From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001 From: jmorganca Date: Thu, 17 Oct 2024 15:18:22 -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 7cae1bbe..122e3cf1 100644 --- a/include/llama.h +++ b/include/llama.h @@ -423,6 +423,10 @@ 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 83b80b59..b189a19a 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -169,6 +169,7 @@ 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, @@ -223,6 +224,7 @@ 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" }, @@ -330,6 +332,7 @@ enum llm_kv { LLM_KV_ATTENTION_SLIDING_WINDOW, LLM_KV_ATTENTION_SCALE, LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, + LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, @@ -439,6 +442,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" }, { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" }, { 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" }, @@ -613,6 +617,14 @@ enum llm_tensor { LLM_TENSOR_CLS, LLM_TENSOR_CLS_OUT, 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 = { @@ -642,6 +654,40 @@ 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, { @@ -2390,6 +2436,7 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_90B, MODEL_236B, MODEL_314B, MODEL_SMALL, @@ -2434,6 +2481,7 @@ 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; @@ -2502,10 +2550,11 @@ 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; @@ -2623,6 +2672,10 @@ 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"); @@ -2806,6 +2859,16 @@ 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, @@ -3452,6 +3515,12 @@ 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 { @@ -3686,6 +3755,18 @@ 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(); @@ -5460,12 +5541,14 @@ 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; @@ -5514,7 +5597,7 @@ 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)); } @@ -5554,6 +5637,16 @@ 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); @@ -7249,6 +7342,55 @@ static bool llm_load_tensors( layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } 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) { @@ -9093,7 +9235,7 @@ 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) { @@ -9178,7 +9320,7 @@ 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); } @@ -9193,6 +9335,22 @@ 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, @@ -10167,6 +10325,7 @@ 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() { @@ -10754,6 +10913,253 @@ 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); @@ -16501,6 +16907,10 @@ 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(); @@ -16773,6 +17183,14 @@ 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; @@ -17455,6 +17873,10 @@ 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 @@ -18648,7 +19070,9 @@ 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; @@ -19744,6 +20168,11 @@ 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; } @@ -19814,6 +20243,7 @@ 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: