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Provides functionality to update a model at execution time (#49)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,112 @@ | ||
| #include <AMS.h> | ||
| #include <ATen/core/interned_strings.h> | ||
| #include <c10/core/TensorOptions.h> | ||
| #include <torch/types.h> | ||
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| #include <cstring> | ||
| #include <iostream> | ||
| #include <ml/surrogate.hpp> | ||
| #include <umpire/ResourceManager.hpp> | ||
| #include <umpire/Umpire.hpp> | ||
| #include <vector> | ||
| #include <wf/resource_manager.hpp> | ||
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| #define SIZE (32L) | ||
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| template <typename T> | ||
| bool inference(SurrogateModel<T> &model, | ||
| AMSResourceType resource, | ||
| std::string update_path) | ||
| { | ||
| using namespace ams; | ||
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| std::vector<const T *> inputs; | ||
| std::vector<T *> outputs; | ||
| auto &ams_rm = ams::ResourceManager::getInstance(); | ||
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| for (int i = 0; i < 2; i++) | ||
| inputs.push_back(ams_rm.allocate<T>(SIZE, resource)); | ||
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| for (int i = 0; i < 4 * 2; i++) | ||
| outputs.push_back(ams_rm.allocate<T>(SIZE, resource)); | ||
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| for (int repeat = 0; repeat < 2; repeat++) { | ||
| model.evaluate( | ||
| SIZE, inputs.size(), 4, inputs.data(), &(outputs.data()[repeat * 4])); | ||
| if (repeat == 0) model.update(update_path); | ||
| } | ||
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| // Verify | ||
| bool errors = false; | ||
| for (int i = 0; i < 4; i++) { | ||
| T *first_model_out = outputs[i]; | ||
| T *second_model_out = outputs[i + 4]; | ||
| if (resource == AMSResourceType::DEVICE) { | ||
| first_model_out = ams_rm.allocate<T>(SIZE, AMSResourceType::HOST); | ||
| second_model_out = ams_rm.allocate<T>(SIZE, AMSResourceType::HOST); | ||
| ams_rm.copy(outputs[i], first_model_out, SIZE * sizeof(T)); | ||
| ams_rm.copy(outputs[i + 4], second_model_out, SIZE * sizeof(T)); | ||
| } | ||
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| for (int j = 0; j < SIZE; j++) { | ||
| if (first_model_out[j] != 1.0) { | ||
| errors = true; | ||
| std::cout << "One Model " << first_model_out << " " << j << " " | ||
| << first_model_out[j] << "\n"; | ||
| } | ||
| if (second_model_out[j] != 0.0) { | ||
| std::cout << "Zero Model " << second_model_out << " " << j << " " | ||
| << second_model_out[j] << "\n"; | ||
| errors = true; | ||
| } | ||
| } | ||
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| if (resource == AMSResourceType::DEVICE) { | ||
| ams_rm.deallocate(first_model_out, resource); | ||
| ams_rm.deallocate(second_model_out, resource); | ||
| } | ||
| } | ||
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| for (int i = 0; i < 2; i++) | ||
| ams_rm.deallocate(const_cast<T *>(inputs[i]), resource); | ||
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| for (int i = 0; i < 4 * 2; i++) | ||
| ams_rm.deallocate(outputs[i], resource); | ||
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| return errors; | ||
| } | ||
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| int main(int argc, char *argv[]) | ||
| { | ||
| using namespace ams; | ||
| auto &ams_rm = ams::ResourceManager::getInstance(); | ||
| int use_device = std::atoi(argv[1]); | ||
| char *data_type = argv[2]; | ||
| char *zero_model = argv[3]; | ||
| char *one_model = argv[4]; | ||
| char *swap; | ||
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| AMSResourceType resource = AMSResourceType::HOST; | ||
| if (use_device == 1) { | ||
| resource = AMSResourceType::DEVICE; | ||
| } | ||
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| ams_rm.init(); | ||
| int ret = 0; | ||
| if (std::strcmp("double", data_type) == 0) { | ||
| std::shared_ptr<SurrogateModel<double>> model = | ||
| SurrogateModel<double>::getInstance(one_model, resource); | ||
| assert(model->is_double()); | ||
| ret = inference<double>(*model, resource, zero_model); | ||
| } else if (std::strcmp("single", data_type) == 0) { | ||
| std::shared_ptr<SurrogateModel<float>> model = | ||
| SurrogateModel<float>::getInstance(one_model, resource); | ||
| assert(!model->is_double()); | ||
| ret = inference<float>(*model, resource, zero_model); | ||
| } | ||
| std::cout << "Zero Model is " << zero_model << "\n"; | ||
| std::cout << "One Model is " << one_model << "\n"; | ||
| return ret; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,64 @@ | ||
| import torch | ||
| import os | ||
| import sys | ||
| import numpy as np | ||
| from torch.autograd import Variable | ||
| from torch import jit | ||
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| class ConstantModel(torch.nn.Module): | ||
| def __init__(self, inputSize, outputSize, constant): | ||
| super(ConstantModel, self).__init__() | ||
| self.linear = torch.nn.Linear(inputSize, outputSize) | ||
| self.linear.weight.data.fill_(0.0) | ||
| self.linear.bias.data.fill_(constant) | ||
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| def forward(self, x): | ||
| y = self.linear(x) | ||
| return y | ||
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| def main(args): | ||
| inputDim = int(args[1]) | ||
| outputDim = int(args[2]) | ||
| device = args[3] | ||
| enable_cuda = True | ||
| if device == "cuda": | ||
| enable_cuda = True | ||
| suffix = '_gpu' | ||
| elif device == "cpu": | ||
| enable_cuda = False | ||
| suffix = '_cpu' | ||
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| model = ConstantModel(inputDim, outputDim, 1.0).double() | ||
| if torch.cuda.is_available() and enable_cuda: | ||
| model = model.cuda() | ||
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| model.eval() | ||
| with torch.jit.optimized_execution(True): | ||
| traced = torch.jit.trace(model, (torch.randn(inputDim, dtype=torch.double), )) | ||
| traced.save(f"ConstantOneModel_{suffix}.pt") | ||
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| model = ConstantModel(inputDim, outputDim, 0.0).double() | ||
| if torch.cuda.is_available() and enable_cuda: | ||
| model = model.cuda() | ||
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| model.eval() | ||
| with torch.jit.optimized_execution(True): | ||
| traced = torch.jit.trace(model, (torch.randn(inputDim, dtype=torch.double), )) | ||
| traced.save(f"ConstantZeroModel_{suffix}.pt") | ||
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| inputs = Variable(torch.from_numpy(np.zeros((1, inputDim)))) | ||
| zero_model = jit.load(f"ConstantZeroModel_{suffix}.pt") | ||
| print("ZeroModel", zero_model(inputs)) | ||
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| one_model = jit.load(f"ConstantOneModel_{suffix}.pt") | ||
| print("OneModel", one_model(inputs)) | ||
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| if __name__ == '__main__': | ||
| main(sys.argv) | ||
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