Merge branch 'main' into server-embedding
This commit is contained in:
Andrei Betlen
commit
922b5b2bfd
5 changed files with 239 additions and 68 deletions
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@ -127,7 +127,6 @@ class Llama:
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self.params = llama_cpp.llama_context_default_params()
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self.params.n_ctx = n_ctx
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self.params.n_parts = n_parts
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self.params.n_gpu_layers = n_gpu_layers
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self.params.seed = seed
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self.params.f16_kv = f16_kv
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@ -149,6 +148,10 @@ class Llama:
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self.lora_base = lora_base
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self.lora_path = lora_path
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### DEPRECATED ###
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self.n_parts = n_parts
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### DEPRECATED ###
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if not os.path.exists(model_path):
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raise ValueError(f"Model path does not exist: {model_path}")
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@ -174,6 +177,30 @@ class Llama:
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if self.verbose:
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print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
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n_vocab = self.n_vocab()
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n_ctx = self.n_ctx()
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data = (llama_cpp.llama_token_data * n_vocab)(
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*[
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llama_cpp.llama_token_data(
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id=llama_cpp.llama_token(i),
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logit=llama_cpp.c_float(0.0),
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p=llama_cpp.c_float(0.0),
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)
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for i in range(n_vocab)
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]
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)
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size = llama_cpp.c_size_t(n_vocab)
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sorted = False
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candidates = llama_cpp.llama_token_data_array(
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data=data,
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size=size,
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sorted=sorted,
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)
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self._candidates = candidates
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self._token_nl = Llama.token_nl()
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self._token_eos = Llama.token_eos()
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def tokenize(self, text: bytes, add_bos: bool = True) -> List[int]:
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"""Tokenize a string.
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@ -293,8 +320,8 @@ class Llama:
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):
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assert self.ctx is not None
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assert len(self.eval_logits) > 0
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n_vocab = int(llama_cpp.llama_n_vocab(self.ctx))
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n_ctx = int(llama_cpp.llama_n_ctx(self.ctx))
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n_vocab = self.n_vocab()
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n_ctx = self.n_ctx()
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top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
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last_n_tokens_size = (
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llama_cpp.c_int(n_ctx)
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@ -302,24 +329,14 @@ class Llama:
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else last_n_tokens_size
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)
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logits = self.eval_logits[-1]
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nl_logit = logits[int(Llama.token_nl())]
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data = (llama_cpp.llama_token_data * n_vocab)(
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*[
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llama_cpp.llama_token_data(
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id=llama_cpp.llama_token(i),
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logit=logits[i],
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p=llama_cpp.c_float(0.0),
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)
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for i in range(n_vocab)
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]
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)
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size = llama_cpp.c_size_t(n_vocab)
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sorted = False
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candidates = llama_cpp.llama_token_data_array(
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data=data,
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size=size,
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sorted=sorted,
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)
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nl_logit = logits[self._token_nl]
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candidates = self._candidates
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for i, logit in enumerate(logits):
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candidates.data[i].id = llama_cpp.llama_token(i)
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candidates.data[i].logit = llama_cpp.c_float(logit)
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candidates.data[i].p = llama_cpp.c_float(0.0)
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candidates.sorted = llama_cpp.c_bool(False)
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candidates.size = llama_cpp.c_size_t(n_vocab)
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llama_cpp.llama_sample_repetition_penalty(
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ctx=self.ctx,
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last_tokens_data=last_n_tokens_data,
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@ -336,7 +353,7 @@ class Llama:
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alpha_presence=presence_penalty,
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)
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if not penalize_nl:
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candidates.data[int(Llama.token_nl())].logit = nl_logit
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candidates.data[self._token_nl].logit = llama_cpp.c_float(nl_logit)
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if temp.value == 0.0:
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return llama_cpp.llama_sample_token_greedy(
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ctx=self.ctx,
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@ -685,7 +702,7 @@ class Llama:
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presence_penalty=presence_penalty,
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repeat_penalty=repeat_penalty,
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):
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if token == Llama.token_eos():
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if token == self._token_eos:
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text = self.detokenize(completion_tokens)
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finish_reason = "stop"
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break
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@ -1237,7 +1254,6 @@ class Llama:
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verbose=self.verbose,
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model_path=self.model_path,
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n_ctx=self.params.n_ctx,
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n_parts=self.params.n_parts,
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n_gpu_layers=self.params.n_gpu_layers,
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seed=self.params.seed,
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f16_kv=self.params.f16_kv,
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@ -1251,6 +1267,9 @@ class Llama:
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n_threads=self.n_threads,
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lora_base=self.lora_base,
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lora_path=self.lora_path,
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### DEPRECATED ###
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n_parts=self.n_parts,
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### DEPRECATED ###
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)
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def __setstate__(self, state):
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@ -1303,6 +1322,21 @@ class Llama:
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if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
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raise RuntimeError("Failed to set llama state data")
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def n_ctx(self) -> int:
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"""Return the context window size."""
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assert self.ctx is not None
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return llama_cpp.llama_n_ctx(self.ctx)
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def n_embd(self) -> int:
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"""Return the embedding size."""
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assert self.ctx is not None
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return llama_cpp.llama_n_embd(self.ctx)
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def n_vocab(self) -> int:
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"""Return the vocabulary size."""
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assert self.ctx is not None
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return llama_cpp.llama_n_vocab(self.ctx)
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@staticmethod
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def token_eos() -> int:
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"""Return the end-of-sequence token."""
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@ -72,31 +72,61 @@ _lib_base_name = "llama"
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# Load the library
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_lib = _load_shared_library(_lib_base_name)
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# C types
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LLAMA_FILE_VERSION = c_int(2)
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LLAMA_FILE_MAGIC = b"ggjt"
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LLAMA_FILE_MAGIC_UNVERSIONED = b"ggml"
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LLAMA_SESSION_MAGIC = b"ggsn"
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# Misc
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c_float_p = POINTER(c_float)
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c_uint8_p = POINTER(c_uint8)
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c_size_t_p = POINTER(c_size_t)
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# llama.h bindings
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# #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
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LLAMA_FILE_MAGIC_GGJT = ctypes.c_uint(0x67676A74)
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# #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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LLAMA_FILE_MAGIC_GGLA = ctypes.c_uint(0x67676C61)
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# #define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
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LLAMA_FILE_MAGIC_GGMF = ctypes.c_uint(0x67676D66)
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# #define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
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LLAMA_FILE_MAGIC_GGML = ctypes.c_uint(0x67676D6C)
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# #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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LLAMA_FILE_MAGIC_GGSN = ctypes.c_uint(0x6767736E)
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# #define LLAMA_FILE_VERSION 3
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LLAMA_FILE_VERSION = c_int(3)
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LLAMA_FILE_MAGIC = LLAMA_FILE_MAGIC_GGJT
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LLAMA_FILE_MAGIC_UNVERSIONED = LLAMA_FILE_MAGIC_GGML
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LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
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LLAMA_SESSION_VERSION = c_int(1)
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# struct llama_context;
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llama_context_p = c_void_p
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# typedef int llama_token;
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llama_token = c_int
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llama_token_p = POINTER(llama_token)
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# typedef struct llama_token_data {
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# llama_token id; // token id
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# float logit; // log-odds of the token
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# float p; // probability of the token
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# } llama_token_data;
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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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("logit", c_float), # log-odds of the token
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("p", c_float), # probability of the token
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("id", llama_token),
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("logit", c_float),
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("p", c_float),
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]
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llama_token_data_p = POINTER(llama_token_data)
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# typedef struct llama_token_data_array {
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# llama_token_data * data;
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# size_t size;
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# bool sorted;
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# } llama_token_data_array;
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class llama_token_data_array(Structure):
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_fields_ = [
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("data", llama_token_data_p),
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@ -107,54 +137,72 @@ class llama_token_data_array(Structure):
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llama_token_data_array_p = POINTER(llama_token_data_array)
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# typedef void (*llama_progress_callback)(float progress, void *ctx);
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llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
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# struct llama_context_params {
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# int n_ctx; // text context
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# int n_gpu_layers; // number of layers to store in VRAM
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# int seed; // RNG seed, -1 for random
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# bool f16_kv; // use fp16 for KV cache
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# bool logits_all; // the llama_eval() call computes all logits, not just the last one
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# bool vocab_only; // only load the vocabulary, no weights
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# bool use_mmap; // use mmap if possible
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# bool use_mlock; // force system to keep model in RAM
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# bool embedding; // embedding mode only
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# // called with a progress value between 0 and 1, pass NULL to disable
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# llama_progress_callback progress_callback;
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# // context pointer passed to the progress callback
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# void * progress_callback_user_data;
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# };
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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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("n_gpu_layers", c_int), # number of layers to store in VRAM
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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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("n_ctx", c_int),
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("n_gpu_layers", c_int),
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("seed", c_int),
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("f16_kv", c_bool),
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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_mmap", c_bool), # use mmap if possible
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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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),
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("vocab_only", c_bool),
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("use_mmap", c_bool),
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("use_mlock", c_bool),
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("embedding", c_bool),
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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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# enum llama_ftype {
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# LLAMA_FTYPE_ALL_F32 = 0,
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# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
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# LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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# };
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LLAMA_FTYPE_ALL_F32 = c_int(0)
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LLAMA_FTYPE_MOSTLY_F16 = c_int(1) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(
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4
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) # tok_embeddings.weight and output.weight are F16
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# LLAMA_FTYPE_MOSTLY_Q4_2 = c_int(5) # except 1d tensors
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# LLAMA_FTYPE_MOSTYL_Q4_3 = c_int(6) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9) # except 1d tensors
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# Misc
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c_float_p = POINTER(c_float)
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c_uint8_p = POINTER(c_uint8)
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c_size_t_p = POINTER(c_size_t)
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# Functions
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LLAMA_FTYPE_MOSTLY_F16 = c_int(1)
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LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2)
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LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3)
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(4)
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LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7)
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LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8)
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LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9)
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# LLAMA_API struct llama_context_params llama_context_default_params();
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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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@ -163,6 +211,7 @@ _lib.llama_context_default_params.argtypes = []
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_lib.llama_context_default_params.restype = llama_context_params
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# LLAMA_API bool llama_mmap_supported();
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def llama_mmap_supported() -> bool:
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return _lib.llama_mmap_supported()
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@ -171,6 +220,7 @@ _lib.llama_mmap_supported.argtypes = []
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_lib.llama_mmap_supported.restype = c_bool
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# LLAMA_API bool llama_mlock_supported();
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def llama_mlock_supported() -> bool:
