diff --git a/llm/ext_server/server.cpp b/llm/ext_server/server.cpp index 6ce457ae..f1751c60 100644 --- a/llm/ext_server/server.cpp +++ b/llm/ext_server/server.cpp @@ -173,6 +173,8 @@ struct server_slot { double t_prompt_processing; // ms double t_token_generation; // ms + float *cross_attn_state = nullptr; + // multitasks int multitask_id = -1; @@ -200,6 +202,11 @@ struct server_slot { img.prefix_prompt = ""; } + if (cross_attn_state) { + free(cross_attn_state); + cross_attn_state = nullptr; + } + images.clear(); } @@ -731,6 +738,27 @@ struct llama_server_context { const std::vector image_buffer = base64_decode(img["data"].get()); + // Check for mllama architecture, which processes images differently than llava + char arch_str[256]; + llama_model_meta_val_str(model, "general.architecture", arch_str, 256); + bool is_mllama = strcmp(arch_str, "mllama") == 0; + + if (is_mllama) { + LOG_INFO("MLLAMA architecture detected, processing first image", {{"slot_id", slot->id}}); + + struct clip_image_f32 *img = clip_image_f32_init(); + clip_image_load_from_data(image_buffer.data(), image_buffer.size(), 560, 560, 3, 4, img); + + const int n = clip_embd_nbytes(clp_ctx); + printf("%s: nbytes %d\n", __func__, n); + + slot->cross_attn_state = (float *)malloc(n); + printf("%s: nbytes %d image_embd: %p\n", __func__, n, slot->cross_attn_state); + clip_image_encode(clp_ctx, 1, img, slot->cross_attn_state); + llama_set_cross_attn_state(ctx, slot->cross_attn_state); + break; + } + slot_image img_sl; img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); img_sl.img_data = clip_image_u8_init(); diff --git a/llm/patches/0009-mllama.patch b/llm/patches/0009-mllama.patch new file mode 100644 index 00000000..8634cd8d --- /dev/null +++ b/llm/patches/0009-mllama.patch @@ -0,0 +1,693 @@ +From 52f526a86b6fdd50784678c02d8212edc2412a5b Mon Sep 17 00:00:00 2001 +From: jmorganca +Date: Tue, 24 Sep 2024 11:53:40 -0700 +Subject: [PATCH] add mllama support + +mllama adds cross-attention layers to the standard llama architecture +it also requires a way to input a new tensor: cross_attention_state +once per generation + +cross-attention layers don't change and so they are cached in the +kv cache once per run + +remaining is to implement the cross attention mask +--- + include/llama.h | 4 + + src/llama.cpp | 456 ++++++++++++++++++++++++++++++++++++++++++++++-- + 2 files changed, 447 insertions(+), 13 deletions(-) + +diff --git a/include/llama.h b/include/llama.h +index bfc37e88..792520cc 100644 +--- a/include/llama.h ++++ b/include/llama.h +@@ -449,6 +449,10 @@ extern "C" { + struct llama_model * model, + struct llama_context_params params); + ++ // TODO (jmorganca): this should most likely be passed in as part of a batch ++ // and not set on the context for all batches. ++ LLAMA_API void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state); ++ + // Frees all allocated memory + LLAMA_API void llama_free(struct llama_context * ctx); + +diff --git a/src/llama.cpp b/src/llama.cpp +index b7771f53..cf70ea90 100644 +--- a/src/llama.cpp ++++ b/src/llama.cpp +@@ -170,6 +170,7 @@ static std::string format(const char * fmt, ...) { + + enum llm_arch { + LLM_ARCH_LLAMA, ++ LLM_ARCH_MLLAMA, + LLM_ARCH_FALCON, + LLM_ARCH_BAICHUAN, + LLM_ARCH_GROK, +@@ -219,6 +220,7 @@ enum llm_arch { + + static const std::map LLM_ARCH_NAMES = { + { LLM_ARCH_LLAMA, "llama" }, ++ { LLM_ARCH_MLLAMA, "mllama" }, + { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GROK, "grok" }, + { LLM_ARCH_GPT2, "gpt2" }, +@@ -317,6 +319,7 @@ enum llm_kv { + LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, + LLM_KV_ATTENTION_SLIDING_WINDOW, + LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, ++ LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, + + LLM_KV_ROPE_DIMENSION_COUNT, + LLM_KV_ROPE_FREQ_BASE, +@@ -422,6 +425,7 @@ static const std::map LLM_KV_NAMES = { + { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" }, + { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" }, + { LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" }, ++ { LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, "%s.attention.cross_attention_layers" }, + + { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, + { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, +@@ -594,6 +598,14 @@ enum llm_tensor { + LLM_TENSOR_ENC_FFN_UP, + LLM_TENSOR_ENC_OUTPUT_NORM, + LLM_TENSOR_BSKCN_TV, ++ LLM_TENSOR_CROSS_ATTN_K_NORM, ++ LLM_TENSOR_CROSS_ATTN_K_PROJ, ++ LLM_TENSOR_CROSS_ATTN_O_PROJ, ++ LLM_TENSOR_CROSS_ATTN_Q_NORM, ++ LLM_TENSOR_CROSS_ATTN_Q_PROJ, ++ LLM_TENSOR_CROSS_ATTN_V_PROJ, ++ LLM_TENSOR_CROSS_ATTN_ATTN_GATE, ++ LLM_TENSOR_CROSS_ATTN_MLP_GATE, + }; + + static const std::map> LLM_TENSOR_NAMES = { +@@ -623,6 +635,40 @@ static const std::map> LLM_TENSOR_NA + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, ++ { ++ LLM_ARCH_MLLAMA, ++ { ++ { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, ++ { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, ++ { LLM_TENSOR_OUTPUT, "output" }, ++ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, ++ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, ++ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, ++ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, ++ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, ++ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, ++ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, ++ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, ++ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, ++ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, ++ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, ++ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, ++ { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, ++ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, ++ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, ++ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, ++ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, ++ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, ++ { LLM_TENSOR_CROSS_ATTN_K_NORM, "blk.%d.cross_attn_k_norm" }, ++ { LLM_TENSOR_CROSS_ATTN_K_PROJ, "blk.%d.cross_attn_k_proj" }, ++ { LLM_TENSOR_CROSS_ATTN_O_PROJ, "blk.%d.cross_attn_o_proj" }, ++ { LLM_TENSOR_CROSS_ATTN_Q_NORM, "blk.%d.cross_attn_q_norm" }, ++ { LLM_TENSOR_CROSS_ATTN_Q_PROJ, "blk.%d.cross_attn_q_proj" }, ++ { LLM_TENSOR_CROSS_ATTN_V_PROJ, "blk.%d.cross_attn_v_proj" }, ++ { LLM_TENSOR_CROSS_ATTN_ATTN_GATE, "blk.%d.cross_attn_attn_gate" }, ++ { LLM_TENSOR_CROSS_ATTN_MLP_GATE, "blk.%d.cross_attn_mlp_gate" }, ++ }, ++ }, + { + LLM_ARCH_BAICHUAN, + { +@@ -2267,6 +2313,7 @@ enum e_model { + MODEL_40B, + MODEL_65B, + MODEL_70B, ++ MODEL_90B, + MODEL_236B, + MODEL_314B, + MODEL_SMALL, +@@ -2309,6 +2356,7 @@ struct llama_hparams { + std::array n_ff_arr; + + std::array, 4> n_bskcn_arr; ++ std::array cross_attn_layers; + + uint32_t n_layer_dense_lead = 0; + uint32_t n_lora_q = 0; +@@ -2372,10 +2420,11 @@ struct llama_hparams { + if (this->n_expert != other.n_expert) return true; + if (this->n_expert_used != other.n_expert_used) return true; + +- if (this->n_head_arr != other.n_head_arr) return true; +- if (this->n_head_kv_arr != other.n_head_kv_arr) return true; +- if (this->n_ff_arr != other.n_ff_arr) return true; +- if (this->n_bskcn_arr != other.n_bskcn_arr) return true; ++ if (this->n_head_arr != other.n_head_arr) return true; ++ if (this->n_head_kv_arr != other.n_head_kv_arr) return true; ++ if (this->n_ff_arr != other.n_ff_arr) return true; ++ if (this->n_bskcn_arr != other.n_bskcn_arr) return true; ++ if (this->cross_attn_layers != other.cross_attn_layers) return true; + + if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true; + if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; +@@ -2490,6 +2539,10 @@ struct llama_hparams { + + GGML_ABORT("fatal error"); + } ++ ++ bool cross_attention_layer(uint32_t il) const { ++ return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end(); ++ } + }; + + static_assert(std::is_trivially_copyable::value, "llama_hparams must be trivially copyable"); +@@ -2672,6 +2725,16 @@ struct llama_layer { + struct ggml_tensor * ffn_down_scale; + + struct ggml_tensor * bskcn_tv; ++ ++ // cross attention ++ struct ggml_tensor * cross_attn_k_norm; ++ struct ggml_tensor * cross_attn_k_proj; ++ struct ggml_tensor * cross_attn_o_proj; ++ struct ggml_tensor * cross_attn_q_norm; ++ struct ggml_tensor * cross_attn_q_proj; ++ struct ggml_tensor * cross_attn_v_proj; ++ struct ggml_tensor * cross_attn_attn_gate; ++ struct ggml_tensor * cross_attn_mlp_gate; + }; + + // very similar to llama_batch, +@@ -3317,6 +3380,12 @@ struct llama_context { + struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch] + struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc] + struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch] ++ ++ // TODO (jmorganca): this should most likely be passed in as part of a batch ++ // and not set on the context for all batches. ++ float * cross_attn_state = nullptr; ++ bool cross_attn_state_first_pass = true; ++ struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061] + }; + + struct llama_lora_weight { +@@ -3543,6 +3612,18 @@ static bool llama_kv_cache_init( + cache.v_l.reserve(n_layer); + + for (int i = 0; i < (int) n_layer; i++) { ++ // for cross attention layers ++ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layer(i)) { ++ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); ++ ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i)); ++ ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i)); ++ ggml_format_name(k, "cache_k_l%d", i); ++ ggml_format_name(v, "cache_v_l%d", i); ++ cache.k_l.push_back(k); ++ cache.v_l.push_back(v); ++ continue; ++ } ++ + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); + +@@ -5312,12 +5393,14 @@ static void llm_load_hparams( + } + + // zero-out the per-layer hparams +- std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0); +- std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0); +- std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0); ++ std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0); ++ std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0); ++ std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0); ++ std::fill(hparams.cross_attn_layers.begin(), hparams.cross_attn_layers.end(), -1); + +- ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer); +- ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer); ++ ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer); ++ ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer); ++ ml.get_arr(LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, hparams.cross_attn_layers, false); + + // n_head_kv is optional, default to n_head + hparams.n_head_kv_arr = hparams.n_head_arr; +@@ -5366,7 +5449,7 @@ static void llm_load_hparams( + + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); + +- if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) { ++ if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_MLLAMA || model.arch == LLM_ARCH_FALCON) { + if (hparams.n_rot != hparams.n_embd_head_k) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k)); + } +@@ -5404,6 +5487,16 @@ static void llm_load_hparams( + } + } + } break; ++ case LLM_ARCH_MLLAMA: ++ { ++ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ++ ++ switch (hparams.n_layer) { ++ case 40: model.type = e_model::MODEL_11B; break; ++ case 100: model.type = e_model::MODEL_90B; break; ++ default: model.type = e_model::MODEL_UNKNOWN; ++ } ++ } break; + case LLM_ARCH_MINICPM: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); +@@ -6918,6 +7011,55 @@ static bool llm_load_tensors( + } + } + } break; ++ case LLM_ARCH_MLLAMA: ++ { ++ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8}); ++ ++ // output ++ { ++ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); ++ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); ++ ++ // if output is NULL, init from the input tok embed ++ if (model.output == NULL) { ++ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); ++ } ++ } ++ ++ for (int i = 0; i < n_layer; ++i) { ++ ggml_context * ctx_layer = ctx_for_layer(i); ++ ggml_context * ctx_split = ctx_for_layer_split(i); ++ ++ auto & layer = model.layers[i]; ++ ++ if (hparams.cross_attention_layer(i)) { ++ layer.cross_attn_k_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128}); ++ layer.cross_attn_k_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024}); ++ layer.cross_attn_o_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd}); ++ layer.cross_attn_q_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128}); ++ layer.cross_attn_q_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd}); ++ layer.cross_attn_v_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024}); ++ layer.cross_attn_attn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1}); ++ layer.cross_attn_mlp_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1}); ++ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); ++ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); ++ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); ++ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); ++ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); ++ } else { ++ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); ++ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); ++ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); ++ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); ++ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); ++ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); ++ layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); ++ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); ++ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); ++ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); ++ } ++ } ++ } break; + case LLM_ARCH_GROK: + { + if (n_expert == 0) { +@@ -8678,7 +8820,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam + + if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE && + model.hparams.n_vocab != model.vocab.id_to_token.size()) { +- throw std::runtime_error("vocab size mismatch"); ++ LLAMA_LOG_WARN("%s: vocab mismatch %u !- %zu ...\n", __func__, model.hparams.n_vocab, model.vocab.id_to_token.size()); + } + + if (params.vocab_only) { +@@ -8759,7 +8901,7 @@ static struct ggml_tensor * llm_build_inp_embd( + + inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); + } else { +- lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); ++ lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + inpL = lctx.inp_embd; + ggml_set_input(lctx.inp_embd); + } +@@ -8769,6 +8911,22 @@ static struct ggml_tensor * llm_build_inp_embd( + return inpL; + } + ++static struct ggml_tensor * llm_build_inp_cross_attn_state( ++ struct ggml_context * ctx, ++ struct llama_context & lctx, ++ const llama_hparams & hparams, ++ const llm_build_cb & cb) { ++ const int64_t n_embd = hparams.n_embd; ++ ++ struct ggml_tensor * inpCAS; ++ lctx.inp_cross_attn_state = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4); ++ cb(lctx.inp_cross_attn_state, "inp_cross_attn_state", -1); ++ ggml_set_input(lctx.inp_cross_attn_state); ++ inpCAS = lctx.inp_cross_attn_state; ++ ++ return inpCAS; ++} ++ + static void llm_build_kv_store( + struct ggml_context * ctx, + const llama_hparams & hparams, +@@ -9743,6 +9901,7 @@ struct llm_build_context { + lctx.inp_pos_bucket = nullptr; + lctx.inp_embd_enc = nullptr; + lctx.inp_KQ_mask_cross = nullptr; ++ lctx.inp_cross_attn_state = nullptr; + } + + void free() { +@@ -10158,6 +10317,253 @@ struct llm_build_context { + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + ++ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); ++ cb(cur, "result_output", -1); ++ ++ ggml_build_forward_expand(gf, cur); ++ ++ return gf; ++ } ++ ++ struct ggml_cgraph * build_mllama() { ++ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); ++ ++ // mutable variable, needed during the last layer of the computation to skip unused tokens ++ int32_t n_tokens = this->n_tokens; ++ ++ const int64_t n_embd_head = hparams.n_embd_head_v; ++ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); ++ GGML_ASSERT(n_embd_head == hparams.n_rot); ++ ++ struct ggml_tensor * cur; ++ struct ggml_tensor * inpL; ++ struct ggml_tensor * inpCAS; ++ ++ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); ++ inpCAS = llm_build_inp_cross_attn_state(ctx0, lctx, hparams, cb); ++ ++ // inp_pos - contains the positions ++ struct ggml_tensor * inp_pos = build_inp_pos(); ++ ++ // KQ_mask (mask for 1 head, it will be broadcasted to all heads) ++ struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); ++ ++ for (int il = 0; il < n_layer; ++il) { ++ struct ggml_tensor * inpSA = inpL; ++ ++ // norm ++ cur = llm_build_norm(ctx0, inpL, hparams, ++ model.layers[il].attn_norm, NULL, ++ LLM_NORM_RMS, cb, il); ++ cb(cur, "attn_norm", il); ++ ++ if (hparams.cross_attention_layer(il)) { ++ if (!lctx.cross_attn_state) { ++ continue; ++ } ++ ++ // cross attention