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c0ed38e644
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import operator_benchmark as op_bench
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import torch
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from torch import nn
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from torch.ao import pruning
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"""Microbenchmarks for sparsifier."""
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sparse_configs_short = op_bench.config_list(
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attr_names=["M", "SL", "SBS", "ZPB"],
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attrs=[
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[(32, 16), 0.3, (4, 1), 2],
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[(32, 16), 0.6, (1, 4), 4],
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[(17, 23), 0.9, (1, 1), 1],
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],
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tags=("short",),
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)
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sparse_configs_long = op_bench.cross_product_configs(
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M=((128, 128), (255, 324)), # Mask shape
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SL=(0.0, 1.0, 0.3, 0.6, 0.9, 0.99), # Sparsity level
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SBS=((1, 4), (1, 8), (4, 1), (8, 1)), # Sparse block shape
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ZPB=(0, 1, 2, 3, 4, None), # Zeros per block
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tags=("long",),
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)
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class WeightNormSparsifierBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, SL, SBS, ZPB):
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weight = torch.ones(M)
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model = nn.Module()
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model.register_buffer("weight", weight)
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sparse_config = [{"tensor_fqn": "weight"}]
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self.sparsifier = pruning.WeightNormSparsifier(
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sparsity_level=SL,
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sparse_block_shape=SBS,
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zeros_per_block=ZPB,
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)
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self.sparsifier.prepare(model, config=sparse_config)
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self.inputs = {} # All benchmarks need inputs :)
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self.set_module_name("weight_norm_sparsifier_step")
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def forward(self):
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self.sparsifier.step()
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all_tests = sparse_configs_short + sparse_configs_long
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op_bench.generate_pt_test(all_tests, WeightNormSparsifierBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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