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llama.cpp
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feat: Construct hybrid recurrent cache for hybrid recurrent models 

This includes a refactor of the create_memory logic to avoid needing to use
the arch enum explicitly unless a model needs explicit cache instantiation
logic beyond the standard logic for recurrent, hybrid, unified, and iswa.

Branch: HybridRecurrentCache

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart 2025-05-28 08:57:18 -06:00
parent c71eaa37a0
commit 423c89401d
1 changed files with 63 additions and 47 deletions
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110
src/llama-model.cpp
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@ -9,6 +9,7 @@
#include "llama-kv-cache-unified.h"
#include "llama-kv-cache-unified-iswa.h"
#include "llama-kv-cache-recurrent.h"
#include "llama-kv-cache-hybrid-recurrent.h"
#include "ggml-cpp.h"
@ -13742,6 +13743,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
llama_memory_i * res;
switch (arch) {
// Models that need specific instantiation should be handled in the
// switch statement
case LLM_ARCH_BERT:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_NOMIC_BERT:
@ -13751,58 +13754,71 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
{
res = nullptr;
} break;
case LLM_ARCH_MAMBA:
case LLM_ARCH_RWKV6:
case LLM_ARCH_RWKV6QWEN2:
case LLM_ARCH_RWKV7:
case LLM_ARCH_ARWKV7:
{
res = new llama_kv_cache_recurrent(
*this,
nullptr,
GGML_TYPE_F32,
GGML_TYPE_F32,
cparams.offload_kqv,
std::max((uint32_t) 1, cparams.n_seq_max),
cparams.n_seq_max);
} break;
// Models that need standard caching should rely on recurrent/hybrid
// checks
default:
{
const auto padding = llama_kv_cache_unified::get_padding(cparams);
cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding);
LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx);
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
GGML_ASSERT(hparams.is_swa_any());
res = new llama_kv_cache_unified_iswa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.n_ctx,
cparams.n_seq_max,
cparams.n_ubatch,
padding);
} else {
GGML_ASSERT(!hparams.is_swa_any());
res = new llama_kv_cache_unified(
if (llm_arch_is_recurrent(arch)) {
res = new llama_kv_cache_recurrent(
*this,
nullptr,
params.type_k,
params.type_v,
!cparams.flash_attn,
GGML_TYPE_F32,
GGML_TYPE_F32,
cparams.offload_kqv,
cparams.n_ctx,
cparams.n_seq_max,
padding,
hparams.n_swa,
hparams.swa_type);
std::max((uint32_t) 1, cparams.n_seq_max),
cparams.n_seq_max);
} else if (llm_arch_is_hybrid_recurrent(arch)) {
res = new llama_kv_cache_hybrid_recurrent(
/* model */ *this,
/* attn_type_k */ params.type_k,
/* attn_type_v */ params.type_v,
/* attn_v_trans */ !cparams.flash_attn,
/* attn_kv_size */ cparams.n_ctx,
/* attn_n_pad */ llama_kv_cache_unified::get_padding(cparams),
/* attn_n_swa */ hparams.n_swa,
/* attn_swa_type */ hparams.swa_type,
/* recurrent_type_k */ GGML_TYPE_F32,
/* recurrent_type_v */ GGML_TYPE_F32,
/* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
/* n_seq_max */ cparams.n_seq_max,
/* offload */ cparams.offload_kqv);
} else {
const auto padding = llama_kv_cache_unified::get_padding(cparams);
cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding);
LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx);
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
GGML_ASSERT(hparams.is_swa_any());
res = new llama_kv_cache_unified_iswa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.n_ctx,
cparams.n_seq_max,
cparams.n_ubatch,
padding);
} else {
GGML_ASSERT(!hparams.is_swa_any());
res = new llama_kv_cache_unified(
*this,
nullptr,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.n_ctx,
cparams.n_seq_max,
padding,
hparams.n_swa,
hparams.swa_type);
}
}
}
}