = "GLOBALS"
+
 rc_name = os.path.expanduser("~/.hippynnrc")
 if os.path.exists(rc_name) and os.path.isfile(rc_name):
     config = configparser.ConfigParser(inline_comment_prefixes="#")
     config.read(rc_name)
-    config_sources["~/.hippynnrc"] = config["GLOBALS"]
+    if SECTION_NAME not in config:
+        warnings.warn(f"Config file {rc_name} does not contain a {SECTION_NAME} section and will be ignored!")
+    else:
+        config_sources["~/.hippynnrc"] = config[SECTION_NAME]
 
 SETTING_PREFIX = "HIPPYNN_"
 hippynn_environment_variables = {
@@ -103,7 +114,10 @@ def kernel_handler(kernel_string):
     if os.path.exists(local_rc_fname) and os.path.isfile(local_rc_fname):
         local_config = configparser.ConfigParser()
         local_config.read(local_rc_fname)
-        config_sources[LOCAL_RC_FILE_KEY] = local_config["GLOBALS"]
+        if SECTION_NAME not in local_config:
+            warnings.warn(f"Config file {local_rc_fname} does not contain a {SECTION_NAME} section and will be ignored!")
+        else:
+            config_sources[LOCAL_RC_FILE_KEY] = local_config[SECTION_NAME]
     else:
         warnings.warn(f"Local configuration file {local_rc_fname} not found.")
 

hippynn/custom_kernels/tensor_wrapper.py

@@ -38,8 +38,8 @@ def _numba_gpu_not_found(*args, **kwargs):
 class NumbaCompatibleTensorFunction:
     def __init__(self):
         if numba.cuda.is_available():
-            self.kernel64 = self.make_kernel(numba.float64)
-            self.kernel32 = self.make_kernel(numba.float32)
+            self.kernel64 = None
+            self.kernel32 = None
         else:
             self.kernel64 = _numba_gpu_not_found
             self.kernel32 = _numba_gpu_not_found
@@ -59,8 +59,12 @@ def __call__(self, *args, **kwargs):
             with numba.cuda.gpus[dev.index]:
                 numba_args = batch_convert_torch_to_numba(*args)
                 if dtype == torch.float64:
+                    if self.kernel64 is None:
+                        self.kernel64 = self.make_kernel(numba.float64)
                     self.kernel64[launch_bounds](*numba_args)
                 elif dtype == torch.float32:
+                    if self.kernel32 is None:
+                        self.kernel32 = self.make_kernel(numba.float32)
                     self.kernel32[launch_bounds](*numba_args)
                 else:
                     raise ValueError("Bad dtype: {}".format(dtype))

hippynn/custom_kernels/test_env_numba.py

@@ -122,6 +122,7 @@ def get_simulated_data(n_molecules, n_atoms, atom_prob, n_features, n_nu, printi
 TEST_LARGE_PARAMS = dict(n_molecules=1000, n_atoms=30, atom_prob=0.7, n_features=80, n_nu=20)
 TEST_MEGA_PARAMS = dict(n_molecules=500, n_atoms=30, atom_prob=0.7, n_features=128, n_nu=100)
 TEST_ULTRA_PARAMS = dict(n_molecules=500, n_atoms=30, atom_prob=0.7, n_features=128, n_nu=320)
+TEST_GIGA_PARAMS = dict(n_molecules=32, n_atoms=30, atom_prob=0.7, n_features=512, n_nu=320)
 
 # reference implementation
 
@@ -434,6 +435,12 @@ def main(env_impl, sense_impl, feat_impl, args=None):
 
         if use_verylarge_gpu:
             if use_ultra:
+
+                print("-" * 80)
+                print("Giga systems:", TEST_GIGA_PARAMS)
+                tester.check_speed(
+                    n_repetitions=20, data_size=TEST_GIGA_PARAMS, device=torch.device("cuda"), compare_against=compare_against
+                )
                 print("-" * 80)
                 print("Ultra systems:", TEST_ULTRA_PARAMS)
                 tester.check_speed(

hippynn/databases/__init__.py

@@ -12,16 +12,25 @@
 from .database import Database
 from .ondisk import DirectoryDatabase, NPZDatabase
 has_ase = False
+has_h5 = False
+
 try: 
     import ase
     has_ase = True
+    import h5py
+    has_h5 = True
 except ImportError:
     pass
 
 if has_ase:
    from ..interfaces.ase_interface import AseDatabase
+   if has_h5:
+       from .h5_pyanitools import PyAniFileDB, PyAniDirectoryDB
 
 all_list = ["Database", "DirectoryDatabase", "NPZDatabase"]
+
 if has_ase:
     all_list += ["AseDatabase"]
+    if has_h5:
+        all_list += ["PyAniFileDB", "PyAniDirectoryDB"]
 __all__ = all_list