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import
sys
import
os
import
ctypes
from
ctypes
import
(
c_double
,
c_int
,
c_float
,
c_char_p
,
c_int32
,
c_uint32
,
c_void_p
,
c_bool
,
POINTER
,
_Pointer
,
# type: ignore
Structure
,
Array
,
c_uint8
,
c_size_t
,
)
import
pathlib
from
typing
import
List
,
Union
# Load the library
def
_load_shared_library
(
lib_base_name
:
str
):
# Construct the paths to the possible shared library names
_base_path
=
pathlib
.
Path
(
__file__
).
parent
.
resolve
()
# Searching for the library in the current directory under the name "libllama" (default name
# for llamacpp) and "llama" (default name for this repo)
_lib_paths
:
List
[
pathlib
.
Path
]
=
[]
# Determine the file extension based on the platform
if
sys
.
platform
.
startswith
(
"linux"
):
_lib_paths
+=
[
_base_path
/
f"lib
{
lib_base_name
}
.so"
,
]
elif
sys
.
platform
==
"darwin"
:
_lib_paths
+=
[
_base_path
/
f"lib
{
lib_base_name
}
.so"
,
_base_path
/
f"lib
{
lib_base_name
}
.dylib"
,
]
elif
sys
.
platform
==
"win32"
:
_lib_paths
+=
[
_base_path
/
f"
{
lib_base_name
}
.dll"
,
]
else
:
raise
RuntimeError
(
"Unsupported platform"
)
if
"LLAMA_CPP_LIB"
in
os
.
environ
:
lib_base_name
=
os
.
environ
[
"LLAMA_CPP_LIB"
]
_lib
=
pathlib
.
Path
(
lib_base_name
)
_base_path
=
_lib
.
parent
.
resolve
()
_lib_paths
=
[
_lib
.
resolve
()]
cdll_args
=
dict
()
# type: ignore
# Add the library directory to the DLL search path on Windows (if needed)
if
sys
.
platform
==
"win32"
and
sys
.
version_info
>=
(
3
,
8
):
os
.
add_dll_directory
(
str
(
_base_path
))
if
"CUDA_PATH"
in
os
.
environ
:
os
.
add_dll_directory
(
os
.
path
.
join
(
os
.
environ
[
"CUDA_PATH"
],
"bin"
))
os
.
add_dll_directory
(
os
.
path
.
join
(
os
.
environ
[
"CUDA_PATH"
],
"lib"
))
cdll_args
[
"winmode"
]
=
0
# Try to load the shared library, handling potential errors
for
_lib_path
in
_lib_paths
:
if
_lib_path
.
exists
():
try
:
return
ctypes
.
CDLL
(
str
(
_lib_path
),
**
cdll_args
)
except
Exception
as
e
:
raise
RuntimeError
(
f"Failed to load shared library '
{
_lib_path
}
':
{
e
}
"
)
raise
FileNotFoundError
(
f"Shared library with base name '
{
lib_base_name
}
' not found"
)
# Specify the base name of the shared library to load
_lib_base_name
=
"llama"
# Load the library
_lib
=
_load_shared_library
(
_lib_base_name
)
# Misc
c_float_p
=
POINTER
(
c_float
)
c_uint8_p
=
POINTER
(
c_uint8
)
c_size_t_p
=
POINTER
(
c_size_t
)
# llama.h bindings
GGML_USE_CUBLAS
=
hasattr
(
_lib
,
"ggml_init_cublas"
)
GGML_CUDA_MAX_DEVICES
=
ctypes
.
c_int
(
16
)
LLAMA_MAX_DEVICES
=
GGML_CUDA_MAX_DEVICES
if
GGML_USE_CUBLAS
else
ctypes
.
c_int
(
1
)
# #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
LLAMA_FILE_MAGIC_GGJT
=
ctypes
.
c_uint
(
0x67676A74
)
# #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
LLAMA_FILE_MAGIC_GGLA
=
ctypes
.
c_uint
(
0x67676C61
)
# #define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
LLAMA_FILE_MAGIC_GGMF
=
ctypes
.
c_uint
(
0x67676D66
)
# #define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
LLAMA_FILE_MAGIC_GGML
=
ctypes
.
c_uint
(
0x67676D6C
)
# #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
LLAMA_FILE_MAGIC_GGSN
=
ctypes
.
