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1674 lines (1511 loc) · 66.8 KB
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from __future__ import annotations
import os
import sys
import uuid
import time
import multiprocessing
from typing import (
List,
Optional,
Union,
Generator,
Sequence,
Iterator,
Deque,
Callable,
)
from collections import deque
import ctypes
import numpy as np
import numpy.typing as npt
from .llama_types import *
from .llama_grammar import LlamaGrammar
from .llama_cache import (
BaseLlamaCache,
LlamaCache, # type: ignore
LlamaDiskCache, # type: ignore
LlamaRAMCache, # type: ignore
)
import llama_cpp.llama_cpp as llama_cpp
import llama_cpp.llama_chat_format as llama_chat_format
from ._internals import (
_LlamaModel, # type: ignore
_LlamaContext, # type: ignore
_LlamaBatch, # type: ignore
_LlamaTokenDataArray, # type: ignore
_LlamaSamplingParams, # type: ignore
_LlamaSamplingContext, # type: ignore
)
from ._utils import suppress_stdout_stderr
class Llama:
"""High-level Python wrapper for a llama.cpp model."""
__backend_initialized = False
def __init__(
self,
model_path: str,
*,
# Model Params
n_gpu_layers: int = 0,
split_mode: int = llama_cpp.LLAMA_SPLIT_LAYER,
main_gpu: int = 0,
tensor_split: Optional[List[float]] = None,
vocab_only: bool = False,
use_mmap: bool = True,
use_mlock: bool = False,
kv_overrides: Optional[Dict[str, Union[bool, int, float]]] = None,
# Context Params
seed: int = llama_cpp.LLAMA_DEFAULT_SEED,
n_ctx: int = 512,
n_batch: int = 512,
n_threads: Optional[int] = None,
n_threads_batch: Optional[int] = None,
rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED,
rope_freq_base: float = 0.0,
rope_freq_scale: float = 0.0,
yarn_ext_factor: float = -1.0,
yarn_attn_factor: float = 1.0,
yarn_beta_fast: float = 32.0,
yarn_beta_slow: float = 1.0,
yarn_orig_ctx: int = 0,
mul_mat_q: bool = True,
logits_all: bool = False,
embedding: bool = False,
offload_kqv: bool = False,
# Sampling Params
last_n_tokens_size: int = 64,
# LoRA Params
lora_base: Optional[str] = None,
lora_scale: float = 1.0,
lora_path: Optional[str] = None,
# Backend Params
numa: bool = False,
# Chat Format Params
chat_format: str = "llama-2",
chat_handler: Optional[llama_chat_format.LlamaChatCompletionHandler] = None,
# Misc
verbose: bool = True,
# Extra Params
**kwargs, # type: ignore
):
"""Load a llama.cpp model from `model_path`.
Examples:
Basic usage
>>> import llama_cpp
>>> model = llama_cpp.Llama(
... model_path="path/to/model",
... )
>>> print(model("The quick brown fox jumps ", stop=["."])["choices"][0]["text"])
the lazy dog
Loading a chat model
>>> import llama_cpp
>>> model = llama_cpp.Llama(
... model_path="path/to/model",
... chat_format="llama-2",
... )
>>> print(model.create_chat_completion(
... messages=[{
... "role": "user",
... "content": "what is the meaning of life?"
... }]
... ))
Args:
model_path: Path to the model.
n_gpu_layers: Number of layers to offload to GPU (-ngl). If -1, all layers are offloaded.
split_mode: How to split the model across GPUs. See llama_cpp.LLAMA_SPLIT_* for options.
main_gpu: main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model. LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results. LLAMA_SPLIT_LAYER: ignored
tensor_split: How split tensors should be distributed across GPUs. If None, the model is not split.
vocab_only: Only load the vocabulary no weights.
use_mmap: Use mmap if possible.
use_mlock: Force the system to keep the model in RAM.
kv_overrides: Key-value overrides for the model.
