model : support for DeepseekV32ForCausalLM with generic DeepSeek Sparse Attention (DSA) implementation (#23346)
* llama : support DeepSeek V3.2 model family (with DSA lightning indexer) * convert : handle DeepseekV32ForCausalLM architecture * ggml : support for f16 GGML_OP_FILL * memory : separate hparams argument in llama_kv_cache constructor * memory : add llama_kv_cache_dsa memory (KV cache + lightning indexer cache) * llama : support for LLM_ARCH_DEEPSEEK32 * model : llama_model_deepseek32 implementation * model : merge two scale operations into one in DSA lightning indexer implementation * chore : remove unused code * model : support NVFP4 in DeepSeek V3.2 Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * memory : refactoring TODO Co-authored-by: ggerganov <ggerganov@users.noreply.github.com> --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
This commit is contained in:
co-authored by
Stanisław Szymczyk
Sigbjørn Skjæret
ggerganov
parent
031ddb2e08
commit
1f0aa2a696
@@ -79,6 +79,7 @@ static ggml_tensor * ggml_mul_mat_aux(
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llama_kv_cache::llama_kv_cache(
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const llama_model & model,
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const llama_hparams & hparams,
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ggml_type type_k,
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ggml_type type_v,
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bool v_trans,
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@@ -91,7 +92,7 @@ llama_kv_cache::llama_kv_cache(
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llama_swa_type swa_type,
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const layer_filter_cb & filter,
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const layer_reuse_cb & reuse) :
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model(model), hparams(model.hparams), v_trans(v_trans),
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model(model), hparams(hparams), v_trans(v_trans),
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n_seq_max(n_seq_max), n_stream(unified ? 1 : n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
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GGML_ASSERT(kv_size % n_pad == 0);
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@@ -253,7 +254,7 @@ llama_kv_cache::llama_kv_cache(
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// allocate tensors and initialize the buffers to avoid NaNs in the padding
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for (auto & [buft, ctx] : ctx_map) {
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ggml_backend_buffer_t buf;
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if (model.hparams.no_alloc) {
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if (hparams.no_alloc) {
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buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
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for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
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t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
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@@ -293,6 +294,11 @@ llama_kv_cache::llama_kv_cache(
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ggml_is_quantized(type_k) &&
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hparams.n_embd_head_k() % 64 == 0;
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// always create Hadamard rotation tensors for DeepSeek V3.2 DSA lightning indexer
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if (model.arch == LLM_ARCH_DEEPSEEK32 && hparams.n_embd_head_k_full == hparams.indexer_head_size) {
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attn_rot_k = true;
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}
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attn_rot_v =
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!attn_rot_disable &&
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n_embd_head_v_all > 0 &&
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