#include "models.h"
void llama_model_rwkv7::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK, hparams.n_lora_decay);
ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK, hparams.n_lora_iclr);
ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK, hparams.n_lora_gate, false);
ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count, false);
switch (hparams.n_layer) {
case 12:
switch (hparams.n_embd) {
case 768: type = LLM_TYPE_190M; break;
default: type = LLM_TYPE_UNKNOWN;
} break;
case 24:
switch (hparams.n_embd) {
case 1024: type = LLM_TYPE_450M; break;
case 2048: type = LLM_TYPE_1_5B; break;
default: type = LLM_TYPE_UNKNOWN;
} break;
case 28:
switch (hparams.n_embd) {
case 1536: type = LLM_TYPE_1_5B; break;
case 3584: type = LLM_TYPE_7B; break;
default: type = LLM_TYPE_UNKNOWN;
} break;
case 32:
switch (hparams.n_embd) {
case 2560: type = LLM_TYPE_2_9B; break;
case 4096: type = LLM_TYPE_7B; break;
default: type = LLM_TYPE_UNKNOWN;
} break;
case 61:
switch (hparams.n_embd) {
case 4096: type = LLM_TYPE_14B; break;
default: type = LLM_TYPE_UNKNOWN;
} break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_rwkv7::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// Block 0, LN0
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias", 0), {n_embd}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
const int n_lora_decay = hparams.n_lora_decay;
const int n_lora_iclr = hparams.n_lora_iclr;
const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
const int n_lora_gate = hparams.n_lora_gate;
const int attn_hidden_size = n_embd;
const int ffn_size = hparams.n_ff_arr[0];
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, 0);
layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
if (i == 0) {
// actually not used
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
} else {
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
}
layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, 0);
layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, 0);
layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_rwkv7::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_rwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_build_rwkv7_base(model, params) {
GGML_ASSERT(hparams.token_shift_count == 2);
ggml_tensor * cur;
ggml_tensor * inpL;
ggml_tensor * v_first = nullptr;
inpL = build_inp_embd(model.tok_embd);
inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, 0);
auto * rs_inp = build_rs_inp();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
const auto n_seqs = ubatch.n_seqs;
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const llama_layer * layer = &model.layers[il];
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
ggml_tensor * att_shift =
ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1],
token_shift->nb[2], n_embd * ggml_element_size(token_shift));
ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
cb(att_norm, "attn_norm", il);
ggml_tensor * x_prev = ggml_concat(
ctx0, att_shift,
ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0), 1);
cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
cb(ffn_norm, "ffn_norm", il);
x_prev = ggml_concat(
ctx0, ffn_shift,
ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0), 1);
token_shift = ggml_concat(ctx0,
ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2],
(n_seq_tokens - 1) * n_embd * ggml_element_size(att_norm)),
ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2],
(n_seq_tokens - 1) * n_embd * ggml_element_size(ffn_norm)),
1);
ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
x_prev = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens);
if (il == n_layer - 1 && inp_out_ids) {
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
x_prev = ggml_get_rows(ctx0, x_prev, inp_out_ids);
}
cur = build_rwkv7_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV7);
cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
