Hands-on Practical: Auto-Tuning a ResNet Block

Optimizing a deep neural network requires moving from theoretical search spaces to concrete implementation. The principles of automated search and cost modeling are applied to a standard ResNet bottleneck block. We utilize the modern generation-based search approach, often referred to as auto-scheduling (specifically the Ansor system within TVM), rather than template-based auto-tuning. This approach allows the compiler to automatically generate search spaces for complex subgraphs involving operator fusion, rather than relying on manually written schedule templates.

Defining the Workload

A ResNet block contains multiple convolution layers, batch normalizations, and activation functions, along with a residual addition. When lowering this to a hardware target, the compiler views this as a subgraph. Our goal is to identify the optimal loop schedule for this entire subgraph, allowing the compiler to perform aggressive operator fusion and tiling.

We begin by defining the network layout using a high-level Intermediate Representation (Relay). While we focus on a specific shape typical of ResNet-50 (Input: $1 \times 64 \times 56 \times 56$), the methodology applies to any tensor computation.

import tvm
from tvm import relay, auto_scheduler
import numpy as np

def get_resnet_block(batch_size=1, dtype="float32"):
    data_shape = (batch_size, 64, 56, 56)
    data = relay.var("data", shape=data_shape, dtype=dtype)
    weight1 = relay.var("weight1", shape=(64, 64, 3, 3), dtype=dtype)

    # Convolution -> BatchNorm -> ReLU
    conv1 = relay.nn.conv2d(data, weight1, padding=(1, 1), kernel_size=(3, 3))
    bn1 = relay.nn.batch_norm(conv1, relay.var("gamma"), relay.var("beta"), 
                              relay.var("mean"), relay.var("var"))[0]
    relu1 = relay.nn.relu(bn1)

    # Simulating the residual add (simplified for demonstration)
    out = relay.add(relu1, data)

    return relay.Function(relay.analysis.free_vars(out), out)

Configuring the Search Policy

The search process involves an iterative feedback loop. The search policy generates a batch of candidate programs (sketches) from the search space defined in the previous section. These candidates are measured on the hardware, and the results update the statistical cost model (XGBoost or LightGBM).

The architecture of this tuning loop drives the optimization. The TaskScheduler orchestrates the process, allocating time resources to different subgraphs if we were tuning a full network. For a single block, it focuses strictly on maximizing the throughput of our defined function.

Flow of the auto-scheduling pipeline. The search policy generates candidates which are filtered by the cost model before physical benchmarking.

Implementing the Tuning Loop

To execute the tuning, we extract the search tasks from the Relay function. The compiler analyzes the graph to identify optimizable subgraphs. We then configure the Tuner.

For production environments, the measure_ctx would typically point to a remote device using an RPC tracker. For this local example, we assume the host machine is the target.

# 1. Extract tasks from the network
target = tvm.target.Target("llvm -mcpu=skylake")
mod = tvm.IRModule.from_expr(get_resnet_block())
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], [], target)

# 2. Configure the search options
log_file = "resnet_block_tuning.json"
measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)

tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
    num_measure_trials=1000,  # Total number of configs to test
    runner=measure_ctx.runner,
    measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
    verbose=1  # Set to 0 for silent operation
)

# 3. Execute the search
# This process may take several minutes to hours depending on hardware
tuner.tune(tune_option)

During execution, the tuner searches the space $S$ defined by loop tiling sizes $T$, vectorization factors, and unrolling depths. Initially, the performance improvements are rapid as the evolutionary search moves away from inefficient default schedules. As the process continues, the gains become incremental, representing micro-optimizations in assembly generation.

The cost model is important here. Without it, the tuner would rely on random search. The model learns the correlation between specific loop structures (features) and execution time (labels), allowing it to prune the search space effectively without running every candidate on hardware.

Analyzing Convergence and Performance

Once the tuning completes, we analyze the improvement. The following chart demonstrates a typical convergence pattern for a convolution kernel. The x-axis represents the number of measurement trials, while the y-axis shows the throughput in GFLOPS.

Performance convergence during the auto-tuning session. The "Best Found" line tracks the highest throughput configuration identified up to that trial.

In the initial 100 trials, the tuner usually discovers the optimal tiling factors (e.g., splitting loops to fit L1/L2 cache). Subsequent improvements often stem from vectorization width adjustments and resolving thread binding conflicts.

Compiling with the Optimized Schedule

The output of the tuning process is a JSON log file containing the parameters for the best schedule found. To use this in a production inference pipeline, we compile the model using ApplyHistoryBest. This pass replaces the default lowering mechanisms with the specific schedules found during our search.

# Compile with the history best context
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=None)

# Create the runtime module
dev = tvm.device(str(target), 0)
module = tvm.contrib.graph_executor.GraphModule(lib["default"](dev))

# Benchmark
print("Optimized inference ready.")

When ApplyHistoryBest is active, the compiler matches the workload signature in the Relay graph against the entries in the log file. If a match is found, the corresponding low-level IR (TIR) transformations are applied. If no match is found (e.g., if the input shape changes), the compiler falls back to a default, unoptimized schedule, which highlights the importance of tuning for the specific shapes encountered in deployment.

Handling Search Instability

Engineers often encounter scenarios where the tuner fails to find a better schedule than the baseline, or the search crashes. Common causes include:

1. Insufficient Search Budget: Complex operators with high-dimensional loop nests (like 3D convolutions) require more trials to traverse the valid space.
2. Invalid Hardware Constraints: If the search space includes vectorization lengths that exceed the hardware's SIMD width, or shared memory usage that exceeds the bank size, candidates will fail during measurement.
3. Cost Model Overfitting: If the model trains on too few samples, it may hallucinate high performance for invalid configurations.

To mitigate these, ensure the target string accurately reflects the processor capabilities (e.g., including specific vector extensions like AVX-512 or tensor core versions). Additionally, using TransferLearning can help; you can load a log file from a similar hardware target to initialize the cost model, giving the search a "warm start."