Fabio
c36510c21e
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108 lines
3.7 KiB
C++
108 lines
3.7 KiB
C++
#pragma once
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#include "engine/models/ming3_audio/assets.h"
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#include "engine/framework/core/backend_weight_store.h"
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#include "engine/framework/core/disk_expert_weight_store.h"
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#include "engine/framework/modules/linear_module.h"
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#include "engine/framework/modules/norm_modules.h"
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#include <array>
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namespace engine::models::ming3_audio {
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struct ThinkerOptions {
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bool disk_experts = false;
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bool pin_experts = true;
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int64_t expert_cache_size = 8;
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int64_t disk_expert_read_threads = 1;
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};
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struct KdaWeights {
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modules::LinearWeights input_proj;
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std::array<core::TensorValue, 3> conv;
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modules::LinearWeights output;
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core::TensorValue a_log;
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core::TensorValue dt_bias;
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modules::NormWeights norm;
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};
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struct MlaWeights {
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modules::LinearWeights query_down;
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modules::LinearWeights query_up;
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modules::NormWeights query_norm;
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modules::LinearWeights kv_down;
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modules::LinearWeights kv_up;
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modules::NormWeights kv_norm;
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modules::LinearWeights gate;
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modules::LinearWeights output;
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};
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struct ThinkerLayerWeights {
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modules::NormWeights input_norm;
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modules::NormWeights post_norm;
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KdaWeights kda;
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MlaWeights mla;
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modules::LinearWeights mlp_gate_up;
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modules::LinearWeights mlp_down;
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core::TensorValue router;
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core::TensorValue router_bias;
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core::TensorValue expert_gate;
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core::TensorValue expert_up;
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core::TensorValue expert_down;
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core::DiskExpertWeightStore * disk_store = nullptr;
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size_t disk_group = 0;
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};
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struct ThinkerWeights {
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ThinkerWeights(core::ExecutionContext & execution, const assets::TensorSource & source,
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const ThinkerConfig & config, const ThinkerOptions & options = {});
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core::BackendWeightStore store;
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std::unique_ptr<core::DiskExpertWeightStore> disk_store;
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core::TensorValue embedding;
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core::TensorValue head;
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core::TensorValue group_identity;
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modules::NormWeights norm;
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std::vector<ThinkerLayerWeights> layers;
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};
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struct KdaOutput {
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core::TensorValue hidden;
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std::array<core::TensorValue, 3> conv_tails;
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core::TensorValue recurrent_state;
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};
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core::TensorValue build_mla(core::ModuleBuildContext & ctx, const core::TensorValue & input,
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const core::TensorValue & positions, const core::TensorValue & attention_mask,
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const core::TensorValue & key_cache, const core::TensorValue & value_cache,
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const MlaWeights & weights, const ThinkerConfig & config);
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KdaOutput build_kda(core::ModuleBuildContext & ctx, const core::TensorValue & input,
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const std::array<core::TensorValue, 3> & tails,
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const core::TensorValue & state, const KdaWeights & weights,
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const ThinkerConfig & config);
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core::TensorValue build_thinker_mlp(core::ModuleBuildContext & ctx, const core::TensorValue & input,
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const ThinkerLayerWeights & weights, const ThinkerConfig & config,
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const core::TensorValue & group_identity, bool routed);
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class Thinker {
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public:
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Thinker(core::ExecutionContext & execution, std::shared_ptr<const assets::TensorSource> source,
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ThinkerConfig config, int64_t capacity, ThinkerOptions options = {});
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~Thinker();
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void reset();
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void reserve(int64_t capacity);
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void release_workspace();
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std::vector<float> prefill(const std::vector<int32_t> & ids,
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const std::vector<float> & audio_embeddings = {},
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int32_t audio_token_id = -1);
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std::vector<float> step(int32_t token);
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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::ming3_audio
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