mirror of
https://github.com/LostRuins/koboldcpp.git
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Merge branch 'master' into concedo_experimental
# Conflicts: # .devops/full-cuda.Dockerfile # .devops/full-rocm.Dockerfile # .devops/full.Dockerfile # .devops/main-rocm.Dockerfile # README.md # flake.lock # flake.nix # ggml-cuda.cu # requirements.txt # tests/CMakeLists.txt
This commit is contained in:
Concedo
commit
fe7c200610
48 changed files with 1838 additions and 500 deletions
234
llama.cpp
234
llama.cpp
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@ -355,6 +355,7 @@ enum llm_tensor {
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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LLM_TENSOR_FFN_UP,
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LLM_TENSOR_FFN_ACT,
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LLM_TENSOR_FFN_DOWN_EXP,
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LLM_TENSOR_FFN_GATE_EXP,
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LLM_TENSOR_FFN_UP_EXP,
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@ -423,6 +424,15 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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LLM_ARCH_GPT2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_POS_EMBD, "position_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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},
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},
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{
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@ -474,6 +484,7 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
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},
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},
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{
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@ -1259,6 +1270,10 @@ enum e_model {
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MODEL_40B,
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MODEL_65B,
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MODEL_70B,
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MODEL_SMALL,
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MODEL_MEDIUM,
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MODEL_LARGE,
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MODEL_XL,
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};
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static const size_t kiB = 1024;
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@ -1290,6 +1305,7 @@ struct llama_hparams {
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float f_clamp_kqv;
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float f_max_alibi_bias;
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bool operator!=(const llama_hparams & other) const {
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if (this->vocab_only != other.vocab_only) return true;
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if (this->n_vocab != other.n_vocab) return true;
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@ -1393,6 +1409,7 @@ struct llama_layer {
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// ff bias
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struct ggml_tensor * ffn_down_b; // b2
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struct ggml_tensor * ffn_up_b; // b3
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struct ggml_tensor * ffn_act;
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};
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struct llama_kv_cell {
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@ -2559,18 +2576,22 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
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static const char * llama_model_type_name(e_model type) {
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switch (type) {
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case MODEL_1B: return "1B";
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case MODEL_3B: return "3B";
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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case MODEL_13B: return "13B";
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case MODEL_15B: return "15B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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default: return "?B";
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case MODEL_1B: return "1B";
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case MODEL_3B: return "3B";
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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case MODEL_13B: return "13B";
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case MODEL_15B: return "15B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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case MODEL_SMALL: return "0.1B";
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case MODEL_MEDIUM: return "0.4B";
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case MODEL_LARGE: return "0.8B";
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case MODEL_XL: return "1.5B";
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default: return "?B";
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}
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}
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@ -2789,6 +2810,17 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_GPT2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 12: model.type = e_model::MODEL_SMALL; break;
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case 24: model.type = e_model::MODEL_MEDIUM; break;
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case 36: model.type = e_model::MODEL_LARGE; break;
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case 48: model.type = e_model::MODEL_XL; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -3500,7 +3532,6 @@ static bool llm_load_tensors(
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case LLM_ARCH_MPT:
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{
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model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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// output
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{
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ggml_backend_type backend_norm;
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@ -3538,6 +3569,9 @@ static bool llm_load_tensors(
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layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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// AWQ ScaleActivation layer
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layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false);
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}
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} break;
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case LLM_ARCH_STABLELM:
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@ -3733,6 +3767,60 @@ static bool llm_load_tensors(
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layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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}
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} break;
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case LLM_ARCH_GPT2:
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{
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model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
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// output
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{
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ggml_backend_type backend_norm;
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ggml_backend_type backend_output;
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if (n_gpu_layers > int(n_layer)) {
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backend_norm = llama_backend_offload;
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backend_output = llama_backend_offload_split;
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} else {
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backend_norm = GGML_BACKEND_CPU;
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backend_output = GGML_BACKEND_CPU;
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}
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
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model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
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model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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}
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const uint32_t n_ff = hparams.n_ff;
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const int i_gpu_start = n_layer - n_gpu_layers;
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model.layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
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const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
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layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
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layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
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layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
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layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
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layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
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layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
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layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
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layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
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layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -4068,6 +4156,7 @@ static struct ggml_tensor * llm_build_ffn(
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struct ggml_tensor * gate_b,
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struct ggml_tensor * down,
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struct ggml_tensor * down_b,
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struct ggml_tensor * act_scales,
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llm_ffn_op_type type_op,
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llm_ffn_gate_type type_gate,
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const llm_build_cb & cb,
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@ -4112,6 +4201,10 @@ static struct ggml_tensor * llm_build_ffn(
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{
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cur = ggml_gelu(ctx, cur);
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cb(cur, "ffn_gelu", il);
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if (act_scales != NULL) {
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cur = ggml_div(ctx, cur, act_scales);
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cb(cur, "ffn_act", il);
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}
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} break;
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case LLM_FFN_RELU:
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{
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@ -4430,6 +4523,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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} else {
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@ -4609,6 +4703,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -4723,6 +4818,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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NULL, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -4827,6 +4923,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, model.layers[il].ffn_up_b,
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NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5031,6 +5128,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, model.layers[il].ffn_up_b,
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NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5117,6 +5215,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5212,6 +5311,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, model.layers[il].ffn_up_b,
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NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5297,11 +5397,11 @@ struct llm_build_context {
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NULL,
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LLM_NORM, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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NULL, NULL,
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model.layers[il].ffn_down, NULL,
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model.layers[il].ffn_act,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5410,6 +5510,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5522,6 +5623,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5629,6 +5731,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, model.layers[il].ffn_up_b,
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NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(ffn_output, "ffn_out", il);
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}
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@ -5732,6 +5835,7 @@ struct llm_build_context {
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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@ -5761,6 +5865,102 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_gpt2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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struct ggml_tensor * cur;
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struct ggml_tensor * pos;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
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cb(inpL, "inp_embd", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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cb(inp_pos, "inp_pos", -1);
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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cb(KQ_mask, "KQ_mask", -1);
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pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
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cb(pos, "pos_embd", -1);
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inpL = ggml_add(ctx0, inpL, pos);
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cb(inpL, "inpL", -1);
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for (int il = 0; il < n_layer; ++il) {
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm,
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model.layers[il].attn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
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cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
|
||||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
|
||||
// add the input
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// FF
|
||||
{
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm,
|
||||
model.layers[il].ffn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
||||
NULL, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||
NULL,
|
||||
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
inpL = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(inpL, "l_out", il);
|
||||
}
|
||||
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.output_norm,
|
||||
model.output_norm_b,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
|
|
@ -5916,6 +6116,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
|
|||
{ "ffn_gate", OFFLOAD_FUNC },
|
||||
{ "ffn_gate_b", OFFLOAD_FUNC },
|
||||
{ "ffn_gate_par", OFFLOAD_FUNC },
|
||||
{ "ffn_act", OFFLOAD_FUNC },
|
||||
{ "ffn_down", OFFLOAD_FUNC },
|
||||
{ "ffn_down_b", OFFLOAD_FUNC },
|
||||
{ "ffn_out", OFFLOAD_FUNC },
|
||||
|
|
@ -6275,6 +6476,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_plamo();
|
||||
} break;
|
||||
case LLM_ARCH_GPT2:
|
||||
{
|
||||
result = llm.build_gpt2();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -10853,6 +11058,7 @@ const char * llama_print_system_info(void) {
|
|||
|
||||
s = "";
|
||||
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
|
||||
s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | ";
|
||||
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
|
||||
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
|
||||
s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue