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