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Sync llama.cpp to 6986c7835adc13ba3f9d933b95671bb1f3984dc6

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Justine Tunney 2023-06-03 10:29:12 -07:00
parent 8f522cb702
commit 1904a3cae8
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3 changed files with 3666 additions and 357 deletions
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77
third_party/ggml/llama.cc vendored
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@ -105,26 +105,26 @@ static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
// 2*n_embd*n_ctx*n_layer*sizeof(float16)
static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
{
static std::map<e_model, size_t> _MEM_REQ_KV_SELF = {
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 1026ull * MB },
{ MODEL_13B, 1608ull * MB },
{ MODEL_30B, 3124ull * MB },
{ MODEL_65B, 5120ull * MB },
};
return _MEM_REQ_KV_SELF;
return k_sizes;
}
// this is mostly needed for temporary mul_mat buffers to dequantize the data
// not actually needed if BLAS is disabled
static const std::map<e_model, size_t> & MEM_REQ_EVAL()
{
static std::map<e_model, size_t> _MEM_REQ_EVAL = {
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 768ull * MB },
{ MODEL_13B, 1024ull * MB },
{ MODEL_30B, 1280ull * MB },
{ MODEL_65B, 1536ull * MB },
};
return _MEM_REQ_EVAL;
return k_sizes;
}
// default hparams (LLaMA 7B)
@ -681,7 +681,7 @@ struct llama_model_loader {
}
}
struct ggml_tensor * get_tensor(const std::string & name, std::vector<uint32_t> ne) {
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne) {
auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) {
Die("llama.cpp: tensor '%s' is missing from model", name.c_str());
@ -1131,7 +1131,7 @@ static bool llama_eval_internal(
const auto & model = lctx.model;
const auto & hparams = model.hparams;
auto & kv_self = model.kv_self;
const auto & kv_self = model.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
@ -1184,8 +1184,8 @@ static bool llama_eval_internal(
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
ggml_set_name(Qcur, "Qcur");
ggml_set_name(Kcur, "Kcur");
@ -1226,17 +1226,19 @@ static bool llama_eval_internal(
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
ggml_set_name(KQ_scale, "1/sqrt(n_embd/n_head)");
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
@ -1337,7 +1339,7 @@ static bool llama_eval_internal(
lctx.use_buf(ctx0, -1);
// logits -> probs
//inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
@ -1375,7 +1377,7 @@ static bool llama_eval_internal(
}
// extract embeddings
if (lctx.embedding.size()) {
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
embedding_out.resize(n_embd);
@ -1426,6 +1428,8 @@ struct llama_sp_symbol {
size_t n;
};
static_assert(std::is_trivially_copyable<llama_sp_symbol>::value, "llama_sp_symbol is not trivially copyable");
struct llama_sp_bigram {
struct comparator {
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
@ -1458,7 +1462,7 @@ struct llama_tokenizer {
sym.prev = index - 1;
sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols_.emplace_back(std::move(sym));
symbols_.emplace_back(sym);
}
// seed the work queue with all possible 2-character tokens.
@ -1549,7 +1553,7 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
llama_tokenizer tokenizer(vocab);
std::vector<llama_vocab::id> output;
if (text.size() == 0) {
if (text.empty()) {
return output;
}
@ -1785,7 +1789,7 @@ void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_dat
const int64_t t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < candidates->size; ++i) {
auto token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
if (token_iter == last_tokens + last_tokens_size) {
continue;
}
@ -1929,7 +1933,7 @@ llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_da
const int64_t t_start_sample_us = ggml_time_us();
// Find max element
auto max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
return a.logit < b.logit;
});
@ -2286,7 +2290,8 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
size_t ctx_size, mmapped_size;
size_t ctx_size;
size_t mmapped_size;
model_loader->calc_sizes(&ctx_size, &mmapped_size);
base_buf.resize(ctx_size);
@ -2325,8 +2330,12 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
}
std::string name(length, 0);
fin.read(&name[0], length);
std::string name;
{
char buf[1024];
fin.read(buf, length);
name = std::string(buf, length);
}
// check for lora suffix and get the type of tensor
const std::string lora_suffix = ".lora";
@ -2341,7 +2350,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
base_name.erase(pos);
// fprintf(stderr, "%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name.data()) == model_tensors.end()) {
if (model_tensors.find(base_name) == model_tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return 1;
}
@ -2421,7 +2430,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
BA = ggml_scale(lora_ctx, BA, scale_tensor);
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
}
ggml_tensor * r;
@ -2443,8 +2452,9 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
lora_tensors.clear();
n_tensors++;
if (n_tensors % 4 == 0)
if (n_tensors % 4 == 0) {
fprintf(stderr, ".");
}
}
}
@ -2462,7 +2472,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
// try {
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
// } catch (const std::string & err) {
// fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str());
// return 1;
@ -2473,7 +2483,7 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
return ctx->model.kv_self.n;
}
#define LLAMA_MAX_RNG_STATE 64*1024
#define LLAMA_MAX_RNG_STATE (64*1024)
void llama_set_rng_seed(struct llama_context * ctx, int seed) {
if (seed < 0) {
@ -2482,7 +2492,7 @@ void llama_set_rng_seed(struct llama_context * ctx, int seed) {
ctx->rng.seed(seed);
}
// Returns the size of the state
// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
@ -2514,8 +2524,8 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
}
// Copies the state to the specified destination address
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
uint8_t * out = dest;
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
uint8_t * out = dst;
// copy rng
{
@ -2575,9 +2585,10 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
if (kv_size) {
const size_t elt_size = ggml_element_size(kv_self.k);
llama_buffer buffer;
buffer.resize(4096);
ggml_context * cpy_ctx = ggml_init({ buffer.size, buffer.addr, /* no_alloc */ true });
char buffer[4096];
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
@ -2600,10 +2611,12 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_free(cpy_ctx);
}
}
const size_t written = out - dest;
const size_t written = out - dst;
const size_t max_size = llama_get_state_size(ctx);
LLAMA_ASSERT(written <= max_size);