benchmark merge layers to run benchmarks see the following command for an example please change the flag to your custom value python3 m benchmarks layerbenchmark mergebenchmark benchmarknamebenchmarkadd numsamples2048 batchsize256 jitcompiletrue this code provides a benchmarking framework for various basic mathematical operations in neural networks using keras and tensorflow it includes benchmark functions for operations such as add average concatenate dot maximum minimum multiply and subtract each function initializes a layerbenchmark object with the respective layer name and input shapes for operations that require two inputs two input shapes are provided the benchmark predict and benchmark train methods are then used to evaluate the performance of each operation in terms of prediction and training the main function uses the absl library to parse command line arguments allowing the user to specify the benchmark name number of samples batch size and whether to use jit compilation it executes the specified benchmark or all benchmarks if none is specified and handles cases where an invalid benchmark name is provided the benchmarks are stored in a dictionary for easy access and execution
from absl import app
from absl import flags

from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark

FLAGS = flags.FLAGS


def benchmark_add(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Add"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_average(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Average"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_concatenate(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Concatenate"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_dot(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Dot"
    init_args = {"axes": [2, 1]}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 32], [32, 64]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_maximum(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Maximum"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_minimum(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Minimum"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_multiply(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Multiply"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 64], [256, 64]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_subtract(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Subtract"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[[256, 256], [256, 256]],
        flat_call_inputs=False,
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


BENCHMARK_NAMES = {
    "benchmark_add": benchmark_add,
    "benchmark_average": benchmark_average,
    "benchmark_concatenate": benchmark_concatenate,
    "benchmark_dot": benchmark_dot,
    "benchmark_maximum": benchmark_maximum,
    "benchmark_minimum": benchmark_minimum,
    "benchmark_multiply": benchmark_multiply,
    "benchmark_subtract": benchmark_subtract,
}


def main(_):
    benchmark_name = FLAGS.benchmark_name
    num_samples = FLAGS.num_samples
    batch_size = FLAGS.batch_size
    jit_compile = FLAGS.jit_compile

    if benchmark_name is None:
        for name, benchmark_fn in BENCHMARK_NAMES.items():
            benchmark_fn(num_samples, batch_size, jit_compile)
        return

    if benchmark_name not in BENCHMARK_NAMES:
        raise ValueError(
            f"Invalid benchmark name: {benchmark_name}, `benchmark_name` must "
            f"be one of {BENCHMARK_NAMES.keys()}"
        )
    benchmark_fn = BENCHMARK_NAMES[benchmark_name]
    benchmark_fn(num_samples, batch_size, jit_compile)


if __name__ == "__main__":
    app.run(main)