[Model] Qwen3.5 dense and MoE support (no vision) (#19435)
* Unified delta net handling * Remove old methods. * Refactor and optimize * Adapt autoregressive version from @ymcki * Change to decay mask approach * Fix bad permute * Qwen 3.5 support * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Further fixes * Use inheritance, remove unneeded conts * Not like this! * Remove ggml.h explicit import * Remove transformers, fix the views * ACTUALLY fix views, make super calls explicit in conversion. * Fix conversion again * Remove extra ggml.h imports --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Piotr Wilkin (ilintar)
GitHub
parent
e06088da0f
commit
39bf692af1
14 changed files with 1532 additions and 399 deletions
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@ -2412,6 +2412,25 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_QWEN3_5:
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case LLM_ARCH_QWEN3_5_MOE:
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{
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// Load linear attention (gated delta net) parameters
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ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
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ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
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ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
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ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
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ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
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// Mark recurrent layers (linear attention layers)
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for (uint32_t i = 0; i < hparams.n_layer; ++i) {
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hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
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}
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} break;
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case LLM_ARCH_MISTRAL3:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -7094,6 +7113,129 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
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// Shared experts
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layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
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}
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} break;
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case LLM_ARCH_QWEN3_5:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
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}
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// Calculate dimensions from hyperparameters
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const int64_t head_k_dim = hparams.ssm_d_state;
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const int64_t head_v_dim = hparams.ssm_d_state;
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const int64_t n_k_heads = hparams.ssm_n_group;
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const int64_t n_v_heads = hparams.ssm_dt_rank;
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const int64_t key_dim = head_k_dim * n_k_heads;
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const int64_t value_dim = head_v_dim * n_v_heads;
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const int64_t conv_dim = key_dim * 2 + value_dim;
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const int64_t ba_dim = n_v_heads * 2;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
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if (!hparams.is_recurrent(i)) {
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// Full attention layers
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
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} else {
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// Linear attention (gated delta net) specific tensors
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
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layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
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layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
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}
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// Dense FFN for all layers
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
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}
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} break;
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case LLM_ARCH_QWEN3_5_MOE:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
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}
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const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
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// Calculate dimensions from hyperparameters
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const int64_t head_k_dim = hparams.ssm_d_state;
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const int64_t head_v_dim = hparams.ssm_d_state;
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const int64_t n_k_heads = hparams.ssm_n_group;
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const int64_t n_v_heads = hparams.ssm_dt_rank;
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const int64_t key_dim = head_k_dim * n_k_heads;
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const int64_t value_dim = head_v_dim * n_v_heads;
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const int64_t conv_dim = key_dim * 2 + value_dim;
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const int64_t ba_dim = n_v_heads * 2;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
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if (!hparams.is_recurrent(i)) {
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// Full attention layers
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
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} else {
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// Linear attention (gated delta net) specific tensors
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
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layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
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layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
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}
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// MoE FFN
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
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// Shared experts
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layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
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@ -7545,6 +7687,8 @@ void llama_model::print_info() const {
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arch == LLM_ARCH_PLAMO2 ||
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arch == LLM_ARCH_GRANITE_HYBRID ||
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arch == LLM_ARCH_QWEN3NEXT ||
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arch == LLM_ARCH_QWEN3_5 ||
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arch == LLM_ARCH_QWEN3_5_MOE ||
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arch == LLM_ARCH_NEMOTRON_H ||
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arch == LLM_ARCH_NEMOTRON_H_MOE) {
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LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
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@ -8343,6 +8487,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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{
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llm = std::make_unique<llm_build_qwen3next>(*this, params);
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} break;
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case LLM_ARCH_QWEN3_5:
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{
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llm = std::make_unique<llm_build_qwen3_5>(*this, params);
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} break;
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case LLM_ARCH_QWEN3_5_MOE:
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{
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llm = std::make_unique<llm_build_qwen3_5_moe>(*this, params);
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} break;
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case LLM_ARCH_MISTRAL3:
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{
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llm = std::make_unique<llm_build_mistral3>(*this, params);
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@ -8603,6 +8755,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_PANGU_EMBED:
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case LLM_ARCH_AFMOE:
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case LLM_ARCH_QWEN3NEXT:
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case LLM_ARCH_QWEN3_5:
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case LLM_ARCH_QWEN3_5_MOE:
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case LLM_ARCH_MIMO2:
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case LLM_ARCH_STEP35:
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return LLAMA_ROPE_TYPE_NEOX;
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