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graph : refactor context to not pass gf explicitly (#14629)

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-07-18 08:29:28 +03:00
committed by GitHub
parent 09651d09ff
commit 8f974bc1e9
5 changed files with 295 additions and 341 deletions
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34
src/llama-graph.cpp
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@@ -486,6 +486,10 @@ llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) {
return inputs.back().get();
}
void llm_graph_result::set_params(const llm_graph_params & params) {
this->params = params;
}
//
// llm_graph_context
//
@@ -527,9 +531,10 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
mctx (params.mctx),
cross (params.cross),
cb_func (params.cb),
res (static_cast<llm_graph_result *>(params.res)),
ctx0 (res->get_ctx()) {
res->params = params;
res (params.res),
ctx0 (res->get_ctx()),
gf (res->get_gf()) {
res->set_params(params);
}
void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
@@ -1119,7 +1124,6 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t
}
ggml_tensor * llm_graph_context::build_attn_mha(
ggml_cgraph * gf,
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
@@ -1253,7 +1257,6 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_no_cache * inp,
ggml_cgraph * gf,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
@@ -1281,7 +1284,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = k_cur;
ggml_tensor * v = v_cur;
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1337,7 +1340,6 @@ llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified()
ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_kv_unified * inp,
ggml_cgraph * gf,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
@@ -1370,7 +1372,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1390,7 +1392,6 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_kv_unified_iswa * inp,
ggml_cgraph * gf,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
@@ -1437,7 +1438,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1470,7 +1471,6 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_cross * inp,
ggml_cgraph * gf,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
@@ -1492,7 +1492,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = k_cur;
ggml_tensor * v = v_cur;
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1550,7 +1550,6 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
}
ggml_tensor * llm_graph_context::build_rs(
ggml_cgraph * gf,
ggml_tensor * s,
ggml_tensor * state_copy,
int32_t state_size,
@@ -1608,21 +1607,19 @@ llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
ggml_tensor * llm_graph_context::build_rs(
llm_graph_input_rs * inp,
ggml_cgraph * gf,
ggml_tensor * s,
int32_t state_size,
int32_t n_seqs,
const llm_graph_get_rows_fn & get_state_rows) const {
const auto * kv_state = inp->mctx;
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
return build_rs(s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
}
ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
llm_graph_input_rs * inp,
ggml_cgraph * gf,
const llama_ubatch & ubatch,
int il) const {
int il) const {
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto token_shift_count = hparams.token_shift_count;
@@ -1632,7 +1629,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_tensor * token_shift_all = mctx_cur->get_r_l(il);
ggml_tensor * token_shift = build_rs(
inp, gf, token_shift_all,
inp, token_shift_all,
hparams.n_embd_r(), n_seqs);
token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs);
@@ -1672,7 +1669,6 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
}
void llm_graph_context::build_pooling(
ggml_cgraph * gf,
ggml_tensor * cls,
ggml_tensor * cls_b,
ggml_tensor * cls_out,