Merge remote-tracking branch 'origin/master' into qwen3_next

pwilkin · pwilkin · commit 1aed3d718a48 · 2025-10-23T19:45:50.000+02:00
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
@@ -1258,6 +1258,58 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_floor(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_floor_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_ceil(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_ceil_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_round(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_round_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+     /**
+     * Truncates the fractional part of each element in the tensor (towards zero).
+     * For example: trunc(3.7) = 3.0, trunc(-2.9) = -2.0
+     * Similar to std::trunc in C/C++.
+     */
+
+    GGML_API struct ggml_tensor * ggml_trunc(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_trunc_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+
+
+    // xIELU activation function
+    // x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
+    // where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions
+    // that constrain the positive and negative source alpha values respectively
+    GGML_API struct ggml_tensor * ggml_xielu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float alpha_n,
+            float alpha_p,
+            float beta,
+            float eps);
+
     // gated linear unit ops
     // A: n columns, r rows,
     // result is n / 2 columns, r rows,
@@ -1732,6 +1784,13 @@ extern "C" {
             float                 scale,
             float                 max_bias);
 
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * mask,
+            float                 scale,
+            float                 max_bias);
+
     GGML_API void ggml_soft_max_add_sinks(
             struct ggml_tensor * a,
             struct ggml_tensor * sinks);
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
@@ -9104,6 +9104,26 @@ void ggml_compute_forward_unary(
             {
                 ggml_compute_forward_trunc(params, dst);
             } break;
+        case GGML_UNARY_OP_XIELU:
+            {
+                ggml_compute_forward_xielu(params, dst);
+            } break;
+        case GGML_UNARY_OP_FLOOR:
+            {
+                ggml_compute_forward_floor(params, dst);
+            } break;
+        case GGML_UNARY_OP_CEIL:
+            {
+                ggml_compute_forward_ceil(params, dst);
+            } break;
+        case GGML_UNARY_OP_ROUND:
+            {
+                ggml_compute_forward_round(params, dst);
+            } break;
+        case GGML_UNARY_OP_TRUNC:
+            {
+                ggml_compute_forward_trunc(params, dst);
+            } break;
         case GGML_UNARY_OP_EXPM1:
             {
                 ggml_compute_forward_expm1(params, dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2343,6 +2343,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_UNARY_OP_ELU:
                     ggml_cuda_op_elu(ctx, dst);
                     break;
+                case GGML_UNARY_OP_XIELU:
+                    ggml_cuda_op_xielu(ctx, dst);
+                    break;
                 case GGML_UNARY_OP_EXPM1:
                     ggml_cuda_op_expm1(ctx, dst);
                     break;
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
@@ -2716,8 +2716,8 @@ struct ggml_tensor * ggml_xielu(
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
     ggml_set_op_params_i32(result, 0, (int32_t) GGML_UNARY_OP_XIELU);
-    ggml_set_op_params_f32(result, 1, beta + ggml_compute_softplus_f32(alpha_n));
-    ggml_set_op_params_f32(result, 2, ggml_compute_softplus_f32(alpha_p));
+    ggml_set_op_params_f32(result, 1, beta + ggml_softplus(alpha_n));
+    ggml_set_op_params_f32(result, 2, ggml_softplus(alpha_p));
     ggml_set_op_params_f32(result, 3, beta);
     ggml_set_op_params_f32(result, 4, eps);
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
@@ -6230,6 +6230,95 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_GROVEMOE:
+                {
+                    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);
+                    }
+
+                    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for GROVEMOE");
+                    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for GROVEMOE");
+                    GGML_ASSERT(hparams.n_group_experts > 0 && "n_group_experts must be > 0 for GROVEMOE");
+
+                    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.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                        // MoE branch
+                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+                        const int64_t n_ff_chexp = hparams.n_ff_chexp ? hparams.n_ff_chexp : n_embd_head_k;
+                        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
+
+                        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);
+
+                        layer.ffn_gate_chexps = create_tensor(tn(LLM_TENSOR_FFN_GATE_CHEXPS, "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+                        layer.ffn_down_chexps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_CHEXPS, "weight", i), {n_ff_chexp,   n_embd, n_chunk_expert}, 0);
+                        layer.ffn_up_chexps   = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS,   "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+                    }
+                } break;
+            case LLM_ARCH_APERTUS:
+                {
+                    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 }, 0);
+
+                    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);
+
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        } else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 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);
+
+                        // optional bias tensors
+                        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.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd },     TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
+
+                        // Q and K layernorms for Apertus
+                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -17404,6 +17493,150 @@ struct llm_build_bailingmoe2 : public llm_graph_context {
     }
 };
 
+struct llm_build_bailingmoe2 : public llm_graph_context {
+    llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        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();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
+        for (int il = 0; il < n_transformer_layers; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
+                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+
+            if (il == n_transformer_layers - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
+            cb(sa_out, "sa_out", il);
+
+            // MoE branch
+            cur = build_norm(sa_out,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                ggml_tensor * moe_out =
+                    build_moe_ffn(cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            model.layers[il].ffn_exp_probs_b,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, hparams.expert_weights_norm,
+                            true, hparams.expert_weights_scale,
+                            (llama_expert_gating_func_type) hparams.expert_gating_func,
+                            il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                {
+                    ggml_tensor * ffn_shexp = build_ffn(cur,
+                            model.layers[il].ffn_up_shexp,   NULL, NULL,
+                            model.layers[il].ffn_gate_shexp, NULL, NULL,
+                            model.layers[il].ffn_down_shexp, NULL, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
+
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
+            }
+
+            cur = ggml_add(ctx0, cur, sa_out);
+
+            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);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 struct llm_build_dots1 : public llm_graph_context {
     llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
@@ -717,9 +717,6 @@ int main(int argc, char ** argv) {
 
             embd.push_back(id);
 
-            // Print cache statistics after each token generation
-            token_count++;
-
             if (params.conversation_mode && !waiting_for_first_input && !llama_vocab_is_eog(vocab, id)) {
                 assistant_ss << common_token_to_piece(ctx, id, false);
             }

Original file line number	Diff line number	Diff line change
`@@ -717,9 +717,6 @@ int main(int argc, char ** argv) {`
`717`	`717`
`718`	`718`	`embd.push_back(id);`
`719`	`719`
`720`		`- // Print cache statistics after each token generation`
`721`		`- token_count++;`
`722`		`-`
`723`	`720`	`if (params.conversation_mode && !waiting_for_first_input && !llama_vocab_is_eog(vocab, id)) {`
`724`	`721`	`assistant_ss << common_token_to_piece(ctx, id, false);`
`725`	`722`	`}`