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llama : minor indentation during tensor loading (#8304)
* llama : minor indentation during tensor loading ggml-ci * llama : use int for layer iterators [no ci]
This commit is contained in:
Georgi Gerganov
GitHub
parent
8e558309dc
commit
2cccbaa008
145
src/llama.cpp
145
src/llama.cpp
@ -5840,8 +5840,8 @@ static bool llm_load_tensors(
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model.main_gpu = main_gpu;
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model.n_gpu_layers = n_gpu_layers;
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const int64_t n_layer = hparams.n_layer;
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const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
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const int n_layer = hparams.n_layer;
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const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
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bool use_mmap_buffer = true;
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// there is very little benefit to offloading the input layer, so always keep it on the CPU
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@ -5851,7 +5851,7 @@ static bool llm_load_tensors(
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model.buft_layer.resize(n_layer);
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// assign cpu layers
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for (int64_t i = 0; i < i_gpu_start; ++i) {
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for (int i = 0; i < i_gpu_start; ++i) {
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model.buft_layer[i] = llama_default_buffer_type_cpu(true);
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}
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@ -5881,7 +5881,7 @@ static bool llm_load_tensors(
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// assign the repeating layers to the devices according to the splits
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int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
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for (int64_t i = i_gpu_start; i < n_layer; ++i) {
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for (int i = i_gpu_start; i < n_layer; ++i) {
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int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
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model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu);
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}
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@ -5901,7 +5901,7 @@ static bool llm_load_tensors(
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split_buft = llama_default_buffer_type_offload(model, main_gpu);
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}
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// assign the repeating layers
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for (int64_t i = i_gpu_start; i < n_layer; ++i) {
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for (int i = i_gpu_start; i < n_layer; ++i) {
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model.buft_layer[i] = {
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split_buft,
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llama_default_buffer_type_offload(model, main_gpu)
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@ -5924,7 +5924,7 @@ static bool llm_load_tensors(
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buft_layer_count[model.buft_input.buft_matrix]++;
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buft_layer_count[model.buft_output.buft]++;
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buft_layer_count[model.buft_output.buft_matrix]++;
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for (int64_t i = 0; i < n_layer; ++i) {
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for (int i = 0; i < n_layer; ++i) {
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buft_layer_count[model.buft_layer[i].buft]++;
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buft_layer_count[model.buft_layer[i].buft_matrix]++;
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}
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@ -5954,15 +5954,21 @@ static bool llm_load_tensors(
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// create tensors for the weights
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{
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// note: cast to int64_t since we will use these for the tensor dimensions
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const int64_t n_head = hparams.n_head();
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const int64_t n_head_kv = hparams.n_head_kv();
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_embd_head = hparams.n_head() > 0 ? n_embd / hparams.n_head() : 0;
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const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
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const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
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const int64_t n_embd_head_k = hparams.n_embd_head_k;
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const int64_t n_embd_head_v = hparams.n_embd_head_v;
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const int64_t n_ff = hparams.n_ff();
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const int64_t n_embd_gqa = n_embd_v_gqa;
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const int64_t n_vocab = hparams.n_vocab;
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const int64_t n_vocab_type = hparams.n_vocab_type;
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const int64_t n_ff = hparams.n_ff();
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const int64_t n_expert = hparams.n_expert;
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const int64_t n_expert_used = hparams.n_expert_used;
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const int64_t n_ctx_train = hparams.n_ctx_train;
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if (n_expert > 0 && hparams.n_expert_used == 0) {
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throw std::runtime_error("model has expert layers but no expert layers are used");
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@ -5971,6 +5977,7 @@ static bool llm_load_tensors(
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ggml_context * ctx_input = ctx_map.at(model.buft_input.buft);
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ggml_context * ctx_output = ctx_map.at(model.buft_output.buft);
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ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
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auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
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auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
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@ -5988,6 +5995,7 @@ static bool llm_load_tensors(
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
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@ -6070,6 +6078,7 @@ static bool llm_load_tensors(
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
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@ -6094,8 +6103,8 @@ static bool llm_load_tensors(
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
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layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
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if (layer.ffn_gate_exps) {
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layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
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layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
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@ -6225,7 +6234,7 @@ static bool llm_load_tensors(
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case LLM_ARCH_STARCODER:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
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// output
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{
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@ -6269,8 +6278,9 @@ static bool llm_load_tensors(
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
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if (model.arch == LLM_ARCH_BERT) {
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
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}
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model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
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@ -6306,7 +6316,6 @@ static bool llm_load_tensors(
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if (model.arch == LLM_ARCH_BERT) {
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layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
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} else {
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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@ -6320,6 +6329,7 @@ static bool llm_load_tensors(
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings
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model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings
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model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
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model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
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@ -6404,7 +6414,7 @@ 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_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// output
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{
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@ -6482,8 +6492,8 @@ static bool llm_load_tensors(
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// optional q and k layernorms, present in StableLM 2 12B
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head()}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv()}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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@ -6594,17 +6604,19 @@ static bool llm_load_tensors(
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layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
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GGML_ASSERT(hparams.n_expert > 0);
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GGML_ASSERT(hparams.n_expert_used > 0);
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GGML_ASSERT(n_expert > 0);
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GGML_ASSERT(n_expert_used > 0);
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// MoE branch
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auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used;
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const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
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layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
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layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
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layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
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// Shared expert branch
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auto n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
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const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
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layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
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layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp});
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layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd});
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@ -6658,6 +6670,8 @@ static bool llm_load_tensors(
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} break;
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case LLM_ARCH_PHI3:
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{
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const int64_t n_embd_head = n_embd / n_head;
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
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// output
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@ -6717,7 +6731,7 @@ static bool llm_load_tensors(
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case LLM_ARCH_GPT2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
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// output
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{
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@ -6854,12 +6868,7 @@ static bool llm_load_tensors(
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
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const int64_t n_ff = hparams.n_ff();
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const int64_t n_embd_head_k = hparams.n_embd_head_k;
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const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
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const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
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for (uint32_t i = 0; i < n_layer; ++i) {
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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@ -6867,10 +6876,10 @@ static bool llm_load_tensors(
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head()});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head(), n_embd});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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@ -6886,12 +6895,7 @@ static bool llm_load_tensors(
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
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const int64_t n_ff = hparams.n_ff();
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const int64_t n_embd_head_k = hparams.n_embd_head_k;
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const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
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const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
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for (uint32_t i = 0; i < n_layer; ++i) {
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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@ -6899,10 +6903,10 @@ static bool llm_load_tensors(
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head()});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
||||
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head(), n_embd});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
||||
layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||
@ -6966,6 +6970,7 @@ static bool llm_load_tensors(
|
||||
const int64_t d_inner = hparams.ssm_d_inner;
|
||||
const int64_t d_state = hparams.ssm_d_state;
|
||||
const int64_t dt_rank = hparams.ssm_dt_rank;
|
||||
|
||||
// only an expansion factor of 2 is supported for now
|
||||
GGML_ASSERT(2 * n_embd == d_inner);
|
||||
|
||||
@ -7016,15 +7021,20 @@ static bool llm_load_tensors(
|
||||
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
||||
|
||||
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
||||
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
@ -7051,8 +7061,8 @@ static bool llm_load_tensors(
|
||||
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
||||
|
||||
if (n_layer >= 64){
|
||||
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head()});
|
||||
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv()});
|
||||
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
|
||||
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
|
||||
}
|
||||
|
||||
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
||||
@ -7088,7 +7098,6 @@ static bool llm_load_tensors(
|
||||
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
@ -7108,8 +7117,6 @@ static bool llm_load_tensors(
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
const int64_t n_head = hparams.n_head(i);
|
||||
const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head;
|
||||
const int64_t n_embd_head = hparams.n_embd_head_k;
|
||||
|
||||
const int64_t n_ff = hparams.n_ff(i);
|
||||
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
@ -7119,10 +7126,10 @@ static bool llm_load_tensors(
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
||||
|
||||
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head});
|
||||
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head});
|
||||
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head, n_embd});
|
||||
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k});
|
||||
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
|
||||
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd});
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
@ -7133,6 +7140,7 @@ static bool llm_load_tensors(
|
||||
case LLM_ARCH_GPTNEOX:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
// output
|
||||
{
|
||||
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
||||
@ -7173,6 +7181,7 @@ static bool llm_load_tensors(
|
||||
{
|
||||
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (model.output == NULL) {
|
||||
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
||||
@ -7207,13 +7216,16 @@ static bool llm_load_tensors(
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
{
|
||||
bool is_lite = (hparams.n_layer == 27);
|
||||
const bool is_lite = (hparams.n_layer == 27);
|
||||
|
||||
const uint32_t n_embd_head_qk_rope = hparams.n_rot;
|
||||
const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
|
||||
const uint32_t q_lora_rank = hparams.n_lora_q;
|
||||
const uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
const uint32_t n_ff_exp = hparams.n_ff_exp;
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot;
|
||||
const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
|
||||
|
||||
const int64_t q_lora_rank = hparams.n_lora_q;
|
||||
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
const int64_t n_expert_shared = hparams.n_expert_shared;
|
||||
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
@ -7233,29 +7245,31 @@ static bool llm_load_tensors(
|
||||
if (!is_lite) {
|
||||
layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
|
||||
}
|
||||
|
||||
layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
|
||||
|
||||
if (!is_lite) {
|
||||
layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
|
||||
layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.n_head() * hparams.n_embd_head_k});
|
||||
layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k});
|
||||
} else {
|
||||
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
}
|
||||
layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope});
|
||||
layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, hparams.n_head() * (n_embd_head_qk_nope + hparams.n_embd_head_v)});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { hparams.n_head() * ( hparams.n_embd_head_v), n_embd});
|
||||
|
||||
layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
|
||||
layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd});
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||
|
||||
if ((uint32_t) i < hparams.n_layer_dense_lead) {
|
||||
if (i < hparams.n_layer_dense_lead) {
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
} else {
|
||||
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
||||
|
||||
GGML_ASSERT(hparams.n_expert > 0);
|
||||
GGML_ASSERT(hparams.n_expert_used > 0);
|
||||
GGML_ASSERT(n_expert > 0);
|
||||
GGML_ASSERT(n_expert_used > 0);
|
||||
|
||||
// MoE branch
|
||||
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
||||
@ -7263,9 +7277,9 @@ static bool llm_load_tensors(
|
||||
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
||||
|
||||
// Shared expert branch
|
||||
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared});
|
||||
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * hparams.n_expert_shared, n_embd});
|
||||
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared});
|
||||
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
|
||||
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd});
|
||||
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
|
||||
}
|
||||
}
|
||||
} break;
|
||||
@ -7309,6 +7323,8 @@ static bool llm_load_tensors(
|
||||
} break;
|
||||
case LLM_ARCH_T5:
|
||||
{
|
||||
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
||||
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
// output
|
||||
@ -7330,7 +7346,7 @@ static bool llm_load_tensors(
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
|
||||
layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
@ -7343,7 +7359,7 @@ static bool llm_load_tensors(
|
||||
layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd});
|
||||
layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
@ -7352,7 +7368,7 @@ static bool llm_load_tensors(
|
||||
|
||||
layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd});
|
||||
// this tensor seems to be unused in HF transformers implementation
|
||||
layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
@ -7375,6 +7391,7 @@ static bool llm_load_tensors(
|
||||
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user