mirror of https://github.com/hpcaitech/ColossalAI
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
import argparse
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import time
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import torch
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import torch.distributed as dist
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import transformers
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from transformers import LlamaForCausalLM, LlamaTokenizer
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import colossalai
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from colossalai.inference import CaiInferEngine
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from colossalai.testing import spawn
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def run_inference(args):
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llama_model_path = args.path
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max_input_len = args.max_input_len
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max_output_len = args.max_output_len
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max_batch_size = args.batch_size
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micro_batch_size = args.micro_batch_size
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tp_size = args.tp_size
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pp_size = args.pp_size
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rank = dist.get_rank()
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tokenizer = LlamaTokenizer.from_pretrained(llama_model_path)
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tokenizer.pad_token_id = tokenizer.unk_token_id
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model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.eos_token_id)
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model = model.half()
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model = transformers.LlamaForCausalLM(
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transformers.LlamaConfig(
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vocab_size=20000, hidden_size=512, intermediate_size=1536, num_attention_heads=4, num_hidden_layers=4
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)
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)
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engine = CaiInferEngine(
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tp_size=tp_size,
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pp_size=pp_size,
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model=model,
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max_output_len=max_output_len,
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micro_batch_size=micro_batch_size,
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)
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input_tokens = {
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"input_ids": torch.randint(1, 1000, (max_batch_size, max_input_len), device="cuda"),
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"attention_mask": torch.ones((max_batch_size, max_input_len), device="cuda"),
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}
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iters = 10
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warmup = 3
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times = []
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for i in range(iters):
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torch.cuda.synchronize()
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start = time.time()
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outputs = engine.generate(input_tokens)
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torch.cuda.synchronize()
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end = time.time()
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if rank == 0:
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out_len = len(outputs[0])
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print("generation time {} s".format(str(end - start)))
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print(out_len)
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times.append((end - start) / out_len)
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if rank == 0:
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times = times[warmup:]
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latency = sum(times) / len(times)
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print("total process latency is : " + str(latency) + " s")
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print("total throughput is : " + str(1 / latency * max_batch_size))
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def run_tp_pipeline_inference(rank, world_size, port, args):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_inference(args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-p", "--path", type=str, help="Model path", required=True)
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parser.add_argument("-tp", "--tp_size", type=int, default=1, help="Tensor parallel size")
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parser.add_argument("-pp", "--pp_size", type=int, default=1, help="Tensor parallel size")
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parser.add_argument("-b", "--batch_size", type=int, default=64, help="Maximum batch size")
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parser.add_argument("--max_input_len", type=int, default=512, help="Maximum input length")
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parser.add_argument("--max_output_len", type=int, default=256, help="Maximum output length")
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parser.add_argument("--micro_batch_size", type=int, default=2, help="Micro batch size")
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args = parser.parse_args()
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spawn(run_tp_pipeline_inference, nprocs=args.tp_size * args.pp_size, args=args)
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