models : optimize qwen3next graph (#19375)
* models : optimizing qwen3next graph * cont * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * cont : remove redundant q, g chunking * minor * minor * avoid passing masks around * avoid concats during chunking * naming + shapes * update names and use prefix to disable CUDA graphs
This commit is contained in:
Georgi Gerganov
GitHub
parent
b7742cf321
commit
1725e316c1
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@ -2872,6 +2872,7 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
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const std::string ffn_moe_down_bias_prefix = "ffn_moe_down_biased";
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const std::string nemotron_h_block_out_prefix = "nemotron_h_block_out";
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const std::string mamba2_y_add_d_prefix = "mamba2_y_add_d";
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const std::string delta_net_prefix = "dnet_add";
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_tensor * node = cgraph->nodes[i];
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@ -2902,7 +2903,8 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
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strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
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strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0 &&
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strncmp(node->name, nemotron_h_block_out_prefix.c_str(), nemotron_h_block_out_prefix.size()) != 0 &&
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strncmp(node->name, mamba2_y_add_d_prefix.c_str(), mamba2_y_add_d_prefix.size()) != 0) {
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strncmp(node->name, mamba2_y_add_d_prefix.c_str(), mamba2_y_add_d_prefix.size()) != 0 &&
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strncmp(node->name, delta_net_prefix.c_str(), delta_net_prefix.size()) != 0) {
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// disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
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// by means of matching node names. See
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// https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
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@ -4544,6 +4546,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
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case GGML_UNARY_OP_CEIL:
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case GGML_UNARY_OP_ROUND:
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case GGML_UNARY_OP_TRUNC:
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// TODO: should become:
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//return ggml_is_contiguous_rows(op->src[0]);
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return ggml_is_contiguous(op->src[0]);
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default:
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return false;
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@ -273,6 +273,7 @@ static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node
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case GGML_OP_DIAG:
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case GGML_OP_MUL:
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case GGML_OP_ADD:
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case GGML_OP_SUB:
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case GGML_OP_DIV:
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case GGML_OP_GLU:
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case GGML_OP_SCALE:
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@ -489,9 +489,6 @@ private:
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ggml_tensor * build_layer_attn_linear(
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llm_graph_input_rs * inp,
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ggml_tensor * cur,
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ggml_tensor * causal_mask,
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ggml_tensor * identity,
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ggml_tensor * diag_mask,
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int il);
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ggml_tensor * build_layer_ffn(
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@ -506,9 +503,6 @@ private:
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ggml_tensor * g,
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ggml_tensor * beta,
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ggml_tensor * state,
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ggml_tensor * causal_mask,
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ggml_tensor * identity,
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ggml_tensor * diag_mask,
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int il);
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// returns pair of output and new state
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@ -16,17 +16,6 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
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ggml_tensor * inp_pos = build_inp_pos();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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ggml_tensor * causal_mask =
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ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
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GGML_TRI_TYPE_LOWER);
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ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
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ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
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ggml_build_forward_expand(gf, causal_mask);
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ggml_build_forward_expand(gf, identity);
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ggml_build_forward_expand(gf, diag_mask);
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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@ -36,7 +25,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
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// Determine layer type and build appropriate attention mechanism
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if (hparams.is_recurrent(il)) {
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// Linear attention layer (gated delta net)
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cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
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cur = build_layer_attn_linear(inp->get_recr(), cur, il);
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} else {
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// Full attention layer
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cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
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@ -99,11 +88,8 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chu
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * beta,
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ggml_tensor * state,
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ggml_tensor * causal_mask,
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ggml_tensor * identity,
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ggml_tensor * diag_mask,
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ggml_tensor * b,
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ggml_tensor * s,
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int il) {
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const int64_t S_k = q->ne[0];
