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code : normalize enum names (#5697)

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* coda : normalize enum names

ggml-ci

* code : cont

* code : cont
This commit is contained in:
Georgi Gerganov
2024-02-25 12:09:09 +02:00
committed by GitHub
parent 69917dfa55
commit ab336a9d5e
20 changed files with 502 additions and 502 deletions
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64
llama.cpp
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@@ -850,9 +850,9 @@ struct LLM_TN {
//
static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
{ LLAMA_ROPE_SCALING_NONE, "none" },
{ LLAMA_ROPE_SCALING_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_YARN, "yarn" },
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
};
static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
@@ -862,7 +862,7 @@ static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
}
}
return LLAMA_ROPE_SCALING_UNSPECIFIED;
return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
}
static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
@@ -1580,7 +1580,7 @@ struct llama_hparams {
bool causal_attn = true;
bool need_kq_pos = false;
uint32_t pooling_type = LLAMA_POOLING_NONE;
uint32_t pooling_type = LLAMA_POOLING_TYPE_NONE;
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
@@ -2345,9 +2345,9 @@ namespace GGUFMeta {
static const char * override_type_to_str(const llama_model_kv_override_type ty) {
switch (ty) {
case LLAMA_KV_OVERRIDE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_INT: return "int";
case LLAMA_KV_OVERRIDE_FLOAT: return "float";
case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
}
return "unknown";
}
@@ -2358,13 +2358,13 @@ namespace GGUFMeta {
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
__func__, override_type_to_str(override->tag), override->key);
switch (override->tag) {
case LLAMA_KV_OVERRIDE_BOOL: {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_INT: {
case LLAMA_KV_OVERRIDE_TYPE_INT: {
LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value);
} break;
case LLAMA_KV_OVERRIDE_FLOAT: {
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
LLAMA_LOG_INFO("%.6f\n", override->float_value);
} break;
default:
@@ -2383,7 +2383,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, override)) {
target = override->bool_value;
return true;
}
@@ -2393,7 +2393,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, override)) {
target = override->int_value;
return true;
}
@@ -2403,7 +2403,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, override)) {
target = override->float_value;
return true;
}
@@ -2999,7 +2999,7 @@ static void llm_load_hparams(
std::string rope_scaling("linear");
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
// rope_freq_scale (inverse of the kv) is optional
float ropescale = 0.0f;
@@ -3643,7 +3643,7 @@ static bool llm_load_tensors(
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
}
if (split_mode == LLAMA_SPLIT_LAYER) {
if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
// calculate the split points
int device_count = llama_get_device_count();
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
@@ -3682,10 +3682,10 @@ static bool llm_load_tensors(
}
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_ROW) {
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
} else {
// LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported
// LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
split_buft = llama_default_buffer_type_offload(main_gpu);
}
// assign the repeating layers
@@ -5070,7 +5070,7 @@ struct llm_build_context {
kv_head (worst_case ? n_ctx - n_tokens : kv_self.head),
n_orig_ctx (cparams.n_yarn_orig_ctx),
do_rope_shift (worst_case || kv_self.has_shift),
pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE),
pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_TYPE_NONE),
cb (cb),
buf_compute_meta (lctx.buf_compute_meta) {
// all initializations should be done in init()
@@ -6050,12 +6050,12 @@ struct llm_build_context {
cur = inpL;
// pooling layer
if (pooling_type == LLAMA_POOLING_MEAN) {
if (pooling_type == LLAMA_POOLING_TYPE_MEAN) {
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
} else if (pooling_type == LLAMA_POOLING_CLS) {
} else if (pooling_type == LLAMA_POOLING_TYPE_CLS) {
cur = ggml_get_rows(ctx0, cur, inp_cls);
} else {
GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type");
GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type");
}
cb(cur, "result_embd", -1);
@@ -7754,7 +7754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) {
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
@@ -7782,7 +7782,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) {
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
@@ -11351,7 +11351,7 @@ static int llama_apply_lora_from_file_internal(
struct llama_model_params llama_model_default_params() {
struct llama_model_params result = {
/*.n_gpu_layers =*/ 0,
/*.split_mode =*/ LLAMA_SPLIT_LAYER,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.progress_callback =*/ nullptr,
@@ -11377,7 +11377,7 @@ struct llama_context_params llama_context_default_params() {
/*.n_batch =*/ 512,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
/*.rope_freq_base =*/ 0.0f,
/*.rope_freq_scale =*/ 0.0f,
/*.yarn_ext_factor =*/ -1.0f,
@@ -11565,16 +11565,16 @@ struct llama_context * llama_new_context_with_model(
cparams.cb_eval_user_data = params.cb_eval_user_data;
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
}
if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
}
if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
}
if (params.seed == LLAMA_DEFAULT_SEED) {
@@ -11608,8 +11608,8 @@ struct llama_context * llama_new_context_with_model(
}
#elif defined(GGML_USE_CUBLAS)
if (model->n_gpu_layers > 0) {
// with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) {
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
@@ -11618,7 +11618,7 @@ struct llama_context * llama_new_context_with_model(
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_LAYER requires a backend for each GPU
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device);
if (backend == nullptr) {