mirror of
https://github.com/LostRuins/koboldcpp.git
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Merge branch 'master' into concedo_experimental
# Conflicts: # CMakeLists.txt # Makefile
This commit is contained in:
Concedo
commit
ac36aee001
10 changed files with 441 additions and 44 deletions
274
llama.cpp
274
llama.cpp
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@ -193,6 +193,7 @@ enum llm_arch {
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LLM_ARCH_REFACT,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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LLM_ARCH_UNKNOWN,
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};
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@ -209,6 +210,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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};
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enum llm_kv {
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@ -519,6 +521,22 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_QWEN,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_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_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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@ -1253,6 +1271,9 @@ struct llama_layer {
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struct ggml_tensor * wqkv;
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// attention bias
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struct ggml_tensor * bq;
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struct ggml_tensor * bk;
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struct ggml_tensor * bv;
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struct ggml_tensor * bo;
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struct ggml_tensor * bqkv;
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@ -1981,10 +2002,13 @@ struct llama_model_loader {
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return tensor;
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}
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struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend) {
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struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend, bool required = true) {
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struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
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if (cur == NULL) {
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if (!required) {
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return NULL;
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}
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throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
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}
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@ -2358,6 +2382,15 @@ 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_QWEN:
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{
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GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 40: model.type = e_model::MODEL_13B; 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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@ -2812,6 +2845,12 @@ static void llm_load_tensors(
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layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
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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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// optional bias tensors
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layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, false);
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layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, false);
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layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, false);
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layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, false);
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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_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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@ -2820,9 +2859,14 @@ static void llm_load_tensors(
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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) +
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(layer.bq ? ggml_nbytes(layer.bq) : 0) +
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(layer.bk ? ggml_nbytes(layer.bk) : 0) +
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(layer.bv ? ggml_nbytes(layer.bv) : 0) +
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(layer.bo ? ggml_nbytes(layer.bo) : 0) +
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ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
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ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
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}
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}
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} break;
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@ -3340,6 +3384,71 @@ static void llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_QWEN:
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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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{
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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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// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
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// on Windows however this is detrimental unless everything is on the GPU
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#ifndef _WIN32
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backend_norm = llama_backend_offload;
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#else
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backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
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#endif // _WIN32
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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 = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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if (backend_norm == GGML_BACKEND_GPU) {
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vram_weights += ggml_nbytes(model.output_norm);
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}
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if (backend_output == GGML_BACKEND_GPU_SPLIT) {
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vram_weights += ggml_nbytes(model.output);
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}
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}
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const uint32_t n_ff = hparams.n_ff / 2;
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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.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
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layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, 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.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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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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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
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ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
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ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
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}
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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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@ -3921,12 +4030,24 @@ struct llm_build_context {
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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@ -3945,7 +4066,7 @@ struct llm_build_context {
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llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
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cur = llm_build_kqv(ctx0, hparams, kv_self,
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model.layers[il].wo, NULL,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
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cb(cur, "kqv_out", il);
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}
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@ -4938,6 +5059,121 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_qwen() {
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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 * 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_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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cb(KQ_scale, "KQ_scale", -1);
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// KQ_mask (mask for 1 head, it wil 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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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, 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);
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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cb(cur, "bqkv", il);
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struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
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struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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// using mode = 2 for neox mode
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Qcur = ggml_rope_custom(
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ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
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freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
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freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
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cur = llm_build_kqv(ctx0, hparams, kv_self,
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model.layers[il].wo, NULL,
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Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
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cb(cur, "kqv_out", il);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward forward
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{
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, 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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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, 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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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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};
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//
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@ -5412,6 +5648,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_stablelm();
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} break;
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case LLM_ARCH_QWEN:
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{
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result = llm.build_qwen();
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} break;
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default:
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GGML_ASSERT(false);
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}
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@ -5534,8 +5774,7 @@ static int llama_decode_internal(
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// a heuristic, to avoid attending the full cache if it is not yet utilized
|
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// after enough generations, the benefit from this heuristic disappears
|
||||
// if we start defragmenting the cache, the benefit from this will be more important
|
||||
//kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)); // TODO: this might be better for CUDA?
|
||||
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));
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||||
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
|
||||
|
||||
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
|
||||
|
||||
|
|
@ -7918,18 +8157,21 @@ static void llama_convert_tensor_internal(
|
|||
return;
|
||||
}
|
||||
|
||||
auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
||||
auto block_size_bytes = ggml_type_size(tensor->type);
|
||||
size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
||||
size_t block_size_bytes = ggml_type_size(tensor->type);
|
||||
|
||||
GGML_ASSERT(nelements % block_size == 0);
|
||||
auto nblocks = nelements / block_size;
|
||||
auto blocks_per_thread = nblocks / nthread;
|
||||
auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
||||
size_t nblocks = nelements / block_size;
|
||||
size_t blocks_per_thread = nblocks / nthread;
|
||||
size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
||||
|
||||
for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
|
||||
auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
||||
auto thr_elems = thr_blocks * block_size; // number of elements for this thread
|
||||
auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
||||
size_t in_buff_offs = 0;
|
||||
size_t out_buff_offs = 0;
|
||||
|
||||
for (int tnum = 0; tnum < nthread; tnum++) {
|
||||
size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
||||
size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
|
||||
size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
||||
|
||||
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
|
||||
if (typ == GGML_TYPE_F16) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue