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347 | class LocalTensorRTLLM(CustomLLM):
r"""本地TensorRT LLM。
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)为用户提供了一个易于使用的Python API，用于定义大型语言模型（LLMs）并构建包含最先进优化的TensorRT引擎，以在NVIDIA GPU上高效执行推断。
由于TensorRT-LLM是用于与本地模型交互的SDK，因此必须遵循一些环境步骤，以确保可以使用TensorRT-LLM设置。
1. 目前需要Nvidia Cuda 12.2或更高版本才能运行TensorRT-LLM
2. 使用`pip3 install tensorrt_llm -U --extra-index-url https://pypi.nvidia.com`通过pip安装`tensorrt_llm`
3. 在本示例中，我们将使用Llama2。需要通过脚本按照说明创建Llama2模型文件
（https://github.com/NVIDIA/trt-llm-rag-windows/blob/release/1.0/README.md#building-trt-engine）
* 遵循上述步骤将创建以下文件
* `Llama_float16_tp1_rank0.engine`：构建脚本的主要输出，包含嵌入模型权重的可执行图操作。
* `config.json`：包括有关模型的详细信息，例如其一般结构和精度，以及有关哪些插件被合并到引擎中的信息。
* `model.cache`：缓存模型编译的一些时间和优化信息，使连续构建更快。
4. `mkdir model`
5. 将上述提到的所有文件移动到model目录。
示例：
`pip install llama-index-llms-nvidia-tensorrt`
```python
from llama_index.llms.nvidia_tensorrt import LocalTensorRTLLM
def completion_to_prompt(completion):
return f"<s> [INST] {completion} [/INST] "
def messages_to_prompt(messages):
content = ""
for message in messages:
content += str(message) + "\n"
return f"<s> [INST] {content} [/INST] "
llm = LocalTensorRTLLM(
model_path="./model",
engine_name="llama_float16_tp1_rank0.engine",
tokenizer_dir="meta-llama/Llama-2-13b-chat",
completion_to_prompt=completion_to_prompt,
messages_to_prompt=messages_to_prompt,
)
resp = llm.complete("Who is Paul Graham?")
print(str(resp))
```"""
model_path: Optional[str] = Field(description="The path to the trt engine.")
temperature: float = Field(description="The temperature to use for sampling.")
max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
context_window: int = Field(
description="The maximum number of context tokens for the model."
)
messages_to_prompt: Callable = Field(
description="The function to convert messages to a prompt.", exclude=True
)
completion_to_prompt: Callable = Field(
description="The function to convert a completion to a prompt.", exclude=True
)
generate_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for generation."
)
model_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for model initialization."
)
verbose: bool = Field(description="Whether to print verbose output.")
_model: Any = PrivateAttr()
_model_config: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_max_new_tokens = PrivateAttr()
_sampling_config = PrivateAttr()
_verbose = PrivateAttr()
def __init__(
self,
model_path: Optional[str] = None,
engine_name: Optional[str] = None,
tokenizer_dir: Optional[str] = None,
temperature: float = 0.1,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
callback_manager: Optional[CallbackManager] = None,
generate_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
verbose: bool = False,
) -> None:
try:
import tensorrt_llm
from tensorrt_llm.runtime import ModelConfig, SamplingConfig
except ImportError:
print(
"Unable to import `tensorrt_llm` module. Please ensure you have\
`tensorrt_llm` installed in your environment. You can run\
`pip3 install tensorrt_llm -U --extra-index-url https://pypi.nvidia.com` to install."
)
model_kwargs = model_kwargs or {}
model_kwargs.update({"n_ctx": context_window, "verbose": verbose})
self._max_new_tokens = max_new_tokens
self._verbose = verbose
# check if model is cached
if model_path is not None:
if not os.path.exists(model_path):
raise ValueError(
"Provided model path does not exist. "
"Please check the path or provide a model_url to download."
