From 8f974bc1e980c06833504276021072e7a4088c81 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 18 Jul 2025 08:29:28 +0300 Subject: [PATCH 1/7] graph : refactor context to not pass gf explicitly (#14629) ggml-ci --- src/llama-context.cpp | 4 +- src/llama-context.h | 4 +- src/llama-graph.cpp | 34 ++- src/llama-graph.h | 64 ++--- src/llama-model.cpp | 530 ++++++++++++++++++++---------------------- 5 files changed, 295 insertions(+), 341 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 4e1d91159..1af19caa3 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -694,7 +694,7 @@ bool llama_context::apply_adapter_cvec( return cvec.apply(model, data, len, n_embd, il_start, il_end); } -llm_graph_result_i * llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) { +llm_graph_result * llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) { if (mctx && !mctx->apply()) { LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__); ret = GGML_STATUS_FAILED; @@ -1363,7 +1363,7 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u } llm_graph_params llama_context::graph_params( - llm_graph_result_i * res, + llm_graph_result * res, const llama_ubatch & ubatch, const llama_memory_context_i * mctx, llm_graph_type gtype) const { diff --git a/src/llama-context.h b/src/llama-context.h index fd480af6e..1601ac682 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -94,7 +94,7 @@ struct llama_context { // if memory_context is provided, it will be applied first to the context's memory // ret contains the status of the graph computation // returns nullptr only if ret != GGML_STATUS_SUCCESS - llm_graph_result_i * process_ubatch( + llm_graph_result * process_ubatch( const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, @@ -199,7 +199,7 @@ public: private: llm_graph_params graph_params( - llm_graph_result_i * res, + llm_graph_result * res, const llama_ubatch & ubatch, const llama_memory_context_i * mctx, llm_graph_type gtype) const; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index e27f78c2a..7ea7fd615 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -486,6 +486,10 @@ llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) { return inputs.back().get(); } +void llm_graph_result::set_params(const llm_graph_params & params) { + this->params = params; +} + // // llm_graph_context // @@ -527,9 +531,10 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : mctx (params.mctx), cross (params.cross), cb_func (params.cb), - res (static_cast(params.res)), - ctx0 (res->get_ctx()) { - res->params = params; + res (params.res), + ctx0 (res->get_ctx()), + gf (res->get_gf()) { + res->set_params(params); } void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const { @@ -1119,7 +1124,6 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t } ggml_tensor * llm_graph_context::build_attn_mha( - ggml_cgraph * gf, ggml_tensor * q, ggml_tensor * k, ggml_tensor * v, @@ -1253,7 +1257,6 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_no_cache * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, @@ -1281,7 +1284,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * k = k_cur; ggml_tensor * v = v_cur; - ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); + ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); if (wo) { @@ -1337,7 +1340,6 @@ llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_kv_unified * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, @@ -1370,7 +1372,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * k = mctx_cur->get_k(ctx0, il); ggml_tensor * v = mctx_cur->get_v(ctx0, il); - ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); + ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); if (wo) { @@ -1390,7 +1392,6 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_kv_unified_iswa * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, @@ -1437,7 +1438,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * k = mctx_cur->get_k(ctx0, il); ggml_tensor * v = mctx_cur->get_v(ctx0, il); - ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); + ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); if (wo) { @@ -1470,7 +1471,6 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const { ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_cross * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, @@ -1492,7 +1492,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * k = k_cur; ggml_tensor * v = v_cur; - ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); + ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); if (wo) { @@ -1550,7 +1550,6 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif } ggml_tensor * llm_graph_context::build_rs( - ggml_cgraph * gf, ggml_tensor * s, ggml_tensor * state_copy, int32_t state_size, @@ -1608,21 +1607,19 @@ llm_graph_input_rs * llm_graph_context::build_rs_inp() const { ggml_tensor * llm_graph_context::build_rs( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * s, int32_t state_size, int32_t n_seqs, const llm_graph_get_rows_fn & get_state_rows) const { const auto * kv_state = inp->mctx; - return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows); + return build_rs(s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows); } ggml_tensor * llm_graph_context::build_rwkv_token_shift_load( llm_graph_input_rs * inp, - ggml_cgraph * gf, const llama_ubatch & ubatch, - int il) const { + int il) const { const auto * mctx_cur = static_cast(mctx); const auto token_shift_count = hparams.token_shift_count; @@ -1632,7 +1629,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load( ggml_tensor * token_shift_all = mctx_cur->get_r_l(il); ggml_tensor * token_shift = build_rs( - inp, gf, token_shift_all, + inp, token_shift_all, hparams.n_embd_r(), n_seqs); token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs); @@ -1672,7 +1669,6 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const { } void llm_graph_context::build_pooling( - ggml_cgraph * gf, ggml_tensor * cls, ggml_tensor * cls_b, ggml_tensor * cls_out, diff --git a/src/llama-graph.h b/src/llama-graph.h index 42e636e0e..a28a8c4bd 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -371,31 +371,11 @@ public: // along with the input tensors, the object also provides commonly used outputs tensors, such as logits, embeddings, etc. // these are used by the llama_context to extact the relevant data, based on the compute parameters -// TODO: this interface seems redundant - remove it -class llm_graph_result_i { -public: - virtual ~llm_graph_result_i() = default; - - virtual ggml_tensor * get_tokens() const = 0; - virtual ggml_tensor * get_logits() const = 0; - virtual ggml_tensor * get_embd() const = 0; - virtual ggml_tensor * get_embd_pooled() const = 0; - - virtual ggml_cgraph * get_gf() = 0; - virtual ggml_context * get_ctx() = 0; - - virtual void reset() = 0; - - virtual void set_inputs(const llama_ubatch * ubatch) = 0; - - virtual bool can_reuse(const llm_graph_params & params) = 0; -}; - -using llm_graph_result_ptr = std::unique_ptr; - // callback that allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) using llm_graph_cb = std::function; +class llm_graph_result; + struct llm_graph_params { llm_arch arch = LLM_ARCH_UNKNOWN; @@ -418,8 +398,7 @@ struct llm_graph_params { llm_graph_cb cb; - // TODO: temporary - llm_graph_result_i * res; + llm_graph_result * res; // return true if the "other" params would result in a graph with the same topology as with the current params // having the same topology allows us to reuse the graph in some cases @@ -464,35 +443,37 @@ struct llm_graph_params { } }; -class llm_graph_result : public llm_graph_result_i { +class llm_graph_result { public: llm_graph_result(int64_t max_nodes); virtual ~llm_graph_result() = default; - ggml_tensor * get_tokens() const override { return t_tokens; } - ggml_tensor * get_logits() const override { return t_logits; } - ggml_tensor * get_embd() const override { return t_embd; } - ggml_tensor * get_embd_pooled() const override { return t_embd_pooled; } + ggml_tensor * get_tokens() const { return t_tokens; } + ggml_tensor * get_logits() const { return t_logits; } + ggml_tensor * get_embd() const { return t_embd; } + ggml_tensor * get_embd_pooled() const { return t_embd_pooled; } - ggml_cgraph * get_gf() override { return gf; } - ggml_context * get_ctx() override { return ctx_compute.get(); } + ggml_cgraph * get_gf() const { return gf; } + ggml_context * get_ctx() const { return ctx_compute.get(); } int64_t get_max_nodes() const; - void reset() override; + void reset(); - void set_inputs(const llama_ubatch * ubatch) override; + void set_inputs(const llama_ubatch * ubatch); // try to update the existing graph result using the new graph parameters in order to reuse it // this can only be done if we determine that the resulting graph using the new graph parameters // would be identical to the existing graph. in that case, we simply have to update the memory // contexts of the input tensors of the graph and we can reuse it for another computation // return true if the graph was updated and can be reused - bool can_reuse(const llm_graph_params & params) override; + bool can_reuse(const llm_graph_params & params); llm_graph_input_i * add_input(llm_graph_input_ptr input); + void set_params(const llm_graph_params & params); + // important graph nodes ggml_tensor * t_tokens = nullptr; ggml_tensor * t_logits = nullptr; @@ -510,6 +491,7 @@ public: int64_t max_nodes; +private: // keep a copy of the previous graph parameters // we will use this to determine whether the graph can be reused by comparing them with the new parameters // note: these are updated after constructing the new graph @@ -519,6 +501,8 @@ public: int debug = 0; }; +using llm_graph_result_ptr = std::unique_ptr; + // // llm_graph_context // @@ -576,6 +560,7 @@ struct llm_graph_context { llm_graph_result * res; ggml_context * ctx0 = nullptr; + ggml_cgraph * gf = nullptr; llm_graph_context(const llm_graph_params & params); virtual ~llm_graph_context() = default; @@ -661,7 +646,6 @@ struct llm_graph_context { // ggml_tensor * build_attn_mha( - ggml_cgraph * gf, ggml_tensor * q, // [n_embd_head_q, n_head_q, n_tokens] ggml_tensor * k, // [n_embd_head_k, n_head_k, n_tokens] ggml_tensor * v, // [n_embd_head_v, n_head_v, n_tokens] (v_trans == false) @@ -674,7 +658,6 @@ struct llm_graph_context { ggml_tensor * build_attn( llm_graph_input_attn_no_cache * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens] @@ -689,7 +672,6 @@ struct llm_graph_context { ggml_tensor * build_attn( llm_graph_input_attn_kv_unified * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens] @@ -705,7 +687,6 @@ struct llm_graph_context { // note: if k_cur or v_cur are not provided, they will not be stored in the memory ggml_tensor * build_attn( llm_graph_input_attn_kv_unified_iswa * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens] @@ -720,7 +701,6 @@ struct llm_graph_context { ggml_tensor * build_attn( llm_graph_input_attn_cross * inp, - ggml_cgraph * gf, ggml_tensor * wo, ggml_tensor * wo_b, ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens] @@ -742,7 +722,6 @@ struct llm_graph_context { // implementation in 2 separate methods. the goal is to avoid calling `ggml_build_forward_expand` in // `llama_memory_recurrent` ggml_tensor * build_rs( - ggml_cgraph * gf, ggml_tensor * s, ggml_tensor * state_copy, int32_t state_size, @@ -757,7 +736,6 @@ struct llm_graph_context { ggml_tensor * build_rs( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * s, int32_t state_size, int32_t n_seqs, @@ -765,9 +743,8 @@ struct llm_graph_context { ggml_tensor * build_rwkv_token_shift_load( llm_graph_input_rs * inp, - ggml_cgraph * gf, const llama_ubatch & ubatch, - int il) const; + int il) const; ggml_tensor * build_rwkv_token_shift_store( ggml_tensor * token_shift, @@ -784,7 +761,6 @@ struct llm_graph_context { // void build_pooling( - ggml_cgraph * gf, ggml_tensor * cls, ggml_tensor * cls_b, ggml_tensor * cls_out, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 589d95936..b88f4ebc5 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -5524,7 +5524,7 @@ ggml_tensor * llama_model::get_rope_factors(const llama_cparams & cparams, int i } struct llm_build_llama : public llm_graph_context { - llm_build_llama(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -5600,7 +5600,7 @@ struct llm_build_llama : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -5680,7 +5680,7 @@ struct llm_build_llama : public llm_graph_context { }; struct llm_build_llama_iswa : public llm_graph_context { - llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -5774,7 +5774,7 @@ struct llm_build_llama_iswa : public llm_graph_context { cb(Kcur, "Kcur_normed", il); } - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -5863,7 +5863,7 @@ struct llm_build_llama_iswa : public llm_graph_context { }; struct llm_build_deci : public llm_graph_context { - llm_build_deci(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -5951,7 +5951,7 @@ struct llm_build_deci : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); } @@ -6019,7 +6019,7 @@ struct llm_build_deci : public llm_graph_context { }; struct llm_build_baichuan : public llm_graph_context { - llm_build_baichuan(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -6083,7 +6083,7 @@ struct llm_build_baichuan : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6141,7 +6141,7 @@ struct llm_build_baichuan : public llm_graph_context { }; struct llm_build_xverse : public llm_graph_context { - llm_build_xverse(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -6198,7 +6198,7 @@ struct llm_build_xverse : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6254,7 +6254,7 @@ struct llm_build_xverse : public llm_graph_context { }; struct llm_build_falcon : public llm_graph_context { - llm_build_falcon(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -6321,7 +6321,7 @@ struct llm_build_falcon : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6376,7 +6376,7 @@ struct llm_build_falcon : public llm_graph_context { }; struct llm_build_grok : public llm_graph_context { - llm_build_grok(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -6451,7 +6451,7 @@ struct llm_build_grok : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -6538,7 +6538,7 @@ struct llm_build_grok : public llm_graph_context { }; struct llm_build_dbrx : public llm_graph_context { - llm_build_dbrx(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -6600,7 +6600,7 @@ struct llm_build_dbrx : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6663,7 +6663,7 @@ struct llm_build_dbrx : public llm_graph_context { }; struct llm_build_starcoder : public llm_graph_context { - llm_build_starcoder(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -6714,7 +6714,7 @@ struct llm_build_starcoder : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6772,7 +6772,7 @@ struct llm_build_starcoder : public llm_graph_context { }; struct llm_build_refact : public llm_graph_context { - llm_build_refact(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -6813,7 +6813,7 @@ struct llm_build_refact : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -6871,7 +6871,7 @@ struct llm_build_refact : public llm_graph_context { }; struct llm_build_bert : public llm_graph_context { - llm_build_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -6970,7 +6970,7 @@ struct llm_build_bert : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); cb(cur, "kqv_out", il); @@ -7057,7 +7057,7 @@ struct llm_build_bert : public llm_graph_context { }; struct llm_build_neo_bert : public llm_graph_context { - llm_build_neo_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -7115,7 +7115,7 @@ struct llm_build_neo_bert : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); cb(cur, "kqv_out", il); @@ -7167,7 +7167,7 @@ struct llm_build_neo_bert : public llm_graph_context { }; struct llm_build_bloom : public llm_graph_context { - llm_build_bloom(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -7215,7 +7215,7 @@ struct llm_build_bloom : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -7273,7 +7273,7 @@ struct llm_build_bloom : public llm_graph_context { }; struct llm_build_mpt : public llm_graph_context { - llm_build_mpt(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -7362,7 +7362,7 @@ struct llm_build_mpt : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -7421,7 +7421,7 @@ struct llm_build_mpt : public llm_graph_context { }; struct llm_build_stablelm : public llm_graph_context { - llm_build_stablelm(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -7508,7 +7508,7 @@ struct llm_build_stablelm : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -7573,7 +7573,7 @@ struct llm_build_stablelm : public llm_graph_context { }; struct llm_build_qwen : public llm_graph_context { - llm_build_qwen(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -7629,7 +7629,7 @@ struct llm_build_qwen : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -7687,7 +7687,7 @@ struct llm_build_qwen : public llm_graph_context { }; struct llm_build_qwen2 : public llm_graph_context { - llm_build_qwen2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -7749,7 +7749,7 @@ struct llm_build_qwen2 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -7809,7 +7809,7 @@ struct llm_build_qwen2 : public llm_graph_context { }; struct llm_build_dream : public llm_graph_context { - llm_build_dream(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : + llm_build_dream(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { //copied from qwen2 const int64_t n_embd_head = hparams.n_embd_head_v; @@ -7865,7 +7865,7 @@ struct llm_build_dream : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); } @@ -7912,7 +7912,7 @@ struct llm_build_dream : public llm_graph_context { }; struct llm_build_qwen2vl : public llm_graph_context { - llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -7977,7 +7977,7 @@ struct llm_build_qwen2vl : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8033,7 +8033,7 @@ struct llm_build_qwen2vl : public llm_graph_context { }; struct llm_build_qwen2moe : public llm_graph_context { - llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -8104,7 +8104,7 @@ struct llm_build_qwen2moe : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8192,7 +8192,7 @@ struct llm_build_qwen2moe : public llm_graph_context { }; struct llm_build_qwen3 : public llm_graph_context { - llm_build_qwen3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -8257,7 +8257,7 @@ struct llm_build_qwen3 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8313,7 +8313,7 @@ struct llm_build_qwen3 : public llm_graph_context { }; struct llm_build_qwen3moe : public llm_graph_context { - llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -8378,7 +8378,7 @@ struct llm_build_qwen3moe : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8441,7 +8441,7 @@ struct llm_build_qwen3moe : public llm_graph_context { }; struct llm_build_phi2 : public llm_graph_context { - llm_build_phi2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -8518,7 +8518,7 @@ struct llm_build_phi2 : public llm_graph_context { // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -8572,7 +8572,7 @@ struct llm_build_phi2 : public llm_graph_context { template struct llm_build_phi3 : public llm_graph_context { - llm_build_phi3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -8655,7 +8655,7 @@ struct llm_build_phi3 : public llm_graph_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -8730,7 +8730,7 @@ struct llm_build_phi3 : public llm_graph_context { }; struct llm_build_plamo : public llm_graph_context { - llm_build_plamo(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -8789,7 +8789,7 @@ struct llm_build_plamo : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8845,7 +8845,7 @@ struct llm_build_plamo : public llm_graph_context { }; struct llm_build_gpt2 : public llm_graph_context { - llm_build_gpt2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -8897,7 +8897,7 @@ struct llm_build_gpt2 : public llm_graph_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -8955,7 +8955,7 @@ struct llm_build_gpt2 : public llm_graph_context { }; struct llm_build_codeshell : public llm_graph_context { - llm_build_codeshell(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -9011,7 +9011,7 @@ struct llm_build_codeshell : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -9069,7 +9069,7 @@ struct llm_build_codeshell : public llm_graph_context { }; struct llm_build_orion : public llm_graph_context { - llm_build_orion(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -9140,7 +9140,7 @@ struct llm_build_orion : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -9196,7 +9196,7 @@ struct llm_build_orion : public llm_graph_context { }; struct llm_build_internlm2 : public llm_graph_context { - llm_build_internlm2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -9267,7 +9267,7 @@ struct llm_build_internlm2 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -9323,7 +9323,7 @@ struct llm_build_internlm2 : public llm_graph_context { }; struct llm_build_minicpm3 : public llm_graph_context { - llm_build_minicpm3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_minicpm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { //TODO: if the model varies, these parameters need to be read from the model const int64_t n_embd_base = 256; const float scale_embd = 12.0f; @@ -9455,7 +9455,7 @@ struct llm_build_minicpm3 : public llm_graph_context { ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0); cb(k_states, "k_states", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, q_states, k_states, v_states, nullptr, nullptr, kq_scale, il); } @@ -9527,7 +9527,7 @@ struct llm_build_minicpm3 : public llm_graph_context { }; struct llm_build_gemma : public llm_graph_context { - llm_build_gemma(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; ggml_tensor * cur; @@ -9585,7 +9585,7 @@ struct llm_build_gemma : public llm_graph_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur_scaled", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -9643,7 +9643,7 @@ struct llm_build_gemma : public llm_graph_context { }; struct llm_build_gemma2_iswa : public llm_graph_context { - llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; ggml_tensor * cur; @@ -9700,7 +9700,7 @@ struct llm_build_gemma2_iswa : public llm_graph_context { Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -9773,7 +9773,7 @@ struct llm_build_gemma2_iswa : public llm_graph_context { }; struct llm_build_gemma3_iswa : public llm_graph_context { - llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; ggml_tensor * cur; @@ -9842,7 +9842,7 @@ struct llm_build_gemma3_iswa : public llm_graph_context { // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315 Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } @@ -9911,7 +9911,6 @@ struct llm_build_gemma3_iswa : public llm_graph_context { struct llm_build_gemma3n_iswa : public llm_graph_context { const llama_model & model; - ggml_cgraph * gf; const int64_t n_embd_head; const int64_t n_embd_altup; @@ -9921,10 +9920,9 @@ struct llm_build_gemma3n_iswa : public llm_graph_context { const int n_layer_sparsity = 10; // number of layers using activation sparsity const float f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95) - llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) + llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model), - gf(gf), n_embd_head(model.hparams.n_embd_head_k), n_embd_altup(model.hparams.n_embd_altup), n_altup(model.hparams.n_altup), @@ -10025,7 +10023,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context { cb(Qcur, "Qcur_pos", il); cb(Kcur, "Kcur_pos", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, hparams.f_attention_scale, il); } else { @@ -10043,7 +10041,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context { ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur_pos", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il); } @@ -10337,7 +10335,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context { // TODO: move up next to build_starcoder struct llm_build_starcoder2 : public llm_graph_context { - llm_build_starcoder2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -10408,7 +10406,7 @@ struct llm_build_starcoder2 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -10469,7 +10467,6 @@ struct llm_graph_context_mamba : public llm_graph_context { ggml_tensor * build_mamba_layer( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, @@ -10500,7 +10497,7 @@ struct llm_graph_context_mamba : public llm_graph_context { ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); - ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs); + ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs); // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} @@ -10581,7 +10578,7 @@ struct llm_graph_context_mamba : public llm_graph_context { return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids); }; - ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); + ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); // store last states ggml_build_forward_expand(gf, @@ -10608,11 +10605,10 @@ struct llm_graph_context_mamba : public llm_graph_context { ggml_tensor * build_mamba2_layer( llm_graph_input_rs * inp, - ggml_cgraph * gf, - ggml_tensor * cur, - const llama_model & model, - const llama_ubatch & ubatch, - int il) const { + ggml_tensor * cur, + const llama_model & model, + const llama_ubatch & ubatch, + int il) const { const auto * mctx_cur = inp->mctx; @@ -10635,7 +10631,7 @@ struct llm_graph_context_mamba : public llm_graph_context { ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); - ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs); + ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs); // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} @@ -10705,7 +10701,7 @@ struct llm_graph_context_mamba : public llm_graph_context { return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids); }; - ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); + ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); // store last states ggml_build_forward_expand(gf, @@ -10741,7 +10737,7 @@ struct llm_graph_context_mamba : public llm_graph_context { }; struct llm_build_mamba : public llm_graph_context_mamba { - llm_build_mamba(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) { + llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -10760,9 +10756,9 @@ struct llm_build_mamba : public llm_graph_context_mamba { cb(cur, "attn_norm", il); if (model.arch == LLM_ARCH_MAMBA2) { - cur = build_mamba2_layer(rs_inp, gf, cur, model, ubatch, il); + cur = build_mamba2_layer(rs_inp, cur, model, ubatch, il); } else { - cur = build_mamba_layer(rs_inp, gf, cur, model, ubatch, il); + cur = build_mamba_layer(rs_inp, cur, model, ubatch, il); } if (il == n_layer - 1 && inp_out_ids) { @@ -10798,7 +10794,7 @@ struct llm_build_mamba : public llm_graph_context_mamba { }; struct llm_build_jamba : public llm_graph_context_mamba { - llm_build_jamba(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) { + llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) { const int64_t n_embd_head = hparams.n_embd_head_v; ggml_tensor * cur; @@ -10818,7 +10814,7 @@ struct llm_build_jamba : public llm_graph_context_mamba { cb(cur, "attn_norm", il); if (n_head_kv == 0) { - cur = build_mamba_layer(inp_hybrid->get_recr(), gf, cur, model, ubatch, il); + cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il); } else { // Attention @@ -10839,7 +10835,7 @@ struct llm_build_jamba : public llm_graph_context_mamba { cb(Vcur, "Vcur", il); // No RoPE :) - cur = build_attn(inp_hybrid->get_attn(), gf, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il); + cur = build_attn(inp_hybrid->get_attn(), model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il); } if (il == n_layer - 1 && inp_out_ids) { @@ -10907,7 +10903,7 @@ struct llm_build_jamba : public llm_graph_context_mamba { }; struct llm_build_command_r : public llm_graph_context { - llm_build_command_r(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_command_r(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -10995,7 +10991,7 @@ struct llm_build_command_r : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11054,7 +11050,7 @@ struct llm_build_command_r : public llm_graph_context { }; struct llm_build_cohere2_iswa : public llm_graph_context { - llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11130,7 +11126,7 @@ struct llm_build_cohere2_iswa : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11190,7 +11186,7 @@ struct llm_build_cohere2_iswa : public llm_graph_context { // * removed bias // * removed MoE struct llm_build_olmo : public llm_graph_context { - llm_build_olmo(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11261,7 +11257,7 @@ struct llm_build_olmo : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11318,7 +11314,7 @@ struct llm_build_olmo : public llm_graph_context { }; struct llm_build_olmo2 : public llm_graph_context { - llm_build_olmo2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11381,7 +11377,7 @@ struct llm_build_olmo2 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11447,7 +11443,7 @@ struct llm_build_olmo2 : public llm_graph_context { // * removed bias // * added q, k norm struct llm_build_olmoe : public llm_graph_context { - llm_build_olmoe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11514,7 +11510,7 @@ struct llm_build_olmoe : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11575,7 +11571,7 @@ struct llm_build_olmoe : public llm_graph_context { }; struct llm_build_openelm : public llm_graph_context { - llm_build_openelm(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11647,7 +11643,7 @@ struct llm_build_openelm : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Qcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11704,7 +11700,7 @@ struct llm_build_openelm : public llm_graph_context { }; struct llm_build_gptneox : public llm_graph_context { - llm_build_gptneox(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -11759,7 +11755,7 @@ struct llm_build_gptneox : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11850,7 +11846,7 @@ struct llm_build_gptneox : public llm_graph_context { }; struct llm_build_arctic : public llm_graph_context { - llm_build_arctic(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -11909,7 +11905,7 @@ struct llm_build_arctic : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -11988,7 +11984,7 @@ struct llm_build_arctic : public llm_graph_context { }; struct llm_build_deepseek : public llm_graph_context { - llm_build_deepseek(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_deepseek(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -12064,7 +12060,7 @@ struct llm_build_deepseek : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); } @@ -12150,7 +12146,7 @@ struct llm_build_deepseek : public llm_graph_context { }; struct llm_build_deepseek2 : public llm_graph_context { - llm_build_deepseek2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { bool is_lite = (hparams.n_layer == 27); const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); @@ -12292,7 +12288,7 @@ struct llm_build_deepseek2 : public llm_graph_context { cb(Vcur, "Vcur", il); // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group) - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, model.layers[il].wv_b, kq_scale, il); } else { @@ -12326,7 +12322,7 @@ struct llm_build_deepseek2 : public llm_graph_context { cb(Kcur, "Kcur", il); // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups) - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); } @@ -12413,7 +12409,7 @@ struct llm_build_deepseek2 : public llm_graph_context { }; struct llm_build_bitnet : public llm_graph_context { - llm_build_bitnet(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -12493,7 +12489,7 @@ struct llm_build_bitnet : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); @@ -12573,7 +12569,7 @@ struct llm_build_bitnet : public llm_graph_context { }; struct llm_build_t5_enc : public llm_graph_context { - llm_build_t5_enc(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -12616,7 +12612,7 @@ struct llm_build_t5_enc : public llm_graph_context { ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc; ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo_enc, nullptr, Qcur, Kcur, Vcur, kq_b, nullptr, 1.0f, il); cb(cur, "kqv_out", il); @@ -12674,7 +12670,7 @@ struct llm_build_t5_enc : public llm_graph_context { }; struct llm_build_t5_dec : public llm_graph_context { - llm_build_t5_dec(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; //const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -12722,7 +12718,7 @@ struct llm_build_t5_dec : public llm_graph_context { ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b; ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b); - cur = build_attn(inp_attn_self, gf, + cur = build_attn(inp_attn_self, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, kq_b, nullptr, 1.0f, il); cb(cur, "kqv_out", il); @@ -12754,7 +12750,7 @@ struct llm_build_t5_dec : public llm_graph_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc); - cur = build_attn(inp_attn_cross, gf, + cur = build_attn(inp_attn_cross, model.layers[il].wo_cross, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); cb(cur, "kqv_out", il); @@ -12844,7 +12840,7 @@ struct llm_build_t5_dec : public llm_graph_context { }; struct llm_build_jais : public llm_graph_context { - llm_build_jais(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -12886,7 +12882,7 @@ struct llm_build_jais : public llm_graph_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/float(n_embd_head), il); } @@ -12939,7 +12935,7 @@ struct llm_build_jais : public llm_graph_context { }; struct llm_build_chatglm : public llm_graph_context { - llm_build_chatglm(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -13018,7 +13014,7 @@ struct llm_build_chatglm : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -13072,7 +13068,7 @@ struct llm_build_chatglm : public llm_graph_context { }; struct llm_build_glm4 : public llm_graph_context { - llm_build_glm4(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -13151,7 +13147,7 @@ struct llm_build_glm4 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -13223,7 +13219,7 @@ struct llm_build_glm4 : public llm_graph_context { }; struct llm_build_nemotron : public llm_graph_context { - llm_build_nemotron(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -13295,7 +13291,7 @@ struct llm_build_nemotron : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -13352,7 +13348,7 @@ struct llm_build_nemotron : public llm_graph_context { }; struct llm_build_exaone : public llm_graph_context { - llm_build_exaone(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_exaone(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -13426,7 +13422,7 @@ struct llm_build_exaone : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -13519,7 +13515,6 @@ struct llm_build_rwkv6_base : public llm_graph_context { ggml_tensor * build_rwkv6_time_mix( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * cur, ggml_tensor * x_prev, const llama_ubatch & ubatch, @@ -13646,7 +13641,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { } ggml_tensor * wkv_state = build_rs( - inp, gf, mctx_cur->get_s_l(il), + inp, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs); ggml_tensor * wkv_output; @@ -13692,7 +13687,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { }; struct llm_build_rwkv6 : public llm_build_rwkv6_base { - llm_build_rwkv6(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_build_rwkv6_base(model, params) { + llm_build_rwkv6(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) { GGML_ASSERT(hparams.token_shift_count == 2); ggml_tensor * cur; @@ -13713,7 +13708,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); - ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, gf, ubatch, il); + ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il); ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0); ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift)); @@ -13728,7 +13723,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base { 1 ); - cur = build_rwkv6_time_mix(rs_inp, gf, att_norm, x_prev, ubatch, il); + cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il); ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); @@ -13793,7 +13788,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base { // ref: https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1/blob/main/modeling_rwkv6qwen2.py struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base { - llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_build_rwkv6_base(model, params) { + llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) { GGML_ASSERT(n_embd == hparams.n_embd_r()); ggml_tensor * cur; @@ -13813,7 +13808,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); - ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, gf, ubatch, il); + ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il); ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il); cb(att_norm, "attn_norm", il); @@ -13825,7 +13820,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base { 1 ); - cur = build_rwkv6_time_mix(rs_inp, gf, att_norm, x_prev, ubatch, il); + cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il); token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm)); ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il)); @@ -13915,7 +13910,6 @@ struct llm_build_rwkv7_base : public llm_graph_context { ggml_tensor * build_rwkv7_time_mix( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * cur, ggml_tensor * x_prev, ggml_tensor *& first_layer_value, @@ -14001,7 +13995,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens); ggml_tensor * wkv_state = build_rs( - inp, gf, mctx_cur->get_s_l(il), + inp, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs); ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state); @@ -14048,7 +14042,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { }; struct llm_build_rwkv7 : public llm_build_rwkv7_base { - llm_build_rwkv7(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_build_rwkv7_base(model, params) { + llm_build_rwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) { GGML_ASSERT(hparams.token_shift_count == 2); ggml_tensor * cur; @@ -14070,7 +14064,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); - ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, gf, ubatch, il); + ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il); ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0); ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift)); @@ -14085,7 +14079,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base { 1 ); - cur = build_rwkv7_time_mix(rs_inp, gf, att_norm, x_prev, v_first, ubatch, il); + cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il); ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); @@ -14144,7 +14138,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base { struct llm_build_arwkv7 : public llm_build_rwkv7_base { - llm_build_arwkv7(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_build_rwkv7_base(model, params) { + llm_build_arwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) { GGML_ASSERT(n_embd == hparams.n_embd_r()); ggml_tensor * cur; @@ -14165,7 +14159,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); - ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, gf, ubatch, il); + ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il); ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il); cb(att_norm, "attn_norm", il); @@ -14177,7 +14171,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { 1 ); - cur = build_rwkv7_time_mix(rs_inp, gf, att_norm, x_prev, v_first, ubatch, il); + cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il); token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm)); ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il)); @@ -14234,8 +14228,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { struct llm_build_granite : public llm_graph_context { llm_build_granite( const llama_model & model, - const llm_graph_params & params, - ggml_cgraph * gf) + const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -14269,7 +14262,7 @@ struct llm_build_granite : public llm_graph_context { // self-attention cur = build_attention_layer( - gf, cur, inp_pos, inp_attn, + cur, inp_pos, inp_attn, model, n_embd_head, il); if (il == n_layer - 1 && inp_out_ids) { @@ -14305,7 +14298,6 @@ struct llm_build_granite : public llm_graph_context { } ggml_tensor * build_attention_layer( - ggml_cgraph * gf, ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv_unified * inp_attn, @@ -14360,7 +14352,7 @@ struct llm_build_granite : public llm_graph_context { cb(Vcur, "Vcur", il); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -14448,11 +14440,9 @@ struct llm_build_granite : public llm_graph_context { }; struct llm_build_granite_hybrid : public llm_graph_context_mamba { - llm_build_granite_hybrid( const llama_model & model, - const llm_graph_params & params, - ggml_cgraph * gf) : + const llm_graph_params & params) : llm_graph_context_mamba(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -14484,11 +14474,11 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { if (hparams.is_recurrent(il)) { // ssm layer // - cur = build_mamba2_layer(inp->get_recr(), gf, cur, model, ubatch, il); + cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il); } else { // attention layer // cur = build_attention_layer( - gf, cur, inp_pos, inp->get_attn(), model, + cur, inp_pos, inp->get_attn(), model, n_embd_head, il); } @@ -14527,7 +14517,6 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { } ggml_tensor * build_attention_layer( - ggml_cgraph * gf, ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv_unified * inp_attn, @@ -14582,7 +14571,7 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { cb(Vcur, "Vcur", il); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -14676,7 +14665,7 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { // * removed bias // * removed MoE struct llm_build_chameleon : public llm_graph_context { - llm_build_chameleon(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -14767,7 +14756,7 @@ struct llm_build_chameleon : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -14853,7 +14842,7 @@ struct llm_build_chameleon : public llm_graph_context { }; struct llm_build_wavtokenizer_dec : public llm_graph_context { - llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -15005,7 +14994,7 @@ struct llm_build_wavtokenizer_dec : public llm_graph_context { }; struct llm_build_plm : public llm_graph_context { - llm_build_plm(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k)); const uint32_t n_embd_head_qk_rope = hparams.n_rot; @@ -15123,7 +15112,7 @@ struct llm_build_plm : public llm_graph_context { ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0); cb(k_states, "k_states", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, q_states, k_states, v_states, nullptr, nullptr, kq_scale, il); } @@ -15177,7 +15166,7 @@ struct llm_build_plm : public llm_graph_context { }; struct llm_build_bailingmoe : public llm_graph_context { - llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -15246,7 +15235,7 @@ struct llm_build_bailingmoe : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il); } @@ -15321,7 +15310,7 @@ struct llm_build_bailingmoe : public llm_graph_context { }; struct llm_build_dots1 : public llm_graph_context { - llm_build_dots1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -15386,7 +15375,7 @@ struct llm_build_dots1 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -15471,7 +15460,7 @@ struct llm_build_dots1 : public llm_graph_context { }; struct llm_build_ernie4_5 : public llm_graph_context { - llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -15541,7 +15530,7 @@ struct llm_build_ernie4_5 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } @@ -15601,7 +15590,7 @@ struct llm_build_ernie4_5 : public llm_graph_context { }; struct llm_build_ernie4_5_moe : public llm_graph_context { - llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -15674,7 +15663,7 @@ struct llm_build_ernie4_5_moe : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); cb(cur, "attn_out", il); @@ -15771,7 +15760,7 @@ struct llm_build_ernie4_5_moe : public llm_graph_context { }; struct llm_build_falcon_h1 : public llm_graph_context_mamba { - llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) { + llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) { const int64_t n_embd_head = hparams.n_embd_head_v; ggml_tensor * cur; @@ -15827,7 +15816,7 @@ struct llm_build_falcon_h1 : public llm_graph_context_mamba { cb(Kcur, "Kcur-post-rope", il); cb(Vcur, "Vcur-post-rope", il); - ggml_tensor * attn_out = build_attn(inp->get_attn(), gf, + ggml_tensor * attn_out = build_attn(inp->get_attn(), model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(attn_out, "attn_out", il); @@ -15838,7 +15827,7 @@ struct llm_build_falcon_h1 : public llm_graph_context_mamba { // Mamba2 layer cb(cur, "ssm_in", il); - ggml_tensor * ssm_out = build_mamba2_layer(inp->get_recr(), gf, cur, model, ubatch, il); + ggml_tensor * ssm_out = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il); cb(ssm_out, "ssm_out", il); // // Aggregation @@ -15897,7 +15886,7 @@ struct llm_build_falcon_h1 : public llm_graph_context_mamba { }; struct llm_build_plamo2 : public llm_graph_context_mamba { - llm_build_plamo2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context_mamba(params) { + llm_build_plamo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -15925,10 +15914,10 @@ struct llm_build_plamo2 : public llm_graph_context_mamba { if (is_mamba_layer) { // PLaMo-2 Mamba layer - cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), gf, cur, model, ubatch, il); + cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il); } else { // PLaMo-2 Attention layer - cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, gf, cur, model, il); + cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, cur, model, il); } // post_mixer_norm @@ -15991,7 +15980,6 @@ private: ggml_tensor * build_plamo2_attn_layer( llm_graph_input_attn_kv_unified * inp, ggml_tensor * inp_pos, - ggml_cgraph * gf, ggml_tensor * cur, const llama_model & model, int il) { @@ -16040,7 +16028,7 @@ private: ext_factor, attn_factor, beta_fast, beta_slow ); - cur = build_attn(inp, gf, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f, il); + cur = build_attn(inp, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f, il); } cb(cur, "attn_out", il); @@ -16050,7 +16038,6 @@ private: ggml_tensor * build_plamo2_mamba_layer( llm_graph_input_rs * inp, - ggml_cgraph * gf, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, @@ -16077,7 +16064,7 @@ private: ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); - ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs); + ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs); // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} @@ -16174,7 +16161,7 @@ private: return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids); }; - ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); + ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); cb(y_ssm, "mamba_ssm_scan", il); // store last states @@ -16211,7 +16198,7 @@ private: }; struct llm_build_arcee : public llm_graph_context { - llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -16287,7 +16274,7 @@ struct llm_build_arcee : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -16346,7 +16333,7 @@ struct llm_build_arcee : public llm_graph_context { }; struct llm_build_hunyuan_moe : public llm_graph_context { - llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -16432,7 +16419,7 @@ struct llm_build_hunyuan_moe : public llm_graph_context { LLM_NORM_RMS, il); cb(Qcur, "Qcur_norm", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -16507,7 +16494,7 @@ struct llm_build_hunyuan_moe : public llm_graph_context { }; struct llm_build_smollm3 : public llm_graph_context { - llm_build_smollm3(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -16584,7 +16571,7 @@ struct llm_build_smollm3 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); @@ -16646,7 +16633,7 @@ struct llm_build_smollm3 : public llm_graph_context { struct llm_build_lfm2 : public llm_graph_context { const llama_model & model; - llm_build_lfm2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) { + llm_build_lfm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model) { ggml_tensor * cur = build_inp_embd(model.tok_embd); cb(cur, "model.embed_tokens", -1); @@ -16661,8 +16648,8 @@ struct llm_build_lfm2 : public llm_graph_context { cb(cur, "model.layers.{}.operator_norm", il); cur = hparams.is_recurrent(il) ? - build_shortconv_block(gf, cur, inp_hybrid->get_recr(), il) : - build_attn_block(gf, cur, inp_pos, inp_hybrid->get_attn(), il) ; + build_shortconv_block(cur, inp_hybrid->get_recr(), il) : + build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il) ; if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); @@ -16705,8 +16692,7 @@ struct llm_build_lfm2 : public llm_graph_context { return cur; } - ggml_tensor * build_attn_block(ggml_cgraph * gf, - ggml_tensor * cur, + ggml_tensor * build_attn_block(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv_unified * inp_attn, int il) const { @@ -16743,7 +16729,7 @@ struct llm_build_lfm2 : public llm_graph_context { ext_factor, attn_factor, beta_fast, beta_slow ); - cur = build_attn(inp_attn, gf, model.layers[il].wo, NULL, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, q, k, v, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); cb(cur, "model.layers.{}.self_attn.out_proj", il); @@ -16751,8 +16737,7 @@ struct llm_build_lfm2 : public llm_graph_context { return cur; } - ggml_tensor * build_shortconv_block(ggml_cgraph * gf, - ggml_tensor * cur, + ggml_tensor * build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il) { const auto * mctx_cur = static_cast(mctx)->get_recr(); @@ -16783,7 +16768,7 @@ struct llm_build_lfm2 : public llm_graph_context { // read conv state auto * conv_state = mctx_cur->get_r_l(il); - auto * conv_rs = build_rs(inp_recr, gf, conv_state, hparams.n_embd_r(), n_seqs); + auto * conv_rs = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs); auto * conv = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs); bx = ggml_concat(ctx0, conv, bx, 0); @@ -16930,235 +16915,232 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, } ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { - // TODO: temporary - will refactor this to keep the "gf" instance in the llm_graph_context and avoid passing it everywhere - auto * gf = params.res->get_gf(); - std::unique_ptr llm; switch (arch) { case LLM_ARCH_LLAMA: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_LLAMA4: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DECI: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_BAICHUAN: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_FALCON: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GROK: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_STARCODER: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_REFACT: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_BERT: case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_NOMIC_BERT_MOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_NEO_BERT: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_BLOOM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_MPT: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_STABLELM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DREAM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN2VL: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN2MOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN3: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_QWEN3MOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_PHI2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_PHI3: case LLM_ARCH_PHIMOE: { if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { - llm = std::make_unique> (*this, params, gf); + llm = std::make_unique> (*this, params); } else { - llm = std::make_unique>(*this, params, gf); + llm = std::make_unique>(*this, params); } } break; case LLM_ARCH_PLAMO: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_PLAMO2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GPT2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_CODESHELL: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ORION: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_INTERNLM2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_MINICPM3: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GEMMA: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GEMMA2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GEMMA3: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GEMMA3N: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_STARCODER2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_MAMBA: case LLM_ARCH_MAMBA2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_JAMBA: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_XVERSE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_COMMAND_R: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_COHERE2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DBRX: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_OLMO: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_OLMO2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_OLMOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_OPENELM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GPTNEOX: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ARCTIC: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DEEPSEEK: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DEEPSEEK2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_CHATGLM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GLM4: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_BITNET: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_T5: { switch (params.gtype) { case LLM_GRAPH_TYPE_ENCODER: - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); break; case LLM_GRAPH_TYPE_DEFAULT: case LLM_GRAPH_TYPE_DECODER: - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); break; default: GGML_ABORT("invalid graph type"); @@ -17166,101 +17148,101 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { } break; case LLM_ARCH_T5ENCODER: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_JAIS: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_NEMOTRON: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_EXAONE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_RWKV6: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_RWKV6QWEN2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_RWKV7: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ARWKV7: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: case LLM_ARCH_MINICPM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_GRANITE_HYBRID: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_CHAMELEON: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_WAVTOKENIZER_DEC: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_PLM: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_BAILINGMOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_DOTS1: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ARCEE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ERNIE4_5: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_ERNIE4_5_MOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_HUNYUAN_MOE: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_SMOLLM3: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_FALCON_H1: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_LFM2: { - llm = std::make_unique(*this, params, gf); + llm = std::make_unique(*this, params); } break; default: GGML_ABORT("fatal error"); } // add on pooling layer - llm->build_pooling(gf, cls, cls_b, cls_out, cls_out_b); + llm->build_pooling(cls, cls_b, cls_out, cls_out_b); return llm->res->get_gf(); } From f9a31eea06a859e34cecb88b4d020c7f03d86cc4 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Fri, 18 Jul 2025 14:54:18 +0800 Subject: [PATCH 2/7] CUDA: set_rows + cpy.cu refactor (#14712) --- ggml/src/ggml-cuda/cpy-utils.cuh | 251 +++++++++++++++++++++++++++++++ ggml/src/ggml-cuda/cpy.cu | 239 +---------------------------- ggml/src/ggml-cuda/ggml-cuda.cu | 5 +- ggml/src/ggml-cuda/set-rows.cu | 145 +++++++++++++++++- 4 files changed, 396 insertions(+), 244 deletions(-) create mode 100644 ggml/src/ggml-cuda/cpy-utils.cuh diff --git a/ggml/src/ggml-cuda/cpy-utils.cuh b/ggml/src/ggml-cuda/cpy-utils.cuh new file mode 100644 index 000000000..e7a0bd2f1 --- /dev/null +++ b/ggml/src/ggml-cuda/cpy-utils.cuh @@ -0,0 +1,251 @@ +#pragma once + +#include "ggml-common.h" + +static __device__ __forceinline__ void convert_f32_f32(const float * src, float * dst) { + *dst = *src; +} + +static __device__ __forceinline__ void convert_f32_f16(const float * src, half * dst) { + *dst = __float2half(*src); +} + +static __device__ __forceinline__ void convert_f32_bf16(const float * src, nv_bfloat16 * dst) { + *dst = *src; +} + +static __device__ __forceinline__ void convert_f16_f16(const half * src, half * dst) { + *dst = *src; +} + +static __device__ __forceinline__ void convert_f16_f32(const half * src, float * dst) { + *dst = *src; +} + +static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) { + if (x <= val[0]) return 0; + if (x >= val[n-1]) return n-1; + int ml = 0, mu = n-1; + while (mu-ml > 1) { + int mav = (ml+mu)/2; + if (x < val[mav]) mu = mav; else ml = mav; + } + return x - val[mu-1] < val[mu] - x ? mu-1 : mu; +} + +static __device__ void quantize_f32_q4_0_block(const float * __restrict__ x, block_q4_0 * __restrict__ y) { + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK4_0; ++j) { + const float v = x[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; + } + } + + const float d = vmax / -8; + const float id = d ? 1.0f/d : 0.0f; + + y->d = d; + + for (int j = 0; j < QK4_0/2; ++j) { + const float x0 = x[0 + j]*id; + const float x1 = x[QK4_0/2 + j]*id; + + const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f)); + const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f)); + + y->qs[j] = xi0; + y->qs[j] |= xi1 << 4; + } +} + +static __device__ void quantize_f32_q4_1_block(const float * __restrict__ x, block_q4_1 * __restrict__ y) { + float vmin = FLT_MAX; + float vmax = -FLT_MAX; + + for (int j = 0; j < QK4_1; ++j) { + const float v = x[j]; + if (v < vmin) vmin = v; + if (v > vmax) vmax = v; + } + + const float d = (vmax - vmin) / ((1 << 4) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y->dm.x = d; + y->dm.y = vmin; + + for (int j = 0; j < QK4_1/2; ++j) { + const float x0 = (x[0 + j] - vmin)*id; + const float x1 = (x[QK4_1/2 + j] - vmin)*id; + + const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f)); + const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f)); + + y->qs[j] = xi0; + y->qs[j] |= xi1 << 4; + } +} + +static __device__ void quantize_f32_q5_0_block(const float * __restrict__ x, block_q5_0 * __restrict__ y) { + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK5_0; ++j) { + const float v = x[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; + } + } + + const float d = vmax / -16; + const float id = d ? 1.0f/d : 0.0f; + + y->d = d; + + uint32_t qh = 0; + for (int j = 0; j < QK5_0/2; ++j) { + const float x0 = x[0 + j]*id; + const float x1 = x[QK5_0/2 + j]*id; + + const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f)); + const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f)); + + y->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); + } + memcpy(y->qh, &qh, sizeof(qh)); +} + +static __device__ void quantize_f32_q5_1_block(const float * __restrict__ x, block_q5_1 * __restrict__ y) { + float min = x[0]; + float max = x[0]; + + for (int j = 1; j < QK5_1; ++j) { + const float v = x[j]; + min = v < min ? v : min; + max = v > max ? v : max; + } + + const float d = (max - min) / 31; + const float id = d ? 1.0f/d : 0.0f; + + y->dm.x = d; + y->dm.y = min; + + uint32_t qh = 0; + for (int j = 0; j < QK5_1/2; ++j) { + const float x0 = (x[0 + j] - min)*id; + const float x1 = (x[QK5_1/2 + j] - min)*id; + + const uint8_t xi0 = (uint8_t)(x0 + 0.5f); + const uint8_t xi1 = (uint8_t)(x1 + 0.5f); + + y->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); + } + memcpy(y->qh, &qh, sizeof(qh)); +} + +static __device__ void quantize_f32_q8_0_block(const float * __restrict__ x, block_q8_0 * __restrict__ y) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + const float v = x[j]; + amax = fmaxf(amax, fabsf(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y->d = d; + + for (int j = 0; j < QK8_0; ++j) { + const float x0 = x[j]*id; + y->qs[j] = roundf(x0); + } +} + +static __device__ void quantize_f32_iq4_nl_block(const float * __restrict__ x, block_iq4_nl * __restrict__ y) { + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK4_NL; ++j) { + const float v = x[j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + vmax = v; + } + } + + float d = vmax / kvalues_iq4nl[0]; + const float id = d ? 1.0f/d : 0.0f; + + float sumqx = 0, sumq2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + const float x0 = x[0 + j]*id; + const float x1 = x[QK4_NL/2 + j]*id; + const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0); + const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1); + y->qs[j] = xi0 | (xi1 << 4); + const float v0 = kvalues_iq4nl[xi0]; + const float v1 = kvalues_iq4nl[xi1]; + const float w0 = x[0 + j]*x[0 + j]; + const float w1 = x[QK4_NL/2 + j]*x[QK4_NL/2 + j]; + sumqx += w0*v0*x[j] + w1*v1*x[QK4_NL/2 + j]; + sumq2 += w0*v0*v0 + w1*v1*v1; + } + + y->d = sumq2 > 0 ? sumqx/sumq2 : d; +} + +// Wrapper functions for cpy.cu compatibility +static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { + quantize_f32_q4_0_block((const float *)cxi, (block_q4_0 *)cdsti); +} + +static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { + quantize_f32_q4_1_block((const float *)cxi, (block_q4_1 *)cdsti); +} + +static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) { + quantize_f32_q5_0_block((const float *)cxi, (block_q5_0 *)cdsti); +} + +static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) { + quantize_f32_q5_1_block((const float *)cxi, (block_q5_1 *)cdsti); +} + +static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { + quantize_f32_q8_0_block((const float *)cxi, (block_q8_0 *)cdsti); +} + +static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) { + quantize_f32_iq4_nl_block((const float *)cxi, (block_iq4_nl *)cdsti); +} + +static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) { + convert_f32_f32((const float *)cxi, (float *)cdsti); +} + +static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { + convert_f32_f16((const float *)cxi, (half *)cdsti); +} + +static __device__ void cpy_1_f32_bf16(const char * cxi, char * cdsti) { + convert_f32_bf16((const float *)cxi, (nv_bfloat16 *)cdsti); +} + +static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { + convert_f16_f16((const half *)cxi, (half *)cdsti); +} + +static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) { + convert_f16_f32((const half *)cxi, (float *)cdsti); +} diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index 2c55d2149..e7d0da087 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -1,46 +1,12 @@ #include "cpy.cuh" #include "dequantize.cuh" +#include "cpy-utils.cuh" #ifdef GGML_USE_MUSA #include "ggml-musa/mudnn.cuh" #endif // GGML_USE_MUSA typedef void (*cpy_kernel_t)(const char * cx, char * cdst); -static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - float * dsti = (float *) cdsti; - - *dsti = *xi; -} - -static __device__ void cpy_1_f32_bf16(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - nv_bfloat16 * dsti = (nv_bfloat16 *) cdsti; - - *dsti = *xi; -} - -static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - half * dsti = (half *) cdsti; - - *dsti = __float2half(*xi); -} - -static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { - const half * xi = (const half *) cxi; - half * dsti = (half *) cdsti; - - *dsti = *xi; -} - -static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) { - const half * xi = (const half *) cxi; - float * dsti = (float *) cdsti; - - *dsti = *xi; -} - template static __global__ void cpy_f32_f16(const char * cx, char * cdst_direct, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, @@ -71,29 +37,6 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst_direct, const in cpy_1(cx + x_offset, cdst + dst_offset); } -static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q8_0 * dsti = (block_q8_0 *) cdsti; - - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - const float v = xi[j]; - amax = fmaxf(amax, fabsf(v)); - } - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dsti->d = d; - - for (int j = 0; j < QK8_0; ++j) { - const float x0 = xi[j]*id; - - dsti->qs[j] = roundf(x0); - } -} - static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) { float * cdstf = (float *)(cdsti); @@ -106,139 +49,6 @@ static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) { } } -static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_0 * dsti = (block_q4_0 *) cdsti; - - float amax = 0.0f; - float vmax = 0.0f; - - for (int j = 0; j < QK4_0; ++j) { - const float v = xi[j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - vmax = v; - } - } - - const float d = vmax / -8; - const float id = d ? 1.0f/d : 0.0f; - - dsti->d = d; - - for (int j = 0; j < QK4_0/2; ++j) { - const float x0 = xi[0 + j]*id; - const float x1 = xi[QK4_0/2 + j]*id; - - const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f)); - const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f)); - - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; - } -} - -static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_1 * dsti = (block_q4_1 *) cdsti; - - float vmin = FLT_MAX; - float vmax = -FLT_MAX; - - for (int j = 0; j < QK4_1; ++j) { - const float v = xi[j]; - - if (v < vmin) vmin = v; - if (v > vmax) vmax = v; - } - - const float d = (vmax - vmin) / ((1 << 4) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dsti->dm.x = d; - dsti->dm.y = vmin; - - for (int j = 0; j < QK4_1/2; ++j) { - const float x0 = (xi[0 + j] - vmin)*id; - const float x1 = (xi[QK4_1/2 + j] - vmin)*id; - - const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f)); - const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f)); - - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; - } -} - -static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q5_0 * dsti = (block_q5_0 *) cdsti; - - float amax = 0.0f; - float vmax = 0.0f; - - for (int j = 0; j < QK5_0; ++j) { - const float v = xi[j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - vmax = v; - } - } - - const float d = vmax / -16; - const float id = d ? 1.0f/d : 0.0f; - - dsti->d = d; - - uint32_t qh = 0; - for (int j = 0; j < QK5_0/2; ++j) { - const float x0 = xi[0 + j]*id; - const float x1 = xi[QK5_0/2 + j]*id; - - const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f)); - const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f)); - - dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); - } - memcpy(dsti->qh, &qh, sizeof(qh)); -} - -static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q5_1 * dsti = (block_q5_1 *) cdsti; - - float min = xi[0]; - float max = xi[0]; - - for (int j = 1; j < QK5_1; ++j) { - const float v = xi[j]; - min = v < min ? v : min; - max = v > max ? v : max; - } - - const float d = (max - min) / 31; - const float id = d ? 1.0f/d : 0.0f; - - dsti->dm.x = d; - dsti->dm.y = min; - - uint32_t qh = 0; - for (int j = 0; j < QK5_1/2; ++j) { - const float x0 = (xi[0 + j] - min)*id; - const float x1 = (xi[QK5_1/2 + j] - min)*id; - - const uint8_t xi0 = (uint8_t)(x0 + 0.5f); - const uint8_t xi1 = (uint8_t)(x1 + 0.5f); - - dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); - } - memcpy(dsti->qh, &qh, sizeof(qh)); -} - template static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) { float * cdstf = (float *)(cdsti); @@ -252,53 +62,6 @@ static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) { } } -static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) { - if (x <= val[0]) return 0; - if (x >= val[n-1]) return n-1; - int ml = 0, mu = n-1; - while (mu-ml > 1) { - int mav = (ml+mu)/2; - if (x < val[mav]) mu = mav; else ml = mav; - } - return x - val[mu-1] < val[mu] - x ? mu-1 : mu; -} - -static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_iq4_nl * dsti = (block_iq4_nl *) cdsti; - - float amax = 0.0f; - float vmax = 0.0f; - - for (int j = 0; j < QK4_NL; ++j) { - const float v = xi[j]; - if (amax < fabsf(v)) { - amax = fabsf(v); - vmax = v; - } - } - - float d = vmax / kvalues_iq4nl[0]; - const float id = d ? 