ColossalAI/examples/inference/gptq_bloom.py

import argparse
import logging
import os
import time

import torch
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from auto_gptq.nn_modules.qlinear import GeneralQuantLinear
from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, LlamaForCausalLM, LlamaTokenizer

import colossalai
from colossalai.inference.tensor_parallel.engine import TPInferEngine
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer import ShardConfig
from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn

os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'


def print_perf_stats(latency_set, config, bs, warmup=3):
    # trim warmup queries
    latency_set = list(latency_set)
    latency_set = latency_set[warmup:]
    count = len(latency_set)

    if count > 0:
        latency_set.sort()
        avg = sum(latency_set) / count
        num_layers = getattr(config, "num_layers", config.num_hidden_layers)
        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
        num_bytes = 2    # float16

        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
        print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))


def bench_bloom(args):

    pretrained_model_dir = args.path
    quantized_model_dir = args.quantized_path
    max_batch_size = args.batch_size
    max_input_len = args.input_len
    max_output_len = args.output_len

    tokenizer = BloomTokenizerFast.from_pretrained(pretrained_model_dir)
    tokenizer.pad_token = tokenizer.eos_token

    # load quantized model to the first GPU
    model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir,
                                               device=torch.cuda.current_device(),
                                               inject_fused_attention=False)

    model = model.half()

    model_config = model.config
    shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False, inference_only=True)
    infer_engine = TPInferEngine(model, shard_config, max_batch_size, max_input_len, max_output_len)
    generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)

    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')
    }

    # init TPInferEngine and shard the original model
    # To benchmark torch original, comment out the line of optimizing model
    shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False,
                               inference_only=True,
                               inference_gptq=True)
    infer_engine = TPInferEngine(model, shard_config, max_batch_size, max_input_len, max_output_len)

    # prepare data for generation
    generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)
    input_tokens = {
        "input_ids": torch.randint(10, 1000, (max_batch_size, max_input_len)),
        "attention_mask": torch.ones((max_batch_size, max_input_len))
    }
    for t in input_tokens:
        if torch.is_tensor(input_tokens[t]):
            input_tokens[t] = input_tokens[t].to(torch.cuda.current_device())
            # print(f" input_tokens[{t}].shape: {input_tokens[t].shape}")

    iters = 10
    times = []
    for i in range(iters):
        torch.cuda.synchronize()
        start = time.time()
        outputs = infer_engine.generate(input_tokens, **generate_kwargs)
        torch.cuda.synchronize()
        end = time.time()
        out_len = outputs.shape[1]
        print(f" iter {i}: out len {str(out_len)}, generation time {str(end - start)} s")
        times.append((end - start) / (out_len - max_input_len))

    print_perf_stats(times, model_config, max_batch_size)


def check_bloom(rank, world_size, port, args):
    disable_existing_loggers()
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    bench_bloom(args)


@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_bloom(args):
    spawn(check_bloom, args.tp_size, args=args)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('-p', '--path', type=str, help='Model path', required=True)
    parser.add_argument('-q', '--quantized_path', type=str, help='Model path', required=True)
    parser.add_argument('-tp', '--tp_size', type=int, default=1, help='Tensor parallel size')
    parser.add_argument('-b', '--batch_size', type=int, default=16, help='Maximum batch size')
    parser.add_argument('--input_len', type=int, default=1024, help='Maximum input length')
    parser.add_argument('--output_len', type=int, default=128, help='Maximum output length')

    args = parser.parse_args()

    test_bloom(args)