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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
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from pt import configs
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import operator_benchmark as op_bench
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import torch
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import torch.ao.nn.quantized as nnq
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import torch.ao.nn.quantized.dynamic as nnqd
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"""
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Microbenchmarks for Quantized Linear operators.
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"""
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class _QLinearBenchmarkBase(op_bench.TorchBenchmarkBase):
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def init(self, N, IN, OUT, linear_under_test):
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scale = torch.tensor(1.0 / 255)
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zero_point = torch.tensor(0)
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self.X = torch.randn(N, IN, dtype=torch.float32)
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self.qX = torch.quantize_per_tensor(
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self.X, scale=scale, zero_point=zero_point, dtype=torch.quint8
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)
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W = torch.randn(OUT, IN, dtype=torch.float32)
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qW = torch.quantize_per_tensor(W, scale=scale, zero_point=0, dtype=torch.qint8)
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# Assume that the `self.qlinear` is set in the child
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self.qlinear = linear_under_test
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self.qlinear.weight = qW
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self.qlinear.scale = scale
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self.qlinear.zero_point = zero_point
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def forward(self, input):
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# Assume that the `self.input` is set in the child
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return self.qlinear(input)
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class QLinearBenchmark(_QLinearBenchmarkBase):
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def init(self, N, IN, OUT, device):
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super().init(N, IN, OUT, nnq.Linear(IN, OUT))
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self.inputs = {"input": self.qX}
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self.set_module_name("QLinear")
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class QDynamicLinearBenchmark(_QLinearBenchmarkBase):
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def init(self, N, IN, OUT, device):
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super().init(N, IN, OUT, nnqd.Linear(IN, OUT))
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self.inputs = {"input": self.X}
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self.set_module_name("QDynamicLinear")
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op_bench.generate_pt_test(
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configs.remove_cuda(configs.linear_configs_short + configs.linear_configs_long),
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QLinearBenchmark,
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)
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op_bench.generate_pt_test(
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configs.remove_cuda(configs.linear_configs_short + configs.linear_configs_long),
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QDynamicLinearBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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