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return _lib.llama_mlock_supported()
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@ -179,9 +229,33 @@ _lib.llama_mlock_supported.argtypes = []
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_lib.llama_mlock_supported.restype = c_bool
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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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# // TODO: not great API - very likely to change
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# // Initialize the llama + ggml backend
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# // Call once at the start of the program
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# LLAMA_API void llama_init_backend();
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def llama_init_backend():
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return _lib.llama_init_backend()
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_lib.llama_init_backend.argtypes = []
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_lib.llama_init_backend.restype = None
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# LLAMA_API int64_t llama_time_us();
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def llama_time_us() -> int:
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return _lib.llama_time_us()
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_lib.llama_time_us.argtypes = []
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_lib.llama_time_us.restype = ctypes.c_int64
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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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# LLAMA_API struct llama_context * llama_init_from_file(
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# const char * path_model,
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# struct llama_context_params params);
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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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@ -193,6 +267,7 @@ _lib.llama_init_from_file.restype = llama_context_p
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# Frees all allocated memory
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# LLAMA_API void llama_free(struct llama_context * ctx);
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def llama_free(ctx: llama_context_p):
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return _lib.llama_free(ctx)
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@ -204,6 +279,11 @@ _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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# nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
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# LLAMA_API int llama_model_quantize(
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# const char * fname_inp,
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# const char * fname_out,
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# enum llama_ftype ftype,
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# int nthread);
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def llama_model_quantize(
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fname_inp: bytes, fname_out: bytes, ftype: c_int, nthread: c_int
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) -> int:
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@ -220,6 +300,11 @@ _lib.llama_model_quantize.restype = c_int
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# The model needs to be reloaded before applying a new adapter, otherwise the adapter
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# will be applied on top of the previous one
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# Returns 0 on success
|
||||
# LLAMA_API int llama_apply_lora_from_file(
|
||||
# struct llama_context * ctx,
|
||||
# const char * path_lora,
|
||||
# const char * path_base_model,
|
||||
# int n_threads);
|
||||
def llama_apply_lora_from_file(
|
||||
ctx: llama_context_p,
|
||||
path_lora: c_char_p,
|
||||
|
|
@ -234,6 +319,7 @@ _lib.llama_apply_lora_from_file.restype = c_int
|
|||
|
||||
|
||||
# Returns the number of tokens in the KV cache
|
||||
# LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
||||
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
|
||||
return _lib.llama_get_kv_cache_token_count(ctx)
|
||||
|
||||
|
|
@ -243,6 +329,7 @@ _lib.llama_get_kv_cache_token_count.restype = c_int
|
|||
|
||||
|
||||
# Sets the current rng seed.
|
||||
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
|
||||
def llama_set_rng_seed(ctx: llama_context_p, seed: c_int):
|
||||
return _lib.llama_set_rng_seed(ctx, seed)
|
||||
|
||||
|
|
@ -253,6 +340,7 @@ _lib.llama_set_rng_seed.restype = None
|
|||
|
||||
# Returns the maximum size in bytes of the state (rng, logits, embedding
|
||||
# and kv_cache) - will often be smaller after compacting tokens
|
||||
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
|
||||
def llama_get_state_size(ctx: llama_context_p) -> int:
|
||||
return _lib.llama_get_state_size(ctx)
|
||||
|
||||
|
|
@ -264,6 +352,7 @@ _lib.llama_get_state_size.restype = c_size_t
|
|||
# Copies the state to the specified destination address.
|
||||
# Destination needs to have allocated enough memory.
|
||||
# Returns the number of bytes copied
|
||||
# LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
|
||||
def llama_copy_state_data(
|
||||
ctx: llama_context_p, dst # type: Array[c_uint8]
|
||||
) -> int:
|
||||
|
|
@ -276,6 +365,7 @@ _lib.llama_copy_state_data.restype = c_size_t
|
|||
|
||||
# Set the state reading from the specified address
|
||||
# Returns the number of bytes read
|
||||
# LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
|
||||
def llama_set_state_data(
|
||||
ctx: llama_context_p, src # type: Array[c_uint8]
|
||||
) -> int:
|
||||
|
|
@ -287,6 +377,7 @@ _lib.llama_set_state_data.restype = c_size_t
|
|||
|
||||
|
||||
# Save/load session file
|
||||
# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
|
||||
def llama_load_session_file(
