Accelerate your hybrid workloads with embedded simulators from PennyLane
Let’s look at how you can use embedded simulators from PennyLane on Amazon Braket Hybrid
Jobs to run hybrid workloads. Pennylane’s GPU-based embedded simulator,
lightning.gpu
, uses the Nvidia cuQuantum librarylightning.gpu
to speed up your hybrid workloads.
Using lightning.gpu
for Quantum
Approximate Optimization Algorithm workloads
Consider the Quantum Approximate Optimization Algorithm (QAOA) examples from this
notebookdevice
argument to be a string of the form:
"local:<provider>/<simulator_name>"
. For example, you would
set "local:pennylane/lightning.gpu"
for lightning.gpu
. The
device string you give to the Hybrid Job when you launch is passed to the job as the
environment variable "AMZN_BRAKET_DEVICE_ARN"
.
device_string = os.environ["AMZN_BRAKET_DEVICE_ARN"] prefix, device_name = device_string.split("/") device = qml.device(simulator_name, wires=n_wires)
In this page, let’s compare the two embedded PennyLane state vector simulators
lightning.qubit
(which is CPU-based) and lightning.gpu
(which is GPU-based). You’ll need to provide the simulators with some custom gate
decompositions in order to compute various gradients.
Now you’re ready to prepare the hybrid job launching script. You’ll run the QAOA algorithm
using two instance types: m5.2xlarge
and p3.2xlarge
. The
m5.2xlarge
instance type is comparable to a standard developer laptop.
The p3.2xlarge
is an accelerated computing instance that has a single
NVIDIA Volta GPU with 16GB of memory.
The hyperparameters
for all your hybrid jobs will be the same. All you need to
do to try out different instances and simulators is change two lines as follows.
# Specify device that the hybrid job will primarily be targeting device = "local:pennylane/lightning.qubit" # Run on a CPU based instance with about as much power as a laptop instance_config = InstanceConfig(instanceType='ml.m5.2xlarge')
or:
# Specify device that the hybrid job will primarily be targeting device = "local:pennylane/lightning.gpu" # Run on an inexpensive GPU based instance instance_config = InstanceConfig(instanceType='ml.p3.2xlarge')
Note
If you specify the instance_config
as using a GPU-based instance, but
choose the device
to be the embedded CPU-based simulator
(lightning.qubit
), the GPU will
not be used. Make sure to use the embedded GPU-based simulator if you
wish to target the GPU!
First, you can create two hybrid jobs and solve Max-Cut with QAOA on a graph with 18
vertices. This translates to an 18-qubit circuit—relatively small and feasible to run
quickly on your laptop or the m5.2xlarge
instance.
num_nodes = 18 num_edges = 24 seed = 1967 graph = nx.gnm_random_graph(num_nodes, num_edges, seed=seed) # And similarly for the p3 job m5_job = AwsQuantumJob.create( device=device, source_module="qaoa_source", job_name="qaoa-m5-" + str(int(time.time())), image_uri=image_uri, # Relative to the source_module entry_point="qaoa_source.qaoa_algorithm_script", copy_checkpoints_from_job=None, instance_config=instance_config, # general parameters hyperparameters=hyperparameters, input_data={"input-graph": input_file_path}, wait_until_complete=True, )
The mean iteration time for the m5.2xlarge
instance is about 25 seconds,
while for the p3.2xlarge
instance it’s about 12 seconds. For this 18-qubit
workflow, the GPU instance gives us a 2x speedup. If you look at the Amazon Braket Hybrid
Jobs pricing
pagem5.2xlarge
instance is $0.00768, while for the p3.2xlarge
instance it’s $0.06375. To
run for 5 total iterations, as you did here, would cost $0.016 using the CPU instance or
$0.06375 using the GPU instance — both pretty inexpensive!
Now let’s make the problem harder, and try solving a Max-Cut problem on a 24-vertex graph, which will translate to 24 qubits. Run the hybrid jobs again on the same two instances and compare the cost.
