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llama.cpp fork for Ternary Bonsai 2 27B: MTP speculation, suffix cache reuse (SCR), q8 KV, two-slot fairness, fused DeltaNet kernels
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at main
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303#include "models.h"
#include "llama-memory-recurrent.h"
#include <algorithm>
llm_build_mamba_base::llm_build_mamba_base(const llm_graph_params & params) : llm_graph_context(params) {}
ggml_tensor * llm_build_mamba_base::build_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 auto & layer = model.layers[il];
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 dt_rank = hparams.ssm_dt_rank; const int64_t n_head = d_inner; const int64_t head_dim = 1; const int64_t n_seqs = ubatch.n_seqs; // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers) const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
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_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, 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);
// {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs} ggml_tensor * xz = build_lora_mm(layer.ssm_in, cur, layer.ssm_in_s); // split the above in two // => {d_inner, n_seq_tokens, n_seqs} ggml_tensor * x = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0); ggml_tensor * z = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], d_inner * ggml_element_size(xz));
// conv { // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs} ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
// 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) * (n_seqs), kv_head * (d_conv - 1) * (d_inner) *ggml_element_size(conv_states_all))));
// 1D convolution // The equivalent is to make a self-overlapping view of conv_x // over d_conv columns at each stride in the 3rd dimension, // then element-wise multiply that with the conv1d weight, // then sum the elements of each row, // (the last two steps are a dot product over rows (also doable with mul_mat)) // then permute away the ne[0] dimension, // and then you're left with the resulting x tensor. // For simultaneous sequences, all sequences need to have the same length. x = ggml_ssm_conv(ctx0, conv_x, layer.ssm_conv1d);
// bias x = ggml_add(ctx0, x, layer.ssm_conv1d_b);
x = ggml_silu(ctx0, x); }
// ssm { // {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_db = build_lora_mm(layer.ssm_x, x); // split ggml_tensor * dt = ggml_view_3d(ctx0, x_db, dt_rank, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], 0); ggml_tensor * B = ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state * x_db->nb[0], x_db->nb[1], x_db->nb[2], ggml_element_size(x_db) * dt_rank); ggml_tensor * C = ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state * x_db->nb[0], x_db->nb[1], x_db->nb[2], ggml_element_size(x_db) * (dt_rank + d_state));
// Some Mamba variants (e.g. FalconMamba, Jamba) apply RMS norm in B, C & Dt layers if (ssm_dt_b_c_rms || (layer.ssm_dt_norm && layer.ssm_b_norm && layer.ssm_c_norm)) { dt = build_norm(dt, layer.ssm_dt_norm, NULL, LLM_NORM_RMS, il); B = build_norm(B, layer.ssm_b_norm, NULL, LLM_NORM_RMS, il); C = build_norm(C, layer.ssm_c_norm, NULL, LLM_NORM_RMS, il); }
// {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs} dt = build_lora_mm(layer.ssm_dt, dt); dt = ggml_add(ctx0, dt, layer.ssm_dt_b);
cur = x; x = ggml_reshape_4d(ctx0, x, head_dim, n_head, n_seq_tokens, n_seqs);
ggml_tensor * A = layer.ssm_a;
// 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_head, 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, /*K=*/1); };
ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
// store last states ggml_build_forward_expand( gf, ggml_cpy(ctx0, ggml_view_1d(ctx0, y_ssm, d_state * d_inner * n_seqs, x->nb[3] * x->ne[3]), ggml_view_1d(ctx0, ssm_states_all, d_state * d_inner * n_seqs, kv_head * d_state * d_inner * ggml_element_size(ssm_states_all))));
ggml_tensor * y = ggml_view_3d(ctx0, y_ssm, d_inner, n_seq_tokens, n_seqs, x->nb[2], x->nb[3], 0);
// TODO: skip computing output earlier for unused tokens
y = ggml_add(ctx0, y, ggml_mul(ctx0, cur, layer.ssm_d)); y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
// {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs} cur = build_lora_mm(layer.ssm_out, y, layer.ssm_out_s); }
// {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);
return cur;}
ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il) const { const auto * mctx_cur = inp->mctx;
const auto kv_head = mctx_cur->get_head(); const auto mem_size = mctx_cur->get_size();
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_head = hparams.ssm_dt_rank; const int64_t head_dim = d_inner / n_head; 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; const int64_t K = cparams.n_rs_seq > 0 ? (int64_t) cparams.n_rs_seq + 1 : 1;
