mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
96 lines
3.4 KiB
96 lines
3.4 KiB
import random
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from transformers import AutoTokenizer, GenerationConfig, LlamaConfig, LlamaForCausalLM
|
|
|
|
import colossalai
|
|
from colossalai.inference.config import _DEFAULT_PROMPT_TEMPLATES, InferenceConfig
|
|
from colossalai.inference.core.engine import InferenceEngine
|
|
from colossalai.inference.flash_decoding_utils import FDIntermTensors
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
|
|
|
|
|
def setup_seed(seed):
|
|
torch.manual_seed(seed)
|
|
torch.cuda.manual_seed_all(seed)
|
|
np.random.seed(seed)
|
|
random.seed(seed)
|
|
|
|
|
|
def check_inference_engine(use_engine=False, prompt_template=None):
|
|
setup_seed(20)
|
|
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
|
|
model = LlamaForCausalLM(
|
|
LlamaConfig(
|
|
vocab_size=50000, hidden_size=512, intermediate_size=1536, num_attention_heads=4, num_hidden_layers=16
|
|
)
|
|
).cuda()
|
|
model = model.eval()
|
|
|
|
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
|
|
do_sample = True
|
|
top_p = 0.5
|
|
top_k = 50
|
|
|
|
if use_engine:
|
|
inference_config = InferenceConfig(max_output_len=output_len, prompt_template=prompt_template, dtype="fp32")
|
|
inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
|
|
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(do_sample=do_sample, 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]
|
|
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,
|
|
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
|
|
|
|
|
|
@parameterize("prompt_template", [None, "llama"])
|
|
def check_output_consistency(prompt_template):
|
|
cai_outputs = check_inference_engine(use_engine=True, prompt_template=prompt_template)
|
|
transformer_outputs = check_inference_engine(use_engine=False, prompt_template=prompt_template)
|
|
|
|
for s1, s2 in zip(cai_outputs, transformer_outputs):
|
|
assert s1 == s2, f"\nColossalAI Output: {s1}\nTransformers Output: {s2}"
|
|
|
|
# clear singleton flash decoding tensors
|
|
FDIntermTensors._instances = {}
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host="localhost")
|
|
check_output_consistency()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_if_address_is_in_use()
|
|
def test_inference_engine():
|
|
spawn(run_dist, 1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_inference_engine()
|