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Merge pull request #633 from abetlen/gguf

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GGUF (Breaking Change to Model Files)
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Andrei 2023-08-25 15:13:12 -04:00 committed by GitHub
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3
README.md
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@ -17,6 +17,9 @@ This package provides:
Documentation is available at [https://llama-cpp-python.readthedocs.io/en/latest](https://llama-cpp-python.readthedocs.io/en/latest). Documentation is available at [https://llama-cpp-python.readthedocs.io/en/latest](https://llama-cpp-python.readthedocs.io/en/latest).
> [!WARNING]
> Starting with version 0.1.79 the model format has changed from `ggmlv3` to `gguf`. Old model files can be converted using the `convert-llama-ggmlv3-to-gguf.py` script in [`llama.cpp`](https://github.com/ggerganov/llama.cpp)
## Installation from PyPI (recommended) ## Installation from PyPI (recommended)

71
llama_cpp/llama.py
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@ -228,7 +228,7 @@ class Llama:
rope_freq_scale: float = 1.0, rope_freq_scale: float = 1.0,
n_gqa: Optional[int] = None, # (TEMPORARY) must be 8 for llama2 70b n_gqa: Optional[int] = None, # (TEMPORARY) must be 8 for llama2 70b
rms_norm_eps: Optional[float] = None, # (TEMPORARY) rms_norm_eps: Optional[float] = None, # (TEMPORARY)
mul_mat_q: Optional[bool] = None, # (TEMPORARY) mul_mat_q: Optional[bool] = None,
verbose: bool = True, verbose: bool = True,
): ):
"""Load a llama.cpp model from `model_path`. """Load a llama.cpp model from `model_path`.
@ -290,11 +290,6 @@ class Llama:
self.params.rope_freq_base = rope_freq_base self.params.rope_freq_base = rope_freq_base
self.params.rope_freq_scale = rope_freq_scale self.params.rope_freq_scale = rope_freq_scale
if n_gqa is not None:
self.params.n_gqa = n_gqa
if rms_norm_eps is not None:
self.params.rms_norm_eps = rms_norm_eps
if mul_mat_q is not None: if mul_mat_q is not None:
self.params.mul_mat_q = mul_mat_q self.params.mul_mat_q = mul_mat_q
@ -371,8 +366,8 @@ class Llama:
sorted=sorted, sorted=sorted,
) )
self._candidates = candidates self._candidates = candidates
self._token_nl = Llama.token_nl() self._token_nl = self.token_nl()
self._token_eos = Llama.token_eos() self._token_eos = self.token_eos()
self._candidates_data_id = np.arange(self._n_vocab, dtype=np.intc) # type: ignore self._candidates_data_id = np.arange(self._n_vocab, dtype=np.intc) # type: ignore
self._candidates_data_p = np.zeros(self._n_vocab, dtype=np.single) self._candidates_data_p = np.zeros(self._n_vocab, dtype=np.single)
@ -413,11 +408,11 @@ class Llama:
Returns: Returns:
A list of tokens. A list of tokens.
""" """
assert self.ctx is not None assert self.model is not None
n_ctx = self._n_ctx n_ctx = self._n_ctx
tokens = (llama_cpp.llama_token * n_ctx)() tokens = (llama_cpp.llama_token * n_ctx)()
n_tokens = llama_cpp.llama_tokenize( n_tokens = llama_cpp.llama_tokenize_with_model(
self.ctx, self.model,
text, text,
tokens, tokens,
llama_cpp.c_int(n_ctx), llama_cpp.c_int(n_ctx),
@ -426,8 +421,8 @@ class Llama:
if n_tokens < 0: if n_tokens < 0:
n_tokens = abs(n_tokens) n_tokens = abs(n_tokens)
tokens = (llama_cpp.llama_token * n_tokens)() tokens = (llama_cpp.llama_token * n_tokens)()
n_tokens = llama_cpp.llama_tokenize( n_tokens = llama_cpp.llama_tokenize_with_model(
self.ctx, self.model,
text, text,
tokens, tokens,
llama_cpp.c_int(n_tokens), llama_cpp.c_int(n_tokens),
@ -448,13 +443,19 @@ class Llama:
Returns: Returns:
The detokenized string. The detokenized string.
""" """
assert self.ctx is not None assert self.model is not None
output = b"" output = b""
size = 8
buffer = (ctypes.c_char * size)()
for token in tokens: for token in tokens:
output += llama_cpp.llama_token_to_str( n = llama_cpp.llama_token_to_str_with_model(
self.ctx, llama_cpp.llama_token(token) self.model, llama_cpp.llama_token(token), buffer, size
) )
return output assert n <= size
output += bytes(buffer[:n])
# NOTE: Llama1 models automatically added a space at the start of the prompt
# this line removes a leading space if the first token is a beginning of sentence token
return output[1:] if len(tokens) > 0 and tokens[0] == self.token_bos() else output
def set_cache(self, cache: Optional[BaseLlamaCache]): def set_cache(self, cache: Optional[BaseLlamaCache]):
"""Set the cache. """Set the cache.
@ -885,7 +886,7 @@ class Llama:
created: int = int(time.time()) created: int = int(time.time())
completion_tokens: List[int] = [] completion_tokens: List[int] = []
# Add blank space to start of prompt to match OG llama tokenizer # Add blank space to start of prompt to match OG llama tokenizer
prompt_tokens: List[int] = self.tokenize(b" " + prompt.encode("utf-8")) prompt_tokens: List[int] = self.tokenize(prompt.encode("utf-8")) if prompt != "" else [self.token_bos()]
text: bytes = b"" text: bytes = b""
returned_tokens: int = 0 returned_tokens: int = 0
stop = ( stop = (
@ -1581,13 +1582,7 @@ class Llama:
lora_base=self.lora_base, lora_base=self.lora_base,
lora_path=self.lora_path, lora_path=self.lora_path,
tensor_split=self.tensor_split, tensor_split=self.tensor_split,
### TEMPORARY ### mul_mat_q=self.params.mul_mat_q,
n_gqa=self.params.n_gqa,
rms_norm_eps=self.params.rms_norm_eps,
### TEMPORARY ###
### DEPRECATED ###
n_parts=self.n_parts,
### DEPRECATED ###
) )
def __setstate__(self, state): def __setstate__(self, state):
@ -1609,14 +1604,8 @@ class Llama:
lora_base=state["lora_base"], lora_base=state["lora_base"],
lora_path=state["lora_path"], lora_path=state["lora_path"],
tensor_split=state["tensor_split"], tensor_split=state["tensor_split"],
mul_mat_q=state["mul_mat_q"],
verbose=state["verbose"], verbose=state["verbose"],
### TEMPORARY ###
n_gqa=state["n_gqa"],
rms_norm_eps=state["rms_norm_eps"],
### TEMPORARY ###
### DEPRECATED ###
n_parts=state["n_parts"],
### DEPRECATED ###
) )
def save_state(self) -> LlamaState: def save_state(self) -> LlamaState:
@ -1681,20 +1670,20 @@ class Llama:
assert self.ctx is not None assert self.ctx is not None
return LlamaTokenizer(self) return LlamaTokenizer(self)
@staticmethod def token_eos(self) -> int:
def token_eos() -> int:
"""Return the end-of-sequence token.""" """Return the end-of-sequence token."""
return llama_cpp.llama_token_eos() assert self.ctx is not None
return llama_cpp.llama_token_eos(self.ctx)
@staticmethod def token_bos(self) -> int:
def token_bos() -> int:
"""Return the beginning-of-sequence token.""" """Return the beginning-of-sequence token."""
return llama_cpp.llama_token_bos() assert self.ctx is not None
return llama_cpp.llama_token_bos(self.ctx)
@staticmethod def token_nl(self) -> int:
def token_nl() -> int:
"""Return the newline token.""" """Return the newline token."""
return llama_cpp.llama_token_nl() assert self.ctx is not None
return llama_cpp.llama_token_nl(self.ctx)
@staticmethod @staticmethod
def logits_to_logprobs(logits: List[float]) -> List[float]: def logits_to_logprobs(logits: List[float]) -> List[float]:

771
llama_cpp/llama_cpp.py
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@ -90,26 +90,17 @@ GGML_USE_CUBLAS = hasattr(_lib, "ggml_init_cublas")
GGML_CUDA_MAX_DEVICES = ctypes.c_int(16) GGML_CUDA_MAX_DEVICES = ctypes.c_int(16)
LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else ctypes.c_int(1) LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else ctypes.c_int(1)
# #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' # define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_FILE_MAGIC_GGJT = ctypes.c_uint(0x67676A74) LLAMA_DEFAULT_SEED = ctypes.c_int(0xFFFFFFFF)
# #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
LLAMA_FILE_MAGIC_GGLA = ctypes.c_uint(0x67676C61) # define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
# #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) LLAMA_FILE_MAGIC_GGSN = ctypes.c_uint(0x6767736E)
# #define LLAMA_FILE_VERSION 3 # define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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_MAGIC = LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_VERSION = c_int(1) # define LLAMA_SESSION_VERSION 1
LLAMA_SESSION_VERSION = ctypes.c_int(1)
# #define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_DEFAULT_SEED = c_int(0xFFFFFFFF)
# struct llama_model; # struct llama_model;
llama_model_p = c_void_p llama_model_p = c_void_p
@ -122,6 +113,82 @@ llama_context_p = c_void_p
llama_token = c_int llama_token = c_int
llama_token_p = POINTER(llama_token) llama_token_p = POINTER(llama_token)
# enum llama_log_level {
# LLAMA_LOG_LEVEL_ERROR = 2,
# LLAMA_LOG_LEVEL_WARN = 3,
# LLAMA_LOG_LEVEL_INFO = 4
# };
LLAMA_LOG_LEVEL_ERROR = c_int(2)
LLAMA_LOG_LEVEL_WARN = c_int(3)
LLAMA_LOG_LEVEL_INFO = c_int(4)
# enum llama_vocab_type {
# LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
# LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
# };
LLAMA_VOCAB_TYPE_SPM = c_int(0)
LLAMA_VOCAB_TYPE_BPE = c_int(1)
# enum llama_token_type {
# LLAMA_TOKEN_TYPE_UNDEFINED = 0,
# LLAMA_TOKEN_TYPE_NORMAL = 1,
# LLAMA_TOKEN_TYPE_UNKNOWN = 2,
# LLAMA_TOKEN_TYPE_CONTROL = 3,
# LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
# LLAMA_TOKEN_TYPE_UNUSED = 5,
# LLAMA_TOKEN_TYPE_BYTE = 6,
# };
LLAMA_TOKEN_TYPE_UNDEFINED = c_int(0)
LLAMA_TOKEN_TYPE_NORMAL = c_int(1)
LLAMA_TOKEN_TYPE_UNKNOWN = c_int(2)
LLAMA_TOKEN_TYPE_CONTROL = c_int(3)
LLAMA_TOKEN_TYPE_USER_DEFINED = c_int(4)
LLAMA_TOKEN_TYPE_UNUSED = c_int(5)
LLAMA_TOKEN_TYPE_BYTE = c_int(6)
# 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_GUESSED = 1024, // not specified in the model file
# };
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)
LLAMA_FTYPE_GUESSED = c_int(1024)
# typedef struct llama_token_data { # typedef struct llama_token_data {
# llama_token id; // token id # llama_token id; // token id
@ -157,35 +224,13 @@ llama_token_data_array_p = POINTER(llama_token_data_array)
# typedef void (*llama_progress_callback)(float progress, void *ctx); # typedef void (*llama_progress_callback)(float progress, void *ctx);
llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p) llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
# enum llama_log_level {
# LLAMA_LOG_LEVEL_ERROR = 2,
# LLAMA_LOG_LEVEL_WARN = 3,
# LLAMA_LOG_LEVEL_INFO = 4
# };
LLAMA_LOG_LEVEL_ERROR = c_int(2)
LLAMA_LOG_LEVEL_WARN = c_int(3)
LLAMA_LOG_LEVEL_INFO = c_int(4)
# // Signature for logging events
# // Note that text includes the new line character at the end for most events.
# // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
# // if it exists.
# // It might not exist for progress report where '.' is output repeatedly.
# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
llama_log_callback = ctypes.CFUNCTYPE(None, c_int, c_char_p, c_void_p)
# struct llama_context_params { # struct llama_context_params {
# uint32_t seed; // RNG seed, -1 for random # uint32_t seed; // RNG seed, -1 for random
# int32_t n_ctx; // text context # int32_t n_ctx; // text context
# int32_t n_batch; // prompt processing batch size # 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 n_gpu_layers; // number of layers to store in VRAM
# int32_t main_gpu; // the GPU that is used for scratch and small tensors # 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) # const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
# // ref: https://github.com/ggerganov/llama.cpp/pull/2054 # // ref: https://github.com/ggerganov/llama.cpp/pull/2054
@ -213,11 +258,9 @@ class llama_context_params(Structure):
("seed", c_uint32), ("seed", c_uint32),
("n_ctx", c_int32), ("n_ctx", c_int32),
("n_batch", c_int32), ("n_batch", c_int32),
("n_gqa", c_int32),
("rms_norm_eps", c_float),
("n_gpu_layers", c_int32), ("n_gpu_layers", c_int32),
("main_gpu", c_int32), ("main_gpu", c_int32),
("tensor_split", POINTER(c_float)), ("tensor_split", c_float_p),
("rope_freq_base", c_float), ("rope_freq_base", c_float),
("rope_freq_scale", c_float), ("rope_freq_scale", c_float),
("progress_callback", llama_progress_callback), ("progress_callback", llama_progress_callback),
@ -235,50 +278,20 @@ class llama_context_params(Structure):
llama_context_params_p = POINTER(llama_context_params) llama_context_params_p = POINTER(llama_context_params)
# enum llama_ftype {
# LLAMA_FTYPE_ALL_F32 = 0, # // Signature for logging events
# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors # // Note that text includes the new line character at the end for most events.
# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors # // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors # // if it exists.
# LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 # // It might not exist for progress report where '.' is output repeatedly.
# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed # typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed llama_log_callback = ctypes.CFUNCTYPE(None, c_int, c_char_p, c_void_p)
# 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 # // model quantization parameters
# typedef struct llama_model_quantize_params { # typedef struct llama_model_quantize_params {
# int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() # 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 # enum llama_ftype ftype; // quantize to this llama_ftype
# bool allow_requantize; // allow quantizing non-f32/f16 tensors # bool allow_requantize; // allow quantizing non-f32/f16 tensors
# bool quantize_output_tensor; // quantize output.weight # bool quantize_output_tensor; // quantize output.weight
# } llama_model_quantize_params; # } llama_model_quantize_params;
@ -370,29 +383,7 @@ class llama_timings(Structure):
] ]
# // Set callback for all future logging events. # LLAMA_API struct llama_context_params llama_context_default_params(void);
# // If this is not called, or NULL is supplied, everything is output on stderr.
# LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
def llama_log_set(
log_callback: "ctypes._FuncPointer", user_data: c_void_p # type: ignore
):
return _lib.llama_log_set(log_callback, user_data)
_lib.llama_log_set.argtypes = [llama_log_callback, c_void_p]
_lib.llama_log_set.restype = None
# 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: def llama_context_default_params() -> llama_context_params:
return _lib.llama_context_default_params() return _lib.llama_context_default_params()
@ -401,7 +392,7 @@ _lib.llama_context_default_params.argtypes = []
_lib.llama_context_default_params.restype = llama_context_params _lib.llama_context_default_params.restype = llama_context_params
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(); # LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
def llama_model_quantize_default_params() -> llama_model_quantize_params: def llama_model_quantize_default_params() -> llama_model_quantize_params:
return _lib.llama_model_quantize_default_params() return _lib.llama_model_quantize_default_params()
@ -410,25 +401,6 @@ _lib.llama_model_quantize_default_params.argtypes = []
_lib.llama_model_quantize_default_params.restype = llama_model_quantize_params _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 # // Initialize the llama + ggml backend
# // If numa is true, use NUMA optimizations # // If numa is true, use NUMA optimizations
# // Call once at the start of the program # // Call once at the start of the program
@ -442,7 +414,7 @@ _lib.llama_backend_init.restype = None
# // Call once at the end of the program - currently only used for MPI # // Call once at the end of the program - currently only used for MPI
# LLAMA_API void llama_backend_free(); # LLAMA_API void llama_backend_free(void);
def llama_backend_free(): def llama_backend_free():
return _lib.llama_backend_free() return _lib.llama_backend_free()
@ -452,7 +424,7 @@ _lib.llama_backend_free.restype = None
# LLAMA_API struct llama_model * llama_load_model_from_file( # LLAMA_API struct llama_model * llama_load_model_from_file(
# const char * path_model, # const char * path_model,
# struct llama_context_params params); # struct llama_context_params params);
def llama_load_model_from_file( def llama_load_model_from_file(
path_model: bytes, params: llama_context_params path_model: bytes, params: llama_context_params
@ -474,7 +446,7 @@ _lib.llama_free_model.restype = None
# LLAMA_API struct llama_context * llama_new_context_with_model( # LLAMA_API struct llama_context * llama_new_context_with_model(
# struct llama_model * model, # struct llama_model * model,
# struct llama_context_params params); # struct llama_context_params params);
def llama_new_context_with_model( def llama_new_context_with_model(
model: llama_model_p, params: llama_context_params model: llama_model_p, params: llama_context_params
@ -486,7 +458,17 @@ _lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_param
_lib.llama_new_context_with_model.restype = llama_context_p _lib.llama_new_context_with_model.restype = llama_context_p
# LLAMA_API int64_t llama_time_us(); # // 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
# LLAMA_API int64_t llama_time_us(void);
def llama_time_us() -> int: def llama_time_us() -> int:
return _lib.llama_time_us() return _lib.llama_time_us()
@ -495,30 +477,124 @@ _lib.llama_time_us.argtypes = []
_lib.llama_time_us.restype = ctypes.c_int64 _lib.llama_time_us.restype = ctypes.c_int64
# // Various functions for loading a ggml llama model. # LLAMA_API int llama_max_devices (void);
# // Allocate (almost) all memory needed for the model. def llama_max_devices() -> int:
# // Return NULL on failure return _lib.llama_max_devices()
# 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_max_devices.argtypes = []
_lib.llama_init_from_file.restype = llama_context_p _lib.llama_max_devices.restype = c_int
# Frees all allocated memory # LLAMA_API bool llama_mmap_supported (void);
# LLAMA_API void llama_free(struct llama_context * ctx); def llama_mmap_supported() -> bool:
def llama_free(ctx: llama_context_p): return _lib.llama_mmap_supported()
return _lib.llama_free(ctx)
_lib.llama_free.argtypes = [llama_context_p] _lib.llama_mmap_supported.argtypes = []
_lib.llama_free.restype = None _lib.llama_mmap_supported.restype = c_bool
# LLAMA_API bool llama_mlock_supported(void);
def llama_mlock_supported() -> bool:
return _lib.llama_mlock_supported()
_lib.llama_mlock_supported.argtypes = []
_lib.llama_mlock_supported.restype = c_bool
# 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 enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
def llama_vocab_type(ctx: llama_context_p) -> int:
return _lib.llama_vocab_type(ctx)
_lib.llama_vocab_type.argtypes = [llama_context_p]
_lib.llama_vocab_type.restype = c_int
# LLAMA_API int llama_model_n_vocab(const struct llama_model * model);
def llama_model_n_vocab(model: llama_model_p) -> int:
return _lib.llama_model_n_vocab(model)
_lib.llama_model_n_vocab.argtypes = [llama_model_p]
_lib.llama_model_n_vocab.restype = c_int
# LLAMA_API int llama_model_n_ctx (const struct llama_model * model);
def llama_model_n_ctx(model: llama_model_p) -> int:
return _lib.llama_model_n_ctx(model)
_lib.llama_model_n_ctx.argtypes = [llama_model_p]
_lib.llama_model_n_ctx.restype = c_int
# LLAMA_API int llama_model_n_embd (const struct llama_model * model);
def llama_model_n_embd(model: llama_model_p) -> int:
return _lib.llama_model_n_embd(model)
_lib.llama_model_n_embd.argtypes = [llama_model_p]
_lib.llama_model_n_embd.restype = c_int
# // Get a string describing the model type
# LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
def llama_model_desc(model: llama_model_p, buf: bytes, buf_size: c_size_t) -> int:
return _lib.llama_model_desc(model, buf, buf_size)
_lib.llama_model_desc.argtypes = [llama_model_p, c_char_p, c_size_t]
_lib.llama_model_desc.restype = c_int
# // Returns the total size of all the tensors in the model in bytes
# LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
def llama_model_size(model: llama_model_p) -> int:
return _lib.llama_model_size(model)
_lib.llama_model_size.argtypes = [llama_model_p]
_lib.llama_model_size.restype = ctypes.c_uint64
# // Returns the total number of parameters in the model
# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
def llama_model_n_params(model: llama_model_p) -> int:
return _lib.llama_model_n_params(model)
_lib.llama_model_n_params.argtypes = [llama_model_p]
_lib.llama_model_n_params.restype = ctypes.c_uint64
# // Returns 0 on success # // Returns 0 on success
@ -737,147 +813,17 @@ _lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int, c_int
_lib.llama_eval_embd.restype = c_int _lib.llama_eval_embd.restype = c_int
# Convert the provided text into tokens. # // Export a static computation graph for context of 511 and batch size of 1
# The tokens pointer must be large enough to hold the resulting tokens. # // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
# Returns the number of tokens on success, no more than n_max_tokens # // parameters here to keep things simple
# Returns a negative number on failure - the number of tokens that would have been returned # // IMPORTANT: do not use for anything else other than debugging and testing!
# TODO: not sure if correct # LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
# LLAMA_API int llama_tokenize( def llama_eval_export(ctx: llama_context_p, fname: bytes) -> int:
# struct llama_context * ctx, return _lib.llama_eval_export(ctx, fname)
# 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_eval_export.argtypes = [llama_context_p, c_char_p]
_lib.llama_tokenize.restype = c_int _lib.llama_eval_export.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() # Token logits obtained from the last call to llama_eval()
@ -909,60 +855,172 @@ _lib.llama_get_embeddings.argtypes = [llama_context_p]
_lib.llama_get_embeddings.restype = c_float_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( # // Vocab
# 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] # LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
_lib.llama_token_to_str.restype = c_char_p def llama_token_get_text(ctx: llama_context_p, token: llama_token) -> bytes:
return _lib.llama_token_get_text(ctx, token)
# LLAMA_API const char * llama_token_to_str_with_model( _lib.llama_token_get_text.argtypes = [llama_context_p, llama_token]
# const struct llama_model * model, _lib.llama_token_get_text.restype = c_char_p
# 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] # LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
_lib.llama_token_to_str_with_model.restype = c_char_p def llama_token_get_score(ctx: llama_context_p, token: llama_token) -> float:
return _lib.llama_token_get_score(ctx, token)
# Special tokens
# LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence _lib.llama_token_get_score.argtypes = [llama_context_p, llama_token]
def llama_token_bos() -> int: _lib.llama_token_get_score.restype = c_float
return _lib.llama_token_bos()
_lib.llama_token_bos.argtypes = [] # LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
def llama_token_get_type(ctx: llama_context_p, token: llama_token) -> int:
return _lib.llama_token_get_type(ctx, token)
_lib.llama_token_get_type.argtypes = [llama_context_p, llama_token]
_lib.llama_token_get_type.restype = ctypes.c_int
# // Special tokens
# LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx); // beginning-of-sentence
def llama_token_bos(ctx: llama_context_p) -> llama_token:
return _lib.llama_token_bos(ctx)
_lib.llama_token_bos.argtypes = [llama_context_p]
_lib.llama_token_bos.restype = llama_token _lib.llama_token_bos.restype = llama_token
# LLAMA_API llama_token llama_token_eos(); // end-of-sentence # LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx); // end-of-sentence
def llama_token_eos() -> int: def llama_token_eos(ctx: llama_context_p) -> llama_token:
return _lib.llama_token_eos() return _lib.llama_token_eos(ctx)
_lib.llama_token_eos.argtypes = [] _lib.llama_token_eos.argtypes = [llama_context_p]
_lib.llama_token_eos.restype = llama_token _lib.llama_token_eos.restype = llama_token
# LLAMA_API llama_token llama_token_nl(); // next-line # LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx); // next-line
def llama_token_nl() -> int: def llama_token_nl(ctx: llama_context_p) -> llama_token:
return _lib.llama_token_nl() return _lib.llama_token_nl(ctx)
_lib.llama_token_nl.argtypes = [] _lib.llama_token_nl.argtypes = [llama_context_p]
_lib.llama_token_nl.restype = llama_token _lib.llama_token_nl.restype = llama_token
# //
# // Tokenization
# //
# 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)
_lib.llama_tokenize_with_model.argtypes = [
llama_model_p,
c_char_p,
llama_token_p,
c_int,
c_bool,
]
_lib.llama_tokenize_with_model.restype = c_int
# // Token Id -> String. Uses the vocabulary in the provided context
# // Does not write null terminator to the buffer
# LLAMA_API int llama_token_to_str(
# const struct llama_context * ctx,
# llama_token token,
# char * buf,
# int length);
def llama_token_to_str(
ctx: llama_context_p, token: llama_token, buf: bytes, length: c_int
) -> int:
return _lib.llama_token_to_str(ctx, token, buf, length)
_lib.llama_tokenize_with_model.argtypes = [
llama_model_p,
c_char_p,
llama_token_p,
c_int,
c_bool,
]
_lib.llama_tokenize_with_model.restype = c_int
# LLAMA_API int llama_token_to_str_with_model(
# const struct llama_model * model,
# llama_token token,
# char * buf,
# int length);
def llama_token_to_str_with_model(
model: llama_model_p, token: llama_token, buf: bytes, length: c_int
) -> int:
return _lib.llama_token_to_str_with_model(model, token, buf, length)
_lib.llama_token_to_str_with_model.argtypes = [
llama_model_p,
llama_token,
c_char_p,
c_int,
]
_lib.llama_token_to_str_with_model.restype = c_int
# //
# // Grammar # // Grammar
# // # //
# LLAMA_API struct llama_grammar * llama_grammar_init( # LLAMA_API struct llama_grammar * llama_grammar_init(
# const llama_grammar_element ** rules, # const llama_grammar_element ** rules,
# size_t n_rules, # size_t n_rules,
@ -991,8 +1049,78 @@ def llama_grammar_free(grammar: llama_grammar_p):
_lib.llama_grammar_free.argtypes = [llama_grammar_p] _lib.llama_grammar_free.argtypes = [llama_grammar_p]
_lib.llama_grammar_free.restype = None _lib.llama_grammar_free.restype = None
# //
# // Beam search
# //
# Sampling functions
# struct llama_beam_view {
# const llama_token * tokens;
# size_t n_tokens;
# float p; // Cumulative beam probability (renormalized relative to all beams)
# bool eob; // Callback should set this to true when a beam is at end-of-beam.
# };
class llama_beam_view(ctypes.Structure):
_fields_ = [
("tokens", llama_token_p),
("n_tokens", c_size_t),
("p", c_float),
("eob", c_bool),
]
# // Passed to beam_search_callback function.
# // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
# // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
# // These pointers are valid only during the synchronous callback, so should not be saved.
# struct llama_beams_state {
# struct llama_beam_view * beam_views;
# size_t n_beams; // Number of elements in beam_views[].
# size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
# bool last_call; // True iff this is the last callback invocation.
# };
class llama_beams_state(ctypes.Structure):
_fields_ = [
("beam_views", POINTER(llama_beam_view)),
("n_beams", c_size_t),
("common_prefix_length", c_size_t),
("last_call", c_bool),
]
# // Type of pointer to the beam_search_callback function.
# // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
# // passed back to beam_search_callback. This avoids having to use global variables in the callback.
# typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state);
llama_beam_search_callback_fn_t = ctypes.CFUNCTYPE(None, c_void_p, llama_beams_state)
# /// @details Deterministically returns entire sentence constructed by a beam search.
# /// @param ctx Pointer to the llama_context.
# /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
# /// @param callback_data A pointer that is simply passed back to callback.
# /// @param n_beams Number of beams to use.
# /// @param n_past Number of tokens already evaluated.
# /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
# /// @param n_threads Number of threads as passed to llama_eval().
# LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
def llama_beam_search(
ctx: llama_context_p,
callback: "ctypes._CFuncPtr[None, c_void_p, llama_beams_state]", # type: ignore
callback_data: c_void_p,
n_beams: c_size_t,
n_past: c_int,
n_predict: c_int,
n_threads: c_int,
):
return _lib.llama_beam_search(
ctx, callback, callback_data, n_beams, n_past, n_predict, n_threads
)
# //
# // Sampling functions
# //
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix. # @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
@ -1351,6 +1479,19 @@ def llama_print_system_info() -> bytes:
_lib.llama_print_system_info.argtypes = [] _lib.llama_print_system_info.argtypes = []
_lib.llama_print_system_info.restype = c_char_p _lib.llama_print_system_info.restype = c_char_p
# // Set callback for all future logging events.
# // If this is not called, or NULL is supplied, everything is output on stderr.
# LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
def llama_log_set(
log_callback: "ctypes._FuncPointer", user_data: c_void_p # type: ignore
):
return _lib.llama_log_set(log_callback, user_data)
_lib.llama_log_set.argtypes = [llama_log_callback, c_void_p]
_lib.llama_log_set.restype = None
################################################################################################### ###################################################################################################

3
pyproject.toml
View file

@ -35,6 +35,9 @@ scikit-build = "0.17.6"
[tool.poetry.extras] [tool.poetry.extras]
server = ["uvicorn", "fastapi", "pydantic-settings", "sse-starlette"] server = ["uvicorn", "fastapi", "pydantic-settings", "sse-starlette"]
[tool.pytest.ini_options]
addopts = "--ignore=vendor"
[build-system] [build-system]
requires = [ requires = [
"setuptools>=42", "setuptools>=42",

22
tests/test_llama.py
View file

@ -1,20 +1,32 @@
import pytest
import llama_cpp import llama_cpp
MODEL = "./vendor/llama.cpp/models/ggml-vocab.bin" MODEL = "./vendor/llama.cpp/models/ggml-vocab-llama.gguf"
def test_llama(): def test_llama_cpp_tokenization():
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True) llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, verbose=False)
assert llama assert llama
assert llama.ctx is not None assert llama.ctx is not None
text = b"Hello World" text = b"Hello World"
assert llama.detokenize(llama.tokenize(text)) == text tokens = llama.tokenize(text)
assert tokens[0] == llama.token_bos()
assert tokens == [1, 15043, 2787]
detokenized = llama.detokenize(tokens)
assert detokenized == text
tokens = llama.tokenize(text, add_bos=False)
assert tokens[0] != llama.token_bos()
assert tokens == [15043, 2787]
detokenized = llama.detokenize(tokens)
assert detokenized != text
# @pytest.mark.skip(reason="need to update sample mocking") @pytest.mark.skip(reason="bug in tokenization where leading space is always inserted even if not after eos")
def test_llama_patch(monkeypatch): def test_llama_patch(monkeypatch):
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True) llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
n_vocab = llama_cpp.llama_n_vocab(llama.ctx) n_vocab = llama_cpp.llama_n_vocab(llama.ctx)

2
vendor/llama.cpp vendored

@ -1 +1 @@
Subproject commit 604b8bdfa6320bbcb018eebcc1252dfede603c6b Subproject commit 232caf3c1581a6cb023571780ff41dc2d66d1ca0