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(d_inner % 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); const int64_t state_slots = ssm_states_all->ne[1];
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);
// d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
// Keep the projection 2D: with a {n_embd, 1, n_seqs} batch the CUDA backend // dispatches a column-batched GEMV for what is a large dense GEMM. // {n_embd, d_in_proj} @ {n_embd, n_tokens} => {d_in_proj, n_tokens} ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur, model.layers[il].ssm_in_s); // {d_in_proj, n_tokens} => {d_in_proj, n_seq_tokens, n_seqs} zxBCdt = ggml_reshape_3d(ctx0, zxBCdt, zxBCdt->ne[0], n_seq_tokens, n_seqs);
// split the above in three ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim * zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0); ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2 * n_group * d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner * ggml_element_size(zxBCdt)); ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2 * d_inner + 2 * n_group * d_state) * ggml_element_size(zxBCdt));
// conv { // => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs} ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
const int64_t row_count = (d_conv - 1) * (d_inner + 2 * n_group * d_state); const size_t row_size = ggml_row_size(conv_states_all->type, row_count); const int64_t n_written = std::min<int64_t>(n_seq_tokens, K);
for (int64_t slot = 0; slot < n_written; ++slot) { ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2 * n_group * d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], (n_seq_tokens - slot) * conv_x->nb[0]);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv, ggml_view_2d(ctx0, conv_states_all, row_count, n_seqs, conv_states_all->nb[1], ((size_t) slot * mem_size + kv_head) * row_size))); }
// 1D convolution // The equivalent is to make a self-overlapping view of conv_x // over d_conv columns at each stride in the 3rd dimension, // then element-wise multiply that with the conv1d weight, // then sum the elements of each row, // (the last two steps are a dot product over rows (also doable with mul_mat)) // then permute away the ne[0] dimension, // and then you're left with the resulting x tensor. // For simultaneous sequences, all sequences need to have the same length. xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
// bias xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
xBC = ggml_silu(ctx0, xBC); }
// ssm { // These correspond to V K Q in SSM/attention duality ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim * xBC->nb[0], xBC->nb[1], xBC->nb[2], 0); ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state * xBC->nb[0], xBC->nb[1], xBC->nb[2], d_inner * ggml_element_size(xBC)); ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state * xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_inner + n_group * d_state) * ggml_element_size(xBC));
// {n_head, n_seq_tokens, n_seqs} dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
ggml_tensor * A = model.layers[il].ssm_a;
// 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_head, state_slots);
// TODO: use semistructured matrices to implement state-space duality // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs} // K > 1 asks the backend to return rollback snapshots in addition to the final state. return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids, K); };
ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows); const int64_t D = d_state * d_inner; const int64_t n_written = std::min<int64_t>(n_seq_tokens, K); const size_t row_size = ggml_row_size(ssm_states_all->type, D); const size_t y_row_size = ggml_row_size(y_ssm->type, D); const size_t state_offset = ggml_nelements(x) * ggml_element_size(x);
ggml_build_forward_expand( gf, ggml_cpy(ctx0, ggml_view_3d(ctx0, y_ssm, D, n_seqs, n_written, y_row_size, y_row_size * n_seqs, state_offset), ggml_view_3d(ctx0, ssm_states_all, D, n_seqs, n_written, ssm_states_all->nb[1], (size_t) mem_size * row_size, kv_head * row_size)));
ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head * x->nb[1], n_seq_tokens * n_head * x->nb[1], 0);
// TODO: skip computing output earlier for unused tokens
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d)); cb(y, "mamba2_y_add_d", il); y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
// grouped RMS norm if (model.layers[il].ssm_norm) { y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs); y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il); }
y = ggml_reshape_2d(ctx0, y, d_inner, n_seq_tokens * n_seqs);
// {d_inner, n_embd} @ {d_inner, n_tokens} => {n_embd, n_tokens} cur = build_lora_mm(model.layers[il].ssm_out, y, model.layers[il].ssm_out_s); }
cb(cur, "mamba_out", il); return cur;}