Feature/#155 add cuda inference example - #210
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artiomn
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Jul 30, 2026
artiomn
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Jul 31, 2026
| network.data_.inference_internal_projection_.end()) | ||
| network.network_.add_projection(std::move(projection)); | ||
| } | ||
| replace_wta_with_projections(network); |
| { | ||
| throw std::runtime_error("Not supported neuron type."); | ||
| } | ||
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| if (model_desc.type_ == SupportedModelType::BLIFAT) | ||
| network.network_.upcast_populations<knp::neuron_traits::BLIFATNeuron>(); | ||
| else if (model_desc.type_ == SupportedModelType::AltAI) | ||
| network.network_.upcast_populations<knp::neuron_traits::AltAILIF>(); |
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По-моему, всё-таки это код Давида, иначе как оно вообще работало?
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| if (!model_desc.model_saving_path_.empty()) | ||
| { | ||
| if (!model_desc.inference_only_) | ||
| { | ||
| save_network(model_desc, network); | ||
| } | ||
| knp::framework::Network new_network = knp::framework::sonata::load_network(model_desc.model_saving_path_); | ||
| if (new_network.populations_count() != network.network_.populations_count() | ||
| || new_network.projections_count() != network.network_.projections_count()) | ||
| { | ||
| std::cout << "Populations " << new_network.populations_count() << " vs. " | ||
| << network.network_.populations_count() << std::endl; | ||
| std::cout << "Projections: " << new_network.projections_count() << " vs. " | ||
| << network.network_.projections_count() << std::endl; | ||
| } | ||
| network.network_ = new_network; | ||
| } |
| */ | ||
| std::filesystem::path model_saving_path_; | ||
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| /// A flag to not do the training. |
| }; | ||
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| struct ValueIndex |
| gather_index_neuron_kernel<<<num_blocks, num_threads>>>(index.view(), inputs, buffer); | ||
| // in-place prefix sum, for each neuron the value is the number of synapses before this, starts with 0. | ||
| thrust::exclusive_scan(thrust::device, buffer, buffer + inputs.size_, buffer); | ||
| return CUDAVector<unsigned long long>{buffer, inputs.size_}; // The vector would take care of releasing |
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| template <typename BaseSynapseType> | ||
| void Network::upcast_projections() |
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Думаю, что в Network этого быть не должно.
| is_converted = true; | ||
| return core::AllProjectionsVariant{res}; | ||
| } | ||
| return core::AllProjectionsVariant{proj}; // TODO: Remove unnecessary copying. |
| }, population); | ||
| if (is_converted) | ||
| { | ||
| population = std::move(new_population); |
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А если она не сконвертирована?
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Adding CUDA inference example. Now CUDA backend works at least with a decent speed.