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memory : handle saving/loading null layers in recurrent memory (#14675)

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* Update llama-memory-recurrent.cpp

handle saving/loading null layers in recurrent memory

* fixed styling issues and updated comments

* fix styling issue

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
l3utterfly
2025-07-23 16:16:41 +08:00
committed by GitHub
parent 6c88b3bb25
commit 7233358d29
1 changed files with 15 additions and 0 deletions
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15
src/llama-memory-recurrent.cpp
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@@ -768,6 +768,8 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
// Iterate and write all the keys first, each row is a cell
// Get whole range at a time
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (r_l[il] == nullptr) continue;
// Write key type
const int32_t r_type_i = (int32_t)r_l[il]->type;
@@ -787,6 +789,8 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
if (!s_trans) {
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (s_l[il] == nullptr) continue;
// Write value type
const int32_t s_type_i = (int32_t)s_l[il]->type;
@@ -807,6 +811,9 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
// When v is transposed, we also need the element size and get the element ranges from each row
const uint32_t mem_size = size;
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (s_l[il] == nullptr) continue;
const uint32_t n_embd_s = hparams.n_embd_s();
// Write value type
@@ -951,6 +958,8 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
// For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (r_l[il] == nullptr) continue;
// Read type of key
int32_t r_type_i_ref;
@@ -978,11 +987,14 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
if (!s_trans) {
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (s_l[il] == nullptr) continue;
// Read type of value
int32_t s_type_i_ref;
io.read_to(&s_type_i_ref, sizeof(s_type_i_ref));
const int32_t s_type_i = (int32_t)s_l[il]->type;
if (s_type_i != s_type_i_ref) {
LLAMA_LOG_ERROR("%s: mismatched s type (%d != %d, layer %d)\n", __func__, s_type_i, s_type_i_ref, il);
return false;
@@ -1005,6 +1017,9 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
} else {
// For each layer, read the values for each cell (transposed)
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (s_l[il] == nullptr) continue;
const uint32_t n_embd_s = hparams.n_embd_s();
// Read type of value