c_uint
(
0x6767736E
)
# #define LLAMA_FILE_VERSION 3
LLAMA_FILE_VERSION
=
c_int
(
3
)
LLAMA_FILE_MAGIC
=
LLAMA_FILE_MAGIC_GGJT
LLAMA_FILE_MAGIC_UNVERSIONED
=
LLAMA_FILE_MAGIC_GGML
LLAMA_SESSION_MAGIC
=
LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_VERSION
=
c_int
(
1
)
# #define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_DEFAULT_SEED
=
c_int
(
0xFFFFFFFF
)
# struct llama_model;
llama_model_p
=
c_void_p
# struct llama_context;
llama_context_p
=
c_void_p
# typedef int llama_token;
llama_token
=
c_int
llama_token_p
=
POINTER
(
llama_token
)
# typedef struct llama_token_data {
# llama_token id; // token id
# float logit; // log-odds of the token
# float p; // probability of the token
# } llama_token_data;
class
llama_token_data
(
Structure
):
_fields_
=
[
(
"id"
,
llama_token
),
(
"logit"
,
c_float
),
(
"p"
,
c_float
),
]
llama_token_data_p
=
POINTER
(
llama_token_data
)
# typedef struct llama_token_data_array {
# llama_token_data * data;
# size_t size;
# bool sorted;
# } llama_token_data_array;
class
llama_token_data_array
(
Structure
):
_fields_
=
[
(
"data"
,
llama_token_data_p
),
(
"size"
,
c_size_t
),
(
"sorted"
,
c_bool
),
]
llama_token_data_array_p
=
POINTER
(
llama_token_data_array
)
# typedef void (*llama_progress_callback)(float progress, void *ctx);
llama_progress_callback
=
ctypes
.
CFUNCTYPE
(
None
,
c_float
,
c_void_p
)
# struct llama_context_params {
# uint32_t seed; // RNG seed, -1 for random
# int32_t n_ctx; // text context
# int32_t n_batch; // prompt processing batch size
# int32_t n_gqa; // grouped-query attention (TEMP - will be moved to model hparams)
# float rms_norm_eps; // rms norm epsilon (TEMP - will be moved to model hparams)
# int32_t n_gpu_layers; // number of layers to store in VRAM
# int32_t main_gpu; // the GPU that is used for scratch and small tensors
#
# const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
# // ref: https://github.com/ggerganov/llama.cpp/pull/2054
# float rope_freq_base; // RoPE base frequency
# float rope_freq_scale; // RoPE frequency scaling factor
# // called with a progress value between 0 and 1, pass NULL to disable
# llama_progress_callback progress_callback;
# // context pointer passed to the progress callback
# void * progress_callback_user_data;
# // Keep the booleans together to avoid misalignment during copy-by-value.
# bool low_vram; // if true, reduce VRAM usage at the cost of performance
# bool f16_kv; // use fp16 for KV cache
# bool logits_all; // the llama_eval() call computes all logits, not just the last one
# bool vocab_only; // only load the vocabulary, no weights
# bool use_mmap; // use mmap if possible
# bool use_mlock; // force system to keep model in RAM
# bool embedding; // embedding mode only
# };
class
llama_context_params
(
Structure
):
_fields_
=
[
(
"seed"
,
c_uint32
),
(
"n_ctx"
,
c_int32
),
(
"n_batch"
,
c_int32
),
(
"n_gqa"
,
c_int32
),
(
"rms_norm_eps"
,
c_float
),
(
"n_gpu_layers"
,
c_int32
),
(
"main_gpu"
,
c_int32
),
(
"tensor_split"
,
POINTER
(
c_float
)),
(
"rope_freq_base"
,
c_float
),
(
"rope_freq_scale"
,
c_float
),
(
"progress_callback"
,
llama_progress_callback
),
(
"progress_callback_user_data"
,
c_void_p
),
(
"low_vram"
,
c_bool
),
(
"f16_kv"
,
c_bool
),
(
"logits_all"
,
c_bool
),
(
"vocab_only"
,
c_bool
),
(
"use_mmap"
,
c_bool
),
(
"use_mlock"
,
c_bool
),
(
"embedding"
,
c_bool
),
]
llama_context_params_p
