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talk-llama : sync llama.cpp

This commit is contained in:
Georgi Gerganov 2026-06-19 10:19:58 +03:00
parent 41cf1278c9
commit 5ed76e9a07
16 changed files with 555 additions and 58 deletions
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1
examples/talk-llama/llama-arch.cpp
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@ -66,6 +66,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_XVERSE, "xverse" },
{ LLM_ARCH_COMMAND_R, "command-r" },
{ LLM_ARCH_COHERE2, "cohere2" },
{ LLM_ARCH_COHERE2MOE, "cohere2moe" },
{ LLM_ARCH_DBRX, "dbrx" },
{ LLM_ARCH_OLMO, "olmo" },
{ LLM_ARCH_OLMO2, "olmo2" },

1
examples/talk-llama/llama-arch.h
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@ -71,6 +71,7 @@ enum llm_arch {
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
LLM_ARCH_COHERE2,
LLM_ARCH_COHERE2MOE,
LLM_ARCH_DBRX,
LLM_ARCH_OLMO,
LLM_ARCH_OLMO2,

2
examples/talk-llama/llama-context.cpp
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@ -1382,7 +1382,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
// eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_embd = hparams.n_embd_inp_enc();
const int64_t n_vocab = model.vocab.n_tokens();
// note: during encode, we always pass the full sequence starting from pos = 0

1
examples/talk-llama/llama-ext.h
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@ -2,6 +2,7 @@
// this is a staging header for new llama.cpp API
// breaking changes and C++ are allowed. everything here should be considered WIP
// try as much as possible to not include this header in the rest of the codebase
#include "llama.h"

103
examples/talk-llama/llama-graph.cpp
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@ -1088,6 +1088,10 @@ ggml_tensor * llm_graph_context::build_lora_mm(
ggml_tensor * w_s) const {
ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
if (w_s) {
res = ggml_mul(ctx0, res, w_s);
}
for (const auto & lora : *loras) {
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
if (lw == nullptr) {
@ -1106,18 +1110,24 @@ ggml_tensor * llm_graph_context::build_lora_mm(
res = ggml_add(ctx0, res, ab_cur);
}
if (w_s) {
res = ggml_mul(ctx0, res, w_s);
}
return res;
}
ggml_tensor * llm_graph_context::build_lora_mm_id(
ggml_tensor * w, // ggml_tensor * as
ggml_tensor * cur, // ggml_tensor * b
ggml_tensor * ids) const {
ggml_tensor * ids,
ggml_tensor * w_s) const {
ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
if (w_s) {
const int64_t n_expert = w_s->ne[0];
const int64_t n_tokens = cur->ne[2];
ggml_tensor * s = ggml_reshape_3d(ctx0, w_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, ids);
res = ggml_mul(ctx0, res, s);
}
for (const auto & lora : *loras) {
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
if (lw == nullptr) {
@ -1269,6 +1279,29 @@ ggml_tensor * llm_graph_context::build_ffn(
llm_ffn_op_type type_op,
llm_ffn_gate_type type_gate,
int il) const {
// NVFP4 support is currently restricted to
// 1) LORA absence (*_s would be applied after LORA residual, which is incorrect)
// 2) bias absense (*_s would be applied after bias addition, which is incorrect)
// TODO: disambiguate LLM-architectural scales (which use *_s) from NVFP4 scale_2 (which also uses *_s currently)
auto has_lora = [this](ggml_tensor * w) {
if (!w) {
return false;
}
for (const auto & lora : *loras) {
if (lora.first->get_weight(w) != nullptr) {
return true;
}
}
return false;
};
GGML_ASSERT(!up_s || !up_b || !up || up->type != GGML_TYPE_NVFP4);
GGML_ASSERT(!gate_s || !gate_b || !gate || gate->type != GGML_TYPE_NVFP4);
GGML_ASSERT(!down_s || !down_b || !down || down->type != GGML_TYPE_NVFP4);
GGML_ASSERT(!up_s || !up || up->type != GGML_TYPE_NVFP4 || !has_lora(up));
GGML_ASSERT(!gate_s || !gate || gate->type != GGML_TYPE_NVFP4 || !has_lora(gate));
GGML_ASSERT(!down_s || !down || down->type != GGML_TYPE_NVFP4 || !has_lora(down));
ggml_tensor * tmp = up ? build_lora_mm(up, cur) : cur;
cb(tmp, "ffn_up", il);
@ -1627,23 +1660,18 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
if (gate_up_exps) {
// merged gate_up path: one mul_mat_id, then split into gate and up views
ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens]
ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts, up_exps_s); // [n_ff*2, n_expert_used, n_tokens]
cb(gate_up, "ffn_moe_gate_up", il);
if (up_exps_s) {
cb(gate_up, "ffn_moe_gate_up_scaled", il);
}
if (gate_up_exps_b) {
gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts);
cb(gate_up, "ffn_moe_gate_up_biased", il);
}
// apply per-expert scale2 to merged gate_up (use up_exps_s since gate and up are fused)
if (up_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
gate_up = ggml_mul(ctx0, gate_up, s);
cb(gate_up, "ffn_moe_gate_up_scaled", il);
}
const int64_t n_ff = gate_up->ne[0] / 2;
cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
cb(cur, "ffn_moe_gate", il);
@ -1651,43 +1679,33 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(up, "ffn_moe_up", il);
} else {
// separate gate and up path
up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
up = build_lora_mm_id(up_exps, cur, selected_experts, up_exps_s); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
if (up_exps_s) {
cb(up, "ffn_moe_up_scaled", il);
}
if (up_exps_b) {
up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
cb(up, "ffn_moe_up_biased", il);
}
// apply per-expert scale2 to up
if (up_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
up = ggml_mul(ctx0, up, s);
cb(up, "ffn_moe_up_scaled", il);
}
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cur = build_lora_mm_id(gate_exps, cur, selected_experts, gate_exps_s); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
} else {
cur = up;
}
if (gate_exps_s) {
cb(cur, "ffn_moe_gate_scaled", il);
}
if (gate_exps_b) {
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
cb(cur, "ffn_moe_gate_biased", il);
}
// apply per-expert scale2 to gate
if (gate_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, gate_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
cur = ggml_mul(ctx0, cur, s);
cb(cur, "ffn_moe_gate_scaled", il);
}
}
const bool has_gate = gate_exps || gate_up_exps;
@ -1759,23 +1777,18 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
GGML_ABORT("fatal error");
}
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
experts = build_lora_mm_id(down_exps, cur, selected_experts, down_exps_s); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
if (down_exps_s) {
cb(experts, "ffn_moe_down_scaled", il);
}
if (down_exps_b) {
experts = ggml_add_id(ctx0, experts, down_exps_b, selected_experts);
cb(experts, "ffn_moe_down_biased", il);
}
// apply per-expert scale2 to down
if (down_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, down_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
experts = ggml_mul(ctx0, experts, s);
cb(experts, "ffn_moe_down_scaled", il);
}
if (!weight_before_ffn) {
experts = ggml_mul(ctx0, experts, weights);
cb(experts, "ffn_moe_weighted", il);

5
examples/talk-llama/llama-graph.h
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@ -853,11 +853,12 @@ struct llm_graph_context {
ggml_tensor * cur,
ggml_tensor * w_s = nullptr) const;
// do mat_mul_id, while optionally apply lora
// do mat_mul_id, while optionally apply lora and per-expert scale
ggml_tensor * build_lora_mm_id(
ggml_tensor * w, // ggml_tensor * as
ggml_tensor * cur, // ggml_tensor * b
ggml_tensor * ids) const;
ggml_tensor * ids,
ggml_tensor * w_s = nullptr) const;
ggml_tensor * build_norm(
ggml_tensor * cur,

4
examples/talk-llama/llama-hparams.cpp
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@ -104,6 +104,10 @@ uint32_t llama_hparams::n_embd_inp() const {
return n_embd_inp;
}
uint32_t llama_hparams::n_embd_inp_enc() const {
return n_embd_inp_enc_impl > 0 ? n_embd_inp_enc_impl : n_embd_inp();
}
uint32_t llama_hparams::n_embd_out() const {
return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd;
}

7
examples/talk-llama/llama-hparams.h
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@ -189,6 +189,10 @@ struct llama_hparams {
// input embedding dimension (0 = use n_embd)
uint32_t n_embd_inp_impl = 0;
// encoder input embedding dimension (0 = use n_embd_inp())
// e.g. the eagle3 encoder fuses target_layers * target_hidden features
uint32_t n_embd_inp_enc_impl = 0;
// output embedding dimension (0 = use n_embd)
uint32_t n_embd_out_impl = 0;
@ -305,6 +309,9 @@ struct llama_hparams {
// dimension of main + auxiliary input embeddings
uint32_t n_embd_inp() const;
// dimension of the encoder input embeddings
uint32_t n_embd_inp_enc() const;
// dimension of output embeddings
uint32_t n_embd_out() const;

1
examples/talk-llama/llama-model-saver.cpp
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@ -18,6 +18,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
case LLM_ARCH_GEMMA3:
case LLM_ARCH_GEMMA3N:
case LLM_ARCH_COHERE2:
case LLM_ARCH_COHERE2MOE:
case LLM_ARCH_OLMO2:
case LLM_ARCH_BITNET:
case LLM_ARCH_T5:

9
examples/talk-llama/llama-model.cpp
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@ -157,6 +157,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_command_r(params);
case LLM_ARCH_COHERE2:
return new llama_model_cohere2(params);
case LLM_ARCH_COHERE2MOE:
return new llama_model_cohere2moe(params);
case LLM_ARCH_DBRX:
return new llama_model_dbrx(params);
case LLM_ARCH_OLMO:
@ -1467,9 +1469,12 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
}
ml.done_getting_tensors();
// Tied NVFP4 output is valid when no separate LM-head scale tensors are present.
// If sidecar scales exist, the output weight must be an actual output tensor.
GGML_ASSERT(!(output && tok_embd &&
strcmp(output->name, tok_embd->name) == 0 &&
output->type == GGML_TYPE_NVFP4));
output->type == GGML_TYPE_NVFP4 &&
(output_s || output_in_s)));
// populate tensors_by_name
for (auto & [_, ctx_ptr] : ml.ctx_map) {
for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) {
@ -1844,6 +1849,7 @@ void llama_model::print_info() const {
}
if (arch == LLM_ARCH_MELLUM ||
arch == LLM_ARCH_COHERE2MOE ||
arch == LLM_ARCH_QWEN3MOE ||
arch == LLM_ARCH_OPENAI_MOE ||
arch == LLM_ARCH_QWEN3VLMOE ||
@ -2389,6 +2395,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
case LLM_ARCH_COHERE2:
case LLM_ARCH_COHERE2MOE:
case LLM_ARCH_OLMO:
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK:

3
examples/talk-llama/llama-vocab.cpp
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@ -2280,7 +2280,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
clean_spaces = false;
ignore_merges = true;
} else if (
tokenizer_pre == "tiny_aya") {
tokenizer_pre == "tiny_aya" ||
tokenizer_pre == "cohere2moe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_TINY_AYA;
clean_spaces = false;
} else if (

2
examples/talk-llama/llama.cpp
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@ -249,7 +249,7 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama
}
// if using single GPU mode, remove all except the main GPU
if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
if (params.split_mode == LLAMA_SPLIT_MODE_NONE && !model->devices.empty()) {
if (params.main_gpu < 0) {
model->devices.clear();
} else {

6
examples/talk-llama/models/cohere2.cpp
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@ -122,9 +122,9 @@ llama_model_cohere2::graph::graph(const llama_model & model, const llm_graph_par
// feed-forward network
{
cur = build_ffn(ffn_inp,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
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);
}

443
examples/talk-llama/models/cohere2moe.cpp Normal file
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@ -0,0 +1,443 @@
#include "models.h"
void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
const bool found_norm = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
const bool found_norm_rms = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
if (!found_norm && !found_norm_rms) {
throw std::runtime_error("missing Cohere2 MoE norm epsilon");
}
if (!found_norm_rms) {
hparams.f_norm_rms_eps = 0.0f;
}
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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");
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
}
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
uint32_t swa_period = 4;
if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
hparams.set_swa_pattern(swa_period, true);
} else {
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
}
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
switch (hparams.n_layer()) {
case 49: type = LLM_TYPE_30B_A3B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
// tensors live in a separate file. Mark MTP tensors NOT_REQUIRED so the
// trunk loads cleanly.
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
// output
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 is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
}
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0 for Cohere2Moe");
}
if (n_expert_used == 0) {
throw std::runtime_error("n_expert_used must be > 0 for Cohere2Moe");
}
auto load_block_trunk = [&](int i, int flags) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
if (static_cast<uint32_t>(i) < 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 {
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, 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);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
if (hparams.n_expert_shared > 0) {
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
}
}
};
auto load_block_mtp = [&](int i, int flags) {
auto & layer = layers[i];
// MTP block looks like a full-attention Cohere2 MoE decoder block.
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
// Routed experts
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, 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);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
if (hparams.n_expert_shared > 0) {
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
// Shared experts
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
}
// NextN-specific tensors that define the MTP block.
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 }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
};
for (int i = 0; i < n_layer; ++i) {
load_block_trunk(i, trunk_flags);
}
// MTP/NextN layers are loaded as extra decoder blocks.
for (int i = n_layer; i < n_layer_all; ++i) {
load_block_mtp(i, mtp_flags);
}
}
std::unique_ptr<llm_graph_context> llama_model_cohere2moe::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
llama_model_cohere2moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
GGML_ASSERT(n_embd_head == n_rot);
const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
const float f_logit_scale = hparams.f_logit_scale;
ggml_tensor * cur;
ggml_tensor * inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
for (int il = 0; il < n_layer; ++il) {
const bool is_swa = hparams.is_swa(il);
// Dense-prefix full-attention layers use RoPE; later layers follow the SWA pattern.
const bool force_rope = static_cast<uint32_t>(il) < hparams.n_layer_dense_lead;
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, cohere2moe_norm_type, il);
cb(cur, "attn_norm", il);
ggml_tensor * ffn_inp = cur;
{
const auto & layer = model.layers[il];
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
n_embd_head, n_head, n_head_kv, il);
if (is_swa || force_rope) {
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
layer.wo, layer.wo_b, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
1.0f / sqrtf(float(n_embd_head)), il);
}
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
}
ggml_tensor * attn_out = cur;
const auto & layer = model.layers[il];
if (layer.ffn_gate_inp == nullptr) {
cur = build_ffn(ffn_inp,
layer.ffn_up, nullptr, layer.ffn_up_s,
layer.ffn_gate, nullptr, layer.ffn_gate_s,
layer.ffn_down, nullptr, layer.ffn_down_s,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
cur = build_moe_ffn(ffn_inp,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
nullptr,
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, layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(cur, "ffn_moe_out", il);
if (layer.ffn_up_shexp) {
ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, cur, ffn_shexp);
cur = ggml_scale(ctx0, cur, 0.5f);
cb(cur, "ffn_out", il);
}
}
cur = ggml_add(ctx0, cur, inpL);
cur = ggml_add(ctx0, cur, attn_out);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, nullptr, cohere2moe_norm_type, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
if (f_logit_scale) {
cur = ggml_scale(ctx0, cur, f_logit_scale);
}
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
llama_model_cohere2moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "COHERE2MOE MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "COHERE2MOE MTP currently only supports a single MTP block");
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
GGML_ASSERT(n_embd_head == n_rot);
const int il = hparams.n_layer();
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
ggml_set_input(inp->embd);
// TODO: make static using `ggml_build_forward_select()`
// see llm_graph_context::build_inp_embd() for reference
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * h_embd = inp->h;
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, cohere2moe_norm_type, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, cohere2moe_norm_type, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpL = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, cohere2moe_norm_type, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * ffn_inp = cur;
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);
cur = build_attn(inp_attn,
layer.wo, layer.wo_b, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
1.0f / sqrtf(float(n_embd_head)), il);
cb(cur, "mtp_attn_out", il);
ggml_tensor * attn_out = cur;
cur = build_moe_ffn(ffn_inp,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
nullptr,
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, layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(cur, "mtp_ffn_moe_out", il);
if (layer.ffn_up_shexp) {
ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
cur = ggml_add(ctx0, cur, ffn_shexp);
cur = ggml_scale(ctx0, cur, 0.5f);
cb(cur, "mtp_ffn_out", il);
}
cur = ggml_add(ctx0, cur, inpL);
cur = ggml_add(ctx0, cur, attn_out);
cb(cur, "mtp_post_ffn", il);
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "COHERE2MOE MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, cohere2moe_norm_type, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
GGML_ASSERT(head_w && "COHERE2MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : nullptr);
if (hparams.f_logit_scale) {
cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
}
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}

8
examples/talk-llama/models/eagle3.cpp
View File

@ -19,7 +19,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
hparams.n_embd_inp_enc_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
// eagle3 norm_before_residual (optional, default false)
// compatible with Readhat eagle3 speculator model
@ -34,7 +34,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_embd_inp = hparams.n_embd_inp();
const int64_t n_embd_inp = hparams.n_embd_inp_enc();
const int64_t n_embd_attn_input = 2 * n_embd;
// Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
@ -109,8 +109,8 @@ ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
// Input: Target model features (3 layers concatenated: low, mid, high)
// Data will be provided via ubatch->embd in encode_eagle3_features()
auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp_enc());
inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp_enc(), n_tokens);
ggml_set_input(inp_target->embd);
cur = inp_target->embd;

17
examples/talk-llama/models/models.h
View File

@ -937,6 +937,23 @@ struct llama_model_cohere2 : public llama_model_base {
};
struct llama_model_cohere2moe : public llama_model_base {
llama_model_cohere2moe(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);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(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_dbrx : public llama_model_base {
llama_model_dbrx(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;