Note
Go to the end to download the full example code.
Profiling your models¶
Do you feel like your model is too slow? Do you want to make it faster? Instead of guessing which part of the code is responsible for any slowdown, you should profile your code to learn how much time is spent in each function and where to focus any optimization efforts.
In this tutorial you’ll learn how to profile your model using PyTorch profiler, how to read the output of the profiler, and how to add your own labels for new functions/steps in your model forward function.
from typing import Dict, List, Optional
import ase.build
import matplotlib.pyplot as plt
import numpy as np
import torch
from metatensor.torch import Labels, TensorBlock, TensorMap
from metatensor.torch.atomistic import (
MetatensorAtomisticModel,
ModelCapabilities,
ModelMetadata,
ModelOutput,
System,
)
from metatensor.torch.atomistic.ase_calculator import MetatensorCalculator
When profiling your code, it is important to run the model on a representative system to ensure you are actually exercising the behavior of your model at the right scale. Here we’ll use a relatively large system with many atoms.
primitive = ase.build.bulk(name="C", crystalstructure="diamond", a=3.567)
atoms = ase.build.make_supercell(primitive, 10 * np.eye(3))
print(f"We have {len(atoms)} atoms in our system")
We have 2000 atoms in our system
We will use the same HarmonicModel as in the previous tutorial as our machine learning potential.
Click to see the definition of HarmonicModel
class HarmonicModel(torch.nn.Module):
def __init__(self, force_constant: float, equilibrium_positions: torch.Tensor):
"""Create an ``HarmonicModel``.
:param force_constant: force constant, in ``energy unit / (length unit)^2``
:param equilibrium_positions: torch tensor with shape ``n x 3``, containing the
equilibrium positions of all atoms
"""
super().__init__()
assert force_constant > 0
self.force_constant = force_constant
self.equilibrium_positions = equilibrium_positions
def forward(
self,
systems: List[System],
outputs: Dict[str, ModelOutput],
selected_atoms: Optional[Labels],
) -> Dict[str, TensorMap]:
if list(outputs.keys()) != ["energy"]:
raise ValueError(
"this model can only compute 'energy', but `outputs` contains other "
f"keys: {', '.join(outputs.keys())}"
)
# we don't want to worry about selected_atoms yet
if selected_atoms is not None:
raise NotImplementedError("selected_atoms is not implemented")
if outputs["energy"].per_atom:
raise NotImplementedError("per atom energy is not implemented")
# compute the energy for each system by adding together the energy for each atom
energy = torch.zeros((len(systems), 1), dtype=systems[0].positions.dtype)
for i, system in enumerate(systems):
assert len(system) == self.equilibrium_positions.shape[0]
r0 = self.equilibrium_positions
energy[i] += torch.sum(self.force_constant * (system.positions - r0) ** 2)
# add metadata to the output
block = TensorBlock(
values=energy,
samples=Labels("system", torch.arange(len(systems)).reshape(-1, 1)),
components=[],
properties=Labels("energy", torch.tensor([[0]])),
)
return {
"energy": TensorMap(keys=Labels("_", torch.tensor([[0]])), blocks=[block])
}
model = HarmonicModel(
force_constant=3.14159265358979323846,
equilibrium_positions=torch.tensor(atoms.positions),
)
capabilities = ModelCapabilities(
outputs={
"energy": ModelOutput(quantity="energy", unit="eV", per_atom=False),
},
atomic_types=[6],
interaction_range=0.0,
length_unit="Angstrom",
supported_devices=["cpu"],
dtype="float32",
)
metadata = ModelMetadata()
wrapper = MetatensorAtomisticModel(model.eval(), metadata, capabilities)
wrapper.export("exported-model.pt")
/home/runner/work/metatensor/metatensor/python/examples/atomistic/4-profiling.py:128: DeprecationWarning: `export()` is deprecated, use `save()` instead
wrapper.export("exported-model.pt")
If you are trying to profile your own model, you can start here and create a
MetatensorCalculator with your own model.
atoms.calc = MetatensorCalculator("exported-model.pt")
Before trying to profile the code, it is a good idea to run it a couple of times to allow torch to warmup internally.
