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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
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import operator_benchmark as op_bench
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import torch
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from torch._ops import ops
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qarithmetic_binary_configs = op_bench.cross_product_configs(
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N=(2, 8, 64, 512),
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dtype=(torch.quint8, torch.qint8, torch.qint32),
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contig=(False, True),
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tags=("short",),
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)
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qarithmetic_binary_ops = op_bench.op_list(
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attrs=(
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("add", ops.quantized.add),
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("add_relu", ops.quantized.add_relu),
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("mul", ops.quantized.mul),
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),
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attr_names=("op_name", "op_func"),
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)
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qarithmetic_binary_scalar_ops = op_bench.op_list(
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attrs=(
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("add_scalar", ops.quantized.add_scalar),
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("mul_scalar", ops.quantized.mul_scalar),
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),
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attr_names=("op_name", "op_func"),
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)
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class _QFunctionalBinaryArithmeticBenchmarkBase(op_bench.TorchBenchmarkBase):
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def setup(self, N, dtype, contig):
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self.qfunctional = torch.ao.nn.quantized.QFunctional()
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# TODO: Consider more diverse shapes
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f_input = (torch.rand(N, N) - 0.5) * 256
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self.scale = 1.0
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self.zero_point = 0
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self.q_input_a = torch.quantize_per_tensor(
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f_input, scale=self.scale, zero_point=self.zero_point, dtype=dtype
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)
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if not contig:
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permute_dims = list(range(f_input.ndim))[::-1]
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self.q_input_a = self.q_input_a.permute(permute_dims)
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class QFunctionalBenchmark(_QFunctionalBinaryArithmeticBenchmarkBase):
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def init(self, N, dtype, contig, op_func):
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super().setup(N, dtype, contig)
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self.inputs = {
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"q_input_a": self.q_input_a,
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"q_input_b": self.q_input_a,
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"scale": self.scale,
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"zero_point": self.zero_point,
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}
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self.op_func = op_func
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def forward(self, q_input_a, q_input_b, scale: float, zero_point: int):
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return self.op_func(q_input_a, q_input_b, scale=scale, zero_point=zero_point)
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op_bench.generate_pt_tests_from_op_list(
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qarithmetic_binary_ops, qarithmetic_binary_configs, QFunctionalBenchmark
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)
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class QFunctionalScalarBenchmark(_QFunctionalBinaryArithmeticBenchmarkBase):
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def init(self, N, dtype, contig, op_func):
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super().setup(N, dtype, contig)
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self.inputs = {"q_input": self.q_input_a, "scalar_input": 42}
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self.op_func = op_func
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def forward(self, q_input, scalar_input: int):
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return self.op_func(q_input, scalar_input)
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op_bench.generate_pt_tests_from_op_list(
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qarithmetic_binary_scalar_ops,
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qarithmetic_binary_configs,
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QFunctionalScalarBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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