1import operator_benchmark as op_bench 2 3import torch 4from torch import nn 5from torch.ao import pruning 6 7 8"""Microbenchmarks for sparsifier.""" 9 10sparse_configs_short = op_bench.config_list( 11 attr_names=["M", "SL", "SBS", "ZPB"], 12 attrs=[ 13 [(32, 16), 0.3, (4, 1), 2], 14 [(32, 16), 0.6, (1, 4), 4], 15 [(17, 23), 0.9, (1, 1), 1], 16 ], 17 tags=("short",), 18) 19 20sparse_configs_long = op_bench.cross_product_configs( 21 M=((128, 128), (255, 324)), # Mask shape 22 SL=(0.0, 1.0, 0.3, 0.6, 0.9, 0.99), # Sparsity level 23 SBS=((1, 4), (1, 8), (4, 1), (8, 1)), # Sparse block shape 24 ZPB=(0, 1, 2, 3, 4, None), # Zeros per block 25 tags=("long",), 26) 27 28 29class WeightNormSparsifierBenchmark(op_bench.TorchBenchmarkBase): 30 def init(self, M, SL, SBS, ZPB): 31 weight = torch.ones(M) 32 model = nn.Module() 33 model.register_buffer("weight", weight) 34 35 sparse_config = [{"tensor_fqn": "weight"}] 36 self.sparsifier = pruning.WeightNormSparsifier( 37 sparsity_level=SL, 38 sparse_block_shape=SBS, 39 zeros_per_block=ZPB, 40 ) 41 self.sparsifier.prepare(model, config=sparse_config) 42 self.inputs = {} # All benchmarks need inputs :) 43 self.set_module_name("weight_norm_sparsifier_step") 44 45 def forward(self): 46 self.sparsifier.step() 47 48 49all_tests = sparse_configs_short + sparse_configs_long 50op_bench.generate_pt_test(all_tests, WeightNormSparsifierBenchmark) 51 52 53if __name__ == "__main__": 54 op_bench.benchmark_runner.main() 55