mirrors/ollama-for-amd
1
0
Fork 0
mirror of https://github.com/likelovewant/ollama-for-amd.git synced 2025-12-21 22:33:56 +00:00
Code Issues Packages Projects Releases Wiki Activity

llama/parsers/renderers: nemotron 3 nano (#13489)

Browse Source 
---------

Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
This commit is contained in:
Parth Sareen
2025-12-15 18:00:08 -08:00
committed by GitHub
parent 7b95087b9d
commit 7e3ea813c1
13 changed files with 2364 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
llama/llama.cpp/src/llama-arch.cpp vendored
View File

@@ -75,6 +75,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_JAIS, "jais" },
{ LLM_ARCH_NEMOTRON, "nemotron" },
{ LLM_ARCH_NEMOTRON_H, "nemotron_h" },
{ LLM_ARCH_NEMOTRON_H_MOE, "nemotron_h_moe" },
{ LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_EXAONE4, "exaone4" },
{ LLM_ARCH_RWKV6, "rwkv6" },
@@ -1765,6 +1766,39 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_NEMOTRON_H_MOE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
// mamba(2) ssm layers
{ LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_D, "blk.%d.ssm_d" },
{ LLM_TENSOR_SSM_NORM, "blk.%d.ssm_norm" },
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
// attention layers
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
// dense FFN
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
// MoE FFN (for MoE layers)
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_EXP_PROBS_B,"blk.%d.exp_probs_b" },
// MoE shared expert layer
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_EXAONE,
{
@@ -2838,6 +2872,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
case LLM_ARCH_QWEN3NEXT:
return true;
default:

1
llama/llama.cpp/src/llama-arch.h vendored
View File

@@ -79,6 +79,7 @@ enum llm_arch {
LLM_ARCH_JAIS,
LLM_ARCH_NEMOTRON,
LLM_ARCH_NEMOTRON_H,
LLM_ARCH_NEMOTRON_H_MOE,
LLM_ARCH_EXAONE,
LLM_ARCH_EXAONE4,
LLM_ARCH_RWKV6,

10
llama/llama.cpp/src/llama-graph.cpp vendored
View File

@@ -1089,6 +1089,16 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cur = ggml_relu(ctx0, cur);
cb(cur, "ffn_moe_relu", il);
} break;
case LLM_FFN_RELU_SQR:
if (gate_exps) {
// TODO: add support for gated squared relu
GGML_ABORT("fatal error: gated squared relu not implemented");
} else {
cur = ggml_relu(ctx0, cur);
cur = ggml_sqr(ctx0, cur);
cb(cur, "ffn_moe_relu_sqr", il);
}
break;
default:
GGML_ABORT("fatal error");
}

50
llama/llama.cpp/src/llama-model.cpp vendored
View File

@@ -120,6 +120,8 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_16B_A1B: return "16B.A1B";
case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_31B_A3_5B: return "31B.A3.5B";
case LLM_TYPE_80B_A3B: return "80B.A3B";
case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
case LLM_TYPE_230B_A10B: return "230B.A10B";
@@ -1788,6 +1790,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
{
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
@@ -1803,7 +1806,14 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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);
switch (hparams.n_layer) {
case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
case 56: type = LLM_TYPE_9B; break;
default: type = LLM_TYPE_UNKNOWN;
}
@@ -5175,6 +5185,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
{
// mamba2 Mixer SSM params
// NOTE: int64_t for tensor dimensions
@@ -5185,6 +5196,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t n_group = hparams.ssm_n_group;
const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_ssm_head;
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_shexp = hparams.n_ff_shexp;
// embeddings
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5234,12 +5248,26 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_k_gqa_i}, TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_v_gqa_i}, TENSOR_NOT_REQUIRED);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
} else {
// mlp layers
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { hparams.n_ff(i), n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, hparams.n_ff(i)}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
} else {
if (n_expert != 0) {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert }, 0);
// MoE branch
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
// Shared expert branch
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
} else {
// mlp layers
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { hparams.n_ff(i), n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, hparams.n_ff(i)}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
}
}
}
} break;
@@ -6870,7 +6898,8 @@ void llama_model::print_info() const {
arch == LLM_ARCH_PLAMO2 ||
arch == LLM_ARCH_GRANITE_HYBRID ||
arch == LLM_ARCH_QWEN3NEXT ||
arch == LLM_ARCH_NEMOTRON_H) {
arch == LLM_ARCH_NEMOTRON_H ||
arch == LLM_ARCH_NEMOTRON_H_MOE) {
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
@@ -6926,7 +6955,8 @@ void llama_model::print_info() const {
if (arch == LLM_ARCH_MINICPM ||
arch == LLM_ARCH_GRANITE ||
arch == LLM_ARCH_GRANITE_MOE ||
arch == LLM_ARCH_GRANITE_HYBRID) {
arch == LLM_ARCH_GRANITE_HYBRID ||
arch == LLM_ARCH_NEMOTRON_H_MOE) {
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -7107,7 +7137,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
if (arch == LLM_ARCH_FALCON_H1) {
filter_attn = [&](int32_t) { return true; };
filter_recr = [&](int32_t) { return true; };
} else if (arch == LLM_ARCH_NEMOTRON_H) {
} else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
filter_attn = [&](int32_t il) {
return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
};
@@ -7478,6 +7508,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
llm = std::make_unique<llm_build_nemotron>(*this, params);
} break;
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
{
llm = std::make_unique<llm_build_nemotron_h>(*this, params);
} break;
@@ -7765,6 +7796,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_ARWKV7:
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_NEMOTRON_H:
case LLM_ARCH_NEMOTRON_H_MOE:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values

1
llama/llama.cpp/src/llama-model.h vendored
View File

@@ -114,6 +114,7 @@ enum llm_type {
LLM_TYPE_16B_A1B,
LLM_TYPE_21B_A3B, // Ernie MoE small
LLM_TYPE_30B_A3B,
LLM_TYPE_31B_A3_5B,
LLM_TYPE_80B_A3B, // Qwen3 Next
LLM_TYPE_100B_A6B,
LLM_TYPE_106B_A12B, // GLM-4.5-Air

41
llama/llama.cpp/src/models/nemotron-h.cpp vendored
View File

@@ -107,12 +107,41 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *
}
ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il) {
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * ffn_inp = cur;
ggml_tensor * moe_out =
build_moe_ffn(ffn_inp,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
nullptr, // no gate
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
true, hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
il);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
model.layers[il].ffn_up_shexp, NULL, NULL,
NULL /* no gate */ , NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
cur = build_cvec(cur, il);
cb(cur, "l_out", il);