return [(self.map_tensor_name(name), data_torch)]
+@ModelBase.register("SmallThinkerForCausalLM")
+class SmallThinkerModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.SMALLTHINKER
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ if (n_experts := self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))) is not None:
+ self.gguf_writer.add_expert_count(n_experts)
+ if (n_experts_used := self.hparams.get("num_experts_per_tok", self.hparams.get("moe_num_active_primary_experts"))) is not None:
+ self.gguf_writer.add_expert_used_count(n_experts_used)
+ if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
+ self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+ self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
+ logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+ if (self.hparams.get('moe_primary_router_apply_softmax')):
+ self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+ else:
+ self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+ # YaRN is not enabled by default
+ # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
+ rope_scaling = self.hparams.get("rope_scaling") or {}
+ if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+ self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+ self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+ self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+ sliding_window_layout = self.hparams.get("sliding_window_layout")
+ if sliding_window_layout:
+ for i in sliding_window_layout:
+ if i != 0:
+ sliding_window = self.hparams.get("sliding_window_size")
+ if sliding_window:
+ self.gguf_writer.add_sliding_window(sliding_window)
+ break
+
+ _experts: list[dict[str, Tensor]] | None = None
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ # process the experts separately
+ if name.find("experts") != -1:
+ n_experts = self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))
+ assert bid is not None
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ tensors: list[tuple[str, Tensor]] = []
+
+ # merge the experts into a single 3d tensor
+ for w_name in ["down", "gate", "up"]:
+ datas: list[Tensor] = []
+
+ for xid in range(n_experts):
+ ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+ datas.append(self._experts[bid][ename])
+ del self._experts[bid][ename]
+
+ data_torch = torch.stack(datas, dim=0)
+
+ merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+
+ new_name = self.map_tensor_name(merged_name)
+
+ tensors.append((new_name, data_torch))
+ return tensors
+ else:
+ return []
+
+ return [(self.map_tensor_name(name), data_torch)]
+
+ def prepare_tensors(self):
+ super().prepare_tensors()
+
+ if self._experts is not None:
+ # flatten `list[dict[str, Tensor]]` into `list[str]`
+ experts = [k for d in self._experts for k in d.keys()]
+ if len(experts) > 0:
+ raise ValueError(f"Unprocessed experts: {experts}")
+
###### CONVERSION LOGIC ######
SMOLLM3 = auto()
LFM2 = auto()
DREAM = auto()
+ SMALLTHINKER = auto()
class VISION_PROJECTOR_TYPE(IntEnum):
MODEL_ARCH.SMOLLM3: "smollm3",
MODEL_ARCH.LFM2: "lfm2",
MODEL_ARCH.DREAM: "dream",
+ MODEL_ARCH.SMALLTHINKER: "smallthinker",
}
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
],
+ MODEL_ARCH.SMALLTHINKER: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_Q,
+ MODEL_TENSOR.ATTN_K,
+ MODEL_TENSOR.ATTN_V,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ MODEL_TENSOR.FFN_GATE_INP,
+ MODEL_TENSOR.FFN_GATE_EXP,
+ MODEL_TENSOR.FFN_DOWN_EXP,
+ MODEL_TENSOR.FFN_UP_EXP,
+ ],
# TODO
}
"model.layers.{bid}.feed_forward.router", # llama4 jamba
"encoder.layers.{bid}.mlp.router.layer", # nomic-bert-moe
"model.layers.{bid}.mlp.gate.wg", # hunyuan
+ "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker
),
MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
"transformer.h.{bid}.mlp.c_fc_1", # exaone
"model.layers.{bid}.feed_forward.up_proj", # llama4 jamba granite-hybrid
"transformer_encoder.{bid}.ffn.w12", # neobert
+ "model.layers.{bid}.block_sparse_moe.up", # smallthinker
),
MODEL_TENSOR.FFN_UP_EXP: (
"model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged)
"model.layers.{bid}.feed_forward.experts.up_proj", # llama4
"encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe
+ "model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker
),
MODEL_TENSOR.FFN_UP_SHEXP: (
"model.layers.{bid}.residual_mlp.w1", # arctic
"transformer.h.{bid}.mlp.c_fc_0", # exaone
"model.layers.{bid}.feed_forward.gate_proj", # llama4 jamba granite-hybrid
+ "model.layers.{bid}.block_sparse_moe.gate", # smallthinker
),
MODEL_TENSOR.FFN_GATE_EXP: (
"model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged) ernie4.5-moe
"model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
"model.layers.{bid}.feed_forward.experts.gate_proj", # llama4
+ "model.layers.{bid}.block_sparse_moe.experts.gate", # smallthinker