seed: RNG seed, -1 for random
n_ctx: Text context, 0 = from model
n_batch: Prompt processing maximum batch size
n_threads: Number of threads to use for generation
n_threads_batch: Number of threads to use for batch processing
rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054
rope_freq_base: RoPE base frequency, 0 = from model
rope_freq_scale: RoPE frequency scaling factor, 0 = from model
yarn_ext_factor: YaRN extrapolation mix factor, negative = from model
yarn_attn_factor: YaRN magnitude scaling factor
yarn_beta_fast: YaRN low correction dim
yarn_beta_slow: YaRN high correction dim
yarn_orig_ctx: YaRN original context size
logits_all: Return logits for all tokens, not just the last token. Must be True for completion to return logprobs.
embedding: Embedding mode only.
offload_kqv: Offload K, Q, V to GPU.
last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
lora_path: Path to a LoRA file to apply to the model.
numa: Enable NUMA support. (NOTE: The initial value of this parameter is used for the remainder of the program as this value is set in llama_backend_init)
chat_format: String specifying the chat format to use when calling create_chat_completion.
chat_handler: Optional chat handler to use when calling create_chat_completion.
verbose: Print verbose output to stderr.
Raises:
ValueError: If the model path does not exist.
Returns:
A Llama instance.
"""
self.verbose = verbose
self.numa = numa
if not Llama.__backend_initialized:
with suppress_stdout_stderr(disable=self.verbose):
llama_cpp.llama_backend_init(self.numa)
Llama.__backend_initialized = True
self.model_path = model_path
# Model Params
self.model_params = llama_cpp.llama_model_default_params()
self.model_params.n_gpu_layers = (
0x7FFFFFFF if n_gpu_layers == -1 else n_gpu_layers
) # 0x7FFFFFFF is INT32 max, will be auto set to all layers
self.model_params.split_mode = split_mode
self.model_params.main_gpu = main_gpu
self.tensor_split = tensor_split
self._c_tensor_split = None
if self.tensor_split is not None:
if len(self.tensor_split) > llama_cpp.LLAMA_MAX_DEVICES:
raise ValueError(
f"Attempt to split tensors that exceed maximum supported devices. Current LLAMA_MAX_DEVICES={llama_cpp.LLAMA_MAX_DEVICES}"
)
# Type conversion and expand the list to the length of LLAMA_MAX_DEVICES
FloatArray = ctypes.c_float * llama_cpp.LLAMA_MAX_DEVICES
self._c_tensor_split = FloatArray(
*tensor_split # type: ignore
) # keep a reference to the array so it is not gc'd
self.model_params.tensor_split = self._c_tensor_split
self.model_params.vocab_only = vocab_only
self.model_params.use_mmap = use_mmap if lora_path is None else False
self.model_params.use_mlock = use_mlock
self.kv_overrides = kv_overrides
if kv_overrides is not None:
n_overrides = len(kv_overrides)
self._kv_overrides_array = llama_cpp.llama_model_kv_override * (n_overrides + 1)
self._kv_overrides_array_keys = []
for k, v in kv_overrides.items():
key_buf = ctypes.create_string_buffer(k.encode("utf-8"))
self._kv_overrides_array_keys.append(key_buf)
self._kv_overrides_array[i].key = key_buf
if isinstance(v, int):
self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_INT
self._kv_overrides_array[i].value.int_value = v
elif isinstance(v, float):
self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_FLOAT
self._kv_overrides_array[i].value.float_value = v
elif isinstance(v, bool):
self._kv_overrides_array[i].tag = llama_cpp.LLAMA_KV_OVERRIDE_BOOL
self._kv_overrides_array[i].value.bool_value = v
else:
raise ValueError(f"Unknown value type for {k}: {v}")
self._kv_overrides_array_sentinel_key = b'\0'
# null array sentinel
self._kv_overrides_array[n_overrides].key = self._kv_overrides_array_sentinel_key
self.model_params.kv_overrides = self._kv_overrides_array
self.n_batch = min(n_ctx, n_batch) # ???
