mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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db4edea1e1
* Update TensorRT-LLM --------- Co-authored-by: Kota Tsuyuzaki <bloodeagle40234@gmail.com> Co-authored-by: Pzzzzz <hello-cd.plus@hotmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com>
503 lines
17 KiB
Python
503 lines
17 KiB
Python
import asyncio
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import json
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import os
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import sys
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import tempfile
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from typing import List
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import pytest
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import torch
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from transformers import AutoTokenizer
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from tensorrt_llm.hlapi.llm import (LLM, BuildConfig, KvCacheConfig,
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ModelConfig, ParallelConfig,
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PretrainedConfig, SamplingParams,
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StreamingLLMParam, TokenizerBase)
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from tensorrt_llm.hlapi.tokenizer import TransformersTokenizer
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from tensorrt_llm.hlapi.utils import get_total_gpu_memory
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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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from utils.util import force_ampere, similar
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from tensorrt_llm.models.llama.model import LLaMAForCausalLM
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# The unittests are based on the tiny-llama, which is fast to build and run.
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# There are other tests based on llama-7B model, such as the end-to-end tests in test_e2e.py, and parallel tests in test_llm_multi_gpu.py.
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def get_model_path(model_name):
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engine_dir = os.environ.get('LLM_ENGINE_DIR', None)
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if engine_dir:
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return engine_dir
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return str(llm_models_root() / model_name)
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default_model_name = "llama-models-v2/TinyLlama-1.1B-Chat-v1.0"
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mixtral_model_name = "Mixtral-8x7B-v0.1"
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llama_model_path = get_model_path(default_model_name)
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llm_engine_dir = os.environ.get('LLM_ENGINE_DIR', './tmp.engine')
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prompts = ["A B C"]
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@force_ampere
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def test_ModelConfig_build_config():
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config = ModelConfig(llama_model_path)
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assert config.build_config is not None
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# change some building parameters
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config.build_config.max_batch_size = 129
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config.build_config.max_beam_width = 4
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config.build_config.builder_opt = 3
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config.build_config.max_num_tokens = 888
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config.build_config.strongly_typed = True
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config.build_config.max_output_len = 333
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llm = LLM(config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4))
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tmpdir = tempfile.TemporaryDirectory()
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llm.save(tmpdir.name)
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with open(os.path.join(tmpdir.name, "config.json"), "r") as f:
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# read the build_config and check if the parameters are correctly saved
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engine_config = json.load(f)
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pretrained_config = PretrainedConfig.from_dict(
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engine_config["pretrained_config"])
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build_config = BuildConfig.from_dict(engine_config["build_config"])
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# Know issue: this will be converted to None after save engine for single-gpu
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build_config.plugin_config.nccl_plugin = 'float16'
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assert build_config.max_batch_size == config.build_config.max_batch_size
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assert build_config.max_beam_width == config.build_config.max_beam_width
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assert build_config.builder_opt == config.build_config.builder_opt
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assert build_config.max_num_tokens == config.build_config.max_num_tokens
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assert build_config.strongly_typed == config.build_config.strongly_typed
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assert build_config.max_output_len == config.build_config.max_output_len
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def test_llm_loading_from_hf():
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config = ModelConfig(llama_model_path)
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# The performance-related flags are turned on eagerly to check the functionality
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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enable_chunked_context=False,
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)
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sampling_params = SamplingParams(max_new_tokens=8)
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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assert output.text == "D E F G H I J K"
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@force_ampere
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def test_llm_loading_from_ckpt():
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tokenizer = TransformersTokenizer.from_pretrained(llama_model_path)
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assert tokenizer is not None
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with tempfile.TemporaryDirectory() as ckpt_dir:
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llama = LLaMAForCausalLM.from_hugging_face(llama_model_path)
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llama.save_checkpoint(ckpt_dir)
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del llama
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config = ModelConfig(ckpt_dir)
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llm = LLM(
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config,
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tokenizer=tokenizer,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(max_new_tokens=8)
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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assert output.text == "D E F G H I J K"
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class MyTokenizer(TokenizerBase):
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''' A wrapper for the Transformers' tokenizer.
