feat(low-level-api): Improve API static type-safety and performance (#1205)
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Andrei
GitHub
parent
0f8aa4ab5c
commit
7f51b6071f
5 changed files with 858 additions and 743 deletions
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@ -108,7 +108,7 @@ class _LlamaModel:
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scale,
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path_base_model.encode("utf-8")
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if path_base_model is not None
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else llama_cpp.c_char_p(0),
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else ctypes.c_char_p(0),
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n_threads,
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)
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@ -303,8 +303,8 @@ class _LlamaContext:
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assert self.ctx is not None
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assert batch.batch is not None
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return_code = llama_cpp.llama_decode(
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ctx=self.ctx,
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batch=batch.batch,
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self.ctx,
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batch.batch,
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)
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if return_code != 0:
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raise RuntimeError(f"llama_decode returned {return_code}")
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@ -493,7 +493,7 @@ class _LlamaBatch:
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def __init__(
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self, *, n_tokens: int, embd: int, n_seq_max: int, verbose: bool = True
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):
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self.n_tokens = n_tokens
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self._n_tokens = n_tokens
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self.embd = embd
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self.n_seq_max = n_seq_max
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self.verbose = verbose
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@ -502,7 +502,7 @@ class _LlamaBatch:
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self.batch = None
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self.batch = llama_cpp.llama_batch_init(
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self.n_tokens, self.embd, self.n_seq_max
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self._n_tokens, self.embd, self.n_seq_max
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)
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def __del__(self):
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@ -570,12 +570,13 @@ class _LlamaTokenDataArray:
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self.candidates.data = self.candidates_data.ctypes.data_as(
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llama_cpp.llama_token_data_p
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)
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self.candidates.sorted = llama_cpp.c_bool(False)
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self.candidates.size = llama_cpp.c_size_t(self.n_vocab)
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self.candidates.sorted = ctypes.c_bool(False)
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self.candidates.size = ctypes.c_size_t(self.n_vocab)
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# Python wrappers over common/common
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def _tokenize(model: _LlamaModel, text: str, add_bos: bool, special: bool) -> list[int]:
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assert model.model is not None
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n_tokens = len(text) + 1 if add_bos else len(text)
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result = (llama_cpp.llama_token * n_tokens)()
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n_tokens = llama_cpp.llama_tokenize(
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@ -1818,7 +1818,7 @@ class Llama:
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self.input_ids = state.input_ids.copy()
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self.n_tokens = state.n_tokens
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state_size = state.llama_state_size
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LLamaStateArrayType = llama_cpp.c_uint8 * state_size
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LLamaStateArrayType = ctypes.c_uint8 * state_size
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llama_state = LLamaStateArrayType.from_buffer_copy(state.llama_state)
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if llama_cpp.llama_set_state_data(self._ctx.ctx, llama_state) != state_size:
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File diff suppressed because it is too large
Load diff
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@ -5,21 +5,15 @@ from ctypes import (
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c_bool,
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c_char_p,
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c_int,
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c_int8,
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c_int32,
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c_uint8,
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c_uint32,
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c_size_t,
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c_float,
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c_double,
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c_void_p,
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POINTER,
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_Pointer, # type: ignore
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Structure,
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Array,
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)
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import pathlib
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from typing import List, Union
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from typing import List, Union, NewType, Optional
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import llama_cpp.llama_cpp as llama_cpp
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@ -67,7 +61,7 @@ def _load_shared_library(lib_base_name: str):
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for _lib_path in _lib_paths:
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if _lib_path.exists():
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try:
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return ctypes.CDLL(str(_lib_path), **cdll_args)
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return ctypes.CDLL(str(_lib_path), **cdll_args) # type: ignore
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except Exception as e:
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raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
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@ -88,7 +82,8 @@ _libllava = _load_shared_library(_libllava_base_name)
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################################################
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# struct clip_ctx;
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clip_ctx_p = c_void_p
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clip_ctx_p = NewType("clip_ctx_p", int)
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clip_ctx_p_ctypes = c_void_p
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# struct llava_image_embed {
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# float * embed;
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@ -102,43 +97,48 @@ class llava_image_embed(Structure):
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# /** sanity check for clip <-> llava embed size match */
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# LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
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def llava_validate_embed_size(ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p) -> bool:
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return _libllava.llava_validate_embed_size(ctx_llama, ctx_clip)
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def llava_validate_embed_size(ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, /) -> bool:
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...
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_libllava.llava_validate_embed_size.argtypes = [llama_cpp.llama_context_p, clip_ctx_p]
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_libllava.llava_validate_embed_size.restype = c_bool
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llava_validate_embed_size = _libllava.llava_validate_embed_size
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llava_validate_embed_size.argtypes = [llama_cpp.llama_context_p_ctypes, clip_ctx_p_ctypes]
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llava_validate_embed_size.restype = c_bool
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# /** build an image embed from image file bytes */
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# LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
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def llava_image_embed_make_with_bytes(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_bytes: bytes, image_bytes_length: Union[c_int, int]) -> "_Pointer[llava_image_embed]":
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return _libllava.llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length)
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def llava_image_embed_make_with_bytes(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_bytes: bytes, image_bytes_length: Union[c_int, int], /) -> "_Pointer[llava_image_embed]":
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...
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_libllava.llava_image_embed_make_with_bytes.argtypes = [clip_ctx_p, c_int, POINTER(c_uint8), c_int]
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_libllava.llava_image_embed_make_with_bytes.restype = POINTER(llava_image_embed)
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llava_image_embed_make_with_bytes = _libllava.llava_image_embed_make_with_bytes
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llava_image_embed_make_with_bytes.argtypes = [clip_ctx_p_ctypes, c_int, POINTER(c_uint8), c_int]
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llava_image_embed_make_with_bytes.restype = POINTER(llava_image_embed)
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# /** build an image embed from a path to an image filename */
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# LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
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def llava_image_embed_make_with_filename(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes) -> "_Pointer[llava_image_embed]":
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return _libllava.llava_image_embed_make_with_filename(ctx_clip, n_threads, image_path)
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def llava_image_embed_make_with_filename(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes, /) -> "_Pointer[llava_image_embed]":
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...
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_libllava.llava_image_embed_make_with_filename.argtypes = [clip_ctx_p, c_int, c_char_p]
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_libllava.llava_image_embed_make_with_filename.restype = POINTER(llava_image_embed)
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llava_image_embed_make_with_filename = _libllava.llava_image_embed_make_with_filename
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llava_image_embed_make_with_filename.argtypes = [clip_ctx_p_ctypes, c_int, c_char_p]
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llava_image_embed_make_with_filename.restype = POINTER(llava_image_embed)
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# LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
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# /** free an embedding made with llava_image_embed_make_* */
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def llava_image_embed_free(embed: "_Pointer[llava_image_embed]"):
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return _libllava.llava_image_embed_free(embed)
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def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /):
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...
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_libllava.llava_image_embed_free.argtypes = [POINTER(llava_image_embed)]
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_libllava.llava_image_embed_free.restype = None
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llava_image_embed_free = _libllava.llava_image_embed_free
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llava_image_embed_free.argtypes = [POINTER(llava_image_embed)]
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llava_image_embed_free.restype = None
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# /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
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# LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
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def llava_eval_image_embed(ctx_llama: llama_cpp.llama_context_p, embed: "_Pointer[llava_image_embed]", n_batch: Union[c_int, int], n_past: "_Pointer[c_int]") -> bool:
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return _libllava.llava_eval_image_embed(ctx_llama, embed, n_batch, n_past)
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def llava_eval_image_embed(ctx_llama: llama_cpp.llama_context_p, embed: "_Pointer[llava_image_embed]", n_batch: Union[c_int, int], n_past: "_Pointer[c_int]", /) -> bool:
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...
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_libllava.llava_eval_image_embed.argtypes = [llama_cpp.llama_context_p, POINTER(llava_image_embed), c_int, POINTER(c_int)]
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_libllava.llava_eval_image_embed.restype = c_bool
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llava_eval_image_embed = _libllava.llava_eval_image_embed
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llava_eval_image_embed.argtypes = [llama_cpp.llama_context_p_ctypes, POINTER(llava_image_embed), c_int, POINTER(c_int)]
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llava_eval_image_embed.restype = c_bool
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################################################
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@ -148,16 +148,18 @@ _libllava.llava_eval_image_embed.restype = c_bool
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# /** load mmproj model */
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# CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity);
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def clip_model_load(fname: bytes, verbosity: Union[c_int, int]) -> clip_ctx_p:
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return _libllava.clip_model_load(fname, verbosity)
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def clip_model_load(fname: bytes, verbosity: Union[c_int, int], /) -> Optional[clip_ctx_p]:
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...
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_libllava.clip_model_load.argtypes = [c_char_p, c_int]
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_libllava.clip_model_load.restype = clip_ctx_p
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clip_model_load = _libllava.clip_model_load
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clip_model_load.argtypes = [c_char_p, c_int]
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clip_model_load.restype = clip_ctx_p_ctypes
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# /** free mmproj model */
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# CLIP_API void clip_free(struct clip_ctx * ctx);
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def clip_free(ctx: clip_ctx_p):
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return _libllava.clip_free(ctx)
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def clip_free(ctx: clip_ctx_p, /):
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...
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_libllava.clip_free.argtypes = [clip_ctx_p]
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_libllava.clip_free.restype = None
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clip_free = _libllava.clip_free
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clip_free.argtypes = [clip_ctx_p_ctypes]
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clip_free.restype = None
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@ -54,7 +54,7 @@ def mock_llama(monkeypatch):
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output_tokens = llama.tokenize(
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output_text.encode("utf-8"), add_bos=True, special=True
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)
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logits = (llama_cpp.c_float * (n_vocab * n_ctx))(-100.0)
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logits = (ctypes.c_float * (n_vocab * n_ctx))(-100.0)
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for i in range(n_ctx):
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output_idx = i + 1 # logits for first tokens predict second token
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if output_idx < len(output_tokens):
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@ -90,9 +90,9 @@ def mock_llama(monkeypatch):
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assert n > 0, "mock_llama_decode not called"
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assert last_n_tokens > 0, "mock_llama_decode not called"
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# Return view of logits for last_n_tokens
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return (llama_cpp.c_float * (last_n_tokens * n_vocab)).from_address(
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return (ctypes.c_float * (last_n_tokens * n_vocab)).from_address(
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ctypes.addressof(logits)
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+ (n - last_n_tokens) * n_vocab * ctypes.sizeof(llama_cpp.c_float)
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+ (n - last_n_tokens) * n_vocab * ctypes.sizeof(ctypes.c_float)
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)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_decode)
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