Maphist0andSigbjørn Skjæret
7775ffb7e4
* model : add SenseNova U1 understanding support Add support for the dense SenseNova U1 understanding branch, including the U1.5-8B-MoT model. - Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation tensors and concatenating temporal and spatial Q/K normalization weights. - Register the sensenova_u1 GGUF architecture and custom spatial RoPE metadata. - Implement temporal/spatial Q/K normalization with 4-axis RoPE. - Keep U1-specific text position handling in the model implementation. - Add synthetic architecture coverage for sensenova_u1. Only the dense understanding branch is supported; generation branches are intentionally rejected during conversion. * conversion : treat one-expert SenseNova U1 configs as dense * mtmd : add SenseNova U1 vision support Add conversion support for the SenseNova U1 vision encoder and implement its CPU mtmd projector graph. The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial downsampling, and dense projection for dynamic-resolution image inputs. * mtmd : fix SenseNova U1 vision token layout * model : clean up SenseNova U1 RoPE metadata Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV definitions used by conversion, model loading, and architecture tests. Clarify why SenseNova U1 retains its custom text-position input: U1 uses [t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0]. * mtmd : simplify SenseNova U1 projector integration Map the SenseNova U1 dense projection to the standard mm.0 weight and bias tensors and reuse the shared projector fields in the loader and graph. Read vision parameters from the checkpoint configuration and remove the U1-specific tensor names and model fields while preserving its strided projection, bias, and row-major token layout. * mtmd : fix SenseNova U1 image positions and attention * fix spacing * mtmd : restore generic test input chunks * mtmd : restore SenseNova U1 integration after upstream merge * Update conversion/sensenova_u1.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * model : remove SenseNova U1 BF16 activation casts Use standard backend precision handling for QKV, FFN, and output projections instead of casting inputs based on the output tensor type. Assisted-by: Codex * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
3025 lines
117 KiB
C++
3025 lines
117 KiB
C++
#pragma once
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#include "llama-model.h"
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#include "llama-graph.h"
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#include "llama-model-loader.h"
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// note: almost all graphs require at least sqrtf, so include cmath globally
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#include <cmath>
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#include <map>
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class llama_memory_hybrid_idx_context;
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// ref: https://github.com/ggml-org/llama.cpp/pull/28068
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static inline ggml_tensor * build_gdn_l2_norm(ggml_context * ctx, ggml_tensor * x, float eps) {
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const float n = x->ne[0];
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return ggml_scale(ctx, ggml_rms_norm(ctx, x, eps/n), 1.0f/sqrtf(n));
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}
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//
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// base classes
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//
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struct llm_build_mamba_base : public llm_graph_context {
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llm_build_mamba_base(const llm_graph_params & params);
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virtual ~llm_build_mamba_base() = default;
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ggml_tensor * build_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
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ggml_tensor * build_mamba2_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il) const;
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};
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struct llm_build_delta_net_base : public llm_graph_context {
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llm_build_delta_net_base(const llm_graph_params & params);
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virtual ~llm_build_delta_net_base() = default;
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// returns pair of output and new state
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std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * b,
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ggml_tensor * s,
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int il);
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// returns pair of output and new state
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std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * b,
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ggml_tensor * s,
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int il);
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// use the ggml_gated_delta_net fused operator (K=1; state has shape [S_v, S_v, H_v, n_seqs])
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std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_fused(
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * b,
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ggml_tensor * s,
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int il);
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// choose one of two implementations above based on the number of tokens
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std::pair<ggml_tensor *, ggml_tensor *> build_delta_net(
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * b,
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ggml_tensor * s,
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int il);
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// read conv state from cache, concat with qkv_mixed, write back (single slot or per-token)
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// qkv_mixed: (qkv_dim, n_seq_tokens, n_seqs); returns conv_input: (kernel_size + n_seq_tokens - 1, channels, n_seqs)
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ggml_tensor * build_conv_state(
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llm_graph_input_rs * inp,
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ggml_tensor * conv_states_all,
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ggml_tensor * qkv_mixed,
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int64_t conv_kernel_size,
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int64_t conv_channels,
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int il);
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// run delta-net attention and write the new recurrent state(s) back to ssm_states_all
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// s: (head_v_dim, head_v_dim, num_v_heads, n_seqs); returns output: (head_v_dim, num_v_heads, n_seq_tokens, n_seqs)
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ggml_tensor * build_recurrent_attn(
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llm_graph_input_rs * inp,
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ggml_tensor * ssm_states_all,
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * b,
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ggml_tensor * s,
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int il);
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};
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struct llm_build_lfm_base : public llm_graph_context {
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llm_build_lfm_base(const llm_graph_params & params);
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virtual ~llm_build_lfm_base() = default;
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ggml_tensor * build_lfm_shortconv(const llama_model & model,
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const llama_memory_recurrent_context * mctx_recr,
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llm_graph_input_rs * inp_recr,
