Update llama.cpp
This commit is contained in:
Andrei Betlen
parent
011c3630f5
commit
eb9c7d4ed8
2 changed files with 74 additions and 66 deletions
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@ -93,6 +93,9 @@ c_size_t_p = POINTER(c_size_t)
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# llama.h bindings
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_lib.llama_max_devices.argtypes = []
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_lib.llama_max_devices.restype = ctypes.c_int32
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LLAMA_MAX_DEVICES = _lib.llama_max_devices()
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# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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@ -481,7 +484,7 @@ It might not exist for progress report where '.' is output repeatedly."""
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# // model quantization parameters
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# typedef struct llama_model_quantize_params {
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# int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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# int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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# enum llama_ftype ftype; // quantize to this llama_ftype
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# bool allow_requantize; // allow quantizing non-f32/f16 tensors
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# bool quantize_output_tensor; // quantize output.weight
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@ -499,7 +502,7 @@ class llama_model_quantize_params(Structure):
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only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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pure (bool): disable k-quant mixtures and quantize all tensors to the same type"""
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_fields_ = [
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("nthread", c_int),
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("nthread", c_int32),
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("ftype", c_int),
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("allow_requantize", c_bool),
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("quantize_output_tensor", c_bool),
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@ -698,13 +701,13 @@ _lib.llama_time_us.argtypes = []
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_lib.llama_time_us.restype = ctypes.c_int64
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# LLAMA_API int llama_max_devices (void);
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# LLAMA_API int32_t llama_max_devices(void);
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def llama_max_devices() -> int:
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return _lib.llama_max_devices()
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_lib.llama_max_devices.argtypes = []
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_lib.llama_max_devices.restype = c_int
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_lib.llama_max_devices.restype = ctypes.c_int32
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# LLAMA_API bool llama_mmap_supported (void);
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@ -734,7 +737,7 @@ _lib.llama_get_model.argtypes = [llama_context_p]
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_lib.llama_get_model.restype = llama_model_p
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# LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
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# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
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def llama_n_ctx(ctx: llama_context_p) -> int:
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return _lib.llama_n_ctx(ctx)
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@ -758,31 +761,31 @@ _lib.llama_vocab_type.argtypes = [llama_model_p]
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_lib.llama_vocab_type.restype = c_int
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# LLAMA_API int llama_n_vocab (const struct llama_model * model);
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# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
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def llama_n_vocab(model: llama_model_p) -> int:
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return _lib.llama_n_vocab(model)
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_lib.llama_n_vocab.argtypes = [llama_model_p]
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_lib.llama_n_vocab.restype = c_int
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_lib.llama_n_vocab.restype = c_int32
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# LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
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# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
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def llama_n_ctx_train(model: llama_model_p) -> int:
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return _lib.llama_n_ctx_train(model)
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_lib.llama_n_ctx_train.argtypes = [llama_model_p]
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_lib.llama_n_ctx_train.restype = c_int
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_lib.llama_n_ctx_train.restype = c_int32
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# LLAMA_API int llama_n_embd (const struct llama_model * model);
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# LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
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def llama_n_embd(model: llama_model_p) -> int:
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return _lib.llama_n_embd(model)
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_lib.llama_n_embd.argtypes = [llama_model_p]
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_lib.llama_n_embd.restype = c_int
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_lib.llama_n_embd.restype = c_int32
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# // Get the model's RoPE frequency scaling factor
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@ -802,7 +805,7 @@ _lib.llama_rope_freq_scale_train.restype = c_float
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# // Get metadata value as a string by key name
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# LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
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# LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
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def llama_model_meta_val_str(
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model: llama_model_p, key: Union[c_char_p, bytes], buf: bytes, buf_size: int
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) -> int:
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@ -811,22 +814,22 @@ def llama_model_meta_val_str(
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_lib.llama_model_meta_val_str.argtypes = [llama_model_p, c_char_p, c_char_p, c_size_t]
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_lib.llama_model_meta_val_str.restype = c_int
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_lib.llama_model_meta_val_str.restype = c_int32
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# // Get the number of metadata key/value pairs
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# LLAMA_API int llama_model_meta_count(const struct llama_model * model);
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# LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
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def llama_model_meta_count(model: llama_model_p) -> int:
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"""Get the number of metadata key/value pairs"""
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return _lib.llama_model_meta_count(model)
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_lib.llama_model_meta_count.argtypes = [llama_model_p]
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_lib.llama_model_meta_count.restype = c_int
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_lib.llama_model_meta_count.restype = c_int32
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# // Get metadata key name by index
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# LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
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# LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
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def llama_model_meta_key_by_index(
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model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int
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) -> int:
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@ -834,12 +837,17 @@ def llama_model_meta_key_by_index(
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return _lib.llama_model_meta_key_by_index(model, i, buf, buf_size)
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_lib.llama_model_meta_key_by_index.argtypes = [llama_model_p, c_int, c_char_p, c_size_t]
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_lib.llama_model_meta_key_by_index.restype = c_int
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_lib.llama_model_meta_key_by_index.argtypes = [
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llama_model_p,
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c_int32,
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c_char_p,
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c_size_t,
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]
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_lib.llama_model_meta_key_by_index.restype = c_int32
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# // Get metadata value as a string by index
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# LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
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# LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
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def llama_model_meta_val_str_by_index(
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model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int
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) -> int:
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@ -849,15 +857,15 @@ def llama_model_meta_val_str_by_index(
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_lib.llama_model_meta_val_str_by_index.argtypes = [
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llama_model_p,
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c_int,
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c_int32,
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c_char_p,
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c_size_t,
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]
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_lib.llama_model_meta_val_str_by_index.restype = c_int
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_lib.llama_model_meta_val_str_by_index.restype = c_int32
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# // Get a string describing the model type
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# LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
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# LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
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def llama_model_desc(
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model: llama_model_p, buf: bytes, buf_size: Union[c_size_t, int]
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) -> int:
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@ -866,7 +874,7 @@ def llama_model_desc(
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_lib.llama_model_desc.argtypes = [llama_model_p, c_char_p, c_size_t]
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_lib.llama_model_desc.restype = c_int
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_lib.llama_model_desc.restype = c_int32
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# // Returns the total size of all the tensors in the model in bytes
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@ -905,7 +913,7 @@ _lib.llama_get_model_tensor.restype = c_void_p
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# // Returns 0 on success
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# LLAMA_API int llama_model_quantize(
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# LLAMA_API uint32_t llama_model_quantize(
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# const char * fname_inp,
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# const char * fname_out,
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# const llama_model_quantize_params * params);
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@ -923,7 +931,7 @@ _lib.llama_model_quantize.argtypes = [
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c_char_p,
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POINTER(llama_model_quantize_params),
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]
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_lib.llama_model_quantize.restype = c_int
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_lib.llama_model_quantize.restype = c_uint32
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# // Apply a LoRA adapter to a loaded model
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@ -932,12 +940,12 @@ _lib.llama_model_quantize.restype = c_int
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# // The model needs to be reloaded before applying a new adapter, otherwise the adapter
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# // will be applied on top of the previous one
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# // Returns 0 on success
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# LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
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# LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
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# struct llama_context * ctx,
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# const char * path_lora,
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# float scale,
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# const char * path_base_model,
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# int n_threads),
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# int32_t n_threads),
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# "use llama_model_apply_lora_from_file instead");
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def llama_apply_lora_from_file(
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ctx: llama_context_p,
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@ -962,17 +970,17 @@ _lib.llama_apply_lora_from_file.argtypes = [
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c_char_p,
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c_float,
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c_char_p,
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c_int,
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c_int32,
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]
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_lib.llama_apply_lora_from_file.restype = c_int
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_lib.llama_apply_lora_from_file.restype = c_int32
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# LLAMA_API int llama_model_apply_lora_from_file(
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# LLAMA_API int32_t llama_model_apply_lora_from_file(
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# const struct llama_model * model,
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# const char * path_lora,
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# float scale,
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# const char * path_base_model,
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# int n_threads);
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# int32_t n_threads);
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def llama_model_apply_lora_from_file(
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model: llama_model_p,
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path_lora: Union[c_char_p, bytes],
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@ -990,9 +998,9 @@ _lib.llama_model_apply_lora_from_file.argtypes = [
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c_char_p,
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c_float,
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c_char_p,
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c_int,
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c_int32,
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]
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_lib.llama_model_apply_lora_from_file.restype = c_int
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_lib.llama_model_apply_lora_from_file.restype = c_int32
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# //
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# // KV cache
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@ -1094,7 +1102,7 @@ _lib.llama_kv_cache_view_update.restype = None
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# // Returns the number of tokens in the KV cache (slow, use only for debug)
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# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
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# LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