atoms.get_forces()
atoms.get_potential_energy()
np.float64(3.770593615115558e-09)
Profiling energy calculation¶
Now we can run code using torch.profiler.profile() to collect statistic on
how long each function takes to run. We randomize the positions to force ASE to
recompute the energy of the system
atoms.positions += np.random.rand(*atoms.positions.shape)
with torch.profiler.profile() as energy_profiler:
atoms.get_potential_energy()
print(energy_profiler.key_averages().table(sort_by="self_cpu_time_total", row_limit=10))
-------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
-------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
ASECalculator::compute_neighbors 21.41% 290.356us 24.93% 338.085us 338.085us 1
ASECalculator::prepare_inputs 20.80% 282.050us 28.81% 390.682us 390.682us 1
ASECalculator::sum_energies 8.44% 114.435us 8.44% 114.435us 114.435us 1
Model::forward 5.93% 80.460us 17.01% 230.683us 230.683us 1
ASECalculator::convert_outputs 4.11% 55.726us 5.44% 73.749us 73.749us 1
aten::copy_ 3.48% 47.250us 3.48% 47.250us 3.635us 13
aten::sort 2.91% 39.394us 5.31% 71.996us 71.996us 1
aten::_to_copy 2.15% 29.148us 5.51% 74.662us 6.787us 11
aten::arange 1.95% 26.459us 3.95% 53.591us 13.398us 4
forward 1.88% 25.530us 31.05% 421.050us 421.050us 1
-------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.356ms
There are a couple of interesting things to see here. First the total runtime of the
code is shown in the bottom; and then the most costly functions are visible on top,
one line per function. For each function, Self CPU refers to the time spent in
this function excluding any called functions; and CPU total refers to the time
spent in this function, including called functions.
For more options to record operations and display the output, please refer to the official documentation for PyTorch profiler.
Here, Model::forward indicates the time taken by your model’s forward().
Anything starting with aten:: comes from operations on torch tensors, typically
with the same function name as the corresponding torch functions (e.g.
aten::arange is torch.arange()). We can also see some internal functions
from metatensor, with the name staring with MetatensorAtomisticModel:: for
MetatensorAtomisticModel; and ASECalculator:: for
ase_calculator.MetatensorCalculator.
If you want to see more details on the internal steps taken by your model, you can add
torch.profiler.record_function()
(https://pytorch.org/docs/stable/generated/torch.autograd.profiler.record_function.html)
inside your model code to give names to different steps in the calculation. This is
how we are internally adding names such as Model::forward or
ASECalculator::prepare_inputs above.
Profiling forces calculation¶
Let’s now do the same, but computing the forces for this system. This mean we should
now see some time spent in the backward() function, on top of everything else.
atoms.positions += np.random.rand(*atoms.positions.shape)
with torch.profiler.profile() as forces_profiler:
atoms.get_forces()
print(forces_profiler.key_averages().table(sort_by="self_cpu_time_total", row_limit=10))
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
torch::jit::(anonymous namespace)::DifferentiableGra... 27.65% 750.067us 31.12% 844.133us 844.133us 1
Model::forward 15.58% 422.666us 19.88% 539.172us 539.172us 1
ASECalculator::prepare_inputs 12.00% 325.353us 18.35% 497.714us 497.714us 1
ASECalculator::convert_outputs 6.06% 164.290us 8.06% 218.640us 218.640us 1
ASECalculator::run_backward 5.59% 151.504us 42.46% 1.152ms 1.152ms 1
aten::mm 3.93% 106.481us 3.96% 107.302us 26.825us 4
ASECalculator::sum_energies 2.68% 72.817us 2.68% 72.817us 72.817us 1
aten::copy_ 2.52% 68.328us 2.52% 68.328us 2.971us 23
ASECalculator::compute_neighbors 2.36% 64.000us 4.39% 119.103us 119.103us 1
aten::mul 1.60% 43.451us 2.07% 56.215us 14.054us 4
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 2.712ms
Let’s visualize this data in an other way:
events = forces_profiler.key_averages()
events = sorted(events, key=lambda u: u.self_cpu_time_total, reverse=True)
total_cpu_time = sum(map(lambda u: u.self_cpu_time_total, events))
bottom = 0.0
for event in events:
self_time = event.self_cpu_time_total
name = event.key
if len(name) > 30:
name = name[:12] + "[...]" + name[-12:]
if self_time > 0.03 * total_cpu_time:
plt.bar(0, self_time, bottom=bottom, label=name)
bottom += self_time
else:
plt.bar(0, total_cpu_time - bottom, bottom=bottom, label="others")
break
plt.legend()
plt.xticks([])
plt.xlim(0, 1)
plt.ylabel("self time / µs")
plt.show()
Total running time of the script: (0 minutes 0.236 seconds)