|
||||
ctx: llama_context_p,
|
||||
path_session: bytes,
|
||||
|
|
@ -309,6 +400,7 @@ _lib.llama_load_session_file.argtypes = [
|
|||
_lib.llama_load_session_file.restype = c_size_t
|
||||
|
||||
|
||||
# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
|
||||
def llama_save_session_file(
|
||||
ctx: llama_context_p,
|
||||
path_session: bytes,
|
||||
|
|
@ -331,6 +423,12 @@ _lib.llama_save_session_file.restype = c_size_t
|
|||
# tokens + n_tokens is the provided batch of new tokens to process
|
||||
# n_past is the number of tokens to use from previous eval calls
|
||||
# Returns 0 on success
|
||||
# LLAMA_API int llama_eval(
|
||||
# struct llama_context * ctx,
|
||||
# const llama_token * tokens,
|
||||
# int n_tokens,
|
||||
# int n_past,
|
||||
# int n_threads);
|
||||
def llama_eval(
|
||||
ctx: llama_context_p,
|
||||
tokens, # type: Array[llama_token]
|
||||
|
|
@ -350,6 +448,12 @@ _lib.llama_eval.restype = c_int
|
|||
# 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,
|
||||
|
|
@ -364,6 +468,7 @@ _lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int,
|
|||
_lib.llama_tokenize.restype = c_int
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
|
|
@ -372,6 +477,7 @@ _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)
|
||||
|
||||
|
|
@ -380,6 +486,7 @@ _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)
|
||||
|
||||
|
|
@ -393,6 +500,7 @@ _lib.llama_n_embd.restype = c_int
|
|||
# Can be mutated in order to change the probabilities of the next token
|
||||
# Rows: n_tokens
|
||||
# Cols: n_vocab
|
||||
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
||||
def llama_get_logits(
|
||||
ctx: llama_context_p,
|
||||
): # type: (...) -> Array[float] # type: ignore
|
||||
|
|
@ -405,6 +513,7 @@ _lib.llama_get_logits.restype = c_float_p
|
|||
|
||||
# Get the embeddings for the input
|
||||
# shape: [n_embd] (1-dimensional)
|
||||
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
||||
def llama_get_embeddings(
|
||||
ctx: llama_context_p,
|
||||
): # type: (...) -> Array[float] # type: ignore
|
||||
|
|
@ -416,6 +525,7 @@ _lib.llama_get_embeddings.restype = c_float_p
|
|||
|
||||
|
||||
# Token Id -> String. Uses the vocabulary in the provided context
|
||||
# LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
|
||||
def llama_token_to_str(ctx: llama_context_p, token: llama_token) -> bytes:
|
||||
return _lib.llama_token_to_str(ctx, token)
|
||||
|
||||
|
|
@ -426,6 +536,7 @@ _lib.llama_token_to_str.restype = c_char_p
|
|||
# Special tokens
|
||||
|
||||
|
||||
# LLAMA_API llama_token llama_token_bos();
|
||||
def llama_token_bos() -> int:
|
||||
return _lib.llama_token_bos()
|
||||
|
||||
|
|
@ -434,6 +545,7 @@ _lib.llama_token_bos.argtypes = []
|
|||
_lib.llama_token_bos.restype = llama_token
|
||||
|
||||
|
||||
# LLAMA_API llama_token llama_token_eos();
|
||||
def llama_token_eos() -> int:
|
||||
return _lib.llama_token_eos()
|
||||
|
||||
|
|
@ -442,6 +554,7 @@ _lib.llama_token_eos.argtypes = []
|
|||
_lib.llama_token_eos.restype = llama_token
|
||||
|
||||
|
||||
# LLAMA_API llama_token llama_token_nl();
|
||||
def llama_token_nl() -> int:
|
||||
return _lib.llama_token_nl()
|
||||
|
||||
|
|
@ -454,6 +567,7 @@ _lib.llama_token_nl.restype = llama_token
|
|||
|
||||
|
||||
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
|
||||
def llama_sample_repetition_penalty(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -477,6 +591,7 @@ _lib.llama_sample_repetition_penalty.restype = None
|
|||
|
||||
|
||||
# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
|
||||
def llama_sample_frequency_and_presence_penalties(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -507,6 +622,7 @@ _lib.llama_sample_frequency_and_presence_penalties.restype = None
|
|||
|
||||
|
||||
# @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
|
||||
# LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
def llama_sample_softmax(
|
||||
ctx: llama_context_p, candidates # type: _Pointer[llama_token_data]
|
||||
):
|
||||
|
|
@ -521,6 +637,7 @@ _lib.llama_sample_softmax.restype = None
|
|||
|
||||
|
||||
# @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
# LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
|
||||
def llama_sample_top_k(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -540,6 +657,7 @@ _lib.llama_sample_top_k.restype = None
|
|||
|
||||
|
||||
# @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
# LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
|
||||
def llama_sample_top_p(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -559,6 +677,7 @@ _lib.llama_sample_top_p.restype = None
|
|||
|
||||
|
||||
# @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
|
||||
# LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
|
||||
def llama_sample_tail_free(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -578,6 +697,7 @@ _lib.llama_sample_tail_free.restype = None
|
|||
|
||||
|
||||
# @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||||
# LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
|
||||
def llama_sample_typical(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -596,6 +716,7 @@ _lib.llama_sample_typical.argtypes = [
|
|||
_lib.llama_sample_typical.restype = None
|
||||
|
||||
|
||||
# LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
||||
def llama_sample_temperature(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -618,6 +739,7 @@ _lib.llama_sample_temperature.restype = None
|
|||
# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
# @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||||
# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
# LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
|
||||
def llama_sample_token_mirostat(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -645,6 +767,7 @@ _lib.llama_sample_token_mirostat.restype = llama_token
|
|||
# @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||||
# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
# LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
|
||||
def llama_sample_token_mirostat_v2(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -666,6 +789,7 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
