691 lines
32 KiB
Diff
691 lines
32 KiB
Diff
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From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
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From: jmorganca <jmorganca@gmail.com>
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Date: Thu, 17 Oct 2024 15:18:22 -0700
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Subject: [PATCH] add mllama support
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mllama adds cross-attention layers to the standard llama architecture
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it also requires a way to input a new tensor: cross_attention_state
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once per generation
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cross-attention layers don't change and so they are cached in the
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kv cache once per run
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remaining is to implement the cross attention mask
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---
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include/llama.h | 4 +
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src/llama.cpp | 456 ++++++++++++++++++++++++++++++++++++++++++++++--
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2 files changed, 447 insertions(+), 13 deletions(-)
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diff --git a/include/llama.h b/include/llama.h
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index 7cae1bbe..122e3cf1 100644
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--- a/include/llama.h
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+++ b/include/llama.h
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@@ -423,6 +423,10 @@ extern "C" {
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struct llama_model * model,
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struct llama_context_params params);
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+ // TODO (jmorganca): this should most likely be passed in as part of a batch
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+ // and not set on the context for all batches.
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+ LLAMA_API void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state);
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+
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// Frees all allocated memory
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LLAMA_API void llama_free(struct llama_context * ctx);
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diff --git a/src/llama.cpp b/src/llama.cpp
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index 83b80b59..b189a19a 100644
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--- a/src/llama.cpp
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+++ b/src/llama.cpp
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@@ -169,6 +169,7 @@ static std::string format(const char * fmt, ...) {
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enum llm_arch {
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LLM_ARCH_LLAMA,
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+ LLM_ARCH_MLLAMA,
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LLM_ARCH_FALCON,
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LLM_ARCH_BAICHUAN,
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LLM_ARCH_GROK,
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@@ -223,6 +224,7 @@ enum llm_arch {
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static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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+ { LLM_ARCH_MLLAMA, "mllama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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@@ -330,6 +332,7 @@ enum llm_kv {
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LLM_KV_ATTENTION_SLIDING_WINDOW,
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LLM_KV_ATTENTION_SCALE,
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LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
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+ LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,
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LLM_KV_ROPE_DIMENSION_COUNT,
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LLM_KV_ROPE_FREQ_BASE,
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@@ -439,6 +442,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
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{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
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{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" },
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+ { LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, "%s.attention.cross_attention_layers" },
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{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
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{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
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@@ -613,6 +617,14 @@ enum llm_tensor {
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LLM_TENSOR_CLS,
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LLM_TENSOR_CLS_OUT,
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LLM_TENSOR_BSKCN_TV,
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+ LLM_TENSOR_CROSS_ATTN_K_NORM,
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+ LLM_TENSOR_CROSS_ATTN_K_PROJ,
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+ LLM_TENSOR_CROSS_ATTN_O_PROJ,
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+ LLM_TENSOR_CROSS_ATTN_Q_NORM,
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+ LLM_TENSOR_CROSS_ATTN_Q_PROJ,
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+ LLM_TENSOR_CROSS_ATTN_V_PROJ,
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+ LLM_TENSOR_CROSS_ATTN_ATTN_GATE,
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+ LLM_TENSOR_CROSS_ATTN_MLP_GATE,
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};
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static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
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@@ -642,6 +654,40 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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},
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},
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+ {
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+ LLM_ARCH_MLLAMA,
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+ {
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+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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+ { LLM_TENSOR_OUTPUT, "output" },
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+ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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+ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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+ { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
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+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
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+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
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+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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+ { LLM_TENSOR_CROSS_ATTN_K_NORM, "blk.%d.cross_attn_k_norm" },
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+ { LLM_TENSOR_CROSS_ATTN_K_PROJ, "blk.%d.cross_attn_k_proj" },
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+ { LLM_TENSOR_CROSS_ATTN_O_PROJ, "blk.%d.cross_attn_o_proj" },
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+ { LLM_TENSOR_CROSS_ATTN_Q_NORM, "blk.%d.cross_attn_q_norm" },
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+ { LLM_TENSOR_CROSS_ATTN_Q_PROJ, "blk.%d.cross_attn_q_proj" },
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+ { LLM_TENSOR_CROSS_ATTN_V_PROJ, "blk.%d.cross_attn_v_proj" },
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+ { LLM_TENSOR_CROSS_ATTN_ATTN_GATE, "blk.%d.cross_attn_attn_gate" },
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+ { LLM_TENSOR_CROSS_ATTN_MLP_GATE, "blk.%d.cross_attn_mlp_gate" },
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+ },
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+ },
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{
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LLM_ARCH_BAICHUAN,
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{
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@@ -2390,6 +2436,7 @@ enum e_model {
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MODEL_40B,
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MODEL_65B,
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MODEL_70B,
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+ MODEL_90B,
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MODEL_236B,
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MODEL_314B,
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MODEL_SMALL,
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@@ -2434,6 +2481,7 @@ struct llama_hparams {
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std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
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std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
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+ std::array<uint32_t, LLAMA_MAX_LAYERS> cross_attn_layers;
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uint32_t n_layer_dense_lead = 0;
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uint32_t n_lora_q = 0;
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@@ -2502,10 +2550,11 @@ struct llama_hparams {
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if (this->n_expert != other.n_expert) return true;
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if (this->n_expert_used != other.n_expert_used) return true;
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- if (this->n_head_arr != other.n_head_arr) return true;
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- if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
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- if (this->n_ff_arr != other.n_ff_arr) return true;
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- if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
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+ if (this->n_head_arr != other.n_head_arr) return true;
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+ if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
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+ if (this->n_ff_arr != other.n_ff_arr) return true;
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+ if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
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+ if (this->cross_attn_layers != other.cross_attn_layers) return true;
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if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
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if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
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@@ -2623,6 +2672,10 @@ struct llama_hparams {
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GGML_ABORT("fatal error");
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}
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+
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+ bool cross_attention_layer(uint32_t il) const {
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+ return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
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+ }
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};
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static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
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@@ -2806,6 +2859,16 @@ struct llama_layer {
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struct ggml_tensor * ffn_down_scale;
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struct ggml_tensor * bskcn_tv;
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+
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+ // cross attention
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+ struct ggml_tensor * cross_attn_k_norm;
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+ struct ggml_tensor * cross_attn_k_proj;
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+ struct ggml_tensor * cross_attn_o_proj;
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+ struct ggml_tensor * cross_attn_q_norm;
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+ struct ggml_tensor * cross_attn_q_proj;
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+ struct ggml_tensor * cross_attn_v_proj;
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+ struct ggml_tensor * cross_attn_attn_gate;
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+ struct ggml_tensor * cross_attn_mlp_gate;
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};
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// very similar to llama_batch,
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@@ -3452,6 +3515,12 @@ struct llama_context {
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struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
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struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
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struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
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+
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+ // TODO (jmorganca): this should most likely be passed in as part of a batch
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+ // and not set on the context for all batches.
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+ float * cross_attn_state = nullptr;
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+ bool cross_attn_state_first_pass = true;
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+ struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
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};
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struct llama_lora_weight {
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@@ -3686,6 +3755,18 @@ static bool llama_kv_cache_init(
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cache.v_l.reserve(n_layer);
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for (int i = 0; i < (int) n_layer; i++) {
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+ // for cross attention layers
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+ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layer(i)) {
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+ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
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+ ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
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+ ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
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+ ggml_format_name(k, "cache_k_l%d", i);
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+ ggml_format_name(v, "cache_v_l%d", i);
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+ cache.k_l.push_back(k);
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+ cache.v_l.push_back(v);
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+ continue;
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+ }
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+
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const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
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@@ -5460,12 +5541,14 @@ static void llm_load_hparams(
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}
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// zero-out the per-layer hparams
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- std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
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- std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
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- std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
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+ std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
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+ std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
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+ std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
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+ std::fill(hparams.cross_attn_layers.begin(), hparams.cross_attn_layers.end(), -1);
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- ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer);
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- ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
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+ ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer);
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+ ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
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+ ml.get_arr(LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, hparams.cross_attn_layers, false);
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// n_head_kv is optional, default to n_head
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hparams.n_head_kv_arr = hparams.n_head_arr;
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@@ -5514,7 +5597,7 @@ static void llm_load_hparams(
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ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
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- if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
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+ if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_MLLAMA || model.arch == LLM_ARCH_FALCON) {
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if (hparams.n_rot != hparams.n_embd_head_k) {
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throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
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}
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@@ -5554,6 +5637,16 @@ static void llm_load_hparams(
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}
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}
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} break;
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+ case LLM_ARCH_MLLAMA:
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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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+
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+ switch (hparams.n_layer) {
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+ case 40: model.type = e_model::MODEL_11B; break;
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+ case 100: model.type = e_model::MODEL_90B; break;
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+ default: model.type = e_model::MODEL_UNKNOWN;
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+ }
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+ } break;
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case LLM_ARCH_MINICPM:
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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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@@ -7249,6 +7342,55 @@ static bool llm_load_tensors(
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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));
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|
}
|
||
|
|
} 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:
|