tqcq/llama.cpp
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memory : rename interface to llama_memory_context_i (#14296)

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* memory : rename interface to llama_memory_context_i

ggml-ci

* cont : fix comments

* cont : use "mctx" for referencing a memory context

ggml-ci
This commit is contained in:
Georgi Gerganov
2025-06-21 08:03:46 +03:00
committed by GitHub
parent b23fa0b3f4
commit 692e3cdd0a
14 changed files with 339 additions and 341 deletions
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28
src/llama-model.cpp
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@ -9171,9 +9171,9 @@ struct llm_build_mamba : public llm_graph_context {
ggml_tensor * cur,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
@ -9191,8 +9191,8 @@ struct llm_build_mamba : public llm_graph_context {
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
ggml_tensor * conv_states_all = kv_state->get_r_l(il);
ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
// (ab)using the KV cache to store the states
ggml_tensor * conv = build_rs(
@ -11916,7 +11916,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
ggml_tensor * x_prev,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
@ -11926,7 +11926,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
const auto n_head = n_embd / head_size;
const auto n_head_kv = hparams.n_head_kv(il);
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
@ -12038,7 +12038,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
}
ggml_tensor * wkv_state = build_rs(
inp, gf, kv_state->get_s_l(il),
inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output;
@ -12057,9 +12057,9 @@ struct llm_build_rwkv6_base : public llm_graph_context {
wkv_state,
ggml_view_1d(
ctx0,
kv_state->get_s_l(il),
mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);
@ -12313,7 +12313,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
@ -12322,7 +12322,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
const auto head_count = n_embd / head_size;
const auto n_seq_tokens = ubatch.n_seq_tokens;
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
@ -12393,7 +12393,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
ggml_tensor * wkv_state = build_rs(
inp, gf, kv_state->get_s_l(il),
inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
@ -12407,9 +12407,9 @@ struct llm_build_rwkv7_base : public llm_graph_context {
wkv_state,
ggml_view_1d(
ctx0,
kv_state->get_s_l(il),
mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);