276 lines
11 KiB
Python
276 lines
11 KiB
Python
import gc
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import json
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import os
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import time
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from pathlib import Path
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from typing import Any, Callable, Dict, Optional, Sequence
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from llama_index.bridge.pydantic import Field, PrivateAttr
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from llama_index.callbacks import CallbackManager
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from llama_index.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
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from llama_index.llms.base import (
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ChatMessage,
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ChatResponse,
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CompletionResponse,
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LLMMetadata,
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llm_chat_callback,
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llm_completion_callback,
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)
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from llama_index.llms.custom import CustomLLM
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from llama_index.llms.generic_utils import completion_response_to_chat_response
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from llama_index.llms.nvidia_tensorrt_utils import (
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generate_completion_dict,
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get_output,
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parse_input,
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)
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EOS_TOKEN = 2
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PAD_TOKEN = 2
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class LocalTensorRTLLM(CustomLLM):
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model_path: Optional[str] = Field(description="The path to the trt engine.")
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temperature: float = Field(description="The temperature to use for sampling.")
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max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
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context_window: int = Field(
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description="The maximum number of context tokens for the model."
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)
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messages_to_prompt: Callable = Field(
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description="The function to convert messages to a prompt.", exclude=True
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)
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completion_to_prompt: Callable = Field(
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description="The function to convert a completion to a prompt.", exclude=True
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)
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generate_kwargs: Dict[str, Any] = Field(
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default_factory=dict, description="Kwargs used for generation."
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)
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model_kwargs: Dict[str, Any] = Field(
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default_factory=dict, description="Kwargs used for model initialization."
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)
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verbose: bool = Field(description="Whether to print verbose output.")
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_model: Any = PrivateAttr()
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_model_config: Any = PrivateAttr()
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_tokenizer: Any = PrivateAttr()
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_max_new_tokens = PrivateAttr()
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_sampling_config = PrivateAttr()
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_verbose = PrivateAttr()
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def __init__(
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self,
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model_path: Optional[str] = None,
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engine_name: Optional[str] = None,
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tokenizer_dir: Optional[str] = None,
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temperature: float = 0.1,
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max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
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context_window: int = DEFAULT_CONTEXT_WINDOW,
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messages_to_prompt: Optional[Callable] = None,
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completion_to_prompt: Optional[Callable] = None,
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callback_manager: Optional[CallbackManager] = None,
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generate_kwargs: Optional[Dict[str, Any]] = None,
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model_kwargs: Optional[Dict[str, Any]] = None,
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verbose: bool = False,
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) -> None:
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try:
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import torch
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from transformers import AutoTokenizer
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except ImportError:
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raise ImportError(
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"nvidia_tensorrt requires `pip install torch` and `pip install transformers`."
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)
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try:
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import tensorrt_llm
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from tensorrt_llm.runtime import ModelConfig, SamplingConfig
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except ImportError:
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print(
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"Unable to import `tensorrt_llm` module. Please ensure you have\
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`tensorrt_llm` installed in your environment. You can run\
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`pip3 install tensorrt_llm -U --extra-index-url https://pypi.nvidia.com` to install."
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)
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model_kwargs = model_kwargs or {}
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model_kwargs.update({"n_ctx": context_window, "verbose": verbose})
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self._max_new_tokens = max_new_tokens
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self._verbose = verbose
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# check if model is cached
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if model_path is not None:
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if not os.path.exists(model_path):
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raise ValueError(
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"Provided model path does not exist. "
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"Please check the path or provide a model_url to download."
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)
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else:
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engine_dir = model_path
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engine_dir_path = Path(engine_dir)
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config_path = engine_dir_path / "config.json"
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# config function
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with open(config_path) as f:
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config = json.load(f)
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use_gpt_attention_plugin = config["plugin_config"][
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"gpt_attention_plugin"
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]
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remove_input_padding = config["plugin_config"]["remove_input_padding"]
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tp_size = config["builder_config"]["tensor_parallel"]
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pp_size = config["builder_config"]["pipeline_parallel"]
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world_size = tp_size * pp_size
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assert (
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world_size == tensorrt_llm.mpi_world_size()
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), f"Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})"
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num_heads = config["builder_config"]["num_heads"] // tp_size
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hidden_size = config["builder_config"]["hidden_size"] // tp_size
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vocab_size = config["builder_config"]["vocab_size"]
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num_layers = config["builder_config"]["num_layers"]
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num_kv_heads = config["builder_config"].get("num_kv_heads", num_heads)
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paged_kv_cache = config["plugin_config"]["paged_kv_cache"]
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if config["builder_config"].get("multi_query_mode", False):
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tensorrt_llm.logger.warning(
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"`multi_query_mode` config is deprecated. Please rebuild the engine."
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)
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num_kv_heads = 1
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num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size
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self._model_config = ModelConfig(
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num_heads=num_heads,
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num_kv_heads=num_kv_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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gpt_attention_plugin=use_gpt_attention_plugin,
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paged_kv_cache=paged_kv_cache,
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remove_input_padding=remove_input_padding,
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)
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assert (
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pp_size == 1
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), "Python runtime does not support pipeline parallelism"
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world_size = tp_size * pp_size
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runtime_rank = tensorrt_llm.mpi_rank()
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runtime_mapping = tensorrt_llm.Mapping(
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world_size, runtime_rank, tp_size=tp_size, pp_size=pp_size
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)
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# TensorRT-LLM must run on a GPU.
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assert (
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torch.cuda.is_available()
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), "LocalTensorRTLLM requires a Nvidia CUDA enabled GPU to operate"
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torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
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self._tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_dir, legacy=False
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)
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self._sampling_config = SamplingConfig(
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end_id=EOS_TOKEN,
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pad_id=PAD_TOKEN,
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num_beams=1,
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temperature=temperature,
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)
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serialize_path = engine_dir_path / (engine_name if engine_name else "")
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with open(serialize_path, "rb") as f:
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engine_buffer = f.read()
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decoder = tensorrt_llm.runtime.GenerationSession(
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self._model_config, engine_buffer, runtime_mapping, debug_mode=False
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)
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self._model = decoder
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generate_kwargs = generate_kwargs or {}
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generate_kwargs.update(
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{"temperature": temperature, "max_tokens": max_new_tokens}
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)
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super().__init__(
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model_path=model_path,
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temperature=temperature,
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context_window=context_window,
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max_new_tokens=max_new_tokens,
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messages_to_prompt=messages_to_prompt,
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completion_to_prompt=completion_to_prompt,
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callback_manager=callback_manager,
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generate_kwargs=generate_kwargs,
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model_kwargs=model_kwargs,
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verbose=verbose,
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)
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@classmethod
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def class_name(cls) -> str:
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"""Get class name."""
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return "LocalTensorRTLLM"
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@property
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def metadata(self) -> LLMMetadata:
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"""LLM metadata."""
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return LLMMetadata(
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context_window=self.context_window,
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num_output=self.max_new_tokens,
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model_name=self.model_path,
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)
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@llm_chat_callback()
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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prompt = self.messages_to_prompt(messages)
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completion_response = self.complete(prompt, formatted=True, **kwargs)
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return completion_response_to_chat_response(completion_response)
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@llm_completion_callback()
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def complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponse:
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try:
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import torch
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except ImportError:
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raise ImportError("nvidia_tensorrt requires `pip install torch`.")
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self.generate_kwargs.update({"stream": False})
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if not formatted:
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prompt = self.completion_to_prompt(prompt)
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input_text = prompt
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input_ids, input_lengths = parse_input(
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input_text, self._tokenizer, EOS_TOKEN, self._model_config
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)
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max_input_length = torch.max(input_lengths).item()
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self._model.setup(
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input_lengths.size(0), max_input_length, self._max_new_tokens, 1
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) # beam size is set to 1
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if self._verbose:
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start_time = time.time()
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output_ids = self._model.decode(input_ids, input_lengths, self._sampling_config)
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torch.cuda.synchronize()
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elapsed_time = -1.0
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if self._verbose:
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end_time = time.time()
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elapsed_time = end_time - start_time
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output_txt, output_token_ids = get_output(
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output_ids, input_lengths, self._max_new_tokens, self._tokenizer
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)
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if self._verbose:
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print(f"Input context length : {input_ids.shape[1]}")
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print(f"Inference time : {elapsed_time:.2f} seconds")
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print(f"Output context length : {len(output_token_ids)} ")
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print(
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f"Inference token/sec : {(len(output_token_ids) / elapsed_time):2f}"
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)
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# call garbage collected after inference
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torch.cuda.empty_cache()
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gc.collect()
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return CompletionResponse(
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text=output_txt,
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raw=generate_completion_dict(output_txt, self._model, self.model_path),
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)
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@llm_completion_callback()
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def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
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raise NotImplementedError(
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"Nvidia TensorRT-LLM does not currently support streaming completion."
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)
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