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Merge commit '3ca19b0e9f3f4f444d22c9f509805d037a611847' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	common/CMakeLists.txt
#	common/chat-peg-parser.cpp
#	docs/backend/SYCL.md
#	docs/ops.md
#	docs/ops/SYCL.csv
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/convert.hpp
#	ggml/src/ggml-sycl/element_wise.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/norm.cpp
#	ggml/src/ggml-sycl/rope.cpp
#	ggml/src/ggml-sycl/rope.hpp
#	ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl
#	ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
#	scripts/compare-llama-bench.py
#	scripts/sync_vendor.py
#	tests/CMakeLists.txt
#	tools/cli/cli.cpp
This commit is contained in:
Concedo
2026-03-15 11:11:31 +08:00
parent 3b9385a627 3ca19b0e9f
commit 67c9798d0b
41 changed files with 836 additions and 212 deletions
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src/llama-model.cpp
+9 -2
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@@ -249,6 +249,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_102B_A12B: return "102B.A12B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
case LLM_TYPE_120B_A12B: return "120B.A12B";
case LLM_TYPE_122B_A10B: return "122B.A10B";
case LLM_TYPE_196B_A11B: return "196B.A11B";
case LLM_TYPE_230B_A10B: return "230B.A10B";
@@ -1975,10 +1976,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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_MOE_LATENT_SIZE, hparams.moe_latent_size, 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;
case 88: type = LLM_TYPE_120B_A12B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@@ -5702,6 +5705,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t n_ssm_head = hparams.ssm_dt_rank;
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 moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
// embeddings
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5761,8 +5765,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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);
layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_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);