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llama: update llama.cpp vendor code to commit d7cfe1ff (#9356)

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This commit is contained in:
Jeffrey Morgan
2025-02-26 20:34:44 -08:00
committed by GitHub
parent 2db96c18e7
commit d7d7e99662
149 changed files with 18215 additions and 11009 deletions
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80
llama/llama.cpp/src/llama-kv-cache.cpp vendored
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@@ -72,39 +72,6 @@ bool llama_kv_cache_init(
cache.v_l.reserve(n_layer);
for (int i = 0; i < n_layer; i++) {
// for cross attention layers
if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const llama_model::buft_list_t * buft_list;
if (offload) {
buft_list = model.dev_layer.at(i).buft_list;
} else {
buft_list = &model.cpu_buft_list;
}
ggml_backend_buffer_type_t buft = select_buft(*buft_list,
[&](ggml_context * ctx) {
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
if (hparams.rope_type == LLAMA_ROPE_TYPE_NONE) {
return k;
}
ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
return ggml_rope(ctx, k, p, hparams.n_rot, hparams.rope_type);
});
ggml_context * ctx = ctx_for_buft(buft);
if (!ctx) {
LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
return false;
}
ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
ggml_format_name(k, "cache_k_l%d", i);
ggml_format_name(v, "cache_v_l%d", i);
cache.k_l.push_back(k);
cache.v_l.push_back(v);
continue;
}
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
@@ -112,7 +79,7 @@ bool llama_kv_cache_init(
ggml_backend_buffer_type_t buft;
if (offload) {
auto * dev = model.dev_layer.at(i).dev;
auto * dev = model.dev_layer(i);
buft = ggml_backend_dev_buffer_type(dev);
} else {
buft = ggml_backend_cpu_buffer_type();
@@ -124,8 +91,17 @@ bool llama_kv_cache_init(
return false;
}
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
ggml_tensor * k, *v;
// for cross attention layers
if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
} else {
k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
}
ggml_format_name(k, "cache_k_l%d", i);
ggml_format_name(v, "cache_v_l%d", i);
cache.k_l.push_back(k);
@@ -152,10 +128,10 @@ bool llama_kv_cache_init(
struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
struct llama_kv_cache & cache,
const struct llama_ubatch & batch) {
const uint32_t n_tokens = batch.n_tokens;
const uint32_t n_seqs = batch.n_seqs;
const uint32_t n_seq_tokens = batch.n_seq_tokens;
const struct llama_ubatch & ubatch) {
const uint32_t n_tokens = ubatch.n_tokens;
const uint32_t n_seqs = ubatch.n_seqs;
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
if (cache.recurrent) {
// For recurrent state architectures (like Mamba or RWKV),
@@ -163,16 +139,16 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
// A slot should be always be contiguous.
// can only process batches with an equal number of new tokens in each sequence
GGML_ASSERT(batch.equal_seqs);
GGML_ASSERT(ubatch.equal_seqs);
int32_t min = cache.size - 1;
int32_t max = 0;
// everything should fit if all seq_ids are smaller than the max
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t n_seq_id = batch.n_seq_id[s];
const uint32_t n_seq_id = ubatch.n_seq_id[s];
for (uint32_t j = 0; j < n_seq_id; ++j) {
const llama_seq_id seq_id = batch.seq_id[s][j];
const llama_seq_id seq_id = ubatch.seq_id[s][j];
if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
// too big seq_id
@@ -231,7 +207,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
// find usable cell range
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
llama_kv_cell & seq_meta = cache.cells[seq_id];
bool has_cell = false;
if (seq_meta.tail >= 0) {
@@ -270,7 +246,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
// gather and re-order
for (uint32_t s = 0; s < n_seqs; ++s) {
int32_t dst_id = s + min;
int32_t src_id = cache.cells[batch.seq_id[s][0]].tail;
int32_t src_id = cache.cells[ubatch.seq_id[s][0]].tail;
if (dst_id != src_id) {
llama_kv_cell & dst_cell = cache.cells[dst_id];
llama_kv_cell & src_cell = cache.cells[src_id];
@@ -291,7 +267,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
// update the pos of the used seqs
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1];
const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
int32_t cell_id = s + min;
llama_kv_cell & cell = cache.cells[cell_id];
@@ -299,12 +275,12 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
// What should happen when the pos backtracks or skips a value?
// Clearing the state mid-batch would require special-casing which isn't done.
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
__func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens);
__func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
}
cell.pos = last_pos;
cell.seq_id.clear();
for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) {
const llama_seq_id seq_id = batch.seq_id[s][j];
for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[s][j];
cell.seq_id.insert(seq_id);
cache.cells[seq_id].tail = cell_id;
}
@@ -358,10 +334,10 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
for (uint32_t s = 0; s < n_seqs; s++) {
for (uint32_t i = 0; i < n_seq_tokens; ++i) {
uint32_t k = s*n_seq_tokens + i;
cache.cells[cache.head + k].pos = batch.pos[k];
cache.cells[cache.head + k].pos = ubatch.pos[k];
for (int32_t j = 0; j < batch.n_seq_id[s]; j++) {
cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]);
for (int32_t j = 0; j < ubatch.n_seq_id[s]; j++) {
cache.cells[cache.head + k].seq_id.insert(ubatch.seq_id[s][j]);
}
}
}