)
else:
engine_dir = model_path
engine_dir_path = Path(engine_dir)
config_path = engine_dir_path / "config.json"
# config function
with open(config_path) as f:
config = json.load(f)
use_gpt_attention_plugin = config["plugin_config"][
"gpt_attention_plugin"
]
remove_input_padding = config["plugin_config"]["remove_input_padding"]
tp_size = config["builder_config"]["tensor_parallel"]
pp_size = 1
if "pipeline_parallel" in config["builder_config"]:
pp_size = config["builder_config"]["pipeline_parallel"]
world_size = tp_size * pp_size
assert (
world_size == tensorrt_llm.mpi_world_size()
), f"Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})"
num_heads = config["builder_config"]["num_heads"] // tp_size
hidden_size = config["builder_config"]["hidden_size"] // tp_size
vocab_size = config["builder_config"]["vocab_size"]
num_layers = config["builder_config"]["num_layers"]
num_kv_heads = config["builder_config"].get("num_kv_heads", num_heads)
paged_kv_cache = config["plugin_config"]["paged_kv_cache"]
if config["builder_config"].get("multi_query_mode", False):
tensorrt_llm.logger.warning(
"`multi_query_mode` config is deprecated. Please rebuild the engine."
)
num_kv_heads = 1
num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size
self._model_config = ModelConfig(
num_heads=num_heads,
num_kv_heads=num_kv_heads,
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
gpt_attention_plugin=use_gpt_attention_plugin,
paged_kv_cache=paged_kv_cache,
remove_input_padding=remove_input_padding,
max_batch_size=config["builder_config"]["max_batch_size"],
)
assert (
pp_size == 1
), "Python runtime does not support pipeline parallelism"
world_size = tp_size * pp_size
runtime_rank = tensorrt_llm.mpi_rank()
runtime_mapping = tensorrt_llm.Mapping(
world_size, runtime_rank, tp_size=tp_size, pp_size=pp_size
)
# TensorRT-LLM must run on a GPU.
assert (
torch.cuda.is_available()
), "LocalTensorRTLLM requires a Nvidia CUDA enabled GPU to operate"
torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
self._tokenizer = AutoTokenizer.from_pretrained(
tokenizer_dir, legacy=False
)
self._sampling_config = SamplingConfig(
end_id=EOS_TOKEN,
pad_id=PAD_TOKEN,
num_beams=1,
temperature=temperature,
)
serialize_path = engine_dir_path / (engine_name if engine_name else "")
with open(serialize_path, "rb") as f:
engine_buffer = f.read()
decoder = tensorrt_llm.runtime.GenerationSession(
self._model_config, engine_buffer, runtime_mapping, debug_mode=False
)
self._model = decoder
generate_kwargs = generate_kwargs or {}
generate_kwargs.update(
{"temperature": temperature, "max_tokens": max_new_tokens}
)
super().__init__(
model_path=model_path,
temperature=temperature,
context_window=context_window,
max_new_tokens=max_new_tokens,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
callback_manager=callback_manager,
generate_kwargs=generate_kwargs,
model_kwargs=model_kwargs,
verbose=verbose,
)
@classmethod
def class_name(cls) -> str:
"""获取类名。"""
return "LocalTensorRTLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM元数据。"""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_path,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try:
import torch
except ImportError:
raise ImportError("nvidia_tensorrt requires `pip install torch`.")
self.generate_kwargs.update({"stream": False})
if not formatted:
prompt = self.completion_to_prompt(prompt)
input_text = prompt
input_ids, input_lengths = parse_input(
input_text, self._tokenizer, EOS_TOKEN, self._model_config
)
max_input_length = torch.max(input_lengths).item()
self._model.setup(
input_lengths.size(0), max_input_length, self._max_new_tokens, 1
) # beam size is set to 1
if self._verbose:
start_time = time.time()
output_ids = self._model.decode(input_ids, input_lengths, self._sampling_config)
torch.cuda.synchronize()
elapsed_time = -1.0
if self._verbose:
end_time = time.time()
elapsed_time = end_time - start_time
output_txt, output_token_ids = get_output(
output_ids, input_lengths, self._max_new_tokens, self._tokenizer
)
if self._verbose:
print(f"Input context length : {input_ids.shape[1]}")
print(f"Inference time : {elapsed_time:.2f} seconds")
print(f"Output context length : {len(output_token_ids)} ")
print(
f"Inference token/sec : {(len(output_token_ids) / elapsed_time):2f}"
)
# call garbage collected after inference
torch.cuda.empty_cache()
gc.collect()
return CompletionResponse(
text=output_txt,
raw=generate_completion_dict(output_txt, self._model, self.model_path),
)
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
raise NotImplementedError(
"Nvidia TensorRT-LLM does not currently support streaming completion."
)
|