feat: Add typechecking for ctypes structure attributes
This commit is contained in:
Andrei Betlen
parent
889d0e8981
commit
1347e1d050
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@ -237,7 +237,7 @@ LLAMA_FILE_MAGIC_GGLA = 0x67676C61
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# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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LLAMA_FILE_MAGIC_GGSN = 0x6767736E
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#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
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# define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
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LLAMA_FILE_MAGIC_GGSQ = 0x67677371
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# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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@ -245,9 +245,9 @@ LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
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# define LLAMA_SESSION_VERSION 5
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LLAMA_SESSION_VERSION = 5
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#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
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# define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
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LLAMA_STATE_SEQ_MAGIC = LLAMA_FILE_MAGIC_GGSQ
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#define LLAMA_STATE_SEQ_VERSION 1
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# define LLAMA_STATE_SEQ_VERSION 1
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LLAMA_STATE_SEQ_VERSION = 1
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# struct llama_model;
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@ -431,6 +431,11 @@ class llama_token_data(ctypes.Structure):
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logit (float): log-odds of the token
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p (float): probability of the token"""
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if TYPE_CHECKING:
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id: llama_token
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logit: float
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p: float
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_fields_ = [
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("id", llama_token),
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("logit", ctypes.c_float),
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@ -454,6 +459,11 @@ class llama_token_data_array(ctypes.Structure):
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size (int): size of the array
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sorted (bool): whether the array is sorted"""
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if TYPE_CHECKING:
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data: CtypesArray[llama_token_data]
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size: int
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sorted: bool
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_fields_ = [
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("data", llama_token_data_p),
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("size", ctypes.c_size_t),
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@ -515,6 +525,15 @@ class llama_batch(ctypes.Structure):
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logits (ctypes.Array[ctypes.ctypes.c_int8]): if zero, the logits for the respective token will not be output
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"""
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if TYPE_CHECKING:
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n_tokens: int
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token: CtypesArray[llama_token]
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embd: CtypesArray[ctypes.c_float]
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pos: CtypesArray[CtypesArray[llama_pos]]
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n_seq_id: CtypesArray[ctypes.c_int]
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seq_id: CtypesArray[CtypesArray[llama_seq_id]]
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logits: CtypesArray[ctypes.c_int8]
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_fields_ = [
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("n_tokens", ctypes.c_int32),
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("token", ctypes.POINTER(llama_token)),
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@ -609,6 +628,18 @@ class llama_model_params(ctypes.Structure):
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use_mmap (bool): use mmap if possible
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use_mlock (bool): force system to keep model in RAM"""
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if TYPE_CHECKING:
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n_gpu_layers: int
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split_mode: int
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main_gpu: int
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tensor_split: CtypesArray[ctypes.c_float]
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progress_callback: Callable[[float, ctypes.c_void_p], bool]
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progress_callback_user_data: ctypes.c_void_p
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kv_overrides: CtypesArray[llama_model_kv_override]
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vocab_only: bool
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use_mmap: bool
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use_mlock: bool
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_fields_ = [
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("n_gpu_layers", ctypes.c_int32),
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("split_mode", ctypes.c_int),
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@ -696,6 +727,34 @@ class llama_context_params(ctypes.Structure):
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abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
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"""
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if TYPE_CHECKING:
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seed: int
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n_ctx: int
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n_batch: int
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n_ubatch: int
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n_seq_max: int
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n_threads: int
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n_threads_batch: int
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rope_scaling_type: int
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pooling_type: int
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rope_freq_base: float
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rope_freq_scale: float
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yarn_ext_factor: float
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yarn_attn_factor: float
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yarn_beta_fast: float
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yarn_beta_slow: float
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yarn_orig_ctx: int
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defrag_thold: float
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cb_eval: Callable[[ctypes.c_void_p, bool], bool]
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cb_eval_user_data: ctypes.c_void_p
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type_k: int
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type_v: int
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logits_all: bool
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embeddings: bool
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offload_kqv: bool
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abort_callback: Callable[[ctypes.c_void_p], bool]
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abort_callback_data: ctypes.c_void_p
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_fields_ = [
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("seed", ctypes.c_uint32),
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("n_ctx", ctypes.c_uint32),
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@ -771,6 +830,18 @@ class llama_model_quantize_params(ctypes.Structure):
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kv_overrides (ctypes.c_void_p): pointer to vector containing overrides
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"""
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if TYPE_CHECKING:
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nthread: int
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ftype: int
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output_tensor_type: int
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token_embedding_type: int
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allow_requantize: bool
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quantize_output_tensor: bool
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only_copy: bool
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pure: bool
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imatrix: ctypes.c_void_p
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kv_overrides: ctypes.c_void_p
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_fields_ = [
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("nthread", ctypes.c_int32),
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("ftype", ctypes.c_int),
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@ -828,6 +899,10 @@ LLAMA_GRETYPE_CHAR_ALT = 6
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# uint32_t value; // Unicode code point or rule ID
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# } llama_grammar_element;
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class llama_grammar_element(ctypes.Structure):
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if TYPE_CHECKING:
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type: int
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value: int
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_fields_ = [
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("type", ctypes.c_int),
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("value", ctypes.c_uint32),
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@ -851,6 +926,17 @@ llama_grammar_element_p = ctypes.POINTER(llama_grammar_element)
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# int32_t n_eval;
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# };
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class llama_timings(ctypes.Structure):
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if TYPE_CHECKING:
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t_start_ms: float
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t_end_ms: float
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t_load_ms: float
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t_sample_ms: float
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t_p_eval_ms: float
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t_eval_ms: float
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n_sample: int
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n_p_eval: int
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n_eval: int
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_fields_ = [
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("t_start_ms", ctypes.c_double),
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("t_end_ms", ctypes.c_double),
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@ -951,7 +1037,8 @@ GGML_NUMA_STRATEGY_COUNT = 5
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[ctypes.c_int],
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None,
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)
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def llama_numa_init(numa: int, /): ...
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def llama_numa_init(numa: int, /):
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...
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# // Call once at the end of the program - currently only used for MPI
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@ -976,7 +1063,8 @@ def llama_backend_free():
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)
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def llama_load_model_from_file(
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path_model: bytes, params: llama_model_params, /
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) -> Optional[llama_model_p]: ...
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) -> Optional[llama_model_p]:
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...
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# LLAMA_API void llama_free_model(struct llama_model * model);
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@ -985,7 +1073,8 @@ def llama_load_model_from_file(
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[llama_model_p_ctypes],
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None,
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)
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def llama_free_model(model: llama_model_p, /): ...
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def llama_free_model(model: llama_model_p, /):
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...
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# LLAMA_API struct llama_context * llama_new_context_with_model(
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@ -998,7 +1087,8 @@ def llama_free_model(model: llama_model_p, /): ...
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)
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def llama_new_context_with_model(
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model: llama_model_p, params: llama_context_params, /
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) -> Optional[llama_context_p]: ...
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) -> Optional[llama_context_p]:
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...
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# // Frees all allocated memory
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@ -1019,82 +1109,98 @@ def llama_free(ctx: llama_context_p, /):
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[],
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ctypes.c_int64,
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)
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def llama_time_us() -> int: ...
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def llama_time_us() -> int:
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...
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# LLAMA_API size_t llama_max_devices(void);
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@ctypes_function("llama_max_devices", [], ctypes.c_size_t)
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def llama_max_devices() -> int: ...
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def llama_max_devices() -> int:
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...
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# LLAMA_API bool llama_supports_mmap (void);
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@ctypes_function("llama_supports_mmap", [], ctypes.c_bool)
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def llama_supports_mmap() -> bool: ...
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def llama_supports_mmap() -> bool:
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...
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# LLAMA_API bool llama_supports_mlock (void);
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@ctypes_function("llama_supports_mlock", [], ctypes.c_bool)
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def llama_supports_mlock() -> bool: ...
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def llama_supports_mlock() -> bool:
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...
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# LLAMA_API bool llama_supports_gpu_offload(void);
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@ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool)
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def llama_supports_gpu_offload() -> bool: ...
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def llama_supports_gpu_offload() -> bool:
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...
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# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
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@ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes)
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def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ...
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def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]:
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...
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# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
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@ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32)
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def llama_n_ctx(ctx: llama_context_p, /) -> int: ...
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def llama_n_ctx(ctx: llama_context_p, /) -> int:
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...
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# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
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@ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32)
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def llama_n_batch(ctx: llama_context_p, /) -> int: ...
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def llama_n_batch(ctx: llama_context_p, /) -> int:
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...
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# LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
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@ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32)
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def llama_n_ubatch(ctx: llama_context_p, /) -> int: ...
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def llama_n_ubatch(ctx: llama_context_p, /) -> int:
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...
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# LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
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@ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32)
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def llama_n_seq_max(ctx: llama_context_p, /) -> int: ...
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def llama_n_seq_max(ctx: llama_context_p, /) -> int:
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...
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# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
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@ctypes_function("llama_vocab_type", [llama_model_p_ctypes], ctypes.c_int)
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def llama_vocab_type(model: llama_model_p, /) -> int: ...
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def llama_vocab_type(model: llama_model_p, /) -> int:
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...
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# LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
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@ctypes_function("llama_rope_type", [llama_model_p_ctypes], ctypes.c_int)
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def llama_rope_type(model: llama_model_p, /) -> int: ...
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def llama_rope_type(model: llama_model_p, /) -> int:
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...
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# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
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@ctypes_function("llama_n_vocab", [llama_model_p_ctypes], ctypes.c_int32)
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def llama_n_vocab(model: llama_model_p, /) -> int: ...
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def llama_n_vocab(model: llama_model_p, /) -> int:
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...
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# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
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@ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
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def llama_n_ctx_train(model: llama_model_p, /) -> int: ...
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def llama_n_ctx_train(model: llama_model_p, /) -> int:
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...
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# LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
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@ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
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def llama_n_embd(model: llama_model_p, /) -> int: ...
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def llama_n_embd(model: llama_model_p, /) -> int:
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...
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# LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
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@ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
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def llama_n_layer(model: llama_model_p, /) -> int: ...
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def llama_n_layer(model: llama_model_p, /) -> int:
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...
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# // Get the model's RoPE frequency scaling factor
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@ -1358,6 +1464,9 @@ class llama_kv_cache_view_cell(ctypes.Structure):
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pos (llama_pos): The position for this cell. Takes KV cache shifts into account.
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May be negative if the cell is not populated."""
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if TYPE_CHECKING:
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pos: llama_pos
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_fields_ = [("pos", llama_pos)]
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@ -1394,6 +1503,16 @@ class llama_kv_cache_view_cell(ctypes.Structure):
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# llama_seq_id * cells_sequences;
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# };
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class llama_kv_cache_view(ctypes.Structure):
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if TYPE_CHECKING:
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n_cells: int
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n_max_seq: int
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token_count: int
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used_cells: int
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max_contiguous: int
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max_contiguous_idx: int
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cells: CtypesArray[llama_kv_cache_view_cell]
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cells_sequences: CtypesArray[llama_seq_id]
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_fields_ = [
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("n_cells", ctypes.c_int32),
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("n_max_seq", ctypes.c_int32),
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@ -1783,7 +1902,8 @@ def llama_state_load_file(
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n_token_capacity: Union[ctypes.c_size_t, int],
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n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
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/,
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) -> bool: ...
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) -> bool:
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...
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# LLAMA_API DEPRECATED(bool llama_load_session_file(
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@ -1811,7 +1931,8 @@ def llama_load_session_file(
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n_token_capacity: Union[ctypes.c_size_t, int],
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n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
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/,
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) -> int: ...
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) -> int:
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...
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# LLAMA_API bool llama_state_save_file(
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@ -1835,7 +1956,8 @@ def llama_state_save_file(
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tokens: CtypesArray[llama_token],
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n_token_count: Union[ctypes.c_size_t, int],
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/,
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) -> bool: ...
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) -> bool:
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...
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# LLAMA_API DEPRECATED(bool llama_save_session_file(
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@ -1860,7 +1982,8 @@ def llama_save_session_file(
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tokens: CtypesArray[llama_token],
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n_token_count: Union[ctypes.c_size_t, int],
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/,
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) -> int: ...
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) -> int:
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...
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# // Get the exact size needed to copy the KV cache of a single sequence
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@ -2233,7 +2356,8 @@ def llama_get_embeddings_seq(
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)
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def llama_token_get_text(
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model: llama_model_p, token: Union[llama_token, int], /
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) -> bytes: ...
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) -> bytes:
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...
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# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
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@ -2242,7 +2366,8 @@ def llama_token_get_text(
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)
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def llama_token_get_score(
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model: llama_model_p, token: Union[llama_token, int], /
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) -> float: ...
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) -> float:
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...
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# LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
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@ -2251,7 +2376,8 @@ def llama_token_get_score(
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)
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def llama_token_get_type(
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model: llama_model_p, token: Union[llama_token, int], /
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) -> int: ...
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) -> int:
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...
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# // Special tokens
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|
|
@ -2318,17 +2444,20 @@ def llama_token_prefix(model: llama_model_p) -> int:
|
|||
|
||||
# LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||||
@ctypes_function("llama_token_middle", [llama_model_p_ctypes], llama_token)
|
||||
def llama_token_middle(model: llama_model_p, /) -> int: ...
|
||||
def llama_token_middle(model: llama_model_p, /) -> int:
|
||||
...
|
||||
|
||||
|
||||
# LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||||
@ctypes_function("llama_token_suffix", [llama_model_p_ctypes], llama_token)
|
||||
def llama_token_suffix(model: llama_model_p, /) -> int: ...
|
||||
def llama_token_suffix(model: llama_model_p, /) -> int:
|
||||
...
|
||||
|
||||
|
||||
# LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
|
||||
@ctypes_function("llama_token_eot", [llama_model_p_ctypes], llama_token)
|
||||
def llama_token_eot(model: llama_model_p, /) -> int: ...
|
||||
def llama_token_eot(model: llama_model_p, /) -> int:
|
||||
...
|
||||
|
||||
|
||||
# //
|
||||
|
|
@ -2459,7 +2588,8 @@ def llama_chat_apply_template(
|
|||
chat: CtypesArray[llama_chat_message],
|
||||
n_msg: int,
|
||||
/,
|
||||
) -> int: ...
|
||||
) -> int:
|
||||
...
|
||||
|
||||
|
||||
# //
|
||||
|
|
@ -2989,6 +3119,12 @@ def llama_grammar_accept_token(
|
|||
# bool eob; // Callback should set this to true when a beam is at end-of-beam.
|
||||
# };
|
||||
class llama_beam_view(ctypes.Structure):
|
||||
if TYPE_CHECKING:
|
||||
tokens: CtypesArray[llama_token]
|
||||
n_tokens: int
|
||||
p: float
|
||||
eob: bool
|
||||
|
||||
_fields_ = [
|
||||
("tokens", llama_token_p),
|
||||
("n_tokens", ctypes.c_size_t),
|
||||
|
|
@ -3008,6 +3144,12 @@ class llama_beam_view(ctypes.Structure):
|
|||
# bool last_call; // True iff this is the last callback invocation.
|
||||
# };
|
||||
class llama_beams_state(ctypes.Structure):
|
||||
if TYPE_CHECKING:
|
||||
beam_views: CtypesArray[llama_beam_view]
|
||||
n_beams: int
|
||||
common_prefix_length: int
|
||||
last_call: bool
|
||||
|
||||
_fields_ = [
|
||||
("beam_views", ctypes.POINTER(llama_beam_view)),
|
||||
("n_beams", ctypes.c_size_t),
|
||||
|
|
@ -3060,7 +3202,8 @@ def llama_beam_search(
|
|||
n_past: Union[ctypes.c_int, int],
|
||||
n_predict: Union[ctypes.c_int, int],
|
||||
/,
|
||||
): ...
|
||||
):
|
||||
...
|
||||
|
||||
|
||||
# /// @details Build a split GGUF final path for this chunk.
|
||||
|
|
@ -3179,4 +3322,5 @@ def llama_log_set(
|
|||
[ctypes.c_void_p, llama_context_p_ctypes],
|
||||
None,
|
||||
)
|
||||
def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p, /): ...
|
||||
def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p, /):
|
||||
...
|
||||
|
|
|
|||
Loading…
Reference in a new issue