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whisper.cpp/examples/talk-llama/models/plamo2.cpp

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#include "models.h"
#include "llama-memory-recurrent.h"
void llama_model_plamo2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
// Load Mamba SSM parameters
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
}
switch (hparams.n_layer) {
case 16: type = LLM_TYPE_1B; break;
case 32:
if (hparams.n_embd == 2048) {
type = LLM_TYPE_2B;
} else if (hparams.n_embd == 4096) {
type = LLM_TYPE_8B;
}
break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_plamo2::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
// mamba parameters
const uint32_t d_conv = hparams.ssm_d_conv;
const uint32_t d_state = hparams.ssm_d_state;
const uint32_t num_heads = hparams.ssm_dt_rank;
const uint32_t intermediate_size = hparams.ssm_d_inner;
const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
// attention parameters
const uint32_t qk_dim = hparams.n_embd_head_k();
const uint32_t v_dim = hparams.n_embd_head_v();
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
bool is_mamba_layer = hparams.is_recurrent(i);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
if (is_mamba_layer) {
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2 * intermediate_size}, 0);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, intermediate_size}, 0);
layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {intermediate_size, dt_dim + 2*d_state}, 0);
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_dim, num_heads}, 0);
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {num_heads}, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {num_heads}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {num_heads}, 0);
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {intermediate_size, n_embd}, 0);
layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, i), {dt_dim}, 0);
layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
} else {
const int64_t num_attention_heads = hparams.n_head(i);
const int64_t q_num_heads = num_attention_heads;
const int64_t num_key_value_heads = hparams.n_head_kv(i);
const int64_t k_num_heads = num_key_value_heads;
const int64_t v_num_heads = num_key_value_heads;
const int64_t q_proj_dim = q_num_heads * qk_dim;
const int64_t k_proj_dim = k_num_heads * qk_dim;
const int64_t v_proj_dim = v_num_heads * v_dim;
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
}
// All layers have post-attention norm, FFN norm, and FFN tensors
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_plamo2::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_plamo2::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_build_mamba_base(params) {
ggml_tensor * cur;
ggml_tensor * inpL;
// {n_embd, n_tokens}
inpL = build_inp_embd(model.tok_embd);
cb(inpL, "embedding_output", -1);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_hybrid = build_inp_mem_hybrid();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * residual = inpL;
// ggml_graph_add_node(gf, model.layers[il].attn_norm);
// cb(model.layers[il].attn_norm, "attn_norm", il);
// pre_mixer_norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
// check if this layer is Mamba or Attention
const bool is_mamba_layer = hparams.is_recurrent(il);
if (is_mamba_layer) {
// PLaMo-2 Mamba layer
cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
} else {
// PLaMo-2 Attention layer
cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, cur, model, il);
}
// post_mixer_norm
cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
// residual connection
cur = ggml_add(ctx0, cur, residual);
cb(cur, "attn_residual", il);
residual = cur;
// pre-ffn norm
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_pre_norm", il);
// feed-forward network
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
cb(cur, "ffn_out", il);
// post ffn norm
cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_post_norm", il);
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
}
// residual connection
cur = ggml_add(ctx0, cur, residual);
cb(cur, "ffn_residual", il);
// input for next layer
inpL = cur;
}
cur = inpL;
// final norm
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
// Explicitly mark as output tensor to ensure proper backend assignment
ggml_set_output(cur);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
ggml_tensor * llama_model_plamo2::graph::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
ggml_tensor * inp_pos,
ggml_tensor * cur,
const llama_model & model,
int il) {
// self-attention
{
// PLaMo-2 uses combined QKV tensor
ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
cb(qkv, "wqkv", il);
// split QKV tensor into Q, K, V
const int64_t n_embd_head_q = hparams.n_embd_head_k();
const int64_t n_embd_head_k = hparams.n_embd_head_k();
const int64_t n_embd_head_v = hparams.n_embd_head_v();
int32_t n_head = hparams.n_head(il);
int32_t n_head_kv = hparams.n_head_kv(il);
const int64_t q_offset = 0;
const int64_t k_offset = n_embd_head_q * n_head;
const int64_t v_offset = k_offset + n_embd_head_k * n_head_kv;
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head, n_tokens, n_embd_head_q * sizeof(float),
qkv->nb[1], q_offset * ggml_element_size(qkv));
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv, n_tokens, n_embd_head_k * sizeof(float),
qkv->nb[1], k_offset * ggml_element_size(qkv));
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv, n_tokens, n_embd_head_v * sizeof(float),
qkv->nb[1], v_offset * ggml_element_size(qkv));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cur = build_attn(inp,
model.layers[il].wo, NULL, model.layers[il].wo_s,
Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f / sqrtf(float(n_embd_head_v)), il);
}
cb(cur, "attn_out", il);
return cur;
}
ggml_tensor * llama_model_plamo2::graph::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
