model: add dots3-note (#27060)
* text: conversion * init impl * address review comments * fix rope * move to a new llama_kv_cache_dsa_iswa
This commit is contained in:
Xuan-Son Nguyen
@@ -25,6 +25,7 @@ add_library(llama
|
||||
llama-kv-cache.cpp
|
||||
llama-kv-cache-iswa.cpp
|
||||
llama-kv-cache-dsa.cpp
|
||||
llama-kv-cache-dsa-iswa.cpp
|
||||
llama-kv-cache-msa.cpp
|
||||
llama-kv-cache-dsv4.cpp
|
||||
llama-memory.cpp
|
||||
|
||||
@@ -110,6 +110,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
||||
{ LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
|
||||
{ LLM_ARCH_BAILINGMOE3, "bailingmoe3" },
|
||||
{ LLM_ARCH_DOTS1, "dots1" },
|
||||
{ LLM_ARCH_DOTS3NOTE, "dots3note" },
|
||||
{ LLM_ARCH_ARCEE, "arcee" },
|
||||
{ LLM_ARCH_AFMOE, "afmoe" },
|
||||
{ LLM_ARCH_LAGUNA, "laguna" },
|
||||
@@ -273,6 +274,9 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
||||
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
|
||||
{ LLM_KV_ATTENTION_KEY_LENGTH_SWA, "%s.attention.key_length_swa" },
|
||||
{ LLM_KV_ATTENTION_VALUE_LENGTH_SWA, "%s.attention.value_length_swa" },
|
||||
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA, "%s.attention.key_length_mla_swa" },
|
||||
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, "%s.attention.value_length_mla_swa" },
|
||||
{ LLM_KV_ATTENTION_KV_LORA_RANK_SWA, "%s.attention.kv_lora_rank_swa" },
|
||||
{ LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" },
|
||||
{ LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" },
|
||||
{ LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" },
|
||||
@@ -1056,6 +1060,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_DEEPSEEK4:
|
||||
case LLM_ARCH_DOTS3NOTE:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
case LLM_ARCH_BITNET:
|
||||
case LLM_ARCH_T5:
|
||||
|
||||
@@ -115,6 +115,7 @@ enum llm_arch {
|
||||
LLM_ARCH_BAILINGMOE2,
|
||||
LLM_ARCH_BAILINGMOE3,
|
||||
LLM_ARCH_DOTS1,
|
||||
LLM_ARCH_DOTS3NOTE,
|
||||
LLM_ARCH_ARCEE,
|
||||
LLM_ARCH_AFMOE,
|
||||
LLM_ARCH_LAGUNA,
|
||||
@@ -278,6 +279,9 @@ enum llm_kv {
|
||||
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
|
||||
LLM_KV_ATTENTION_KEY_LENGTH_SWA,
|
||||
LLM_KV_ATTENTION_VALUE_LENGTH_SWA,
|
||||
LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,
|
||||
LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA,
|
||||
LLM_KV_ATTENTION_KV_LORA_RANK_SWA,
|
||||
LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
|
||||
LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
|
||||
LLM_KV_ATTENTION_INDEXER_TOP_K,
|
||||
|
||||
+60
-6
@@ -9,6 +9,7 @@
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cache-iswa.h"
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
#include "llama-kv-cache-dsa-iswa.h"
|
||||
#include "llama-kv-cache-msa.h"
|
||||
#include "llama-kv-cache-dsv4.h"
|
||||
#include "llama-memory-hybrid.h"
|
||||
@@ -507,10 +508,12 @@ void llm_graph_input_attn_k::set_input(const llama_ubatch * ubatch) {
|
||||
}
|
||||
|
||||
bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) {
|
||||
const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
|
||||
mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
|
||||
|
||||
this->mctx = mctx;
|
||||
return can_reuse_impl(params);
|
||||
}
|
||||
|
||||
bool llm_graph_input_attn_k::can_reuse_impl(const llm_graph_params & params) {
|
||||
bool res = true;
|
||||
|
||||
res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
|
||||
@@ -567,10 +570,12 @@ void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
|
||||
}
|
||||
|
||||
bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
|
||||
const auto * mctx = static_cast<const llama_kv_cache_dsa_context *>(params.mctx);
|
||||
mctx = static_cast<const llama_kv_cache_dsa_context *>(params.mctx);
|
||||
|
||||
this->mctx = mctx;
|
||||
return can_reuse_impl(params);
|
||||
}
|
||||
|
||||
bool llm_graph_input_attn_k_dsa::can_reuse_impl(const llm_graph_params & params) {
|
||||
bool res = true;
|
||||
|
||||
res &= self_k_idxs_mla->ne[0] == params.ubatch.n_tokens;
|
||||
@@ -582,6 +587,25 @@ bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
|
||||
return res;
|
||||
}
|
||||
|
||||
void llm_graph_input_attn_k_dsa_iswa::set_input(const llama_ubatch * ubatch) {
|
||||
inp_dsa->set_input(ubatch);
|
||||
inp_swa->set_input(ubatch);
|
||||
}
|
||||
|
||||
bool llm_graph_input_attn_k_dsa_iswa::can_reuse(const llm_graph_params & params) {
|
||||
mctx = static_cast<const llama_kv_cache_dsa_iswa_context *>(params.mctx);
|
||||
|
||||
inp_dsa->mctx = mctx->get_dsa();
|
||||
inp_swa->mctx = mctx->get_swa();
|
||||
|
||||
bool res = true;
|
||||
|
||||
res &= inp_dsa->can_reuse_impl(params);
|
||||
res &= inp_swa->can_reuse_impl(params);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
|
||||
// base tensors may not be allocated if there are no non-SWA attention layers
|
||||
if (self_k_idxs && self_k_idxs->buffer) {
|
||||
@@ -3210,8 +3234,12 @@ ggml_tensor * llm_graph_context::build_attn(
|
||||
return cur;
|
||||
}
|
||||
|
||||
llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
|
||||
const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_context *>(mctx);
