Remove OP_DELTA_NET, fix flake8 and editorchecker because why not

convert_hf_to_gguf.py

@@ -3748,6 +3748,7 @@ class Qwen3MoeModel(Qwen2MoeModel):
 
         super().set_vocab()
 
+
 @ModelBase.register("Qwen3NextForCausalLM")
 class Qwen3NextModel(Qwen3MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3NEXT

ggml/include/ggml.h

@@ -539,8 +539,7 @@ extern "C" {
         GGML_OP_RWKV_WKV6,
         GGML_OP_GATED_LINEAR_ATTN,
         GGML_OP_RWKV_WKV7,
-        GGML_OP_DELTA_NET,
-    
+
         GGML_OP_UNARY,
 
         GGML_OP_MAP_CUSTOM1,

ggml/src/ggml-cpu/ggml-cpu.c

@@ -1656,172 +1656,6 @@ static void ggml_compute_forward_mul_mat_id(
     }
 }
 
-// ggml_compute_forward_delta_net
-
-static void ggml_compute_forward_delta_net(
-        const struct ggml_compute_params * params,
-        struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * src0 = dst->src[0]; // query
-    const struct ggml_tensor * src1 = dst->src[1]; // key
-    const struct ggml_tensor * src2 = dst->src[2]; // value
-    const struct ggml_tensor * src3 = dst->src[3]; // gate
-    const struct ggml_tensor * src4 = dst->src[4]; // beta
-    const struct ggml_tensor * src5 = dst->src[5]; // state
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src2->type == GGML_TYPE_F32);
-    GGML_ASSERT(src3->type == GGML_TYPE_F32);
-    GGML_ASSERT(src4->type == GGML_TYPE_F32);
-    GGML_ASSERT(src5->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-
-    GGML_TENSOR_TERNARY_OP_LOCALS;
-    GGML_TENSOR_LOCALS(int64_t, ne3, src3, ne);
-    GGML_TENSOR_LOCALS(size_t,  nb3, src3, nb);
-    GGML_TENSOR_LOCALS(int64_t, ne4, src4, ne);
-    GGML_TENSOR_LOCALS(size_t,  nb4, src4, nb);
-    GGML_TENSOR_LOCALS(int64_t, ne5, src5, ne);
-    GGML_TENSOR_LOCALS(size_t,  nb5, src5, nb);
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int64_t S = src0->ne[0]; // head dimension
-    const int64_t H = src0->ne[1]; // number of heads
-    const int64_t n_tokens = src0->ne[2];
-    const int64_t n_seqs = src0->ne[3];
-
-    GGML_ASSERT(ne00 == S && ne01 == H && ne02 == n_tokens && ne03 == n_seqs);
-    GGML_ASSERT(ne10 == S && ne11 == H && ne12 == n_tokens && ne13 == n_seqs);
-    GGML_ASSERT(ne20 == S && ne21 == H && ne22 == n_tokens && ne23 == n_seqs);
-    GGML_ASSERT(ne30 == S && ne31 == H && ne32 == n_tokens && ne33 == n_seqs);
-    GGML_ASSERT(ne40 == H && ne41 == n_tokens && ne42 == n_seqs && ne43 == 1);
-    GGML_ASSERT(ne50 == S && ne51 == S && ne52 == H && ne53 == n_seqs);
-
-    // Get operation parameters
-    bool use_qk_l2norm = ggml_get_op_params_i32(dst, 1) != 0;
-    float scale;
-    memcpy(&scale, ((int32_t*)dst->op_params) + 4, sizeof(float));
-
-    GGML_ASSERT(ne0 == S * H);
-    GGML_ASSERT(ne1 == n_tokens + S * n_seqs);
-
-    // Parallelize over sequences and heads
-    const int64_t n_total = n_seqs * H;
-    const int64_t n_per_thread = (n_total + nth - 1) / nth;
-    const int64_t n_start = ith * n_per_thread;
-    const int64_t n_end = MIN(n_start + n_per_thread, n_total);
-
-    for (int64_t n = n_start; n < n_end; ++n) {
-        const int64_t seq_idx = n / H;
-        const int64_t head_idx = n % H;
-
-        // Get pointers to current sequence and head
-        float * q_ptr = (float *)((char *)src0->data + seq_idx * nb03 + head_idx * nb01);
-        float * k_ptr = (float *)((char *)src1->data + seq_idx * nb13 + head_idx * nb11);
-        float * v_ptr = (float *)((char *)src2->data + seq_idx * nb23 + head_idx * nb21);
-        float * g_ptr = (float *)((char *)src3->data + seq_idx * nb33 + head_idx * nb31);
-        float * beta_ptr = (float *)((char *)src4->data + seq_idx * nb43);
-        float * state_ptr = (float *)((char *)src5->data + seq_idx * nb53 + head_idx * nb51);
-
-        float * out_ptr = (float *)((char *)dst->data + n * ne0 * sizeof(float));
-        float * new_state_ptr = out_ptr + n_tokens * S;
-
-        // Apply L2 normalization if requested
-        if (use_qk_l2norm) {
-            // Normalize query and key
-            for (int64_t t = 0; t < n_tokens; ++t) {
-                float q_sum = 0.0f, k_sum = 0.0f;
-                for (int64_t s = 0; s < S; ++s) {
-                    float q_val = q_ptr[t * nb02 / sizeof(float) + s];