1.0f/d : 0.0f; - - float sumqx = 0, sumq2 = 0; - for (int j = 0; j < QK4_NL/2; ++j) { - const float x0 = xi[0 + j]*id; - const float x1 = xi[QK4_NL/2 + j]*id; - const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0); - const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1); - dsti->qs[j] = xi0 | (xi1 << 4); - const float v0 = kvalues_iq4nl[xi0]; - const float v1 = kvalues_iq4nl[xi1]; - const float w0 = xi[0 + j]*xi[0 + j]; - const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j]; - sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j]; - sumq2 += w0*v0*v0 + w1*v1*v1; - } - - dsti->d = sumq2 > 0 ? sumqx/sumq2 : d; -} - template static __global__ void cpy_f32_q(const char * cx, char * cdst_direct, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 778d5a48b..50a977c30 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -3226,8 +3226,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } break; case GGML_OP_SET_ROWS: { -#pragma message("TODO: implement Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, IQ4_NL support (https://github.com/ggml-org/llama.cpp/pull/14661)") - return (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16) && + return (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 || + op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 || + op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_I64; } break; diff --git a/ggml/src/ggml-cuda/set-rows.cu b/ggml/src/ggml-cuda/set-rows.cu index 58cee9244..560604d09 100644 --- a/ggml/src/ggml-cuda/set-rows.cu +++ b/ggml/src/ggml-cuda/set-rows.cu @@ -1,4 +1,5 @@ #include "set-rows.cuh" +#include "cpy-utils.cuh" typedef void (*set_rows_kernel_t)(const char * src, char * dst); @@ -10,17 +11,93 @@ __device__ void set_rows_1(const src_t * src_f, dst_t * dst_f) { template<> __device__ __forceinline__ void set_rows_1(const float * src_f, half * dst_h) { - *dst_h = __float2half(*src_f); + convert_f32_f16(src_f, dst_h); } template<> __device__ __forceinline__ void set_rows_1(const float * src_f, nv_bfloat16 * dst_b) { - *dst_b = *src_f; + convert_f32_bf16(src_f, dst_b); } template<> __device__ __forceinline__ void set_rows_1(const float * src_f, float * dst_f) { - *dst_f = *src_f; + convert_f32_f32(src_f, dst_f); +} + +// Generic quantized set_rows kernel template +template +static __global__ void k_set_rows_quant( + const float * __restrict__ src0, const int64_t * __restrict__ src1, block_type * __restrict__ dst, + const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, + const int64_t s01, const int64_t s02, const int64_t s03, + const int64_t s10, const int64_t s11, const int64_t s12, + const int64_t s1, const int64_t s2, const int64_t s3) { + + const int64_t i = int64_t(blockDim.x) * blockIdx.x + threadIdx.x; + const int64_t ne_total = (ne00 * ne01 * ne02 * ne03) / qk; + + if (i >= ne_total) { + return; + } + + const int64_t i_base = i * qk; + const int64_t i03 = i_base / (ne00 * ne01 * ne02); + const int64_t i02 = (i_base - i03 * ne00 * ne01 * ne02) / (ne00 * ne01); + const int64_t i01 = (i_base - i03 * ne00 * ne01 * ne02 - i02 * ne00 * ne01) / ne00; + const int64_t i00 = i_base - i03 * ne00 * ne01 * ne02 - i02 * ne00 * ne01 - i01 * ne00; + + const int64_t i12 = i03 % ne12; + const int64_t i11 = i02 % ne11; + const int64_t i10 = i01; + + const int64_t dst_row = *(src1 + i10*s10 + i11*s11 + i12*s12); + + const float * src0_row = src0 + i01*s01 + i02*s02 + i03*s03; + block_type * dst_row_ptr = dst + (dst_row*s1 + i02*s2 + i03*s3) / sizeof(block_type); + + const float * src_block = src0_row + i00; + block_type * dst_block = dst_row_ptr + i00 / qk; + + quantize_func(src_block, dst_block); +} + +// Template dispatch function for quantized set_rows +template +static void set_rows_cuda_quant( + const float * src0_d, const int64_t * src1_d, block_type * dst_d, + const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, + const size_t nb01, const size_t nb02, const size_t nb03, + const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { + + GGML_ASSERT(ne00 % qk == 0); + const int64_t ne_total = (ne00 * ne01 * ne02 * ne03) / qk; + const int num_blocks = (ne_total + CUDA_SET_ROWS_BLOCK_SIZE - 1) / CUDA_SET_ROWS_BLOCK_SIZE; + const dim3 block_size(CUDA_SET_ROWS_BLOCK_SIZE); + const dim3 grid_size(num_blocks); + + const int64_t s01 = nb01/sizeof(float); + const int64_t s02 = nb02/sizeof(float); + const int64_t s03 = nb03/sizeof(float); + const int64_t s10 = nb10/sizeof(int64_t); + const int64_t s11 = nb11/sizeof(int64_t); + const int64_t s12 = nb12/sizeof(int64_t); + const int64_t s1 = nb1; + const int64_t s2 = nb2; + const int64_t s3 = nb3; + + if (ne_total > 0) { + k_set_rows_quant<<>>( + src0_d, src1_d, dst_d, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + s01, s02, s03, + s10, s11, s12, + s1, s2, s3); + } } template @@ -145,7 +222,67 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { nb1, nb2, nb3, stream ); + } else if (dst->type == GGML_TYPE_Q4_0) { + set_rows_cuda_quant( + src0_d, src1_d, (block_q4_0*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); + } else if (dst->type == GGML_TYPE_Q4_1) { + set_rows_cuda_quant( + src0_d, src1_d, (block_q4_1*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); + } else if (dst->type == GGML_TYPE_Q5_0) { + set_rows_cuda_quant( + src0_d, src1_d, (block_q5_0*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); + } else if (dst->type == GGML_TYPE_Q5_1) { + set_rows_cuda_quant( + src0_d, src1_d, (block_q5_1*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); + } else if (dst->type == GGML_TYPE_Q8_0) { + set_rows_cuda_quant( + src0_d, src1_d, (block_q8_0*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); + } else if (dst->type == GGML_TYPE_IQ4_NL) { + set_rows_cuda_quant( + src0_d, src1_d, (block_iq4_nl*)dst->data, + ne00, ne01, ne02, ne03, + ne10, ne11, ne12, ne13, + nb01, nb02, nb03, + nb10, nb11, nb12, + nb1, nb2, nb3, + stream + ); } else { - GGML_ABORT("unsupported type"); + GGML_ABORT("unsupported type %s", ggml_type_name(dst->type)); } } From e0cb5c5cb8a61ac232130cf6bf878035f93824d9 Mon Sep 17 00:00:00 2001 From: lgai-exaone Date: Fri, 18 Jul 2025 17:45:49 +0900 Subject: [PATCH 3/7] model : add EXAONE 4.0 support (#14630) --- convert_hf_to_gguf.py | 72 +++++++++++++ convert_hf_to_gguf_update.py | 1 + gguf-py/gguf/constants.py | 19 ++++ src/llama-arch.cpp | 21 ++++ src/llama-arch.h | 1 + src/llama-chat.cpp | 20 ++++ src/llama-chat.h | 1 + src/llama-model.cpp | 195 +++++++++++++++++++++++++++++++++++ src/llama-vocab.cpp | 3 + 9 files changed, 333 insertions(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d9185c806..c8bf3c538 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -843,6 +843,9 @@ class TextModel(ModelBase): if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51": # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer res = "lfm2" + if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb": + # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B + res = "exaone4" if res is None: logger.warning("\n") @@ -6780,6 +6783,75 @@ class ExaoneModel(TextModel): yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) +@ModelBase.register("Exaone4ForCausalLM") +class Exaone4Model(TextModel): + model_arch = gguf.MODEL_ARCH.EXAONE4 + + def set_vocab(self): + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if hparams.get("sliding_window") is not None: + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + if "layer_types" in hparams: + self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]]) + elif "sliding_window_pattern" in hparams: + sliding_window_pattern = [] + if isinstance(hparams["sliding_window_pattern"], str): # e.g. LLLG + for i in range(hparams["num_hidden_layers"]): + sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L") + if isinstance(hparams["sliding_window_pattern"], int): # e.g. 4 + for i in range(hparams["num_hidden_layers"]): + sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0) + if len(sliding_window_pattern) == hparams["num_hidden_layers"]: + self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) + + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10_000.0) + if (dim := self.hparams.get("head_dim")) is None: + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 16.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + @ModelBase.register("GraniteForCausalLM") class GraniteModel(LlamaModel): """Conversion for IBM's GraniteForCausalLM""" diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index f7b6d97b1..abaf2ea9a 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -129,6 +129,7 @@ models = [ {"name": "a.x-4.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", }, {"name": "midm-2.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", }, {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"}, + {"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", }, ] # some models are known to be broken upstream, so we will skip them as exceptions diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a8f5947ac..40e809f1a 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -354,6 +354,7 @@ class MODEL_ARCH(IntEnum): JAIS = auto() NEMOTRON = auto() EXAONE = auto() + EXAONE4 = auto() GRANITE = auto() GRANITE_MOE = auto() GRANITE_HYBRID = auto() @@ -671,6 +672,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.JAIS: "jais", MODEL_ARCH.NEMOTRON: "nemotron", MODEL_ARCH.EXAONE: "exaone", + MODEL_ARCH.EXAONE4: "exaone4", MODEL_ARCH.GRANITE: "granite", MODEL_ARCH.GRANITE_MOE: "granitemoe", MODEL_ARCH.GRANITE_HYBRID: "granitehybrid", @@ -2197,6 +2199,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.EXAONE4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_POST_NORM, + ], MODEL_ARCH.GRANITE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index df3fc5d3e..814ac93a6 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -68,6 +68,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_JAIS, "jais" }, { LLM_ARCH_NEMOTRON, "nemotron" }, { LLM_ARCH_EXAONE, "exaone" }, + { LLM_ARCH_EXAONE4, "exaone4" }, { LLM_ARCH_RWKV6, "rwkv6" }, { LLM_ARCH_RWKV6QWEN2, "rwkv6qwen2" }, { LLM_ARCH_RWKV7, "rwkv7" }, @@ -1510,6 +1511,26 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_EXAONE4, + { + { 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_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_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_POST_NORM, "blk.%d.post_ffw_norm" }, + } + }, { LLM_ARCH_RWKV6, { diff --git a/src/llama-arch.h b/src/llama-arch.h index 3bffe359e..d09b7d781 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -72,6 +72,7 @@ enum llm_arch { LLM_ARCH_JAIS, LLM_ARCH_NEMOTRON, LLM_ARCH_EXAONE, + LLM_ARCH_EXAONE4, LLM_ARCH_RWKV6, LLM_ARCH_RWKV6QWEN2, LLM_ARCH_RWKV7, diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 240937ece..80072ad27 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -56,6 +56,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "glmedge", LLM_CHAT_TEMPLATE_GLMEDGE }, { "minicpm", LLM_CHAT_TEMPLATE_MINICPM }, { "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 }, + { "exaone4", LLM_CHAT_TEMPLATE_EXAONE_4 }, { "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD }, { "granite", LLM_CHAT_TEMPLATE_GRANITE }, { "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT }, @@ -168,6 +169,9 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { } else if (tmpl_contains(LU8("<｜Assistant｜>")) && tmpl_contains(LU8("<｜User｜>")) && tmpl_contains(LU8("<｜end▁of▁sentence｜>"))) { return LLM_CHAT_TEMPLATE_DEEPSEEK_3; } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) { + if (tmpl_contains("[|tool|]")) { + return LLM_CHAT_TEMPLATE_EXAONE_4; + } // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb // EXAONE-3.0-7.8B-Instruct return LLM_CHAT_TEMPLATE_EXAONE_3; @@ -532,6 +536,22 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "[|assistant|]"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_4) { + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n"; + } else if (role == "user") { + ss << "[|user|]" << trim(message->content) << "\n"; + } else if (role == "assistant") { + ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n"; + } else if (role == "tool") { + ss << "[|tool|]" << trim(message->content) << "[|endofturn|]\n"; + } + } + if (add_ass) { + ss << "[|assistant|]"; + } } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) { // this template requires the model to have "\n\n" as EOT token for (size_t i = 0; i < chat.size(); i++) { diff --git a/src/llama-chat.h b/src/llama-chat.h index cab053348..6968a19fb 100644 --- a/src/llama-chat.h +++ b/src/llama-chat.h @@ -35,6 +35,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_GLMEDGE, LLM_CHAT_TEMPLATE_MINICPM, LLM_CHAT_TEMPLATE_EXAONE_3, + LLM_CHAT_TEMPLATE_EXAONE_4, LLM_CHAT_TEMPLATE_RWKV_WORLD, LLM_CHAT_TEMPLATE_GRANITE, LLM_CHAT_TEMPLATE_GIGACHAT, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index b88f4ebc5..cd3e45694 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1490,6 +1490,23 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; + case LLM_ARCH_EXAONE4: + { + if (hparams.n_layer == 64) { // 32B + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + hparams.n_swa = 4096; + hparams.set_swa_pattern(4); + } + + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 30: type = LLM_TYPE_1_2B; break; + case 64: type = LLM_TYPE_32B; break; + default: type = LLM_TYPE_UNKNOWN; + } + } break; case LLM_ARCH_RWKV6: case LLM_ARCH_RWKV6QWEN2: { @@ -4355,6 +4372,39 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; + case LLM_ARCH_EXAONE4: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); + } + } break; case LLM_ARCH_RWKV6: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -13478,6 +13528,142 @@ struct llm_build_exaone : public llm_graph_context { } }; +template +struct llm_build_exaone4 : public llm_graph_context { + llm_build_exaone4(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_k; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + using inp_attn_type = std::conditional_t; + inp_attn_type * inp_attn = nullptr; + + if constexpr (iswa) { + inp_attn = build_attn_inp_kv_unified_iswa(); + } else { + inp_attn = build_attn_inp_kv_unified(); + } + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // use RoPE for SWA layers or non-SWA models + const bool use_rope = hparams.is_swa(il) || hparams.swa_type == LLAMA_SWA_TYPE_NONE; + + cur = inpL; + + // self-attention + { + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + cb(Kcur, "Kcur_normed", il); + + if (use_rope) { + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, 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); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, NULL, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + cur = build_norm(cur, + model.layers[il].attn_post_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_post_norm", il); + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = build_ffn(ffn_inp, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = build_norm(cur, + model.layers[il].ffn_post_norm, NULL, + LLM_NORM_RMS, -1); + cb(cur, "ffn_post_norm", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + struct llm_build_rwkv6_base : public llm_graph_context { const llama_model & model; @@ -17163,6 +17349,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_EXAONE4: + { + if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) { + llm = std::make_unique>(*this, params, gf); + } else { + llm = std::make_unique>(*this, params, gf); + } + } break; case LLM_ARCH_RWKV6: { llm = std::make_unique(*this, params); @@ -17430,6 +17624,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_ORION: case LLM_ARCH_NEMOTRON: case LLM_ARCH_EXAONE: + case LLM_ARCH_EXAONE4: case LLM_ARCH_MINICPM3: case LLM_ARCH_DOTS1: case LLM_ARCH_HUNYUAN_MOE: diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 2181c01e3..e8bae6450 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1925,6 +1925,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } else if ( tokenizer_pre == "exaone") { pre_type = LLAMA_VOCAB_PRE_TYPE_EXAONE; + } else if ( + tokenizer_pre == "exaone4") { + pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "chameleon") { pre_type = LLAMA_VOCAB_PRE_TYPE_CHAMELEON; From eacdeb5bfcb6c6cd54461fd0e9f04cab78bf975b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 18 Jul 2025 11:53:55 +0300 Subject: [PATCH 4/7] model : fix build after merge conflict (#14754) --- src/llama-model.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index cd3e45694..2d90ec1ac 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -13530,7 +13530,7 @@ struct llm_build_exaone : public llm_graph_context { template struct llm_build_exaone4 : public llm_graph_context { - llm_build_exaone4(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + llm_build_exaone4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); @@ -13603,7 +13603,7 @@ struct llm_build_exaone4 : public llm_graph_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, gf, + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); cb(cur, "attn_out", il); @@ -17352,9 +17352,9 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { case LLM_ARCH_EXAONE4: { if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) { - llm = std::make_unique>(*this, params, gf); + llm = std::make_unique>(*this, params); } else { - llm = std::make_unique>(*this, params, gf); + llm = std::make_unique>(*this, params); } } break; case LLM_ARCH_RWKV6: From d498af3d5a00f96bdd37b534860f03a6d9e98d39 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 18 Jul 2025 14:31:15 +0300 Subject: [PATCH 5/7] graph : avoid huge warm-up graphs for MoE models (#14753) * graph : avoid huge warm-up graphs for MoE models ggml-ci * cont : bump max nodes to 8x model tensors --- src/llama-context.cpp | 2 +- src/llama-graph.cpp | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 1af19caa3..6eb344736 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1312,7 +1312,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) { // uint32_t llama_context::graph_max_nodes() const { - return std::max(65536u, 5u*model.n_tensors()); + return std::max(1024u, 8u*model.n_tensors()); } llm_graph_result * llama_context::get_gf_res_reserve() const { diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 7ea7fd615..7cac3b98f 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -906,8 +906,11 @@ ggml_tensor * llm_graph_context::build_moe_ffn( } // aggregate experts + // note: here we explicitly use hparams.n_expert_used instead of n_expert_used + // to avoid potentially a large number of add nodes during warmup + // ref: https://github.com/ggml-org/llama.cpp/pull/14753 ggml_tensor * moe_out = nullptr; - for (int i = 0; i < n_expert_used; ++i) { + for (uint32_t i = 0; i < hparams.n_expert_used; ++i) { ggml_tensor * cur_expert = ggml_view_2d(ctx0, experts, n_embd, n_tokens, experts->nb[2], i*experts->nb[1]); @@ -918,7 +921,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( } } - if (n_expert_used == 1) { + if (hparams.n_expert_used == 1) { // avoid returning a non-contiguous tensor moe_out = ggml_cont(ctx0, moe_out); } From 021cc28bef4dd7d0bf9c91dbbd0803caa6cb15f2 Mon Sep 17 00:00:00 2001 From: Oliver Simons Date: Fri, 18 Jul 2025 13:35:32 +0200 Subject: [PATCH 6/7] cuda : Fix Gemma3n not executed as CUDA_GRAPH on NVGPUs (#14741) * Fix Gemma3n not executed as CUDA_GRAPH on NVGPUs Gemma3n uses Matrix-Matrix addition as part of their input processing, wrongly triggering CUDA_GRAPH disablement on NVGPUs even when batch-size of 1 is used. * Exclude `project_per_layer_input` by matching node names This ensures that all other graphs which don't exhibit this pattern do not have their behavior changed. * Revert unnecessary formatting changes --- ggml/src/ggml-cuda/ggml-cuda.cu | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 50a977c30..dfc50ef0d 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -2590,6 +2590,9 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud // Loop over nodes in GGML graph to obtain info needed for CUDA graph cuda_ctx->cuda_graph->cpy_dest_ptrs.clear(); + const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected"; + const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj"; + for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; @@ -2611,9 +2614,12 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud #endif } - if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) { - // disable CUDA graphs for batch size > 1 for now. - // Changes in batch size or context size can cause changes to the grid size of some kernels. + if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1 && (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) && (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true)) { + // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation + // by means of matching node names. See + // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and + // https://github.com/huggingface/transformers/blob/bda75b4011239d065de84aa3e744b67ebfa7b245/src/transformers/models/gemma3n/modeling_gemma3n.py#L1773, + // Generally, changes in batch size or context size can cause changes to the grid size of some kernels. use_cuda_graph = false; #ifndef NDEBUG GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); From 2adf8d83acdb9b1bf58db6c9729ac9dc6847a58b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 18 Jul 2025 17:33:41 +0300 Subject: [PATCH 7/7] parallel : add option for different RNG seeds (#14757) ggml-ci --- examples/parallel/parallel.cpp | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 46fb451ba..e48f48fc3 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -184,6 +184,9 @@ int main(int argc, char ** argv) { // extra text to insert in each client's prompt in order to make it larger const int32_t n_junk = std::max(1, params.n_junk); + // signed seed, use negative values to indicate different seeds for the different clients + const int32_t & sseed = params.sampling.seed; + // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); @@ -219,12 +222,21 @@ int main(int argc, char ** argv) { const int n_ctx = llama_n_ctx(ctx); + if (sseed >= 0) { + LOG_INF("%s: initializing all samplers with the same RNG seed: %d (use a negative seed to have different seeds)\n", __func__, sseed); + } else { + LOG_INF("%s: initializing samplers with different RNG seeds, starting from %d\n", __func__, sseed); + } + std::vector clients(n_clients); for (size_t i = 0; i < clients.size(); ++i) { auto & client = clients[i]; client.id = i; client.smpl = common_sampler_init(model, params.sampling); - //params.sampling.seed++; + + if (sseed < 0) { + params.sampling.seed--; + } } std::vector tokens_system;