happyz
/
llama.cpp
mirror of https://github.com/ggerganov/llama.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge e9ad184926 into 2634ed207a

This commit is contained in:
Piotr Wilkin (ilintar) 2026-02-01 20:13:05 +08:00 committed by GitHub
parent 2634ed207a e9ad184926
commit 0eead3b761
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 669 additions and 360 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
src/CMakeLists.txt
View File

@ -57,6 +57,7 @@ add_library(llama
models/deci.cpp
models/deepseek.cpp
models/deepseek2.cpp
models/delta.cpp
models/dots1.cpp
models/dream.cpp
models/ernie4-5-moe.cpp

614
src/models/delta.cpp Normal file
View File

@ -0,0 +1,614 @@
#include "models.h"
#include "ggml.h"
#include <cmath>
#include <utility>
#include <cassert>
llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
/**
* Unified Delta Net implementation supporting both GDA and KDA modes.
*
* GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
* - Per-head gating, broadcasts over K dimension
*
* KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
* - Per-key gating
*
* The mode is auto-detected based on g's dimensionality.
*
* Tensor dimension convention:
* GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
* PyTorch: dim 0 is outermost, dim -1 is innermost
* So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
*/
// Helper to get a slice along dimension 2 (n_chunks dimension)
static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
return ggml_view_4d(ctx, t,
t->ne[0], t->ne[1], 1, t->ne[3],
t->nb[1], t->nb[2], t->nb[3],
chunk * t->nb[2]);
}
/**
* Unified chunked Delta Net implementation.
*
* Input tensor format matches qwen3next conventions:
* @param q Query tensor [S_k, H_k, n_tokens, n_seqs]
* @param k Key tensor [S_k, H_k, n_tokens, n_seqs]
* @param v Value tensor [S_v, H_v, n_tokens, n_seqs]
* @param g Gate tensor:
* GDA: [H_v, n_tokens, n_seqs]
* KDA: [S_k, H_v, n_tokens, n_seqs]
* @param beta Beta tensor [H_v, 1, n_tokens, n_seqs]
* @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
* @param causal_mask Lower triangular mask [chunk_size, chunk_size]
* @param identity Identity matrix [chunk_size, chunk_size]
* @param diag_mask Diagonal mask [chunk_size, chunk_size]
* @param il Layer index (for debugging callbacks)
* @param chunk_size Chunk size for chunked processing
* @param eps_norm Epsilon for L2 normalization
*
* @return Pair of (output_tokens, new_state)
*/
std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_chunking(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state_reshaped,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il,
int64_t chunk_size,
float eps_norm) {
// Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
const int64_t n_seqs = q->ne[3];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
// Detect KDA vs GDA based on g's shape
// GDA: g has shape [H_v, n_tokens, n_seqs]
// KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
// Validate tensor shapes
GGML_ASSERT(v->ne[2] == n_tokens);
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
GGML_ASSERT(H_k == H_v);
if (is_kda) {
// KDA: g shape [S_k, H_v, n_tokens, n_seqs]
GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
} else {
// GDA: g shape [H_v, n_tokens, n_seqs]
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
}
// L2 normalize q and k
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf((float)S_v);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
cb(beta, "beta_in", il);
cb(g, "g_in", il);
// Permute tensors to working format [S, n_tokens, H, n_seqs]
// Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), is_kda ? S_k : 1, n_tokens, H_k, n_seqs);
beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
cb(q, "q_perm", il);
cb(k, "k_perm", il);
cb(v, "v_perm", il);
cb(beta, "beta_perm", il);
cb(g, "g_perm", il);
cb(state_reshaped, "state_in", il);
// Padding for chunk processing
const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
const int64_t n_chunks = (n_tokens + pad) / chunk_size;
q = ggml_pad(ctx0, q, 0, pad, 0, 0);
k = ggml_pad(ctx0, k, 0, pad, 0, 0);
v = ggml_pad(ctx0, v, 0, pad, 0, 0);
beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
g = ggml_pad(ctx0, g, 0, pad, 0, 0);
cb(q, "q_pad", il);
cb(k, "k_pad", il);
cb(v, "v_pad", il);
cb(beta, "beta_pad", il);
cb(g, "g_pad", il);
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
cb(v_beta, "v_beta", il);
cb(k_beta, "k_beta", il);
// Reshape to chunks
q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
// Reshape g for chunks
ggml_tensor * g_cumsum;
ggml_tensor * g_cumsum_t;
if (is_kda) {
// KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
// Cumsum along chunk_size dimension (ne[1])
// GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
g = ggml_cont(ctx0, ggml_transpose(ctx0, g)); // [chunk_size, S_k, n_chunks, H_k * n_seqs]
g_cumsum_t = ggml_cumsum(ctx0, g);
g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t)); // [S_k, chunk_size, n_chunks, H_k * n_seqs]
} else {
// GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
g_cumsum = ggml_cumsum(ctx0, g);
g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
}