|
||||
static std::unique_ptr<llm_graph_input_attn_k_dsa> build_attn_inp_k_dsa_impl(
|
||||
ggml_context * ctx0,
|
||||
const llama_ubatch & ubatch,
|
||||
const llama_hparams & hparams,
|
||||
const llama_cparams & cparams,
|
||||
const llama_kv_cache_dsa_context * mctx_cur) {
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_attn_k_dsa>(hparams, cparams, mctx_cur);
|
||||
|
||||
@@ -3235,9 +3263,35 @@ llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
|
||||
inp->self_k_rot_lid = mctx_cur->get_lid()->build_input_k_rot(ctx0);
|
||||
}
|
||||
|
||||
return inp;
|
||||
}
|
||||
|
||||
llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
|
||||
const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_context *>(mctx);
|
||||
|
||||
auto inp = build_attn_inp_k_dsa_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
|
||||
|
||||
return (llm_graph_input_attn_k_dsa *) res->add_input(std::move(inp));
|
||||
}
|
||||
|
||||
llm_graph_input_attn_k_dsa_iswa * llm_graph_context::build_attn_inp_k_dsa_iswa() const {
|
||||
const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_iswa_context *>(mctx);
|
||||
|
||||
auto inp_dsa = build_attn_inp_k_dsa_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_dsa());
|
||||
|
||||
// build_attn_inp_k_impl rejects SWA caches, so construct the input directly
|
||||
auto inp_swa = std::make_unique<llm_graph_input_attn_k>(hparams, cparams, mctx_cur->get_swa());
|
||||
|
||||
inp_swa->self_k_idxs = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
|
||||
|
||||
inp_swa->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_cur->get_swa(), ubatch, cparams);
|
||||
inp_swa->self_kq_mask_cnv = inp_swa->self_kq_mask;
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_attn_k_dsa_iswa>(std::move(inp_dsa), std::move(inp_swa), mctx_cur);
|
||||
|
||||
return (llm_graph_input_attn_k_dsa_iswa *) res->add_input(std::move(inp));
|
||||
}
|
||||
|
||||
llm_graph_input_attn_kv_msa * llm_graph_context::build_attn_inp_kv_msa(bool msa_enabled) const {
|
||||
const auto * mctx_cur = static_cast<const llama_kv_cache_msa_context *>(mctx);
|
||||
|
||||
|
||||
@@ -23,6 +23,7 @@ struct llama_memory_context_i;
|
||||
|
||||
class llama_kv_cache_context;
|
||||
class llama_kv_cache_dsa_context;
|
||||
class llama_kv_cache_dsa_iswa_context;
|
||||
class llama_kv_cache_msa_context;
|
||||
class llama_kv_cache_dsv4_raw_context;
|
||||
class llama_kv_cache_dsv4_context;
|
||||
@@ -374,6 +375,9 @@ public:
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override;
|
||||
|
||||
// like can_reuse, but does not re-bind mctx
|
||||
bool can_reuse_impl(const llm_graph_params & params);
|
||||
|
||||
ggml_tensor * get_k_idxs() const { return self_k_idxs; }
|
||||
|
||||
ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
|
||||
@@ -405,6 +409,9 @@ public:
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override;
|
||||
|
||||
// like can_reuse, but does not re-bind mctx
|
||||
bool can_reuse_impl(const llm_graph_params & params);
|
||||
|
||||
ggml_tensor * get_k_idxs_mla() const { return self_k_idxs_mla; }
|
||||
ggml_tensor * get_k_idxs_lid() const { return self_k_idxs_lid; }
|
||||
|
||||
@@ -427,6 +434,32 @@ public:
|
||||
const llama_kv_cache_dsa_context * mctx;
|
||||
};
|
||||
|
||||
// DSA input (full-attention layers + indexer) with K-only input for the SWA layers
|
||||
class llm_graph_input_attn_k_dsa_iswa : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_attn_k_dsa_iswa(
|
||||
std::unique_ptr<llm_graph_input_attn_k_dsa> inp_dsa,
|
||||
std::unique_ptr<llm_graph_input_attn_k> inp_swa,
|
||||
const llama_kv_cache_dsa_iswa_context * mctx) :
|
||||
inp_dsa(std::move(inp_dsa)),
|
||||
inp_swa(std::move(inp_swa)),
|
||||
mctx(mctx) {
|
||||
}
|
||||
~llm_graph_input_attn_k_dsa_iswa() = default;
|
||||
|
||||
void set_input(const llama_ubatch * ubatch) override;
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override;
|
||||
|
||||
llm_graph_input_attn_k_dsa * get_dsa() const { return inp_dsa.get(); }
|
||||
llm_graph_input_attn_k * get_swa() const { return inp_swa.get(); }
|
||||
|
||||
std::unique_ptr<llm_graph_input_attn_k_dsa> inp_dsa;
|
||||
std::unique_ptr<llm_graph_input_attn_k> inp_swa;
|
||||
|
||||
const llama_kv_cache_dsa_iswa_context * mctx;
|
||||
};
|
||||
|
||||
// standard K/V attention input against the base cache, plus destination indices for the indexer key cache
|
||||
class llm_graph_input_attn_kv_msa : public llm_graph_input_attn_kv {
|
||||
public:
|
||||
@@ -1191,6 +1224,8 @@ struct llm_graph_context {
|
||||
|
||||
llm_graph_input_attn_k_dsa * build_attn_inp_k_dsa() const;
|
||||
|
||||
llm_graph_input_attn_k_dsa_iswa * build_attn_inp_k_dsa_iswa() const;
|
||||
|
||||
llm_graph_input_attn_kv_msa * build_attn_inp_kv_msa(bool msa_enabled) const;
|
||||
|
||||
ggml_tensor * build_attn(
|
||||
|
||||
@@ -101,6 +101,11 @@ struct llama_hparams {
|
||||
uint32_t n_group_used = 0;
|
||||
uint32_t n_group_experts = 0;
|
||||
|
||||
// MLA + SWA (i.e. dots3note)
|
||||
uint32_t n_lora_kv_swa = 0;
|
||||
uint32_t n_embd_head_k_mla_swa = 0;
|
||||
uint32_t n_embd_head_v_mla_swa = 0;
|
||||
|
||||
float expert_group_scale = 0.05f;
|
||||
float expert_weights_scale = 0.0f;
|
||||
bool expert_weights_norm = false;
|
||||
|
||||
@@ -0,0 +1,341 @@
|
||||
#include "llama-kv-cache-dsa-iswa.h"
|
||||
|
||||
#include "llama-impl.h"
|
||||
#include "llama-batch.h"
|
||||
#include "llama-model.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cassert>
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_iswa
|
||||
//
|
||||
|
||||
llama_kv_cache_dsa_iswa::llama_kv_cache_dsa_iswa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool swa_full,
|
||||
bool unified,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_ubatch,
|
||||
uint32_t n_pad,
|
||||
const layer_filter_cb & filter_mla,
|
||||
const layer_filter_cb & filter_lid,
|
||||
const layer_reuse_cb & reuse) : unified(unified) {
|
||||
|
||||
const auto & hparams = model.hparams;
|
||||
|
||||
// chain filters
|
||||
const layer_filter_cb filter_dsa = [&](int32_t il) {
|
||||
if (filter_mla && !filter_mla(il)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return !hparams.is_swa(il);
|
||||
};
|
||||
|
||||
const layer_filter_cb filter_swa = [&](int32_t il) {
|
||||
if (filter_mla && !filter_mla(il)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return hparams.is_swa(il);
|
||||
};
|
||||
|
||||
const uint32_t size_dsa = kv_size;
|
||||
|
||||
// note: the SWA cache is always padded to 256 for performance
|
||||
// https://github.com/ggml-org/llama.cpp/issues/17037
|
||||
uint32_t size_swa = GGML_PAD(std::min(size_dsa, hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch), 256);
|
||||
|
||||
// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
|
||||
if (swa_full) {
|
||||
LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
|
||||
__func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
|
||||
|
||||
size_swa = size_dsa;
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("%s: creating DSA KV cache, size = %u cells\n", __func__, size_dsa);
|
||||
|
||||
kv_dsa = std::make_unique<llama_kv_cache_dsa>(
|
||||
model, type_k, type_v,
|
||||
v_trans, offload, unified, size_dsa, n_seq_max, n_pad,
|
||||
0, LLAMA_SWA_TYPE_NONE, filter_dsa, filter_lid, reuse);
|
||||
|
||||
LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
|
||||
|
||||
kv_swa = std::make_unique<llama_kv_cache>(
|
||||
model, hparams, type_k, type_v,
|
||||
v_trans, offload, unified, size_swa, n_seq_max, n_pad,
|
||||
hparams.n_swa, hparams.swa_type, nullptr, filter_swa, reuse, nullptr);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::clear(bool data) {
|
||||
kv_dsa->clear(data);
|
||||
kv_swa->clear(data);
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
||||
bool res = true;
|
||||
|
||||
res = res & kv_dsa->seq_rm(seq_id, p0, p1);
|
||||
res = res & kv_swa->seq_rm(seq_id, p0, p1);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
||||
kv_dsa->seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
kv_swa->seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::seq_keep(llama_seq_id seq_id) {
|
||||
kv_dsa->seq_keep(seq_id);
|
||||
kv_swa->seq_keep(seq_id);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
|
||||
kv_dsa->seq_add(seq_id, p0, p1, shift);
|
||||
kv_swa->seq_add(seq_id, p0, p1, shift);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
|
||||
kv_dsa->seq_div(seq_id, p0, p1, d);
|
||||
kv_swa->seq_div(seq_id, p0, p1, d);
|
||||
}
|
||||
|
||||
llama_pos llama_kv_cache_dsa_iswa::seq_pos_min(llama_seq_id seq_id) const {
|
||||
// the DSA cache is a superset of the SWA cache, so we can just check the SWA cache
|
||||
return kv_swa->seq_pos_min(seq_id);
|
||||
}
|
||||
|
||||
llama_pos llama_kv_cache_dsa_iswa::seq_pos_max(llama_seq_id seq_id) const {
|
||||
return kv_swa->seq_pos_max(seq_id);
|
||||
}
|
||||
|
||||
std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsa_iswa::memory_breakdown() const {
|
||||
std::map<ggml_backend_buffer_type_t, size_t> mb = kv_dsa->memory_breakdown();
|
||||
for (const auto & buft_size : kv_swa->memory_breakdown()) {
|
||||
mb[buft_size.first] += buft_size.second;
|
||||
}
|
||||
return mb;
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
|
||||
GGML_UNUSED(embd_all);
|
||||
|
||||
// first try simple split
|
||||
do {
|
||||
if (!unified) {
|
||||
// requires equal splits, so we skip the simple split
|
||||
break;
|
||||
}
|
||||
|
||||
balloc.split_reset();
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
while (true) {
|
||||
auto ubatch = balloc.split_simple(n_ubatch);
|
||||
|
||||
if (ubatch.n_tokens == 0) {
|
||||
break;
|
||||
}
|
||||
|
||||
ubatches.push_back(std::move(ubatch)); // NOLINT
|
||||
}
|
||||
|
||||
if (balloc.get_n_used() < balloc.get_n_tokens()) {
|
||||
// failed to find a suitable split
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_mla = kv_dsa->get_mla()->prepare(ubatches);
|
||||
if (sinfos_mla.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_lid = kv_dsa->get_lid()->prepare(ubatches);
|
||||
if (sinfos_lid.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_swa = kv_swa->prepare(ubatches);
|
||||
if (sinfos_swa.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
assert(sinfos_mla.size() == sinfos_swa.size());
|
||||
|
||||
return std::make_unique<llama_kv_cache_dsa_iswa_context>(
|
||||
this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(sinfos_swa), std::move(ubatches));
|
||||
} while (false);
|
||||
|
||||
// if it fails, try equal split
|
||||
do {
|
||||
balloc.split_reset();
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
while (true) {
|
||||
auto ubatch = balloc.split_equal(n_ubatch, !unified, 0);
|
||||
|
||||
if (ubatch.n_tokens == 0) {
|
||||
break;
|
||||
}
|
||||
|
||||
ubatches.push_back(std::move(ubatch)); // NOLINT
|
||||
}
|
||||
|
||||
if (balloc.get_n_used() < balloc.get_n_tokens()) {
|
||||
// failed to find a suitable split
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_mla = kv_dsa->get_mla()->prepare(ubatches);
|
||||
if (sinfos_mla.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_lid = kv_dsa->get_lid()->prepare(ubatches);
|
||||
if (sinfos_lid.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_swa = kv_swa->prepare(ubatches);
|
||||
if (sinfos_swa.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
assert(sinfos_mla.size() == sinfos_swa.size());
|
||||
|
||||
return std::make_unique<llama_kv_cache_dsa_iswa_context>(
|
||||
this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(sinfos_swa), std::move(ubatches));
|
||||
} while (false);
|
||||
|
||||
return std::make_unique<llama_kv_cache_dsa_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_full() {
|
||||
return std::make_unique<llama_kv_cache_dsa_iswa_context>(this);
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa_iswa::init_update(llama_context * lctx, bool optimize) {
|
||||
return std::make_unique<llama_kv_cache_dsa_iswa_context>(this, lctx, optimize);
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa_iswa::get_can_shift() const {
|
||||
return kv_dsa->get_can_shift() &&
|
||||
kv_swa->get_can_shift() &&
|
||||
kv_dsa->get_mla()->get_size() == kv_swa->get_size();
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
|
||||
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
|
||||
kv_dsa->state_write(io, seq_id, flags);
|
||||
}
|
||||
|
||||
kv_swa->state_write(io, seq_id, flags);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
|
||||
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
|
||||
kv_dsa->state_read(io, seq_id, flags);
|
||||
}
|
||||
|
||||
kv_swa->state_read(io, seq_id, flags);
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa * llama_kv_cache_dsa_iswa::get_dsa() const {
|
||||
return kv_dsa.get();
|
||||
}
|
||||
|
||||
llama_kv_cache * llama_kv_cache_dsa_iswa::get_swa() const {
|
||||
return kv_swa.get();
|
||||
}
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_iswa_context
|
||||
//
|
||||
|
||||
llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(llama_memory_status status) : status(status) {}
|
||||
|
||||
llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv) :
|
||||
ctx_dsa(kv->get_dsa()->init_full()),
|
||||
ctx_swa(kv->get_swa()->init_full()),
|
||||
status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv,
|
||||
llama_context * lctx,
|
||||
bool optimize) :
|
||||
ctx_dsa(kv->get_dsa()->init_update(lctx, optimize)),
|
||||
ctx_swa(kv->get_swa()->init_update(lctx, optimize)),
|
||||
status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_iswa_context::llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv,
|
||||
slot_info_vec_t sinfos_mla,
|
||||
slot_info_vec_t sinfos_lid,
|
||||
slot_info_vec_t sinfos_swa,
|
||||
std::vector<llama_ubatch> ubatches) :
|
||||
ubatches(std::move(ubatches)),
|
||||
// note: here we copy the ubatches. not sure if this is ideal
|
||||
ctx_dsa(new llama_kv_cache_dsa_context(kv->get_dsa(), std::move(sinfos_mla), std::move(sinfos_lid), this->ubatches)),
|
||||
ctx_swa(new llama_kv_cache_context(kv->get_swa(), std::move(sinfos_swa), this->ubatches)),
|
||||
status(llama_memory_status_combine(ctx_dsa->get_status(), ctx_swa->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_iswa_context:: ~llama_kv_cache_dsa_iswa_context() = default;
|
||||
|
||||
bool llama_kv_cache_dsa_iswa_context::next() {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
ctx_dsa->next();
|
||||
ctx_swa->next();
|
||||
|
||||
if (++i_next >= ubatches.size()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa_iswa_context::apply() {
|
||||
assert(!llama_memory_status_is_fail(status));
|
||||
|
||||
bool res = true;
|
||||
|
||||
res = res & ctx_dsa->apply();
|
||||
res = res & ctx_swa->apply();
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
llama_memory_status llama_kv_cache_dsa_iswa_context::get_status() const {
|
||||
return status;
|
||||
}
|
||||
|
||||
const llama_ubatch & llama_kv_cache_dsa_iswa_context::get_ubatch() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return ubatches[i_next];
|
||||
}
|
||||
|
||||
const llama_kv_cache_dsa_context * llama_kv_cache_dsa_iswa_context::get_dsa() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return static_cast<const llama_kv_cache_dsa_context *>(ctx_dsa.get());
|
||||
}
|
||||
|
||||
const llama_kv_cache_context * llama_kv_cache_dsa_iswa_context::get_swa() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return static_cast<const llama_kv_cache_context *>(ctx_swa.get());
|
||||
}
|
||||
@@ -0,0 +1,134 @@
|
||||
#pragma once
|
||||
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
|
||||
#include <vector>
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_iswa
|
||||
//
|
||||
|
||||
// utilizes two child memories: llama_kv_cache_dsa for the full-attention (DSA) layers and llama_kv_cache for the SWA layers
|
||||
|
||||
class llama_kv_cache_dsa_iswa : public llama_memory_i {
|
||||
public:
|
||||
llama_kv_cache_dsa_iswa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool swa_full,
|
||||
bool unified,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_ubatch,
|
||||
uint32_t n_pad,
|
||||
const layer_filter_cb & filter_mla,
|
||||
const layer_filter_cb & filter_lid,
|
||||
const layer_reuse_cb & reuse);
|
||||
|
||||
~llama_kv_cache_dsa_iswa() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
llama_memory_context_ptr init_batch(
|
||||
llama_batch_allocr & balloc,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_all) override;
|
||||
|
||||
llama_memory_context_ptr init_full() override;
|
||||
|
||||
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
void clear(bool data) override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_iswa specific API
|
||||
//
|
||||
|
||||
llama_kv_cache_dsa * get_dsa() const;
|
||||
llama_kv_cache * get_swa() const;
|
||||
|
||||
private:
|
||||
const bool unified;
|
||||
|
||||
std::unique_ptr<llama_kv_cache_dsa> kv_dsa;
|
||||
std::unique_ptr<llama_kv_cache> kv_swa;
|
||||
};
|
||||
|
||||
class llama_kv_cache_dsa_iswa_context : public llama_memory_context_i {
|
||||
public:
|
||||
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
|
||||
|
||||
// used for errors
|
||||
llama_kv_cache_dsa_iswa_context(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache context
|
||||
llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv);
|
||||
|
||||
// used to create an update context
|
||||
llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv,
|
||||
llama_context * lctx,
|
||||
bool optimize);
|
||||
|
||||
// used to create a batch processing context from a batch
|
||||
llama_kv_cache_dsa_iswa_context(
|
||||
llama_kv_cache_dsa_iswa * kv,
|
||||
slot_info_vec_t sinfos_mla,
|
||||
slot_info_vec_t sinfos_lid,
|
||||
slot_info_vec_t sinfos_swa,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_dsa_iswa_context();
|
||||
|
||||
//
|
||||
// llama_memory_context_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_iswa_context specific API
|
||||
//
|
||||
|
||||
const llama_kv_cache_dsa_context * get_dsa() const;
|
||||
const llama_kv_cache_context * get_swa() const;
|
||||
|
||||
private:
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
const llama_memory_context_ptr ctx_dsa;
|
||||
const llama_memory_context_ptr ctx_swa;
|
||||
|
||||
const llama_memory_status status;
|
||||
};
|
||||
@@ -323,7 +323,8 @@ llama_kv_cache::llama_kv_cache(
|
||||
hparams.n_embd_head_k() % 64 == 0;
|
||||
|
||||
// always create Hadamard rotation tensors for DeepSeek lightning indexers
|
||||
if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) &&
|
||||
if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 ||
|
||||
model.arch == LLM_ARCH_GLM_DSA || model.arch == LLM_ARCH_DOTS3NOTE) &&
|
||||
hparams.n_embd_head_k_full == hparams.indexer_head_size) {
|
||||
attn_rot_k = true;
|
||||
}
|
||||
|
||||
@@ -31,6 +31,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
|
||||
case LLM_ARCH_MELLUM:
|
||||
case LLM_ARCH_LAGUNA:
|
||||
case LLM_ARCH_GRANITE_SWA:
|
||||
case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config
|
||||
return false;
|
||||
default:
|
||||
return true;
|
||||
|
||||
+59
-1
@@ -11,6 +11,7 @@
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cache-iswa.h"
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
#include "llama-kv-cache-dsa-iswa.h"
|
||||
#include "llama-kv-cache-msa.h"
|
||||
#include "llama-kv-cache-dsv4.h"
|
||||
#include "llama-memory-hybrid.h"
|
||||
@@ -194,6 +195,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
|
||||
return new llama_model_deepseek2ocr(params);
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
return new llama_model_deepseek32(params);
|
||||
case LLM_ARCH_DOTS3NOTE:
|
||||
return new llama_model_dots3note(params);
|
||||
case LLM_ARCH_DEEPSEEK4:
|
||||
return new llama_model_deepseek4(params);
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
@@ -851,6 +854,7 @@ const char * llm_type_name(llm_type type) {
|
||||
case LLM_TYPE_230B_A10B: return "230B.A10B";
|
||||
case LLM_TYPE_428B_A23B: return "428B.A23B";
|
||||
case LLM_TYPE_235B_A22B: return "235B.A22B";
|
||||
case LLM_TYPE_288B_A19B: return "288B.A19B";
|
||||
case LLM_TYPE_300B_A47B: return "300B.A47B";
|
||||
case LLM_TYPE_310B_A15B: return "310B.A15B";
|
||||
case LLM_TYPE_355B_A32B: return "355B.A32B";
|
||||
@@ -1924,7 +1928,9 @@ void llama_model::print_info() const {
|
||||
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
||||
}
|
||||
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR ||
|
||||
arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA ||
|
||||
arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4) {
|
||||
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
||||
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
|
||||
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
|
||||
@@ -2193,6 +2199,57 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
||||
nullptr);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DOTS3NOTE:
|
||||
{
|
||||
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
|
||||
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) {
|
||||
// MTP draft context: plain attention KV cache holding only the nextn layer
|
||||
llama_kv_cache::layer_filter_cb filter =
|
||||
[&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
|
||||
res = new llama_kv_cache(
|
||||
*this,
|
||||
hparams,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
nullptr,
|
||||
filter,
|
||||
nullptr,
|
||||
nullptr);
|
||||
} else {
|
||||
// main context: DSA cache for the trunk full-attention layers plus a window-sized SWA cache
|
||||
llama_kv_cache::layer_filter_cb filter_mla = nullptr;
|
||||
if (hparams.n_layer_nextn > 0) {
|
||||
filter_mla = [&](uint32_t il) { return il < hparams.n_layer(); };
|
||||
}
|
||||
llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return il < hparams.n_layer() && hparams.is_indexer_full(il); };
|
||||
|
||||
res = new llama_kv_cache_dsa_iswa(
|
||||
*this,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
params.swa_full,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
cparams.n_ubatch,
|
||||
1,
|
||||
filter_mla,
|
||||
filter_lid,
|
||||
nullptr);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK4:
|
||||
{
|
||||
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
|
||||
@@ -2661,6 +2718,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_LLAMA_EMBED:
|
||||
case LLM_ARCH_MAINCODER:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
case LLM_ARCH_DOTS3NOTE:
|
||||
case LLM_ARCH_NANBEIGE:
|
||||
case LLM_ARCH_POCKETTTS:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
@@ -140,6 +140,7 @@ enum llm_type {
|
||||
LLM_TYPE_230B_A10B, // Minimax M2
|
||||
LLM_TYPE_428B_A23B, // Minimax M3
|
||||
LLM_TYPE_235B_A22B,
|
||||
LLM_TYPE_288B_A19B, // dots3-note
|
||||
LLM_TYPE_300B_A47B, // Ernie MoE big
|
||||
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
|
||||
LLM_TYPE_355B_A32B, // GLM-4.5
|
||||
|
||||
@@ -0,0 +1,480 @@
|
||||
#include "models.h"
|
||||
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
|
||||
// note: code adapted from deepseek32.cpp (DSA indexer + absorbed MLA) and step35.cpp (head-wise output gate)
|
||||
|
||||
void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
hparams.f_norm_eps = 1e-6; // eps for the indexer k_norm layer norm
|
||||
|
||||
// TODO: use MTP layer
|
||||
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
|
||||
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
|
||||
|
||||
// MoE parameters
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
|
||||
|
||||
// MLA parameters of the full-attention layers
|
||||
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
|
||||
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
||||
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
|
||||
|
||||
// MLA parameters of the sliding-window layers
|
||||
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK_SWA, hparams.n_lora_kv_swa);
|
||||
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA, hparams.n_embd_head_k_mla_swa);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, hparams.n_embd_head_v_mla_swa);
|
||||
|
||||
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
||||
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
||||
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
|
||||
ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
|
||||
|
||||
// DSA parameters
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
|
||||
ml.get_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl);
|
||||
|
||||
switch (hparams.n_layer()) {
|
||||
case 46: type = LLM_TYPE_288B_A19B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_dots3note::load_arch_tensors(llama_model_loader & ml) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
GGML_UNUSED(ml);
|
||||
|
||||
if (!hparams.is_mla()) {
|
||||
throw std::runtime_error("DOTS3NOTE architecture requires MLA");
|
||||
}
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
|
||||
const int64_t q_lora_rank = hparams.n_lora_q;
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
const int64_t n_expert_shared = hparams.n_expert_shared;
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
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) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer_all; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
const bool is_mtp = i >= n_layer;
|
||||
// the NextN/MTP block uses the sliding-attention geometry
|
||||
const bool is_swa = is_mtp || hparams.is_swa(i);
|
||||
|
||||
// MTP tensors are preserved in the GGUF but there is no MTP graph yet
|
||||
const int flags = is_mtp ? TENSOR_SKIP | TENSOR_NOT_REQUIRED : 0;
|
||||
|
||||
const int64_t n_head_l = hparams.n_head(i);
|
||||
|
||||
const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv;
|
||||
const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
||||
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
|
||||
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
|
||||
// norm applied on the shared rope key before rope
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_qk_rope}, flags);
|
||||
|
||||
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
|
||||
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head_l * n_embd_head_k_mla}, flags);
|
||||
|
||||
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
|
||||
|
||||
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head_l}, flags);
|
||||
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head_l}, flags);
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head_l * n_embd_head_v_mla, n_embd}, flags);
|
||||
|
||||
// head-wise sigmoid output gate
|
||||
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, flags);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
// DSA indexer
|
||||
if (!is_mtp && hparams.is_indexer_full(i)) {
|
||||
layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
|
||||
layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
|
||||
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
|
||||
}
|
||||
|
||||
if (is_mtp || i < (int) hparams.n_layer_dense_lead) {
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
|
||||
} else {
|
||||
if (n_expert == 0 || n_expert_used == 0) {
|
||||
throw std::runtime_error("n_expert and n_expert_used must be > 0");
|
||||
}
|
||||
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
|
||||
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
|
||||
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
}
|
||||
|
||||
if (is_mtp) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags);
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_dots3note::build_arch_graph(const llm_graph_params & params) const {
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
llama_model_dots3note::graph::graph(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
GGML_ASSERT(hparams.is_mla());
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
|
||||
const int64_t n_indexer_head = hparams.indexer_n_head;
|
||||
const int64_t n_embd_indexer_head = hparams.indexer_head_size;
|
||||
const uint32_t n_indexer_top_k = hparams.indexer_top_k;
|
||||
|
||||
// the indexer head layout is [rope | nope]
|
||||
GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
llm_graph_input_attn_k_dsa_iswa * inp_attn = build_attn_inp_k_dsa_iswa();
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
const bool is_swa = hparams.is_swa(il);
|
||||
|
||||
const int64_t n_head_l = hparams.n_head(il);
|
||||
|
||||
const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv;
|
||||
const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
|
||||
|
||||
const float kq_scale = 1.0f/sqrtf(float(n_embd_head_k_mla));
|
||||
const float freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
|
||||
// norm
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self_attention
|
||||
{
|
||||
ggml_tensor * attn_inp = cur;
|
||||
|
||||
ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
|
||||
cb(qr, "qr", il);
|
||||
|
||||
qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(qr, "qr", il);
|
||||
|
||||
ggml_tensor * top_k = nullptr;
|
||||
|
||||
// lightning indexer (full-attention layers only)
|
||||
if (!is_swa) {
|
||||
ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
|
||||
// {n_embd_indexer_head, n_indexer_head, n_tokens}
|
||||
indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);
|
||||
indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,
|
||||
LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
|
||||
ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// {n_embd_indexer_head, 1, n_tokens}
|
||||
indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);
|
||||
indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,
|
||||
LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// perform Hadamard transform on indexer q and k
|
||||
indexer_q = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_q);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
indexer_k = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_k);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// store indexer keys to KV cache
|
||||
const auto * mctx_lid = inp_attn->get_dsa()->mctx->get_lid();
|
||||
const auto & k_idxs_lid = inp_attn->get_dsa()->get_k_idxs_lid();
|
||||
ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
|
||||
|
||||
ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);
|
||||
cb(indexer_weights, "indexer_weights", il);
|
||||
|
||||
indexer_k = mctx_lid->get_k(ctx0, il);
|
||||
|
||||
// split the batch into streams if needed
|
||||
const auto n_stream = indexer_k->ne[3];
|
||||
indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
|
||||
indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
|
||||
|
||||
// pre-scale weights to avoid scaling operations on huge indexer_score tensor
|
||||
indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
|
||||
cb(indexer_weights, "indexer_weights", il);
|
||||
|
||||
ggml_tensor * indexer_score = nullptr;
|
||||
if (cparams.fused_lid) {
|
||||
indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn->get_dsa()->get_kq_mask_lid());
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
|
||||
} else {
|
||||
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
|
||||
cb(indexer_kq, "indexer_kq", il);
|
||||
|
||||
// ReLU requires contiguous tensors
|
||||
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
|
||||
cb(indexer_kq, "indexer_kq", il);
|
||||
|
||||
indexer_score = ggml_relu(ctx0, indexer_kq);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// sum by q n_indexer_head dimension
|
||||
indexer_score = ggml_sum_rows(ctx0, indexer_score);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// permute result to match KQ mask
|
||||
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
ggml_tensor * indexer_kq_mask = inp_attn->get_dsa()->get_kq_mask_lid();
|
||||
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
}
|
||||
|
||||
// get indices of top k indexer scores
|
||||
uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
|
||||
top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
|
||||
cb(top_k, "top_k", il);
|
||||
}
|
||||
|
||||
ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);
|
||||
cb(q, "q", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head_l, n_tokens}
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
|
||||
ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, 0);
|
||||
cb(q_nope, "q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head_l, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, q, n_embd_head_qk_rope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
|
||||
ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "kv_cmpr_pe", il);
|
||||
|
||||
// split into {kv_lora_rank, n_tokens}
|
||||
ggml_tensor * kv_cmpr =
|
||||
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, 1, n_tokens}
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
// norm on the shared rope key, applied before rope
|
||||
k_pe = build_norm(k_pe, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
// MLA attention with the absorption optimization
|
||||
{
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head_l}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head_l} x {n_embd_head_qk_nope, n_tokens, n_head_l}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head_l, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head_l, n_tokens}
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// apply the head-wise output gate before o_proj, so wo stays out of build_attn
|
||||
if (is_swa) {
|
||||
cur = build_attn(inp_attn->get_swa(),
|
||||
nullptr, nullptr, nullptr,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
|
||||
} else {
|
||||
cur = build_attn(inp_attn->get_dsa(),
|
||||
nullptr, nullptr, nullptr,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
|
||||
}
|
||||
cb(cur, "attn_out", il);
|
||||
|
||||
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
|
||||
cb(gate, "attn_gate", il);
|
||||
|
||||
gate = ggml_sigmoid(ctx0, gate);
|
||||
cb(gate, "attn_gate_sigmoid", il);
|
||||
|
||||
// broadcast the per-head gate over the head dimension
|
||||
ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, cur, n_embd_head_v_mla, n_head_l, n_tokens);
|
||||
ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens);
|
||||
attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
|
||||
cb(attn_3d, "attn_gated", il);
|
||||
|
||||
cur = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v_mla * n_head_l, n_tokens);
|
||||
|
||||
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
|
||||
cb(cur, "attn_output", il);
|
||||
}
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
if ((uint32_t) il < hparams.n_layer_dense_lead) {
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
|
||||
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
|
||||
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
model.layers[il].ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr,
|
||||
model.layers[il].ffn_gate_up_exps,
|
||||
model.layers[il].ffn_up_exps_s,
|
||||
model.layers[il].ffn_gate_exps_s,
|
||||
model.layers[il].ffn_down_exps_s);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
|
||||
model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
|
||||
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
@@ -1156,6 +1156,18 @@ struct llama_model_deepseek32 : public llama_model_base {
|
||||
};
|
||||
|
||||
|
||||
struct llama_model_dots3note : public llama_model_base {
|
||||
llama_model_dots3note(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
void load_arch_tensors(llama_model_loader & ml) override;
|
||||
|
||||
struct graph : public llm_graph_context {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
struct llama_model_deepseek4 : public llama_model_base {
|
||||
llama_model_deepseek4(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
|
||||
Reference in New Issue
Block a user