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This is the default tokenizer for LLM. '''
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@classmethod
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def from_pretrained(cls, pretrained_model_dir: str, **kwargs):
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir,
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**kwargs)
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return MyTokenizer(tokenizer)
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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@property
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def eos_token_id(self) -> int:
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return self.tokenizer.eos_token_id
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@property
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def pad_token_id(self) -> int:
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return self.tokenizer.pad_token_id
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def encode(self, text: str, **kwargs) -> List[int]:
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return self.tokenizer.encode(text, **kwargs)
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def decode(self, token_ids: List[int], **kwargs) -> str:
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return self.tokenizer.decode(token_ids, **kwargs)
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def batch_encode_plus(self, texts: List[str], **kwargs) -> dict:
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return self.tokenizer.batch_encode_plus(texts, **kwargs)
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def test_llm_with_customized_tokenizer():
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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# a customized tokenizer is passed to override the default one
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tokenizer=MyTokenizer.from_pretrained(config.model_dir),
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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for output in llm.generate(prompts):
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print(output)
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def test_llm_without_tokenizer():
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(end_id=2, pad_id=2, max_new_tokens=8)
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prompts = [[23, 14, 3]]
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for output in llm.generate(prompts, sampling_params=sampling_params):
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assert not output.text, "The output should be empty since the tokenizer is missing"
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print(output)
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# TODO[chunweiy]: Move mixtral test to the e2e test
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def is_memory_enough_for_mixtral():
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if torch.cuda.device_count() < 2:
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return False
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try:
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total_memory = get_total_gpu_memory(0) + get_total_gpu_memory(1)
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if total_memory >= 160 * 1024**3:
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return True
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except:
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return False
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def test_llm_generate_async():
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_test_llm_generate_async()
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def _test_llm_generate_async(model_name=default_model_name,
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tp_size: int = 1,
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use_auto_parallel: bool = False,
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tokenizer=None):
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if "Mixtral" in model_name and use_auto_parallel:
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pytest.skip("Auto parallel is not supported for Mixtral models")
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config = ModelConfig(llama_model_path)
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if use_auto_parallel:
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config.parallel_config.auto_parallel = True
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config.parallel_config.world_size = tp_size
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else:
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config.parallel_config.tp_size = tp_size
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kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.4)
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devices = config.parallel_config.devices
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if torch.cuda.get_device_properties(devices[0]).major >= 8:
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kv_cache_config.enable_block_reuse = True
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llm = LLM(
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config,
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tokenizer=tokenizer,
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kv_cache_config=kv_cache_config,
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)
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llm.save("./tmp.engine.8") # DEBUG
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sampling_params = SamplingParams(max_new_tokens=6)
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def test_async(streaming: bool):
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async def task(prompt: str):
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outputs = []
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async for output in llm.generate_async(
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prompt, streaming=streaming,
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sampling_params=sampling_params):
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print('output', output)
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outputs.append(output.text)
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print(' '.join(outputs))
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async def main():
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tasks = [task(prompt) for prompt in prompts]
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await asyncio.gather(*tasks)
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asyncio.run(main())
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def test_wait(streaming: bool):
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for prompt in prompts:
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future = llm.generate_async(prompt,
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streaming=streaming,
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sampling_params=sampling_params)
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for output in future:
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print('wait', output)
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def test_non_streaming_usage_wait():
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for prompt in prompts:
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output = llm.generate_async(prompt,
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streaming=False,
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sampling_params=sampling_params)
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print(output.text)
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def test_future(streaming: bool):
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for prompt in prompts:
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future = llm.generate_async(prompt,
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streaming=streaming,
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sampling_params=sampling_params)
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if streaming is True:
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for output in future:
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# Do something else and then wait for the result if needed
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output = output.result(timeout=10)
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print('future', output.text)
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else:
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# Do something else and then wait for the result if needed
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output = future.result(timeout=10)
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print('future', output.text)
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def test_future_async():
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async def task(prompt: str):
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future = llm.generate_async(prompt,
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streaming=False,
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sampling_params=sampling_params)
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output = await future.aresult()
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print('future', output.text)
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async def main():
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tasks = [task(prompt) for prompt in prompts]
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await asyncio.gather(*tasks)
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asyncio.run(main())
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test_async(streaming=True)
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test_async(streaming=False)
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test_wait(streaming=True)
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test_wait(streaming=False)
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test_future(streaming=True)
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test_future(streaming=False)
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test_future_async()
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test_non_streaming_usage_wait()
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@pytest.fixture(scope="module")
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def llm_for_sampling_params() -> LLM:
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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return llm
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@force_ampere
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def test_generate_with_sampling_params_per_prompt(llm_for_sampling_params: LLM):
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llm = llm_for_sampling_params
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sampling_params_list = [
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SamplingParams(end_id=-1, pad_id=-1) for _ in range(2)
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]
