const llama_model & model;
};
-// TODO: derive llm_build_delta_net_base instead
-struct llm_build_qwen35 : public llm_graph_context {
+struct llm_build_qwen35 : public llm_build_delta_net_base {
llm_build_qwen35(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_layer_attn(
ggml_tensor * build_layer_attn_linear(
llm_graph_input_rs * inp,
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);
- // returns pair of output and new state
- std::pair<ggml_tensor *, ggml_tensor *> 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);
-
- // returns pair of output and new state
- std::pair<ggml_tensor *, 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(
ggml_tensor * input,
ggml_tensor * weights,
};
// TODO: derive llm_build_delta_net_base instead
-struct llm_build_qwen35moe : public llm_graph_context {
+struct llm_build_qwen35moe : public llm_build_delta_net_base {
llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_layer_attn(
ggml_tensor * build_layer_attn_linear(
llm_graph_input_rs * inp,
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);
- // returns pair of output and new state
- std::pair<ggml_tensor *, ggml_tensor *> 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);
-
- // returns pair of output and new state
- std::pair<ggml_tensor *, 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(
ggml_tensor * input,
ggml_tensor * weights,
#include "llama-memory-recurrent.h"
-#define CHUNK_SIZE 64
-
llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) :
- llm_graph_context(params), model(model) {
+ llm_build_delta_net_base(params), model(model) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
- ggml_tensor * causal_mask =
- ggml_tri(ctx0, ggml_fill(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(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;
// 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, diag_mask, il);
+ cur = build_layer_attn_linear(inp->get_recr(), cur, il);
} else {
// Full attention layer
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
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_qwen35::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 * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
- 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
-
- ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
- cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
- ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
- cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, 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_t = get_slice_2d(ctx0, key_gdiff_t, 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, k_gdiff_t);
-
- // 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_qwen35::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};
-}
-
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_qkvz(
ggml_tensor * input,
int il) {
ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
llm_graph_input_rs * inp,
ggml_tensor * cur,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
int il) {
const auto * mctx_cur = inp->mctx;
ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
cb(beta, "beta", il);
+
+ beta = ggml_sigmoid(ctx0, beta);
+
ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
cb(alpha, "alpha", il);
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);
+
ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
cb(gate, "gate", il);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
- // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
-
// Build the convolution states tensor
ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
cb(conv_states, "conv_states", il);
ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
const int64_t conv_kernel_size = conv_kernel->ne[0];
const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
- conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+
+ conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
cb(conv_states, "conv_states_reshaped", il);
- qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
- cb(qkv_mixed, "qkv_mixed_permuted", il);
+ qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
+ cb(qkv_mixed, "qkv_mixed_transposed", il);
ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
cb(conv_input, "conv_input", il);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
cb(conv_states_all, "conv_states_updated", il);
- // Apply SSM convolution
+ 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, n_seqs);
+ cb(state, "state_predelta", il);
+
ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
cb(conv_output_proper, "conv_output_raw", il);
int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
// Extract the convolved Q, K, V from conv_output
- ggml_tensor * q_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+ ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ 0);
+
+ ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+
+ ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_v_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
+
cb(q_conv, "q_conv", il);
- ggml_tensor * k_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
- head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(k_conv, "k_conv", il);
- ggml_tensor * v_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
- 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(v_conv, "v_conv", il);
- // Unsqueeze them
- q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+ const float eps_norm = hparams.f_norm_rms_eps;
- 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);
- cb(state, "state_predelta", il);
+ q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
+ k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
+
+ //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
- // if head keys and value keys are different, repeat Q/K to match V's head count
- // V heads are in tiled order (from conversion), so simple tiled repeat works
+ // if head keys and value keys are different, repeat to force tensors into matching shapes
if (num_k_heads != num_v_heads) {
GGML_ASSERT(num_v_heads % num_k_heads == 0);
+ // TODO: try to avoid these explicit repeats by utilizing op broadcast
q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
}
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);
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
}
ggml_tensor * output = attn_out.first;
ggml_tensor * new_state = attn_out.second;
// Update the recurrent states
ggml_build_forward_expand(gf,
- ggml_cpy(ctx0, new_state,
- ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
- kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
- // Reshape both attn_out_final and z to 2D tensors for normalization
- // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
// Apply gated normalization: self.norm(core_attn_out, z)
- ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+ ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
// Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
cb(cur, "linear_attn_out", il);
// Reshape back to original dimensions
- cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+ cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+
return cur;
}
#include "llama-memory-recurrent.h"
-#define CHUNK_SIZE 64
-
llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
- llm_graph_context(params), model(model) {
+ llm_build_delta_net_base(params), model(model) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
- ggml_tensor * causal_mask =
- ggml_tri(ctx0, ggml_fill(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(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;
// 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, diag_mask, il);
+ cur = build_layer_attn_linear(inp->get_recr(), cur, il);
} else {
// Full attention layer
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
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_qwen35moe::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 * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
- 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
-
- ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
- cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
- ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
- cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, 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_t = get_slice_2d(ctx0, key_gdiff_t, 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, k_gdiff_t);
-
- // 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_qwen35moe::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};
-}
-
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
ggml_tensor * input,
int il) {
ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
llm_graph_input_rs * inp,
ggml_tensor * cur,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
int il) {
const auto * mctx_cur = inp->mctx;
ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
cb(beta, "beta", il);
+
+ beta = ggml_sigmoid(ctx0, beta);
+
ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
cb(alpha, "alpha", il);
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);
+
ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
cb(gate, "gate", il);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
- // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
-
// Build the convolution states tensor
ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
cb(conv_states, "conv_states", il);
ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
const int64_t conv_kernel_size = conv_kernel->ne[0];
const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
- conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+
+ conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
cb(conv_states, "conv_states_reshaped", il);
- qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
- cb(qkv_mixed, "qkv_mixed_permuted", il);
+ qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
+ cb(qkv_mixed, "qkv_mixed_transposed", il);
ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
cb(conv_input, "conv_input", il);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
cb(conv_states_all, "conv_states_updated", il);
- // Apply SSM convolution
+ 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, n_seqs);
+ cb(state, "state_predelta", il);
+
ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
cb(conv_output_proper, "conv_output_raw", il);
int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
// Extract the convolved Q, K, V from conv_output
- ggml_tensor * q_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+ ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ 0);
+
+ ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+
+ ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_v_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
+
cb(q_conv, "q_conv", il);
- ggml_tensor * k_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
- head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(k_conv, "k_conv", il);
- ggml_tensor * v_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
- 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(v_conv, "v_conv", il);
- // Unsqueeze them
- q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+ const float eps_norm = hparams.f_norm_rms_eps;
- 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);
- cb(state, "state_predelta", il);
+ q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
+ k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
- // if head keys and value keys are different, repeat Q/K to match V's head count
- // V heads are in tiled order (from conversion), so simple tiled repeat works
+ //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+ // if head keys and value keys are different, repeat to force tensors into matching shapes
if (num_k_heads != num_v_heads) {
GGML_ASSERT(num_v_heads % num_k_heads == 0);
+ // TODO: try to avoid these explicit repeats by utilizing op broadcast
q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
}
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);
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
}
ggml_tensor * output = attn_out.first;
ggml_tensor * new_state = attn_out.second;
// Update the recurrent states
ggml_build_forward_expand(gf,
- ggml_cpy(ctx0, new_state,
- ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
- kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
- // Reshape both attn_out_final and z to 2D tensors for normalization
- // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
// Apply gated normalization: self.norm(core_attn_out, z)
- ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+ ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
// Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
cb(cur, "linear_attn_out", il);
// Reshape back to original dimensions
- cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+ cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+
return cur;
}
overview / pkg / ggml / sources / llama.cpp / commitdiff