427 lines
18 KiB
C++
427 lines
18 KiB
C++
#include "models.h"
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#include <cmath>
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void llama_model_granite_switch::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale, false);
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ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, false);
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ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale, false);
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bool rope_finetuned = true;
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ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
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hparams.rope_finetuned = rope_finetuned;
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switch (hparams.n_layer()) {
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case 40: type = hparams.n_embd == 4096 ? LLM_TYPE_8B : LLM_TYPE_3B; break;
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case 64: type = LLM_TYPE_30B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
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ml.get_key(LLM_KV_ADAPTER_COUNT, n_adapters);
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ml.get_key(LLM_KV_ADAPTER_LORA_RANK, max_lora_rank);
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ml.get_key(LLM_KV_ADAPTER_ROUTER_GAIN, router_gain, /* required */ false);
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// bound counts that size tensors
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if (n_adapters > 4096) {
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throw std::runtime_error(format("graniteswitch: invalid adapter count %u", n_adapters));
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}
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if (max_lora_rank > 4096) {
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throw std::runtime_error(format("graniteswitch: invalid lora rank %u", max_lora_rank));
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}
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std::vector<llama_token> token_ids;
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std::vector<llama_token> substitute_ids;
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ml.get_arr(LLM_KV_ADAPTER_TOKEN_IDS_ACTIVATE, token_ids);
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ml.get_arr(LLM_KV_ADAPTER_TOKEN_IDS_SUBSTITUTE, substitute_ids);
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if (token_ids.size() != n_adapters || substitute_ids.size() != n_adapters) {
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throw std::runtime_error(format(
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"graniteswitch: adapter token id arrays (%zu activate, %zu substitute) do not match adapter count %u",
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token_ids.size(), substitute_ids.size(), n_adapters));
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}
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adapter_token_to_slot.clear();
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adapter_token_to_substitute.clear();
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for (uint32_t i = 0; i < n_adapters; ++i) {
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// adapter i -> stacked slot i+1 (slot 0 is the base/zero delta)
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adapter_token_to_slot[token_ids[i]] = (int32_t) (i + 1);
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adapter_token_to_substitute[token_ids[i]] = substitute_ids[i];
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}
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// extra single-head attention layer at the END (index n_real) holds the router
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// K/V. reusing n_layer_nextn keeps n_layer() == n_real, so the regular layers
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// keep their indices and the KV cache shift/defrag skips the router layer.
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// n_layer_nextn is repurposed here (no MTP): it leaks as 1 into the
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// llama_model_n_layer_nextn() getter and a re-saved nextn_predict_layers
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const uint32_t n_real = hparams.n_layer();
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if (n_real >= LLAMA_MAX_LAYERS) {
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throw std::runtime_error(format("graniteswitch: block count %u exceeds LLAMA_MAX_LAYERS", n_real));
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}
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hparams.router_layer = (int32_t) n_real;
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hparams.n_layer_all = n_real + 1;
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hparams.n_layer_nextn = 1;
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hparams.n_head_arr[n_real] = 1;
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hparams.n_head_kv_arr[n_real] = 1;
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hparams.n_ff_arr[n_real] = 0;
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}
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void llama_model_granite_switch::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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const int64_t n_slots = (int64_t) n_adapters + 1; // slot 0 = base/zero delta
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const int64_t n_rank = (int64_t) max_lora_rank;
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const int64_t n_embd_q = n_embd_head_k * n_head;
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const int64_t n_embd_kv = n_embd_k_gqa;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// substitute ids index tok_embd rows directly; range-check against n_vocab
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for (const auto & kv : adapter_token_to_substitute) {
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const llama_token sub = kv.second;
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if (sub < 0 || (int64_t) sub >= n_vocab) {
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throw std::runtime_error(format(
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"graniteswitch: substitute token id %d out of range [0, %d)", sub, (int) n_vocab));
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}
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}
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd_q + 2*n_embd_kv}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q, n_embd}, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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auto & sl = layer.switch_lora;