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const int64_t H_k = q->ne[1];
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@ -113,134 +99,123 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chu
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const int64_t S_v = v->ne[0];
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const int64_t H_v = v->ne[1];
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GGML_ASSERT(v->ne[2] == n_tokens);
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GGML_ASSERT(k->ne[2] == n_tokens);
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GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
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GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
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GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
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GGML_ASSERT(S_k == S_v);
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GGML_ASSERT(H_v % H_k == 0);
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GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
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GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
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GGML_ASSERT(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
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GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
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GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
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GGML_ASSERT(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
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GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
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const float eps_norm = hparams.f_norm_rms_eps;
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q = ggml_l2_norm(ctx0, q, eps_norm);
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k = ggml_l2_norm(ctx0, k, eps_norm);
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const float scale = 1.0f / sqrtf(S_v);
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const float scale = 1.0f / sqrtf(S_k);
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q = ggml_scale(ctx0, q, scale);
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beta = ggml_sigmoid(ctx0, beta);
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cb(q, "q_in", il);
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cb(k, "k_in", il);
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cb(v, "v_in", il);
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cb(beta, "beta_in", il);
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cb(b, "b_in", il);
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cb(g, "g_in", il);
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q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
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q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
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k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
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v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
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g = ggml_permute(ctx0, g, 2, 1, 3, 0); // [ 1, n_tokens, H_v, n_seqs]
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b = ggml_permute(ctx0, b, 2, 0, 1, 3); // [ 1, n_tokens, H_v, n_seqs]
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beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
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state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
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const int CS = CHUNK_SIZE;
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cb(q, "q_perm", il);
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cb(k, "k_perm", il);
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cb(v, "v_perm", il);
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cb(beta, "beta_perm", il);
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cb(g, "g_perm", il);
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cb(state, "state_in", il);
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GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
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GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
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GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
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GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
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// Do padding
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const int64_t chunk_size = CHUNK_SIZE;
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const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
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const int64_t n_chunks = (n_tokens + pad) / chunk_size;
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const int pad = (CS - n_tokens % CS) % CS;
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const int n_chunks = (n_tokens + pad) / CS;
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q = ggml_pad(ctx0, q, 0, pad, 0, 0);
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k = ggml_pad(ctx0, k, 0, pad, 0, 0);
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v = ggml_pad(ctx0, v, 0, pad, 0, 0);
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g = ggml_pad(ctx0, g, pad, 0, 0, 0);
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beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
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g = ggml_pad(ctx0, g, 0, pad, 0, 0);
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b = ggml_pad(ctx0, b, 0, pad, 0, 0);
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cb(q, "q_pad", il);
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cb(k, "k_pad", il);
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cb(v, "v_pad", il);
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cb(beta, "beta_pad", il);
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cb(g, "g_pad", il);
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ggml_tensor * v_b = ggml_mul(ctx0, v, b);
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ggml_tensor * k_b = ggml_mul(ctx0, k, b);
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ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
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ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
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cb(v_b, "v_b", il);
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cb(k_b, "k_b", il);
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cb(v_beta, "v_beta", il);
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cb(k_beta, "k_beta", il);
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q = ggml_reshape_4d(ctx0, q, S_k, CS, n_chunks, H_k * n_seqs);
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k = ggml_reshape_4d(ctx0, k, S_k, CS, n_chunks, H_k * n_seqs);
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k_b = ggml_reshape_4d(ctx0, k_b, S_k, CS, n_chunks, H_v * n_seqs);
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v = ggml_reshape_4d(ctx0, v, S_v, CS, n_chunks, H_v * n_seqs);
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v_b = ggml_reshape_4d(ctx0, v_b, S_v, CS, n_chunks, H_v * n_seqs);
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q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
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k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