-                    float k_val = k_ptr[t * nb12 / sizeof(float) + s];
-                    q_sum += q_val * q_val;
-                    k_sum += k_val * k_val;
-                }
-                float q_norm = sqrtf(q_sum + 1e-6f);
-                float k_norm = sqrtf(k_sum + 1e-6f);
-                
-                for (int64_t s = 0; s < S; ++s) {
-                    q_ptr[t * nb02 / sizeof(float) + s] /= q_norm;
-                    k_ptr[t * nb12 / sizeof(float) + s] /= k_norm;
-                }
-            }
-        }
-
-        // Apply scaling to query
-        for (int64_t i = 0; i < n_tokens * S; ++i) {
-            q_ptr[i] *= scale;
-        }
-
-        // Apply sigmoid to beta
-        float * beta_sigmoid = (float *)alloca(n_tokens * sizeof(float));
-        for (int64_t t = 0; t < n_tokens; ++t) {
-            beta_sigmoid[t] = 1.0f / (1.0f + expf(-beta_ptr[t * nb42 / sizeof(float)]));
-        }
-
-        // Complete implementation of gated delta rule
-        // Based on torch_recurrent_gated_delta_rule from the reference implementation
-        
-        // Process each token sequentially for recurrent computation
-        for (int64_t t = 0; t < n_tokens; ++t) {
-            // Get pointers to current token data
-            float * q_t = q_ptr + t * (nb02 / sizeof(float));
-            float * k_t = k_ptr + t * (nb12 / sizeof(float));
-            float * v_t = v_ptr + t * (nb22 / sizeof(float));
-            float * g_t = g_ptr + t * (nb32 / sizeof(float));
-            
-            // Apply exponential to gate and multiply by beta
-            float g_exp = expf(g_t[0]);  // g is per-head, not per-dimension
-            float beta_t = beta_sigmoid[t];
-            
-            // Update recurrent state: state = state * g_exp
-            for (int64_t i = 0; i < S * S; ++i) {
-                state_ptr[i] *= g_exp;
-            }
-            
-            // Compute kv_mem = (state * k_t^T).sum(dim=-1)
-            // This is a matrix-vector multiplication: state[S×S] @ k_t[S]
-            float kv_mem[S];
-            for (int64_t i = 0; i < S; ++i) {
-                kv_mem[i] = 0.0f;
-                for (int64_t j = 0; j < S; ++j) {
-                    kv_mem[i] += state_ptr[i * S + j] * k_t[j];
-                }
-            }
-            
-            // Compute delta = (v_t - kv_mem) * beta_t
-            float delta[S];
-            for (int64_t i = 0; i < S; ++i) {
-                delta[i] = (v_t[i] - kv_mem[i]) * beta_t;
-            }
-            
-            // Update state: state = state + k_t * delta^T
-            // This is an outer product: k_t[S] ⊗ delta[S]
-            for (int64_t i = 0; i < S; ++i) {
-                for (int64_t j = 0; j < S; ++j) {
-                    state_ptr[i * S + j] += k_t[i] * delta[j];
-                }
-            }
-            
-            // Compute output: out = (state * q_t^T).sum(dim=-1)
-            // This is a matrix-vector multiplication: state[S×S] @ q_t[S]
-            float * out_t = out_ptr + t * S;
-            for (int64_t i = 0; i < S; ++i) {
-                out_t[i] = 0.0f;
-                for (int64_t j = 0; j < S; ++j) {
-                    out_t[i] += state_ptr[i * S + j] * q_t[j];
-                }
-            }
-        }
-
-        // Copy final state to new_state
-        memcpy(new_state_ptr, state_ptr, S * S * sizeof(float));
-    }
-}
-
-
 /////////////////////////////////
 
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@@ -2164,10 +1998,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_rwkv_wkv7(params, tensor);
             } break;
-        case GGML_OP_DELTA_NET:
-            {
-                ggml_compute_forward_delta_net(params, tensor);
-            } break;
         case GGML_OP_MAP_CUSTOM1:
             {
                 ggml_compute_forward_map_custom1(params, tensor);
@@ -2461,7 +2291,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_GATED_LINEAR_ATTN:
         case GGML_OP_RWKV_WKV7:
-        case GGML_OP_DELTA_NET:
             {
                 n_tasks = n_threads;
             } break;

ggml/src/ggml.c

@@ -1002,7 +1002,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "RWKV_WKV6",
     "GATED_LINEAR_ATTN",
     "RWKV_WKV7",
-    "DELTA_NET",
 
     "UNARY",
 
@@ -1020,7 +1019,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 91, "GGML_OP_COUNT != 91");
+static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1107,7 +1106,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "rwkv_wkv6(k, v, r, tf, td, s)",
     "gated_linear_attn(k, v, q, gate, s)",
     "rwkv_wkv7(r, w, k, v, a, b, s)",
-    "delta_net(k, v, q, g, conv_w, conv_b, beta, state, chunk_size, use_qk_l2norm, scale)",
 
     "unary(x)",
 
@@ -1125,7 +1123,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 91, "GGML_OP_COUNT != 91");
+static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 

src/llama-model.cpp

@@ -18958,6 +18958,7 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
         ggml_tensor * inpL;
 
         inpL = build_inp_embd(model.tok_embd);
+        cb(inpL, "model.embed_tokens", -1);
 
         auto * inp = build_inp_mem_hybrid();
 
@@ -19259,21 +19260,25 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
         ggml_tensor * query = ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads,
                                                            n_tokens, n_seqs, split_sizes_qkvz[0] * sizeof(float),
                                                            mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], 0));
+        cb(query, "q", il);
 
         ggml_tensor * key =
             ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_tokens, n_seqs,
                                          split_sizes_qkvz[1] * sizeof(float), mixed_qkvz_reshaped->nb[1],
                                          mixed_qkvz_reshaped->nb[2], split_sizes_qkvz[0] * sizeof(float)));
+        cb(query, "k", il);
 
         ggml_tensor * value =
             ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_tokens, n_seqs,
                          split_sizes_qkvz[2] * sizeof(float), mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2],
                          (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * sizeof(float));
+        cb(query, "v", il);
 
         ggml_tensor * z =
             ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_tokens, n_seqs,
                          split_sizes_qkvz[3] * sizeof(float), mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2],
                          (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
+        cb(query, "z", il);
 
         // Reshape value and z to merge head dimensions: [batch, seq_len, num_k_heads, head_v_dim*num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads, head_v_dim]
         ggml_tensor * value_reshaped =
@@ -19293,10 +19298,12 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
         ggml_tensor * b =
             ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_tokens, n_seqs,
                          split_sizes_ba[0] * sizeof(float), mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], 0);
+        cb(query, "b", il);
 
         ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_tokens, n_seqs,
                                        split_sizes_ba[1] * sizeof(float), mixed_ba_reshaped->nb[1],
                                        mixed_ba_reshaped->nb[2], split_sizes_ba[0] * sizeof(float));
+        cb(query, "a", il);
 
         // Reshape b and a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads]
         ggml_tensor * beta  = ggml_reshape_3d(ctx0, ggml_cont(ctx0, b), num_v_heads, n_tokens, n_seqs);
@@ -19305,9 +19312,13 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
         GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
 
         ggml_tensor * alpha_softplus = softplus(alpha, model.layers[il].ssm_dt);
+        cb(alpha_softplus, "a_softplus", il);
         ggml_tensor * A_log_exp      = ggml_exp(ctx0, model.layers[il].ssm_a);     // A_log.exp()
+        cb(A_log_exp, "a_logexp", il);
         ggml_tensor * gate_scaled    = ggml_mul(ctx0, alpha_softplus, A_log_exp);  // A_log.exp() * softplus
+        cb(gate_scaled, "gate_scaled", il);
         ggml_tensor * gate           = ggml_scale(ctx0, gate_scaled, -1.0f);       // - (A_log.exp() * softplus)
+        cb(gate, "gate", il);
 
         // Get convolution states from cache
         ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
@@ -19315,6 +19326,7 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
 
         // Build the convolution states tensor
         ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+        cb(conv_states, "conv_states", il);
 
         // Calculate convolution kernel size
         const int64_t conv_kernel_size = model.layers[il].ssm_conv1d->ne[0];
@@ -19396,7 +19408,6 @@ struct llm_build_qwen3next : public llm_graph_context_mamba {
         ggml_tensor * target_gate     = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, head_dim, n_heads, n_tokens, n_seqs);
         ggml_tensor * gate_broadcast  = ggml_reshape_4d(ctx0, gate, 1, n_heads, n_tokens, n_seqs);
         gate                          = ggml_repeat(ctx0, gate_broadcast, target_gate);
-        cb(gate, "gate", il);
 
         // Call the new ggml_delta_net function with the corrected flow
         ggml_tensor * output = ggml_delta_net(k_conv, v_conv, q_conv, gate, beta, state_broadcast, true, 1.0f, il);
@@ -20190,6 +20201,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_ARCEE:
         case LLM_ARCH_ERNIE4_5:
         case LLM_ARCH_ERNIE4_5_MOE:
+        case LLM_ARCH_QWEN3NEXT:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2
@@ -20209,7 +20221,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_QWEN2MOE:
         case LLM_ARCH_QWEN3:
         case LLM_ARCH_QWEN3MOE:
-        case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_LLADA_MOE:
         case LLM_ARCH_OLMO2:
         case LLM_ARCH_OLMOE: