mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 11:24:35 +00:00
add patch tensor function
This commit is contained in:
ngxson
parent
67c5e14d06
commit
e9d7b6c05f
211
src/llama.cpp
211
src/llama.cpp
@ -2702,6 +2702,10 @@ struct llama_model {
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int64_t t_load_us = 0;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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int64_t t_start_us = 0;
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// used by lora, to save model's original tensors
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std::vector<struct ggml_tensor *> orig_tensors;
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std::vector<llama_layer> orig_layers;
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~llama_model() {
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~llama_model() {
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for (struct ggml_context * ctx : ctxs) {
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for (struct ggml_context * ctx : ctxs) {
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ggml_free(ctx);
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ggml_free(ctx);
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@ -13491,6 +13495,10 @@ static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
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return result;
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return result;
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}
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}
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// forward declaration
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static int32_t llama_lora_patch_tensors(struct llama_context & lctx, struct ggml_context * ctx_build);
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static int32_t llama_lora_restore_tensors(struct llama_context & lctx);
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static struct ggml_cgraph * llama_build_graph(
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static struct ggml_cgraph * llama_build_graph(
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llama_context & lctx,
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llama_context & lctx,
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const llama_batch & batch,
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const llama_batch & batch,
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@ -13534,6 +13542,11 @@ static struct ggml_cgraph * llama_build_graph(
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llm.init();
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llm.init();
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if (!lctx.lora_adapters.empty()) {
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llama_lora_restore_tensors(lctx);
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llama_lora_patch_tensors(lctx, llm.ctx0);
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}
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switch (model.arch) {
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switch (model.arch) {
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case LLM_ARCH_LLAMA:
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case LLM_ARCH_LLAMA:
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{
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{
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@ -18304,10 +18317,12 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
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printf("n_tensors_per_layer %d\n", n_tensors_per_layer);
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printf("n_tensors_per_layer %d\n", n_tensors_per_layer);
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// count layer buffer types
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// count layer buffer types
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std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
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std::map<ggml_backend_buffer_type_t, int> buft_tensor_count;
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for (int64_t i = 0; i < model.hparams.n_layer; i++) {
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for (int64_t i = 0; i < model.hparams.n_layer; i++) {
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buft_layer_count[model.buft_layer[i].buft]++;
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buft_tensor_count[model.buft_layer[i].buft] += n_tensors_per_layer;
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}
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}
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buft_tensor_count[model.buft_input.buft] += n_inp_tensors;
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buft_tensor_count[model.buft_output.buft] += n_out_tensors;
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// allocate contexts
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// allocate contexts
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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@ -18320,13 +18335,11 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
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};
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};
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return ggml_init(params);
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return ggml_init(params);
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};
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};
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for (auto & it : buft_layer_count) {
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for (auto & it : buft_tensor_count) {
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int n_layers = it.second;
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int n_tensors = it.second;
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printf("buf %p layers %d\n", it.first, it.second);
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// LLAMA_LOG_INFO("buf %p layers %d\n", it.first, it.second);
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ctx_map[it.first] = new_ggml_ctx(2*n_layers*n_tensors_per_layer);
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ctx_map[it.first] = new_ggml_ctx(2*n_tensors); // for a+b tensors
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}
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}
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//ctx_map[model.buft_input.buft] = new_ggml_ctx(2*n_inp_tensors);
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//ctx_map[model.buft_output.buft] = new_ggml_ctx(2*n_out_tensors);
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}
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}
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// bundle lora_a and lora_b into pairs
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// bundle lora_a and lora_b into pairs
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@ -18356,22 +18369,29 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
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// add tensors
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// add tensors
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for (auto & it : ab_map) {
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for (auto & it : ab_map) {
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std::string name = it.first;
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std::string name = it.first;
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const char * cname = name.c_str();
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lora_weight & w = it.second;
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lora_weight & w = it.second;
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GGML_ASSERT(w.a != nullptr);
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GGML_ASSERT(w.a != nullptr);
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GGML_ASSERT(w.b != nullptr);
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GGML_ASSERT(w.b != nullptr);
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int il = -1;
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int il = -1;
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sscanf(name.c_str(), "blk.%d.", &il);
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sscanf(cname, "blk.%d.", &il);
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struct ggml_context * dev_ctx; // device ctx
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if (il >= 0) {
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if (il >= 0) {
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printf("%s %p %p\n", name.c_str(), w.a, w.b);
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dev_ctx = ctx_map.at(model.buft_layer[il].buft);
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struct ggml_context * dev_ctx = ctx_map.at(model.buft_layer[il].buft);
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} else if (strstr(cname, "tok") == 0) {
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struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
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dev_ctx = ctx_map.at(model.buft_input.buft);
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struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
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} else if (strstr(cname, "output") == 0) {
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ggml_set_name(tensor_a, w.a->name);
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dev_ctx = ctx_map.at(model.buft_output.buft);
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ggml_set_name(tensor_b, w.b->name);
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adapter.ab_map[name] = lora_weight(tensor_a, tensor_b);
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} else {
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} else {
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// TODO: process output & token_embd tensors
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LLAMA_LOG_WARN("%s: discard tensor '%s'\n", __func__, cname);
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continue;
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}
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}
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// LLAMA_LOG_INFO("%s %p %p\n", cname, w.a, w.b);
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struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
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struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
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ggml_set_name(tensor_a, w.a->name);
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ggml_set_name(tensor_b, w.b->name);
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adapter.ab_map[name] = lora_weight(tensor_a, tensor_b);
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}
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}
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// allocate tensors / buffers and zero
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// allocate tensors / buffers and zero
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@ -18402,8 +18422,9 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
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if (read_buf.size() < size) {
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if (read_buf.size() < size) {
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read_buf.resize(size);
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read_buf.resize(size);
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}
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}
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gguf_file.seek(offs, SEEK_SET);
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gguf_file.read_raw(read_buf.data(), size);
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gguf_file.read_raw(read_buf.data(), size);
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printf("%s: %s size=%ld\n", __func__, orig->name, size);
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// LLAMA_LOG_INFO("%s: %s size=%ld\n", __func__, orig->name, size);
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return ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
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return ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
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};
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};
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for (auto & it : adapter.ab_map) {
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for (auto & it : adapter.ab_map) {
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@ -18414,11 +18435,165 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
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}
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}
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}
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}
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LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2);
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// free ctx for reading gguf
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// free ctx for reading gguf
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ggml_free(ctx);
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ggml_free(ctx);
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return 0;
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return 0;
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}
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}
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static int32_t llama_lora_restore_tensors(struct llama_context & lctx) {
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// TODO @ngxson : not ideal, but "const" is discarded to make it work
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struct llama_model & model = const_cast<struct llama_model &>(lctx.model);
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if (!model.orig_tensors.empty()) {
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size_t i = 0;
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model.tok_embd = model.orig_tensors[i++];
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model.type_embd = model.orig_tensors[i++];
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model.pos_embd = model.orig_tensors[i++];
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model.tok_norm = model.orig_tensors[i++];
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model.tok_norm_b = model.orig_tensors[i++];
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model.output_norm = model.orig_tensors[i++];
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model.output_norm_b = model.orig_tensors[i++];
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model.output = model.orig_tensors[i++];
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model.output_b = model.orig_tensors[i++];
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model.output_norm_enc = model.orig_tensors[i++];
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for (size_t il = 0; il < model.orig_layers.size(); il++) {
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model.layers[il] = model.orig_layers[il]; // copy
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}
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}
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}
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static int32_t llama_lora_patch_tensors(struct llama_context & lctx, struct ggml_context * ctx_build) {
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GGML_ASSERT(!lctx.lora_adapters.empty());
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// TODO @ngxson : not ideal, but "const" is discarded to make it work
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struct llama_model & model = const_cast<struct llama_model &>(lctx.model);
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// save all original tensors
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if (model.orig_tensors.empty()) {
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model.orig_tensors.push_back(model.tok_embd);
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model.orig_tensors.push_back(model.type_embd);
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model.orig_tensors.push_back(model.pos_embd);
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model.orig_tensors.push_back(model.tok_norm);
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model.orig_tensors.push_back(model.tok_norm_b);
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model.orig_tensors.push_back(model.output_norm);
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model.orig_tensors.push_back(model.output_norm_b);
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model.orig_tensors.push_back(model.output);
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model.orig_tensors.push_back(model.output_b);
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model.orig_tensors.push_back(model.output_norm_enc);
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model.orig_layers.reserve(model.layers.size());
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for (llama_layer layer : model.layers) {
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model.orig_layers.push_back(layer); // copy
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}
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}
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// patch tensors
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auto patch_tensor = [&](struct llama_lora_adapter * adapter, struct ggml_tensor ** tensor) {
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if (*tensor == nullptr) {
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return;