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k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
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v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
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v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
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g = ggml_reshape_4d(ctx0, g, CS, 1, n_chunks, H_v * n_seqs);
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b = ggml_reshape_4d(ctx0, b, 1, CS, n_chunks, H_v * n_seqs);
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g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
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beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
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// [CS, 1, n_chunks, H_v * n_seqs]
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ggml_tensor * g_cs = ggml_cumsum(ctx0, g);
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cb(g_cs, "g_cs", il);
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ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
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cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * g_cs_i = g_cs;
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ggml_tensor * g_cs_j = ggml_reshape_4d(ctx0, g_cs, 1, CS, n_chunks, H_v * n_seqs);
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ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
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ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
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g_cs_j = ggml_repeat_4d(ctx0, g_cs_j, CS, CS, n_chunks, H_v * n_seqs);
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ggml_tensor * gcs_j_broadcast =
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ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
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ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
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cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
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// [CS, CS, n_chunks, H_v * n_seqs]
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ggml_tensor * decay_mask;
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decay_mask = ggml_sub(ctx0, g_cs_j, g_cs_i);
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decay_mask = ggml_tri(ctx0, decay_mask, GGML_TRI_TYPE_LOWER_DIAG);
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decay_mask = ggml_exp(ctx0, decay_mask);
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decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
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cb(decay_mask, "decay_mask", il);
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ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
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// [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * kb;
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kb = ggml_mul_mat(ctx0, k, k_b);
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kb = ggml_mul (ctx0, kb, decay_mask);
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ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
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ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
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cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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// [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * attn;
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attn = ggml_tri(ctx0, kb, GGML_TRI_TYPE_LOWER);
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ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
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ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
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ggml_tensor * identity;
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identity = ggml_view_1d(ctx0, attn, CS, 0);
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identity = ggml_fill (ctx0, identity, 1.0f);
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identity = ggml_diag (ctx0, identity);
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ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
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attn = ggml_mul(ctx0, lin_solve, causal_mask);
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attn = ggml_add(ctx0, attn, identity);
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cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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ggml_tensor * lhs = ggml_add(ctx0, attn, identity);
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cb(lhs, "dnet_add_ch_lhs", il);
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v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
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attn = ggml_neg(ctx0, attn);
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ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
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ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
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ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
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attn = ggml_add(ctx0, lin_solve, identity);
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cb(attn, "dnet_add_ch_attn_solved", il); // [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
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cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
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// [S_v, CS, n_chunks, H_v * n_seqs]
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v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_b)), attn);
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ggml_tensor * k_cumdecay =
|
||||
ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
|
||||
cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
|
||||
// [CS, 1, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * g_exp = ggml_exp(ctx0, g_cs);
|
||||
|
||||
ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
|
||||
attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
|
||||
attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
|
||||
cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
|
||||
k_b = ggml_cont(ctx0, ggml_transpose(ctx0, k_b));
|
||||
|
||||
// [CS, S_k, n_chunks, H_k * n_seqs]
|
||||
ggml_tensor * kbg = ggml_mul(ctx0, k_b, g_exp);
|
||||
cb(kbg, "k_beta_g_exp", il);
|
||||
|
||||
// [S_k, CS, n_chunks, H_k * n_seqs]
|
||||
ggml_tensor * k_cd = ggml_mul_mat(ctx0, kbg, attn);
|
||||
cb(k_cd, "k_cumdecay", il);
|
||||
|
||||
// [S_k, CS, n_chunks, H_k * n_seqs]
|
||||
ggml_tensor * g_exp_t = ggml_transpose(ctx0, g_exp);
|
||||
ggml_tensor * q_g_exp = ggml_mul(ctx0, q, g_exp_t);
|
||||
|
||||
// [CS, CS, n_chunks, H_k * n_seqs]
|
||||
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
|
||||
kq = ggml_mul(ctx0, kq, decay_mask);
|
||||
kq = ggml_tri(ctx0, kq, GGML_TRI_TYPE_LOWER_DIAG);
|
||||
cb(kq, "kq", il);
|
||||
|
||||
// vectorized calculation of key_gdiff
|
||||
// improved from the chunked version:
|
||||
|
|
@ -250,109 +225,98 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chu
|
|||
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
|
||||
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
|
||||
|
||||
// get last element in g_cumsum along chunk_size dimension (ne0)
|
||||
// get last element in g_cumsum along CS dimension (ne0)
|
||||
// example: [[x, y, z, ..., last], ...] -> [[last], ...]
|
||||
ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
|
||||
g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
|
||||
(g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
|
||||
// [1, 1, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * g_last = ggml_view_4d(ctx0, g_cs, 1, 1, g_cs->ne[2], g_cs->ne[3],
|
||||
g_cs->nb[1],
|
||||
g_cs->nb[2],
|
||||
g_cs->nb[3],
|
||||
ggml_row_size(g_cs->type, g_cs->ne[0] - 1));
|
||||
cb(g_last, "g_last", il);
|
||||
|
||||
// TODO: remove this cont when CUDA supports non-cont unary ops
|
||||
g_last = ggml_cont(ctx0, g_last);
|
||||
cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
|
||||
|
||||
// [1, 1, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
|
||||
cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
|
||||
cb(g_last_exp, "g_last_exp", il);
|
||||
|
||||
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
|
||||
cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
|
||||
// [CS, 1, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cs, g_last));
|
||||
cb(g_diff, "g_diff", il);
|
||||
|
||||
ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
|
||||
ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
|
||||
1, chunk_size, n_chunks, g_diff_exp->ne[3]);
|
||||
ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
|
||||
ggml_tensor * g_diff_exp_t = ggml_transpose(ctx0, g_diff_exp);
|
||||
|
||||
ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
|
||||
cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
|
||||
// [S_k, CS, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * kg = ggml_mul(ctx0, k, g_diff_exp_t);
|
||||
cb(kg, "key_gdiff", il);
|
||||
|
||||
ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
|
||||
cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
|
||||
// [CS, S_k, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * kg_t = ggml_cont(ctx0, ggml_transpose(ctx0, kg));
|
||||
cb(kg_t, "key_gdiff_t", il);
|
||||
|
||||
ggml_tensor * s_t = ggml_transpose(ctx0, s);
|
||||
s_t = ggml_cont_4d(ctx0, s_t, S_v, S_v, 1, H_v * n_seqs);
|
||||
cb(s_t, "dnet_add_ch_state", il);
|
||||
|
||||
// state to be updated per chunk
|
||||
ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
|
||||
cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
|
||||
|
||||
// shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
|
||||
ggml_tensor * core_attn_out = nullptr;
|
||||
// [CS, S_v, n_chunks, H_v * n_seqs]
|
||||
ggml_tensor * v_t = ggml_cont(ctx0, ggml_transpose(ctx0, v));
|
||||
|
||||
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
|
||||
// shape: (S_k, chunk_size, 1, H_k * n_seqs)
|
||||
ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
|
||||
ggml_tensor * ch_k_cd = get_slice_2d(ctx0, k_cd, chunk); // [S_k, CS, 1, H_k * n_seqs]
|
||||
ggml_tensor * ch_v_t = get_slice_2d(ctx0, v_t, chunk); // [ CS, S_v, 1, H_v * n_seqs]
|
||||
ggml_tensor * ch_kq = get_slice_2d(ctx0, kq, chunk); // [ CS, CS, 1, H_k * n_seqs]
|
||||
ggml_tensor * ch_q_g_exp = get_slice_2d(ctx0, q_g_exp, chunk); // [S_k, CS, 1, H_k * n_seqs]
|
||||
ggml_tensor * ch_kg_t = get_slice_2d(ctx0, kg_t, chunk); // [ CS, S_k, 1, H_v * n_seqs]
|
||||
|
||||
// shape: (S_v, chunk_size, 1, H_v * n_seqs)
|
||||
ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
|
||||
// [CS, S_v, 1, H_v * n_seqs]
|
||||
ggml_tensor * v_t_p = ggml_mul_mat(ctx0, ch_k_cd, s_t);
|
||||
cb(v_t_p, "v_prime", il);
|
||||
|
||||
// shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
|
||||
ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
|
||||
// [CS, S_v, 1, H_v * n_seqs]
|
||||
ggml_tensor * v_t_new = ggml_sub(ctx0, ch_v_t, v_t_p);
|
||||
cb(v_t_new, "v_t_new", il);
|
||||
|
||||
// shape: (chunk_size, 1, H_v * n_seqs)
|
||||
ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
|
||||
// [S_v, CS, 1, H_v * n_seqs]
|
||||
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_t_new, ch_kq);
|
||||
cb(v_attn, "v_attn", il);
|
||||
|
||||
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
|
||||
// replaced by precomputed attn_kq
|
||||
ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
|
||||
cb(attn_chunk, "attn_chunk", il);
|
||||
// [S_v, CS, 1, H_v * n_seqs]
|
||||
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, s_t, ch_q_g_exp);
|
||||
cb(attn_inter, "attn_inter", il);
|
||||
|
||||
ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
|
||||
// [S_v, CS, 1, H_v * n_seqs]
|
||||
ggml_tensor * o_ch = ggml_add(ctx0, attn_inter, v_attn);
|
||||
cb(o_ch, "dnet_add_ch_attn_out", il);
|
||||
|
||||
// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
|
||||
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
|
||||
cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
|
||||
|
||||
// v_new = v_i - v_prime
|
||||
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
|
||||
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
|
||||
cb(v_new, "v_new_chunk", il);
|
||||
|
||||
// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
|
||||
ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
|
||||
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
|
||||
cb(attn_inter, "attn_inter_chunk", il);
|
||||
|
||||
// core_attn_out[:, :, i] = attn_inter + attn @ v_new
|
||||
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
|
||||
cb(v_attn, "v_attn_chunk", il);
|
||||
|
||||
ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
|
||||
cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
|
||||
|
||||
core_attn_out = core_attn_out == nullptr
|
||||
? core_attn_out_chunk
|
||||
: ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
|
||||
v = ggml_set_inplace(ctx0, v, o_ch, v->nb[1], v->nb[2], v->nb[3], chunk * v->nb[2]);
|
||||
|
||||
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
|
||||
ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
|
||||
//ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
|
||||
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
|
||||
// TODO: head broadcast might not work here - probably will need a transpose
|
||||
ggml_tensor * kgv = ggml_mul_mat(ctx0, ch_kg_t, v_t_new); // [S_k, S_v, 1, H_k * n_seqs]
|
||||
|
||||
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
|
||||
ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
|
||||
new_state = ggml_add(ctx0,
|
||||
ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
|
||||
ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
|
||||
ggml_tensor * ch_g_last_exp = get_slice_2d(ctx0, g_last_exp, chunk);
|
||||
s_t = ggml_mul(ctx0, s_t, ch_g_last_exp);
|
||||
s_t = ggml_add(ctx0, s_t, kgv);
|
||||
cb(s_t, "dnet_add_ch_state", il);
|
||||
}
|
||||
|
||||
s_t = ggml_reshape_4d(ctx0, s_t, S_v, S_v, H_v, n_seqs);
|
||||
|
||||
// truncate padded tokens
|
||||
ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
|
||||
ggml_tensor * o = ggml_view_4d(ctx0, v,
|
||||
S_v, n_tokens, H_v, n_seqs,
|
||||
ggml_row_size(core_attn_out->type, S_v),
|
||||
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
|
||||
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
|
||||
output_tokens = ggml_cont(ctx0, output_tokens);
|
||||
cb(output_tokens, "output_tokens", il);
|
||||
ggml_row_size(v->type, S_v),
|
||||
ggml_row_size(v->type, S_v * CS * n_chunks),
|
||||
ggml_row_size(v->type, S_v * CS * n_chunks * H_v), 0);
|
||||
|
||||
// permute back to (S_v, H_v, n_tokens, n_seqs)
|
||||
output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
|
||||
output_tokens = ggml_cont(ctx0, output_tokens);
|
||||
o = ggml_permute (ctx0, o, 0, 2, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
|
||||
s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
|
||||
|
||||
return {output_tokens, new_state};
|
||||
return {o, s};
|
||||
}
|
||||
|
||||
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
|
||||
|
|
@ -360,8 +324,8 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_aut
|
|||
ggml_tensor * k,
|
||||
ggml_tensor * v,
|
||||
ggml_tensor * g,
|
||||
ggml_tensor * beta,
|
||||
ggml_tensor * state,
|
||||
ggml_tensor * b, // beta
|
||||
ggml_tensor * s, // state
|
||||
int il) {
|
||||
const int64_t S_k = q->ne[0];
|
||||
const int64_t H_k = q->ne[1];
|
||||
|
|
@ -371,75 +335,72 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_aut
|
|||
const int64_t S_v = v->ne[0];
|
||||
const int64_t H_v = v->ne[1];
|
||||
|
||||
GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
|
||||
GGML_ASSERT(v->ne[2] == n_tokens);
|
||||
GGML_ASSERT(k->ne[2] == n_tokens);
|
||||
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
|
||||
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
|
||||
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
|
||||
GGML_ASSERT(n_tokens == 1);
|
||||
|
||||
GGML_ASSERT(S_k == S_v);
|
||||
GGML_ASSERT(H_v % H_k == 0);
|
||||
|
||||
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
|
||||
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
|
||||
GGML_ASSERT(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
|
||||
|
||||
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
|
||||
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
|
||||
GGML_ASSERT(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
|
||||
GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
|
||||
|
||||
const float eps_norm = hparams.f_norm_rms_eps;
|
||||
const float scale = 1.0f / sqrtf(S_k);
|
||||
|
||||
q = ggml_l2_norm(ctx0, q, eps_norm);
