mirror of https://github.com/hpcaitech/ColossalAI
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
|
import os
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
import torch.distributed as dist
|
||
|
|
from packaging import version
|
||
|
|
from transformers import AutoTokenizer
|
||
|
|
|
||
|
|
import colossalai
|
||
|
|
from colossalai.inference.tensor_parallel.engine import TPInferEngine
|
||
|
|
from colossalai.logging import disable_existing_loggers
|
||
|
|
from colossalai.shardformer import ShardConfig
|
||
|
|
from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration
|
||
|
|
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
|
||
|
|
from tests.kit.model_zoo.transformers.chatglm2 import infer_config
|
||
|
|
|
||
|
|
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
|
||
|
|
TPSIZE = 1
|
||
|
|
BATCH_SIZE = 8
|
||
|
|
MAX_INPUT_LEN = 12
|
||
|
|
MAX_OUTPUT_LEN = 100
|
||
|
|
CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
|
||
|
|
|
||
|
|
|
||
|
|
@parameterize(
|
||
|
|
"test_config",
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"tp_size": TPSIZE,
|
||
|
|
}
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def run_chatglm2_test(test_config):
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
|
||
|
|
# pad_token_id = 0
|
||
|
|
model_fn = lambda: ChatGLMForConditionalGeneration(infer_config, empty_init=False)
|
||
|
|
orig_model = model_fn()
|
||
|
|
orig_model = orig_model.half()
|
||
|
|
text = ["how is the weather today?"]
|
||
|
|
input_ids = tokenizer.batch_encode_plus(text, return_tensors="pt", padding=True)
|
||
|
|
shard_config = ShardConfig(
|
||
|
|
enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True
|
||
|
|
)
|
||
|
|
infer_engine = TPInferEngine(orig_model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
|
||
|
|
|
||
|
|
generate_kwargs = dict(max_new_tokens=MAX_OUTPUT_LEN, do_sample=False)
|
||
|
|
outputs = infer_engine.generate(input_ids, **generate_kwargs)
|
||
|
|
assert outputs is not None
|
||
|
|
|
||
|
|
# print("outputs.shape: ", outputs[0].shape)
|
||
|
|
# print("outputs: ", outputs[0])
|
||
|
|
if not dist.is_initialized() or dist.get_rank() == 0:
|
||
|
|
for o in outputs:
|
||
|
|
output_text = tokenizer.decode(o)
|
||
|
|
print(output_text)
|
||
|
|
|
||
|
|
|
||
|
|
def check_chatglm2(rank, world_size, port):
|
||
|
|
disable_existing_loggers()
|
||
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||
|
|
run_chatglm2_test()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
|
||
|
|
@pytest.mark.dist
|
||
|
|
@rerun_if_address_is_in_use()
|
||
|
|
@clear_cache_before_run()
|
||
|
|
def test_chatglm2():
|
||
|
|
spawn(check_chatglm2, TPSIZE)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
test_chatglm2()
|