jdelony/ik_llama.cpp
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Kawrakow 2026-05-11 12:22:50 +00:00
parent f435fc1d73
commit b28ddd49e3
3 changed files with 8 additions and 36 deletions
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4
ggml/src/ggml-cuda/delta-net.cu
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@ -245,7 +245,7 @@ void ggml_cuda_op_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
const ggml_tensor * src3 = dst->src[3]; // g
const ggml_tensor * src4 = dst->src[4]; // beta
const ggml_tensor * src5 = dst->src[5]; // state
const ggml_tensor * src6 = dst->src[6]; // state
const ggml_tensor * src6 = dst->src[6]; // when not null, state for token 0...n_token-1
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
@ -275,13 +275,11 @@ void ggml_cuda_op_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
const int64_t state_size = head_dim * head_dim * n_heads * n_seqs;
int repeat_type = dst->op_params[0];
//int save_all_states = dst->op_params[1];
if (src6) {
GGML_ASSERT(src6->type == GGML_TYPE_F32);
GGML_ASSERT(src6->ne[0] >= (n_tokens - 1)*state_size);
}
//const int64_t expected_size = save_all_states ? (output_size + n_tokens * state_size) : (output_size + state_size);
const int64_t expected_size = output_size + state_size;
GGML_ASSERT(ggml_nelements(dst) == expected_size);

35
src/llama-delta-net.cpp
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@ -80,8 +80,7 @@ std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_fused_delta_net(ggml_co
ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
int il, const llm_build_cb & cb, int repeat_type,
bool save_all_steps,
ggml_cgraph * gf, ggml_tensor * per_step_ckpt) {
ggml_tensor * per_step_ckpt) {
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[2];
@ -137,13 +136,6 @@ std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_fused_delta_net(ggml_co
ggml_row_size(fused_result->type, S_v * H_v * n_tokens), 0);
//output_tokens = ggml_cont_4d(ctx0, output_tokens, S_v, H_v, n_tokens, n_seqs);
// per-step states are at [output_size, output_size + n_tokens*state_size)
//const int64_t last_state_offset = save_all_steps
// ? (output_size + (n_tokens - 1) * state_size)
// : output_size;
//ggml_tensor * new_state_flat = ggml_view_1d(ctx0, fused_result, state_size,
// last_state_offset * ggml_element_size(fused_result));
ggml_tensor * new_state_flat = ggml_view_1d(ctx0, fused_result, state_size,
output_size * ggml_element_size(fused_result));
ggml_tensor * new_state = ggml_reshape_4d(ctx0, new_state_flat, S_v, S_v, H_v, n_seqs);
@ -151,22 +143,6 @@ std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_fused_delta_net(ggml_co
cb(output_tokens, "output_tokens", il);
cb(new_state, "new_state", il);
// Copy all per-step SSM states to persistent checkpoint tensor
//if (save_all_steps && per_step_ckpt != nullptr && gf != nullptr && n_tokens > 1) {
// const int64_t per_step_total = n_tokens * state_size;
// if (per_step_total <= ggml_nelements(per_step_ckpt)) {
// ggml_tensor * all_steps_src = ggml_view_1d(ctx0, fused_result, per_step_total,
// output_size * ggml_element_size(fused_result));
// ggml_tensor * ckpt_dst = ggml_view_1d(ctx0, per_step_ckpt, per_step_total, 0);
// auto ckpt_cpy = ggml_cpy(ctx0, all_steps_src, ckpt_dst);
// cb(ckpt_cpy, "per_step_ckpt_cpy", il);
// ggml_build_forward_expand(gf, ckpt_cpy);
// } else {
// LLAMA_LOG_WARN("%s: per-step checkpoint tensor too small for %lld tokens (need %lld, have %lld), skipping per-step save\n",
// __func__, (long long)n_tokens, (long long)per_step_total, (long long)ggml_nelements(per_step_ckpt));
