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llama.cpp
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Optimization: Qwen3 next autoregressive pass (#17996) 

* It's Qwen3 Next, the lean mean token generation machine!

* Apply patches from thread

* Remove recurrent version, only keep chunked and autoregressive

* Remove unnecessary conts and asserts

* Remove more extra conts and asserts

* Cleanup masking
This commit is contained in:
Piotr Wilkin (ilintar) 2025-12-16 11:59:53 +01:00 committed by GitHub
parent fb644247de
commit a5251ca11d
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2 changed files with 85 additions and 270 deletions
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22
src/models/models.h
View File

@ -441,23 +441,13 @@ private:
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_layer_ffn(
ggml_tensor * cur,
int il);
ggml_tensor * build_delta_net_recurrent(
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,
int il);
ggml_tensor * build_delta_net_chunking(
ggml_tensor * q,
ggml_tensor * k,
@ -467,6 +457,16 @@ private:
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il);
ggml_tensor * build_norm_gated(

319
src/models/qwen3next.cpp
View File

@ -17,13 +17,15 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
ggml_tensor * inp_out_ids = build_inp_out_ids();
ggml_tensor * causal_mask =
ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens, ubatch.n_seq_tokens), 1.0f),
ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
GGML_TRI_TYPE_LOWER);
ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens), 1.0f));
ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
ggml_build_forward_expand(gf, causal_mask);
ggml_build_forward_expand(gf, identity);
ggml_build_forward_expand(gf, diag_mask);
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@ -34,7 +36,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
// Determine layer type and build appropriate attention mechanism
if (hparams.is_recurrent(il)) {
// Linear attention layer (gated delta net)
cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, il);
cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
} else {
// Full attention layer
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
@ -93,14 +95,8 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il) {
GGML_ASSERT(ggml_is_contiguous(q));
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(g));
GGML_ASSERT(ggml_is_contiguous(beta));
GGML_ASSERT(ggml_is_contiguous(state));
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
@ -120,15 +116,10 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
// TODO: can this ever be false?
const bool use_qk_l2norm = true;
if (use_qk_l2norm) {
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);
@ -136,8 +127,6 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
beta = ggml_sigmoid(ctx0, beta);
ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
@ -188,36 +177,21 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
cb(v_beta, "v_beta", il);
cb(k_beta, "k_beta", il);
ggml_tensor * chunked_mask =
ggml_view_4d(ctx0, causal_mask, chunk_size,
chunk_size, causal_mask->ne[2], causal_mask->ne[3],
causal_mask->nb[1], causal_mask->nb[2], causal_mask->nb[3], 0);
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);
ggml_tensor * chunked_diag_mask =
ggml_view_4d(ctx0, causal_diag_mask, chunk_size,
chunk_size, causal_diag_mask->ne[2], causal_diag_mask->ne[3],
causal_diag_mask->nb[1], causal_diag_mask->nb[2], causal_diag_mask->nb[3], 0);
ggml_tensor * chunked_identity =
ggml_view_4d(ctx0, identity, chunk_size,
chunk_size, identity->ne[2], identity->ne[3],
identity->nb[1], identity->nb[2], identity->nb[3], 0);
q = ggml_cont_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
k = ggml_cont_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
v = ggml_cont_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
g = ggml_cont_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * 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);
ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
ggml_tensor * gcs_i = 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);
@ -226,23 +200,23 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
cb(decay_mask, "decay_mask", il);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_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, chunked_mask));
ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
cb(attn, "attn_pre_solve", il);
ggml_tensor * attn_lower = ggml_mul(ctx0, attn, chunked_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
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, chunked_mask);
attn = ggml_add(ctx0, attn, chunked_identity);
attn = ggml_mul(ctx0, lin_solve, causal_mask);
attn = ggml_add(ctx0, attn, identity);
cb(attn, "attn_solved", il);
@ -291,7 +265,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
attn = ggml_mul(ctx0, attn, decay_mask_chunk);
attn = ggml_mul(ctx0, attn, ggml_add(ctx0, chunked_identity, chunked_mask));
attn = ggml_mul(ctx0, attn, diag_mask);
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);
@ -361,23 +335,14 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
return ggml_concat(ctx0, flat_output, flat_state, 0);
}
ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
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,
ggml_tensor * causal_mask,
ggml_tensor * identity,
int il) {
GGML_ASSERT(ggml_is_contiguous(q));
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(g));
GGML_ASSERT(ggml_is_contiguous(beta));
GGML_ASSERT(ggml_is_contiguous(state));
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];
@ -386,6 +351,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
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);
@ -397,215 +363,65 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
// TODO: can this ever be false?
const bool use_qk_l2norm = true;
if (use_qk_l2norm) {
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);
ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
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_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);
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);
// Apply exponential to g_t
g_t = ggml_exp(ctx0, g_t);
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
// Apply the gated delta rule for the single timestep
// last_recurrent_state = last_recurrent_state * g_t
state = ggml_mul(ctx0, state, g_t);
ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
// 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))));
cb(k_beta, "k_beta", il);
