From fcc2df11df003002000bf3111fa24ec92d46b0b6 Mon Sep 17 00:00:00 2001 From: Kawrakow Date: Wed, 3 Dec 2025 11:01:21 +0100 Subject: [PATCH] Adding ministral3: this seems to work (#1030) Co-authored-by: Iwan Kawrakow --- src/llama-arch.cpp | 2 + src/llama-arch.h | 2 + src/llama-build-context.cpp | 136 ++++++++++++++++++++++++++++++++++++ src/llama-build-context.h | 2 + src/llama-hparams.cpp | 36 ++++++++++ src/llama-hparams.h | 2 +- src/llama-load-tensors.cpp | 1 + src/llama-model.cpp | 26 +++++++ src/llama.cpp | 1 + 9 files changed, 207 insertions(+), 1 deletion(-) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 83280c3b..d4406dfc 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -68,6 +68,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_BAILINGMOE2, "bailingmoe2" }, { LLM_ARCH_MINIMAX_M2, "minimax-m2" }, { LLM_ARCH_SMOLLM3, "smollm3" }, + { LLM_ARCH_MISTRAL3, "mistral3" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -142,6 +143,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" }, { LLM_KV_ATTENTION_OUTPUT_SCALE, "%s.attention.output_scale" }, { LLM_KV_ATTENTION_TEMPERATURE_LENGTH, "%s.attention.temperature_length" }, + { LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" }, { LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" }, { LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index a872e3ce..bfde21a1 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -67,6 +67,7 @@ enum llm_arch { LLM_ARCH_BAILINGMOE2, LLM_ARCH_MINIMAX_M2, LLM_ARCH_SMOLLM3, + LLM_ARCH_MISTRAL3, LLM_ARCH_UNKNOWN, }; @@ -135,6 +136,7 @@ enum llm_kv { LLM_KV_ATTENTION_SCALE, LLM_KV_ATTENTION_OUTPUT_SCALE, LLM_KV_ATTENTION_TEMPERATURE_LENGTH, + LLM_KV_ATTENTION_TEMPERATURE_SCALE, LLM_KV_ATTENTION_KEY_LENGTH_MLA, LLM_KV_ATTENTION_VALUE_LENGTH_MLA, diff --git a/src/llama-build-context.cpp b/src/llama-build-context.cpp index 23ef445f..277da085 100644 --- a/src/llama-build-context.cpp +++ b/src/llama-build-context.cpp @@ -1880,6 +1880,138 @@ ggml_cgraph * llm_build_context::build_llama() { return gf; } +ggml_cgraph * llm_build_context::build_mistral3() { + auto gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false); + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // (optional) temperature tuning + ggml_tensor * inp_attn_scale = nullptr; + if (hparams.f_attn_temp_scale != 0.0f) { + inp_attn_scale = build_input_scale(n_tokens); + } + + ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + //const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : 1.f; + + // ==================================== + + //auto * inp_attn = build_attn_inp_kv(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + auto rope_factors = build_rope_factors(il); + + // self-attention + if (!inp_attn_scale) { + cur = build_std_attention(gf, inpL, inp_pos, rope_factors, KQ_mask, nullptr, kq_scale, hparams.f_attention_scale, 0, il); + } + else { + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, + model.layers[il].wqkv, model.layers[il].bqkv, + model.layers[il].wqk, model.layers[il].bqk, + model.layers[il].wq, model.layers[il].bq, + model.layers[il].wk, model.layers[il].bk, + model.layers[il].wv, model.layers[il].bv, + nullptr, nullptr, hparams.f_attention_scale, il); + + 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); + + if (inp_attn_scale) { + Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale); + cb(Qcur, "Qcur_temp_scaled", il); + } + + cur = llm_build_kv(ctx0, lctx, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il, nullptr, 0); + } + + 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); + cb(cur, "last_attn", il); + cb(inpSA, "last_ffn_inp", il); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network (non-MoE) + if (model.layers[il].ffn_gate_inp == nullptr) { + // non-MoE + cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, nullptr, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf); + cb(cur, "ffn_out", il); + } else { + // MoE branch + cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp, + model.layers[il].ffn_gate_inp, nullptr, + model.layers[il].ffn_up_exps, nullptr, + model.layers[il].ffn_gate_exps, nullptr, + model.layers[il].ffn_down_exps, nullptr, + model.layers[il].ffn_exp_probs_b, + nullptr, nullptr, // we don't have shared experts + nullptr, nullptr, + nullptr, nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, true, false, 0.0f, + LLM_EXPERT_GATING_FUNC_SOFTMAX, + LLM_FFN_SILU, cb, il, gf); + } + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; +} + ggml_cgraph * llm_build_context::build_deci() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false); @@ -9173,6 +9305,10 @@ ggml_cgraph * llm_build_context::llama_build_graph( { result = llm.build_smollm3(); } break; + case LLM_ARCH_MISTRAL3: + { + result = llm.build_mistral3(); + } break; default: GGML_ABORT("fatal error"); } diff --git a/src/llama-build-context.h b/src/llama-build-context.h index 328b51ce..41597248 100644 --- a/src/llama-build-context.h +++ b/src/llama-build-context.h @@ -160,6 +160,8 @@ struct llm_build_context { ggml_cgraph * build_llama(); + ggml_cgraph * build_mistral3(); + ggml_cgraph * build_deci(); ggml_cgraph * build_baichuan(); diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index f0bc0493..81afc62b 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -1037,6 +1037,42 @@ void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MISTRAL3: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); + + ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false); + + if (hparams.f_attn_temp_scale != 0.0f) { + hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn; + if (hparams.n_attn_temp_floor_scale == 0) { + throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling"); + } + } + + // TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f + // but may need further verification with other values + if (hparams.rope_yarn_log_mul != 0.0f) { + float factor = 1.0f / hparams.rope_freq_scale_train; + float mscale = 1.0f; + float mscale_all_dims = hparams.rope_yarn_log_mul; + static auto get_mscale = [](float scale, float mscale) { + return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f); + }; + hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims); + } + + switch (hparams.n_layer) { + case 26: model.type = e_model::MODEL_3B; break; + case 34: model.type = e_model::MODEL_8B; break; + case 40: model.type = e_model::MODEL_14B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + default: (void)0; } diff --git a/src/llama-hparams.h b/src/llama-hparams.h index e8494703..9ef3eefc 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -110,7 +110,7 @@ struct llama_hparams { uint32_t n_no_rope_layer_step = 4; uint32_t n_attn_temp_floor_scale = 8192; float f_attn_temp_scale = 0.1; - + // qwen3vl deepstack uint32_t n_deepstack_layers = 0; diff --git a/src/llama-load-tensors.cpp b/src/llama-load-tensors.cpp index a0f0f0bf..493aec56 100644 --- a/src/llama-load-tensors.cpp +++ b/src/llama-load-tensors.cpp @@ -2820,6 +2820,7 @@ bool create_tensors_helper::create_tensors() { case LLM_ARCH_MINICPM: case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: + case LLM_ARCH_MISTRAL3: use_mmap_buffer = create_llama_tensors(tn); break; case LLM_ARCH_DECI: use_mmap_buffer = create_deci_tensors(tn); break; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 8ff29891..94c30ee7 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1268,6 +1268,32 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MISTRAL3, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_UNKNOWN, { diff --git a/src/llama.cpp b/src/llama.cpp index 6ea2b4cf..1a0480bb 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4833,6 +4833,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_ERNIE4_5: case LLM_ARCH_ERNIE4_5_MOE: case LLM_ARCH_SMOLLM3: + case LLM_ARCH_MISTRAL3: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2