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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	CMakeLists.txt
#	Makefile
#	README.md
#	docs/BLIS.md
#	llama.cpp
#	tests/test-quantize-fns.cpp
This commit is contained in:
Concedo 2023-06-06 23:12:01 +08:00
commit ed603dcafc
24 changed files with 4869 additions and 141 deletions
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283
llama.cpp
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@ -16,6 +16,10 @@
#include "ggml-opencl.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <array>
#include <ctime>
#include <cinttypes>
@ -49,7 +53,6 @@ enum e_model {
MODEL_65B,
};
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
@ -243,6 +246,10 @@ struct llama_context {
llama_ctx_buffer buf_compute;
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
#endif
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
@ -282,15 +289,15 @@ template <typename T>
static T checked_mul(T a, T b) {
T ret = a * b;
if (a != 0 && ret / a != b) {
throw format("overflow multiplying %llu * %llu",
(unsigned long long) a, (unsigned long long) b);
throw std::runtime_error(format("overflow multiplying %llu * %llu",
(unsigned long long) a, (unsigned long long) b));
}
return ret;
}
static size_t checked_div(size_t a, size_t b) {
if (b == 0 || a % b != 0) {
throw format("error dividing %zu / %zu", a, b);
throw std::runtime_error(format("error dividing %zu / %zu", a, b));
}
return a / b;
}
@ -354,7 +361,7 @@ struct llama_load_tensor {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.type != first_shard.type) {
throw format("inconsistent tensor shard type in '%s'", name.c_str());
throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
}
}
type = first_shard.type;
@ -377,8 +384,8 @@ struct llama_load_tensor {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.ne != first_shard.ne) {
throw format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str());
throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
}
}
ne = first_shard.ne;
@ -456,8 +463,8 @@ struct llama_file_loader {
}
}
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version);
throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version));
}
void read_hparams() {
hparams.n_vocab = file.read_u32();
@ -497,7 +504,7 @@ struct llama_file_loader {
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
}
switch (shard.type) {
case GGML_TYPE_F32:
@ -507,9 +514,14 @@ struct llama_file_loader {
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
break;
default: {
throw format("unrecognized tensor type %u\n", shard.type);
throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
}
}
@ -582,6 +594,11 @@ struct llama_file_saver {
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
break;
default: LLAMA_ASSERT(false);
}
@ -613,7 +630,7 @@ struct llama_model_loader {
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
file_loaders.emplace_back(ith_file);
if (ith_file->hparams != first_file->hparams) {
throw format("llama.cpp: hparams inconsistent between files");
throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
}
}
if (!llama_mmap::SUPPORTED) {
@ -643,7 +660,7 @@ struct llama_model_loader {
uint32_t guess_n_parts() const {
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
if (it == tensors_map.name_to_idx.end()) {
throw std::string("missing tok_embeddings.weight");
throw std::runtime_error(std::string("missing tok_embeddings.weight"));
}
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
@ -660,12 +677,12 @@ struct llama_model_loader {
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) {
throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
}
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
if (lt.ne != ne) {
throw format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str());
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
}
return get_tensor_for(lt, backend);
@ -689,7 +706,7 @@ struct llama_model_loader {
void done_getting_tensors() const {
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
throw std::string("llama.cpp: file contained more tensors than expected");
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
}
}
@ -898,6 +915,16 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
default: return "unknown, may not work";
}
}
@ -1006,7 +1033,7 @@ static void llama_model_load_internal(
model.ctx = ggml_init(params);
if (!model.ctx) {
throw format("ggml_init() failed");
throw std::runtime_error(format("ggml_init() failed"));
}
}
@ -1088,7 +1115,7 @@ static void llama_model_load_internal(
mmapped_size - vram_total + // weights in VRAM not in memory
MEM_REQ_SCRATCH0().at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
MEM_REQ_EVAL().at (model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
@ -1187,25 +1214,27 @@ static bool llama_model_load(
llama_model_load_internal(fname, lctx, n_ctx, n_gpu_layers, memory_type, use_mmap, use_mlock,
vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::string & err) {
fprintf(stderr, "error loading model: %s\n", err.c_str());
} catch (const std::exception & err) {
fprintf(stderr, "error loading model: %s\n", err.what());
return false;
}
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
// - cgraph_fname: filename of the exported computation graph
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads) {
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads,
const char * cgraph_fname) {
// // enforce that the first token is BOS
// if (n_past == 0 && tokens[0] != llama_token_bos()) {
@ -1251,13 +1280,12 @@ static bool llama_eval_internal(
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
struct ggml_tensor * cur;
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
lctx.use_buf(ctx0, 0);
// norm
@ -1271,6 +1299,7 @@ static bool llama_eval_internal(
// self-attention
{
// compute Q and K and RoPE them
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");
@ -1280,6 +1309,7 @@ static bool llama_eval_internal(
{
// compute the transposed [N, n_embd] V matrix
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
ggml_set_name(Vcur, "Vcur");
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
@ -1325,7 +1355,6 @@ static bool llama_eval_internal(
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,
@ -1407,26 +1436,55 @@ static bool llama_eval_internal(
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
inpL = ggml_rms_norm(ctx0, inpL);
// cur = cur*norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.norm);
// inpL = inpL*norm(broadcasted)
inpL = ggml_mul(ctx0, inpL, model.norm);
embeddings = inpL;
embeddings = cur;
}
// lm_head
inpL = ggml_mul_mat(ctx0, model.output, inpL);
cur = ggml_mul_mat(ctx0, model.output, cur);
lctx.use_buf(ctx0, -1);
// logits -> probs
//inpL = ggml_soft_max_inplace(ctx0, inpL);
//cur = ggml_soft_max_inplace(ctx0, cur);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_build_forward_expand(&gf, cur);
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
ggml_metal_graph_compute(lctx.ctx_metal, &gf);
ggml_metal_get_tensor (lctx.ctx_metal, cur);
} else {
// IMPORTANT:
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
// coprocessor.
//
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
// But for now, we have focused only on Matrix x Vector Metal multiplication.
//
// TODO: avoid these syncs via shared memory (ref #1696)
//
if (lctx.ctx_metal) {
// We need to sync the GPU KV cache with the CPU KV cache
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
}
ggml_graph_compute(ctx0, &gf);
}
#else
ggml_graph_compute(ctx0, &gf);
#endif
if (cgraph_fname) {
ggml_graph_export(&gf, cgraph_fname);
}
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
@ -1440,7 +1498,7 @@ static bool llama_eval_internal(
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
// update kv token count
lctx.model.kv_self.n = n_past + N;
@ -1451,11 +1509,11 @@ static bool llama_eval_internal(
if (lctx.logits_all) {
logits_out.resize(n_vocab * N);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
memcpy(logits_out.data(), (float *) ggml_get_data(cur), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(cur) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
}
@ -2062,8 +2120,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
default: throw format("invalid output file type %d\n", ftype);
};
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
@ -2073,6 +2142,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
int n_attention_wv = 0;
int n_feed_forward_w2 = 0;
for (auto& tensor : model_loader->tensors_map.tensors) {
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
++n_attention_wv;
}
else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
++n_feed_forward_w2;
}
}
int i_attention_wv = 0;
int i_feed_forward_w2 = 0;
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
@ -2115,6 +2198,32 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
// TODO: temporary disabled until Metal / OpenCL support is available
// ref: https://github.com/ggerganov/llama.cpp/issues/1711
//if (tensor.name == "output.weight") {
// new_type = GGML_TYPE_Q6_K;
//}
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 ||
(i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
++i_attention_wv;
}
if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
(i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 ||
(i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
++i_feed_forward_w2;
}
if (tensor.name.find("attention.wo.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
}
float * f32_data;
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
llama_buffer f32_conv_buf;
@ -2128,7 +2237,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
}
} else {
throw format("type %s unsupported for integer quantization", ggml_type_name(tensor.type));
throw std::runtime_error(format("type %s unsupported for integer quantization", ggml_type_name(tensor.type)));
}
printf("quantizing .. ");
@ -2182,12 +2291,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
tot_count += hist_cur[i];
}
for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements));
if (tot_count > 0) {
for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements));
}
}
printf("\n");
}
@ -2205,11 +2318,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all));
if (sum_all > 0) {
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all));
}
printf("\n");
}
printf("\n");
}
}
@ -2251,8 +2366,8 @@ struct llama_context * llama_init_from_file(
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_gpu_layers, memory_type,
params.use_mmap, params.use_mlock, params.vocab_only,
params.progress_callback, params.progress_callback_user_data)) {
params.use_mmap, params.use_mlock, params.vocab_only,
params.progress_callback, params.progress_callback_user_data)) {
fprintf(stderr, "%s: failed to load model\n", __func__);
llama_free(ctx);
return nullptr;
@ -2290,6 +2405,38 @@ struct llama_context * llama_init_from_file(
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
}
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
// this allocates all Metal resources and memory buffers
ctx->ctx_metal = ggml_metal_init();
void *data_ptr = NULL;
size_t data_size = 0;
if (params.use_mmap) {
data_ptr = ctx->model.mapping->addr;
data_size= ctx->model.mapping->size;
} else {
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
data_size= ggml_get_mem_size(ctx->model.ctx);
}
#define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
llama_free(ctx); \
return NULL; \
}
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size));
#undef LLAMA_METAL_CHECK_BUF
}
#endif
return ctx;
}
@ -2305,8 +2452,8 @@ int llama_model_quantize(
try {
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
return 1;
}
}
@ -2559,8 +2706,8 @@ 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);
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str());
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
@ -2905,7 +3052,7 @@ int llama_eval(
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
@ -2920,6 +3067,20 @@ int llama_eval(
return 0;
}
int llama_eval_export(struct llama_context * ctx, const char * fname) {
const int n_batch = 1;
const int n_ctx = 512 - n_batch;
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
return 0;
}
int llama_tokenize(
struct llama_context * ctx,
const char * text,