Note
This tutorial can be used interactively with Google Colab! You can also click here to run the Jupyter notebook locally.
Auto-scheduling a Neural Network for x86 CPU¶
Author: Lianmin Zheng, Chengfan Jia
Auto-tuning for specific devices and workloads is critical for getting the best performance. This is a tutorial on how to tune a whole neural network for x86 CPU with the auto-scheduler.
To auto-tune a neural network, we partition the network into small subgraphs and tune them independently. Each subgraph is treated as one search task. A task scheduler slices the time and dynamically allocates time resources to these tasks. The task scheduler predicts the impact of each task on the end-to-end execution time and prioritizes the one that can reduce the execution time the most.
For each subgraph, we use the compute declaration in tvm/python/topi to
get the computational DAG in the tensor expression form.
We then use the auto-scheduler to construct a search space of this DAG and search
for good schedules (low-level optimizations).
Different from the template-based autotvm which relies on
manual templates to define the search space, the auto-scheduler does not require any
schedule templates. In other words, the auto-scheduler only uses the compute declarations
in tvm/python/topi and does not use existing schedule templates.
Note that this tutorial will not run on Windows or recent versions of macOS. To
get it to run, you will need to wrap the body of this tutorial in a if
__name__ == "__main__": block.
import numpy as np
import tvm
from tvm import relay, auto_scheduler
from tvm.relay import data_dep_optimization as ddo
import tvm.relay.testing
from tvm.contrib import graph_executor
Define a Network¶
First, we need to define the network with relay frontend API.
We can load some pre-defined network from tvm.relay.testing.
We can also load models from MXNet, ONNX, PyTorch, and TensorFlow
(see front end tutorials).
For convolutional neural networks, although auto-scheduler can work correctly with any layout, we found the best performance is typically achieved with NHWC layout. We also implemented more optimizations for NHWC layout with the auto-scheduler. So it is recommended to convert your models to NHWC layout to use the auto-scheduler. You can use ConvertLayout pass to do the layout conversion in TVM.
def get_network(name, batch_size, layout="NHWC", dtype="float32", use_sparse=False):
"""Get the symbol definition and random weight of a network"""
# auto-scheduler prefers NHWC layout
if layout == "NHWC":
image_shape = (224, 224, 3)
elif layout == "NCHW":
image_shape = (3, 224, 224)
else:
raise ValueError("Invalid layout: " + layout)
input_shape = (batch_size,) + image_shape
output_shape = (batch_size, 1000)
if name.startswith("resnet-"):
n_layer = int(name.split("-")[1])
mod, params = relay.testing.resnet.get_workload(
num_layers=n_layer,
batch_size=batch_size,
layout=layout,
dtype=dtype,
image_shape=image_shape,
)
elif name.startswith("resnet3d-"):
n_layer = int(name.split("-")[1])
mod, params = relay.testing.resnet.get_workload(
num_layers=n_layer,
batch_size=batch_size,
layout=layout,
dtype=dtype,
image_shape=image_shape,
)
elif name == "mobilenet":
mod, params = relay.testing.mobilenet.get_workload(
batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
)
elif name == "squeezenet_v1.1":
assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
mod, params = relay.testing.squeezenet.get_workload(
version="1.1",
batch_size=batch_size,
dtype=dtype,
image_shape=image_shape,
)
elif name == "inception_v3":
input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3)
mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
elif name == "mxnet":
# an example for mxnet model
from mxnet.gluon.model_zoo.vision import get_model
assert layout == "NCHW"
block = get_model("resnet50_v1", pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
net = mod["main"]
net = relay.Function(
net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
)
mod = tvm.IRModule.from_expr(net)
elif name == "mlp":
mod, params = relay.testing.mlp.get_workload(
batch_size=batch_size, dtype=dtype, image_shape=image_shape, num_classes=1000
)
else:
raise ValueError("Network not found.")
if use_sparse:
from tvm.topi.sparse.utils import convert_model_dense_to_sparse
mod, params = convert_model_dense_to_sparse(mod, params, bs_r=4, random_params=True)
return mod, params, input_shape, output_shape
# Define the neural network and compilation target.
# If the target machine supports avx512 instructions, replace the
# "llvm -mcpu=core-avx2" with "llvm -mcpu=skylake-avx512"
network = "resnet-50"
use_sparse = False
batch_size = 1
layout = "NHWC"
target = tvm.target.Target("llvm -mcpu=core-avx2")
dtype = "float32"
log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
Extract Search Tasks¶
Next, we extract the search tasks and their weights from a network.
The weight of a task is the number of appearances of the task’s subgraph
in the whole network.
By using the weight, we can approximate the end-to-end latency of the network
as sum(latency[t] * weight[t]), where latency[t] is the
latency of a task and weight[t] is the weight of the task.
The task scheduler will just optimize this objective.
# Extract tasks from the network
print("Get model...")
mod, params, input_shape, output_shape = get_network(
network,
batch_size,
layout,
dtype=dtype,
use_sparse=use_sparse,
)
print("Extract tasks...")
