mirror of https://github.com/hpcaitech/ColossalAI
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import os
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import pytest
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import torch
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from packaging import version
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import colossalai
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from colossalai.inference.tensor_parallel 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.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
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from tests.kit.model_zoo import model_zoo
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TP_SIZE = 2
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MAX_BATCH_SIZE = 4
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MAX_INPUT_LEN = 16
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MAX_OUTPUT_LEN = 32
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CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.5')
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@parameterize('test_config', [{
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'tp_size': TP_SIZE,
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}])
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def run(test_config):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom_for_causal_lm')
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for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
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orig_model = model_fn()
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orig_model = orig_model.half()
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data = data_gen_fn()
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shard_config = ShardConfig(enable_tensor_parallelism=True if test_config['tp_size'] > 1 else False,
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inference_only=True)
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infer_engine = TPInferEngine(orig_model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
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generate_kwargs = dict(do_sample=False)
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outputs = infer_engine.generate(data, **generate_kwargs)
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assert outputs is not None
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def check_bloom(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()
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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_bloom_infer():
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spawn(check_bloom, TP_SIZE)
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if __name__ == '__main__':
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test_bloom_infer()
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