// }
//}
return {output_tokens, new_state};
}
@ -309,7 +285,7 @@ ggml_tensor * delta_net::build_qkv(ggml_context * ctx0, ggml_tensor * state_stor
int64_t head_k_dim, int64_t num_k_heads, int64_t head_v_dim, int64_t num_v_heads, int64_t ssm_d_conv,
int64_t state_seq_id_local, uint32_t qnext_state_slots, bool reset_state_local,
float eps_norm, int repeat_type, int il, const llm_build_cb & cb, ggml_cgraph * gf,
bool save_per_step_states, ggml_tensor * per_step_ckpt) {
ggml_tensor * per_step_ckpt) {
const int64_t key_dim = head_k_dim * num_k_heads;
const int64_t value_dim = head_v_dim * num_v_heads;
const int64_t conv_dim = key_dim * 2 + value_dim;
@ -395,7 +371,7 @@ ggml_tensor * delta_net::build_qkv(ggml_context * ctx0, ggml_tensor * state_stor
cb(k_conv, "k_conv_normed", il);
auto [output, new_state] = build_fused_delta_net(ctx0, q_conv, k_conv, v_conv, gate, beta, state, il, cb, repeat_type,
save_per_step_states, gf, per_step_ckpt);
per_step_ckpt);
cb(output, "attn_output", il);
cb(new_state, "new_state", il);
@ -559,7 +535,7 @@ ggml_tensor * delta_net::build_layer_attn_linear_core(ggml_context * ctx0, ggml_
auto output = build_qkv(ctx0, split_s_l->splits[id], split_ssm_conv1d->splits[id], qkv_mixed, inp_s_seq_qnext, beta, gate,
head_k_dim, num_k_heads_id, head_v_dim, num_v_heads_id, hparams.ssm_d_conv,
state_seq_id_local, qnext_state_slots, reset_state_local, hparams.f_norm_rms_eps,
l.ssm_beta_alpha ? 0 : 1, il, cb, gf, save_per_step_states, per_step_ckpt);
l.ssm_beta_alpha ? 0 : 1, il, cb, gf, per_step_ckpt);
split_norm = (ggml_split_tensor_t *)l.ssm_norm->extra;
GGML_ASSERT(split_norm && split_norm->splits[id]);
auto split_ssm_out = (ggml_split_tensor_t *)l.ssm_out->extra;
@ -631,8 +607,7 @@ ggml_tensor * delta_net::build_layer_attn_linear_core(ggml_context * ctx0, ggml_
qkv_mixed, inp_s_seq_qnext, beta, gate,
head_k_dim, num_k_heads, head_v_dim, num_v_heads, hparams.ssm_d_conv,
state_seq_id_local, qnext_state_slots, reset_state_local, hparams.f_norm_rms_eps,
model.layers[il].ssm_beta_alpha ? 0 : 1, il, cb, gf,
save_per_step_states, per_step_ckpt);
model.layers[il].ssm_beta_alpha ? 0 : 1, il, cb, gf, per_step_ckpt);
auto gated_output = build_gated_output(lctx, ctx0, model.layers[il].ssm_norm, model.layers[il].ssm_out, output, z, head_v_dim, num_v_heads, n_tok, il, cb);
if (inp_out_ids) {

5
src/llama-delta-net.h
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@ -15,8 +15,7 @@ struct delta_net {
ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
int il, const llm_build_cb & cb, int repeat_type,
bool save_all_steps = false,
ggml_cgraph * gf = nullptr, ggml_tensor * per_step_ckpt = nullptr);
ggml_tensor * per_step_ckpt = nullptr);
ggml_tensor * build_layer_attn_linear_core(ggml_context * ctx0, ggml_cgraph * gf,
ggml_tensor * cur, ggml_tensor * inp_s_seq_qnext, ggml_tensor * inp_out_ids,
@ -52,7 +51,7 @@ private:
int64_t head_k_dim, int64_t num_k_heads, int64_t head_v_dim, int64_t num_v_heads, int64_t ssm_d_conv,
int64_t state_seq_id_local, uint32_t qnext_state_slots, bool reset_state_local,
float eps_norm, int repeat_type, int il, const llm_build_cb & cb, ggml_cgraph * gf,
bool save_per_step_states = false, ggml_tensor * per_step_ckpt = nullptr);
ggml_tensor * per_step_ckpt = nullptr);
static ggml_tensor * build_gated_output(llama_context & lctx, ggml_context * ctx0, ggml_tensor * ssm_norm, ggml_tensor * ssm_out,
ggml_tensor * output, ggml_tensor * z, int64_t head_v_dim, int64_t num_v_heads, int64_t n_tok, int il, const llm_build_cb & cb);