wehub-resource-sync
155 lines
3.4 KiB
Python
155 lines
3.4 KiB
Python
"""Benchmark normalization layers.
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To run benchmarks, see the following command for an example, please change the
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flag to your custom value:
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```
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python3 -m benchmarks.layer_benchmark.normalization_benchmark \
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--benchmark_name=benchmark_batch_normalization \
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--num_samples=2048 \
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--batch_size=256 \
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--jit_compile=True
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```
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"""
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from absl import app
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from absl import flags
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from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark
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FLAGS = flags.FLAGS
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def benchmark_batch_normalization(
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num_samples,
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batch_size,
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jit_compile=True,
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):
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layer_name = "BatchNormalization"
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init_args = {}
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benchmark = LayerBenchmark(
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layer_name,
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init_args,
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input_shape=[256, 256, 4],
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jit_compile=jit_compile,
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)
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benchmark.benchmark_predict(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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benchmark.benchmark_train(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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def benchmark_group_normalization(
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num_samples,
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batch_size,
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jit_compile=True,
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):
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layer_name = "GroupNormalization"
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init_args = {
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"groups": 2,
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}
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benchmark = LayerBenchmark(
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layer_name,
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init_args,
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input_shape=[256, 256, 4],
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jit_compile=jit_compile,
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)
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benchmark.benchmark_predict(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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benchmark.benchmark_train(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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def benchmark_layer_normalization(
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num_samples,
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batch_size,
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jit_compile=True,
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):
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layer_name = "LayerNormalization"
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init_args = {}
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benchmark = LayerBenchmark(
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layer_name,
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init_args,
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input_shape=[256, 128, 4],
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jit_compile=jit_compile,
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)
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benchmark.benchmark_predict(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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benchmark.benchmark_train(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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def benchmark_unit_normalization(
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num_samples,
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batch_size,
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jit_compile=True,
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):
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layer_name = "UnitNormalization"
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init_args = {}
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benchmark = LayerBenchmark(
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layer_name,
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init_args,
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input_shape=[256, 128, 4],
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jit_compile=jit_compile,
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)
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benchmark.benchmark_predict(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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benchmark.benchmark_train(
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num_samples=num_samples,
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batch_size=batch_size,
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)
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BENCHMARK_NAMES = {
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"benchmark_batch_normalization": benchmark_batch_normalization,
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"benchmark_group_normalization": benchmark_group_normalization,
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"benchmark_layer_normalization": benchmark_layer_normalization,
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"benchmark_unit_normalization": benchmark_unit_normalization,
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}
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def main(_):
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benchmark_name = FLAGS.benchmark_name
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num_samples = FLAGS.num_samples
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batch_size = FLAGS.batch_size
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jit_compile = FLAGS.jit_compile
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if benchmark_name is None:
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for name, benchmark_fn in BENCHMARK_NAMES.items():
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benchmark_fn(num_samples, batch_size, jit_compile)
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return
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if benchmark_name not in BENCHMARK_NAMES:
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raise ValueError(
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f"Invalid benchmark name: {benchmark_name}, `benchmark_name` must "
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f"be one of {BENCHMARK_NAMES.keys()}"
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)
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benchmark_fn = BENCHMARK_NAMES[benchmark_name]
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benchmark_fn(num_samples, batch_size, jit_compile)
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if __name__ == "__main__":
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app.run(main)
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