diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 1041ba29f..e4af50778 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1311,29 +1311,23 @@ ggml_tensor * llm_graph_context::build_attn( return cur; } -llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified( - bool causal, - bool swa) const { +llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const { const llama_kv_cache_unified * kv_self = static_cast(memory); auto inp = std::make_unique(hparams, cparams, kv_self); const auto n_kv = kv_self->n; - inp->self_kq_mask = causal - ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)) - : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); + inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask, "KQ_mask", -1); ggml_set_input(inp->self_kq_mask); inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; - if (swa) { + if (hparams.n_swa_pattern > 1) { GGML_ASSERT(hparams.n_swa > 0); - inp->self_kq_mask_swa = causal - ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)) - : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); + inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1); ggml_set_input(inp->self_kq_mask_swa); diff --git a/src/llama-graph.h b/src/llama-graph.h index b7a66d189..c4328e6f9 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -509,9 +509,7 @@ struct llm_graph_context { float kq_scale, int il) const; - llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified( - bool causal, - bool swa) const; + llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const; ggml_tensor * build_attn( llm_graph_input_attn_kv_unified * inp, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index cce943df0..750a702ff 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -784,9 +784,11 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.n_swa = 2047; } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) { // default value for Phi-3-mini-128k-instruct + // note: this seems incorrect because the window is bigger than the train context? hparams.n_swa = 262144; } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) { // default value for Phi-3-medium-128k-instruct + // note: this seems incorrect because the window is equal to the train context? hparams.n_swa = 131072; } bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); @@ -3710,6 +3712,7 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa); + LLAMA_LOG_INFO("%s: n_swa_pattern = %u\n", __func__, hparams.n_swa_pattern); LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str()); @@ -3871,7 +3874,7 @@ struct llm_build_llama : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; for (int il = 0; il < n_layer; ++il) { @@ -4034,7 +4037,7 @@ struct llm_build_deci : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; for (int il = 0; il < n_layer; ++il) { @@ -4192,7 +4195,7 @@ struct llm_build_baichuan : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr; - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -4310,7 +4313,7 @@ struct llm_build_xverse : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -4418,7 +4421,7 @@ struct llm_build_falcon : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * attn_norm; @@ -4543,7 +4546,7 @@ struct llm_build_grok : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -4697,7 +4700,7 @@ struct llm_build_dbrx : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -4821,7 +4824,7 @@ struct llm_build_starcoder : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); cb(pos, "pos_embd", -1); @@ -4924,7 +4927,7 @@ struct llm_build_refact : public llm_graph_context { inpL = build_inp_embd(model.tok_embd); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5187,7 +5190,7 @@ struct llm_build_bloom : public llm_graph_context { inpL = build_inp_embd(model.tok_embd); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); inpL = build_norm(inpL, model.tok_norm, @@ -5292,7 +5295,7 @@ struct llm_build_mpt : public llm_graph_context { inpL = build_inp_embd(model.tok_embd); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); if (model.pos_embd) { // inp_pos - contains the positions @@ -5436,7 +5439,7 @@ struct llm_build_stablelm : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { // norm @@ -5587,7 +5590,7 @@ struct llm_build_qwen : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5703,7 +5706,7 @@ struct llm_build_qwen2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5818,7 +5821,7 @@ struct llm_build_qwen2vl : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -5938,7 +5941,7 @@ struct llm_build_qwen2moe : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -6087,7 +6090,7 @@ struct llm_build_phi2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { attn_norm_output = build_norm(inpL, @@ -6211,7 +6214,7 @@ struct llm_build_phi3 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, true); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { auto * residual = inpL; @@ -6357,7 +6360,7 @@ struct llm_build_plamo : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { @@ -6465,7 +6468,7 @@ struct llm_build_gpt2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); cb(pos, "pos_embd", -1); @@ -6573,7 +6576,7 @@ struct llm_build_codeshell : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, @@ -6686,7 +6689,7 @@ struct llm_build_orion : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -6807,7 +6810,7 @@ struct llm_build_internlm2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -6937,7 +6940,7 @@ struct llm_build_minicpm3 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7141,7 +7144,7 @@ struct llm_build_gemma : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { // norm @@ -7251,7 +7254,7 @@ struct llm_build_gemma2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, true); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { // norm @@ -7386,7 +7389,7 @@ struct llm_build_gemma3 : public llm_graph_context { ggml_tensor * inp_pos = build_inp_pos(); // TODO: is causal == true correct? might need some changes - auto * inp_attn = build_attn_inp_kv_unified(true, true); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { const bool is_swa = hparams.is_swa(il); @@ -7515,7 +7518,7 @@ struct llm_build_starcoder2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7828,7 +7831,7 @@ struct llm_build_command_r : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { @@ -7978,7 +7981,7 @@ struct llm_build_cohere2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, true); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { const bool is_swa = hparams.is_swa(il); @@ -8110,7 +8113,7 @@ struct llm_build_olmo : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8232,7 +8235,7 @@ struct llm_build_olmo2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8358,7 +8361,7 @@ struct llm_build_olmoe : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8481,7 +8484,7 @@ struct llm_build_openelm : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { const int64_t n_head = hparams.n_head(il); @@ -8611,7 +8614,7 @@ struct llm_build_gptneox : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, @@ -8757,7 +8760,7 @@ struct llm_build_arctic : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8889,7 +8892,7 @@ struct llm_build_deepseek : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; @@ -9054,7 +9057,7 @@ struct llm_build_deepseek2 : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9274,7 +9277,7 @@ struct llm_build_bitnet : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9532,7 +9535,7 @@ struct llm_build_t5_dec : public llm_graph_context { const int64_t n_outputs_enc = embd_enc->ne[1]; - auto * inp_attn_self = build_attn_inp_kv_unified(true, false); + auto * inp_attn_self = build_attn_inp_kv_unified(); auto * inp_attn_cross = build_attn_inp_cross(); for (int il = 0; il < n_layer; ++il) { @@ -9698,7 +9701,7 @@ struct llm_build_jais : public llm_graph_context { inpL = build_inp_embd(model.tok_embd); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, @@ -9794,7 +9797,7 @@ struct llm_build_chatglm : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9926,7 +9929,7 @@ struct llm_build_nemotron : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10049,7 +10052,7 @@ struct llm_build_exaone : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10565,7 +10568,7 @@ struct llm_build_chameleon : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(true, false); + auto * inp_attn = build_attn_inp_kv_unified(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL;