import os import pytest import torch from packaging import version from transformers import LlamaForCausalLM from transformers.models.llama.configuration_llama import LlamaConfig import colossalai from colossalai.inference.tensor_parallel.engine import TPInferEngine from colossalai.logging import disable_existing_loggers from colossalai.shardformer import ShardConfig from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true" TPSIZE = 2 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_llama_test(test_config): llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024) model = LlamaForCausalLM(llama_config) model = model.half() shard_config = ShardConfig( enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True ) infer_engine = TPInferEngine(model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN) generate_kwargs = dict(max_new_tokens=MAX_OUTPUT_LEN, do_sample=False) input_tokens = { "input_ids": torch.randint(1, 1000, (BATCH_SIZE, MAX_INPUT_LEN), device="cuda"), "attention_mask": torch.ones((BATCH_SIZE, MAX_INPUT_LEN), device="cuda"), } outputs = infer_engine.generate(input_tokens, **generate_kwargs) assert outputs is not None def check_llama(rank, world_size, port): disable_existing_loggers() colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") run_llama_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_llama(): spawn(check_llama, TPSIZE) if __name__ == "__main__": test_llama()