cb(g_cumsum, "g_cumsum", il);
// Build attention matrix A for the WY representation solve
// For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
// For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
// KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
ggml_tensor * k_decay;
ggml_tensor * decay_mask = nullptr;
ggml_tensor * g_exp_pos = nullptr;
if (is_kda) {
// KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
// A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
// By putting S_k in dim 0, mul_mat implicitly sums over it
const int64_t CHB = n_chunks * H_k * n_seqs;
// g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
// Reshape to [chunk_size, S_k, CHB] then build decay mask
ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
// Build decay mask: [chunk_size, chunk_size, S_k, CHB]
ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
cb(decay_mask, "decay_mask_kda", il);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
// Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
// Reshape k and k_beta for broadcasting with decay_mask
// k_i: indexed at position i (dim 2 of decay_mask)
// k_beta_j: indexed at position j (dim 1 of decay_mask)
ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
// decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
// mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
// g_exp_pos is still needed for later (kbeta_gexp, etc.)
g_exp_pos = ggml_exp(ctx0, g_cumsum);
} else {
// GDA: Use decay mask approach (g broadcasts over K dimension)
// g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
ggml_tensor * gcs_i = g_cumsum;
ggml_tensor * gcs_j = g_cumsum_t;
g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
cb(decay_mask, "decay_mask", il);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
}
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
cb(attn, "attn_pre_solve", il);
// Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
attn = ggml_mul(ctx0, lin_solve, causal_mask);
attn = ggml_add(ctx0, attn, identity);
cb(attn, "attn_solved", il);
// Compute u = A @ v and w = A @ (g.exp() * k)
v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
cb(kbeta_gexp, "kbeta_gexp", il);
ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
cb(k_cumdecay, "k_cumdecay", il);
// Attention scores q @ k^T with decay
// For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
// For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
ggml_tensor * attn_kq;
if (is_kda) {
// KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
const int64_t CHB = n_chunks * H_k * n_seqs;
// Rebuild decay mask (same structure as k_decay)
ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
// Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
// q_j: indexed at position j, k_i: indexed at position i
ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
// decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
// mul_mat sums over S_k
attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
} else {
// GDA: Use decay mask
attn_kq = ggml_mul_mat(ctx0, k, q);
attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
}
cb(attn_kq, "attn_kq", il);
// Compute g_last and g_diff for state updates
ggml_tensor * g_last;
ggml_tensor * g_diff_exp;
ggml_tensor * g_last_exp;
if (is_kda) {
// KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
// Get last element along chunk_size dimension (ne[1])
g_last = ggml_view_4d(ctx0, g_cumsum,
g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
(g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
g_last = ggml_cont(ctx0, g_last);
g_last_exp = ggml_exp(ctx0, g_last);
// g_diff = g_last - g_cumsum
ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
g_diff_exp = ggml_exp(ctx0, g_diff);
} else {
// GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
g_last = ggml_view_4d(ctx0, g_cumsum,
1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
(g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
g_last = ggml_cont(ctx0, g_last);
g_last_exp = ggml_exp(ctx0, g_last);
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
g_diff_exp = ggml_exp(ctx0, g_diff);
}
cb(g_last, "g_last", il);
cb(g_last_exp, "g_last_exp", il);
ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
cb(key_gdiff, "key_gdiff", il);
// Process chunks
ggml_tensor * new_state = state_reshaped;
ggml_tensor * core_attn_out = nullptr;
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
cb(attn_chunk, "attn_chunk", il);
ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
S_v, S_v, 1, H_v * n_seqs);
// v_prime = k_cumdecay @ state
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
cb(v_prime, "v_prime_chunk", il);
// v_new = v - v_prime
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
cb(v_new, "v_new_chunk", il);
// attn_inter = (q * g.exp()) @ state
ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
cb(attn_inter, "attn_inter_chunk", il);
// output = attn_inter + attn @ v_new
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
cb(v_attn, "v_attn_chunk", il);
ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
cb(core_attn_out_chunk, "core_attn_out_chunk", il);
core_attn_out = core_attn_out == nullptr