layer ++ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_q_proj, cur); ++ cb(Qcur, "Qcur", il); ++ ++ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); ++ cb(Qcur, "Qcur", il); ++ ++ Qcur = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); ++ cb(Qcur, "Qcur", il); ++ ++ // TODO: is this required? ++ Qcur = ggml_cont(ctx0, Qcur); ++ cb(Qcur, "Qcur", il); ++ ++ Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].cross_attn_q_norm, NULL, LLM_NORM_RMS, cb, il); ++ cb(Qcur, "Qcur", il); ++ ++ struct ggml_tensor * Kcur; ++ if (lctx.cross_attn_state_first_pass) { ++ Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS); ++ cb(Kcur, "Kcur", il); ++ ++ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, 6404); ++ cb(Kcur, "Kcur", il); ++ ++ Kcur = ggml_permute(ctx0, Kcur, 0, 2, 1, 3); ++ cb(Kcur, "Kcur", il); ++ ++ // TODO: is this required? ++ Kcur = ggml_cont(ctx0, Kcur); ++ cb(Kcur, "Kcur", il); ++ ++ Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].cross_attn_k_norm, NULL, LLM_NORM_RMS, cb, il); ++ cb(Kcur, "Kcur", il); ++ ++ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, kv_self.k_l[il])); ++ } else { ++ Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]); ++ cb(Kcur, "Kcur (view)", il); ++ } ++ ++ struct ggml_tensor * Vcur; ++ if (lctx.cross_attn_state_first_pass) { ++ Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_v_proj, inpCAS); ++ cb(Vcur, "Vcur", il); ++ ++ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, 6404); ++ cb(Vcur, "Vcur", il); ++ ++ Vcur = ggml_permute(ctx0, Vcur, 0, 2, 1, 3); ++ cb(Vcur, "Vcur", il); ++ ++ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, kv_self.v_l[il])); ++ } else { ++ Vcur = ggml_view_tensor(ctx0, kv_self.v_l[il]); ++ cb(Vcur, "Vcur (view)", il); ++ } ++ ++ struct ggml_tensor * kq = ggml_mul_mat(ctx0, Kcur, Qcur); ++ cb(kq, "kq", il); ++ ++ kq = ggml_scale_inplace(ctx0, kq, 1.0f/sqrtf(float(n_embd_head))); ++ cb(kq, "kq_scaled", il); ++ ++ // TODO: apply causal masks ++ struct ggml_tensor * kq_soft_max = ggml_soft_max_inplace(ctx0, kq); ++ cb(kq_soft_max, "kq_soft_max", il); ++ ++ Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, Vcur)); ++ cb(Vcur, "Vcur", il); ++ ++ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, Vcur, kq_soft_max); ++ cb(kqv, "kqv", il); ++ ++ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); ++ cb(kqv_merged, "kqv_merged", il); ++ ++ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens); ++ cb(cur, "kqv_merged_cont", il); ++ ++ cur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_o_proj, cur); ++ cb(cur, "cur", il); ++ ++ // TODO: do this in place once? ++ cur = ggml_mul(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_attn_gate)); ++ ++ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); ++ cb(ffn_inp, "ffn_inp", il); ++ ++ // feed-forward network ++ cur = llm_build_norm(ctx0, ffn_inp, hparams, ++ model.layers[il].ffn_norm, NULL, ++ LLM_NORM_RMS, cb, il); ++ cb(cur, "ffn_norm", il); ++ ++ cur = llm_build_ffn(ctx0, lctx, cur, ++ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, ++ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, ++ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, ++ NULL, ++ LLM_FFN_SILU, LLM_FFN_PAR, cb, il); ++ cb(cur, "ffn_out", il); ++ ++ // TODO: do this inplace once? ++ cur = ggml_add_inplace(ctx0, ggml_mul_inplace(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_mlp_gate)), ffn_inp); ++ cb(cur, "ffn_out", il); ++ ++ cur = lctx.cvec.apply_to(ctx0, cur, il); ++ cb(cur, "l_out", il); ++ ++ // input for next layer ++ inpL = cur; ++ } else { ++ // self attention layer ++ ++ // rope freq factors for llama3; may return nullptr for llama2 and other models ++ struct ggml_tensor * rope_factors = build_rope_factors(il); ++ ++ // compute Q and K and RoPE them ++ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); ++ cb(Qcur, "Qcur", il); ++ if (model.layers[il].bq) { ++ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); ++ cb(Qcur, "Qcur", il); ++ } ++ ++ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); ++ cb(Kcur, "Kcur", il); ++ if (model.layers[il].bk) { ++ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); ++ cb(Kcur, "Kcur", il); ++ } ++ ++ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); ++ cb(Vcur, "Vcur", il); ++ if (model.layers[il].bv) { ++ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); ++ cb(Vcur, "Vcur", il); ++ } ++ ++ Qcur = ggml_rope_ext( ++ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors, ++ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ++ ext_factor, attn_factor, beta_fast, beta_slow ++ ); ++ cb(Qcur, "Qcur", il); ++ ++ Kcur = ggml_rope_ext( ++ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors, ++ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ++ ext_factor, attn_factor, beta_fast, beta_slow ++ ); ++ cb(Kcur, "Kcur", il); ++ ++ 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); ++ ++ ++ if (il == n_layer - 1) { ++ // skip computing output for unused tokens ++ struct ggml_tensor * inp_out_ids = build_inp_out_ids(); ++ n_tokens = n_outputs; ++ cur = ggml_get_rows(ctx0, cur, inp_out_ids); ++ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); ++ } ++ ++ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); ++ cb(ffn_inp, "ffn_inp", il); ++ ++ // feed-forward network ++ cur = llm_build_norm(ctx0, ffn_inp, hparams, ++ model.layers[il].ffn_norm, NULL, ++ LLM_NORM_RMS, cb, il); ++ cb(cur, "ffn_norm", il); ++ ++ cur = llm_build_ffn(ctx0, lctx, cur, ++ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, ++ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, ++ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, ++ NULL, ++ LLM_FFN_SILU, LLM_FFN_PAR, cb, il); ++ cb(cur, "ffn_out", il); ++ ++ cur = ggml_add(ctx0, cur, ffn_inp); ++ cb(cur, "ffn_out", il); ++ ++ cur = lctx.cvec.apply_to(ctx0, cur, il); ++ cb(cur, "l_out", il); ++ ++ // input for next layer ++ inpL = cur; ++ } ++ } ++ ++ cur = inpL; ++ ++ cur = llm_build_norm(ctx0, cur, hparams, ++ model.output_norm, NULL, ++ LLM_NORM_RMS, cb, -1); ++ cb(cur, "result_norm", -1); ++ + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_output", -1); +@@ -15493,6 +15899,10 @@ static struct ggml_cgraph * llama_build_graph( + { + result = llm.build_llama(); + } break; ++ case LLM_ARCH_MLLAMA: ++ { ++ result = llm.build_mllama(); ++ } break; + case LLM_ARCH_BAICHUAN: + { + result = llm.build_baichuan(); +@@ -15753,6 +16163,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { + ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + } + ++ // TODO (jmorganca): this might copy a lot of data on every request of a ++ // single generation even though it doesn't change, so we should ++ // find a way to not set this more than one time per image ++ if (lctx.inp_cross_attn_state && ++ lctx.inp_cross_attn_state->buffer) { ++ ggml_backend_tensor_set(lctx.inp_cross_attn_state, lctx.cross_attn_state, 0, hparams.n_embd * 1601 * 4 * ggml_element_size(lctx.inp_cross_attn_state)); ++ } ++ + if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { + GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); + const int64_t n_tokens = batch.n_tokens; +@@ -16430,6 +16848,10 @@ static int llama_decode_internal( + + llama_set_inputs(lctx, ubatch); + ++ // TODO: replace with something better to find out if its ++ // our first actual pass ++ lctx.cross_attn_state_first_pass = false; ++ + llama_graph_compute(lctx, gf, n_threads, threadpool); + + // update the kv ring buffer +@@ -17586,7 +18008,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s + if (llama_model_has_encoder(&model)) { + n_attn_layer *= 3; + } +- GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected"); ++ if (qs.n_attention_wv != n_attn_layer) { ++ LLAMA_LOG_WARN("%s: n_attention_wv is unexpected, expected: %d, found: %d\n", __func__, n_attn_layer, qs.n_attention_wv); ++ } + } + + size_t total_size_org = 0; +@@ -18681,6 +19105,11 @@ struct llama_context * llama_new_context_with_model( + return ctx; + } + ++void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state) { ++ ctx->cross_attn_state_first_pass = true; ++ ctx->cross_attn_state = cross_attn_state; ++} ++ + void llama_free(struct llama_context * ctx) { + delete ctx; + } +@@ -18731,6 +19160,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { + + // use what we call a normal RoPE, operating on pairs of consecutive head values + case LLM_ARCH_LLAMA: ++ case LLM_ARCH_MLLAMA: + case LLM_ARCH_BAICHUAN: + case LLM_ARCH_STARCODER: + case LLM_ARCH_PLAMO: +-- +2.39.3 (Apple Git-146) +