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https://github.com/NVIDIA/TensorRT-LLM.git
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5955b8afba
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import os
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import sys
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import torch
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from mpi4py.futures import MPIPoolExecutor
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import tensorrt_llm
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from tensorrt_llm import Mapping
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from tensorrt_llm._utils import mpi_barrier
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from tensorrt_llm.models import LLaMAForCausalLM
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.llm_data import llm_models_root
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tensorrt_llm.logger.set_level('verbose')
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# 76s on ipp1-1197, loading weights 18s (varies based on network speed), network/engine creation 27s
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def build_and_run_tp2(rank):
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'''Do not save the engine, all in one LLaMAForCausalLM object
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'''
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input_text = [
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'Born in north-east France, Soyer trained as a',
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"What is large language model?"
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]
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expected_output = [
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"chef in Paris and London before moving to New York",
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"\nLarge language model is a model that is"
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]
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tensorrt_llm.logger.set_level('verbose')
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TP_SIZE = 2
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torch.cuda.set_device(rank)
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max_batch_size, max_isl, max_osl = 8, 256, 256
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hf_model_dir = llm_models_root() / "llama-models/llama-7b-hf"
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tokenizer_dir = hf_model_dir
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mapping = Mapping(world_size=TP_SIZE, rank=rank, tp_size=TP_SIZE)
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# build and run by one llama object
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llama = LLaMAForCausalLM.from_hugging_face(hf_model_dir,
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'float16',
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mapping=mapping)
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llama.to_trt(max_batch_size, max_isl, max_osl)
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mpi_barrier()
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tensorrt_llm.logger.warning(f"Build finished for rank {rank}")
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for idx, (inp, output) in enumerate(
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llama._generate(input_text, 10, tokenizer_dir=tokenizer_dir)):
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# print(f"Input: {inp}")
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tensorrt_llm.logger.info(f"{rank} input: {inp}")
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# print(f'Output: {output}')
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tensorrt_llm.logger.info(f"{rank} output: {output}")
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assert output == expected_output[
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idx], f"Expecting {expected_output[idx]}, got {output}"
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mpi_barrier()
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return True
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def test_multi_gpu():
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if torch.cuda.device_count() < 2:
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print("The test needs at least 2 GPUs, skipping")
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return
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with MPIPoolExecutor(max_workers=2) as executor:
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results = executor.map(build_and_run_tp2, (0, 1))
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for r in results:
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assert r == True
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if __name__ == "__main__":
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test_multi_gpu()
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