Lumpiasty/llama.cpp
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Deepseek 4: -sm tensor (#26490)

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* DSV4: sm tensor

* set coarser granularity for head splits

* fix dspark

* add model saving for dsv4 + allow dflash to return on specific device

* add comment about dsv4 seq_rm

* simplify

* add shared expert delayed allreduce

* remove special test for dsv4
This commit is contained in:
Aman Gupta
2026-08-24 09:20:25 +03:00
committed by GitHub
parent c060ca974c
commit bf0a29cc16
7 changed files with 314 additions and 48 deletions
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src/llama-model-saver.cpp
+13 -4
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@@ -296,11 +296,18 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, hparams.dsv4_o_group_count);
add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK, hparams.dsv4_o_lora_rank);
add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, hparams.dsv4_compress_rope_base);
add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, true);
add_kv(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
if (model->arch == LLM_ARCH_DEEPSEEK4 || hparams.dsv4_hc_mult > 0) {
// the loader requires one compress ratio per layer, including nextn layers
const std::vector<uint32_t> compress_ratios(
hparams.dsv4_compress_ratios.begin(), hparams.dsv4_compress_ratios.begin() + hparams.n_layer_all);
add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, compress_ratios);
} else {
add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, true);
}
add_kv(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
add_kv(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
add_kv(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count);
add_kv(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
add_kv(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count);
const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
@@ -425,6 +432,8 @@ void llama_model_saver::add_tensors_from_model() {
add_tensor(model->output_s);
add_tensor(model->output_in_s);
add_tensor(model->output_res_score);
add_tensor(model->nextn_proj_pre);
add_tensor(model->nextn_proj_post);
add_tensor(model->cls);
add_tensor(model->cls_b);
add_tensor(model->cls_out);