model: move load_hparams and load_tensors to per-model definition (#22004)
* git-friendly migration * add build_graph * nits * exclude old code from build * wip * add llm_arch_model_i * prepare downstream functions * nits * nits * wip * wip * add back create_tensor_qkv * fix files missing include * enforce one llm_build per arch * cmake: use glob * missing model params * nits * wip * wip (2) * wip (3) * test-llama-archs is happy * improve switch case * move more stuff into llm_arch_model_i * fix downstream code * nits * nits (2) * fix order * llama_model_base * LLAMA_LOAD_LOCALS * small fix * fix build errors * auto * rm migration script and ifdef
This commit is contained in:
Xuan-Son Nguyen
+105
-4
@@ -1,8 +1,109 @@
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#include "models.h"
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#include "llama-memory-recurrent.h"
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llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_params & params) :
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void llama_model_plamo2::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// Load Mamba SSM parameters
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ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
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ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
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ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
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ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
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ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
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for (uint32_t i = 0; i < hparams.n_layer; ++i) {
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hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
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}
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switch (hparams.n_layer) {
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case 16: type = LLM_TYPE_1B; break;
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case 32:
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if (hparams.n_embd == 2048) {
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type = LLM_TYPE_2B;
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} else if (hparams.n_embd == 4096) {
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type = LLM_TYPE_8B;
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}
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break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_plamo2::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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// mamba parameters
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const uint32_t d_conv = hparams.ssm_d_conv;
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const uint32_t d_state = hparams.ssm_d_state;
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const uint32_t num_heads = hparams.ssm_dt_rank;
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const uint32_t intermediate_size = hparams.ssm_d_inner;
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const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
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// attention parameters
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const uint32_t qk_dim = hparams.n_embd_head_k();
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const uint32_t v_dim = hparams.n_embd_head_v();
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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bool is_mamba_layer = hparams.is_recurrent(i);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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if (is_mamba_layer) {
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2 * intermediate_size}, 0);
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, intermediate_size}, 0);
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layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {intermediate_size, dt_dim + 2*d_state}, 0);
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layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_dim, num_heads}, 0);
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layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {num_heads}, 0);
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {num_heads}, 0);
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layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {num_heads}, 0);
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {intermediate_size, n_embd}, 0);
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layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, i), {dt_dim}, 0);
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layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
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layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
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} else {
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const int64_t num_attention_heads = hparams.n_head(i);
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const int64_t q_num_heads = num_attention_heads;
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const int64_t num_key_value_heads = hparams.n_head_kv(i);
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const int64_t k_num_heads = num_key_value_heads;
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const int64_t v_num_heads = num_key_value_heads;
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const int64_t q_proj_dim = q_num_heads * qk_dim;
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const int64_t k_proj_dim = k_num_heads * qk_dim;
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const int64_t v_proj_dim = v_num_heads * v_dim;
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
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}
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// All layers have post-attention norm, FFN norm, and FFN tensors
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);
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layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_plamo2::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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llama_model_plamo2::graph::graph(const llama_model & model, const llm_graph_params & params) :
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llm_build_mamba_base(params) {
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ggml_tensor * cur;
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ggml_tensor * inpL;
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@@ -95,7 +196,7 @@ llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_pa
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ggml_build_forward_expand(gf, cur);
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}
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ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
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ggml_tensor * llama_model_plamo2::graph::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
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ggml_tensor * inp_pos,
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ggml_tensor * cur,
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const llama_model & model,
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@@ -150,7 +251,7 @@ ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv
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return cur;
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}
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ggml_tensor * llm_build_plamo2::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
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ggml_tensor * llama_model_plamo2::graph::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
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ggml_tensor * cur,
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const llama_model & model,
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const llama_ubatch & ubatch,
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