self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
self.n_threads_batch = n_threads_batch or max(
multiprocessing.cpu_count() // 2, 1
)
# Context Params
self.context_params = llama_cpp.llama_context_default_params()
self.context_params.seed = seed
self.context_params.n_ctx = n_ctx
self.context_params.n_batch = self.n_batch
self.context_params.n_threads = self.n_threads
self.context_params.n_threads_batch = self.n_threads_batch
self.context_params.rope_scaling_type = (
rope_scaling_type
if rope_scaling_type is not None
else llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED
)
self.context_params.rope_freq_base = (
rope_freq_base if rope_freq_base != 0.0 else 0
)
self.context_params.rope_freq_scale = (
rope_freq_scale if rope_freq_scale != 0.0 else 0
)
self.context_params.yarn_ext_factor = (
yarn_ext_factor if yarn_ext_factor != 0.0 else 0
)
self.context_params.yarn_attn_factor = (
yarn_attn_factor if yarn_attn_factor != 0.0 else 0
)
self.context_params.yarn_beta_fast = (
yarn_beta_fast if yarn_beta_fast != 0.0 else 0
)
self.context_params.yarn_beta_slow = (
yarn_beta_slow if yarn_beta_slow != 0.0 else 0
)
self.context_params.yarn_orig_ctx = yarn_orig_ctx if yarn_orig_ctx != 0 else 0
self.context_params.mul_mat_q = mul_mat_q
self.context_params.logits_all = logits_all
self.context_params.embedding = embedding
self.context_params.offload_kqv = offload_kqv
# Sampling Params
self.last_n_tokens_size = last_n_tokens_size
self.cache: Optional[BaseLlamaCache] = None
self.lora_base = lora_base
self.lora_scale = lora_scale
self.lora_path = lora_path
if not os.path.exists(model_path):
raise ValueError(f"Model path does not exist: {model_path}")
self._model = _LlamaModel(
path_model=self.model_path, params=self.model_params, verbose=self.verbose
)
# Set the default value for the context and correct the batch
if n_ctx == 0:
n_ctx = self._model.n_ctx_train()
self.n_batch = min(n_ctx, n_batch)
self.context_params.n_ctx = self._model.n_ctx_train()
self.context_params.n_batch = self.n_batch
self._ctx = _LlamaContext(
model=self._model,
params=self.context_params,
verbose=self.verbose,
)
self._batch = _LlamaBatch(
n_tokens=self.n_batch,
embd=0,
n_seq_max=self.context_params.n_ctx,
verbose=self.verbose,
)
if self.lora_path:
if self._model.apply_lora_from_file(
self.lora_path,
self.lora_scale,
self.lora_base,
self.n_threads,
):
raise RuntimeError(
f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}"
)
if self.verbose:
print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
self.chat_format = chat_format
self.chat_handler = chat_handler
self._n_vocab = self.n_vocab()
self._n_ctx = self.n_ctx()
self._token_nl = self.token_nl()
self._token_eos = self.token_eos()
self._candidates = _LlamaTokenDataArray(n_vocab=self._n_vocab)
self.n_tokens = 0
self.input_ids: npt.NDArray[np.intc] = np.ndarray((n_ctx,), dtype=np.intc)
self.scores: npt.NDArray[np.single] = np.ndarray(
(n_ctx, self._n_vocab), dtype=np.single
)
@property
def ctx(self) -> llama_cpp.llama_context_p:
assert self._ctx.ctx is not None
return self._ctx.ctx
@property
def model(self) -> llama_cpp.llama_model_p:
assert self._model.model is not None
return self._model.model
@property
def _input_ids(self) -> npt.NDArray[np.intc]:
return self.input_ids[: self.n_tokens]
@property
def _scores(self) -> npt.NDArray[np.single]:
return self.scores[: self.n_tokens, :]
@property
def eval_tokens(self) -> Deque[int]:
return deque(self.input_ids[: self.n_tokens].tolist(), maxlen=self._n_ctx)
@property
def eval_logits(self) -> Deque[List[float]]:
return deque(
self.scores[: self.n_tokens, :].tolist(),
maxlen=self._n_ctx if self.context_params.logits_all else 1,
)
def tokenize(
self, text: bytes, add_bos: bool = True, special: bool = False
) -> List[int]:
"""Tokenize a string.