|
||||
k = ggml_l2_norm(ctx0, k, eps_norm);
|
||||
q = ggml_scale(ctx0, q, scale);
|
||||
|
||||
const float scale = 1.0f / sqrtf(S_v);
|
||||
|
||||
q = ggml_scale(ctx0, q, scale);
|
||||
beta = ggml_sigmoid(ctx0, beta);
|
||||
q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
|
||||
k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
|
||||
v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
|
||||
|
||||
cb(q, "q_in", il);
|
||||
cb(k, "k_in", il);
|
||||
cb(v, "v_in", il);
|
||||
cb(beta, "beta_in", il);
|
||||
cb(b, "b_in", il);
|
||||
cb(g, "g_in", il);
|
||||
|
||||
state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
|
||||
g = ggml_reshape_4d(ctx0, g, 1, 1, H_v, n_seqs);
|
||||
b = ggml_reshape_4d(ctx0, b, 1, 1, H_v, n_seqs);
|
||||
|
||||
ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
|
||||
ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
|
||||
// [S_v, S_v, H_v, n_seqs]
|
||||
g = ggml_exp(ctx0, g);
|
||||
s = ggml_mul(ctx0, s, g);
|
||||
|
||||
// Apply exponential to g_t
|
||||
g_t = ggml_exp(ctx0, g_t);
|
||||
ggml_tensor * s_t = ggml_cont(ctx0, ggml_transpose(ctx0, s));
|
||||
|
||||
// Apply the gated delta rule for the single timestep
|
||||
// last_recurrent_state = last_recurrent_state * g_t
|
||||
state = ggml_mul(ctx0, state, g_t);
|
||||
// [1, S_v, H_v, n_seqs]
|
||||
ggml_tensor * sk;
|
||||
sk = ggml_mul (ctx0, s_t, k);
|
||||
sk = ggml_sum_rows(ctx0, sk);
|
||||
|
||||
// kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
|
||||
ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
|
||||
ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
|
||||
// we need to sum over dim=-2, so we transpose, sum, then transpose again
|
||||
kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
|
||||
// [S_v, 1, H_v, n_seqs]
|
||||
ggml_tensor * d;
|
||||
d = ggml_sub(ctx0, v, ggml_transpose(ctx0, sk));
|
||||
d = ggml_mul(ctx0, d, b);
|
||||
|
||||
// v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
|
||||
ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
|
||||
// delta = (v_t - kv_mem) * beta_t
|
||||
ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
|
||||
ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
|
||||
// [1, S_v, H_v, n_seqs]
|
||||
ggml_tensor * d_t;
|
||||
d_t = ggml_transpose(ctx0, d);
|
||||
|
||||
// last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
|
||||
ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
|
||||
state = ggml_add(ctx0, state, k_t_delta);
|
||||
// [S_v, S_v, H_v, n_seqs]
|
||||
ggml_tensor * kd;
|
||||
k = ggml_repeat(ctx0, k, s);
|
||||
kd = ggml_mul (ctx0, k, d_t);
|
||||
|
||||
// Compute the attention output
|
||||
// core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
|
||||
ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
|
||||
ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
|
||||
// again, since it's over dim = -2, transpose, sum, transpose back
|
||||
ggml_tensor * core_attn_out =
|
||||
ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
|
||||
s_t = ggml_add(ctx0, s_t, kd);
|
||||
|
||||
// core_attn_out should be [S_v, 1, H_v, n_seqs] after this
|
||||
cb(core_attn_out, "output_tokens", il);
|
||||
cb(state, "new_state", il);
|
||||
cb(s_t, "dnet_add_ar_state", il);
|
||||
|
||||
return {core_attn_out, state};
|
||||
ggml_tensor * s_q = ggml_mul (ctx0, s_t, q);
|
||||
ggml_tensor * o = ggml_sum_rows(ctx0, s_q);
|
||||
|
||||
o = ggml_permute (ctx0, o, 2, 0, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
|
||||
s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
|
||||
|
||||
return {o, s};
|
||||
}
|
||||
|
||||
ggml_tensor * llm_build_qwen3next::build_norm_gated(
|
||||
|
|
@ -472,39 +433,29 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
|
|||
// Split Q projection into query and gate
|
||||
// The split should be along dimension 0 (the feature dimension)
|
||||
ggml_tensor * Qcur = ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1,
|
||||
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], 0);
|
||||
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], 0);
|
||||
cb(Qcur, "Qcur_view", il);
|
||||
|
||||
ggml_tensor * gate =
|
||||
ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1,
|
||||
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full));
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(gate, "gate", il);
|
||||
|
||||
// Now reshape Qcur to [n_embd_head, n_head, n_tokens] for multi-head attention
|
||||
Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
cb(Qcur, "Qcur_reshaped", il);
|
||||
|
||||
// Apply Q normalization
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// Apply K normalization