=
POINTER
(
llama_context_params
)
# enum llama_ftype {
# LLAMA_FTYPE_ALL_F32 = 0,
# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
# LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
# };
LLAMA_FTYPE_ALL_F32
=
c_int
(
0
)
LLAMA_FTYPE_MOSTLY_F16
=
c_int
(
1
)
LLAMA_FTYPE_MOSTLY_Q4_0
=
c_int
(
2
)
LLAMA_FTYPE_MOSTLY_Q4_1
=
c_int
(
3
)
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16
=
c_int
(
4
)
LLAMA_FTYPE_MOSTLY_Q8_0
=
c_int
(
7
)
LLAMA_FTYPE_MOSTLY_Q5_0
=
c_int
(
8
)
LLAMA_FTYPE_MOSTLY_Q5_1
=
c_int
(
9
)
LLAMA_FTYPE_MOSTLY_Q2_K
=
c_int
(
10
)
LLAMA_FTYPE_MOSTLY_Q3_K_S
=
c_int
(
11
)
LLAMA_FTYPE_MOSTLY_Q3_K_M
=
c_int
(
12
)
LLAMA_FTYPE_MOSTLY_Q3_K_L
=
c_int
(
13
)
LLAMA_FTYPE_MOSTLY_Q4_K_S
=
c_int
(
14
)
LLAMA_FTYPE_MOSTLY_Q4_K_M
=
c_int
(
15
)
LLAMA_FTYPE_MOSTLY_Q5_K_S
=
c_int
(
16
)
LLAMA_FTYPE_MOSTLY_Q5_K_M
=
c_int
(
17
)
LLAMA_FTYPE_MOSTLY_Q6_K
=
c_int
(
18
)
# // model quantization parameters
# typedef struct llama_model_quantize_params {
# int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
# enum llama_ftype ftype; // quantize to this llama_ftype
# bool allow_requantize; // allow quantizing non-f32/f16 tensors
# bool quantize_output_tensor; // quantize output.weight
# } llama_model_quantize_params;
class
llama_model_quantize_params
(
Structure
):
_fields_
=
[
(
"nthread"
,
c_int
),
(
"ftype"
,
c_int
),
(
"allow_requantize"
,
c_bool
),
(
"quantize_output_tensor"
,
c_bool
),
]
# // grammar types
# struct llama_grammar;
llama_grammar_p
=
c_void_p
# // grammar element type
# enum llama_gretype {
# // end of rule definition
# LLAMA_GRETYPE_END = 0,
# // start of alternate definition for rule
# LLAMA_GRETYPE_ALT = 1,
# // non-terminal element: reference to rule
# LLAMA_GRETYPE_RULE_REF = 2,
# // terminal element: character (code point)
# LLAMA_GRETYPE_CHAR = 3,
# // inverse char(s) ([^a], [^a-b] [^abc])
# LLAMA_GRETYPE_CHAR_NOT = 4,
# // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
# // be an inclusive range ([a-z])
# LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
# // modifies a preceding LLAMA_GRETYPE_CHAR or
# // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
# LLAMA_GRETYPE_CHAR_ALT = 6,
# };
LLAMA_GRETYPE_END
=
c_int
(
0
)
LLAMA_GRETYPE_ALT
=
c_int
(
1
)
LLAMA_GRETYPE_RULE_REF
=
c_int
(
2
)
LLAMA_GRETYPE_CHAR
=
c_int
(
3
)
LLAMA_GRETYPE_CHAR_NOT
=
c_int
(
4
)
LLAMA_GRETYPE_CHAR_RNG_UPPER
=
c_int
(
5
)
LLAMA_GRETYPE_CHAR_ALT
=
c_int
(
6
)
# typedef struct llama_grammar_element {
# enum llama_gretype type;
# uint32_t value; // Unicode code point or rule ID
# } llama_grammar_element;
class
llama_grammar_element
(
Structure
):
_fields_
=
[
(
"type"
,
c_int
),
(
"value"
,
c_uint32
),
]
llama_grammar_element_p
=
POINTER
(
llama_grammar_element
)
# // performance timing information
# struct llama_timings {
# double t_start_ms;
# double t_end_ms;
# double t_load_ms;
# double t_sample_ms;
# double t_p_eval_ms;
# double t_eval_ms;
# int32_t n_sample;
# int32_t n_p_eval;
# int32_t n_eval;
# };
class
llama_timings
(
Structure
):
_fields_
=
[
(
"t_start_ms"
,
c_double
),
(
"t_end_ms"
,
c_double
),
(
"t_load_ms"
,
c_double
),
(
"t_sample_ms"
,
c_double
),
(
"t_p_eval_ms"
,
c_double
),
(
"t_eval_ms"
,
c_double
),
(
"n_sample"
,
c_int32
),
(
"n_p_eval"
,
c_int32
),
(
"n_eval"
,
c_int32
),
]
# LLAMA_API int llama_max_devices();
def
llama_max_devices
()
->
int
:
return
_lib
.
llama_max_devices
()
_lib
.
llama_max_devices
.
argtypes
=
[]
_lib
.
llama_max_devices
.
restype
=
c_int
# LLAMA_API struct llama_context_params llama_context_default_params();
def
llama_context_default_params
()
->
llama_context_params
:
return
_lib
.
llama_context_default_params
()
_lib
.
llama_context_default_params
.
argtypes
=
[]
_lib
.
llama_context_default_params
.
restype
=
llama_context_params
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
def
llama_model_quantize_default_params
()
->
llama_model_quantize_params
:
return
_lib
.
llama_model_quantize_default_params
()
_lib
.
llama_model_quantize_default_params
.
argtypes
=
[]
_lib
.
llama_model_quantize_default_params
.
restype
=
llama_model_quantize_params
# LLAMA_API bool llama_mmap_supported();
def
llama_mmap_supported
()
->
bool
:
return
_lib
.
llama_mmap_supported
()
_lib
.
llama_mmap_supported
.
argtypes
=
[]
_lib
.
llama_mmap_supported
.
restype
=
c_bool
# LLAMA_API bool llama_mlock_supported();
def
llama_mlock_supported
()
->
bool
:
return
_lib
.
llama_mlock_supported
()
_lib
.
llama_mlock_supported
.
argtypes
=
[]
_lib
.
llama_mlock_supported
.
restype
=
c_bool
# // TODO: not great API - very likely to change
# // Initialize the llama + ggml backend
# // If numa is true, use NUMA optimizations
# // Call once at the start of the program
# LLAMA_API void llama_backend_init(bool numa);
def
llama_backend_init
(
numa
:
c_bool
):
return
_lib
.
llama_backend_init
(
numa
)
_lib
.
llama_backend_init
.
argtypes
=
[
c_bool
]
_lib
.
llama_backend_init
.
restype
=
None
# // Call once at the end of the program - currently only used for MPI
# LLAMA_API void llama_backend_free();
def
llama_backend_free
():
return
_lib
.
llama_backend_free
()
_lib
.
llama_backend_free
.
argtypes
=
[]
_lib
.
llama_backend_free
.
restype
=
None
# LLAMA_API struct llama_model * llama_load_model_from_file(
# const char * path_model,
# struct llama_context_params params);
def
llama_load_model_from_file
(
path_model
:
bytes
,
params
:
llama_context_params
)
->
llama_model_p
:
return
_lib
.
llama_load_model_from_file
(
path_model
,
params
)
_lib
.
llama_load_model_from_file
.
argtypes
=
[
c_char_p
,
llama_context_params
]
_lib
.
llama_load_model_from_file
.
restype
=
llama_model_p
# LLAMA_API void llama_free_model(struct llama_model * model);
def
llama_free_model
(
model
:
llama_model_p
):
return
_lib
.
llama_free_model
(
model
)
_lib
.
llama_free_model
.
argtypes
=
[
llama_model_p
]
_lib
.
llama_free_model
.
restype
=
None
# LLAMA_API struct llama_context * llama_new_context_with_model(
# struct llama_model * model,
# struct llama_context_params params);
def
llama_new_context_with_model
(
model
:
llama_model_p
,
params
:
llama_context_params
)
->
llama_context_p
:
return
_lib
.
llama_new_context_with_model
(
model
,
params
)
_lib
.
llama_new_context_with_model
.
argtypes
=
[
llama_model_p
,
llama_context_params
]
_lib
.
llama_new_context_with_model
.
restype
=
llama_context_p
# LLAMA_API int64_t llama_time_us();
def
llama_time_us
()
->
int
:
return
_lib
.
llama_time_us
()
_lib
.
llama_time_us
.
argtypes
=
[]
_lib
.
llama_time_us
.
restype
=
ctypes
.
c_int64
# // Various functions for loading a ggml llama model.
# // Allocate (almost) all memory needed for the model.
# // Return NULL on failure
# LLAMA_API struct llama_context * llama_init_from_file(
# const char * path_model,
# struct llama_context_params params);
def
llama_init_from_file
(
path_model
:
bytes
,
params
:
llama_context_params
)
->
llama_context_p
:
return
_lib
.
llama_init_from_file
(
path_model
,
params
)
_lib
.
llama_init_from_file
.
argtypes
=
[
c_char_p
,
llama_context_params
]
_lib
.
llama_init_from_file
.
restype
=
llama_context_p
# Frees all allocated memory
# LLAMA_API void llama_free(struct llama_context * ctx);
def
llama_free
(
ctx
:
llama_context_p
):
return
_lib
.
llama_free
(
ctx
)
_lib
.
llama_free
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_free
.
restype
=
None
# // Returns 0 on success
# LLAMA_API int llama_model_quantize(
# const char * fname_inp,
# const char * fname_out,
# const llama_model_quantize_params * params);
def
llama_model_quantize
(
fname_inp
:
bytes
,
fname_out
:
bytes
,
params
,
# type: POINTER(llama_model_quantize_params) # type: ignore
)
->
int
:
return
_lib
.
llama_model_quantize
(
fname_inp
,
fname_out
,
params
)
_lib
.
llama_model_quantize
.
argtypes
=
[
c_char_p
,
c_char_p
,
POINTER
(
llama_model_quantize_params
),
]
_lib
.
llama_model_quantize
.
restype
=
c_int
# Apply a LoRA adapter to a loaded model
# path_base_model is the path to a higher quality model to use as a base for
# the layers modified by the adapter. Can be NULL to use the current loaded model.
# The model needs to be reloaded before applying a new adapter, otherwise the adapter
# will be applied on top of the previous one
# Returns 0 on success
# LLAMA_API int llama_apply_lora_from_file(
# struct llama_context * ctx,
# const char * path_lora,
# const char * path_base_model,
# int n_threads);
def
llama_apply_lora_from_file
(
ctx
:
llama_context_p
,
path_lora
:
c_char_p
,
path_base_model
:
c_char_p
,
n_threads
:
c_int
,
)
->
int
:
return
_lib
.
llama_apply_lora_from_file
(
ctx
,
path_lora
,
path_base_model
,
n_threads
)
_lib
.
llama_apply_lora_from_file
.
argtypes
=
[
llama_context_p
,
c_char_p
,
c_char_p
,
c_int
]
_lib
.
llama_apply_lora_from_file
.
restype
=
c_int
# LLAMA_API int llama_model_apply_lora_from_file(
# const struct llama_model * model,
# const char * path_lora,
# const char * path_base_model,
# int n_threads);
def
llama_model_apply_lora_from_file
(
model
:
llama_model_p
,
path_lora
:
Union
[
c_char_p
,
bytes
],
path_base_model
:
Union
[
c_char_p
,
bytes
],
n_threads
:
c_int
,
)
->
int
:
return
_lib
.
llama_model_apply_lora_from_file
(
model
,
path_lora
,
path_base_model
,
n_threads
)
_lib
.
llama_model_apply_lora_from_file
.
argtypes
=
[
llama_model_p
,
c_char_p
,
c_char_p
,
c_int
,
]
_lib
.
llama_model_apply_lora_from_file
.
restype
=
c_int
# Returns the number of tokens in the KV cache
# LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
def
llama_get_kv_cache_token_count
(
ctx
:
llama_context_p
)
->
int
:
return
_lib
.
llama_get_kv_cache_token_count
(
ctx
)
_lib
.
llama_get_kv_cache_token_count
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_get_kv_cache_token_count
.
restype
=
c_int
# Sets the current rng seed.
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
def
llama_set_rng_seed
(
ctx
:
llama_context_p
,
seed
:
c_uint32
):
return
_lib
.
llama_set_rng_seed
(
ctx
,
seed
)
_lib
.
llama_set_rng_seed
.
argtypes
=
[
llama_context_p
,
c_int
]
_lib
.
llama_set_rng_seed
.
restype
=
None
# Returns the maximum size in bytes of the state (rng, logits, embedding
# and kv_cache) - will often be smaller after compacting tokens
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
def
llama_get_state_size
(
ctx
:
llama_context_p
)
->
int
:
return
_lib
.
llama_get_state_size
(
ctx
)
_lib
.
llama_get_state_size
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_get_state_size
.
restype
=
c_size_t
# Copies the state to the specified destination address.
# Destination needs to have allocated enough memory.
# Returns the number of bytes copied
# LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
def
llama_copy_state_data
(
ctx
:
llama_context_p
,
dst
# type: Array[c_uint8]
)
->
int
:
return
_lib
.
llama_copy_state_data
(
ctx
,
dst
)
_lib
.
llama_copy_state_data
.
argtypes
=
[
llama_context_p
,
c_uint8_p
]
_lib
.
llama_copy_state_data
.
restype
=
c_size_t
# Set the state reading from the specified address
# Returns the number of bytes read
# LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
def
llama_set_state_data
(
ctx
:
llama_context_p
,
src
# type: Array[c_uint8]
)
->
int
:
return
_lib
.
llama_set_state_data
(
ctx
,
src
)
_lib
.
llama_set_state_data
.
argtypes
=
[
llama_context_p
,
c_uint8_p
]
_lib
.
llama_set_state_data
.
restype
=
c_size_t
# Save/load session file
# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
def
llama_load_session_file
(
ctx
:
llama_context_p
,
path_session
:
bytes
,
tokens_out
,
# type: Array[llama_token]
n_token_capacity
:
c_size_t
,
n_token_count_out
,
# type: _Pointer[c_size_t]
)
->
int
:
return
_lib
.
llama_load_session_file
(
ctx
,
path_session
,
tokens_out
,
n_token_capacity
,
n_token_count_out
)
_lib
.
llama_load_session_file
.
argtypes
=
[
llama_context_p
,
c_char_p
,
llama_token_p
,
c_size_t
,
c_size_t_p
,
]
_lib
.
llama_load_session_file
.
restype
=
c_size_t
# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
def
llama_save_session_file
(
ctx
:
llama_context_p
,
path_session
:
bytes
,
tokens
,
# type: Array[llama_token]
n_token_count
:
c_size_t
,
)
->
int
:
return
_lib
.
llama_save_session_file
(
ctx
,
path_session
,
tokens
,
n_token_count
)
_lib
.
llama_save_session_file
.
argtypes
=
[
llama_context_p
,
c_char_p
,
llama_token_p
,
c_size_t
,
]
_lib
.
llama_save_session_file
.
restype
=
c_size_t
# Run the llama inference to obtain the logits and probabilities for the next token.
# tokens + n_tokens is the provided batch of new tokens to process
# n_past is the number of tokens to use from previous eval calls
# Returns 0 on success
# LLAMA_API int llama_eval(
# struct llama_context * ctx,
# const llama_token * tokens,
# int n_tokens,
# int n_past,
# int n_threads);
def
llama_eval
(
ctx
:
llama_context_p
,
tokens
,
# type: Array[llama_token]
n_tokens
:
c_int
,
n_past
:
c_int
,
n_threads
:
c_int
,
)
->
int
:
return
_lib
.
llama_eval
(
ctx
,
tokens
,
n_tokens
,
n_past
,
n_threads
)
_lib
.
llama_eval
.
argtypes
=
[
llama_context_p
,
llama_token_p
,
c_int
,
c_int
,
c_int
]
_lib
.
llama_eval
.
restype
=
c_int
# // Same as llama_eval, but use float matrix input directly.
# LLAMA_API int llama_eval_embd(
# struct llama_context * ctx,
# const float * embd,
# int n_tokens,
# int n_past,
# int n_threads);
def
llama_eval_embd
(
ctx
:
llama_context_p
,
embd
,
# type: Array[c_float]
n_tokens
:
c_int
,
n_past
:
c_int
,
n_threads
:
c_int
,
)
->
int
:
return
_lib
.
llama_eval_embd
(
ctx
,
embd
,
n_tokens
,
n_past
,
n_threads
)
_lib
.
llama_eval_embd
.
argtypes
=
[
llama_context_p
,
c_float_p
,
c_int
,
c_int
,
c_int
]
_lib
.
llama_eval_embd
.
restype
=
c_int
# Convert the provided text into tokens.
# The tokens pointer must be large enough to hold the resulting tokens.
# Returns the number of tokens on success, no more than n_max_tokens
# Returns a negative number on failure - the number of tokens that would have been returned
# TODO: not sure if correct
# LLAMA_API int llama_tokenize(
# struct llama_context * ctx,
# const char * text,
# llama_token * tokens,
# int n_max_tokens,
# bool add_bos);
def
llama_tokenize
(
ctx
:
llama_context_p
,
text
:
bytes
,
tokens
,
# type: Array[llama_token]
n_max_tokens
:
c_int
,
add_bos
:
c_bool
,
)
->
int
:
return
_lib
.
llama_tokenize
(
ctx
,
text
,
tokens
,
n_max_tokens
,
add_bos
)
_lib
.
llama_tokenize
.
argtypes
=
[
llama_context_p
,
c_char_p
,
llama_token_p
,
c_int
,
c_bool
]
_lib
.
llama_tokenize
.
