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https://github.com/NVIDIA/TensorRT-LLM.git
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112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
import json
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from functools import partial
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from typing import List, TextIO, Tuple
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from transformers import AutoTokenizer, PreTrainedTokenizer
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from tensorrt_llm.bench.dataclasses.general import (DatasetMetadata,
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InferenceRequest)
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def initialize_tokenizer(model_name: str) -> PreTrainedTokenizer:
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"""Initialize a tokenizer.
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Args:
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model_name (str): The name of the HuggingFace model to pull a
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tokenizer from.
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Returns:
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PreTrainedTokenizer: An initialized HuggingFace tokenizer.
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"""
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# Initialize the tokenizer specific to the model that we are planning
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# to benchmark.
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tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
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if tokenizer.pad_token_id is None:
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tokenizer.add_special_tokens({"pad_token": "[PAD]"})
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return tokenizer
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def create_dataset_from_stream(
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tokenizer: PreTrainedTokenizer,
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stream: TextIO,
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max_input_length: int = 0,
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max_output_length: int = 0,
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num_requests: int = 0,
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) -> Tuple[DatasetMetadata, List[InferenceRequest]]:
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"""Generate metadata and a list of requests to drive benchmarking.
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Args:
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tokenizer (PreTrainedTokenizer): HuggingFace tokenizer.
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stream (TextIO): Stream of input requests.
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max_input_length (int, optional): Maximum input length to cap prompts to. Defaults to 0.
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max_output_length (int, optional): Maximum output length to cap prompts to.. Defaults to 0.
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num_requests (int, optional): Number of requests to limit to. Defaults to 0.
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Returns:
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Tuple[DatasetMetadata, List[InferenceRequest]]: A tuple containing a dataclass of dataset
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statistics and a list of inference requests for benchmarking.
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"""
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# Initialize dataset list, and metadata tracking variables.
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dataset = []
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max_isl = 0
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max_osl = 0
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max_sequence = 0
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max_requests = num_requests if num_requests > 0 else float("inf")
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# If we're limiting the input length to a certain size, then set up
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# a partial to truncate the data down to size. Otherwise, just use the
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# unmodified tokenizer callable.
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tokenize = (partial(
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tokenizer,
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padding="max_length",
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max_length=max_input_length,
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truncation=True,
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) if max_input_length > 0 else tokenizer)
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# If we need to limit the output length, fill in a partial callable
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# for max, otherwise a lambda that just returns x with no bounds.
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output_limiter = (partial(max, max_output_length)
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if max_output_length > 0 else lambda x: x)
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# For each line in the standard input, parse out the JSON string we expect
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# to see.
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# Note the := walrus -- we're assigning and checking the condition.
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while (line := stream.readline()) and len(dataset) < max_requests:
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# We expect the data to come in as a JSON string.
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# For example:
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# {"prompt": "Generate an infinite response to the following:
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# There once was a man who.", "output_tokens": 1000}
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# Each line should be a complete JSON dictionary with no indentation
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# or newline characters.
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data = json.loads(line)
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logits = data.get("input_ids", None)
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prompt = data.get("prompt", None)
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task_id = data["task_id"]
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osl = data["output_tokens"]
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# If the request comes in with logits, just use the provided.
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# Otherwise we need to tokenize it.
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logits = tokenize(prompt)["input_ids"] if logits is None else logits
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request = InferenceRequest(
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task_id=task_id,
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prompt=prompt,
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output_tokens=output_limiter(osl),
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input_ids=logits,
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)
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max_isl = max(max_isl, len(logits))
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max_osl = max(max_osl, osl)
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max_sequence = max(max_sequence, len(logits) + osl)
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dataset.append(request)
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# Fill in basic dataset metrics here
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# TODO: Maybe fill this out to be more complete?
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metadata = DatasetMetadata(
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max_isl=max_isl,
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max_osl=max_osl,
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max_sequence_length=max_sequence,
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num_requests=len(dataset),
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)
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return metadata, dataset
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