Was verursachte den großen Unterschied in der Ausführungszeit zwischen diesen beiden Vorwärtspässen? ⇐ C++

[Anonymous]

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for i in range(263):
states = torch.rand(128, 4, 20, 20, device = device)
# time.sleep(1)
start_time = time.time()
policy, value, action_value = model(states)
end_time = time.time()
total_time = end_time - start_time
print(f"time cost：{total_time:.4f} second")

es wird gedruckt als:

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time cost：0.0003 second
time cost：0.0003 second
time cost：0.0003 second
...

Aber wenn ich time.sleep(1) auskommentiere, wird es langsam:

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time cost：0.0008 second
time cost：0.0009 second
time cost：0.0009 second

In meinem C++-Programm mit libtorch, das mehr als 200 Zeilen hat, also schreibe ich es hier nicht auf, ist der Unterschied noch größer

Code: Select all

std::shared_ptr results_tensor = std::make_shared();
std::shared_ptr model;
while(true)
{
auto start = std::chrono::high_resolution_clock::now();

*results_tensor = nn.model->forward({input});

auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration duration = end - start;
{
std::lock_guard lock(cout_mutex);
std::cout forward(x);

return std::make_tuple(policy, value, action_values);
}
};

int main() {
// Initialize the network
Net net;

net.to(torch::kCUDA);

// Create a random input tensor (batch size 1, 4 channels, 20x20 size)
at::Tensor input_tensor = torch::rand({128, 4, 20, 20}, torch::kCUDA);

// Pass the network through the input data and get the output
for(int i = 0; i < 10; i++)
{
std::this_thread::sleep_for(std::chrono::seconds(60)); // pretend some calculations are being performed
// forward pass I
{
auto cpu_start = std::chrono::high_resolution_clock::now();

cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);

cudaEventRecord(start, 0);  // Start timing

auto output = net.forward(input_tensor);

cudaEventRecord(stop, 0);  // End timing

cudaEventSynchronize(stop);  // Wait for GPU to finish all operations
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);  // Calculate time

std::cout