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llama : consistently catch and throw only exceptions deriving from std::exception (#1599)

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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
mgroeber9110
2023-06-05 22:24:29 +02:00
committed by GitHub
parent 9d0693bce3
commit c2df36d60d
1 changed files with 30 additions and 29 deletions
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llama.cpp
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@@ -289,15 +289,15 @@ template <typename T>
static T checked_mul(T a, T b) { static T checked_mul(T a, T b) {
T ret = a * b; T ret = a * b;
if (a != 0 && ret / a != b) { if (a != 0 && ret / a != b) {
throw format("overflow multiplying %llu * %llu", throw std::runtime_error(format("overflow multiplying %llu * %llu",
(unsigned long long) a, (unsigned long long) b); (unsigned long long) a, (unsigned long long) b));
} }
return ret; return ret;
} }
static size_t checked_div(size_t a, size_t b) { static size_t checked_div(size_t a, size_t b) {
if (b == 0 || a % b != 0) { 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; return a / b;
} }
@@ -361,7 +361,7 @@ struct llama_load_tensor {
const auto & first_shard = shards.at(0); const auto & first_shard = shards.at(0);
for (const auto & shard : shards) { for (const auto & shard : shards) {
if (shard.type != first_shard.type) { 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; type = first_shard.type;
@@ -384,8 +384,8 @@ struct llama_load_tensor {
const auto & first_shard = shards.at(0); const auto & first_shard = shards.at(0);
for (const auto & shard : shards) { for (const auto & shard : shards) {
if (shard.ne != first_shard.ne) { if (shard.ne != first_shard.ne) {
throw format("inconsistent tensor shard shape in '%s': first was %s, other was %s", 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()); name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
} }
} }
ne = first_shard.ne; ne = first_shard.ne;
@@ -463,8 +463,8 @@ struct llama_file_loader {
} }
} }
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?", throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version); magic, version));
} }
void read_hparams() { void read_hparams() {
hparams.n_vocab = file.read_u32(); hparams.n_vocab = file.read_u32();
@@ -504,7 +504,7 @@ struct llama_file_loader {
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims); file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
std::string name = file.read_string(name_len); std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) { 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) { switch (shard.type) {
case GGML_TYPE_F32: case GGML_TYPE_F32:
@@ -521,7 +521,7 @@ struct llama_file_loader {
case GGML_TYPE_Q6_K: case GGML_TYPE_Q6_K:
break; break;
default: { default: {
throw format("unrecognized tensor type %u\n", shard.type); throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
} }
} }
@@ -630,7 +630,7 @@ struct llama_model_loader {
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map); auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
file_loaders.emplace_back(ith_file); file_loaders.emplace_back(ith_file);
if (ith_file->hparams != first_file->hparams) { 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) { if (!llama_mmap::SUPPORTED) {
@@ -660,7 +660,7 @@ struct llama_model_loader {
uint32_t guess_n_parts() const { uint32_t guess_n_parts() const {
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight"); auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
if (it == tensors_map.name_to_idx.end()) { 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); 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); return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
@@ -677,12 +677,12 @@ struct llama_model_loader {
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) { 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); auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) { 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); llama_load_tensor & lt = tensors_map.tensors.at(it->second);
if (lt.ne != ne) { if (lt.ne != ne) {
throw format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s", 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()); name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
} }
return get_tensor_for(lt, backend); return get_tensor_for(lt, backend);
@@ -706,7 +706,7 @@ struct llama_model_loader {
void done_getting_tensors() const { void done_getting_tensors() const {
if (num_ggml_tensors_created != tensors_map.tensors.size()) { 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"));
} }
} }
@@ -994,7 +994,7 @@ static void llama_model_load_internal(
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 && if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 && hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) { hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"); throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"));
} }
} }
@@ -1002,7 +1002,7 @@ static void llama_model_load_internal(
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 || hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) { hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"); throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
} }
} }
@@ -1033,7 +1033,7 @@ static void llama_model_load_internal(
model.ctx = ggml_init(params); model.ctx = ggml_init(params);
if (!model.ctx) { if (!model.ctx) {
throw format("ggml_init() failed"); throw std::runtime_error(format("ggml_init() failed"));
} }
} }
@@ -1214,8 +1214,8 @@ static bool llama_model_load(
llama_model_load_internal(fname, lctx, n_ctx, n_gpu_layers, memory_type, use_mmap, use_mlock, 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); vocab_only, progress_callback, progress_callback_user_data);
return true; return true;
} catch (const std::string & err) { } catch (const std::exception & err) {
fprintf(stderr, "error loading model: %s\n", err.c_str()); fprintf(stderr, "error loading model: %s\n", err.what());
return false; return false;
} }
} }
@@ -2120,8 +2120,9 @@ 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_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_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break; case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
// K-quants // K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; 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_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M: 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_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
@@ -2129,8 +2130,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break; 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_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; 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; case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
default: throw format("invalid output file type %d\n", ftype); default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
} }
if (nthread <= 0) { if (nthread <= 0) {
@@ -2231,7 +2232,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]); f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
} }
} else { } 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 .. "); printf("quantizing .. ");
@@ -2433,8 +2434,8 @@ int llama_model_quantize(
try { try {
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread); llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
return 0; return 0;
} catch (const std::string & err) { } catch (const std::exception & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str()); fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
return 1; return 1;
} }
} }
@@ -2687,8 +2688,8 @@ 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) { int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
try { 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) { } catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str()); fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1; return 1;
} }
} }