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}
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std::string name = ggml_get_name(*tensor);
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if (adapter->ab_map.find(name) != adapter->ab_map.end()) {
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auto lora_w = adapter->ab_map[name];
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struct ggml_tensor * cur = ggml_mul_mat(ctx_build, lora_w.a, lora_w.b);
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cur = ggml_add(ctx_build, cur, *tensor);
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// TODO: scale
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ggml_format_name(cur, "%s.merged", name.c_str());
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// LLAMA_LOG_INFO("LORA %s\n", cur->name);
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tensor = &cur;
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}
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};
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for (auto adapter : lctx.lora_adapters) {
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patch_tensor(adapter, &model.tok_embd);
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patch_tensor(adapter, &model.type_embd);
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patch_tensor(adapter, &model.pos_embd);
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patch_tensor(adapter, &model.tok_norm);
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patch_tensor(adapter, &model.tok_norm_b);
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patch_tensor(adapter, &model.output_norm);
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patch_tensor(adapter, &model.output_norm_b);
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patch_tensor(adapter, &model.output);
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patch_tensor(adapter, &model.output_b);
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patch_tensor(adapter, &model.output_norm_enc);
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for (llama_layer & layer : model.layers) {
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patch_tensor(adapter, &layer.attn_norm);
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patch_tensor(adapter, &layer.attn_norm_b);
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patch_tensor(adapter, &layer.attn_norm_2);
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patch_tensor(adapter, &layer.attn_norm_2_b);
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patch_tensor(adapter, &layer.attn_q_norm);
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patch_tensor(adapter, &layer.attn_q_norm_b);
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patch_tensor(adapter, &layer.attn_k_norm);
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patch_tensor(adapter, &layer.attn_k_norm_b);
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patch_tensor(adapter, &layer.attn_out_norm);
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patch_tensor(adapter, &layer.attn_out_norm_b);
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patch_tensor(adapter, &layer.attn_q_a_norm);
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patch_tensor(adapter, &layer.attn_kv_a_norm);
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patch_tensor(adapter, &layer.attn_sub_norm);
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patch_tensor(adapter, &layer.attn_post_norm);
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patch_tensor(adapter, &layer.ffn_sub_norm);
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patch_tensor(adapter, &layer.attn_norm_cross);
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patch_tensor(adapter, &layer.attn_norm_enc);
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patch_tensor(adapter, &layer.wq);
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patch_tensor(adapter, &layer.wk);
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patch_tensor(adapter, &layer.wv);
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patch_tensor(adapter, &layer.wo);
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patch_tensor(adapter, &layer.wqkv);
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patch_tensor(adapter, &layer.wq_a);
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patch_tensor(adapter, &layer.wq_b);
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patch_tensor(adapter, &layer.wkv_a_mqa);
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patch_tensor(adapter, &layer.wkv_b);
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patch_tensor(adapter, &layer.wq_cross);
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patch_tensor(adapter, &layer.wk_cross);
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patch_tensor(adapter, &layer.wv_cross);
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patch_tensor(adapter, &layer.wo_cross);
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patch_tensor(adapter, &layer.wq_enc);
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patch_tensor(adapter, &layer.wk_enc);
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patch_tensor(adapter, &layer.wv_enc);
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patch_tensor(adapter, &layer.wo_enc);
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patch_tensor(adapter, &layer.bq);
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patch_tensor(adapter, &layer.bk);
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patch_tensor(adapter, &layer.bv);
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patch_tensor(adapter, &layer.bo);
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patch_tensor(adapter, &layer.bqkv);
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patch_tensor(adapter, &layer.attn_rel_b);
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patch_tensor(adapter, &layer.attn_rel_b_enc);
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patch_tensor(adapter, &layer.attn_rel_b_cross);
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patch_tensor(adapter, &layer.ffn_norm);
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patch_tensor(adapter, &layer.ffn_norm_b);
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patch_tensor(adapter, &layer.ffn_post_norm);
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patch_tensor(adapter, &layer.layer_out_norm);
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patch_tensor(adapter, &layer.layer_out_norm_b);
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patch_tensor(adapter, &layer.ffn_norm_exps);
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patch_tensor(adapter, &layer.ffn_norm_enc);
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patch_tensor(adapter, &layer.ffn_gate);
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patch_tensor(adapter, &layer.ffn_down);
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patch_tensor(adapter, &layer.ffn_up);
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patch_tensor(adapter, &layer.ffn_gate_enc);
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patch_tensor(adapter, &layer.ffn_down_enc);
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patch_tensor(adapter, &layer.ffn_up_enc);
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patch_tensor(adapter, &layer.ffn_gate_inp);
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patch_tensor(adapter, &layer.ffn_gate_exps);
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patch_tensor(adapter, &layer.ffn_down_exps);
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patch_tensor(adapter, &layer.ffn_up_exps );
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patch_tensor(adapter, &layer.ffn_gate_inp_shexp);
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patch_tensor(adapter, &layer.ffn_gate_shexp);
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patch_tensor(adapter, &layer.ffn_down_shexp);
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patch_tensor(adapter, &layer.ffn_up_shexp);
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patch_tensor(adapter, &layer.ffn_gate_b);
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patch_tensor(adapter, &layer.ffn_down_b);
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patch_tensor(adapter, &layer.ffn_up_b);
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patch_tensor(adapter, &layer.ffn_act);
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}
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}
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return 0;
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}
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//
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//
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// interface implementation
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// interface implementation
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//
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//
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