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llama : fix session saving/loading (#3400)

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* llama : fix session saving/loading

* llama : temp fix for clearing "future" tokens from the KV cache

* llama : fix handling of "future" tokens when loading sessions

* llama : fix comments for llama_kv_cache API
This commit is contained in:
Georgi Gerganov
2023-10-03 21:04:01 +03:00
committed by GitHub
parent 48be797ffb
commit ac2219fef3
7 changed files with 106 additions and 59 deletions
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134
llama.cpp
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@@ -1283,8 +1283,8 @@ static bool llama_kv_cache_init(
// find an empty slot of size "n_tokens" in the cache
// updates the cache head
static bool llama_kv_cache_find_slot(
struct llama_kv_cache & cache,
const struct llama_batch & batch) {
struct llama_kv_cache & cache,
const struct llama_batch & batch) {
const uint32_t n_ctx = cache.size;
const uint32_t n_tokens = batch.n_tokens;
@@ -1352,10 +1352,13 @@ static void llama_kv_cache_tokens_rm(struct llama_kv_cache & cache, int32_t c0,
}
static void llama_kv_cache_seq_rm(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1) {
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1) {
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.cells[i].seq_id.erase(seq_id);
@@ -1367,11 +1370,14 @@ static void llama_kv_cache_seq_rm(
}
static void llama_kv_cache_seq_cp(
struct llama_kv_cache & cache,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1) {
struct llama_kv_cache & cache,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1) {
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.cells[i].seq_id.insert(seq_id_dst);
@@ -1389,11 +1395,14 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id
}
static void llama_kv_cache_seq_shift(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta) {
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta) {
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.cells[i].pos += delta;
@@ -7209,16 +7218,6 @@ struct llama_data_file_context : llama_data_context {
*
*/
static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
// TODO: does not support multi-sequence states
{
const auto & kv_self = ctx->kv_self;
for (uint32_t i = 0; i < kv_self.head; ++i) {
GGML_ASSERT(kv_self.cells[i].pos == (int32_t) i);
GGML_ASSERT(kv_self.cells[i].seq_id.size() == 1);
GGML_ASSERT(kv_self.cells[i].has_seq_id(0));
}
}
// copy rng
{
std::stringstream rng_ss;
@@ -7271,36 +7270,38 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
const auto & hparams = ctx->model.hparams;
const auto & cparams = ctx->cparams;
const int n_layer = hparams.n_layer;
const int n_embd = hparams.n_embd_gqa();
const int n_ctx = cparams.n_ctx;
const auto n_layer = hparams.n_layer;
const auto n_embd = hparams.n_embd_gqa();
const auto n_ctx = cparams.n_ctx;
const size_t kv_size = kv_self.buf.size;
const int kv_ntok = kv_self.head;
const size_t kv_buf_size = kv_self.buf.size;
const uint32_t kv_head = kv_self.head;
const uint32_t kv_size = kv_self.size;
data_ctx->write(&kv_size, sizeof(kv_size));
data_ctx->write(&kv_ntok, sizeof(kv_ntok));
data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
data_ctx->write(&kv_head, sizeof(kv_head));
data_ctx->write(&kv_size, sizeof(kv_size));
if (kv_size) {
if (kv_buf_size) {
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
kout3d->data = kout3d_data.data();
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
vout3d->data = vout3d_data.data();
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
n_embd, kv_head, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
kv_head, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
@@ -7314,6 +7315,20 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
data_ctx->write(kout3d_data.data(), kout3d_data.size());
data_ctx->write(vout3d_data.data(), vout3d_data.size());
}
for (uint32_t i = 0; i < kv_size; ++i) {
const auto & cell = kv_self.cells[i];
const llama_pos pos = cell.pos;
const size_t seq_id_size = cell.seq_id.size();
data_ctx->write(&pos, sizeof(pos));
data_ctx->write(&seq_id_size, sizeof(seq_id_size));
for (auto seq_id : cell.seq_id) {
data_ctx->write(&seq_id, sizeof(seq_id));
}
}
}
}
@@ -7385,34 +7400,36 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
const int n_embd = hparams.n_embd_gqa();
const int n_ctx = cparams.n_ctx;
size_t kv_size;
int kv_ntok;
size_t kv_buf_size;
uint32_t kv_head;
uint32_t kv_size;
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
memcpy(&kv_ntok, inp, sizeof(kv_ntok)); inp += sizeof(kv_ntok);
memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head);
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
if (kv_size) {
GGML_ASSERT(kv_self.buf.size == kv_size);
if (kv_buf_size) {
GGML_ASSERT(kv_self.buf.size == kv_buf_size);
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
kin3d->data = (void *) inp;
inp += ggml_nbytes(kin3d);
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
vin3d->data = (void *) inp;
inp += ggml_nbytes(vin3d);
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
n_embd, kv_head, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
kv_head, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
@@ -7422,8 +7439,27 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
ggml_free(cpy_ctx);
}
ctx->kv_self.head = kv_ntok;
ctx->kv_self.head = kv_head;
ctx->kv_self.size = kv_size;
ctx->kv_self.cells.resize(kv_size);
for (uint32_t i = 0; i < kv_size; ++i) {
llama_pos pos;
size_t seq_id_size;
memcpy(&pos, inp, sizeof(pos)); inp += sizeof(pos);
memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
ctx->kv_self.cells[i].pos = pos;
llama_seq_id seq_id;
for (size_t j = 0; j < seq_id_size; ++j) {
memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
ctx->kv_self.cells[i].seq_id.insert(seq_id);
}
}
}
const size_t nread = inp - src;