restype
=
c_int
# LLAMA_API int llama_tokenize_with_model(
# const struct llama_model * model,
# const char * text,
# llama_token * tokens,
# int n_max_tokens,
# bool add_bos);
def
llama_tokenize_with_model
(
model
:
llama_model_p
,
text
:
bytes
,
tokens
,
# type: Array[llama_token]
n_max_tokens
:
c_int
,
add_bos
:
c_bool
,
)
->
int
:
return
_lib
.
llama_tokenize_with_model
(
model
,
text
,
tokens
,
n_max_tokens
,
add_bos
)
# LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
def
llama_n_vocab
(
ctx
:
llama_context_p
)
->
int
:
return
_lib
.
llama_n_vocab
(
ctx
)
_lib
.
llama_n_vocab
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_n_vocab
.
restype
=
c_int
# LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
def
llama_n_ctx
(
ctx
:
llama_context_p
)
->
int
:
return
_lib
.
llama_n_ctx
(
ctx
)
_lib
.
llama_n_ctx
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_n_ctx
.
restype
=
c_int
# LLAMA_API int llama_n_embd (const struct llama_context * ctx);
def
llama_n_embd
(
ctx
:
llama_context_p
)
->
int
:
return
_lib
.
llama_n_embd
(
ctx
)
_lib
.
llama_n_embd
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_n_embd
.
restype
=
c_int
# LLAMA_API int llama_n_vocab_from_model(const struct llama_model * model);
def
llama_n_vocab_from_model
(
model
:
llama_model_p
)
->
int
:
return
_lib
.
llama_n_vocab_from_model
(
model
)
_lib
.
llama_n_vocab_from_model
.
argtypes
=
[
llama_model_p
]
_lib
.
llama_n_vocab_from_model
.
restype
=
c_int
# LLAMA_API int llama_n_ctx_from_model (const struct llama_model * model);
def
llama_n_ctx_from_model
(
model
:
llama_model_p
)
->
int
:
return
_lib
.
llama_n_ctx_from_model
(
model
)
_lib
.
llama_n_ctx_from_model
.
argtypes
=
[
llama_model_p
]
_lib
.
llama_n_ctx_from_model
.
restype
=
c_int
# LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
def
llama_n_embd_from_model
(
model
:
llama_model_p
)
->
int
:
return
_lib
.
llama_n_embd_from_model
(
model
)
_lib
.
llama_n_embd_from_model
.
argtypes
=
[
llama_model_p
]
_lib
.
llama_n_embd_from_model
.
restype
=
c_int
# // Get the vocabulary as output parameters.
# // Returns number of results.
# LLAMA_API int llama_get_vocab(
# const struct llama_context * ctx,
# const char * * strings,
# float * scores,
# int capacity);
def
llama_get_vocab
(
ctx
:
llama_context_p
,
strings
,
# type: Array[c_char_p] # type: ignore
scores
,
# type: Array[c_float] # type: ignore
capacity
:
c_int
,
)
->
int
:
return
_lib
.
llama_get_vocab
(
ctx
,
strings
,
scores
,
capacity
)
_lib
.
llama_get_vocab
.
argtypes
=
[
llama_context_p
,
POINTER
(
c_char_p
),
POINTER
(
c_float
),
c_int
,
]
_lib
.
llama_get_vocab
.
restype
=
c_int
# LLAMA_API int llama_get_vocab_from_model(
# const struct llama_model * model,
# const char * * strings,
# float * scores,
# int capacity);
def
llama_get_vocab_from_model
(
model
:
llama_model_p
,
strings
,
# type: Array[c_char_p] # type: ignore
scores
,
# type: Array[c_float] # type: ignore
capacity
:
c_int
,
)
->
int
:
return
_lib
.
llama_get_vocab_from_model
(
model
,
strings
,
scores
,
capacity
)
_lib
.
llama_get_vocab_from_model
.
argtypes
=
[
llama_model_p
,
POINTER
(
c_char_p
),
POINTER
(
c_float
),
c_int
,
]
_lib
.
llama_get_vocab_from_model
.
restype
=
c_int
# Token logits obtained from the last call to llama_eval()
# The logits for the last token are stored in the last row
# Can be mutated in order to change the probabilities of the next token
# Rows: n_tokens
# Cols: n_vocab
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
def
llama_get_logits
(
ctx
:
llama_context_p
,
):
# type: (...) -> Array[float] # type: ignore
return
_lib
.
llama_get_logits
(
ctx
)
_lib
.
llama_get_logits
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_get_logits
.
restype
=
c_float_p
# Get the embeddings for the input
# shape: [n_embd] (1-dimensional)
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
def
llama_get_embeddings
(
ctx
:
llama_context_p
,
):
# type: (...) -> Array[float] # type: ignore
return
_lib
.
llama_get_embeddings
(
ctx
)
_lib
.
llama_get_embeddings
.
argtypes
=
[
llama_context_p
]
_lib
.
llama_get_embeddings
.
restype
=
c_float_p
# // Token Id -> String. Uses the vocabulary in the provided context
# LLAMA_API const char * llama_token_to_str(
# const struct llama_context * ctx,
# llama_token token);
def
llama_token_to_str
(
ctx
:
llama_context_p
,
token
:
llama_token
)
->
bytes
:
return
_lib
.
llama_token_to_str
(
ctx
,
token
)
_lib
.
llama_token_to_str
.
argtypes
=
[
llama_context_p
,
llama_token
]
_lib
.
llama_token_to_str
.
restype
=
c_char_p
# LLAMA_API const char * llama_token_to_str_with_model(
# const struct llama_model * model,
# llama_token token);
def
llama_token_to_str_with_model
(
model
:
llama_model_p
,
token
:
llama_token
)
->
bytes
:
return
_lib
.
llama_token_to_str_with_model
(
model
,
token
)
_lib
.
llama_token_to_str_with_model
.
argtypes
=
[
llama_model_p
,
llama_token
]
_lib
.
llama_token_to_str_with_model
.
restype
=
c_char_p
# Special tokens
# LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
def
llama_token_bos
()
->
int
:
return
_lib
.
llama_token_bos
()
_lib
.
llama_token_bos
.
argtypes
=
[]
_lib
.
llama_token_bos
.
restype
=
llama_token
# LLAMA_API llama_token llama_token_eos(); // end-of-sentence
def
llama_token_eos
()
->
int
:
return
_lib
.
llama_token_eos
()
_lib
.
llama_token_eos
.
argtypes
=
[]
_lib
.
llama_token_eos
.
restype
=
llama_token
# LLAMA_API llama_token llama_token_nl(); // next-line
def
llama_token_nl
()
->
int
:
return
_lib
.
llama_token_nl
()
_lib
.
llama_token_nl
.
argtypes
=
[]
_lib
.
llama_token_nl
.
restype
=
llama_token
# // Grammar
# //
# LLAMA_API struct llama_grammar * llama_grammar_init(
# const llama_grammar_element ** rules,
# size_t n_rules,
# size_t start_rule_index);
def
llama_grammar_init
(
rules
,
# type: Array[llama_grammar_element_p] # type: ignore
n_rules
:
c_size_t
,
start_rule_index
:
c_size_t
,
)
->
llama_grammar_p
:
return
_lib
.
llama_grammar_init
(
rules
,
n_rules
,
start_rule_index
)
_lib
.
llama_grammar_init
.
argtypes
=
[
POINTER
(
llama_grammar_element_p
),
c_size_t
,
c_size_t
,
]
_lib
.
llama_grammar_init
.
restype
=
llama_grammar_p
# LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
def
llama_grammar_free
(
grammar
:
llama_grammar_p
):
return
_lib
.
llama_grammar_free
(
grammar
)
_lib
.
llama_grammar_free
.
argtypes
=
[
llama_grammar_p
]
_lib
.
llama_grammar_free
.
restype
=
None
# Sampling functions
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
def
llama_sample_repetition_penalty
(
ctx
:
llama_context_p
,
candidates
,
# type: _Pointer[llama_token_data_array]
last_tokens_data
,
# type: Array[llama_token]
last_tokens_size
:
c_int
,
penalty
:
c_float
,
):
return
_lib
.
llama_sample_repetition_penalty
(
ctx
,
candidates
,
last_tokens_data
,
last_tokens_size
,
penalty
)
_lib
.
llama_sample_repetition_penalty
.
argtypes
=
[
llama_context_p
,
llama_token_data_array_p
,
llama_token_p
,
c_int
,
c_float
,
]
_lib
.
llama_sample_repetition_penalty
.
restype
=
None
# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
def
llama_sample_frequency_and_presence_penalties
(
ctx
:
llama_context_p
,
candidates
,
# type: _Pointer[llama_token_data_array]
last_tokens_data
,
# type: Array[llama_token]
last_tokens_size
:
c_int
,
alpha_frequency
:
c_float
,
alpha_presence
:
c_float
,
):
return
_lib
.
llama_sample_frequency_and_presence_penalties
(
ctx
,
candidates
,
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