),
MODEL_TENSOR.FFN_GATE_SHEXP: (
"model.layers.h.{bid}.mlp.c_proj", # exaone
"model.layers.{bid}.feed_forward.down_proj", # llama4 jamba granite-hybrid
"transformer_encoder.{bid}.ffn.w3", # neobert
+ "model.layers.{bid}.block_sparse_moe.down", # smallthinker
),
MODEL_TENSOR.FFN_DOWN_EXP: (
"model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged)
"model.layers.{bid}.feed_forward.experts.down_proj", # llama4
"encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe
+ "model.layers.{bid}.block_sparse_moe.experts.down", # smallthinker
),
MODEL_TENSOR.FFN_DOWN_SHEXP: (
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_LFM2, "lfm2" },
{ LLM_ARCH_DREAM, "dream" },
+ { LLM_ARCH_SMALLTHINKER, "smallthinker" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
}
},
+ {
+ LLM_ARCH_SMALLTHINKER,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { 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" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }
+ },
+ },
{
LLM_ARCH_DREAM,
{
LLM_ARCH_SMOLLM3,
LLM_ARCH_LFM2,
LLM_ARCH_DREAM,
+ LLM_ARCH_SMALLTHINKER,
LLM_ARCH_UNKNOWN,
};
return moe_out;
}
+ggml_tensor * llm_graph_context::build_moe_ffn_from_probs(
+ ggml_tensor * cur,
+ ggml_tensor * probs,
+ ggml_tensor * up_exps,
+ ggml_tensor * gate_exps,
+ ggml_tensor * down_exps,
+ ggml_tensor * exp_probs_b,
+ int64_t n_expert,
+ int64_t n_expert_used,
+ llama_expert_gating_func_type gating_op,
+ int il) const {
+ const int64_t n_embd = cur->ne[0];
+ const int64_t n_tokens = cur->ne[1];
+
+ // add experts selection bias - introduced in DeepSeek V3
+ // leave probs unbiased as it's later used to get expert weights
+ ggml_tensor * selection_probs = probs;
+ if (exp_probs_b != nullptr) {
+ selection_probs = ggml_add(ctx0, probs, exp_probs_b);
+ cb(selection_probs, "ffn_moe_probs_biased", il);
+ }
+
+ // select experts
+ ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
+ cb(selected_experts->src[0], "ffn_moe_argsort", il);
+ cb(selected_experts, "ffn_moe_topk", il);
+
+ ggml_tensor * weights = ggml_get_rows(ctx0,
+ ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
+ cb(weights, "ffn_moe_weights", il);
+
+ weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens);
+ if (gating_op == LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX) {
+ weights = ggml_soft_max(ctx0, weights);
+ } else {
+ weights = ggml_sigmoid(ctx0, weights);
+ ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); // [1, n_tokens]
+ cb(weights_sum, "ffn_moe_weights_sum", il);
+
+ weights = ggml_div(ctx0, weights, weights_sum); // [n_expert_used, n_tokens]
+ cb(weights, "ffn_moe_weights_norm", il);
+ }
+
+ weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
+
+ cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
+
+ ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ cb(up, "ffn_moe_up", il);
+
+ ggml_tensor * experts = nullptr;
+ cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ cb(cur, "ffn_moe_gate", il);
+
+ cur = ggml_reglu_split(ctx0, cur, up);
+ cb(cur, "ffn_moe_reglu", il);
+
+ experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
+ cb(experts, "ffn_moe_down", il);
+
+ experts = ggml_mul(ctx0, experts, weights);
+ cb(cur, "ffn_moe_weighted", il);
+
+ ggml_tensor * cur_experts[LLAMA_MAX_EXPERTS] = { nullptr };
+
+ assert(n_expert_used > 0);
+
+ // order the views before the adds
+ for (uint32_t i = 0; i < hparams.n_expert_used; ++i) {
+ cur_experts[i] = ggml_view_2d(ctx0, experts, n_embd, n_tokens, experts->nb[2], i*experts->nb[1]);
+
+ ggml_build_forward_expand(gf, cur_experts[i]);
+ }
+
+ // aggregate experts
+ // note: here we explicitly use hparams.n_expert_used instead of n_expert_used
+ // to avoid potentially a large number of add nodes during warmup
+ // ref: https://github.com/ggml-org/llama.cpp/pull/14753
+ ggml_tensor * moe_out = cur_experts[0];
+
+ for (uint32_t i = 1; i < hparams.n_expert_used; ++i) {
+ moe_out = ggml_add(ctx0, moe_out, cur_experts[i]);
+ }
+
+ if (n_expert_used == 1) {
+ // avoid returning a non-contiguous tensor
+ moe_out = ggml_cont(ctx0, moe_out);
+ }
+
+ cb(moe_out, "ffn_moe_out", il);
+
+ return moe_out;
+}
+
// input embeddings with optional lora
ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
const int64_t n_embd = hparams.n_embd;
llama_expert_gating_func_type gating_op,
int il) const;
+ ggml_tensor * build_moe_ffn_from_probs(
+ ggml_tensor * cur,
+ ggml_tensor * probs,
+ ggml_tensor * up_exps,
+ ggml_tensor * gate_exps,
+ ggml_tensor * down_exps,
+ ggml_tensor * exp_probs_b,
+ int64_t n_expert,
+ int64_t n_expert_used,
+ llama_expert_gating_func_type gating_op,
+ int il) const;
+
//
// inputs
//
#include "ggml.h"
-void llama_hparams::set_swa_pattern(uint32_t n_pattern) {
- for (uint32_t il = 0; il < n_layer; ++il) {
- swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
+void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
+ if (dense_first) {
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ swa_layers[il] = n_pattern == 0 || (il % n_pattern != 0);
+ }
+ } else {
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
+ }
}
}
// for Classifiers
uint32_t n_cls_out = 1;
- // llama4
+ // llama4 smallthinker
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 8192;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
// this value n_pattern means that every nth layer is dense (i.e. non-SWA)
+ // dense_first means whether the pattern is start with a dense layer
// note that if n_pattern == 0, all layers are SWA
// if n_pattern == 1, all layers are dense
- // example: n_pattern = 3
+ // example 1: n_pattern = 3, dense_first = false
// il == 0: swa
// il == 1: swa
// il == 2: dense
// il == 5: dense
// il == 6: swa
// etc ...
- void set_swa_pattern(uint32_t n_pattern);
+ // example 2: n_pattern = 2, dense_first = true
+ // il == 0: dense
+ // il == 1: swa
+ // il == 2: dense
+ // il == 3: swa
+ // etc ...
+ void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);
// return true if one of the layers is SWA
bool is_swa_any() const;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_SMALLTHINKER:
+ {
+ const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+
+ if (found_swa && hparams.n_swa > 0) {
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+ hparams.n_swa = 4096;
+ hparams.set_swa_pattern(4, true);
+ } else {
+ hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+ hparams.n_no_rope_layer_step = hparams.n_layer;
+ }
+
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
+
+ switch (hparams.n_layer) {
+ case 32: type = LLM_TYPE_4B; break;
+ case 52: type = LLM_TYPE_20B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
default: throw std::runtime_error("unsupported model architecture");
}
}
}
} break;
+ case LLM_ARCH_SMALLTHINKER:
+ {
+ 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);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+
+ GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");
+ GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
+
+ // MoE branch
+ const int64_t n_ff_exp = hparams.n_ff_exp;
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+ 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);
+ }
+ } break;
default:
throw std::runtime_error("unknown architecture");
}
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
}
+ if (arch == LLM_ARCH_SMALLTHINKER) {
+ LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
+ LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
+ }
+
vocab.print_info();
}
}
};
+template <bool iswa>
+struct llm_build_smallthinker : public llm_graph_context{
+ llm_build_smallthinker(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 == hparams.n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_unified_iswa, llm_graph_input_attn_kv_unified>;
+ inp_attn_type * inp_attn = nullptr;
+
+ if constexpr (iswa) {
+ inp_attn = build_attn_inp_kv_unified_iswa();
+ } else {
+ inp_attn = build_attn_inp_kv_unified();
+ }
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+ ggml_tensor * probs = nullptr;
+
+ probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL); // [n_expert, n_tokens]
+ cb(probs, "ffn_moe_logits", il);
+
+ // norm
+ cur = build_norm(inpL,model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
+ Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, 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, nullptr, 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);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, model.layers[il].bo,
+ Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), 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);
+ probs = ggml_get_rows(ctx0, probs, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // MoE branch
+ cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ ggml_tensor * ffn_out = build_moe_ffn_from_probs(cur, probs, model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+ nullptr, n_expert, n_expert_used,
+ static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func), il);
+
+ cb(ffn_out, "ffn_out", il);
+ cur = ffn_out;
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
llama_memory_i * res;
{
llm = std::make_unique<llm_build_lfm2>(*this, params);
} break;
+ case LLM_ARCH_SMALLTHINKER:
+ {
+ if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+ llm = std::make_unique<llm_build_smallthinker<true>> (*this, params);
+ } else {
+ llm = std::make_unique<llm_build_smallthinker<false>>(*this, params);
+ }
+ } break;
default:
GGML_ABORT("fatal error");
}
case LLM_ARCH_DOTS1:
case LLM_ARCH_HUNYUAN_MOE:
case LLM_ARCH_LFM2:
+ case LLM_ARCH_SMALLTHINKER:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
overview / pkg / ggml / sources / llama.cpp / commitdiff