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Set quantized engine backend for mobile in speed_benchmark_torch (#26911)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26911

Check if QNNPACK is present as a backend (should always be present on mobile).
If it is present then set the backend to QNNPACK

Test Plan:
Test on mobile
./speed_benchmark_torch --model mobilenet_quantized_scripted.pt  --input_dims="1,3,224,224" --input_type=float --warmup=5 --iter 20 --print_output True

Imported from OSS

Differential Revision: D17613908

fbshipit-source-id: af96722570a0111f13d69c38ccca52416ea5e460
This commit is contained in:
Supriya Rao 2019-09-26 16:25:23 -07:00 committed by Facebook Github Bot
parent 638c4375de
commit 8d5c2aa71c
1 changed files with 5 additions and 0 deletions
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5
binaries/speed_benchmark_torch.cc
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@ -82,10 +82,15 @@ int main(int argc, char** argv) {
} }
} }
auto qengines = at::globalContext().supportedQEngines();
if (std::find(qengines.begin(), qengines.end(), at::QEngine::QNNPACK) != qengines.end()) {
at::globalContext().setQEngine(at::QEngine::QNNPACK);
}
torch::autograd::AutoGradMode guard(false); torch::autograd::AutoGradMode guard(false);
auto module = torch::jit::load(FLAGS_model); auto module = torch::jit::load(FLAGS_model);
at::AutoNonVariableTypeMode non_var_type_mode(true); at::AutoNonVariableTypeMode non_var_type_mode(true);
module.eval();
if (FLAGS_print_output) { if (FLAGS_print_output) {
std::cout << module.forward(inputs) << std::endl; std::cout << module.forward(inputs) << std::endl;
} }