Fabio
c36510c21e
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54 lines
1.5 KiB
C++
54 lines
1.5 KiB
C++
#pragma once
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#include "engine/framework/assets/tensor_source.h"
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#include "engine/framework/core/execution_context.h"
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#include "engine/framework/io/json.h"
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#include <memory>
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#include <vector>
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namespace engine::models::plapre {
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struct PlapreLlamaConfig {
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int64_t hidden = 0;
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int64_t intermediate = 0;
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int64_t layers = 0;
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int64_t heads = 0;
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int64_t kv_heads = 0;
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int64_t head_dim = 0;
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int64_t vocabulary = 0;
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int64_t context_length = 0;
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int64_t speaker_dim = 128;
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float norm_eps = 1.0e-5f;
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float rope_theta = 10000.0f;
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static PlapreLlamaConfig from_json(const io::json::Value & value);
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};
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struct PlapreSamplingOptions {
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int64_t max_tokens = 500;
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int64_t min_tokens = 0;
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float temperature = 0.8f;
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float top_p = 0.95f;
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int64_t top_k = 50;
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uint64_t seed = 0;
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std::vector<int32_t> stop_ids;
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};
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class PlapreLlamaGenerator {
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public:
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PlapreLlamaGenerator(std::shared_ptr<const assets::TensorSource> source,
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std::shared_ptr<const assets::TensorSource> speaker_projection,
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PlapreLlamaConfig config, core::ExecutionContext & execution,
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assets::TensorStorageType storage = assets::TensorStorageType::Native);
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~PlapreLlamaGenerator();
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std::vector<float> prepare_inputs(const std::vector<int32_t> & prompt, const std::vector<float> & speaker);
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std::vector<int32_t> generate(const std::vector<float> & embeddings, const PlapreSamplingOptions & options);
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private:
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struct Impl;
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std::unique_ptr<Impl> impl_;
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};
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} // namespace engine::models::plapre
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