? core_attn_out_chunk
: ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
// State update: state = state * g_last_exp + key_gdiff^T @ v_new
ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
if (is_kda) {
// KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
// State: [S_v, S_v, H_v, n_seqs]
// Need to reshape g_last_exp to broadcast correctly over V dimension only
gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
1, gexp_last_chunk->ne[0], H_v, n_seqs); // [1, S_k, H_v, n_seqs]
// Transpose to [S_k, 1, H_v, n_seqs] then broadcast
gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
} else {
// GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
// Broadcasts over both K and V dimensions
gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
}
new_state = ggml_add(ctx0,
ggml_mul(ctx0, new_state, gexp_last_chunk),
ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
}
// Truncate padding and permute back
ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
S_v, n_tokens, H_v, n_seqs,
ggml_row_size(core_attn_out->type, S_v),
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
output_tokens = ggml_cont(ctx0, output_tokens);
cb(output_tokens, "output_tokens", il);
output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
output_tokens = ggml_cont(ctx0, output_tokens);
return {output_tokens, new_state};
}
/**
* Unified autoregressive Delta Net implementation (single token processing).
*
* This implementation uses matrix multiplication instead of elementwise operations + summation,
* which is more efficient and mathematically equivalent. See inline comments for equivalences.
*
* Input tensor format matches qwen3next conventions:
* @param q Query tensor [S_k, H_k, 1, n_seqs]
* @param k Key tensor [S_k, H_k, 1, n_seqs]
* @param v Value tensor [S_v, H_v, 1, n_seqs]
* @param g Gate tensor:
* GDA: [H_v, 1, n_seqs]
* KDA: [S_k, H_v, 1, n_seqs]
* @param beta Beta tensor [H_v, 1, 1, n_seqs]
* @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
* @param il Layer index (for debugging callbacks)
* @param eps_norm Epsilon for L2 normalization
*
* @return Pair of (output_tokens, new_state)
*/
std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified_autoregressive(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il,
float eps_norm) {
// Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
const int64_t n_seqs = q->ne[3];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
GGML_ASSERT(n_tokens == 1); // Autoregressive mode is for single token
// Detect KDA vs GDA based on g's shape
// GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
// KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
// Validate shapes
GGML_ASSERT(v->ne[2] == n_tokens);
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
GGML_ASSERT(H_k == H_v);
if (is_kda) {
GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
} else {
GGML_ASSERT(g->ne[0] == H_v);
}
// L2 normalize q and k
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf((float)S_v);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
cb(beta, "beta_in", il);
cb(g, "g_in", il);
// Reshape g and beta for broadcasting
ggml_tensor * g_t;
ggml_tensor * beta_t;
if (is_kda) {
// KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
// For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
} else {
// GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
// For state multiplication, broadcasts over both K and V
g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
}
beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
// Apply exponential to g_t
g_t = ggml_exp(ctx0, g_t);
// State decay: state = state * exp(g)
if (is_kda) {
// KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
// Need to broadcast g_t over V dimension (ne[0] of state)
// Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
state = ggml_mul(ctx0, state, g_broadcast);
} else {
// GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
state = ggml_mul(ctx0, state, g_t);
}
// Equivalence to previous version:
// Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
// Current: k_state = state_t @ k_t using matrix multiplication
// These are equivalent because: sum_k(A * B) = A @ B when dimensions align
ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
// v_diff = v - k_state (equivalent to v - kv_mem in previous version)
ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
// Equivalence to previous version:
// Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
// Current: state += v_diff^T @ k_beta^T using matrix multiplication
// These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
// Equivalence to previous version:
// Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
// Current: core_attn_out = state_t @ q using matrix multiplication
// These are equivalent because: sum_k(A * B) = A @ B when dimensions align
q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
// core_attn_out should be [S_v, 1, H_v, n_seqs] after this
cb(core_attn_out, "output_tokens", il);
cb(state, "new_state", il);
return {core_attn_out, state};
}
/**
* Main entry point that dispatches to chunked or autoregressive based on n_tokens.
*
* Input tensor format matches qwen3next conventions:
* @param q Query tensor [S_k, H_k, n_tokens, n_seqs]
* @param k Key tensor [S_k, H_k, n_tokens, n_seqs]
* @param v Value tensor [S_v, H_v, n_tokens, n_seqs]
* @param g Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
* @param beta Beta tensor [H_v, 1, n_tokens, n_seqs]
* @param state State tensor [S_v, S_v * H_v, 1, n_seqs]
*/
std::pair<ggml_tensor *, ggml_tensor *> llm_graph_context_delta::build_delta_net_unified(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il,
int64_t chunk_size,
float eps_norm) {
// Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
const int64_t n_tokens = q->ne[2];
if (n_tokens == 1) {
return build_delta_net_unified_autoregressive(
ctx0, q, k, v, g, beta, state, il, eps_norm);
}
return build_delta_net_unified_chunking(
ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
il, chunk_size, eps_norm);
}

49
src/models/models.h
View File

@ -17,6 +17,53 @@ struct llm_graph_context_mamba : public llm_graph_context {
};
struct llm_graph_context_delta : public llm_graph_context_mamba {
llm_graph_context_delta(const llm_graph_params & params);
virtual ~llm_graph_context_delta() = default;
std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_chunking(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il,
int64_t chunk_size,
float eps_norm);
std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified_autoregressive(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il,
float eps_norm);
std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_unified(
ggml_context * ctx0,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il,
int64_t chunk_size,
float eps_norm);
};
// Base class for RWKV-related models
struct llm_build_rwkv6_base : public llm_graph_context {
const llama_model & model;
@ -449,7 +496,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
struct llm_build_qwen3vlmoe : public llm_graph_context {
llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3next : public llm_graph_context_mamba {
struct llm_build_qwen3next : public llm_graph_context_delta {
llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_layer_attn(

365
src/models/qwen3next.cpp
View File

@ -4,7 +4,7 @@
#define CHUNK_SIZE 64
llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
llm_graph_context_mamba(params), model(model) {
llm_graph_context_delta(params), model(model) {
ggml_tensor * cur;
ggml_tensor * inpL;
@ -86,356 +86,6 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
ggml_build_forward_expand(gf, cur);
}
// utility to get one slice from the third dimension
// input dim: [x, y, c, b]
// output dim: [x, y, 1, b]
static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
}
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il) {
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
const int64_t n_seqs = q->ne[3];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
GGML_ASSERT(v->ne[2] == n_tokens);
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
const float eps_norm = hparams.f_norm_rms_eps;
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf(S_v);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
cb(beta, "beta_in", il);
cb(g, "g_in", il);
q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
cb(q, "q_perm", il);
cb(k, "k_perm", il);
cb(v, "v_perm", il);
cb(beta, "beta_perm", il);
cb(g, "g_perm", il);
cb(state, "state_in", il);
GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
// Do padding
const int64_t chunk_size = CHUNK_SIZE;
const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
const int64_t n_chunks = (n_tokens + pad) / chunk_size;
q = ggml_pad(ctx0, q, 0, pad, 0, 0);
k = ggml_pad(ctx0, k, 0, pad, 0, 0);
v = ggml_pad(ctx0, v, 0, pad, 0, 0);
g = ggml_pad(ctx0, g, pad, 0, 0, 0);
beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
cb(q, "q_pad", il);
cb(k, "k_pad", il);
cb(v, "v_pad", il);
cb(beta, "beta_pad", il);
cb(g, "g_pad", il);
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
cb(v_beta, "v_beta", il);
cb(k_beta, "k_beta", il);
q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j_broadcast =
ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
attn = ggml_mul(ctx0, lin_solve, causal_mask);
attn = ggml_add(ctx0, attn, identity);
cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
ggml_tensor * k_cumdecay =
ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
// vectorized calculation of key_gdiff
// improved from the chunked version:
// g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
// g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
// key_gdiff = key * g_diff.unsqueeze(-1)
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
// get last element in g_cumsum along chunk_size dimension (ne0)
// example: [[x, y, z, ..., last], ...] -> [[last], ...]
ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
(g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
g_last = ggml_cont(ctx0, g_last);
cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
// state to be updated per chunk
ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
// shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
ggml_tensor * core_attn_out = nullptr;
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
// shape: (S_k, chunk_size, 1, H_k * n_seqs)
ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
// shape: (S_v, chunk_size, 1, H_v * n_seqs)
ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
// shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
// shape: (chunk_size, 1, H_v * n_seqs)
ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
// replaced by precomputed attn_kq
ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
cb(attn_chunk, "attn_chunk", il);
ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
// v_new = v_i - v_prime
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
cb(v_new, "v_new_chunk", il);
// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
cb(attn_inter, "attn_inter_chunk", il);
// core_attn_out[:, :, i] = attn_inter + attn @ v_new
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
cb(v_attn, "v_attn_chunk", il);
ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
core_attn_out = core_attn_out == nullptr
? core_attn_out_chunk
: ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
//ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
new_state = ggml_add(ctx0,
ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
}
// truncate padded tokens
ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
S_v, n_tokens, H_v, n_seqs,
ggml_row_size(core_attn_out->type, S_v),
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
output_tokens = ggml_cont(ctx0, output_tokens);
cb(output_tokens, "output_tokens", il);
// permute back to (S_v, H_v, n_tokens, n_seqs)
output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
output_tokens = ggml_cont(ctx0, output_tokens);
return {output_tokens, new_state};
}
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il) {
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
const int64_t n_seqs = q->ne[3];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
GGML_ASSERT(v->ne[2] == n_tokens);
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
const float eps_norm = hparams.f_norm_rms_eps;
q = ggml_l2_norm(ctx0, q, eps_norm);
k = ggml_l2_norm(ctx0, k, eps_norm);
const float scale = 1.0f / sqrtf(S_v);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
cb(beta, "beta_in", il);
cb(g, "g_in", il);
state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
// Apply exponential to g_t
g_t = ggml_exp(ctx0, g_t);
// Apply the gated delta rule for the single timestep
// last_recurrent_state = last_recurrent_state * g_t
state = ggml_mul(ctx0, state, g_t);
// kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
// we need to sum over dim=-2, so we transpose, sum, then transpose again
kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
// v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
// delta = (v_t - kv_mem) * beta_t
ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
// last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
state = ggml_add(ctx0, state, k_t_delta);
// Compute the attention output
// core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
// again, since it's over dim = -2, transpose, sum, transpose back
ggml_tensor * core_attn_out =
ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
// core_attn_out should be [S_v, 1, H_v, n_seqs] after this
cb(core_attn_out, "output_tokens", il);
cb(state, "new_state", il);
return {core_attn_out, state};
}
ggml_tensor * llm_build_qwen3next::build_norm_gated(
ggml_tensor * input,
ggml_tensor * weights,
@ -746,7 +396,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
cb(state, "state_predelta", il);
// if head keys and value keys are different, repeat to force tensors into matching shapes
@ -775,13 +425,10 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
cb(k_conv, "k_conv_predelta", il);
cb(v_conv, "v_conv_predelta", il);
// Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
if (n_seq_tokens == 1) {
attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
} else {
attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
}
std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
gate, beta, state, causal_mask, identity, diag_mask,
il, CHUNK_SIZE, hparams.f_norm_rms_eps);
ggml_tensor * output = attn_out.first;
ggml_tensor * new_state = attn_out.second;
cb(output, "attn_output", il);