268 lines
7 KiB
Python
268 lines
7 KiB
Python
import ctypes
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from ctypes import c_int, c_float, c_char_p, c_void_p, c_bool, POINTER, Structure, Array
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import pathlib
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from itertools import chain
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# Load the library
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# TODO: fragile, should fix
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_base_path = pathlib.Path(__file__).parent
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(_lib_path,) = chain(
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_base_path.glob("*.so"), _base_path.glob("*.dylib"), _base_path.glob("*.dll")
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)
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_lib = ctypes.CDLL(str(_lib_path))
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# C types
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llama_context_p = c_void_p
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llama_token = c_int
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llama_token_p = POINTER(llama_token)
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class llama_token_data(Structure):
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_fields_ = [
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("id", llama_token), # token id
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("p", c_float), # probability of the token
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("plog", c_float), # log probability of the token
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]
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llama_token_data_p = POINTER(llama_token_data)
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llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
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class llama_context_params(Structure):
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_fields_ = [
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("n_ctx", c_int), # text context
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("n_parts", c_int), # -1 for default
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("seed", c_int), # RNG seed, 0 for random
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("f16_kv", c_bool), # use fp16 for KV cache
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(
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"logits_all",
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c_bool,
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), # the llama_eval() call computes all logits, not just the last one
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("vocab_only", c_bool), # only load the vocabulary, no weights
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("use_mlock", c_bool), # force system to keep model in RAM
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("embedding", c_bool), # embedding mode only
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# called with a progress value between 0 and 1, pass NULL to disable
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("progress_callback", llama_progress_callback),
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# context pointer passed to the progress callback
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("progress_callback_user_data", c_void_p),
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]
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llama_context_params_p = POINTER(llama_context_params)
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# Functions
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def llama_context_default_params() -> llama_context_params:
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return _lib.llama_context_default_params()
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_lib.llama_context_default_params.argtypes = []
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_lib.llama_context_default_params.restype = llama_context_params
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# Various functions for loading a ggml llama model.
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# Allocate (almost) all memory needed for the model.
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# Return NULL on failure
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def llama_init_from_file(
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path_model: bytes, params: llama_context_params
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) -> llama_context_p:
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return _lib.llama_init_from_file(path_model, params)
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_lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params]
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_lib.llama_init_from_file.restype = llama_context_p
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# Frees all allocated memory
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def llama_free(ctx: llama_context_p):
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_lib.llama_free(ctx)
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_lib.llama_free.argtypes = [llama_context_p]
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_lib.llama_free.restype = None
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# TODO: not great API - very likely to change
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# Returns 0 on success
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def llama_model_quantize(
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fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int
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) -> c_int:
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return _lib.llama_model_quantize(fname_inp, fname_out, itype, qk)
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_lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
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_lib.llama_model_quantize.restype = c_int
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# Run the llama inference to obtain the logits and probabilities for the next token.
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# tokens + n_tokens is the provided batch of new tokens to process
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# n_past is the number of tokens to use from previous eval calls
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# Returns 0 on success
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def llama_eval(
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ctx: llama_context_p,
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tokens, # type: Array[llama_token]
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n_tokens: c_int,
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n_past: c_int,
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n_threads: c_int,
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) -> c_int:
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return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
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_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
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_lib.llama_eval.restype = c_int
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# Convert the provided text into tokens.
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# The tokens pointer must be large enough to hold the resulting tokens.
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# Returns the number of tokens on success, no more than n_max_tokens
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# Returns a negative number on failure - the number of tokens that would have been returned
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# TODO: not sure if correct
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def llama_tokenize(
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ctx: llama_context_p,
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text: bytes,
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tokens, # type: Array[llama_token]
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n_max_tokens: c_int,
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add_bos: c_bool,
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) -> c_int:
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return _lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
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_lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool]
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_lib.llama_tokenize.restype = c_int
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def llama_n_vocab(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_vocab(ctx)
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_lib.llama_n_vocab.argtypes = [llama_context_p]
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_lib.llama_n_vocab.restype = c_int
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def llama_n_ctx(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_ctx(ctx)
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_lib.llama_n_ctx.argtypes = [llama_context_p]
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_lib.llama_n_ctx.restype = c_int
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def llama_n_embd(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_ctx(ctx)
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_lib.llama_n_embd.argtypes = [llama_context_p]
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_lib.llama_n_embd.restype = c_int
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# Token logits obtained from the last call to llama_eval()
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# The logits for the last token are stored in the last row
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# Can be mutated in order to change the probabilities of the next token
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# Rows: n_tokens
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# Cols: n_vocab
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def llama_get_logits(ctx: llama_context_p):
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return _lib.llama_get_logits(ctx)
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_lib.llama_get_logits.argtypes = [llama_context_p]
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_lib.llama_get_logits.restype = POINTER(c_float)
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# Get the embeddings for the input
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# shape: [n_embd] (1-dimensional)
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def llama_get_embeddings(ctx: llama_context_p):
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return _lib.llama_get_embeddings(ctx)
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_lib.llama_get_embeddings.argtypes = [llama_context_p]
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_lib.llama_get_embeddings.restype = POINTER(c_float)
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# Token Id -> String. Uses the vocabulary in the provided context
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def llama_token_to_str(ctx: llama_context_p, token: llama_token) -> bytes:
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return _lib.llama_token_to_str(ctx, token)
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_lib.llama_token_to_str.argtypes = [llama_context_p, llama_token]
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_lib.llama_token_to_str.restype = c_char_p
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# Special tokens
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def llama_token_bos() -> llama_token:
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return _lib.llama_token_bos()
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_lib.llama_token_bos.argtypes = []
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_lib.llama_token_bos.restype = llama_token
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def llama_token_eos() -> llama_token:
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return _lib.llama_token_eos()
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_lib.llama_token_eos.argtypes = []
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_lib.llama_token_eos.restype = llama_token
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# TODO: improve the last_n_tokens interface ?
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def llama_sample_top_p_top_k(
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ctx: llama_context_p,
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last_n_tokens_data, # type: Array[llama_token]
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last_n_tokens_size: c_int,
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top_k: c_int,
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top_p: c_float,
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temp: c_float,
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repeat_penalty: c_float,
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) -> llama_token:
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return _lib.llama_sample_top_p_top_k(
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ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty
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)
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_lib.llama_sample_top_p_top_k.argtypes = [
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llama_context_p,
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llama_token_p,
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c_int,
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c_int,
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c_float,
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c_float,
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c_float,
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]
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_lib.llama_sample_top_p_top_k.restype = llama_token
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# Performance information
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def llama_print_timings(ctx: llama_context_p):
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_lib.llama_print_timings(ctx)
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_lib.llama_print_timings.argtypes = [llama_context_p]
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_lib.llama_print_timings.restype = None
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def llama_reset_timings(ctx: llama_context_p):
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_lib.llama_reset_timings(ctx)
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_lib.llama_reset_timings.argtypes = [llama_context_p]
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_lib.llama_reset_timings.restype = None
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# Print system information
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def llama_print_system_info() -> bytes:
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return _lib.llama_print_system_info()
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_lib.llama_print_system_info.argtypes = []
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_lib.llama_print_system_info.restype = c_char_p
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