Args:
text: The utf-8 encoded string to tokenize.
Raises:
RuntimeError: If the tokenization failed.
Returns:
A list of tokens.
"""
return self._model.tokenize(text, add_bos, special)
def detokenize(self, tokens: List[int]) -> bytes:
"""Detokenize a list of tokens.
Args:
tokens: The list of tokens to detokenize.
Returns:
The detokenized string.
"""
return self._model.detokenize(tokens)
def set_cache(self, cache: Optional[BaseLlamaCache]):
"""Set the cache.
Args:
cache: The cache to set.
"""
self.cache = cache
def set_seed(self, seed: int):
"""Set the random seed.
Args:
seed: The random seed.
"""
assert self._ctx.ctx is not None
llama_cpp.llama_set_rng_seed(self._ctx.ctx, seed)
def reset(self):
"""Reset the model state."""
self.n_tokens = 0
def eval(self, tokens: Sequence[int]):
"""Evaluate a list of tokens.
Args:
tokens: The list of tokens to evaluate.
"""
assert self._ctx.ctx is not None
assert self._batch.batch is not None
self._ctx.kv_cache_seq_rm(-1, self.n_tokens, -1)
for i in range(0, len(tokens), self.n_batch):
batch = tokens[i : min(len(tokens), i + self.n_batch)]
n_past = self.n_tokens
n_tokens = len(batch)
self._batch.set_batch(
batch=batch, n_past=n_past, logits_all=self.context_params.logits_all
)
self._ctx.decode(self._batch)
# Save tokens
self.input_ids[n_past : n_past + n_tokens] = batch
# Save logits
rows = n_tokens
cols = self._n_vocab
offset = (
0 if self.context_params.logits_all else n_tokens - 1
) # NOTE: Only save the last token logits if logits_all is False
self.scores[n_past + offset : n_past + n_tokens, :].reshape(-1)[
:
] = self._ctx.get_logits()[offset * cols : rows * cols]
# Update n_tokens
self.n_tokens += n_tokens
def sample(
self,
top_k: int = 40,
top_p: float = 0.95,
min_p: float = 0.05,
typical_p: float = 1.0,
temp: float = 0.80,
repeat_penalty: float = 1.1,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_eta: float = 0.1,
mirostat_tau: float = 5.0,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
grammar: Optional[LlamaGrammar] = None,
):
"""Sample a token from the model.
Args:
top_k: The top-k sampling parameter.
top_p: The top-p sampling parameter.
temp: The temperature parameter.
repeat_penalty: The repeat penalty parameter.
Returns:
The sampled token.
"""
assert self._ctx is not None
assert self.n_tokens > 0
logits: npt.NDArray[np.single] = self._scores[-1, :]
if logits_processor is not None:
logits[:] = logits_processor(self._input_ids, logits)
sampling_params = _LlamaSamplingParams(
top_k=top_k,
top_p=top_p,
min_p=min_p,
tfs_z=tfs_z,
typical_p=typical_p,
temp=temp,
penalty_last_n=self.last_n_tokens_size,
penalty_repeat=repeat_penalty,
penalty_freq=frequency_penalty,
penalty_present=presence_penalty,
mirostat=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
penalize_nl=penalize_nl,
)
sampling_context = _LlamaSamplingContext(
params=sampling_params,
grammar=grammar,
)
sampling_context.prev = list(self.eval_tokens)
id = sampling_context.sample(ctx_main=self._ctx, logits_array=logits)
sampling_context.accept(
ctx_main=self._ctx,
id=id,
apply_grammar=grammar is not None,
)
return id
def generate(
self,
tokens: Sequence[int],
top_k: int = 40,
top_p: float = 0.95,
min_p: float = 0.05,
typical_p: float = 1.0,
temp: float = 0.80,
repeat_penalty: float = 1.1,
reset: bool = True,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
grammar: Optional[LlamaGrammar] = None,
) -> Generator[int, Optional[Sequence[int]], None]:
"""Create a generator of tokens from a prompt.
