mirror of https://github.com/hpcaitech/ColossalAI
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
import argparse
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from transformers import LlamaForCausalLM, LlamaTokenizer
|
|
|
|
import colossalai
|
|
from colossalai.inference import InferenceEngine
|
|
from colossalai.testing import spawn
|
|
|
|
|
|
def run_inference(args):
|
|
llama_model_path = args.model_path
|
|
llama_tokenize_path = args.tokenizer_path
|
|
|
|
max_input_len = args.max_input_len
|
|
max_output_len = args.max_output_len
|
|
max_batch_size = args.batch_size
|
|
micro_batch_size = args.micro_batch_size
|
|
tp_size = args.tp_size
|
|
pp_size = args.pp_size
|
|
rank = dist.get_rank()
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained(llama_tokenize_path, padding_side="left")
|
|
tokenizer.pad_token_id = tokenizer.unk_token_id
|
|
|
|
if args.quant is None:
|
|
model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.unk_token_id)
|
|
model = model.half()
|
|
elif args.quant == "gptq":
|
|
from auto_gptq import AutoGPTQForCausalLM
|
|
|
|
model = AutoGPTQForCausalLM.from_quantized(
|
|
llama_model_path, inject_fused_attention=False, device=torch.cuda.current_device()
|
|
)
|
|
elif args.quant == "smoothquant":
|
|
from colossalai.inference.quant.smoothquant.models.llama import SmoothLlamaForCausalLM
|
|
|
|
model = SmoothLlamaForCausalLM.from_quantized(llama_model_path, model_basename=args.smoothquant_base_name)
|
|
model = model.cuda()
|
|
|
|
engine = InferenceEngine(
|
|
tp_size=tp_size,
|
|
pp_size=pp_size,
|
|
model=model,
|
|
max_input_len=max_input_len,
|
|
max_output_len=max_output_len,
|
|
micro_batch_size=micro_batch_size,
|
|
quant=args.quant,
|
|
)
|
|
|
|
input_tokens = {
|
|
"input_ids": torch.randint(1, 1000, (max_batch_size, max_input_len), device="cuda"),
|
|
"attention_mask": torch.ones((max_batch_size, max_input_len), device="cuda"),
|
|
}
|
|
|
|
outputs = engine.generate(input_tokens)
|
|
if rank == 0:
|
|
print(tokenizer.batch_decode(outputs))
|
|
|
|
|
|
def run_tp_pipeline_inference(rank, world_size, port, args):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_inference(args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-p", "--model_path", type=str, help="Model path", required=True)
|
|
parser.add_argument("--tokenizer_path", type=str, help="Tokenizer path", required=True)
|
|
|
|
parser.add_argument(
|
|
"-q",
|
|
"--quant",
|
|
type=str,
|
|
choices=["gptq", "smoothquant"],
|
|
default=None,
|
|
help="quantization type: 'gptq' or 'smoothquant'",
|
|
)
|
|
parser.add_argument("--smoothquant_base_name", type=str, default=None, help="soothquant base name")
|
|
parser.add_argument("-tp", "--tp_size", type=int, default=2, help="Tensor parallel size")
|
|
parser.add_argument("-pp", "--pp_size", type=int, default=2, help="Pipeline parallel size")
|
|
parser.add_argument("-b", "--batch_size", type=int, default=4, help="Maximum batch size")
|
|
parser.add_argument("--max_input_len", type=int, default=32, help="Maximum input length")
|
|
parser.add_argument("--max_output_len", type=int, default=16, help="Maximum output length")
|
|
parser.add_argument("--micro_batch_size", type=int, default=1, help="Micro batch size")
|
|
|
|
args = parser.parse_args()
|
|
spawn(run_tp_pipeline_inference, nprocs=args.tp_size * args.pp_size, args=args)
|