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llama.cpp
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naive chunking form implemented

This commit is contained in:
Yee Man Chan 2026-01-06 11:23:53 +08:00
parent aba181ebad
commit cfed14e31b
1 changed files with 214 additions and 9 deletions
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223
src/models/kimi-linear.cpp
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@ -265,7 +265,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// Choose between build_kda_chunking and build_kda_recurrent based on n_tokens
// TODO: Currently only build_kda_recurrent is implemented
ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
build_kda_recurrent(Qcur, Kcur, Vcur, g1, beta, state, causal_mask, identity, il) :
build_kda_chunking(Qcur, Kcur, Vcur, g1, beta, state, causal_mask, identity, il) :
build_kda_recurrent(Qcur, Kcur, Vcur, g1, beta, state, causal_mask, identity, il);
cb(attn_out, "attn_out", il);
@ -485,7 +485,7 @@ ggml_tensor * llm_build_kimi_linear::build_kda_chunking(
GGML_ASSERT(k->ne[2] == n_tokens);
GGML_ASSERT(gk->ne[0] == S_v && gk->ne[1] == H_v && gk->ne[2] == n_tokens && gk->ne[3] == 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(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);
@ -504,8 +504,6 @@ ggml_tensor * llm_build_kimi_linear::build_kda_chunking(
const float scale = 1.0f / sqrtf(S_v);
q = ggml_scale(ctx0, q, scale);
beta = ggml_sigmoid(ctx0, beta);
cb(q, "q_in", il);
@ -514,8 +512,8 @@ ggml_tensor * llm_build_kimi_linear::build_kda_chunking(
cb(beta, "beta_in", il);
cb(gk, "gk_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);
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);
gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
@ -530,20 +528,227 @@ ggml_tensor * llm_build_kimi_linear::build_kda_chunking(
cb(beta, "beta_perm", il);
cb(gk, "gk_perm", il);
cb(state, "state_in", il);
cb(causal_diag_mask, "causal_diag_mask", 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);
gk = ggml_pad(ctx0, gk, 0, pad, 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(gk, "gk_pad", il);
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
cb(k_beta, "k_beta", il);
cb(v_beta, "v_beta", il);
cb(k_beta, "k_beta", il);
return nullptr;
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);
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);
const int64_t HB = H_k * n_seqs;
q = ggml_cont_4d(ctx0, q, S_k, chunk_size, n_chunks, HB);
k = ggml_cont_4d(ctx0, k, S_k, chunk_size, n_chunks, HB);
k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, HB);
v = ggml_cont_4d(ctx0, v, S_v, chunk_size, n_chunks, HB);
v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, HB);
gk = ggml_cont_4d(ctx0, gk, S_k, chunk_size, n_chunks, HB);
beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, HB);
// switch for cumsum
gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 1, 0, 2, 3), chunk_size, S_k, n_chunks, HB);
ggml_tensor * gk_cumsum = ggml_cumsum(ctx0, gk);
cb(gk_cumsum, "gk_cumsum", il);
const int64_t CHB = n_chunks * H_v * n_seqs;
ggml_tensor * g_i = ggml_reshape_4d(ctx0, gk_cumsum, chunk_size, 1, S_k, CHB);
ggml_tensor * g_j = ggml_reshape_4d(ctx0, gk_cumsum, 1, chunk_size, S_k, CHB);
ggml_tensor * g_j_bc = ggml_repeat_4d(ctx0, g_j, chunk_size, chunk_size, S_k, CHB);
ggml_tensor * decay_mask = ggml_sub(ctx0, g_j_bc, g_i);
cb(decay_mask, "decay_mask", il);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
decay_mask = ggml_exp(ctx0, decay_mask);
decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
cb(decay_mask, "decay_mask_exp", il);
// k [S,BT,NT,H*B] k_per [BT,S,NT,H*B]
ggml_tensor * k_per = ggml_cont(ctx0, ggml_permute(ctx0, k, 1, 0, 2, 3));
ggml_tensor * k_i = ggml_reshape_4d(ctx0, k_per, chunk_size, 1, S_k, CHB);
ggml_tensor * k_i_bc = ggml_repeat_4d(ctx0, k_i, chunk_size, chunk_size, S_k, CHB);
ggml_tensor * k_j = ggml_reshape_4d(ctx0, k_per, 1, chunk_size, S_k, CHB);
ggml_tensor * k_j_bc = ggml_repeat_4d(ctx0, k_j, chunk_size, chunk_size, S_k, CHB);
ggml_tensor * Akk = ggml_mul(ctx0, decay_mask, k_j_bc);
Akk = ggml_mul(ctx0, Akk, k_i_bc);
Akk = ggml_cont(ctx0, ggml_permute(ctx0, Akk, 1, 2, 0, 3));
Akk = ggml_sum_rows(ctx0, Akk);
Akk = ggml_reshape_4d(ctx0, Akk, chunk_size, chunk_size, n_chunks, H_k * n_seqs);
Akk = ggml_mul(ctx0, Akk, beta);
Akk = ggml_neg(ctx0, ggml_mul(ctx0, Akk, chunked_mask));
cb(Akk, "attn_pre_solve", il);
ggml_tensor * attn_lower = ggml_mul(ctx0, Akk, chunked_mask);
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, Akk, true, true, false);
Akk = ggml_mul(ctx0, lin_solve, chunked_mask);
Akk = ggml_add(ctx0, Akk, chunked_identity);
cb(Akk, "attn_solved", il);
ggml_tensor * vb = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), Akk);
gk_cumsum = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk_cumsum, 1, 0, 2, 3), S_k, chunk_size, n_chunks, HB);
ggml_tensor * gkexp = ggml_exp(ctx0, gk_cumsum);
ggml_tensor * kbeta_gkexp = ggml_mul(ctx0, k_beta, gkexp);
cb(kbeta_gkexp, "kbeta_gkexp", il);
ggml_tensor * k_cumdecay = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gkexp)), Akk);
cb(k_cumdecay, "k_cumdecay", il);
ggml_tensor * core_attn_out = nullptr;
ggml_tensor * new_state = ggml_dup(ctx0, state);
cb(new_state, "new_state", il);
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
// for (int64_t chunk = 0; chunk < 1; chunk++) {
// extract one chunk worth of data
auto chunkify = [=](ggml_tensor * t) {
return ggml_cont(ctx0, ggml_view_4d(ctx0, t, t->ne[0], chunk_size, 1, t->ne[3],
t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
};
// k [S,BT,NT,H*B] => k_chunk [S,BT,1,H*B]
ggml_tensor * k_chunk = chunkify(k);
ggml_tensor * q_chunk = chunkify(q);
ggml_tensor * vb_chunk = chunkify(vb);
// Since decay_mask now has dimension of [BT,BT,S,NT*H*B], it can't be chunkified
// decay_mask_chunk needs to be recomputed
// gk_cumsum [S,BT,NT,H*B] => gk_cs_chunk [S,BT,1,H*B]
ggml_tensor * gk_cs_chunk = chunkify(gk_cumsum);
ggml_tensor * gk_cs_chunk_i = ggml_cont(ctx0, ggml_permute(ctx0, gk_cs_chunk, 2, 0, 1, 3));
ggml_tensor * gk_cs_chunk_j = ggml_cont(ctx0, ggml_permute(ctx0, gk_cs_chunk, 2, 1, 0, 3));
ggml_tensor * gk_cs_chunk_j_bc = ggml_repeat_4d(ctx0, gk_cs_chunk_j, chunk_size, chunk_size, S_k, HB);
ggml_tensor * decay_mask_chunk = ggml_sub(ctx0, gk_cs_chunk_j_bc, gk_cs_chunk_i);
cb(decay_mask_chunk, "decay_mask_chunk", il);
decay_mask_chunk = ggml_mul(ctx0, decay_mask_chunk, chunked_diag_mask);
decay_mask_chunk = ggml_exp(ctx0, decay_mask_chunk);
decay_mask_chunk = ggml_mul(ctx0, decay_mask_chunk, chunked_diag_mask);
cb(decay_mask_chunk, "decay_mask_chunk_exp", il);
ggml_tensor * k_cumdecay_chunk = chunkify(k_cumdecay);
ggml_tensor * gkexp_chunk = ggml_exp(ctx0, gk_cs_chunk);
ggml_tensor * k_chunk_i = ggml_cont(ctx0, ggml_permute(ctx0, k_chunk, 2, 0, 1, 3));
ggml_tensor * k_chunk_i_bc = ggml_repeat_4d(ctx0, k_chunk_i, chunk_size, chunk_size, S_k, HB);
ggml_tensor * q_chunk_j = ggml_cont(ctx0, ggml_permute(ctx0, q_chunk, 2, 1, 0, 3));
ggml_tensor * q_chunk_j_bc = ggml_repeat_4d(ctx0, q_chunk_j, chunk_size, chunk_size, S_k, HB);
ggml_tensor * kq = ggml_mul(ctx0, decay_mask_chunk, q_chunk_j_bc);
kq = ggml_mul(ctx0, kq, k_chunk_i_bc);
ggml_tensor * Aqk = ggml_mul(ctx0, kq, decay_mask_chunk);
Aqk = ggml_mul(ctx0, Aqk, ggml_add(ctx0, chunked_identity, chunked_mask));
Aqk = ggml_cont(ctx0, ggml_permute(ctx0, Aqk, 1, 2, 0, 3));
Aqk = ggml_sum_rows(ctx0, Aqk);
Aqk = ggml_scale(ctx0, Aqk, scale); // scale q
Aqk = ggml_reshape_4d(ctx0, Aqk, chunk_size, chunk_size, 1, HB);
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);
// new_state [S,S,1,H*B] k_cumdecay_chunk [S,BT,1,H*B]
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, vb_chunk, v_prime), v_prime);
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
// q_chunk [S,BT,1,H*B] gkexp_chunk [S,BT,1,H*B]
ggml_tensor * q_gk_exp = ggml_mul(ctx0, q_chunk, gkexp_chunk);
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_gk_exp);
attn_inter = ggml_scale(ctx0, attn_inter, scale); // scale q
// v_new_t [S,BT,1,H*B] Aqk [BT,BT,1,H*B]
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, Aqk);
ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 1);
ggml_tensor * gk_cum_last =
ggml_cont(ctx0, ggml_view_4d(ctx0, gk_cs_chunk, gk_cs_chunk->ne[0], 1, gk_cs_chunk->ne[2], gk_cs_chunk->ne[3],
gk_cs_chunk->nb[1], gk_cs_chunk->nb[2], gk_cs_chunk->nb[3],
gk_cs_chunk->nb[1] * (gk_cs_chunk->ne[1] - 1)));
ggml_tensor * gkexp_last = ggml_exp(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, gk_cum_last)));
ggml_tensor * gk_diff = ggml_neg(ctx0, ggml_sub(ctx0, gk_cs_chunk, gk_cum_last));
ggml_tensor * gk_diff_exp = ggml_exp(ctx0, gk_diff);
ggml_tensor * key_gkdiff = ggml_mul(ctx0, k_chunk, gk_diff_exp);
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gkdiff)));
new_state = ggml_add(ctx0,
ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gkexp_last, gkexp_last->ne[0], gkexp_last->ne[1], H_v, n_seqs)),
ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
}
core_attn_out = ggml_cont_4d(ctx0, core_attn_out, S_v, chunk_size * n_chunks, H_v, n_seqs);
ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, S_v, n_tokens, H_v, n_seqs, core_attn_out->nb[1], core_attn_out->nb[2], core_attn_out->nb[3], 0);
cb(output_tokens, "output_tokens", il);
// flatten output
ggml_tensor * flat_output =
ggml_cont_1d(ctx0, ggml_permute(ctx0, output_tokens, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
ggml_tensor * flat_state = ggml_cont_1d(ctx0, new_state, S_v * S_v * H_v * n_seqs);
cb(new_state, "output_state", il);
return ggml_concat(ctx0, flat_output, flat_state, 0);
}
ggml_tensor * llm_build_kimi_linear::build_kda_recurrent(