Note
You’ll see that the time to run this hybrid job on the CPU instance may be about five hours!
num_nodes = 24 num_edges = 36 seed = 1967 graph = nx.gnm_random_graph(num_nodes, num_edges, seed=seed) # And similarly for the p3 job m5_big_job = AwsQuantumJob.create( device=device, source_module="qaoa_source", job_name="qaoa-m5-big-" + str(int(time.time())), image_uri=image_uri, # Relative to the source_module entry_point="qaoa_source.qaoa_algorithm_script", copy_checkpoints_from_job=None, instance_config=instance_config, # general parameters hyperparameters=hyperparameters, input_data={"input-graph": input_file_path}, wait_until_complete=True, )
The mean iteration time for the m5.2xlarge
instance is roughly an hour,
while for the p3.2xlarge
instance it’s roughly two minutes. For this larger
problem, the GPU instance is an order of magnitude faster! All you had to do to benefit
from this speedup was to change two lines of code, swapping out the instance type and
the local simulator used. To run for 5 total iterations, as was done here, would cost
about $2.27072 using the CPU instance or about $0.775625 using the GPU instance. The CPU
usage is not only more expensive, but also takes more time to run. Accelerating this
workflow with a GPU instance available on AWS, using PennyLane’s embedded simulator
backed by NVIDIA CuQuantum, allows you to run workflows with intermediate qubit counts
(between 20 and 30) for less total cost and in less time. This means you can experiment
with quantum computing even for problems that are too big to run quickly on your laptop
or a similarly-sized instance.
Quantum machine learning and data parallelism
If your workload type is quantum machine learning (QML) that trains on datasets, you can further accelerate your workload using data parallelism. In QML, the model contains one or more quantum circuits. The model may or may not also contain classical neural nets. When training the model with the dataset, the parameters in the model are updated to minimize the loss function. A loss function is usually defined for a single data point, and the total loss for the average loss over the whole dataset. In QML, the losses are usually computed in serial before averaging to total loss for gradient computations. This procedure is time consuming, especially when there are hundreds of data points.
Because the loss from one data point does not depend on other data points, the losses can be evaluated in parallel! Losses and gradients associated with different data points can be evaluated at the same time. This is known as data parallelism. With SageMaker’s distributed data parallel library, Amazon Braket Hybrid Jobs make it easier for you to leverage data parallelism to accelerate your training.
Consider the following QML workload for data parallelism which uses the Sonar datasetlightning.gpu
to improve the performance over embedded CPU-based
simulators.
To create a hybrid job, you can call AwsQuantumJob.create
and specify the
algorithm script, device, and other configurations through its keyword arguments.
instance_config = InstanceConfig(instanceType='ml.p3.2xlarge') hyperparameters={"nwires": "10", "ndata": "32", ... } job = AwsQuantumJob.create( device="local:pennylane/lightning.gpu", source_module="qml_source", entry_point="qml_source.train_single", hyperparameters=hyperparameters, instance_config=instance_config, ... )
In order to use data parallelism, you need to modify few lines of code in the
algorithm script for the SageMaker distributed library to correctly parallelize the
training. First, you import the smdistributed
package which does most of
the heavy-lifting for distributing your workloads across multiple GPUs and multiple
instances. This package is preconfigured in the Braket PyTorch and TensorFlow
containers. The dist
module tells our algorithm script what the total
number of GPUs for the training (world_size
) is as well as the
rank
and local_rank
of a GPU core. rank
is the
absolute index of a GPU across all instances, while local_rank
is the index
of a GPU within an instance. For example, if there are four instances each with eight
GPUs allocated for the training, the rank
ranges from 0 to 31 and the
local_rank
ranges from 0 to 7.
import smdistributed.dataparallel.torch.distributed as dist dp_info = { "world_size": dist.get_world_size(), "rank": dist.get_rank(), "local_rank": dist.get_local_rank(), } batch_size //= dp_info["world_size"] // 8 batch_size = max(batch_size, 1)
Next, you define a DistributedSampler
according to the
world_size
and rank
and then pass it into the data loader.
This sampler avoids GPUs accessing the same slice of a dataset.
train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset, num_replicas=dp_info["world_size"], rank=dp_info["rank"] ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True, sampler=train_sampler, )
Next, you use the DistributedDataParallel
class to enable data
parallelism.
from smdistributed.dataparallel.torch.parallel.distributed import DistributedDataParallel as DDP model = DressedQNN(qc_dev).to(device) model = DDP(model) torch.cuda.set_device(dp_info["local_rank"]) model.cuda(dp_info["local_rank"])
The above are the changes you need to use data parallelism. In QML, you often want to
save results and print training progress. If each GPU runs the saving and printing
command, the log will be flooded with the repeated information and the results will
overwrite each other. To avoid this, you can only save and print from the GPU that has
rank
0.
if dp_info["rank"]==0: print('elapsed time: ', elapsed) torch.save(model.state_dict(), f"{output_dir}/test_local.pt") save_job_result({"last loss": loss_before})
Amazon Braket Hybrid Jobs supports ml.p3.16xlarge
instance types for the
SageMaker distributed data parallel library. You configure the instance type through the
InstanceConfig
argument in Hybrid Jobs. For the SageMaker distributed
data parallel library to know that data parallelism is enabled, you need to add two
additional hyperparameters, "sagemaker_distributed_dataparallel_enabled"
setting to "true"
and "sagemaker_instance_type"
setting to the
instance type you are using. These two hyperparameters are used by
smdistributed
package. Your algorithm script does not need to explicitly
use them. In Amazon Braket SDK, it provides a convenient keyword argument
distribution
. With distribution="data_parallel"
in hybrid job
creation, the Amazon Braket SDK automatically inserts the two hyperparameters for you. If
you use the Amazon Braket API, you need to include these two hyperparameters.
With the instance and data parallelism configured, you can now submit your hybrid job. There
are 8 GPUs in a ml.p3.16xlarge
instance. When you set
instanceCount=1
, the workload is distributed across the 8 GPUs in the
instance. When you set instanceCount
greater than one, the workload is
distributed across GPUs available in all instances. When using multiple instances, each
instance incurs a charge based on how much time you use it. For example, when you use
four instances, the billable time is four times the run time per instance because there
are four instances running your workloads at the same time.
instance_config = InstanceConfig(instanceType='ml.p3.16xlarge', instanceCount=1, ) hyperparameters={"nwires": "10", "ndata": "32", ..., } job = AwsQuantumJob.create( device="local:pennylane/lightning.gpu", source_module="qml_source", entry_point="qml_source.train_dp", hyperparameters=hyperparameters, instance_config=instance_config, distribution="data_parallel", ... )
Note
In the above hybrid job creation, train_dp.py
is the modified algorithm
script for using data parallelism. Keep in mind that data parallelism only works
correctly when you modify your algorithm script according to the above section. If
the data parallelism option is enabled without a correctly modified algorithm script,
the hybrid job may throw errors, or each GPU may repeatedly process the same data slice,
which is inefficient.
Let’s compare the run time and cost in an example where when train a model with a
26-qubit quantum circuit for the binary classification problem mentioned above. The
ml.p3.16xlarge
instance used in this example costs $0.4692 per minute.
Without data parallelism, it takes the simulator about 45 minutes to train the model for
1 epoch (i.e., over 208 data points) and it costs about $20. With data parallelism
across 1 instance and 4 instances, it only takes 6 minutes and 1.5 minutes respectively,
which translates to roughly $2.8 for both. By using data parallelism across 4 instances,
you not only improve the run time by 30x, but also reduce costs by an order of
magnitude!