ColossalAI/tests/test_infer/test_rpc_engine.py

108 lines
4.1 KiB
Python

import random
import numpy as np
import pytest
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from colossalai.inference.config import _DEFAULT_PROMPT_TEMPLATES, InferenceConfig
from colossalai.inference.core.rpc_engine import RPCInferenceEngine
from colossalai.inference.modeling.policy import NoPaddingLlamaModelInferPolicy
from colossalai.testing import parameterize, rerun_if_address_is_in_use
def setup_seed(seed):
torch.manual_seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def check_inference_engine(tp_size, use_engine=False, prompt_template=None, do_sample=True, policy=None):
setup_seed(20)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
model = "meta-llama/Llama-2-7b-hf" # remote mode path
inputs = [
"介绍一下今天的北京,比如故宫,天安门,长城或者其他的一些景点,",
"介绍一下武汉,",
]
output_len = 38
top_p = 0.5
top_k = 50
if use_engine:
inference_config = InferenceConfig(
max_output_len=output_len,
prompt_template=prompt_template,
dtype="fp32",
use_cuda_kernel=True,
tp_size=tp_size,
)
inference_engine = RPCInferenceEngine(model, tokenizer, inference_config, verbose=True, model_policy=policy)
assert inference_engine.generation_config.max_new_tokens == output_len
inference_engine.add_request(prompts=inputs)
assert inference_engine.request_handler._has_waiting()
generation_config = GenerationConfig(
max_new_tokens=output_len, do_sample=do_sample, dtype="fp32", top_p=top_p, top_k=top_k
)
outputs = inference_engine.generate(generation_config=generation_config)
else:
if prompt_template:
# apply prompt template
inputs = [_DEFAULT_PROMPT_TEMPLATES[prompt_template].format(input_text=input_text) for input_text in inputs]
model = AutoModelForCausalLM.from_pretrained(model).cuda()
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
inputs = tokenizer.batch_encode_plus(inputs, padding=True, return_tensors="pt")["input_ids"]
inputs = inputs.cuda()
generation_config = GenerationConfig(
do_sample=do_sample,
dtype="fp32",
top_p=top_p,
top_k=top_k,
pad_token_id=tokenizer.pad_token_id,
max_new_tokens=output_len,
)
outputs = model.generate(inputs, generation_config=generation_config)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return outputs
def run_engine(tp_size, **kwargs):
return check_inference_engine(tp_size=tp_size, **kwargs)
# TODO: fix the test
@pytest.mark.skip("model is too large")
@pytest.mark.largedist
@parameterize("prompt_template", [None, "llama"])
@parameterize("do_sample", [False])
@rerun_if_address_is_in_use()
def test_tp_engine(prompt_template, do_sample):
if torch.multiprocessing.get_start_method(allow_none=True) is None:
torch.multiprocessing.set_start_method("spawn")
kwargs1 = {
"use_engine": True,
"prompt_template": prompt_template,
"do_sample": do_sample,
"policy": NoPaddingLlamaModelInferPolicy(),
}
kwargs2 = {"use_engine": False, "prompt_template": prompt_template, "do_sample": do_sample, "policy": None}
colossal_tp_1_output = run_engine(1, **kwargs1)
colossal_tp_2_output = run_engine(2, **kwargs1)
transformer_tp_1_output = run_engine(1, **kwargs2)
for s1, s2, s3 in zip(colossal_tp_1_output, colossal_tp_2_output, transformer_tp_1_output):
assert s1 == s3, f"\nColossalAI TP=1 Output: {s1}\nTransformers Output: {s3}"
assert s1 == s2, f"\nColossalAI TP=1 Output: {s1}\nColossalAI TP=2 Output: {s2}"
if __name__ == "__main__":
torch.multiprocessing.set_start_method("spawn") # this code will not be ok for settings to fork to subprocess
test_tp_engine()