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ggml_tensor * cur,
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int il);
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ggml_tensor * build_lfm_dense_ffn(const llama_model & model,
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ggml_tensor * cur,
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int il);
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ggml_tensor * build_lfm_moe_ffn(const llama_model & model,
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ggml_tensor * cur,
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int il);
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};
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struct llm_build_rwkv6_base : public llm_graph_context {
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const llama_model & model;
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llm_build_rwkv6_base(const llama_model & model, const llm_graph_params & params);
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virtual ~llm_build_rwkv6_base() = default;
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ggml_tensor * build_rwkv6_channel_mix(const llama_layer * layer,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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llm_arch arch) const;
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ggml_tensor * build_rwkv6_time_mix(llm_graph_input_rs * inp,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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const llama_ubatch & ubatch,
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int il) const;
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};
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// Base class for RWKV7-related models
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struct llm_build_rwkv7_base : public llm_graph_context {
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const llama_model & model;
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llm_build_rwkv7_base(const llama_model & model, const llm_graph_params & params);
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virtual ~llm_build_rwkv7_base() = default;
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// RWKV7-specific graph building methods
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ggml_tensor * build_rwkv7_channel_mix(const llama_layer * layer,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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llm_arch arch) const;
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ggml_tensor * build_rwkv7_time_mix(llm_graph_input_rs * inp,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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ggml_tensor *& first_layer_value,
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const llama_ubatch & ubatch,
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int il) const;
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};
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//
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// models
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//
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struct llama_model_llama : public llama_model_base {
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llama_model_llama(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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template <bool embed>
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_llama4 : public llama_model_base {
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llama_model_llama4(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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template <bool iswa>
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_llama_embed : public llama_model_llama {
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llama_model_llama_embed(const struct llama_model_params & params) : llama_model_llama(params) {}
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// reuse load_arch_hparams and load_arch_tensors from llama_model_llama
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template <bool embed>
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using graph = llama_model_llama::graph<embed>;
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_maincoder : public llama_model_base {
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llama_model_maincoder(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_maion_coder : public llama_model_base {
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llama_model_maion_coder(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_talkie : public llama_model_base {
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llama_model_talkie(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_deci : public llama_model_base {
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llama_model_deci(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_baichuan : public llama_model_base {
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llama_model_baichuan(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_falcon : public llama_model_base {
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llama_model_falcon(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_grok : public llama_model_base {
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llama_model_grok(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_starcoder : public llama_model_base {
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llama_model_starcoder(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_refact : public llama_model_base {
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llama_model_refact(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_bert : public llama_model_base {
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llama_model_bert(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_jina_bert_v2 : public llama_model_base {
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llama_model_jina_bert_v2(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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using graph = llama_model_bert::graph;
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_jina_bert_v3 : public llama_model_base {
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llama_model_jina_bert_v3(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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using graph = llama_model_bert::graph;
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_nomic_bert : public llama_model_base {
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llama_model_nomic_bert(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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using graph = llama_model_bert::graph;
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_nomic_bert_moe : public llama_model_base {
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llama_model_nomic_bert_moe(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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using graph = llama_model_bert::graph;
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_modern_bert : public llama_model_base {
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llama_model_modern_bert(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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ggml_tensor * build_decision_head(
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const llama_model & model,
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ggml_tensor * inp,
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llm_graph_input_attn_no_cache * inp_attn,
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ggml_tensor * inp_out_ids);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_neo_bert : public llama_model_base {
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llama_model_neo_bert(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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struct graph : public llm_graph_context {
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|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_eurobert : public llama_model_base {
|
|
llama_model_eurobert(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_bloom : public llama_model_base {
|
|
llama_model_bloom(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
// Quant-only stub for mmproj GGUFs
|
|
// none of these are ever called, they only exist to satisfy the llama_model_base interface
|
|
struct llama_model_clip : public llama_model_base {
|
|
llama_model_clip(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
|
|
[[noreturn]]
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
|
|
[[noreturn]]
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
[[noreturn]]
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mpt : public llama_model_base {
|
|
llama_model_mpt(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_stablelm : public llama_model_base {
|
|
llama_model_stablelm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_mellum : public llama_model_base {
|
|
llama_model_mellum(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
template <bool iswa>
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_nanbeige : public llama_model_base {
|
|
llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
int n_loops = 1;
|
|
int n_layer_phys = 0;
|
|
bool skip_loop_final_norm = false;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_qwen : public llama_model_base {
|
|
llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen2 : public llama_model_base {
|
|
llama_model_qwen2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_dream : public llama_model_base {
|
|
llama_model_dream(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_llada : public llama_model_base {
|
|
llama_model_llada(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_llada_moe : public llama_model_base {
|
|
llama_model_llada_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_rnd1 : public llama_model_base {
|
|
llama_model_rnd1(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen2vl : public llama_model_base {
|
|
llama_model_qwen2vl(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen2moe : public llama_model_base {
|
|
llama_model_qwen2moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_sensenova_u1 : public llama_model_base {
|
|
llama_model_sensenova_u1(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
float rope_freq_base_spatial = 10000.0f;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model_sensenova_u1 & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3 : public llama_model_base {
|
|
llama_model_qwen3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
protected:
|
|
// the helpers alone, a derived graph builds its own body
|
|
struct no_build {};
|
|
graph(const llm_graph_params & params, no_build) : llm_graph_context(params) {}
|
|
|
|
// the layers and the final norm: [n_embd, n_outputs]
|
|
ggml_tensor * build_backbone(const llama_model & model);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
// Qwen3 with the decision head of vllm-sr Decision 2.0: one score per option
|
|
struct llama_model_vllm_decision2 : public llama_model_qwen3 {
|
|
llama_model_vllm_decision2(const struct llama_model_params & params) : llama_model_qwen3(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
ggml_tensor * option_norm = nullptr;
|
|
ggml_tensor * option_norm_b = nullptr;
|
|
ggml_tensor * query_norm = nullptr;
|
|
ggml_tensor * query_norm_b = nullptr;
|
|
ggml_tensor * key = nullptr;
|
|
ggml_tensor * query = nullptr;
|
|
ggml_tensor * option_mlp = nullptr;
|
|
ggml_tensor * option_mlp_b = nullptr;
|
|
ggml_tensor * query_mlp = nullptr;
|
|
ggml_tensor * scorer_out = nullptr;
|
|
|
|
class input_decision;
|
|
|
|
struct graph : public llama_model_qwen3::graph {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
// hidden: [n_embd, n_outputs], returns one score per option: [1, n_options]
|
|
ggml_tensor * build_head(ggml_tensor * hidden, input_decision * inp);
|
|
|
|
const llama_model_vllm_decision2 & model;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3moe : public llama_model_base {
|
|
llama_model_qwen3moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3vl : public llama_model_base {
|
|
llama_model_qwen3vl(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3vlmoe : public llama_model_base {
|
|
llama_model_qwen3vlmoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3tts : public llama_model_qwen3vl {
|
|
llama_model_qwen3tts(const struct llama_model_params & params) : llama_model_qwen3vl(params) {}
|
|
};
|
|
|
|
|
|
struct llama_model_phi2 : public llama_model_base {
|
|
llama_model_phi2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_phi3 : public llama_model_base {
|
|
llama_model_phi3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_phimoe : public llama_model_base {
|
|
llama_model_phimoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
using graph = llama_model_phi3::graph<iswa>;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_plamo : public llama_model_base {
|
|
llama_model_plamo(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_plamo2 : public llama_model_base {
|
|
llama_model_plamo2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
ggml_tensor * build_plamo2_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
|
|
ggml_tensor * build_plamo2_attn_layer(llm_graph_input_attn_kv * inp, ggml_tensor * inp_pos, ggml_tensor * cur,
|
|
const llama_model & model, int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_plamo3 : public llama_model_base {
|
|
llama_model_plamo3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gpt2 : public llama_model_base {
|
|
llama_model_gpt2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_pockettts : public llama_model_base {
|
|
llama_model_pockettts(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_codeshell : public llama_model_base {
|
|
llama_model_codeshell(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_orion : public llama_model_base {
|
|
llama_model_orion(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_internlm2 : public llama_model_base {
|
|
llama_model_internlm2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_minicpm3 : public llama_model_base {
|
|
llama_model_minicpm3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma : public llama_model_base {
|
|
llama_model_gemma(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma2 : public llama_model_base {
|
|
llama_model_gemma2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma3 : public llama_model_base {
|
|
llama_model_gemma3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma3n : public llama_model_base {
|
|
llama_model_gemma3n(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
const llama_model & model;
|
|
|
|
const int64_t n_embd_head;
|
|
const int64_t n_embd_altup;
|
|
const int64_t n_altup;
|
|
const int i_altup_act;
|
|
const int n_layer_sparsity = 10; // number of layers using activation sparsity
|
|
const float f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
|
|
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
ggml_tensor * calc_magnitude(ggml_tensor * x);
|
|
|
|
// TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
|
|
ggml_tensor * build_inp_per_layer();
|
|
ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
|
|
|
|
ggml_tensor * gaussian_topk(ggml_tensor * x);
|
|
ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il);
|
|
ggml_tensor * altup_predict(ggml_tensor * cur, int il);
|
|
ggml_tensor * laurel(ggml_tensor * cur, int il);
|
|
ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma4 : public llama_model_base {
|
|
llama_model_gemma4(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
const llama_model & model;
|
|
|
|
const int64_t n_embd_per_layer;
|
|
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
// TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
|
|
ggml_tensor * build_inp_per_layer();
|
|
ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma4_assistant : public llama_model_base {
|
|
llama_model_gemma4_assistant(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma_embedding : public llama_model_base {
|
|
llama_model_gemma_embedding(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gemma_embedding2 : public llama_model_base {
|
|
llama_model_gemma_embedding2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
const llama_model & model;
|
|
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_inp_per_layer(ggml_tensor * inpL);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_starcoder2 : public llama_model_base {
|
|
