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Add Qwen2.5-32B-BitNet support with per-linear RMSNorm #522

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Add Qwen2.5-32B-BitNet support with per-linear RMSNorm #522

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159 changes: 159 additions & 0 deletions qwen2-per-linear-rmsnorm.patch
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> // per-linear RMSNorm (BitNet QAT)
> LLM_TENSOR_ATTN_Q_RMS_NORM,
> LLM_TENSOR_ATTN_K_RMS_NORM,
> LLM_TENSOR_ATTN_V_RMS_NORM,
> LLM_TENSOR_ATTN_OUT_RMS_NORM,
> LLM_TENSOR_FFN_GATE_RMS_NORM,
> LLM_TENSOR_FFN_DOWN_RMS_NORM,
> LLM_TENSOR_FFN_UP_RMS_NORM,
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> // per-linear RMSNorm (BitNet QAT)
> { LLM_TENSOR_ATTN_Q_RMS_NORM, "blk.%d.attn_q.rms_norm" },
> { LLM_TENSOR_ATTN_K_RMS_NORM, "blk.%d.attn_k.rms_norm" },
> { LLM_TENSOR_ATTN_V_RMS_NORM, "blk.%d.attn_v.rms_norm" },
> { LLM_TENSOR_ATTN_OUT_RMS_NORM, "blk.%d.attn_output.rms_norm" },
> { LLM_TENSOR_FFN_GATE_RMS_NORM, "blk.%d.ffn_gate.rms_norm" },
> { LLM_TENSOR_FFN_DOWN_RMS_NORM, "blk.%d.ffn_down.rms_norm" },
> { LLM_TENSOR_FFN_UP_RMS_NORM, "blk.%d.ffn_up.rms_norm" },
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>
> // per-linear RMSNorm weights (BitNet QAT)
> struct ggml_tensor * wq_rms_norm = nullptr;
> struct ggml_tensor * wk_rms_norm = nullptr;
> struct ggml_tensor * wv_rms_norm = nullptr;
> struct ggml_tensor * wo_rms_norm = nullptr;
> struct ggml_tensor * ffn_gate_rms_norm = nullptr;
> struct ggml_tensor * ffn_down_rms_norm = nullptr;
> struct ggml_tensor * ffn_up_rms_norm = nullptr;
7925a7951,7959
>
> // optional per-linear RMSNorm weights (BitNet QAT)
> layer.wq_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.wk_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.wv_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.wo_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.ffn_gate_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.ffn_down_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN_RMS_NORM, "weight", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
> layer.ffn_up_rms_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP_RMS_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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> // per-linear RMSNorm before Q projection (BitNet QAT)
> struct ggml_tensor * cur_q = cur;
> if (model.layers[il].wq_rms_norm) {
> cur_q = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur_q = ggml_mul(ctx0, cur_q, model.layers[il].wq_rms_norm);
> cb(cur_q, "wq_rms_norm", il);
> }
>
> // per-linear RMSNorm before K projection (BitNet QAT)
> struct ggml_tensor * cur_k = cur;
> if (model.layers[il].wk_rms_norm) {
> cur_k = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur_k = ggml_mul(ctx0, cur_k, model.layers[il].wk_rms_norm);
> cb(cur_k, "wk_rms_norm", il);
> }
>
> // per-linear RMSNorm before V projection (BitNet QAT)
> struct ggml_tensor * cur_v = cur;
> if (model.layers[il].wv_rms_norm) {
> cur_v = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur_v = ggml_mul(ctx0, cur_v, model.layers[il].wv_rms_norm);
> cb(cur_v, "wv_rms_norm", il);
> }
>
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< struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
---
> struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur_q);
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< struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
---
> struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur_k);
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< struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
---
> struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur_v);
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< cur = llm_build_kv(ctx0, lctx, kv_self, gf,
< model.layers[il].wo, model.layers[il].bo,
< Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
---
> // For wo: if per-linear RMSNorm exists, pass NULL for wo to llm_build_kv
> // and apply RMSNorm + wo manually afterward
> if (model.layers[il].wo_rms_norm) {
> cur = llm_build_kv(ctx0, lctx, kv_self, gf,
> NULL, NULL,
> Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
>
> // per-linear RMSNorm before wo projection (BitNet QAT)
> cur = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur = ggml_mul(ctx0, cur, model.layers[il].wo_rms_norm);
> cb(cur, "wo_rms_norm", il);
>
> cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
> if (model.layers[il].bo) {
> cb(cur, "kqv_wo", il);
> cur = ggml_add(ctx0, cur, model.layers[il].bo);
> }
> cb(cur, "kqv_out", il);
> } else {
> cur = llm_build_kv(ctx0, lctx, kv_self, gf,
> model.layers[il].wo, model.layers[il].bo,
> Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
> }
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< cur = llm_build_ffn(ctx0, lctx, 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, cb, il);
---
> // If per-linear RMSNorm exists for FFN, handle projections manually
> if (model.layers[il].ffn_gate_rms_norm || model.layers[il].ffn_up_rms_norm || model.layers[il].ffn_down_rms_norm) {
> // per-linear RMSNorm before gate projection
> struct ggml_tensor * cur_gate = cur;
> if (model.layers[il].ffn_gate_rms_norm) {
> cur_gate = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur_gate = ggml_mul(ctx0, cur_gate, model.layers[il].ffn_gate_rms_norm);
> cb(cur_gate, "ffn_gate_rms_norm", il);
> }
>
> // per-linear RMSNorm before up projection
> struct ggml_tensor * cur_up = cur;
> if (model.layers[il].ffn_up_rms_norm) {
> cur_up = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur_up = ggml_mul(ctx0, cur_up, model.layers[il].ffn_up_rms_norm);
> cb(cur_up, "ffn_up_rms_norm", il);
> }
>
> // gate and up projections (parallel: SwiGLU)
> struct ggml_tensor * gate_out = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate, cur_gate);
> cb(gate_out, "ffn_gate", il);
>
> struct ggml_tensor * up_out = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_up, cur_up);
> cb(up_out, "ffn_up", il);
>
> // SiLU activation on gate, then element-wise multiply with up
> gate_out = ggml_silu(ctx0, gate_out);
> cb(gate_out, "ffn_silu", il);
>
> cur = ggml_mul(ctx0, gate_out, up_out);
> cb(cur, "ffn_gate_par", il);
>
> // per-linear RMSNorm before down projection
> if (model.layers[il].ffn_down_rms_norm) {
> cur = ggml_rms_norm(ctx0, cur, hparams.f_norm_rms_eps);
> cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_rms_norm);
> cb(cur, "ffn_down_rms_norm", il);
> }
>
> cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, cur);
> } else {
> cur = llm_build_ffn(ctx0, lctx, 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, cb, il);
> }