mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-19 11:57:07 +00:00
kv_cache : move state read/write to llama_kv_cache
ggml-ci
This commit is contained in:
Georgi Gerganov
@@ -8,6 +8,7 @@
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#include <algorithm>
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#include <limits>
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#include <map>
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#include <stdexcept>
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static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
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@@ -696,6 +697,383 @@ size_t llama_kv_cache::size_v_bytes() const {
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return size_v_bytes;
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}
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void llama_kv_cache::state_write(const io & io, const llama_hparams & hparams, llama_seq_id seq_id) const {
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std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
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uint32_t cell_count = 0;
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// Count the number of cells with the specified seq_id
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// Find all the ranges of cells with this seq id (or all, when -1)
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uint32_t cell_range_begin = size;
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for (uint32_t i = 0; i < size; ++i) {
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const auto & cell = cells[i];
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if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
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++cell_count;
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if (cell_range_begin == size) {
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cell_range_begin = i;
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}
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} else {
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if (cell_range_begin != size) {
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cell_ranges.emplace_back(cell_range_begin, i);
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cell_range_begin = size;
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}
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}
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}
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if (cell_range_begin != size) {
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cell_ranges.emplace_back(cell_range_begin, size);
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}
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// DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
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uint32_t cell_count_check = 0;
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for (const auto & range : cell_ranges) {
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cell_count_check += range.second - range.first;
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}
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GGML_ASSERT(cell_count == cell_count_check);
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io.write(&cell_count, sizeof(cell_count));
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state_write_meta(io, cell_ranges, seq_id);
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state_write_data(io, cell_ranges, hparams);
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}
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void llama_kv_cache::state_read(const io & io, const llama_hparams & hparams, llama_seq_id seq_id) {
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uint32_t cell_count;
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io.read_to(&cell_count, sizeof(cell_count));
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bool res = true;
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res = res && state_read_meta(io, cell_count, seq_id);
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res = res && state_read_data(io, hparams, cell_count);
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if (!res) {
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if (seq_id == -1) {
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clear();
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} else {
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seq_rm(seq_id, -1, -1);
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}
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throw std::runtime_error("failed to restore kv cache");
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}
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}
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void llama_kv_cache::state_write_meta(const io & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id) const {
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for (const auto & range : cell_ranges) {
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for (uint32_t i = range.first; i < range.second; ++i) {
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const auto & cell = cells[i];
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const llama_pos pos = cell.pos;
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const uint32_t n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
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io.write(&pos, sizeof(pos));
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io.write(&n_seq_id, sizeof(n_seq_id));
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if (n_seq_id) {
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for (auto seq_id : cell.seq_id) {
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io.write(&seq_id, sizeof(seq_id));
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}
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}
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}
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}
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}
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void llama_kv_cache::state_write_data(const io & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, const llama_hparams & hparams) const {
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const uint32_t v_trans = this->v_trans ? 1 : 0;
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const uint32_t n_layer = hparams.n_layer;
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io.write(&v_trans, sizeof(v_trans));
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io.write(&n_layer, sizeof(n_layer));
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std::vector<uint8_t> tmp_buf;
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// Iterate and write all the keys first, each row is a cell
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// Get whole range at a time
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
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// Write key type
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const int32_t k_type_i = (int32_t)k_l[il]->type;
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io.write(&k_type_i, sizeof(k_type_i));
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// Write row size of key
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const uint64_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
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io.write(&k_size_row, sizeof(k_size_row));
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// Read each range of cells of k_size length each into tmp_buf and write out
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for (const auto & range : cell_ranges) {
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const size_t range_size = range.second - range.first;
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const size_t buf_size = range_size * k_size_row;
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io.write_tensor_data(k_l[il], range.first * k_size_row, buf_size);
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}
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}
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if (!v_trans) {
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
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// Write value type
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const int32_t v_type_i = (int32_t)v_l[il]->type;
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io.write(&v_type_i, sizeof(v_type_i));
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// Write row size of value
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const uint64_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa);
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io.write(&v_size_row, sizeof(v_size_row));
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// Read each range of cells of v_size length each into tmp_buf and write out
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for (const auto & range : cell_ranges) {
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const size_t range_size = range.second - range.first;
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const size_t buf_size = range_size * v_size_row;
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io.write_tensor_data(v_l[il], range.first * v_size_row, buf_size);
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}
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}
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} else {
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// When v is transposed, we also need the element size and get the element ranges from each row
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const uint32_t kv_size = size;
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
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// Write value type
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const int32_t v_type_i = (int32_t)v_l[il]->type;
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io.write(&v_type_i, sizeof(v_type_i));
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// Write element size
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const uint32_t v_size_el = ggml_type_size(v_l[il]->type);
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io.write(&v_size_el, sizeof(v_size_el));
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// Write GQA embedding size
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io.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
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// For each row, we get the element values of each cell
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for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
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// Read each range of cells of v_size_el length each into tmp_buf and write out
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for (const auto & range : cell_ranges) {
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const size_t range_size = range.second - range.first;
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const size_t src_offset = (range.first + j * kv_size) * v_size_el;
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const size_t buf_size = range_size * v_size_el;
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io.write_tensor_data(v_l[il], src_offset, buf_size);
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}
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}
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}
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}
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}
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bool llama_kv_cache::state_read_meta(const io & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
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if (dest_seq_id != -1) {
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// single sequence
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seq_rm(dest_seq_id, -1, -1);
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llama_sbatch sbatch;
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llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
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batch.n_tokens = cell_count;
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batch.n_seq_tokens = cell_count;
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batch.n_seqs = 1;
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for (uint32_t i = 0; i < cell_count; ++i) {
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llama_pos pos;
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uint32_t n_seq_id;
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io.read_to(&pos, sizeof(pos));
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io.read_to(&n_seq_id, sizeof(n_seq_id));
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if (n_seq_id != 0) {
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LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
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return false;
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}
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batch.pos[i] = pos;
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}
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batch.n_seq_id[0] = 1;
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batch.seq_id[0] = &dest_seq_id;
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if (!find_slot(batch)) {
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LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
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return false;
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}
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// DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
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// Assume that this is one contiguous block of cells
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GGML_ASSERT(head + cell_count <= size);
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GGML_ASSERT(cells[head].pos == batch.pos[0]);
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GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]);
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GGML_ASSERT(cells[head].has_seq_id(dest_seq_id));
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GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id));
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} else {
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// whole KV cache restore
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if (cell_count > size) {
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LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
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return false;
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}
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clear();
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for (uint32_t i = 0; i < cell_count; ++i) {
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llama_kv_cell & cell = cells[i];
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llama_pos pos;
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uint32_t n_seq_id;
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io.read_to(&pos, sizeof(pos));
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io.read_to(&n_seq_id, sizeof(n_seq_id));
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cell.pos = pos;
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for (uint32_t j = 0; j < n_seq_id; ++j) {
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llama_seq_id seq_id;
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io.read_to(&seq_id, sizeof(seq_id));
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// TODO: llama_kv_cache should have a notion of max sequences
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//if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
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if (seq_id < 0) {
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//LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
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LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, seq_id);
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return false;
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}
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cell.seq_id.insert(seq_id);
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if (recurrent) {
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int32_t & tail = cells[seq_id].tail;
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if (tail != -1) {
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LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
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return false;
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}
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tail = i;
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}
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}
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}
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head = 0;
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used = cell_count;
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}
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if (recurrent) {
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for (uint32_t i = 0; i < cell_count; ++i) {
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uint32_t cell_id = head + i;
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// make sure the recurrent states will keep their restored state
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cells[cell_id].src = cell_id;
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}
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}
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return true;
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}
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bool llama_kv_cache::state_read_data(const io & io, const llama_hparams & hparams, uint32_t cell_count) {
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uint32_t v_trans;
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uint32_t n_layer;
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io.read_to(&v_trans, sizeof(v_trans));
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io.read_to(&n_layer, sizeof(n_layer));
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if (n_layer != hparams.n_layer) {
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LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
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return false;
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}
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if (cell_count > size) {
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LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size);
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return false;
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}
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if (v_trans != (bool) v_trans) {
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LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
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return false;
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}
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// For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
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// Read type of key
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int32_t k_type_i_ref;
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io.read_to(&k_type_i_ref, sizeof(k_type_i_ref));
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const int32_t k_type_i = (int32_t) k_l[il]->type;
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if (k_type_i != k_type_i_ref) {
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LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
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return false;
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}
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// Read row size of key
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uint64_t k_size_row_ref;
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io.read_to(&k_size_row_ref, sizeof(k_size_row_ref));
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const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
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if (k_size_row != k_size_row_ref) {
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LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
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return false;
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}
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if (cell_count) {
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// Read and set the keys for the whole cell range
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ggml_backend_tensor_set(k_l[il], io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
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}
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}
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if (!v_trans) {
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
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// Read type of value
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int32_t v_type_i_ref;
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io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
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const int32_t v_type_i = (int32_t)v_l[il]->type;
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if (v_type_i != v_type_i_ref) {
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LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
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return false;
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}
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// Read row size of value
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uint64_t v_size_row_ref;
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io.read_to(&v_size_row_ref, sizeof(v_size_row_ref));
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const size_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa);
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if (v_size_row != v_size_row_ref) {
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LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
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return false;
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}
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if (cell_count) {
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// Read and set the values for the whole cell range
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ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row);
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}
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}
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} else {
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// For each layer, read the values for each cell (transposed)
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for (uint32_t il = 0; il < n_layer; ++il) {
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
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// Read type of value
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int32_t v_type_i_ref;
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io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
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const int32_t v_type_i = (int32_t)v_l[il]->type;
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if (v_type_i != v_type_i_ref) {
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LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
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return false;
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}
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// Read element size of value
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uint32_t v_size_el_ref;
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io.read_to(&v_size_el_ref, sizeof(v_size_el_ref));
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const size_t v_size_el = ggml_type_size(v_l[il]->type);
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if (v_size_el != v_size_el_ref) {
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LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
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return false;
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}
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// Read GQA embedding size
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uint32_t n_embd_v_gqa_ref;
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io.read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
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if (n_embd_v_gqa != n_embd_v_gqa_ref) {
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LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
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return false;
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}
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if (cell_count) {
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// For each row in the transposed matrix, read the values for the whole cell range
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for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
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const size_t dst_offset = (head + j * size) * v_size_el;
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ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
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}
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}
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}
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}
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return true;
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}
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/////////////
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void llama_kv_cache_clear(llama_kv_cache * kv) {
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kv->clear();
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}
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