llama_model_starcoder2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mamba : public llama_model_base {
|
|
llama_model_mamba(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mamba2 : public llama_model_base {
|
|
llama_model_mamba2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
using graph = llama_model_mamba::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_maple : public llama_model_base {
|
|
llama_model_maple(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_jamba : public llama_model_base {
|
|
llama_model_jamba(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_xverse : public llama_model_base {
|
|
llama_model_xverse(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_command_r : public llama_model_base {
|
|
llama_model_command_r(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_cohere2 : public llama_model_base {
|
|
llama_model_cohere2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_cohere2moe : public llama_model_base {
|
|
llama_model_cohere2moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_dbrx : public llama_model_base {
|
|
llama_model_dbrx(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_olmo : public llama_model_base {
|
|
llama_model_olmo(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_olmo2 : public llama_model_base {
|
|
llama_model_olmo2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_olmoe : public llama_model_base {
|
|
llama_model_olmoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_muse_spark : public llama_model_base {
|
|
llama_model_muse_spark(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_muse_glimmer : public llama_model_base {
|
|
llama_model_muse_glimmer(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_openelm : public llama_model_base {
|
|
llama_model_openelm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_gptneox : public llama_model_base {
|
|
llama_model_gptneox(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_arctic : public llama_model_base {
|
|
llama_model_arctic(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_deepseek : public llama_model_base {
|
|
llama_model_deepseek(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_deepseek2 : public llama_model_base {
|
|
llama_model_deepseek2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_deepseek32 : public llama_model_base {
|
|
llama_model_deepseek32(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_dots3note : public llama_model_base {
|
|
llama_model_dots3note(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_deepseek4 : public llama_model_base {
|
|
llama_model_deepseek4(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llm_graph_params & params) : llm_graph_context(params) {}
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
// collapse the hc streams with per-stream weights
|
|
ggml_tensor * build_hc_pre(
|
|
ggml_tensor * x,
|
|
ggml_tensor * weights,
|
|
int il) const;
|
|
|
|
// mean over the hyper-connection streams: [n_embd, hc, n_tokens] -> [n_embd, n_tokens]
|
|
ggml_tensor * build_hc_mean(ggml_tensor * x) const;
|
|
|
|
// returns the collapsed input and fills the post / comb weights
|
|
ggml_tensor * build_hc_pre(
|
|
ggml_tensor * x,
|
|
ggml_tensor * hc_fn,
|
|
ggml_tensor * hc_scale,
|
|
ggml_tensor * hc_base,
|
|
ggml_tensor ** post,
|
|
ggml_tensor ** comb,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_post(
|
|
ggml_tensor * x,
|
|
ggml_tensor * residual,
|
|
ggml_tensor * post,
|
|
ggml_tensor * comb,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_sinkhorn(
|
|
ggml_tensor * comb,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_head(
|
|
ggml_tensor * x,
|
|
ggml_tensor * hc_fn,
|
|
ggml_tensor * hc_scale,
|
|
ggml_tensor * hc_base) const;
|
|
|
|
ggml_tensor * build_attention(
|
|
const llama_model & model,
|
|
llm_graph_input_dsv4 * inp_dsv4,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_attention(
|
|
const llama_model & model,
|
|
llm_graph_input_attn_k_iswa * inp_mtp,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_attention_impl(
|
|
const llama_model & model,
|
|
llm_graph_input_dsv4 * inp_dsv4,
|
|
llm_graph_input_attn_k_iswa * inp_mtp,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hca_compressed_kv_from_state(
|
|
ggml_tensor * kv_state,
|
|
ggml_tensor * score_state,
|
|
ggml_tensor * state_read_idxs,
|
|
ggml_tensor * comp_pos,
|
|
ggml_tensor * norm,
|
|
int64_t n_embd_head,
|
|
const char * name,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_overlap_compressed_kv_from_state(
|
|
ggml_tensor * kv_state,
|
|
ggml_tensor * score_state,
|
|
ggml_tensor * state_read_idxs,
|
|
ggml_tensor * comp_pos,
|
|
ggml_tensor * norm,
|
|
int64_t ratio,
|
|
int64_t n_embd_head,
|
|
const char * name,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_lid_top_k(
|
|
const llama_model & model,
|
|
llm_graph_input_dsv4 * inp_dsv4,
|
|
ggml_tensor * qr,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_top_k_mask(
|
|
ggml_tensor * kq_mask,
|
|
ggml_tensor * top_k,
|
|
const char * name,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_csa_lid_attention(
|
|
const llama_model & model,
|
|
llm_graph_input_dsv4 * inp_dsv4,
|
|
llm_graph_input_dsv4_raw * inp_attn,
|
|
ggml_tensor * q,
|
|
ggml_tensor * kv,
|
|
ggml_tensor * qr,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
ggml_tensor * sinks,
|
|
float kq_scale,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hca_attention(
|
|
llm_graph_input_dsv4 * inp_dsv4,
|
|
llm_graph_input_dsv4_raw * inp_attn,
|
|
ggml_tensor * q,
|
|
ggml_tensor * kv,
|
|
ggml_tensor * sinks,
|
|
float kq_scale,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_raw_attention(
|
|
llm_graph_input_dsv4_raw * inp_attn,
|
|
ggml_tensor * q,
|
|
ggml_tensor * kv,
|
|
ggml_tensor * sinks,
|
|
float kq_scale,
|
|
int il) const;
|
|
|
|
};
|
|
|
|
struct graph_mtp : public graph {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_deepseek2ocr : public llama_model_base {
|
|
llama_model_deepseek2ocr(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
using graph = llama_model_deepseek2::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_glm_dsa : public llama_model_base {
|
|
llama_model_glm_dsa(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_eagle3 : public llama_model_base {
|
|
llama_model_eagle3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool is_enc>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_inp_embd_enc() const;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_dflash : public llama_model_base {
|
|
llama_model_dflash(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool is_enc>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_inp_embd_enc() const;
|
|
};
|
|
|
|
struct graph_dsv4 : public llama_model_deepseek4::graph {
|
|
graph_dsv4(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mistral4 : public llama_model_deepseek2 {
|
|
llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}
|
|
// reuse load_arch_hparams and load_arch_tensors from llama_model_deepseek2
|
|
|
|
using graph = llama_model_deepseek2::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_chatglm : public llama_model_base {
|
|
llama_model_chatglm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_glm4 : public llama_model_base {
|
|
llama_model_glm4(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_glm4_moe : public llama_model_base {
|
|
llama_model_glm4_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_bitnet : public llama_model_base {
|
|
llama_model_bitnet(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_t5 : public llama_model_base {
|
|
llama_model_t5(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool is_enc>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_t5encoder : public llama_model_base {
|
|
llama_model_t5encoder(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
using graph = llama_model_t5::graph<true>;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_jais : public llama_model_base {
|
|
llama_model_jais(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_jais2 : public llama_model_base {
|
|
llama_model_jais2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_nemotron : public llama_model_base {
|
|
llama_model_nemotron(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_nemotron_h : public llama_model_base {
|
|
llama_model_nemotron_h(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
ggml_tensor * build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il);
|
|
ggml_tensor * build_attention_layer(ggml_tensor * cur, llm_graph_input_attn_kv * inp_attn,
|
|
const llama_model & model, int64_t n_embd_head, int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_nemotron_h_moe : public llama_model_nemotron_h {
|
|
llama_model_nemotron_h_moe(const struct llama_model_params & params) : llama_model_nemotron_h(params) {}
|
|
// reuse load_arch_hparams and load_arch_tensors from llama_model_nemotron_h
|
|
|
|
using graph = llama_model_nemotron_h::graph;
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_exaone : public llama_model_base {
|
|
llama_model_exaone(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_exaone4 : public llama_model_base {
|
|
llama_model_exaone4(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_exaone_moe : public llama_model_base {
|
|
llama_model_exaone_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_rwkv6 : public llama_model_base {
|
|
llama_model_rwkv6(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_rwkv6_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_rwkv6qwen2 : public llama_model_base {
|
|
llama_model_rwkv6qwen2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_rwkv6_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_rwkv7 : public llama_model_base {
|
|
llama_model_rwkv7(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_rwkv7_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_arwkv7 : public llama_model_base {
|
|
llama_model_arwkv7(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_rwkv7_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_granite : public llama_model_base {
|
|
llama_model_granite(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
private:
|
|
ggml_tensor * build_attention_layer(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
const llama_model & model,
|
|
const int64_t n_embd_head,
|
|
const int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inpSA,
|
|
const llama_model & model,
|
|
const int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_granite_moe : public llama_model_base {
|
|
llama_model_granite_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
using graph = llama_model_granite::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_granite_switch : public llama_model_base {
|
|
llama_model_granite_switch(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
uint32_t n_adapters = 0;
|
|
uint32_t max_lora_rank = 0;
|
|
float router_gain = 15.0f;
|
|
|
|
std::unordered_map<llama_token, int32_t> adapter_token_to_slot;
|
|
std::unordered_map<llama_token, llama_token> adapter_token_to_substitute;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
private:
|
|
ggml_tensor * build_switched_lora_delta(
|
|
ggml_tensor * lora_a,
|
|
ggml_tensor * lora_b,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * ids);
|
|
|
|
ggml_tensor * build_switched_lora_mm(
|
|
ggml_tensor * w,
|
|
ggml_tensor * lora_a,
|
|
ggml_tensor * lora_b,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * ids);
|
|
|
|
ggml_tensor * build_attention_layer(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
ggml_tensor * adapter_ids,
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
const llama_model & model,
|
|
const int64_t n_embd_head,
|
|
const int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inpSA,
|
|
ggml_tensor * adapter_ids,
|
|
const llama_model & model,
|
|
const int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_minicpm : public llama_model_base {
|
|
llama_model_minicpm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
using graph = llama_model_granite::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_granite_hybrid : public llama_model_base {
|
|
llama_model_granite_hybrid(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
ggml_tensor * build_layer_ffn(ggml_tensor * cur, ggml_tensor * inpSA, const llama_model & model, const int il);
|
|
ggml_tensor * build_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn,
|
|
const llama_model & model,const int64_t n_embd_head, const int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_granite_swa : public llama_model_base {
|
|
llama_model_granite_swa(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
private:
|
|
ggml_tensor * build_attention_layer(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
llm_graph_input_attn_kv_iswa * inp_attn,
|
|
const llama_model & model,
|
|
const int64_t n_embd_head,
|
|
const int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inpSA,
|
|
const llama_model & model,
|
|
const int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_chameleon : public llama_model_base {
|
|
llama_model_chameleon(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_wavtokenizer_dec : public llama_model_base {
|
|
llama_model_wavtokenizer_dec(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_plm : public llama_model_base {
|
|
llama_model_plm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_hrm_text : public llama_model_base {
|
|
llama_model_hrm_text(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
const llama_model & model;
|
|
|
|
ggml_tensor * build_stack(
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
ggml_tensor * inp_pos,
|
|
ggml_tensor * cur,
|
|
int slot_base) const;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_bailingmoe : public llama_model_base {
|
|
llama_model_bailingmoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_bailingmoe2 : public llama_model_base {
|
|
llama_model_bailingmoe2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_bailingmoe3 : public llama_model_base {
|
|
llama_model_bailingmoe3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_seed_oss : public llama_model_base {
|
|
llama_model_seed_oss(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_dots1 : public llama_model_base {
|
|
llama_model_dots1(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_arcee : public llama_model_base {
|
|
llama_model_arcee(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_afmoe : public llama_model_base {
|
|
llama_model_afmoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_beam : public llama_model_base {
|
|
llama_model_beam(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_laguna : public llama_model_base {
|
|
llama_model_laguna(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_ernie4_5 : public llama_model_base {
|
|
llama_model_ernie4_5(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_ernie4_5_moe : public llama_model_ernie4_5 {
|
|
llama_model_ernie4_5_moe(const struct llama_model_params & params) : llama_model_ernie4_5(params) {}
|
|
// reuse load_arch_hparams and load_arch_tensors from llama_model_ernie4_5
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_paddleocr : public llama_model_ernie4_5 {
|
|
llama_model_paddleocr(const struct llama_model_params & params) : llama_model_ernie4_5(params) {}
|
|
// reuse load_arch_hparams and load_arch_tensors from llama_model_ernie4_5
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_hunyuan_moe : public llama_model_base {
|
|
llama_model_hunyuan_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_hy_v3 : public llama_model_base {
|
|
llama_model_hy_v3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_hy_v4 : public llama_model_base {
|
|
llama_model_hy_v4(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
// iHC (independent Hyper-Connections): pre reduces the hc streams to one and returns the
|
|
// per-stream post gates, post writes the sublayer output back into the streams, head
|
|
// collapses the streams before the final norm.
|
|
ggml_tensor * build_hc_pre(
|
|
ggml_tensor * x,
|
|
ggml_tensor * hc_fn,
|
|
ggml_tensor * hc_scale,
|
|
ggml_tensor * hc_base,
|
|
ggml_tensor ** post,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_post(
|
|
ggml_tensor * x,
|
|
ggml_tensor * residual,
|
|
ggml_tensor * post,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_head(
|
|
ggml_tensor * x,
|
|
ggml_tensor * hc_fn,
|
|
ggml_tensor * hc_scale,
|
|
ggml_tensor * hc_base) const;
|
|
|
|
ggml_tensor * build_attention(
|
|
const llama_model & model,
|
|
llm_graph_input_attn_k * inp_attn,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
float kq_scale,
|
|
int il) const;
|
|
|
|
// DSA lightning indexer: top-k KV positions for this layer. Only "full" layers compute
|
|
// it, "shared" layers reuse the last preceding full layer result through last_top_k.
|
|
ggml_tensor * build_indexer_top_k(
|
|
const llama_model & model,
|
|
llm_graph_input_attn_k_dsa * inp_attn_dsa,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * qr,
|
|
ggml_tensor * inp_pos,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_attention_dsa(
|
|
const llama_model & model,
|
|
llm_graph_input_attn_k_dsa * inp_attn_dsa,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
ggml_tensor ** last_top_k,
|
|
float kq_scale,
|
|
int il) const;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_hunyuan_vl : public llama_model_base {
|
|
llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_hunyuan_dense : public llama_model_hunyuan_vl {
|
|
llama_model_hunyuan_dense(const struct llama_model_params & params) : llama_model_hunyuan_vl(params) {}
|
|
// reuse load_arch_hparams and load_arch_tensors from llama_model_hunyuan_vl
|
|
|
|
using graph = llama_model_hunyuan_vl::graph;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_smollm3 : public llama_model_base {
|
|
llama_model_smollm3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_openai_moe : public llama_model_base {
|
|
llama_model_openai_moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_falcon_h1 : public llama_model_base {
|
|
llama_model_falcon_h1(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_mamba_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_lfm2 : public llama_model_base {
|
|
llama_model_lfm2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_build_lfm_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
// non-causal trunk without memory, then the decision head
|
|
struct graph_decision : public llm_graph_context {
|
|
graph_decision(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_decision_head(
|
|
const llama_model & model,
|
|
ggml_tensor * inp,
|
|
llm_graph_input_attn_no_cache * inp_attn,
|
|
ggml_tensor * inp_out_ids);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_lfm2moe : public llama_model_base {
|
|
llama_model_lfm2moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
using graph = llama_model_lfm2::graph<iswa>;
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_smallthinker : public llama_model_base {
|
|
llama_model_smallthinker(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
template <bool iswa>
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_grovemoe : public llama_model_base {
|
|
llama_model_grovemoe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_apertus : public llama_model_base {
|
|
llama_model_apertus(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_minimax_01 : public llama_model_base {
|
|
llama_model_minimax_01(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_minimax_m2 : public llama_model_base {
|
|
llama_model_minimax_m2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct msa_params {
|
|
int blk;
|
|
int topk_blocks;
|
|
int local;
|
|
};
|
|
|
|
struct llama_model_minimax_m3 : public llama_model_base {
|
|
llama_model_minimax_m3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
msa_params msa_p;
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_attn_msa_fa(
|
|
ggml_tensor * q_cur, // [D, HQ, S] f32
|
|
ggml_tensor * k, // [D, n_keys, 1, C] C = HKV or HKV*n_stream
|
|
ggml_tensor * v, // [D, n_keys, 1, C]
|
|
ggml_tensor * mask, // [n_keys, R, 1, C] f16, R = HQ*T/(Gp*C)
|
|
int64_t Gp, float kq_scale, int il) const;
|
|
};
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_cogvlm : public llama_model_base {
|
|
llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_pangu_embed : public llama_model_base {
|
|
llama_model_pangu_embed(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen3next : public llama_model_base {
|
|
llama_model_qwen3next(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
ggml_tensor * build_layer_attn(
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn_linear(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_norm_gated(
|
|
ggml_tensor * input,
|
|
ggml_tensor * weights,
|
|
ggml_tensor * gate,
|
|
int layer);
|
|
|
|
// returns pair of qkv, z
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
|
|
ggml_tensor * input,
|
|
int il);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen35 : public llama_model_base {
|
|
llama_model_qwen35(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
ggml_tensor * build_layer_attn(
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int * sections,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn_linear(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_norm_gated(
|
|
ggml_tensor * input,
|
|
ggml_tensor * weights,
|
|
ggml_tensor * gate,
|
|
int layer);
|
|
|
|
// returns pair of qkv, z
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
|
|
ggml_tensor * input,
|
|
int il);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
// Qwen3.5 backbone evaluated without memory, with a joint decision head on top of its hidden states
|
|
struct llama_model_clef : public llama_model_qwen35 {
|
|
llama_model_clef(const struct llama_model_params & params) : llama_model_qwen35(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct attn {
|
|
ggml_tensor * wq = nullptr;
|
|
ggml_tensor * bq = nullptr;
|
|
ggml_tensor * wk = nullptr;
|
|
ggml_tensor * bk = nullptr;
|
|
ggml_tensor * wv = nullptr;
|
|
ggml_tensor * bv = nullptr;
|
|
ggml_tensor * wo = nullptr;
|
|
ggml_tensor * bo = nullptr;
|
|
};
|
|
|
|
struct norm {
|
|
ggml_tensor * w = nullptr;
|
|
ggml_tensor * b = nullptr;
|
|
};
|
|
|
|
// the first n_layer_routing blocks have no self attention
|
|
struct head_layer {
|
|
norm self_norm;
|
|
attn self_attn;
|
|
norm cross_norm;
|
|
norm cross_norm_kv; // routing blocks only
|
|
attn cross_attn;
|
|
norm ffn_norm;
|
|
ggml_tensor * ffn_up = nullptr;
|
|
ggml_tensor * ffn_up_b = nullptr;
|
|
ggml_tensor * ffn_down = nullptr;
|
|
ggml_tensor * ffn_down_b = nullptr;
|
|
};
|
|
|
|
uint32_t n_layer_routing = 0;
|
|
uint32_t n_layer_joint = 0;
|
|
uint32_t n_head_decision = 0;
|
|
|
|
