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Merge commit '9963b81f63' into concedo_experimental

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# Conflicts:
#	.github/workflows/server.yml
#	SECURITY.md
#	docs/backend/SYCL.md
#	examples/model-conversion/README.md
#	examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/matmul-ops.c
#	tests/CMakeLists.txt
#	tests/test-chat.cpp
#	tests/test-json-schema-to-grammar.cpp
This commit is contained in:
Concedo 2025-12-17 20:30:34 +08:00
commit 1daeed5d4d
65 changed files with 2904 additions and 1006 deletions
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55
src/llama-model.cpp
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@ -225,6 +225,7 @@ 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";
@ -1902,6 +1903,7 @@ 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);
@ -1917,7 +1919,14 @@ 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;
}
@ -3546,9 +3555,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
// optional bias tensors
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
@ -5317,6 +5326,7 @@ 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
@ -5327,6 +5337,9 @@ 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);
@ -5376,12 +5389,26 @@ 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;
@ -7009,7 +7036,8 @@ 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);
@ -7064,7 +7092,8 @@ 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);
@ -7248,7 +7277,7 @@ 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;
};
@ -7619,6 +7648,7 @@ 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;
@ -7903,6 +7933,7 @@ 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