ggml_tensor * cur,
const llama_model & model,
const llama_ubatch & ubatch,
int il) {
const auto * mctx_cur = inp->mctx;
const auto kv_head = mctx_cur->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
const int64_t d_state = hparams.ssm_d_state;
const int64_t n_heads = hparams.ssm_dt_rank;
const int64_t head_dim = d_inner / n_heads;
const int64_t n_group = hparams.ssm_n_group;
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs());
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
GGML_ASSERT(d_inner % n_head == 0);
GGML_ASSERT(n_group == 0);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2 * n_group * d_state, n_seqs);
// {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
// in_proj: {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
ggml_tensor * zx = build_lora_mm(model.layers[il].ssm_in, cur);
cb(zx, "mamba_in_proj", il);
// {8192, 5, 1, 1} -> {8192, 1, 5, 1}
zx = ggml_permute(ctx0, zx, 0, 2, 1, 3);
zx = ggml_cont_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
cb(zx, "mamba_in_proj_out", il);
// split into z and x
// => {head_dim * n_heads, n_seq_tokens, n_seqs}
ggml_tensor * x = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3],
head_dim * ggml_element_size(zx));
x = ggml_cont_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
// x = ggml_permute(ctx0, x, 0, 2, 1, 3);
cb(x, "mamba_x_split", il);
ggml_tensor * z =
ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], 0);
cb(z, "mamba_z_split", il);
// conv1d
{
// => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
cb(conv_x, "mamba_conv1d_input", il);
// copy last (d_conv - 1) columns back into the state cache
ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2],
n_seq_tokens * (conv_x->nb[0]));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv,
ggml_view_1d(ctx0, conv_states_all,
(d_conv - 1) * (d_inner + 2 * n_group * d_state) * (n_seqs),
kv_head * (d_conv - 1) * (d_inner + 2 * n_group * d_state) *
ggml_element_size(conv_states_all))));
cb(conv_states_all, "mamba_conv1d_state", il);
// 1D convolution
x = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
cb(x, "mamba_conv1d", il);
x = ggml_silu(ctx0, x);
cb(x, "mamba_conv1d_silu", il);
}
// SSM
{
// bcdt_proj: {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
ggml_tensor * x_bcdt = build_lora_mm(model.layers[il].ssm_x, x);
cb(x_bcdt, "mamba_bcdt_proj", il);
// split into dt, B, C
const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
ggml_tensor * B = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], 0);
ggml_tensor * C = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2],
ggml_element_size(x_bcdt) * d_state);
ggml_tensor * dt = ggml_view_3d(ctx0, x_bcdt, dt_dim, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2],
ggml_element_size(x_bcdt) * (2 * d_state));
cb(B, "mamba_B_raw", il);
cb(C, "mamba_C_raw", il);
cb(dt, "mamba_dt_raw", il);
// Apply RMS norm to dt, B, C (PLaMo-2 specific)
B = build_norm(B, model.layers[il].ssm_b_norm, NULL, LLM_NORM_RMS, il);
C = build_norm(C, model.layers[il].ssm_c_norm, NULL, LLM_NORM_RMS, il);
dt = build_norm(dt, model.layers[il].ssm_dt_norm, NULL, LLM_NORM_RMS, il);
cb(B, "mamba_B_normed", il);
cb(C, "mamba_C_normed", il);
cb(dt, "mamba_dt_normed", il);
// dt_proj: {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
dt = build_lora_mm(model.layers[il].ssm_dt, dt);
dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
cb(dt, "mamba_dt_proj", il);
ggml_tensor * A = ggml_reshape_2d(ctx0, model.layers[il].ssm_a, 1, n_heads);
cb(A, "mamba_A", il);
x = ggml_view_4d(ctx0, x, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x),
head_dim * n_heads * ggml_element_size(x),
head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
B = ggml_view_4d(ctx0, B, d_state, 1, n_seq_tokens, n_seqs, d_state * B->nb[0], B->nb[1], B->nb[2], 0);
C = ggml_view_4d(ctx0, C, d_state, 1, n_seq_tokens, n_seqs, d_state * C->nb[0], C->nb[1], C->nb[2], 0);
// use the states and the indices provided by build_recurrent_state
// (this is necessary in order to properly use the states before they are overwritten,
// while avoiding to make unnecessary copies of the states)
auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_heads, mctx_cur->get_size());
// Custom operator to optimize the parallel associative scan
// as described in the Annex D of the Mamba paper.
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
};
ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
cb(y_ssm, "mamba_ssm_scan", il);
// store last states
ggml_build_forward_expand(
gf, ggml_cpy(
ctx0,
ggml_view_1d(ctx0, y_ssm, n_heads * head_dim * d_state * n_seqs,
n_heads * head_dim * n_seq_tokens * n_seqs * ggml_element_size(y_ssm)),
ggml_view_1d(ctx0, ssm_states_all, n_heads * head_dim * d_state * n_seqs,
kv_head * n_seqs * n_heads * head_dim * d_state * ggml_element_size(ssm_states_all))));
cb(ssm_states_all, "mamba_ssm_states", il);
ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_heads, n_seq_tokens, n_seqs,
head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x),
head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
cb(y, "mamba_y_view", il);
// Add D parameter and apply gating with z
// {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs}
ggml_tensor * D = ggml_reshape_2d(ctx0, model.layers[il].ssm_d, 1, n_heads);
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, D));
cb(y, "mamba_y_add_d", il);
y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
cb(y, "mamba_y_swiglu_z", il);
// out_proj: {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
y = ggml_view_3d(ctx0, y, head_dim * n_heads, n_seq_tokens, n_seqs, y->nb[2], y->nb[3], 0);
cur = build_lora_mm(model.layers[il].ssm_out, y);
cb(cur, "mamba_out_proj", il);
}
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
cb(cur, "mamba_out", il);
return cur;
}
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