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[AI Bench] Add support for nlu model

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Summary: add support for nlu specific input

Test Plan:
tested

```
buck run aibench:run_bench -- -b aibench/specifications/models/pytorch/fbnet/assistant_mobile_inference.json --platform android/full_jit --framework pytorch --remote --devices  SM-G950U-7.0-24
```
make sure it compatible with previous test
```
buck run aibench:run_bench -- -b aibench/specifications/models/pytorch/fbnet/fbnet_mobile_inference.json --platform android/full_jit --framework pytorch --remote --devices  SM-G950U-7.0-24
```

```
{
  "model": {
    "category": "CNN",
    "description": "Assistant Mobile Inference",
    "files": {
      "model": {
        "filename": "model.pt1",
        "location": "//everstore/GICWmAB2Znbi_mAAAB0P51IPW8UrbllgAAAP/model.pt1",
        "md5": "c0f4b29c442bbaeb0007fb0ce513ccb3"
      },
      "data": {
        "filename": "input.txt",
        "location": "/home/pengxia/test/input.txt",
        "md5": "c0f4b29c442bbaeb0007fb0ce513ccb3"
      }
    },
    "format": "pytorch",
    "framework": "pytorch",
    "kind": "deployment",
    "name": "Assistant Mobile Inference"
  },
  "tests": [
    {
      "command": "{program} --model {files.model}  --input_dims \"1\" --input_type NLUType --warmup {warmup} --iter {iter} --input_file {files.data} --report_pep true",
      "identifier": "{ID}",
      "metric": "delay",
      "iter": 5,
      "warmup": 2,
      "log_output": true
    }
  ]
}

```
input.txt
```
what is weather today
what time it is
set a reminder for tomorrow
```

result
https://our.intern.facebook.com/intern/aibench/details/137241352201417

Reviewed By: kimishpatel

Differential Revision: D20300947

fbshipit-source-id: 7c1619541a2e9514a560a9acb9029cfc4669f37a
This commit is contained in:
Peng Xia 2020-03-09 10:33:53 -07:00 committed by Facebook Github Bot
parent bcfd348858
commit 91e922a338
1 changed files with 47 additions and 20 deletions
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67
binaries/speed_benchmark_torch.cc
View File

@ -14,6 +14,7 @@
* limitations under the License.
*/
#include <fstream>
#include <string>
#include <vector>
@ -37,6 +38,7 @@ C10_DEFINE_string(
"semicolon to separate the dimension of different "
"tensors.");
C10_DEFINE_string(input_type, "", "Input type (uint8_t/float)");
C10_DEFINE_string(input_file, "", "Input file");
C10_DEFINE_bool(
print_output,
false,
@ -63,6 +65,22 @@ split(char separator, const std::string& string, bool ignore_empty = true) {
return pieces;
}
std::vector<std::vector<c10::IValue>> nlu_process(std::string file_path) {
std::vector<std::vector<c10::IValue>> nlu_inputs;
std::ifstream input_file(FLAGS_input_file);
for (std::string line; getline(input_file, line);) {
std::vector<c10::IValue> nlu_input;
c10::List<std::string> tokens(split(' ', line));
nlu_input.push_back(tokens);
auto len = torch::jit::IValue(static_cast<int64_t>(tokens.size()));
nlu_input.push_back({});
nlu_input.push_back(len);
nlu_inputs.emplace_back(std::move(nlu_input));
std::cout << line << std::endl;
}
return nlu_inputs;
}
int main(int argc, char** argv) {
c10::SetUsageMessage(
"Run speed benchmark for pytorch model.\n"
@ -88,27 +106,32 @@ int main(int argc, char** argv) {
input_type_list.size(),
"Input dims and type should have the same number of items.");
std::vector<c10::IValue> inputs;
for (size_t i = 0; i < input_dims_list.size(); ++i) {
auto input_dims_str = split(',', input_dims_list[i]);
std::vector<int64_t> input_dims;
for (const auto& s : input_dims_str) {
input_dims.push_back(c10::stoi(s));
}
if (input_type_list[i] == "float") {
inputs.push_back(torch::ones(input_dims, at::ScalarType::Float));
} else if (input_type_list[i] == "uint8_t") {
inputs.push_back(torch::ones(input_dims, at::ScalarType::Byte));
} else if (input_type_list[i] == "int64") {
inputs.push_back(torch::ones(input_dims, torch::kI64));
} else {
CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
std::vector<std::vector<c10::IValue>> inputs;
if (input_type_list[0] == "NLUType"){
inputs = nlu_process(FLAGS_input_file);
} else {
inputs.push_back(std::vector<c10::IValue>());
for (size_t i = 0; i < input_dims_list.size(); ++i) {
auto input_dims_str = split(',', input_dims_list[i]);
std::vector<int64_t> input_dims;
for (const auto& s : input_dims_str) {
input_dims.push_back(c10::stoi(s));
}
if (input_type_list[i] == "float") {
inputs[0].push_back(torch::ones(input_dims, at::ScalarType::Float));
} else if (input_type_list[i] == "uint8_t") {
inputs[0].push_back(torch::ones(input_dims, at::ScalarType::Byte));
} else if (input_type_list[i] == "int64") {
inputs[0].push_back(torch::ones(input_dims, torch::kI64));
} else {
CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
}
}
}
if (FLAGS_pytext_len > 0) {
auto stensor = FLAGS_pytext_len * at::ones({1}, torch::kI64);
inputs.push_back(stensor);
inputs[0].push_back(stensor);
}
auto qengines = at::globalContext().supportedQEngines();
@ -121,7 +144,7 @@ int main(int argc, char** argv) {
module.eval();
if (FLAGS_print_output) {
std::cout << module.forward(inputs) << std::endl;
std::cout << module.forward(inputs[0]) << std::endl;
}
std::cout << "Starting benchmark." << std::endl;
@ -131,8 +154,10 @@ int main(int argc, char** argv) {
"Number of warm up runs should be non negative, provided ",
FLAGS_warmup,
".");
for (int i = 0; i < FLAGS_warmup; ++i) {
module.forward(inputs);
for (unsigned int i = 0; i < FLAGS_warmup; ++i) {
for (const auto& input : inputs) {
module.forward(input);
}
}
std::cout << "Main runs." << std::endl;
@ -146,7 +171,9 @@ int main(int argc, char** argv) {
auto millis = timer.MilliSeconds();
for (int i = 0; i < FLAGS_iter; ++i) {
auto start = high_resolution_clock::now();
module.forward(inputs);
for (const std::vector<c10::IValue>& input: inputs) {
module.forward(input);
}
auto stop = high_resolution_clock::now();
auto duration = duration_cast<milliseconds>(stop - start);
times.push_back(duration.count());