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sl.a_q = create_tensor(tn(LLM_TENSOR_ATTN_Q, "lora_a", i), {n_embd, n_rank, n_slots}, 0);
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sl.b_q = create_tensor(tn(LLM_TENSOR_ATTN_Q, "lora_b", i), {n_rank, n_embd_q, n_slots}, 0);
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sl.a_k = create_tensor(tn(LLM_TENSOR_ATTN_K, "lora_a", i), {n_embd, n_rank, n_slots}, 0);
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sl.b_k = create_tensor(tn(LLM_TENSOR_ATTN_K, "lora_b", i), {n_rank, n_embd_kv, n_slots}, 0);
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sl.a_v = create_tensor(tn(LLM_TENSOR_ATTN_V, "lora_a", i), {n_embd, n_rank, n_slots}, 0);
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sl.b_v = create_tensor(tn(LLM_TENSOR_ATTN_V, "lora_b", i), {n_rank, n_embd_kv, n_slots}, 0);
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sl.a_o = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "lora_a", i), {n_embd_q, n_rank, n_slots}, 0);
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sl.b_o = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "lora_b", i), {n_rank, n_embd, n_slots}, 0);
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sl.a_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "lora_a", i), {n_embd, n_rank, n_slots}, 0);
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sl.b_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "lora_b", i), {n_rank, n_ff, n_slots}, 0);
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sl.a_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "lora_a", i), {n_embd, n_rank, n_slots}, 0);
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sl.b_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "lora_b", i), {n_rank, n_ff, n_slots}, 0);
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sl.a_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "lora_a", i), { n_ff, n_rank, n_slots}, 0);
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sl.b_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "lora_b", i), {n_rank, n_embd, n_slots}, 0);
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}
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}
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class llm_graph_input_switch : public llm_graph_input_i {
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public:
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llm_graph_input_switch(const llama_model_granite_switch & smodel) : smodel(smodel) {}
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virtual ~llm_graph_input_switch() = default;
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void set_input(const llama_ubatch * ubatch) override;
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ggml_tensor * sub_tokens = nullptr; // I32 [n_tokens] adapter-substituted token ids
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ggml_tensor * router_ksig = nullptr; // F32 [n_tokens] router K signal (+/-gain)
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ggml_tensor * router_vval = nullptr; // F32 [n_tokens] router V value (adapter slot / 0)
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ggml_tensor * router_q = nullptr; // F32 [n_tokens] router Q value (constant 1.0)
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const llama_model_granite_switch & smodel;
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};
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// K dim-0 is +gain for an adapter token, -gain otherwise; the causal softmax then
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// lets a single visible adapter token dominate so the readback recovers its slot.
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void llm_graph_input_switch::set_input(const llama_ubatch * ubatch) {
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if (!ubatch->token) {
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return;
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}
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const int64_t n_tokens = ubatch->n_tokens;
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std::vector<int32_t> sub (n_tokens);
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std::vector<float> ksig(n_tokens);
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std::vector<float> vval(n_tokens);
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std::vector<float> q (n_tokens, 1.0f);
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for (int64_t i = 0; i < n_tokens; ++i) {
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const llama_token tok = ubatch->token[i];
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const auto it = smodel.adapter_token_to_slot.find(tok);
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if (it != smodel.adapter_token_to_slot.end()) {
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ksig[i] = +smodel.router_gain;
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vval[i] = (float) it->second;
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} else {
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ksig[i] = -smodel.router_gain;
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vval[i] = 0.0f;
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}
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const auto sit = smodel.adapter_token_to_substitute.find(tok);
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sub[i] = (sit != smodel.adapter_token_to_substitute.end())
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? (int32_t) sit->second
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: (int32_t) tok;
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}
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ggml_backend_tensor_set(sub_tokens, sub.data(), 0, n_tokens*ggml_element_size(sub_tokens));
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ggml_backend_tensor_set(router_ksig, ksig.data(), 0, n_tokens*ggml_element_size(router_ksig));
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ggml_backend_tensor_set(router_vval, vval.data(), 0, n_tokens*ggml_element_size(router_vval));
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ggml_backend_tensor_set(router_q, q.data(), 0, n_tokens*ggml_element_size(router_q));
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}
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std::unique_ptr<llm_graph_context> llama_model_granite_switch::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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// per-token switched LoRA delta: B_a*(A_a*x), adapter selected per token via ids.
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// cur: {n_in, n_tokens}, ids: {n_tokens} -> {n_out, n_tokens}
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ggml_tensor * llama_model_granite_switch::graph::build_switched_lora_delta(
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ggml_tensor * lora_a,
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ggml_tensor * lora_b,
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ggml_tensor * cur,
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ggml_tensor * ids) {
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const int64_t n_in = cur->ne[0];
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const int64_t n_tokens = cur->ne[1];
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ggml_tensor * x = ggml_reshape_3d(ctx0, cur, n_in, 1, n_tokens);
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ggml_tensor * ids2 = ggml_reshape_2d(ctx0, ids, 1, n_tokens);
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ggml_tensor * a = ggml_mul_mat_id(ctx0, lora_a, x, ids2); // {max_rank, 1, n_tokens}
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ggml_tensor * d = ggml_mul_mat_id(ctx0, lora_b, a, ids2); // {n_out, 1, n_tokens}
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return ggml_reshape_2d(ctx0, d, d->ne[0], n_tokens);
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}
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ggml_tensor * llama_model_granite_switch::graph::build_switched_lora_mm(
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ggml_tensor * w,
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ggml_tensor * lora_a,
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ggml_tensor * lora_b,
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ggml_tensor * cur,
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ggml_tensor * ids) {
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ggml_tensor * base = ggml_mul_mat(ctx0, w, cur);
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ggml_tensor * delta = build_switched_lora_delta(lora_a, lora_b, cur, ids);
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return ggml_add(ctx0, base, delta);
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}
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llama_model_granite_switch::graph::graph(
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const llama_model & model,
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const llm_graph_params & params)
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: llm_graph_context(params) {
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const auto & smodel = static_cast<const llama_model_granite_switch &>(model);
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// TODO: support raw embedding input (multimodal / pre-embedded tokens) when needed
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GGML_ASSERT(ubatch.token && "granite-switch requires token input");
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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GGML_ASSERT(n_embd_head == n_rot);
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auto inp_switch = std::make_unique<llm_graph_input_switch>(smodel);
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inp_switch->sub_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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inp_switch->router_ksig = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens);
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inp_switch->router_vval = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens);
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inp_switch->router_q = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens);
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ggml_set_input(inp_switch->sub_tokens);
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ggml_set_input(inp_switch->router_ksig);
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ggml_set_input(inp_switch->router_vval);
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ggml_set_input(inp_switch->router_q);
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ggml_tensor * sub_tokens = inp_switch->sub_tokens;
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ggml_tensor * router_ksig = inp_switch->router_ksig;
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ggml_tensor * router_vval = inp_switch->router_vval;
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ggml_tensor * router_q = inp_switch->router_q;
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res->add_input(std::move(inp_switch));
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// embed the substituted ids directly; build_inp_embd would embed the raw tokens
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ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embd, sub_tokens);
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if (hparams.f_embedding_scale != 0.0f) {
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inpL = ggml_scale(ctx0, inpL, hparams.f_embedding_scale);
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}
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cb(inpL, "inp_embd", -1);
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ggml_tensor * inp_pos = nullptr;
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if (hparams.rope_finetuned) {
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inp_pos = build_inp_pos();
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}
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auto * inp_attn = build_attn_inp_kv();
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// single causal head at layer R recovers the adapter index in-graph: only dim 0
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// carries signal (Q[0]=1, K[0]=+/-gain, V[0]=slot/0), the rest is zero-padded.
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const int R = hparams.router_layer;
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GGML_ASSERT(R >= 0);
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auto router_lane = [&](ggml_tensor * sig1d) {
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ggml_tensor * t = ggml_reshape_3d(ctx0, sig1d, 1, 1, n_tokens);
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return ggml_pad(ctx0, t, (int) n_embd_head - 1, 0, 0, 0);
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};
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ggml_tensor * Qr = router_lane(router_q);
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ggml_tensor * Kr = router_lane(router_ksig);
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ggml_tensor * Vr = router_lane(router_vval);
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ggml_tensor * router_out = build_attn(inp_attn,
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nullptr, nullptr, nullptr,
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Qr, Kr, Vr, nullptr, nullptr, nullptr, /*kq_scale=*/1.0f, /*il=*/R);
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cb(router_out, "router_out", R);
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// row 0 of router_out is the attended slot; clamp+round to an I32 index
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ggml_tensor * slot_f = ggml_cont(ctx0,
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ggml_view_2d(ctx0, router_out, 1, n_tokens, router_out->nb[1], 0));
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slot_f = ggml_reshape_1d(ctx0, slot_f, n_tokens);
