mirror of
https://github.com/hpcaitech/ColossalAI.git
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77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import random
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import numpy as np
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import pytest
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import torch
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import transformers
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from transformers import AutoTokenizer, GenerationConfig
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import colossalai
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from colossalai.inference.config import InferenceConfig
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from colossalai.inference.core.engine import InferenceEngine
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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def check_inference_engine(test_cai=False):
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setup_seed(20)
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
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model = transformers.LlamaForCausalLM(
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transformers.LlamaConfig(
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vocab_size=50000, hidden_size=512, intermediate_size=1536, num_attention_heads=4, num_hidden_layers=16
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)
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).cuda()
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inputs = [
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"介绍一下今天的北京,",
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"介绍一下武汉,",
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]
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output_len = 16
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do_sample = True
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if test_cai:
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inference_config = InferenceConfig(max_output_len=output_len)
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inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
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inference_engine.add_request(prompts=inputs)
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assert inference_engine.request_handler._has_waiting()
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generation_config = GenerationConfig(do_sample=do_sample, top_p=0.5, top_k=50)
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outputs = inference_engine.generate(generation_config)
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else:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.pad_token_id = tokenizer.eos_token_id
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inputs = tokenizer.batch_encode_plus(inputs, padding=True, return_tensors="pt")["input_ids"]
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inputs = inputs.cuda()
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generation_config = GenerationConfig(
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do_sample=do_sample, top_p=0.5, top_k=50, pad_token_id=tokenizer.pad_token_id, max_new_tokens=output_len
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)
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outputs = model.generate(inputs, generation_config=generation_config)
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outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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return outputs
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host="localhost")
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cai_outputs = check_inference_engine(True)
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transformer_outputs = check_inference_engine(False)
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for s1, s2 in zip(cai_outputs, transformer_outputs):
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assert s1 == s2
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_inference_engine():
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spawn(run_dist, 1)
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if __name__ == "__main__":
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test_inference_engine()
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