model: move load_hparams and load_tensors to per-model definition (#22004)

* git-friendly migration * add build_graph * nits * exclude old code from build * wip * add llm_arch_model_i * prepare downstream functions * nits * nits * wip * wip * add back create_tensor_qkv * fix files missing include * enforce one llm_build per arch * cmake: use glob * missing model params * nits * wip * wip (2) * wip (3) * test-llama-archs is happy * improve switch case * move more stuff into llm_arch_model_i * fix downstream code * nits * nits (2) * fix order * llama_model_base * LLAMA_LOAD_LOCALS * small fix * fix build errors * auto * rm migration script and ifdef
2026-05-04 12:36:59 +02:00
parent c84e6d6db5
commit 994118a183
129 changed files with 10667 additions and 8117 deletions
@@ -0,0 +1,87 @@
+#include "models.h"
+
+void llama_model_mamba2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 24:
+            switch (hparams.n_embd) {
+                case 768: type = LLM_TYPE_SMALL; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 48:
+            switch (hparams.n_embd) {
+                case 1024: type = LLM_TYPE_MEDIUM; break;
+                case 1536: type = LLM_TYPE_LARGE; break;
+                case 2048: type = LLM_TYPE_XL; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 64:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mamba2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t d_conv  = hparams.ssm_d_conv;
+    const int64_t d_inner = hparams.ssm_d_inner;
+    const int64_t d_state = hparams.ssm_d_state;
+    const int64_t n_group = hparams.ssm_n_group;
+    const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_head;
+
+    // only an expansion factor of 2 is supported for now
+    GGML_ASSERT(2 * n_embd == d_inner);
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+        if (output == NULL) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+
+        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, 0);
+
+        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_head}, 0);
+
+        // no "weight" suffix for these
+        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
+        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_head}, 0);
+
+        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+
+        // out_proj
+        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mamba2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+