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sampling_params_list[0].max_new_tokens = 4
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sampling_params_list[1].max_new_tokens = 8
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for i, output in enumerate(
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llm.generate(prompts, sampling_params=sampling_params_list)):
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output_len = len(output.token_ids)
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print(f"output_len: {output_len}")
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assert output_len <= sampling_params_list[i].max_new_tokens
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@force_ampere
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@pytest.fixture(scope="module")
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@pytest.mark.parametrize(
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"sampling_params",
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[
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# temperature
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SamplingParams(
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max_new_tokens=6, temperature=0.5, beam_search_diversity_rate=0.5),
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# topK
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SamplingParams(max_new_tokens=6, top_k=10, top_p=0.92),
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# topP
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SamplingParams(max_new_tokens=6, top_p=0.92),
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# penalty
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SamplingParams(max_new_tokens=8,
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length_penalty=1.0,
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presence_penalty=0.0,
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repetition_penalty=1.0,
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min_length=5),
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# early stopping
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SamplingParams(max_new_tokens=6, early_stopping=5),
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])
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def test_generate_with_SamplingConfig(llm_for_sampling_params: LLM,
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sampling_params: SamplingParams):
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llm = llm_for_sampling_params
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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@force_ampere
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def test_generate_with_beam_search():
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config = ModelConfig(llama_model_path)
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config.build_config.max_beam_width = 2
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config.build_config.max_num_tokens = 20
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(max_new_tokens=6, beam_width=2)
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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assert len(output.text) == 2
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assert len(output.token_ids) == 2
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assert similar(output.text[0], "D E F G H I")
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assert similar(output.text[1], "D E F G I J")
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@force_ampere
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def test_generate_with_streaming_llm():
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config = ModelConfig(llama_model_path)
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# TODO[chunweiy]: Test with larger size when the underlying support is ready
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llm = LLM(config, streaming_llm=StreamingLLMParam(64, 4))
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# Check the plugin config is correctly set
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assert config.build_config.plugin_config.streamingllm is True
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assert config.build_config.plugin_config.use_paged_context_fmha is False
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sampling_params = SamplingParams(max_new_tokens=4)
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for output in llm.generate(prompts, sampling_params=sampling_params):
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assert output.text == "D E F G"
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print(output)
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def test_parallel_config():
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config = ParallelConfig()
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config.tp_size = 2
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config.pp_size = 2
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assert config.world_size == 4
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config.world_size = 4 # should not raise exception
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with pytest.raises(ValueError):
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config.world_size = 5
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@force_ampere # Save H100 resource
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@pytest.mark.parametrize("gather_context_logits", [True, False])
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@pytest.mark.parametrize("gather_generation_logits", [True, False])
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@pytest.mark.parametrize("return_log_probs", [True]) # prune space
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def test_generate_with_OutputConfig(gather_context_logits: bool,
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gather_generation_logits: bool,
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return_log_probs: bool):
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if not (gather_context_logits or gather_generation_logits): # prune space
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return
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config = ModelConfig(llama_model_path)
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config.build_config.gather_context_logits = gather_context_logits
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config.build_config.gather_generation_logits = gather_generation_logits
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config.build_config.return_log_probs = return_log_probs
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(
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max_new_tokens=8,
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return_context_logits=gather_context_logits,
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return_generation_logits=gather_generation_logits,
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return_log_probs=return_log_probs)
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for output in llm.generate(prompts, sampling_params=sampling_params):
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if gather_context_logits:
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assert output.context_logits is not None
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assert len(prompts[0].split()) + 1 == output.context_logits.shape[0]
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if gather_generation_logits:
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assert output.generation_logits is not None
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assert sampling_params.max_new_tokens == output.generation_logits.shape[
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1]
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if return_log_probs:
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assert output.log_probs is not None
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print(output)
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@force_ampere
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def test_generate_with_stop_words():
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(max_new_tokens=6, stop_words=[[11]])
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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assert output.text == "D E F G H I"
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@force_ampere
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def test_generate_with_bad_words():
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4),
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)
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sampling_params = SamplingParams(max_new_tokens=6)
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tokenizer = AutoTokenizer.from_pretrained(llama_model_path,
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add_prefix_space=False)
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# TODO[chunweiy]: Consider to make the generate api accept bad_words as a list of strings
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bad_words = tokenizer(["H", "I"]).input_ids
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bad_words = [row[1] for row in tokenizer(["H", "I"]).input_ids]
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bad_words = [bad_words]
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print('bad_words:', bad_words)
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sampling_params.bad_words = bad_words
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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assert output.text == "D E F G H J"
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@force_ampere
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def test_generate_block_reuse():
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config = ModelConfig(llama_model_path)
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llm = LLM(
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config,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4,
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enable_block_reuse=True),
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)
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# Check the configurations are correctly set
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assert config.build_config.plugin_config.use_paged_context_fmha is True
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assert config.build_config.plugin_config.paged_kv_cache is True
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sampling_params = SamplingParams(max_new_tokens=6)
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prompts = ["A B C", "A B C D"]
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for output in llm.generate(prompts, sampling_params=sampling_params):
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print(output)
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# TODO[chunweiy]: Add test for loading inmemory model
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if __name__ == '__main__':
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test_llm_loading_from_hf()
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