|
||||
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);
|
||||
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
|
||||
// Reshape gate to [n_embd, n_tokens] for the sigmoid gating (flatten the heads)
|
||||
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
|
||||
cb(gate, "gate_reshaped", il);
|
||||
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
// Apply RoPE
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
|
|
@ -519,7 +470,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
|
|||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// Attention computation
|
||||
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
|
||||
|
||||
cur = build_attn(inp,
|
||||
|
|
@ -527,10 +477,15 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
|
|||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_pregate", il);
|
||||
|
||||
ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
|
||||
cb(gate_sigmoid, "gate_sigmoid", il);
|
||||
// TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
|
||||
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
|
||||
|
||||
cur = ggml_mul(ctx0, cur, gate_sigmoid);
|
||||
gate = ggml_sigmoid(ctx0, gate);
|
||||
cb(gate, "gate_sigmoid", il);
|
||||
|
||||
gate = ggml_reshape_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
|
||||
|
||||
cur = ggml_mul(ctx0, cur, gate);
|
||||
cb(cur, "attn_gated", il);
|
||||
|
||||
cur = build_lora_mm(model.layers[il].wo, cur);
|
||||
|
|
@ -560,7 +515,6 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_qkvz(
|
|||
cb(z, "z", il);
|
||||
|
||||
return { qkv_mixed, z };
|
||||
|
||||
} else {
|
||||
// legacy (slower) path
|
||||
ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
|
||||
|
|
@ -624,9 +578,6 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_qkvz(
|
|||
ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
||||
llm_graph_input_rs * inp,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * causal_mask,
|
||||
ggml_tensor * identity,
|
||||
ggml_tensor * diag_mask,
|
||||
int il) {
|
||||
const auto * mctx_cur = inp->mctx;
|
||||
|
||||
|
|
@ -671,7 +622,12 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
|
||||
cb(a, "a", il);
|
||||
|
||||
ggml_tensor * beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
|
||||
// TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
|
||||
b = ggml_cont(ctx0, b);
|
||||
|
||||
ggml_tensor * beta = ggml_sigmoid(ctx0, b);
|
||||
|
||||
beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
|
||||
|
||||
// Reshape 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 * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
|
||||
|
|
@ -679,6 +635,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
|
||||
ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
|
||||
cb(alpha_softplus, "a_softplus", il);
|
||||
|
||||
ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
|
||||
cb(gate, "gate", il);
|
||||
|
||||
|
|
@ -686,8 +643,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
|
||||
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
|
||||
|
||||
// bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
|
||||
|
||||
// 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);
|
||||
|
|
@ -696,11 +651,12 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
|
||||
const int64_t conv_kernel_size = conv_kernel->ne[0];
|
||||
const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
|
||||
conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
|
||||
|
||||
conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
|
||||
cb(conv_states, "conv_states_reshaped", il);
|
||||
|
||||
qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
|
||||
cb(qkv_mixed, "qkv_mixed_permuted", il);
|
||||
qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
|
||||
cb(qkv_mixed, "qkv_mixed_transposed", il);
|
||||
|
||||
ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
|
||||
cb(conv_input, "conv_input", il);
|
||||
|
|
@ -720,7 +676,10 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
|
||||
cb(conv_states_all, "conv_states_updated", il);
|
||||
|
||||
// Apply SSM convolution
|
||||
ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
|
||||
state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
|
||||
cb(state, "state_predelta", il);
|
||||
|
||||
ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
|
||||
cb(conv_output_proper, "conv_output_raw", il);
|
||||
|
||||
|
|
@ -734,26 +693,36 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
|
||||
|
||||
// Extract the convolved Q, K, V from conv_output
|
||||
ggml_tensor * q_conv =
|
||||
ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
|
||||
ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
|
||||
ggml_row_size(conv_qkv_mix->type, head_k_dim),
|
||||
nb1_qkv,
|
||||
nb1_qkv * n_seq_tokens,
|
||||
0);
|
||||
|
||||
ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
|
||||
ggml_row_size(conv_qkv_mix->type, head_k_dim),
|
||||
nb1_qkv,
|
||||
nb1_qkv * n_seq_tokens,
|
||||
head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
||||
|
||||
ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
|
||||
ggml_row_size(conv_qkv_mix->type, head_v_dim),
|
||||
nb1_qkv,
|
||||
nb1_qkv * n_seq_tokens,
|
||||
ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
|
||||
|
||||
cb(q_conv, "q_conv", il);
|
||||
ggml_tensor * k_conv =
|
||||
ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
|
||||
head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
||||
cb(k_conv, "k_conv", il);
|
||||
ggml_tensor * v_conv =
|
||||
ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
|
||||
2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
||||
cb(v_conv, "v_conv", il);
|
||||
|
||||
// Unsqueeze them
|
||||
q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
|
||||
k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
|
||||
v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
|
||||
const float eps_norm = hparams.f_norm_rms_eps;
|
||||
|
||||
ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
|
||||
state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
|
||||
cb(state, "state_predelta", il);
|
||||
q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
|
||||
k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
|
||||
|
||||
//q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
|
||||
//k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
|
||||
//v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
|
||||
|
||||
// if head keys and value keys are different, repeat to force tensors into matching shapes
|
||||
if (num_k_heads != num_v_heads) {
|
||||
|
|
@ -786,7 +755,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
if (n_seq_tokens == 1) {
|
||||
attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
|
||||
} else {
|
||||
attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
|
||||
attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
|
||||
}
|
||||
ggml_tensor * output = attn_out.first;
|
||||
ggml_tensor * new_state = attn_out.second;
|
||||
|
|
@ -795,19 +764,15 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
|
||||
// Update the recurrent states
|
||||
ggml_build_forward_expand(gf,
|
||||
ggml_cpy(ctx0, new_state,
|
||||
ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
|
||||
kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
|
||||
|
||||
// Reshape both attn_out_final and z to 2D tensors for normalization
|
||||
// attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
|
||||
ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
|
||||
ggml_cpy(ctx0, new_state,
|
||||
ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
|
||||
kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
|
||||
|
||||
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
|
||||
ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
|
||||
ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
|
||||
|
||||
// Apply gated normalization: self.norm(core_attn_out, z)
|
||||
ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
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||||
ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
|
||||
|
||||
// Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
|
||||
ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
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||||
|
|
@ -818,7 +783,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|||
cb(cur, "linear_attn_out", il);
|
||||
|
||||
// Reshape back to original dimensions
|
||||
cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
|
|
@ -839,7 +805,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int
|
|||
if (model.layers[il].ffn_up_shexp != nullptr) {
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, NULL, NULL,
|
||||
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,
|
||||
|
|
@ -852,11 +818,9 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int
|
|||
ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
|
||||
cb(shared_gate, "shared_expert_gate", il);
|
||||
|
||||
// Apply sigmoid to the gate
|
||||
shared_gate = ggml_sigmoid(ctx0, shared_gate);
|
||||
cb(shared_gate, "shared_expert_gate_sigmoid", il);
|
||||
|
||||
// Apply the gate to the shared expert output
|
||||
ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
|
||||
cb(ffn_shexp, "ffn_shexp_gated", il);
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue