mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-04-29 02:41:47 +00:00
Port of Qwen3-VL support from mainline (#883)
* Port of Qwen3-VL for latest ik_llama.cpp - convert_hf_to_gguf.py - Not touched, use llama.cpp to convert model instead - sysl and metal support for imrope not added - Vulkan support for imrope not tested - Code not tested * Bugfix n_embd was declared multiple times https://github.com/ikawrakow/ik_llama.cpp/pull/883#issuecomment-3471179655 * Fix n_embd issue with qwen3vl * model.output tensor not required https://github.com/ikawrakow/ik_llama.cpp/pull/883#discussion_r2480388389 * Improved logic for qkv combined tensors59ceaf8fcb (r2480395800)59ceaf8fcb (r2480398187)* Fix n_embd for merge_qkv() + cleaner code https://github.com/ikawrakow/ik_llama.cpp/pull/883#discussion_r2481227395 * Revert TENSOR_NOT_REQUIRED
This commit is contained in:
Thireus ☠
@@ -241,7 +241,7 @@ ggml_cgraph * llm_build_context::build_defrag(const std::vector<uint32_t> & ids)
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}
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ggml_tensor * llm_build_context::build_inp_pos() {
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int n_pos_per_embd = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
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int n_pos_per_embd = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE ? 4 : 1;
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lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, int64_t(n_tokens)*n_pos_per_embd);
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cb(lctx.inp_pos, "inp_pos", -1);
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ggml_set_input(lctx.inp_pos);
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@@ -3561,6 +3561,288 @@ ggml_cgraph * llm_build_context::build_qwen3moe() {
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return gf;
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}
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ggml_cgraph * llm_build_context::build_qwen3vl() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
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const size_t n_deepstack_layers = hparams.n_deepstack_layers;
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const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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int sections[4];
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std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
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std::vector<struct ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
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if (batch.embd) {
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// Image input: split main embd and deepstack embds
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struct ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
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for (size_t i = 0; i < n_deepstack_layers; i++) {
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deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
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}
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inpL = inpL_main;
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}
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wq, nullptr,
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model.layers[il].wk, nullptr,
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model.layers[il].wv, nullptr,
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0, il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Qcur, "Qcur_normed", il);
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Qcur = ggml_rope_multi(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Kcur, "Kcur_normed", il);
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Kcur = ggml_rope_multi(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = llm_build_kv(ctx0, lctx, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_ffn(ctx0, lctx, cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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if (batch.embd && (size_t)il < n_deepstack_layers) {
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cur = ggml_add(ctx0, cur, deepstack_features[il]);
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cb(cur, "deepstack_out", il);
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}
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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ggml_cgraph * llm_build_context::build_qwen3vlmoe() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
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const size_t n_deepstack_layers = hparams.n_deepstack_layers;
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const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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int sections[4];
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std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
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std::vector<struct ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
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if (batch.embd) {
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// Image input: split main embd and deepstack embds
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struct ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
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for (size_t i = 0; i < n_deepstack_layers; i++) {
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deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
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}
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inpL = inpL_main;
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}
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self_attention
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{
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wq, nullptr,
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model.layers[il].wk, nullptr,
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model.layers[il].wv, nullptr,
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0, il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Qcur, "Qcur_normed", il);
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Qcur = ggml_rope_multi(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(Kcur, "Kcur_normed", il);
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Kcur = ggml_rope_multi(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = llm_build_kv(ctx0, lctx, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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n_tokens = n_outputs;
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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cur =
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llm_build_moe_ffn(ctx0, lctx, cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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LLM_EXPERT_GATING_FUNC_SOFTMAX,
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cb, il, gf);
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cb(cur, "ffn_moe_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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if (batch.embd && (size_t)il < n_deepstack_layers) {
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cur = ggml_add(ctx0, cur, deepstack_features[il]);
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cb(cur, "deepstack_out", il);
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}
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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ggml_cgraph * llm_build_context::build_phi2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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@@ -8220,6 +8502,14 @@ ggml_cgraph * llm_build_context::llama_build_graph(
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{
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result = llm.build_qwen3moe();
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} break;
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case LLM_ARCH_QWEN3VL:
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{
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result = llm.build_qwen3vl();
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} break;
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case LLM_ARCH_QWEN3VLMOE:
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{
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result = llm.build_qwen3vlmoe();
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} break;
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case LLM_ARCH_PHI2:
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{
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result = llm.build_phi2();
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