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