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slot_f = ggml_clamp(ctx0, slot_f, 0.0f, (float) smodel.n_adapters);
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slot_f = ggml_round(ctx0, slot_f);
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ggml_tensor * adapter_ids = ggml_cast(ctx0, slot_f, GGML_TYPE_I32);
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cb(adapter_ids, "adapter_ids", -1);
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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ggml_tensor * cur;
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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cur = build_attention_layer(cur, inp_pos, adapter_ids, inp_attn, model, n_embd_head, il);
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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// keep adapter_ids aligned to the kept rows (2D round-trip for get_rows)
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const int64_t n_out = inp_out_ids->ne[0];
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adapter_ids = ggml_get_rows(ctx0,
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ggml_reshape_2d(ctx0, adapter_ids, 1, adapter_ids->ne[0]), inp_out_ids);
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adapter_ids = ggml_reshape_1d(ctx0, adapter_ids, n_out);
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}
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cur = build_layer_ffn(cur, inpSA, adapter_ids, model, il);
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inpL = cur;
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}
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cur = inpL;
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cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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cur = build_lora_mm(model.output, cur, model.output_s);
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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ggml_tensor * llama_model_granite_switch::graph::build_attention_layer(
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ggml_tensor * cur,
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ggml_tensor * inp_pos,
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ggml_tensor * adapter_ids,
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llm_graph_input_attn_kv * inp_attn,
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const llama_model & model,
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const int64_t n_embd_head,
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const int il) {
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const auto & layer = model.layers[il];
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const auto & sl = layer.switch_lora;
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const int64_t n_head = hparams.n_head(il);
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const int64_t n_head_kv = hparams.n_head_kv(il);
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ggml_tensor * qkv = ggml_mul_mat(ctx0, layer.wqkv, cur);
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cb(qkv, "wqkv", il);
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const int64_t n_embd_q = n_embd_head * n_head;
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const int64_t n_embd_kv = n_embd_head * n_head_kv;
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// slice fused qkv into Q/K/V, made contiguous so LoRA deltas can be added
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ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_q, qkv->ne[1], qkv->nb[1], 0));
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ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_kv, qkv->ne[1], qkv->nb[1], n_embd_q*ggml_element_size(qkv)));
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ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_kv, qkv->ne[1], qkv->nb[1], (n_embd_q + n_embd_kv)*ggml_element_size(qkv)));
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Qcur = ggml_add(ctx0, Qcur, build_switched_lora_delta(sl.a_q, sl.b_q, cur, adapter_ids));
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Kcur = ggml_add(ctx0, Kcur, build_switched_lora_delta(sl.a_k, sl.b_k, cur, adapter_ids));
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Vcur = ggml_add(ctx0, Vcur, build_switched_lora_delta(sl.a_v, sl.b_v, cur, adapter_ids));
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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if (hparams.rope_finetuned) {
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ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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const float kq_scale = hparams.f_attention_scale == 0.0f
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? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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// wo = nullptr so build_attn returns concatenated heads; o-proj is switched below
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ggml_tensor * attn = build_attn(inp_attn,
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nullptr, nullptr, nullptr,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(attn, "attn_pre_o", il);
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|
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cur = build_switched_lora_mm(layer.wo, sl.a_o, sl.b_o, attn, adapter_ids);
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cb(cur, "attn_out", il);
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return cur;
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}
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|
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ggml_tensor * llama_model_granite_switch::graph::build_layer_ffn(
|
|
ggml_tensor * cur,
|
|
ggml_tensor * inpSA,
|
|
ggml_tensor * adapter_ids,
|
|
const llama_model & model,
|
|
const int il) {
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|
|
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const auto & layer = model.layers[il];
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const auto & sl = layer.switch_lora;
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|
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
|
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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|
|
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cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
|
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cb(cur, "ffn_norm", il);
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|
|
|
ggml_tensor * g = build_switched_lora_mm(layer.ffn_gate, sl.a_gate, sl.b_gate, cur, adapter_ids);
|
|
ggml_tensor * u = build_switched_lora_mm(layer.ffn_up, sl.a_up, sl.b_up, cur, adapter_ids);
|
|
g = ggml_silu(ctx0, g);
|
|
ggml_tensor * gu = ggml_mul(ctx0, g, u);
|
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cur = build_switched_lora_mm(layer.ffn_down, sl.a_down, sl.b_down, gu, adapter_ids);
|
|
cb(cur, "ffn_out", il);
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|
|
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if (hparams.f_residual_scale) {
|
|
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
|
|
}
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
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|
|
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cur = build_cvec(cur, il);
|
|
cb(cur, "l_out", il);
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|
|
|
return cur;
|
|
}
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