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.
130 lines
4.3 KiB
130 lines
4.3 KiB
import importlib.util
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from packaging import version
|
|
|
|
import colossalai
|
|
from colossalai.inference import InferenceEngine
|
|
from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
|
|
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
|
|
|
|
CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
|
|
HAS_LIGHTLLM_KERNEL = True
|
|
|
|
if importlib.util.find_spec("lightllm") is None:
|
|
HAS_LIGHTLLM_KERNEL = False
|
|
|
|
|
|
def data_gen():
|
|
input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779, 318, 13779]], dtype=torch.int64)
|
|
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
|
|
return dict(input_ids=input_ids, attention_mask=attention_mask)
|
|
|
|
|
|
inputs = data_gen()
|
|
for k, v in inputs.items():
|
|
if torch.is_tensor(v) or "Tensor" in v.__class__.__name__:
|
|
new_shape = [1] * v.dim()
|
|
new_shape[0] = 16
|
|
inputs[k] = v.to("cuda").repeat(*new_shape)
|
|
|
|
|
|
def pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
|
|
chatglm_config = ChatGLMConfig(
|
|
num_layers=2,
|
|
vocab_size=20000,
|
|
use_cache=True,
|
|
multi_query_attention=True,
|
|
multi_query_group_num=2,
|
|
num_attention_heads=8,
|
|
hidden_size=1024,
|
|
)
|
|
model = ChatGLMForConditionalGeneration(chatglm_config)
|
|
|
|
engine = InferenceEngine(
|
|
tp_size=tp_size,
|
|
pp_size=pp_size,
|
|
model=model,
|
|
max_output_len=max_output_len,
|
|
micro_batch_size=micro_batch_size,
|
|
)
|
|
output = engine.generate(inputs)
|
|
if dist.get_rank() == 0:
|
|
assert len(output[0]) == max_output_len, f"{len(output)}, {max_output_len}"
|
|
|
|
|
|
@parameterize("tp_size", [1])
|
|
@parameterize("pp_size", [2])
|
|
@parameterize("max_output_len", [4])
|
|
@parameterize("micro_batch_size", [1])
|
|
@clear_cache_before_run()
|
|
def run_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
|
|
pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
@parameterize("tp_size", [2])
|
|
@parameterize("pp_size", [2])
|
|
@parameterize("max_output_len", [4])
|
|
@parameterize("micro_batch_size", [1])
|
|
@clear_cache_before_run()
|
|
def run_tp_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
|
|
pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
@parameterize("tp_size", [2])
|
|
@parameterize("pp_size", [1])
|
|
@parameterize("max_output_len", [2])
|
|
@parameterize("micro_batch_size", [1])
|
|
@clear_cache_before_run()
|
|
def run_tp_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
|
|
pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
@parameterize("tp_size", [1])
|
|
@parameterize("pp_size", [1])
|
|
@parameterize("max_output_len", [2])
|
|
@parameterize("micro_batch_size", [1])
|
|
@clear_cache_before_run()
|
|
def run_single_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
|
|
pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
def check_tp_pp_inference(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_tp_pipeline_inference_test()
|
|
|
|
|
|
def check_tp_or_pp_inference(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_tp_inference_test()
|
|
run_pipeline_inference_test()
|
|
|
|
|
|
def check_single_inference(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_single_inference_test
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
not CUDA_SUPPORT or not HAS_LIGHTLLM_KERNEL,
|
|
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_pipeline_inference():
|
|
spawn(check_tp_pp_inference, nprocs=4)
|
|
spawn(check_tp_or_pp_inference, nprocs=2)
|
|
spawn(check_single_inference, nprocs=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_pipeline_inference()
|