mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-13 05:42:22 +01:00
Merge branch 'master' into convert-py
This commit is contained in:
teleprint-me
commit
acf8f4b20f
@ -118,6 +118,7 @@ as the main playground for developing new features for the [ggml](https://github
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- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
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- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
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- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
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- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
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- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
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- Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
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- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
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@ -138,6 +138,7 @@ struct cmd_params {
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std::vector<int> n_threads;
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std::vector<int> n_gpu_layers;
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std::vector<int> main_gpu;
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std::vector<bool> no_kv_offload;
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std::vector<bool> mul_mat_q;
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std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
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int reps;
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@ -155,6 +156,7 @@ static const cmd_params cmd_params_defaults = {
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/* n_threads */ {get_num_physical_cores()},
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/* n_gpu_layers */ {99},
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/* main_gpu */ {0},
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/* no_kv_offload */ {false},
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/* mul_mat_q */ {true},
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/* tensor_split */ {{}},
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/* reps */ 5,
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@ -176,6 +178,7 @@ static void print_usage(int /* argc */, char ** argv) {
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printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
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printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
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printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
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printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
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printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
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printf(" -ts, --tensor_split <ts0/ts1/..> \n");
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printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
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@ -309,6 +312,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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break;
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}
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params.main_gpu = split<int>(argv[i], split_delim);
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} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<bool>(argv[i], split_delim);
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params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
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} else if (arg == "-mmq" || arg == "--mul-mat-q") {
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if (++i >= argc) {
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invalid_param = true;
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@ -383,6 +393,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
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if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
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if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
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if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
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if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
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if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
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if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
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@ -400,6 +411,7 @@ struct cmd_params_instance {
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int n_threads;
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int n_gpu_layers;
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int main_gpu;
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bool no_kv_offload;
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bool mul_mat_q;
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std::array<float, LLAMA_MAX_DEVICES> tensor_split;
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@ -428,6 +440,7 @@ struct cmd_params_instance {
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cparams.type_k = type_k;
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cparams.type_v = type_v;
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cparams.mul_mat_q = mul_mat_q;
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cparams.offload_kqv = !no_kv_offload;
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return cparams;
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}
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@ -444,6 +457,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
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for (const auto & tk : params.type_k)
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for (const auto & tv : params.type_v)
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for (const auto & mmq : params.mul_mat_q)
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for (const auto & nkvo : params.no_kv_offload)
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for (const auto & nt : params.n_threads) {
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cmd_params_instance instance = {
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/* .model = */ m,
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@ -455,6 +469,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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/* .no_kv_offload= */ nkvo,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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};
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@ -476,6 +491,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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for (const auto & tk : params.type_k)
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for (const auto & tv : params.type_v)
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for (const auto & mmq : params.mul_mat_q)
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for (const auto & nkvo : params.no_kv_offload)
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for (const auto & nt : params.n_threads) {
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for (const auto & n_prompt : params.n_prompt) {
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if (n_prompt == 0) {
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@ -491,6 +507,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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/* .no_kv_offload= */ nkvo,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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||||
};
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@ -511,6 +528,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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||||
/* .no_kv_offload= */ nkvo,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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||||
};
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@ -559,6 +577,7 @@ struct test {
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ggml_type type_v;
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int n_gpu_layers;
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int main_gpu;
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bool no_kv_offload;
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bool mul_mat_q;
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std::array<float, LLAMA_MAX_DEVICES> tensor_split;
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int n_prompt;
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@ -579,6 +598,7 @@ struct test {
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type_v = inst.type_v;
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n_gpu_layers = inst.n_gpu_layers;
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main_gpu = inst.main_gpu;
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no_kv_offload = inst.no_kv_offload;
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mul_mat_q = inst.mul_mat_q;
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tensor_split = inst.tensor_split;
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n_prompt = inst.n_prompt;
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@ -640,7 +660,8 @@ struct test {
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"cpu_info", "gpu_info",
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"model_filename", "model_type", "model_size", "model_n_params",
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"n_batch", "n_threads", "type_k", "type_v",
|
||||
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
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"n_gpu_layers", "main_gpu", "no_kv_offload",
|
||||
"mul_mat_q", "tensor_split",
|
||||
"n_prompt", "n_gen", "test_time",
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"avg_ns", "stddev_ns",
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"avg_ts", "stddev_ts"
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@ -659,7 +680,7 @@ struct test {
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||||
return INT;
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}
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if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
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field == "f16_kv" || field == "mul_mat_q") {
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field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") {
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return BOOL;
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}
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if (field == "avg_ts" || field == "stddev_ts") {
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@ -690,7 +711,8 @@ struct test {
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cpu_info, gpu_info,
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model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
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std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
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std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
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std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload),
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std::to_string(mul_mat_q), tensor_split_str,
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std::to_string(n_prompt), std::to_string(n_gen), test_time,
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std::to_string(avg_ns()), std::to_string(stdev_ns()),
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std::to_string(avg_ts()), std::to_string(stdev_ts())
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@ -851,6 +873,9 @@ struct markdown_printer : public printer {
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if (field == "mul_mat_q") {
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return "mmq";
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}
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if (field == "no_kv_offload") {
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return "nkvo";
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}
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if (field == "tensor_split") {
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return "ts";
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}
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@ -885,6 +910,9 @@ struct markdown_printer : public printer {
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if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
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fields.push_back("mul_mat_q");
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}
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if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
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fields.push_back("no_kv_offload");
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}
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if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
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fields.push_back("tensor_split");
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}
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|
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@ -1,8 +1,5 @@
|
||||
import Foundation
|
||||
|
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// To use this in your own project, add llama.cpp as a swift package dependency
|
||||
// and uncomment this import line.
|
||||
// import llama
|
||||
import llama
|
||||
|
||||
enum LlamaError: Error {
|
||||
case couldNotInitializeContext
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
//
|
||||
// Use this file to import your target's public headers that you would like to expose to Swift.
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||||
//
|
||||
|
||||
#import "llama.h"
|
||||
@ -7,52 +7,31 @@
|
||||
objects = {
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||||
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549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
|
||||
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
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@ -69,30 +49,10 @@
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@ -157,7 +108,6 @@
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8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
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@ -198,6 +148,7 @@
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@ -244,9 +195,7 @@
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8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
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8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
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@ -258,18 +207,12 @@
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buildActionMask = 2147483647;
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8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
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542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
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8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
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8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
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@ -399,11 +342,9 @@
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ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
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ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
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DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
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@ -420,11 +361,12 @@
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MARKETING_VERSION = 1.0;
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PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
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@ -432,11 +374,9 @@
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DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
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DEVELOPMENT_TEAM = STLSG3FG8Q;
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GENERATE_INFOPLIST_FILE = YES;
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@ -453,10 +393,11 @@
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MARKETING_VERSION = 1.0;
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PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
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SWIFT_VERSION = 5.0;
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@ -482,6 +423,13 @@
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DF810E122B4A5BA200301144 /* llama */ = {
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productName = llama;
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/* End XCSwiftPackageProductDependency section */
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rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
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@ -1,11 +0,0 @@
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{
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"colors" : [
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{
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"idiom" : "universal"
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}
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],
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"info" : {
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"author" : "xcode",
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"version" : 1
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}
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@ -1,6 +0,0 @@
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{
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"info" : {
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||||
"author" : "xcode",
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||||
"version" : 1
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}
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}
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||||
@ -447,8 +447,14 @@ struct llama_client_slot
|
||||
}
|
||||
|
||||
bool has_budget(gpt_params &global_params) {
|
||||
if (params.n_predict == -1 && global_params.n_predict == -1)
|
||||
{
|
||||
return true; // limitless
|
||||
}
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||||
|
||||
n_remaining = -1;
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||||
if(params.n_predict != -1)
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||||
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||||
if (params.n_predict != -1)
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||||
{
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||||
n_remaining = params.n_predict - n_decoded;
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||||
}
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||||
@ -456,7 +462,8 @@ struct llama_client_slot
|
||||
{
|
||||
n_remaining = global_params.n_predict - n_decoded;
|
||||
}
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||||
return n_remaining > 0 || n_remaining == -1; // no budget || limitless
|
||||
|
||||
return n_remaining > 0; // no budget
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||||
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||||
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||||
bool available() const {
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||||
@ -1102,7 +1109,7 @@ struct llama_server_context
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||||
}
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||||
|
||||
// check the limits
|
||||
if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
|
||||
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
|
||||
{
|
||||
slot.stopped_limit = true;
|
||||
slot.has_next_token = false;
|
||||
@ -1703,7 +1710,6 @@ struct llama_server_context
|
||||
|
||||
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
|
||||
|
||||
slot.n_decoded += 1;
|
||||
slot.n_past += 1;
|
||||
}
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||||
|
||||
@ -1921,6 +1927,7 @@ struct llama_server_context
|
||||
|
||||
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
||||
|
||||
slot.n_decoded += 1;
|
||||
if (slot.n_decoded == 1)
|
||||
{
|
||||
slot.t_start_genereration = ggml_time_us();
|
||||
|
||||
37
ggml-cuda.cu
37
ggml-cuda.cu
@ -183,7 +183,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
|
||||
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
|
||||
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
|
||||
c = __builtin_amdgcn_sdot4(a, b, c, false);
|
||||
#elif defined(__gfx1100__)
|
||||
#elif defined(RDNA3)
|
||||
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
|
||||
#elif defined(__gfx1010__) || defined(__gfx900__)
|
||||
int tmp1;
|
||||
@ -1872,14 +1872,6 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
|
||||
v.y = x[ib + iqs + 1];
|
||||
}
|
||||
|
||||
static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
||||
const float * x = (const float *) vx;
|
||||
|
||||
// automatic half -> float type cast if dfloat == float
|
||||
v.x = x[ib + iqs + 0];
|
||||
v.y = x[ib + iqs + 1];
|
||||
}
|
||||
|
||||
static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
|
||||
const int ix = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
@ -1983,7 +1975,7 @@ static __global__ void k_get_rows_float(
|
||||
|
||||
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
|
||||
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
|
||||
const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
|
||||
const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
|
||||
|
||||
if (i >= k) {
|
||||
return;
|
||||
@ -2002,6 +1994,19 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
|
||||
y[iybs + iqs + y_offset] = v.y;
|
||||
}
|
||||
|
||||
template <typename src_t, typename dst_t>
|
||||
static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
|
||||
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
if (i >= k) {
|
||||
return;
|
||||
}
|
||||
|
||||
const src_t * x = (src_t *) vx;
|
||||
|
||||
y[i] = x[i];
|
||||
}
|
||||
|
||||
// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
|
||||
// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
|
||||
|
||||
@ -5609,7 +5614,7 @@ static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, con
|
||||
|
||||
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
|
||||
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
|
||||
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
||||
const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
|
||||
dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
||||
}
|
||||
|
||||
@ -5659,6 +5664,12 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu
|
||||
#endif
|
||||
}
|
||||
|
||||
template <typename src_t, typename dst_t>
|
||||
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
|
||||
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
||||
convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
||||
}
|
||||
|
||||
static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
@ -5682,7 +5693,7 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
|
||||
case GGML_TYPE_Q6_K:
|
||||
return dequantize_row_q6_K_cuda;
|
||||
case GGML_TYPE_F32:
|
||||
return dequantize_block_cuda<1, 1, convert_f32>;
|
||||
return convert_unary_cuda<float>;
|
||||
default:
|
||||
return nullptr;
|
||||
}
|
||||
@ -5711,7 +5722,7 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
||||
case GGML_TYPE_Q6_K:
|
||||
return dequantize_row_q6_K_cuda;
|
||||
case GGML_TYPE_F16:
|
||||
return dequantize_block_cuda<1, 1, convert_f16>;
|
||||
return convert_unary_cuda<half>;
|
||||
default:
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
16
llama.cpp
16
llama.cpp
@ -2180,7 +2180,11 @@ struct llama_model_loader {
|
||||
type_max = type;
|
||||
}
|
||||
|
||||
// LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
|
||||
// TODO: make runtime configurable
|
||||
#if 0
|
||||
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
|
||||
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
|
||||
#endif
|
||||
}
|
||||
|
||||
switch (type_max) {
|
||||
@ -4772,7 +4776,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@ -4896,7 +4899,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * pos;
|
||||
@ -4995,9 +4997,7 @@ struct llm_build_context {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
const int64_t n_rot = n_embd_head_k / 2;
|
||||
|
||||
@ -5209,9 +5209,7 @@ struct llm_build_context {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@ -5304,7 +5302,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@ -5400,7 +5397,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@ -5727,7 +5723,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * attn_norm_output;
|
||||
@ -5951,7 +5946,6 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * pos;
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user