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Merged
maorz1998 merged 2 commits into from
Oct 28, 2023
Merged

Gpu dev #381

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084bccb
pass libtorch version
maorz1998 Oct 27, 2023
00d3974
add batch size
maorz1998 Oct 28, 2023
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5 changes: 3 additions & 2 deletions src_gpu/dfChemistrySolver.H
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Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,10 @@ private:
double *Xmu_, *Xstd_, *Ymu_, *Ystd_;

double *init_input_, *y_input_BCT;
int dim_input_, num_cells_, num_species_;
int dim_input_, num_cells_, num_species_, num_modules_;
int batch_size_;
public:
dfChemistrySolver(int num_cells, int num_species);
dfChemistrySolver(int num_cells, int num_species, int batch_size);
~dfChemistrySolver();

void loadModels(const std::string dir);
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69 changes: 43 additions & 26 deletions src_gpu/dfChemistrySolver.cu
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Expand Up @@ -29,15 +29,15 @@ __global__ void normalize_input(int num_cells, int dim, const double *input,
}
}

__global__ void calculate_y_new(int num_cells, int num_species, const double *output_init,
__global__ void calculate_y_new(int num_cells, int num_modules, const double *output_init,
const double *y_input_BCT, const double *Ymu, const double *Ystd, double *output)
{
int index = blockDim.x * blockIdx.x + threadIdx.x;
if (index >= num_cells)
return;

double RR_tmp;
for (int i = 0; i < num_species; ++i) {
for (int i = 0; i < num_modules; ++i) {
RR_tmp = output_init[i * num_cells + index] * Ystd[i] + Ymu[i] + y_input_BCT[i * num_cells + index];
RR_tmp = pow((RR_tmp * 0.1 + 1), 10); // rev-BCT: lambda = 0.1
output[i * num_cells + index] = RR_tmp;
Expand All @@ -62,17 +62,18 @@ __global__ void calculate_RR(int num_cells, int num_species, double delta_t,
}
}

dfChemistrySolver::dfChemistrySolver(int num_cells, int num_species)
: device_(torch::kCUDA), num_cells_(num_cells), num_species_(num_species)
dfChemistrySolver::dfChemistrySolver(int num_cells, int num_species, int batch_size)
: device_(torch::kCUDA), num_cells_(num_cells), num_species_(num_species), batch_size_(batch_size)
{
dim_input_ = num_species + 2; // p, T, y
num_modules_ = num_species_ - 1;
cudaMalloc(&init_input_, sizeof(double) * num_cells * dim_input_);
cudaMalloc(&y_input_BCT, sizeof(double) * num_cells * num_species_);
cudaMalloc(&Xmu_, sizeof(double) * dim_input_);
cudaMalloc(&Xstd_, sizeof(double) * dim_input_);
cudaMalloc(&Ymu_, sizeof(double) * num_species_);
cudaMalloc(&Ystd_, sizeof(double) * num_species_);
modules_.reserve(num_species_);
cudaMalloc(&Ymu_, sizeof(double) * num_modules_);
cudaMalloc(&Ystd_, sizeof(double) * num_modules_);
modules_.reserve(num_modules_);

// now norm paras are set in constructor manually
at::TensorOptions opts = at::TensorOptions().dtype(at::kDouble).device(at::kCUDA);
Expand All @@ -84,13 +85,28 @@ dfChemistrySolver::dfChemistrySolver(int num_cells, int num_species)
7.9704668543e-01, 5.1327160125e-01, 4.6827591193e-03};
std::vector<double> Ymu_vec = {0.0085609265, -0.0082998877, 0.0030108739,
-0.0067360325, 0.0037464590, 0.0024258509};
std::vector<double> Ystd_vec = {0.0085609265, -0.0082998877, 0.0030108739,
-0.0067360325, 0.0037464590, 0.0024258509};
std::vector<double> Ystd_vec = {0.05887569, 0.12253898, 0.01643904,
0.0963155, 0.07127831, 0.04373392};