cb(v_beta, "v_beta", il);
cb(g_cumsum, "g_cumsum", il);
// 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);
ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, n_tokens, 1, H_v, n_seqs); // [chunk_size, 1, n_tokens, n_seqs]
ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, n_tokens, H_v, n_seqs); // [1, chunk_size, n_tokens, n_seqs]
// 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);
// Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs]
// ggml_tensor * gcs_i_broadcast =
// ggml_repeat_4d(ctx0, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v,
// n_seqs); // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
// Don't need this, this one will get auto-broadcast
ggml_tensor * gcs_j_broadcast =
ggml_repeat_4d(ctx0, gcs_j, n_tokens, n_tokens, H_v, n_seqs); // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
// Apply lower triangular mask to ensure attention is causal (only past tokens influence current)
decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
// Apply exponential to get the decay mask values
decay_mask = ggml_exp(ctx0, decay_mask);
// Apply lower triangular mask again to ensure only lower triangular values remain
decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
cb(decay_mask, "decay_mask", il);
// attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
cb(kmulkbeta, "kmulkbeta", il);
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_rec", il);
// for i in range(1, chunk_size):
// row = attn[..., i, :i].clone()
// sub = attn[..., :i, :i].clone()
// attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
// attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
//
// We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
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);
// value = attn @ v_beta
v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
cb(v, "value_beta", il);
// k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
cb(gexp, "g_cum_exp", il);
ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
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);
// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
attn = ggml_mul_mat(ctx0, k, q);
attn = ggml_mul(ctx0, attn, decay_mask);
attn = ggml_mul(ctx0, attn, ggml_add(ctx0, identity, causal_mask));
cb(attn, "attn_decay_key", il);
ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay);
cb(v_prime, "v_prime", il);
// v_new = v_i - v_prime
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v, v_prime), v_prime);
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
cb(v_new, "v_new", il);
// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
ggml_tensor * q_g_exp = ggml_mul(ctx0, q, gexp);
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
cb(attn_inter, "attn_inter", il);
// core_attn_out[:, :, i] = attn_inter + attn @ v_new
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
cb(v_attn, "v_attn", il);
ggml_tensor * core_attn_out = ggml_add(ctx0, attn_inter, v_attn);
cb(core_attn_out, "core_attn_out", il);
// 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
ggml_tensor * g_cum_last =
ggml_cont(ctx0, ggml_view_4d(ctx0, g_cumsum_t, g_cumsum_t->ne[0], 1, g_cumsum_t->ne[2], g_cumsum_t->ne[3],
g_cumsum_t->nb[1], g_cumsum_t->nb[2], g_cumsum_t->nb[3],
g_cumsum_t->nb[0] * (g_cumsum_t->ne[1] - 1)));
cb(g_cum_last, "g_cum_last", il);
ggml_tensor * gexp_last =
ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
cb(gexp_last, "gexp_last", il);
ggml_tensor * g_cum_last_3d =
ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
cb(g_cum_last_3d, "g_cum_last_3d", il);
ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cumsum, g_cumsum->ne[0], g_cumsum->ne[2], g_cumsum->ne[3]);
cb(g_cumsum_3d, "g_cumsum_3d", il);
ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
cb(g_diff, "g_diff", il);
ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
cb(g_diff_exp, "g_diff_exp", il);
ggml_tensor * key_gdiff = ggml_mul(ctx0, k,
ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
g_diff_exp->ne[2] * g_diff_exp->ne[3]));
cb(key_gdiff, "key_gdiff", il);
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
cb(kgdmulvnew, "kgdmulvnew", il);
state = ggml_add(ctx0, ggml_mul(ctx0, state, gexp_last), kgdmulvnew);
// 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);
// flatten output
ggml_tensor * flat_output =
ggml_cont_1d(ctx0, ggml_permute(ctx0, core_attn_out, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
ggml_tensor * flat_state = ggml_cont_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
// flatten output, no need to permute since n_tokens is 1 so [S_v, 1, H_v, n_seqs] and [S_v, H_v, 1, n_seqs] are equivalent memory-layout wise
ggml_tensor * flat_output = ggml_reshape_1d(ctx0, core_attn_out, S_v * H_v * n_tokens * n_seqs);
ggml_tensor * flat_state = ggml_reshape_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
return ggml_concat(ctx0, flat_output, flat_state, 0);
}
@ -712,6 +528,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il) {
const auto * mctx_cur = inp->mctx;
@ -737,11 +554,11 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
cb(mixed_ba, "linear_attn_mixed_ba", il);
int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
// Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
int64_t ba_new_dim = 2 * num_v_heads / num_k_heads;
ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
// Split mixed_ba into b and a (beta and alpha parameters)
int64_t split_sizes_ba[2] = {
@ -762,8 +579,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
ggml_tensor * beta = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
cb(alpha_softplus, "a_softplus", il);
@ -799,9 +614,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
(split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
cb(z, "z", il);
GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value) + ggml_nelements(z) ==
ggml_nelements(mixed_qkvz));
// After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
// query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
@ -925,10 +737,13 @@ 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 and build_delta_net_recurrent based on n_tokens
ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
build_delta_net_chunking (q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il) :
build_delta_net_recurrent(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il);
// Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
ggml_tensor * attn_out;
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);
}
cb(attn_out, "attn_out", il);
// The tensors were concatenated 1d, so we need to extract them 1d as well