ikawrakow/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-04-28 18:32:04 +00:00
Code Issues Packages Projects Releases Wiki Activity

Mimo-V2-Flash support (#1096)

Browse Source 
* Mimo-2 support

* Fix bug for head sizes not being the same

It still does not solve the Mimo-2 quantized cache issue.

* Fix quantized cache

* Minor

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2026-01-05 08:00:01 +02:00
committed by GitHub
parent 56dceefd6b
commit ab50c6cdcb
12 changed files with 251 additions and 54 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
src/llama-load-tensors.cpp
View File

@@ -133,6 +133,8 @@ struct create_tensors_helper : public create_tensors_helper_interface {
bool create_smollm3_tensors(const LLM_TN & tn);
bool create_mimo2_tensors(const LLM_TN & tn);
llama_model_loader & ml;
llama_model & model;
@@ -1194,6 +1196,49 @@ bool create_tensors_helper::create_qwen3_moe_tensors(const LLM_TN & tn) {
return use_mmap_buffer;
}
bool create_tensors_helper::create_mimo2_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
create_embd_output(tn, n_embd, n_vocab, true, false); //true);
for (int i = 0; i < n_layer; ++i) {
uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
uint32_t n_head = hparams.n_head(i);
//printf("Layer %2d: n_head = %u, n_embd_head_k = %d, n_embd_head_v = %d, n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", i, n_head, (int)n_embd_head_k, (int)n_embd_head_v, n_embd_k_gqa, n_embd_v_gqa);
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_sinks = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, 0);
auto ffn_ctx = model.split_mode == LLAMA_SPLIT_MODE_GRAPH ? ctx_split : ctx_layer;
layer.ffn_norm = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
// non-MoE branch
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
layer.ffn_gate_inp = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_gate_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_exp_probs_b = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
return use_mmap_buffer;
}
bool create_tensors_helper::create_phi2_tensors(const LLM_TN & tn) {
LOADING_PRELUDE
@@ -2947,6 +2992,8 @@ bool create_tensors_helper::create_tensors() {
use_mmap_buffer = create_minimaxm2_tensors(tn); break;
case LLM_ARCH_SMOLLM3:
use_mmap_buffer = create_smollm3_tensors(tn); break;
case LLM_ARCH_MIMO2:
use_mmap_buffer = create_mimo2_tensors(tn); break;
default:
throw std::runtime_error("unknown architecture");
}
@@ -2955,7 +3002,6 @@ bool create_tensors_helper::create_tensors() {
printf("================================ max_gpu = %d\n", model.max_gpu);
std::vector<size_t> mem_used(model.splits.size(), 0);
const auto & hparams = model.hparams;
int gqa_ratio = hparams.n_head() / hparams.n_head_kv();
auto cur_splits = model.splits;
int adjust_step = std::max(1, int(n_layer / (2*model.splits.size())));
if (model.max_gpu > 1 && model.max_gpu < int(cur_splits.size())) {
@@ -2976,6 +3022,7 @@ bool create_tensors_helper::create_tensors() {
}
}
for (int il = 0; il < n_layer; ++il) {
int gqa_ratio = hparams.n_head(il) / hparams.n_head_kv(il);
if (ggml_backend_buft_is_host(model.buft_layer[il].buft_matrix)) {
LLAMA_LOG_INFO("%s: not splitting layer %d because buffer type is host\n", __func__, il);
continue;
@@ -2996,19 +3043,26 @@ bool create_tensors_helper::create_tensors() {
if (layer.attn_norm) {
auto split = create_split(ggml_nrows(layer.attn_norm), -1, cur_splits, mem_used);
prepare_split_tensors(-1, ctx_split, layer.attn_norm, layer.split_attn_norm, split, mem_used);
if (layer.attn_sinks) {
prepare_split_tensors(-1, ctx_split, layer.attn_sinks, layer.split_attn_sinks, split, mem_used);
}
}
if (layer.rope_freqs) {
auto split = create_split(ggml_nrows(layer.rope_freqs), -1, cur_splits, mem_used);
prepare_split_tensors(-1, ctx_split, layer.rope_freqs, layer.split_rope_freqs, split, mem_used);
}
if (layer.wo && layer.wq && layer.wk && layer.wv) {
int attn_granularity = hparams.n_embd_head_k * gqa_ratio;
// TODO: fix this logic. It only works whe K and V head size is the same
//printf("Layer %d: q = %ld x %ld, k = %ld x %ld, v = %ld x %ld, qo = %ld x %ld\n", il, layer.wq->ne[0], layer.wq->ne[1],
// layer.wk->ne[0], layer.wk->ne[1], layer.wv->ne[0], layer.wv->ne[1], layer.wo->ne[0], layer.wo->ne[1]);
int attn_granularity = hparams.n_embd_head_v * gqa_ratio;
if (ggml_is_quantized(layer.wo->type)) {
auto tt = ggml_internal_get_type_traits(layer.wo->type);
if (tt.blck_size > attn_granularity) attn_granularity = tt.blck_size;
}
GGML_ASSERT(attn_granularity % hparams.n_embd_head_k == 0);
GGML_ASSERT(attn_granularity % hparams.n_embd_head_v == 0);
auto split = create_split(layer.wo->ne[0], attn_granularity, cur_splits, mem_used);
//printf("Split:"); for (auto s : split) printf(" %d", s); printf("\n");
prepare_split_tensors(0, ctx_split, layer.wo, layer.split_wo, split, mem_used);
prepare_split_tensors(1, ctx_split, layer.wq, layer.split_wq, split, mem_used);
if (layer.bo) {