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
+110
-1
@@ -1,6 +1,115 @@
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#include "models.h"
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llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) :
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void llama_model_falcon_h1::load_arch_hparams(llama_model_loader & ml) {
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// Common parameters
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// 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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std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
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switch (hparams.n_layer) {
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case 36:
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type = LLM_TYPE_0_5B; break;
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case 24:
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type = LLM_TYPE_1_5B; break;
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case 66:
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type = LLM_TYPE_1B; break;
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case 32:
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type = LLM_TYPE_3B; break;
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case 44:
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type = LLM_TYPE_7B; break;
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case 72:
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type = LLM_TYPE_34B; break;
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default:
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type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_falcon_h1::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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// Common
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const int64_t hidden_size = hparams.n_embd; // hidden_size
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// mamba2 Mixer SSM params
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const int64_t ssm_conv_kernel_size = hparams.ssm_d_conv; // ssm_conv_kernel_size
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const int64_t ssm_n_groups = hparams.ssm_n_group; // ssm_n_groups
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const int64_t ssm_state_size = hparams.ssm_d_state; // ssm_state_size
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const int64_t ssm_intermediate_size = hparams.ssm_d_inner; // TODO expand
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const int64_t ssm_num_heads = hparams.ssm_dt_rank; // ssm_num_heads
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const int64_t ssm_conv_dim = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
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const int64_t ssm_projection_size = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
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// attn params
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const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
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const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
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// ffn params
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const int64_t ffn_intermediate_size = hparams.n_ff(0);
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// embeddings
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, 0);
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// output
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, n_vocab}, TENSOR_NOT_REQUIRED);
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
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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"), {hidden_size, 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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/*SSM LAYERS*/
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// ssm in
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
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// ssm 1d conv
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
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layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, TENSOR_NOT_REQUIRED);
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// ssm_dt
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layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
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// no "weight" suffix for these
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
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layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
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// ssm_norm
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layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, TENSOR_NOT_REQUIRED);
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// out_proj
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
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/*ATTENTION LAYERS*/
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// attention layers (with optional bias)
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create_tensor_qkv(layer, i, hidden_size, n_embd_head_k * attn_num_attention_head, attn_num_key_value_head * n_embd_head_k, attn_num_key_value_head * n_embd_head_v, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * attn_num_attention_head, hidden_size}, 0);
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layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
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// feed forward (w/ optional biases)
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, i), {hidden_size}, 0);
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layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { ffn_intermediate_size, hidden_size}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
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layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
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layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_falcon_h1::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_falcon_h1::graph::graph(const llama_model & model, const llm_graph_params & params) :
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llm_build_mamba_base(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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