Examples:
>>> llama = Llama("models/ggml-7b.bin")
>>> tokens = llama.tokenize(b"Hello, world!")
>>> for token in llama.generate(tokens, top_k=40, top_p=0.95, temp=1.0, repeat_penalty=1.1):
... print(llama.detokenize([token]))
Args:
tokens: The prompt tokens.
top_k: The top-k sampling parameter.
top_p: The top-p sampling parameter.
temp: The temperature parameter.
repeat_penalty: The repeat penalty parameter.
reset: Whether to reset the model state.
Yields:
The generated tokens.
"""
if reset and self.n_tokens > 0:
longest_prefix = 0
for a, b in zip(self._input_ids, tokens[:-1]):
if a == b:
longest_prefix += 1
else:
break
if longest_prefix > 0:
if self.verbose:
print("Llama.generate: prefix-match hit", file=sys.stderr)
reset = False
tokens = tokens[longest_prefix:]
self.n_tokens = longest_prefix
if reset:
self.reset()
if grammar is not None:
grammar.reset()
while True:
self.eval(tokens)
token = self.sample(
top_k=top_k,
top_p=top_p,
min_p=min_p,
typical_p=typical_p,
temp=temp,
repeat_penalty=repeat_penalty,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
logits_processor=logits_processor,
grammar=grammar,
penalize_nl=penalize_nl,
)
if stopping_criteria is not None and stopping_criteria(
self._input_ids, self._scores[-1, :]
):
return
tokens_or_none = yield token
tokens = [token]
if tokens_or_none is not None:
tokens.extend(tokens_or_none)
def create_embedding(
self, input: Union[str, List[str]], model: Optional[str] = None
) -> CreateEmbeddingResponse:
"""Embed a string.
Args:
input: The utf-8 encoded string to embed.
Returns:
An embedding object.
"""
assert self._ctx.ctx is not None
assert self._model.model is not None
model_name: str = model if model is not None else self.model_path
if self.context_params.embedding == False:
raise RuntimeError(
"Llama model must be created with embedding=True to call this method"
)
if self.verbose:
llama_cpp.llama_reset_timings(self._ctx.ctx)
if isinstance(input, str):
inputs = [input]
else:
inputs = input
data: List[Embedding] = []
total_tokens = 0
for index, input in enumerate(inputs):
tokens = self.tokenize(input.encode("utf-8"), special=True)
self.reset()
self.eval(tokens)
n_tokens = len(tokens)
total_tokens += n_tokens
embedding = llama_cpp.llama_get_embeddings(self._ctx.ctx)[
: llama_cpp.llama_n_embd(self._model.model)
]
data.append(
{
"object": "embedding",
"embedding": embedding,
"index": index,
}
)
if self.verbose:
llama_cpp.llama_print_timings(self._ctx.ctx)
return {
"object": "list",
"data": data,
"model": model_name,
"usage": {
"prompt_tokens": total_tokens,
"total_tokens": total_tokens,
},
}
def embed(self, input: str) -> List[float]:
"""Embed a string.
Args:
input: The utf-8 encoded string to embed.
