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import operator_benchmark as op_bench
import torch
import torch.ao.quantization.observer as obs
qobserver_short_configs_dict = {
'attr_names': ('C', 'M', 'N', 'dtype', 'device'),
'attrs': (
(3, 512, 512, torch.quint8, 'cpu'),
(3, 512, 512, torch.quint8, 'cuda'),
),
'tags': ('short',),
}
q_hist_observer_short_configs_dict = {
'attr_names': ('C', 'M', 'N', 'dtype', 'device'),
'attrs': (
(3, 512, 512, torch.quint8, 'cpu'),
),
'tags': ('short',),
}
qobserver_long_configs_dict = {
'C': (32, 64),
'M': (256, 1024),
'N': (256, 1024),
'device': ('cpu', 'cuda'),
'dtype': (torch.quint8,), # dtype doesn't change the timing, keep the same
'tags': ('long',),
}
q_hist_observer_long_configs_dict = {
'C': (1, 3, 8),
'M': (256, 1024),
'N': (256, 1024),
'device': ('cpu',),
'dtype': (torch.quint8,), # dtype doesn't change the timing, keep the same
'tags': ('long',),
}
qobserver_per_tensor_configs_short = op_bench.config_list(
cross_product_configs={
'qscheme': (torch.per_tensor_affine, torch.per_tensor_symmetric)
},
**qobserver_short_configs_dict,
)
qobserver_per_tensor_configs_long = op_bench.cross_product_configs(
qscheme=(torch.per_tensor_affine, torch.per_tensor_symmetric),
**qobserver_long_configs_dict,
)
qobserver_per_channel_configs_short = op_bench.config_list(
cross_product_configs={
'qscheme': (torch.per_channel_affine, torch.per_channel_symmetric)
},
**qobserver_short_configs_dict,
)
qobserver_per_channel_configs_long = op_bench.cross_product_configs(
qscheme=(torch.per_channel_affine, torch.per_channel_symmetric),
**qobserver_long_configs_dict,
)
q_hist_observer_per_tensor_configs_short = op_bench.config_list(
cross_product_configs={
'qscheme': (torch.per_tensor_affine, torch.per_tensor_symmetric)
},
**q_hist_observer_short_configs_dict,
)
q_hist_observer_per_tensor_configs_long = op_bench.cross_product_configs(
qscheme=(torch.per_tensor_affine, torch.per_tensor_symmetric),
**q_hist_observer_long_configs_dict,
)
qobserver_per_tensor_list = op_bench.op_list(
attr_names=['op_name', 'op_func'],
attrs=[
['MinMaxObserver', obs.MinMaxObserver],
['MovingAverageMinMaxObserver', obs.MovingAverageMinMaxObserver],
]
)
qobserver_per_channel_list = op_bench.op_list(
attr_names=['op_name', 'op_func'],
attrs=[
['PerChannelMinMaxObserver', obs.PerChannelMinMaxObserver],
['MovingAveragePerChannelMinMaxObserver',
obs.MovingAveragePerChannelMinMaxObserver],
]
)
q_hist_observer_list = op_bench.op_list(
attr_names=['op_name', 'op_func'],
attrs=[
['HistogramObserver', obs.HistogramObserver],
['HistogramObserverCalculateQparams', obs.HistogramObserver],
]
)
class QObserverBenchmark(op_bench.TorchBenchmarkBase):
def init(self, C, M, N, dtype, qscheme, op_func, device):
self.inputs = {
"f_input": torch.rand(C, M, N, device=device)
}
self.op_func = op_func(dtype=dtype, qscheme=qscheme).to(device)
def forward(self, f_input):
self.op_func(f_input)
return self.op_func.calculate_qparams()
class QObserverBenchmarkCalculateQparams(op_bench.TorchBenchmarkBase):
def init(self, C, M, N, dtype, qscheme, op_func, device):
self.f_input = torch.rand(C, M, N, device=device)
self.q_observer = op_func(dtype=dtype, qscheme=qscheme).to(device)
self.q_observer(self.f_input)
self.inputs = {}
def forward(self):
return self.q_observer.calculate_qparams()
op_bench.generate_pt_tests_from_op_list(
qobserver_per_tensor_list,
qobserver_per_tensor_configs_short + qobserver_per_tensor_configs_long,
QObserverBenchmark)
op_bench.generate_pt_tests_from_op_list(
qobserver_per_channel_list,
qobserver_per_channel_configs_short + qobserver_per_channel_configs_long,
QObserverBenchmark)
op_bench.generate_pt_tests_from_op_list(
q_hist_observer_list,
q_hist_observer_per_tensor_configs_short + q_hist_observer_per_tensor_configs_long,
QObserverBenchmarkCalculateQparams)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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