std::vector<head_layer> head_layers; // routing blocks, then joint blocks
|
|
|
|
norm hidden_norm;
|
|
norm option_summary_norm;
|
|
norm field_norm;
|
|
norm option_norm;
|
|
|
|
ggml_tensor * proj_memory = nullptr;
|
|
ggml_tensor * proj_question = nullptr;
|
|
ggml_tensor * proj_option_question = nullptr;
|
|
ggml_tensor * proj_global = nullptr;
|
|
ggml_tensor * proj_option_context = nullptr;
|
|
ggml_tensor * proj_option_lexical = nullptr;
|
|
ggml_tensor * scales = nullptr; // prior scale, joint scale, residual gate
|
|
ggml_tensor * scorer = nullptr;
|
|
ggml_tensor * scorer_b = nullptr;
|
|
ggml_tensor * scorer_out = nullptr;
|
|
ggml_tensor * scorer_out_b = nullptr;
|
|
|
|
class input_decision;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
llm_graph_input_attn_no_cache * build_attn_inp_causal();
|
|
|
|
ggml_tensor * build_layer_attn(
|
|
llm_graph_input_attn_no_cache * inp_attn,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int * sections,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn_linear(
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
// hidden: [n_embd, n_tokens], returns one score per option: [1, n_options]
|
|
ggml_tensor * build_head(
|
|
ggml_tensor * hidden,
|
|
input_decision * inp);
|
|
|
|
ggml_tensor * build_head_norm(ggml_tensor * cur, const norm & n);
|
|
ggml_tensor * build_head_attn(ggml_tensor * q, ggml_tensor * kv, const attn & a);
|
|
ggml_tensor * build_head_ffn (ggml_tensor * cur, const head_layer & layer);
|
|
|
|
const llama_model_clef & model;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_qwen4exp : public llama_model_base {
|
|
llama_model_qwen4exp(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
|
|
class llm_graph_input_kpool;
|
|
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
protected:
|
|
// the helpers alone, graph_mtp builds its own body
|
|
struct no_build {};
|
|
graph(const llama_model & model, const llm_graph_params & params, no_build) :
|
|
llm_build_delta_net_base(params), model(model) {}
|
|
|
|
// HC replaces every layer norm: residual is [n_embd, hc, n_tokens]
|
|
ggml_tensor * build_hc_mix(
|
|
ggml_tensor * x,
|
|
ggml_tensor * w_norm,
|
|
ggml_tensor * w_down,
|
|
ggml_tensor * w_up,
|
|
ggml_tensor * w_inject,
|
|
ggml_tensor ** inject,
|
|
int il);
|
|
|
|
ggml_tensor * build_hc_combine(
|
|
ggml_tensor * residual,
|
|
ggml_tensor * block_out,
|
|
ggml_tensor * inject,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn(
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
const llama_memory_hybrid_idx_context * mctx_hyb,
|
|
llm_graph_input_kpool * inp_kpool,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int * sections,
|
|
int il);
|
|
|
|
// dense self-attention over the cells the QSA mask keeps
|
|
ggml_tensor * build_attn_qsa(
|
|
llm_graph_input_attn_kv * inp,
|
|
ggml_tensor * q_cur,
|
|
ggml_tensor * k_cur,
|
|
ggml_tensor * v_cur,
|
|
ggml_tensor * sel,
|
|
int64_t n_sel,
|
|
float kq_scale,
|
|
int il);
|
|
|
|
// the QSA layers share one set of k-pool inputs, see llama_memory_hybrid_idx
|
|
llm_graph_input_kpool * build_inp_kpool(const llama_memory_hybrid_idx_context * mctx_hyb);
|
|
|
|
// QSA: the additive mask [n_kv, n_tokens] of the top blocks and the tail, kq_mask included
|
|
ggml_tensor * build_qsa_sel(
|
|
const llama_memory_hybrid_idx_context * mctx_hyb,
|
|
llm_graph_input_kpool * inp_kpool,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
ggml_tensor * kq_mask,
|
|
int * sections,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn_linear(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_norm_gated(
|
|
ggml_tensor * input,
|
|
ggml_tensor * weights,
|
|
ggml_tensor * gate,
|
|
int layer);
|
|
|
|
// build_rs writes the state tensor in place, so one gather per cache tensor is reused
|
|
std::map<ggml_tensor *, ggml_tensor *> rs_rows;
|
|
|
|
// one conv history per cache tensor: delta-net and PLE each have their own
|
|
ggml_tensor * build_conv_state_at(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * conv_states_all,
|
|
ggml_tensor * x,
|
|
int64_t state_cols,
|
|
int64_t channels,
|
|
int il);
|
|
|
|
ggml_tensor * build_inp_ple(
|
|
const llama_memory_hybrid_idx_context * mctx_hyb);
|
|
|
|
ggml_tensor * build_ple(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * emb,
|
|
ggml_tensor * hidden,
|
|
int il);
|
|
|
|
// returns pair of qkv, z
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
|
|
ggml_tensor * input,
|
|
int il);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
// MTP draft head: one QSA block after the trunk, fed by the trunk's hc-wide residual
|
|
struct graph_mtp : public graph {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_qwen35moe : public llama_model_base {
|
|
llama_model_qwen35moe(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
private:
|
|
ggml_tensor * build_layer_attn(
|
|
llm_graph_input_attn_kv * inp_attn,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inp_pos,
|
|
int * sections,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_attn_linear(
|
|
llm_graph_input_rs * inp,
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
int il);
|
|
|
|
ggml_tensor * build_norm_gated(
|
|
ggml_tensor * input,
|
|
ggml_tensor * weights,
|
|
ggml_tensor * gate,
|
|
int layer);
|
|
|
|
// returns pair of qkv, z
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
|
|
ggml_tensor * input,
|
|
int il);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mistral3 : public llama_model_base {
|
|
llama_model_mistral3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
class input_t_cond; // voxtral realtime: time conditioning of the ada rms norm
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_mimo2 : public llama_model_base {
|
|
llama_model_mimo2(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_kimi_k3 : public llama_model_base {
|
|
llama_model_kimi_k3(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
const llama_model & model;
|
|
|
|
// Cross-layer residual attention (K3's `_apply_attn_res`).
|
|
ggml_tensor * resi_stack = nullptr;
|
|
|
|
void res_push(ggml_tensor * cur, int64_t n_embd, int64_t n_tokens);
|
|
ggml_tensor * res_mix(ggml_tensor * cur, ggml_tensor * score_w,
|
|
int64_t n_tokens, int il);
|
|
|
|
ggml_tensor * build_kda_layer(ggml_tensor * cur, const llama_layer & layer,
|
|
llm_graph_input_rs * inp_rs,
|
|
int64_t d_conv, int64_t head_dim, int64_t n_head_kda,
|
|
int64_t d_inner, int64_t n_seq_tokens, int64_t n_seqs, int il);
|
|
|
|
ggml_tensor * build_mla_layer(ggml_tensor * cur, const llama_layer & layer,
|
|
llm_graph_input_attn_k * inp_attn_k,
|
|
llm_graph_input_attn_kv * inp_attn_kv,
|
|
int64_t n_embd_head_k_mla, int64_t n_embd_head_v_mla,
|
|
int64_t kv_lora_rank, int64_t n_embd_head_qk_rope,
|
|
int64_t n_embd_head_qk_nope, float kq_scale, int il);
|
|
|
|
ggml_tensor * build_latent_moe(ggml_tensor * cur, const llama_layer & layer,
|
|
int64_t n_embd_latent, int il);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_glm5_next : public llama_model_base {
|
|
llama_model_glm5_next(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
// k-pool indexer inputs on top of the generic hybrid input
|
|
class llm_graph_input_kpool;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
// build the helpers without the trunk, so graph_mtp can reuse them
|
|
struct no_build {};
|
|
graph(const llama_model & model, const llm_graph_params & params, no_build);
|
|
|
|
// collapse the hc streams with per-stream weights
|
|
ggml_tensor * build_hc_pre(
|
|
ggml_tensor * x,
|
|
ggml_tensor * weights,
|
|
int il) const;
|
|
|
|
// mean over the hyper-connection streams: [n_embd, hc, n_tokens] -> [n_embd, n_tokens]
|
|
ggml_tensor * build_hc_mean(ggml_tensor * x) const;
|
|
|
|
// returns the collapsed input and fills the post / comb weights
|
|
ggml_tensor * build_hc_pre(
|
|
ggml_tensor * x,
|
|
ggml_tensor * hc_fn,
|
|
ggml_tensor * hc_scale,
|
|
ggml_tensor * hc_base,
|
|
ggml_tensor ** post,
|
|
ggml_tensor ** comb,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_post(
|
|
ggml_tensor * x,
|
|
ggml_tensor * residual,
|
|
ggml_tensor * post,
|
|
ggml_tensor * comb,
|
|
int il) const;
|
|
|
|
ggml_tensor * build_hc_sinkhorn(
|
|
ggml_tensor * comb,
|
|
int il) const;
|
|
|
|
const llama_model & model;
|
|
|
|
llm_graph_input_kpool * build_inp_kpool(const llama_memory_hybrid_idx_context * mctx_hyb);
|
|
|
|
ggml_tensor * build_kda_layer(ggml_tensor * cur, const llama_layer & layer,
|
|
llm_graph_input_rs * inp_rs,
|
|
int64_t d_conv, int64_t head_dim, int64_t n_head_kda,
|
|
int64_t d_inner, int64_t n_seq_tokens, int64_t n_seqs, int il);
|
|
|
|
ggml_tensor * build_kpool_select(ggml_tensor * cur, ggml_tensor * qr, ggml_tensor * kq_mask, const llama_layer & layer,
|
|
const llama_memory_hybrid_idx_context * mctx_hyb, llm_graph_input_kpool * inp_kpool, int il);
|
|
|
|
ggml_tensor * build_dsa_layer(ggml_tensor * cur, const llama_layer & layer,
|
|
const llama_memory_hybrid_idx_context * mctx_hyb, llm_graph_input_attn_k * inp_attn,
|
|
llm_graph_input_kpool * inp_kpool, ggml_tensor ** prev_sel, int il);
|
|
|
|
};
|
|
|
|
// the NextN / MTP draft head: one DSA block appended after the trunk
|
|
struct graph_mtp : public graph {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_kimi_linear : public llama_model_base {
|
|
llama_model_kimi_linear(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_build_delta_net_base {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_kda_autoregressive(
|
|
ggml_tensor * q,
|
|
ggml_tensor * k,
|
|
ggml_tensor * v,
|
|
ggml_tensor * gk,
|
|
ggml_tensor * beta,
|
|
ggml_tensor * state,
|
|
int il);
|
|
|
|
std::pair<ggml_tensor *, ggml_tensor *> build_kda_chunking(
|
|
ggml_tensor * q,
|
|
ggml_tensor * k,
|
|
ggml_tensor * v,
|
|
ggml_tensor * gk,
|
|
ggml_tensor * beta,
|
|
ggml_tensor * state,
|
|
ggml_tensor * causal_mask,
|
|
ggml_tensor * identity,
|
|
ggml_tensor * diag_mask,
|
|
int il);
|
|
|
|
const llama_model & model;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_step35 : public llama_model_base {
|
|
llama_model_step35(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
struct graph_mtp : public llm_graph_context {
|
|
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_spark2_5 : public llama_model_base {
|
|
llama_model_spark2_5(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
struct llama_model_k2_horizon : public llama_model_base {
|
|
llama_model_k2_horizon(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
|
|
ggml_tensor * build_routed_value(const llama_layer & layer, ggml_tensor * cur, int il) const;
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|
|
|
|
|
|
struct llama_model_kolibri1 : public llama_model_base {
|
|
llama_model_kolibri1(const struct llama_model_params & params) : llama_model_base(params) {}
|
|
void load_arch_hparams(llama_model_loader & ml) override;
|
|
void load_arch_tensors(llama_model_loader & ml) override;
|
|
|
|
struct graph : public llm_graph_context {
|
|
graph(const llama_model & model, const llm_graph_params & params);
|
|
};
|
|
|
|
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
|
};
|