Returns:
A list of embeddings
"""
return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
def _create_completion(
self,
prompt: Union[str, List[int]],
suffix: Optional[str] = None,
max_tokens: Optional[int] = 16,
temperature: float = 0.8,
top_p: float = 0.95,
min_p: float = 0.05,
typical_p: float = 1.0,
logprobs: Optional[int] = None,
echo: bool = False,
stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
repeat_penalty: float = 1.1,
top_k: int = 40,
stream: bool = False,
seed: Optional[int] = None,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None,
grammar: Optional[LlamaGrammar] = None,
logit_bias: Optional[Dict[str, float]] = None,
) -> Union[
Iterator[CreateCompletionResponse], Iterator[CreateCompletionStreamResponse]
]:
assert self._ctx is not None
assert suffix is None or suffix.__class__ is str
completion_id: str = f"cmpl-{str(uuid.uuid4())}"
created: int = int(time.time())
# If prompt is empty, initialize completion with BOS token to avoid
# detokenization including a space at the beginning of the completion
completion_tokens: List[int] = [] if len(prompt) > 0 else [self.token_bos()]
# Add blank space to start of prompt to match OG llama tokenizer
prompt_tokens: List[int] = (
(
self.tokenize(prompt.encode("utf-8"), special=True)
if prompt != ""
else [self.token_bos()]
)
if isinstance(prompt, str)
else prompt
)
text: bytes = b""
returned_tokens: int = 0
stop = (
stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
)
model_name: str = model if model is not None else self.model_path
# NOTE: This likely doesn't work correctly for the first token in the prompt
# because of the extra space added to the start of the prompt_tokens
if logit_bias is not None:
logit_bias_map = {int(k): float(v) for k, v in logit_bias.items()}
def logit_bias_processor(
input_ids: npt.NDArray[np.intc],
scores: npt.NDArray[np.single],
) -> npt.NDArray[np.single]:
new_scores = np.copy(
scores
) # Does it make sense to copy the whole array or can we just overwrite the original one?
for input_id, score in logit_bias_map.items():
new_scores[input_id] = score + scores[input_id]
return new_scores
_logit_bias_processor = LogitsProcessorList([logit_bias_processor])
if logits_processor is None:
logits_processor = _logit_bias_processor
else:
logits_processor = logits_processor.extend(_logit_bias_processor)
if self.verbose:
self._ctx.reset_timings()
if len(prompt_tokens) >= self._n_ctx:
raise ValueError(
f"Requested tokens ({len(prompt_tokens)}) exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
)
if max_tokens is None or max_tokens <= 0:
# Unlimited, depending on n_ctx.
max_tokens = self._n_ctx - len(prompt_tokens)
# Truncate max_tokens if requested tokens would exceed the context window
max_tokens = (
max_tokens
if max_tokens + len(prompt_tokens) < self._n_ctx
else (self._n_ctx - len(prompt_tokens))
)
if stop != []:
stop_sequences = [s.encode("utf-8") for s in stop]
else:
stop_sequences = []
if logprobs is not None and self.context_params.logits_all is False:
raise ValueError(
"logprobs is not supported for models created with logits_all=False"
)
if self.cache:
try:
cache_item = self.cache[prompt_tokens]
cache_prefix_len = Llama.longest_token_prefix(
cache_item.input_ids.tolist(), prompt_tokens
)
eval_prefix_len = Llama.longest_token_prefix(
self._input_ids.tolist(), prompt_tokens
)
if cache_prefix_len > eval_prefix_len:
self.load_state(cache_item)
if self.verbose:
print("Llama._create_completion: cache hit", file=sys.stderr)
except KeyError:
if self.verbose:
print("Llama._create_completion: cache miss", file=sys.stderr)
if seed is not None:
self._ctx.set_rng_seed(seed)
def _completion_stream_response(
text: str,
logprobs_or_none: Optional[CompletionLogprobs] = None,
finish_reason: Optional[Literal["stop", "length"]] = None,
) -> CreateCompletionStreamResponse:
return {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": text,
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": finish_reason,
}
],
}
def _completion_response(
text: str,
finish_reason: Literal["stop", "length"],
logprobs_or_none: Optional[CompletionLogprobs] = None,
) -> CreateCompletionResponse:
return {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": text,
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": finish_reason,
}
],
"usage": {
"prompt_tokens": len(prompt_tokens),
"completion_tokens": len(completion_tokens),
"total_tokens": len(prompt_tokens) + len(completion_tokens),
},
}
finish_reason = "length"
multibyte_fix = 0
for token in self.generate(
prompt_tokens,
top_k=top_k,
top_p=top_p,
min_p=min_p,
typical_p=typical_p,
temp=temperature,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
repeat_penalty=repeat_penalty,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
grammar=grammar,
):
if token == self._token_eos:
text = self.detokenize(completion_tokens)
finish_reason = "stop"
break
completion_tokens.append(token)
all_text = self.detokenize(completion_tokens)
# Contains multi-byte UTF8
for k, char in enumerate(all_text[-3:]):
k = 3 - k
for num, pattern in [(2, 192), (3, 224), (4, 240)]:
# Bitwise AND check
if num > k and pattern & char == pattern:
multibyte_fix = num - k
# Stop incomplete bytes from passing
if multibyte_fix > 0:
multibyte_fix -= 1
continue
any_stop = [s for s in stop_sequences if s in all_text]
if len(any_stop) > 0:
first_stop = any_stop[0]
text = all_text[: all_text.index(first_stop)]
finish_reason = "stop"
break
if stream:
remaining_tokens = completion_tokens[returned_tokens:-1]
remaining_text = self.detokenize(remaining_tokens)
remaining_length = len(remaining_text)
# We want to avoid yielding any characters from
# the generated text if they are part of a stop
# sequence.
first_stop_position = 0
for s in stop_sequences:
for i in range(min(len(s), remaining_length), 0, -1):
if remaining_text.endswith(s[:i]):
if i > first_stop_position:
first_stop_position = i
break
token_end_position = 0
if logprobs is not None:
# not sure how to handle this branch when dealing
# with CJK output, so keep it unchanged
for token in remaining_tokens:
if token == self.token_bos():
continue
token_end_position += len(self.detokenize([token]))
# Check if stop sequence is in the token
if token_end_position > (
remaining_length - first_stop_position
):
break
token_str = self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
text_offset = len(prompt) + len(
self.detokenize(completion_tokens[:returned_tokens]).decode(
"utf-8", errors="ignore"
)
)
token_offset = len(prompt_tokens) + returned_tokens
logits = self._scores[token_offset - 1, :]
token_logprob = Llama.logits_to_logprobs(logits).tolist()
sorted_logprobs = list(
sorted(
zip(token_logprob, range(len(token_logprob))),
reverse=True,
)
)
top_logprob = {
self.detokenize([i]).decode(
"utf-8", errors="ignore"
): logprob
for logprob, i in sorted_logprobs[:logprobs]
}
top_logprob.update({token_str: token_logprob[int(token)]})
logprobs_or_none = {
"tokens": [
self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
],
"text_offset": [text_offset],
"token_logprobs": [token_logprob[int(token)]],
"top_logprobs": [top_logprob],
}
returned_tokens += 1
yield _completion_stream_response(
self.detokenize([token]).decode("utf-8", errors="ignore"),
logprobs_or_none,
)
else:
while len(remaining_tokens) > 0:
decode_success = False
for i in range(1, len(remaining_tokens) + 1):
try:
bs = self.detokenize(remaining_tokens[:i])
ts = bs.decode("utf-8")
decode_success = True
break
except UnicodeError:
pass
else:
break
if not decode_success:
# all remaining tokens cannot be decoded to a UTF-8 character
break
token_end_position += len(bs)
if token_end_position > (
remaining_length - first_stop_position
):
break
remaining_tokens = remaining_tokens[i:]
returned_tokens += i
yield _completion_stream_response(text=ts)
if len(completion_tokens) >= max_tokens:
text = self.detokenize(completion_tokens)
finish_reason = "length"
break
if stopping_criteria is not None and stopping_criteria(
self._input_ids, self._scores[-1, :]
):
text = self.detokenize(completion_tokens)
finish_reason = "stop"
if self.verbose:
self._ctx.print_timings()
if stream:
remaining_tokens = completion_tokens[returned_tokens:]
all_text = self.detokenize(remaining_tokens)
any_stop = [s for s in stop_sequences if s in all_text]
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