cudaMemcpy(Xmu_, Xmu_vec.data(), sizeof(double) * dim_input_, cudaMemcpyHostToDevice);
cudaMemcpy(Xstd_, Xstd_vec.data(), sizeof(double) * dim_input_, cudaMemcpyHostToDevice);
cudaMemcpy(Ymu_, Ymu_vec.data(), sizeof(double) * num_species_, cudaMemcpyHostToDevice);
cudaMemcpy(Ystd_, Ystd_vec.data(), sizeof(double) * num_species_, cudaMemcpyHostToDevice);
cudaMemcpy(Ymu_, Ymu_vec.data(), sizeof(double) * num_modules_, cudaMemcpyHostToDevice);
cudaMemcpy(Ystd_, Ystd_vec.data(), sizeof(double) * num_modules_, cudaMemcpyHostToDevice);

// input modules
std::string prefix = "new_Temporary_Chemical_";
std::string suffix = ".pt";
for (int i = 0; i < num_modules_; ++i) {
std::string model_path = prefix + std::to_string(i) + suffix;
try {
modules_.push_back(torch::jit::load(model_path));
}
catch (const c10::Error& e) {
std::cerr << "error loading the model\n";
exit(-1);
}
modules_[i].to(device_);
}
}

dfChemistrySolver::~dfChemistrySolver() {
Expand All @@ -106,23 +122,24 @@ void dfChemistrySolver::Inference(const double *T, const double *p, const double
normalize_input<<<blocks_per_grid, threads_per_block>>>(num_cells_, dim_input_, init_input_, Xmu_, Xstd_, init_input_);

// inference by torch
at::Tensor torch_input = torch::from_blob(init_input_, {num_cells_, dim_input_}, device_);
torch_input = torch_input.to(at::kFloat);
std::vector<torch::jit::IValue> INPUTS;
INPUTS.push_back(torch_input);
double *d_output;
for (int sample_start = 0; sample_start < num_cells_; sample_start += batch_size_) {
int sample_end = std::min(sample_start + batch_size_, num_cells_);
int sample_len = sample_end - sample_start;
at::Tensor torch_input = torch::from_blob(init_input_ + sample_start * dim_input_, {sample_len, dim_input_}, torch::TensorOptions().device(device_).dtype(torch::kDouble));
torch_input = torch_input.to(at::kFloat);
std::vector<torch::jit::IValue> INPUTS;
INPUTS.push_back(torch_input);
std::vector<at::Tensor> output(num_modules_);

std::vector<at::Tensor> output(num_species_);
for (int i = 0; i < num_species_; ++i) {
output[i] = modules_[i].forward(INPUTS).toTensor();
output[i] = output[i].to(at::kDouble);
for (int i = 0; i < num_modules_; ++i) {
output[i] = modules_[i].forward(INPUTS).toTensor();
output[i] = output[i].to(at::kDouble);
d_output = output[i].data_ptr<double>();
cudaMemcpy(RR + (i * num_cells_ + sample_start), d_output, sizeof(double) * sample_len, cudaMemcpyDeviceToDevice);
}
}

// post process
double *d_output;
for (int i = 0; i < num_species_; ++i) {
d_output = output[i].data_ptr<double>();
cudaMemcpy(RR + i * num_cells_, d_output, sizeof(double) * num_cells_, cudaMemcpyDeviceToDevice);
}
calculate_y_new<<<blocks_per_grid, threads_per_block>>>(num_cells_, num_species_, RR, y_input_BCT, Ymu_, Ystd_, RR);
calculate_y_new<<<blocks_per_grid, threads_per_block>>>(num_cells_, num_modules_, RR, y_input_BCT, Ymu_, Ystd_, RR);
calculate_RR<<<blocks_per_grid, threads_per_block>>>(num_cells_, num_species_, 1e-6, rho, y, RR, RR);
}