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import operator_benchmark as op_bench
import torch
from torch import nn
from torch.ao import pruning
"""Microbenchmarks for sparsifier."""
sparse_configs_short = op_bench.config_list(
attr_names=["M", "SL", "SBS", "ZPB"],
attrs=[
[(32, 16), 0.3, (4, 1), 2],
[(32, 16), 0.6, (1, 4), 4],
[(17, 23), 0.9, (1, 1), 1],
],
tags=("short",),
)
sparse_configs_long = op_bench.cross_product_configs(
M=((128, 128), (255, 324)), # Mask shape
SL=(0.0, 1.0, 0.3, 0.6, 0.9, 0.99), # Sparsity level
SBS=((1, 4), (1, 8), (4, 1), (8, 1)), # Sparse block shape
ZPB=(0, 1, 2, 3, 4, None), # Zeros per block
tags=("long",),
)
class WeightNormSparsifierBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, SL, SBS, ZPB):
weight = torch.ones(M)
model = nn.Module()
model.register_buffer("weight", weight)
sparse_config = [{"tensor_fqn": "weight"}]
self.sparsifier = pruning.WeightNormSparsifier(
sparsity_level=SL,
sparse_block_shape=SBS,
zeros_per_block=ZPB,
)
self.sparsifier.prepare(model, config=sparse_config)
self.inputs = {} # All benchmarks need inputs :)
self.set_module_name("weight_norm_sparsifier_step")
def forward(self):
self.sparsifier.step()
all_tests = sparse_configs_short + sparse_configs_long
op_bench.generate_pt_test(all_tests, WeightNormSparsifierBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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