memory profiling

modelforge/tests/test_profiling.py

@@ -0,0 +1,56 @@
+import torch
+import pytest
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_profiling_function():
+    from modelforge.tests.helper_functions import setup_potential_for_test
+    import torch
+    from modelforge.utils.profiling import (
+        start_record_memory_history,
+        export_memory_snapshot,
+        stop_record_memory_history,
+        setup_waterbox_testsystem,
+    )
+
+    # define the potential, device and precision
+    potential_name = "AimNet2"
+    precision = torch.float32
+    device = "cuda"
+
+    # setup the input and model
+    nnp_input = setup_waterbox_testsystem(2.5, device=device, precision=precision)
+    model = setup_potential_for_test(
+        potential_name,
+        "inference",
+        potential_seed=42,
+        use_training_mode_neighborlist=True,
+        simulation_environment="PyTorch",
+    ).to(device, precision)
+    # Disable gradients for model parameters
+    for param in model.parameters():
+        param.requires_grad = False
+    # Set model to eval
+    model.eval()
+
+    # this is the function that will be profiled
+    def loop_to_record():
+        for _ in range(5):
+            # perform the forward pass through each of the models
+            r = model(nnp_input)["per_system_energy"]
+            # Compute the gradient (forces) from the predicted energies
+            grad = torch.autograd.grad(
+                r,
+                nnp_input.positions,
+                grad_outputs=torch.ones_like(r),
+                create_graph=False,
+                retain_graph=False,
+            )[0]
+
+    # Start recording memory snapshot history
+    start_record_memory_history()
+    loop_to_record()
+    # Create the memory snapshot file
+    export_memory_snapshot()
+    # Stop recording memory snapshot history
+    stop_record_memory_history()

modelforge/utils/io.py

@@ -159,6 +159,14 @@
     conda install conda-forge::wandb
     
 """
+MESSAGES[
+    "openmmtools"
+] = """
+A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.
+OpenMMTools can be installed via conda:
+    conda install conda-forge::openmmtools
+    
+"""
 
 
 def import_(module: str):