|
|||
|
||||
|
||||
# @details Selects the token with the highest probability.
|
||||
# LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
def llama_sample_token_greedy(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -681,6 +805,7 @@ _lib.llama_sample_token_greedy.restype = llama_token
|
|||
|
||||
|
||||
# @details Randomly selects a token from the candidates based on their probabilities.
|
||||
# LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
def llama_sample_token(
|
||||
ctx: llama_context_p,
|
||||
candidates, # type: _Pointer[llama_token_data_array]
|
||||
|
|
@ -698,6 +823,7 @@ _lib.llama_sample_token.restype = llama_token
|
|||
# Performance information
|
||||
|
||||
|
||||
# LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
def llama_print_timings(ctx: llama_context_p):
|
||||
_lib.llama_print_timings(ctx)
|
||||
|
||||
|
|
@ -706,6 +832,7 @@ _lib.llama_print_timings.argtypes = [llama_context_p]
|
|||
_lib.llama_print_timings.restype = None
|
||||
|
||||
|
||||
# LLAMA_API void llama_reset_timings(struct llama_context * ctx);
|
||||
def llama_reset_timings(ctx: llama_context_p):
|
||||
_lib.llama_reset_timings(ctx)
|
||||
|
||||
|
|
@ -715,9 +842,19 @@ _lib.llama_reset_timings.restype = None
|
|||
|
||||
|
||||
# Print system information
|
||||
# LLAMA_API const char * llama_print_system_info(void);
|
||||
def llama_print_system_info() -> bytes:
|
||||
return _lib.llama_print_system_info()
|
||||
|
||||
|
||||
_lib.llama_print_system_info.argtypes = []
|
||||
_lib.llama_print_system_info.restype = c_char_p
|
||||
|
||||
###################################################################################################
|
||||
|
||||
|
||||
_llama_initialized = False
|
||||
|
||||
if not _llama_initialized:
|
||||
llama_init_backend()
|
||||
_llama_initialized = True
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "llama_cpp_python"
|
||||
version = "0.1.51"
|
||||
version = "0.1.53"
|
||||
description = "Python bindings for the llama.cpp library"
|
||||
authors = ["Andrei Betlen <abetlen@gmail.com>"]
|
||||
license = "MIT"
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -10,7 +10,7 @@ setup(
|
|||
description="A Python wrapper for llama.cpp",
|
||||
long_description=long_description,
|
||||
long_description_content_type="text/markdown",
|
||||
version="0.1.51",
|
||||
version="0.1.53",
|
||||
author="Andrei Betlen",
|
||||
author_email="abetlen@gmail.com",
|
||||
license="MIT",
|
||||
|
|
|
|||
2
vendor/llama.cpp
vendored
2
vendor/llama.cpp
vendored
|
|
@ -1 +1 @@
|
|||
Subproject commit c238b5873a1ea496db03ffcfe124c9d0d83afbc6
|
||||
Subproject commit 7e4ea5beff567f53be92f75f9089e6f11fa5dabd
|
||||
Loading…
Reference in a new issue