wehub-resource-sync
139 lines
3.0 KiB
Python
139 lines
3.0 KiB
Python
"""Benchmark core 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.core_benchmark \
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--benchmark_name=benchmark_dense \
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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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import numpy as np
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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_dense(
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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 = "Dense"
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init_args = {"units": 256}
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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],
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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_einsum_dense(
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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 = "EinsumDense"
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init_args = {
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"equation": "abc,cd->abd",
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"output_shape": (None, 256),
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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],
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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_embedding(
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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 = "Embedding"
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init_args = {
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"input_dim": 128,
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"output_dim": 256,
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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=[
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256,
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],
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jit_compile=jit_compile,
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)
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data = [np.random.randint(30, size=(num_samples, 256))]
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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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data=data,
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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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data=data,
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)
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BENCHMARK_NAMES = {
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"benchmark_dense": benchmark_dense,
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"benchmark_einsum_dense": benchmark_einsum_dense,
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"benchmark_embedding": benchmark_embedding,
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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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