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123 lines
3.4 KiB
123 lines
3.4 KiB
import random
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import numpy as np
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import torch
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from torch.multiprocessing import Manager
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from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM
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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 data_gen(batch_size: int = 4, seq_len: int = 512):
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input_ids = torch.randint(10, 30000, (batch_size, seq_len), device=torch.cuda.current_device())
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return input_ids
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.random.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_streamingllm():
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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 = LlamaForCausalLM(
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LlamaConfig(
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vocab_size=50000,
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hidden_size=512,
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intermediate_size=1536,
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num_attention_heads=4,
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num_key_value_heads=2,
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num_hidden_layers=16,
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)
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).cuda()
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model = model.eval()
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input_token_ids = data_gen(1, 4)
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output_len = 128
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inference_config = InferenceConfig(
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max_batch_size=1,
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max_output_len=output_len,
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dtype="fp32",
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use_cuda_kernel=True,
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enable_streamingllm=True,
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start_token_size=4,
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generated_token_size=32,
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)
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inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
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assert inference_engine.generation_config.max_new_tokens == output_len
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inference_engine.add_request(prompts_token_ids=input_token_ids)
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assert inference_engine.request_handler._has_waiting()
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assert inference_config.start_token_size == inference_config.block_size
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request_handler = inference_engine.request_handler
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running_bb = request_handler.running_bb
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for _ in range(12):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, -1, -1, -1]
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assert running_bb.seq_lengths[0].item() == 16
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for _ in range(16):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, 1, -1, -1]
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assert running_bb.seq_lengths[0].item() == 32
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for _ in range(16):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, 1, 2, -1]
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assert running_bb.seq_lengths[0].item() == 48
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for _ in range(16):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, 2, 3, -1]
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assert running_bb.seq_lengths[0].item() == 48
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for _ in range(1):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, 2, 3, 1]
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assert running_bb.seq_lengths[0].item() == 49
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for _ in range(15):
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inference_engine.step()
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assert running_bb.block_tables[0].tolist() == [0, 3, 1, -1]
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assert running_bb.seq_lengths[0].item() == 48
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def run_dist(rank, world_size, port, func_to_run, ret=None, **kwargs):
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colossalai.launch(rank=rank, world_size=world_size, port=port, host="localhost")
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if ret:
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ret[rank] = func_to_run(**kwargs)
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else:
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func_to_run(**kwargs)
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@rerun_if_address_is_in_use()
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def test_engine():
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manager = Manager()
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result_list = manager.list([-1] * 1) # Create a shared list
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spawn(run_dist, 1, func_to_run=check_streamingllm, ret=result_list)
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return result_list[0]
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if __name__ == "__main__":
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test_engine()
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