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# LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
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def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
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"""Returns the number of tokens in the KV cache (slow, use only for debug)
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If a KV cell has multiple sequences assigned to it, it will be counted multiple times
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@ -1103,18 +1111,18 @@ def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
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_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
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_lib.llama_get_kv_cache_token_count.restype = c_int
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_lib.llama_get_kv_cache_token_count.restype = c_int32
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# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
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# LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
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# LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
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def llama_get_kv_cache_used_cells(ctx: llama_context_p) -> int:
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"""Returns the number of used KV cells (i.e. have at least one sequence assigned to them)"""
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return _lib.llama_get_kv_cache_used_cells(ctx)
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_lib.llama_get_kv_cache_used_cells.argtypes = [llama_context_p]
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_lib.llama_get_kv_cache_used_cells.restype = c_int
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_lib.llama_get_kv_cache_used_cells.restype = c_int32
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# // Clear the KV cache
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@ -1361,7 +1369,7 @@ _lib.llama_save_session_file.restype = c_size_t
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# struct llama_context * ctx,
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# llama_token * tokens,
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# int32_t n_tokens,
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# int n_past),
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# int32_t n_past),
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# "use llama_decode() instead");
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def llama_eval(
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ctx: llama_context_p,
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@ -1377,7 +1385,7 @@ def llama_eval(
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return _lib.llama_eval(ctx, tokens, n_tokens, n_past)
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_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int]
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_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int32, c_int32]
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_lib.llama_eval.restype = c_int
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@ -1387,7 +1395,7 @@ _lib.llama_eval.restype = c_int
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# struct llama_context * ctx,
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# float * embd,
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# int32_t n_tokens,
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# int n_past),
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# int32_t n_past),
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# "use llama_decode() instead");
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def llama_eval_embd(
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ctx: llama_context_p,
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@ -1400,7 +1408,7 @@ def llama_eval_embd(
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return _lib.llama_eval_embd(ctx, embd, n_tokens, n_past)
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_lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int]
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_lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int32, c_int32]
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_lib.llama_eval_embd.restype = c_int
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@ -1480,7 +1488,7 @@ _lib.llama_batch_free.restype = None
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# // 0 - success
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# // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
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# // < 0 - error
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# LLAMA_API int llama_decode(
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# LLAMA_API int32_t llama_decode(
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# struct llama_context * ctx,
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# struct llama_batch batch);
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def llama_decode(ctx: llama_context_p, batch: llama_batch) -> int:
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@ -1492,7 +1500,7 @@ def llama_decode(ctx: llama_context_p, batch: llama_batch) -> int:
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_lib.llama_decode.argtypes = [llama_context_p, llama_batch]
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_lib.llama_decode.restype = c_int
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_lib.llama_decode.restype = c_int32
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# // Set the number of threads used for decoding
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@ -1634,25 +1642,25 @@ _lib.llama_token_nl.restype = llama_token
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# // Returns -1 if unknown, 1 for true or 0 for false.
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# LLAMA_API int llama_add_bos_token(const struct llama_model * model);
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# LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
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def llama_add_bos_token(model: llama_model_p) -> int:
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"""Returns -1 if unknown, 1 for true or 0 for false."""
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return _lib.llama_add_bos_token(model)
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_lib.llama_add_bos_token.argtypes = [llama_model_p]
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_lib.llama_add_bos_token.restype = c_int
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_lib.llama_add_bos_token.restype = c_int32
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# // Returns -1 if unknown, 1 for true or 0 for false.
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# LLAMA_API int llama_add_eos_token(const struct llama_model * model);
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# LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
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def llama_add_eos_token(model: llama_model_p) -> int:
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"""Returns -1 if unknown, 1 for true or 0 for false."""
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return _lib.llama_add_eos_token(model)
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_lib.llama_add_eos_token.argtypes = [llama_model_p]
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_lib.llama_add_eos_token.restype = c_int
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_lib.llama_add_eos_token.restype = c_int32
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# // codellama infill tokens
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@ -1704,12 +1712,12 @@ _lib.llama_token_eot.restype = llama_token
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# /// @return Returns a negative number on failure - the number of tokens that would have been returned
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# /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
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# /// Does not insert a leading space.
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# LLAMA_API int llama_tokenize(
|
||||
# LLAMA_API int32_t llama_tokenize(
|
||||
# const struct llama_model * model,
|
||||
# const char * text,
|
||||
# int text_len,
|
||||
# int32_t text_len,
|
||||
# llama_token * tokens,
|
||||
# int n_max_tokens,
|
||||
# int32_t n_max_tokens,
|
||||
# bool add_bos,
|
||||
# bool special);
|
||||
def llama_tokenize(
|
||||
|
|
@ -1730,24 +1738,24 @@ def llama_tokenize(
|
|||
_lib.llama_tokenize.argtypes = [
|
||||
llama_model_p,
|
||||
c_char_p,
|
||||
c_int,
|
||||
c_int32,
|
||||
llama_token_p,
|
||||
c_int,
|
||||
c_int32,
|
||||
c_bool,
|
||||
c_bool,
|
||||
]
|
||||
_lib.llama_tokenize.restype = c_int
|
||||
_lib.llama_tokenize.restype = c_int32
|
||||
|
||||
|
||||
# // Token Id -> Piece.
|
||||
# // Uses the vocabulary in the provided context.
|
||||
# // Does not write null terminator to the buffer.
|
||||
# // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
# LLAMA_API int llama_token_to_piece(
|
||||
# LLAMA_API int32_t llama_token_to_piece(
|
||||
# const struct llama_model * model,
|
||||
# llama_token token,
|
||||
# char * buf,
|
||||
# int length);
|
||||
# int32_t length);
|
||||
def llama_token_to_piece(
|
||||
model: llama_model_p,
|
||||
token: Union[llama_token, int],
|
||||
|
|
@ -1762,8 +1770,8 @@ def llama_token_to_piece(
|
|||
return _lib.llama_token_to_piece(model, token, buf, length)
|
||||
|
||||
|
||||
_lib.llama_token_to_piece.argtypes = [llama_model_p, llama_token, c_char_p, c_int]
|
||||
_lib.llama_token_to_piece.restype = c_int
|
||||
_lib.llama_token_to_piece.argtypes = [llama_model_p, llama_token, c_char_p, c_int32]
|
||||
_lib.llama_token_to_piece.restype = c_int32
|
||||
|
||||
|
||||
# //
|
||||
|
|
@ -1924,7 +1932,7 @@ _lib.llama_sample_softmax.restype = None
|
|||
# LLAMA_API void llama_sample_top_k(
|
||||
# struct llama_context * ctx,
|
||||
# llama_token_data_array * candidates,
|
||||
# int k,
|
||||
# int32_t k,
|
||||
# size_t min_keep);
|
||||
def llama_sample_top_k(
|
||||
ctx: llama_context_p,
|
||||
|
|
@ -1939,7 +1947,7 @@ def llama_sample_top_k(
|
|||
_lib.llama_sample_top_k.argtypes = [
|
||||
llama_context_p,
|
||||
llama_token_data_array_p,
|
||||
c_int,
|
||||
c_int32,
|
||||
c_size_t,
|
||||
]
|
||||
_lib.llama_sample_top_k.restype = None
|
||||
|
|
@ -2129,7 +2137,7 @@ _lib.llama_sample_grammar.restype = None
|
|||
# llama_token_data_array * candidates,
|
||||
# float tau,
|
||||
# float eta,
|
||||
# int m,
|
||||
# int32_t m,
|
||||
# float * mu);
|
||||
def llama_sample_token_mirostat(
|
||||
ctx: llama_context_p,
|
||||
|
|
@ -2155,7 +2163,7 @@ _lib.llama_sample_token_mirostat.argtypes = [
|
|||
llama_token_data_array_p,
|
||||
c_float,
|
||||
c_float,
|
||||
c_int,
|
||||
c_int32,
|
||||
c_float_p,
|
||||
]
|
||||
_lib.llama_sample_token_mirostat.restype = llama_token
|
||||
|
|
@ -2320,8 +2328,8 @@ llama_beam_search_callback_fn_t = ctypes.CFUNCTYPE(None, c_void_p, llama_beams_s
|
|||
# llama_beam_search_callback_fn_t callback,
|
||||
# void * callback_data,
|
||||
# size_t n_beams,
|
||||
# int n_past,
|
||||
# int n_predict);
|
||||
# int32_t n_past,
|
||||
# int32_t n_predict);
|
||||
def llama_beam_search(
|
||||
ctx: llama_context_p,
|
||||
callback: "ctypes._CFuncPtr[None, c_void_p, llama_beams_state]", # type: ignore
|
||||
|
|
@ -2340,8 +2348,8 @@ _lib.llama_beam_search.argtypes = [
|
|||
llama_beam_search_callback_fn_t,
|
||||
c_void_p,
|
||||
c_size_t,
|
||||
c_int,
|
||||
c_int,
|
||||
c_int32,
|
||||
c_int32,
|
||||
]
|
||||
_lib.llama_beam_search.restype = None
|
||||
|
||||
|
|
|
|||
2
vendor/llama.cpp
vendored
2
vendor/llama.cpp
vendored
|
|
@ -1 +1 @@
|
|||
Subproject commit f6793491b5af6da75edad34d6f503ef86d31b09f
|
||||
Subproject commit cb1e2818e0e12ec99f7236ec5d4f3ffd8bcc2f4a
|
||||
Loading…
Reference in a new issue