from typing import List, Optional, Union
import torch
from tqdm import tqdm
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
from aphrodite.common.outputs import EmbeddingRequestOutput, RequestOutput
from aphrodite.common.pooling_params import PoolingParams
from aphrodite.common.sampling_params import SamplingParams
from aphrodite.common.sequence import MultiModalData
from aphrodite.common.utils import Counter
from aphrodite.engine.aphrodite_engine import AphroditeEngine
from aphrodite.engine.args_tools import EngineArgs
from aphrodite.lora.request import LoRARequest
class LLM:
"""An LLM for generating texts from given prompts and sampling parameters.
This class includes a tokenizer, a language model (possibly distributed
across multiple GPUs), and GPU memory space allocated for intermediate
states (aka KV cache). Given a batch of prompts and sampling parameters,
this class generates texts from the model, using an intelligent batching
mechanism and efficient memory management.
NOTE: This class is intended to be used for offline inference. For online
serving, use the `AsyncLLMEngine` class instead.
NOTE: For the comprehensive list of arguments, see `EngineArgs`.
Args:
model: The name or path of a HuggingFace Transformers model.
tokenizer: The name or path of a HuggingFace Transformers tokenizer.
tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
if available, and "slow" will always use the slow tokenizer.
trust_remote_code: Trust remote code (e.g., from HuggingFace) when
downloading the model and tokenizer.
tensor_parallel_size: The number of GPUs to use for distributed
execution with tensor parallelism.
dtype: The data type for the model weights and activations. Currently,
we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
the `torch_dtype` attribute specified in the model config file.
However, if the `torch_dtype` in the config is `float32`, we will
use `float16` instead.
quantization: The method used to quantize the model weights. Currently,
we support "awq", "gptq", "quip" and "squeezellm". If None,
we first check the `quantization_config` attribute in the model
config file. If that is None, we assume the model weights are not
quantized and use `dtype` to determine the data type of the weights.
revision: The specific model version to use. It can be a branch name,
a tag name, or a commit id.
seed: The seed to initialize the random number generator for sampling.
gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
reserve for the model weights, activations, and KV cache. Higher
values will increase the KV cache size and thus improve the model's
throughput. However, if the value is too high, it may cause out-of-
memory (OOM) errors.
swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
This can be used for temporarily storing the states of the requests
when their `best_of` sampling parameters are larger than 1. If all
requests will have `best_of=1`, you can safely set this to 0.
Otherwise, too small values may cause out-of-memory (OOM) errors.
enforce_eager: Whether to enforce eager execution. If True, we will
disable CUDA graph and always execute the model in eager mode.
If False, we will use CUDA graph and eager execution in hybrid.
max_context_len_to_capture: Maximum context len covered by CUDA graphs.
When a sequence has context length larger than this, we fall back
to eager mode (DEPRECATED. Use `max_seq_len_to_capture` instead).
max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
When a sequence has context length larger than this, we fall back
to eager mode.
disable_custom_all_reduce: See ParallelConfig.
"""
def __init__(
self,
model: str,
tokenizer: Optional[str] = None,
tokenizer_mode: str = "auto",
trust_remote_code: bool = False,
tensor_parallel_size: int = 1,
dtype: str = "auto",
quantization: Optional[str] = None,
revision: Optional[str] = None,
seed: int = 0,
gpu_memory_utilization: float = 0.9,
swap_space: int = 4,
enforce_eager: bool = True,
max_context_len_to_capture: Optional[int] = None,
max_seq_len_to_capture: int = 8192,
disable_custom_all_reduce: bool = False,
enable_prefix_caching: bool = False,
**kwargs,
) -> None:
if "disable_log_stats" not in kwargs:
kwargs["disable_log_stats"] = True
engine_args = EngineArgs(
model=model,
tokenizer=tokenizer,
tokenizer_mode=tokenizer_mode,
trust_remote_code=trust_remote_code,
tensor_parallel_size=tensor_parallel_size,
dtype=dtype,
quantization=quantization,
revision=revision,
seed=seed,
gpu_memory_utilization=gpu_memory_utilization,
swap_space=swap_space,
enforce_eager=enforce_eager,
max_context_len_to_capture=max_context_len_to_capture,
max_seq_len_to_capture=max_seq_len_to_capture,
disable_custom_all_reduce=disable_custom_all_reduce,
enable_prefix_caching=enable_prefix_caching,
**kwargs,
)
self.llm_engine = AphroditeEngine.from_engine_args(engine_args)
self.request_counter = Counter()
def get_tokenizer(
self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
return self.llm_engine.tokenizer
def set_tokenizer(
self,
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
) -> None:
self.llm_engine.tokenizer = tokenizer
def generate(
self,
prompts: Optional[Union[str, List[str]]] = None,
sampling_params: Optional[Union[SamplingParams,
List[SamplingParams]]] = None,
prompt_token_ids: Optional[List[List[int]]] = None,
use_tqdm: bool = True,
lora_request: Optional[LoRARequest] = None,
multi_modal_data: Optional[MultiModalData] = None,
) -> List[RequestOutput]:
"""Generates the completions for the input prompts.
NOTE: This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: A list of prompts to generate completions for.
sampling_params: The sampling parameters for text generation. If
None, we use the default sampling parameters.
When it's a single value, it's applied to every prompt.
When it's a list, the list must have the same length as
the prompts and it's paired one-to-one with the prompts.
prompt_token_ids: A list of token IDs for the prompts. If None, we
use the tokenizer to convert the prompts to token IDs.
use_tqdm: Whether to use tqdm to display the progress bar.
lora_request: LoRA request to use for generation, if any.
multi_modal_data: Multi-modal data to use for generation, if any.
Returns:
A list of `RequestOutput` objects containing the
generated completions in the same order as the input prompts.
"""
if sampling_params is None:
# Use default sampling params.
sampling_params = SamplingParams()
requests_data = self._validate_and_prepare_requests(
prompts,
sampling_params,
prompt_token_ids,
lora_request,
multi_modal_data,
)
# Add requests to the engine and run the engine
for request_data in requests_data:
self._add_request(**request_data)
return self._run_engine(use_tqdm)
def encode(
self,
prompts: Optional[Union[str, List[str]]] = None,
pooling_params: Optional[Union[PoolingParams,
List[PoolingParams]]] = None,
prompt_token_ids: Optional[List[List[int]]] = None,
use_tqdm: bool = True,
lora_request: Optional[LoRARequest] = None,
multi_modal_data: Optional[MultiModalData] = None,
) -> List[EmbeddingRequestOutput]:
"""Generates the completions for the input prompts.
NOTE: This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: A list of prompts to generate completions for.
pooling_params: The pooling parameters for pooling. If None, we
use the default pooling parameters.
prompt_token_ids: A list of token IDs for the prompts. If None, we
use the tokenizer to convert the prompts to token IDs.
use_tqdm: Whether to use tqdm to display the progress bar.
lora_request: LoRA request to use for generation, if any.
multi_modal_data: Multi modal data.
Returns:
A list of `EmbeddingRequestOutput` objects containing the
generated embeddings in the same order as the input prompts.
"""
if pooling_params is None:
# Use default pooling params.
pooling_params = PoolingParams()
requests_data = self._validate_and_prepare_requests(
prompts,
pooling_params,
prompt_token_ids,
lora_request,
multi_modal_data,
)
# Add requests to the engine and run the engine
for request_data in requests_data:
self._add_request(**request_data)
return self._run_engine(use_tqdm)
def _validate_and_prepare_requests(
self,
prompts: Optional[Union[str, List[str]]],
params: Union[Union[SamplingParams, PoolingParams],
List[Union[SamplingParams,
PoolingParams]]], # Unified parameter
prompt_token_ids: Optional[List[List[int]]] = None,
lora_request: Optional[LoRARequest] = None,
multi_modal_data: Optional[MultiModalData] = None,
) -> List[dict]:
"""Validates and prepares request data for adding to the engine.
Ensures prompts and token IDs are consistent, and returns a list of
dictionaries with request data for further processing.
"""
if prompts is None and prompt_token_ids is None:
raise ValueError("Either prompts or prompt_token_ids must be "
"provided.")
if isinstance(prompts, str):
# Convert a single prompt to a list.
prompts = [prompts]
if prompts is not None and prompt_token_ids is not None and len(
prompts) != len(prompt_token_ids):
raise ValueError(
"The lengths of prompts and prompt_token_ids must "
"be the same.")
if prompts is not None:
num_requests = len(prompts)
else:
assert prompt_token_ids is not None
num_requests = len(prompt_token_ids)
if isinstance(params, list) and len(params) != num_requests:
raise ValueError("The lengths of prompts and params "
"be the same.")
if multi_modal_data:
multi_modal_data.data = multi_modal_data.data.to(torch.float16)
# Add requests to the engine.
requests_data = []
num_requests = len(prompts) if prompts is not None else len(
prompt_token_ids)
for i in range(num_requests):
prompt = prompts[i] if prompts is not None else None
token_ids = None if prompt_token_ids is None else prompt_token_ids[
i]
multi_modal_item = MultiModalData(
type=multi_modal_data.type,
data=multi_modal_data.data[i].unsqueeze(0),
) if multi_modal_data else None
requests_data.append({
"prompt":
prompt,
"params":
params[i] if isinstance(params, list) else params,
"prompt_token_ids":
token_ids,
"lora_request":
lora_request,
"multi_modal_data":
multi_modal_item,
})
return requests_data
def _add_request(
self,
prompt: Optional[str],
params: Union[SamplingParams, PoolingParams],
prompt_token_ids: Optional[List[int]],
lora_request: Optional[LoRARequest] = None,
multi_modal_data: Optional[MultiModalData] = None,
) -> None:
request_id = str(next(self.request_counter))
self.llm_engine.add_request(request_id,
prompt,
params,
prompt_token_ids,
lora_request=lora_request,
multi_modal_data=multi_modal_data)
def _run_engine(
self, use_tqdm: bool
) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
# Initialize tqdm.
if use_tqdm:
num_requests = self.llm_engine.get_num_unfinished_requests()
pbar = tqdm(
total=num_requests,
desc="Processed prompts",
dynamic_ncols=True,
postfix=f"Generation Speed: {0:.2f} toks/s",
)
# Run the engine.
outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
total_toks = 0
while self.llm_engine.has_unfinished_requests():
step_outputs = self.llm_engine.step()
for output in step_outputs:
if output.finished:
outputs.append(output)
if use_tqdm:
if isinstance(output, RequestOutput):
# Calculate tokens only for RequestOutput
total_toks += sum(
len(stp.token_ids) for stp in output.outputs)
spd = total_toks / pbar.format_dict["elapsed"]
pbar.postfix = f"Generation Speed: {spd:.2f} toks/s"
pbar.update(1)
if use_tqdm:
pbar.close()
# Sort the outputs by request ID.
# This is necessary because some requests may be finished earlier than
# its previous requests.
outputs = sorted(outputs, key=lambda x: int(x.request_id))
return outputs