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
for idx, task in enumerate(tasks):
print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))
print(task.compute_dag)
Begin Tuning¶
Now, we set some options for tuning and launch the search tasks
num_measure_trialsis the number of measurement trials we can use during the tuning. You can set it to a small number (e.g., 200) for a fast demonstrative run. In practice, we recommend setting it around800 * len(tasks), which is typically enough for the search to converge. For example, there are 29 tasks in resnet-50, so we can set it as 20000. You can adjust this parameter according to your time budget.In addition, we use
RecordToFileto dump measurement records into a log file, The measurement records can be used to query the history best, resume the search, and do more analyses later.see
auto_scheduler.TuningOptions,auto_scheduler.LocalRunnerfor more parameters.
def run_tuning():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200, # change this to 20000 to achieve the best performance
runner=auto_scheduler.LocalRunner(repeat=10, enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
if use_sparse:
from tvm.topi.sparse.utils import sparse_sketch_rules
search_policy = [
auto_scheduler.SketchPolicy(
task,
program_cost_model=auto_scheduler.XGBModel(),
init_search_callbacks=sparse_sketch_rules(),
)
for task in tasks
]
tuner.tune(tune_option, search_policy=search_policy)
else:
tuner.tune(tune_option)
# We do not run the tuning in our webpage server since it takes too long.
# Uncomment the following line to run it by yourself.
# run_tuning()
Note
Explain the printed information during tuning
During the tuning, a lot of information will be printed on the console. They are used for debugging purposes. The most important info is the output of the task scheduler. The following table is a sample output.
----------------------------------------------------------------------
------------------------------ [ Task Scheduler ]
----------------------------------------------------------------------
| ID | Latency (ms) | Speed (GFLOPS) | Trials |
-------------------------------------------------
| 0 | 0.010 | 0.40 | 64 |
| 1 | 0.087 | 47.19 | 64 |
| 2 | 0.008 | -0.00 | 64 |
| 3 | 0.177 | 582.07 | 64 |
| 4 | 0.268 | 862.37 | 256 |
| 5 | 0.166 | 621.13 | 128 |
| 6 | 0.170 | 605.10 | 128 |
| 7 | 0.128 | 403.20 | 64 |
| 8 | 0.189 | 545.71 | 64 |
| 9 | 0.231 | 1001.01 | 448 |
| 10 | 0.155 | 664.80 | 256 |
| 11 | 0.155 | 662.86 | 256 |
| 12 | 0.119 | 434.08 | 64 |
| 13 | 0.199 | 522.13 | 64 |
| 14 | 0.235 | 986.56 | 320 |
| 15 | 0.149 | 689.13 | 128 |
| 16 | 0.155 | 664.80 | 192 |
| 17 | 0.151 | 340.64 | 64 |
| 18 | 0.176 | 597.55 | 128 |
| 19 | 0.220 | 1054.37 | 192 |
| 20 | 0.150 | 686.01 | 128 |
| 21 | 0.159 | 650.88 | 128 |
| 22 | 0.073 | 358.19 | 64 |
| 23 | 0.031 | 70.63 | 64 |
| 24 | 0.251 | 947.73 | 128 |
| 25 | 0.157 | 652.47 | 128 |
| 26 | 0.215 | 954.84 | 128 |
| 27 | 0.237 | 868.92 | 128 |
| 28 | 0.266 | 774.06 | 128 |
-------------------------------------------------
Estimated total latency: 10.016 ms Trials: 3992 Used time : 1131 s Next ID: 15
This table lists the latency and (estimated) speed of all tasks. It also lists the allocation of measurement trials for all tasks. The last line prints the total weighted latency of these tasks, which can be a rough estimation of the end-to-end execution time of the network. The last line also prints the total number of measurement trials, total time spent on auto-tuning and the id of the next task to tune.
There will also be some “tvm::Error”s errors, because the auto-scheduler will try some invalid schedules. You can safely ignore them if the tuning can continue, because these errors are isolated from the main process.
Note
Terminate the tuning earlier
You can terminate the tuning earlier by forcibly killing this process. As long as you get at least one valid schedule for each task in the log file, you should be able to do the compilation (the secion below).
Compile and Evaluate¶
After auto-tuning, we can compile the network with the best schedules we found. All measurement records are dumped into the log file during auto-tuning, so we can read the log file and load the best schedules.
# Compile with the history best
print("Compile...")
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, params=params)
# Create graph executor
dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))
data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
module.set_input("data", data_tvm)
# Evaluate
print("Evaluate inference time cost...")
print(module.benchmark(dev, repeat=3, min_repeat_ms=500))
Other Tips¶
During the tuning, the auto-scheduler needs to compile many programs and extract feature from them. This part is CPU-intensive, so a high-performance CPU with many cores is recommended for faster search.
You can use
python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.jsonto distill the large log file and only save the best useful records.You can resume a search from the previous log file. You just need to add a new argument
load_log_filewhen creating the task scheduler in functionrun_tuning. Say,tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)If you have multiple target CPUs, you can use all of them for measurements to parallelize the measurements. Check this section to learn how to use the RPC Tracker and RPC Server. To use the RPC Tracker in auto-scheduler, replace the runner in
TuningOptionswithauto_scheduler.RPCRunner.