modelforge/utils/profiling.py

@@ -0,0 +1,279 @@
+import torch
+from loguru import logger as log
+import socket
+from datetime import datetime
+from modelforge.dataset.dataset import NNPInput
+
+TIME_FORMAT_STR: str = "%b_%d_%H_%M_%S"
+MAX_NUM_OF_MEM_EVENTS_PER_SNAPSHOT: int = 100000
+
+
+def setup_waterbox_testsystem(
+    edge_size_in_nm: float,
+    device: torch.device,
+    precision: torch.dtype,
+) -> NNPInput:
+    from modelforge.utils.io import import_
+
+    openmmtools = import_("openmmtools")
+    from simtk import unit
+    from modelforge.dataset.dataset import NNPInput
+
+    test_system = openmmtools.testsystems.WaterBox(
+        box_edge=edge_size_in_nm * unit.nanometer
+    )
+    positions = test_system.positions  # Positions in nanometers
+    topology = test_system.topology
+
+    # Extract atomic numbers and residue indices
+    atomic_numbers = []
+    residue_indices = []
+    for residue_index, residue in enumerate(topology.residues()):
+        for atom in residue.atoms():
+            atomic_numbers.append(atom.element.atomic_number)
+            residue_indices.append(residue_index)
+    num_waters = len(list(topology.residues()))
+    positions_in_nanometers = positions.value_in_unit(unit.nanometer)
+
+    # Convert to torch tensors and move to GPU
+    torch_atomic_numbers = torch.tensor(atomic_numbers, dtype=torch.long, device=device)
+    torch_positions = torch.tensor(
+        positions_in_nanometers, dtype=torch.float32, device=device, requires_grad=True
+    )
+    torch_atomic_subsystem_indices = torch.zeros_like(
+        torch_atomic_numbers, dtype=torch.long, device=device
+    )
+    torch_total_charge = torch.zeros((1, 1), dtype=torch.float32, device=device)
+
+    log.info(f"Waterbox system setup with {num_waters} waters")
+    return NNPInput(
+        atomic_numbers=torch_atomic_numbers,
+        positions=torch_positions,
+        atomic_subsystem_indices=torch_atomic_subsystem_indices,
+        per_system_total_charge=torch_total_charge,
+    ).to_dtype(dtype=precision)
+
+
+from typing import List
+import time
+
+
+def measure_performance_for_edge_sizes(
+    edge_sizes: List[float],
+    potential_names: List[str],
+):
+    """
+    Measures GPU memory utilization and computation time for force calculations
+    for water boxes of different edge sizes across multiple potentials.
+    Parameters
+    ----------
+    edge_sizes : List[float]
+        A list of edge sizes (in nanometers) for the water boxes.
+    potential_names : List[str]
+        A list of potential names to use in the model setup.
+    Returns
+    -------
+    List[dict]
+        A list of dictionaries containing edge size, number of water molecules,
+        potential name, memory usage in bytes, and computation time in seconds.
+    """
+    results = []
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    precicion = torch.float32
+    for potential_name in potential_names:
+        for edge_size in edge_sizes:
+
+            nnp_input = setup_waterbox_testsystem(
+                edge_size,
+                device,
+                precicion,
+            )
+
+            # Import your model setup function
+            from modelforge.tests.helper_functions import setup_potential_for_test
+
+            # Setup model
+            model = setup_potential_for_test(
+                potential_name,
+                "inference",
+                potential_seed=42,
+                use_training_mode_neighborlist=False,
+                simulation_environment="PyTorch",
+            )
+
+            model.to(device)
+            model.to(precicion)
+            total_params = sum(p.numel() for p in model.parameters())
+
+            # Measure GPU memory usage and computation time
+            torch.cuda.reset_peak_memory_stats(device=device)
+            torch.cuda.synchronize()
+
+            # Run forward pass and time it
+            start_time = time.perf_counter()
+            try:
+                output = model(nnp_input.as_namedtuple())["per_molecule_energy"]
+            except:
+                print("Out of memory error during forward pass")
+                continue
+
+            try:
+                F_training = -torch.autograd.grad(
+                    output.sum(),
+                    nnp_input.positions,
+                    create_graph=False,
+                    retain_graph=False,
+                )[0]
+            except:
+                print("Out of memory error during backward pass")
+                continue
+            torch.cuda.synchronize()
+            end_time = time.perf_counter()
+
+            max_memory_allocated = torch.cuda.max_memory_allocated(device=device)
+            computation_time = end_time - start_time
+
+            results.append(
+                {
+                    "potential_name": f"{potential_name}: {total_params:.1e} params",
+                    "edge_size_nm": edge_size,
+                    "num_waters": num_waters,
+                    "memory_usage_bytes": max_memory_allocated,
+                    "computation_time_s": computation_time,
+                }
+            )
+
+            # Clean up
+            del (
+                nnp_input,
+                output,
+                model,
+            )
+            try:
+                del F_training
+            except:
+                pass
+            torch.cuda.empty_cache()
+            time.sleep(1)  # Sleep for a second to allow GPU memory to be freed
+
+    return results
+
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+def plot_computation_time(results):
+    """
+    Plots computation time against the number of water molecules for multiple potentials.
+    Parameters
+    ----------
+    results : List[dict]
+        A list of dictionaries containing edge size, number of water molecules,
+        potential name, memory usage in bytes, and computation time in seconds.
+    """
+    # Create a DataFrame for plotting
+    df = pd.DataFrame(results)
+    df["computation_time_ms"] = (
+        df["computation_time_s"] * 1000
+    )  # Convert seconds to milliseconds
+
+    # Plot using seaborn
+    sns.set(style="whitegrid")
+    plt.figure(figsize=(10, 6))
+    sns.lineplot(
+        data=df,
+        x="num_waters",
+        y="computation_time_ms",
+        hue="potential_name",
+        units="potential_name",
+        estimator=None,  # Do not aggregate data
+        marker="o",
+        linewidth=2,
+        markersize=8,
+    )
+    plt.title("Computation Time vs Number of Water Molecules for Different Potentials")
+    plt.xlabel("Number of Water Molecules")
+    plt.ylabel("Computation Time (ms)")
+    plt.xticks(sorted(df["num_waters"].unique()))
+    plt.legend(title="Potential Name")
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_gpu_memory_usage(results):
+    """
+    Plots GPU memory usage against the number of water molecules for multiple potentials.
+    Parameters
+    ----------
+    results : List[dict]
+        A list of dictionaries containing edge size, number of water molecules,
+        potential name, and memory usage in bytes.
+    """
+    # Create a DataFrame for plotting
+    df = pd.DataFrame(results)
+    df["memory_usage_mb"] = df["memory_usage_bytes"] / 1e6  # Convert bytes to megabytes
+
+    # Plot using seaborn
+    sns.set(style="whitegrid")
+    plt.figure(figsize=(10, 6))
+    sns.lineplot(
+        data=df,
+        x="num_waters",
+        y="memory_usage_mb",
+        units="potential_name",
+        estimator=None,  # Do not aggregate data
+        hue="potential_name",
+        marker="o",
+        linewidth=2,
+        markersize=8,
+    )
+    plt.title(
+        "Backward pass: GPU Memory Usage vs Number of Water Molecules for Different Potentials"
+    )
+    plt.xlabel("Number of Water Molecules")
+    plt.ylabel("GPU Memory Usage (MB)")
+    plt.xticks(sorted(df["num_waters"].unique()))
+    plt.legend(title="Potential Name")
+    plt.tight_layout()
+    plt.show()
+
+
+def start_record_memory_history() -> None:
+    if not torch.cuda.is_available():
+        log.info("CUDA unavailable. Not recording memory history")
+        return
+
+    log.info("Starting snapshot record_memory_history")
+    torch.cuda.memory._record_memory_history(
+        max_entries=MAX_NUM_OF_MEM_EVENTS_PER_SNAPSHOT
+    )
+
+
+def stop_record_memory_history() -> None:
+    if not torch.cuda.is_available():
+        log.info("CUDA unavailable. Not stopping memory history")
+        return
+
+    log.info("Stopping snapshot record_memory_history")
+    torch.cuda.memory._record_memory_history(enabled=None)
+
+
+def export_memory_snapshot() -> None:
+    if not torch.cuda.is_available():
+        log.info("CUDA unavailable. Not exporting memory snapshot")
+        return
+
+    # Prefix for file names.
+    host_name = socket.gethostname()
+    timestamp = datetime.now().strftime(TIME_FORMAT_STR)
+    file_prefix = f"{host_name}_{timestamp}"
+
+    try:
+        log.info(f"Saving snapshot to local file: {file_prefix}.pickle")
+        torch.cuda.memory._dump_snapshot(f"{file_prefix}.pickle")
+    except Exception as e:
+        log.error(f"Failed to capture memory snapshot {e}")
+        return
+    return f"{file_prefix}.pickle"