mixtral_vllm.py

#
# Copyright (c) 2025 Chenpeng Wu (cpwu_sjtu@sjtu.edu.cn), Qiqi Gu (qiqi.gu@sjtu.edu.cn). 
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import argparse
import time
import os

import torch
import torch.distributed
import torch.nn as nn

from mixtral.configuration_mixtral import MixtralConfig
from mixtral.modeling_mixtral_vLLM import MixtralDecoderLayer
from vllm.model_executor.models.mixtral import MixtralMoE
from vllm.model_executor.parallel_utils.parallel_state import initialize_model_parallel
# from vllm.distributed.parallel_state import initialize_model_parallel

parser = argparse.ArgumentParser()
parser.add_argument('--profile', action='store_true', default=False)
parser.add_argument('--time', action='store_true', default=False)

parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--seq_len', type=int, default=4096)

parser.add_argument('--mlp', action='store_true', default=False)
parser.add_argument('--layer', action='store_true', default=False)
parser.add_argument('--model', action='store_true', default=False)

parser.add_argument('--hidden_size', type=int, default=4096)
parser.add_argument('--intermediate_size', type=int, default=14336)

parser.add_argument('--experts', type=int, default=8)

parser.add_argument('--flash', action='store_true', default=False)

args = parser.parse_args()

m = args.intermediate_size
k = args.hidden_size
n = args.batch_size * args.seq_len
expert_num = args.experts
use_flash = args.flash

WARMUP = 10
ITER = 100

configuration = MixtralConfig(
        vocab_size=32000,  # default 32000
        hidden_size=k,  # default 4096
        intermediate_size=m,  # default 14336
        num_hidden_layers=1,  # default 32
        num_attention_heads=32,  # default 32
        num_key_value_heads=8,  # default 8
        hidden_act="silu",  # default "silu"
        max_position_embeddings=4096 * 32,  # default 4096 * 32
        initializer_range=0.02,  # default 0.02
        rms_norm_eps=1e-5,  # default 1e-5
        use_cache=True,  # default True
        pad_token_id=None,  # default None
        bos_token_id=1,  # default 1
        eos_token_id=2,  # default 2
        tie_word_embeddings=False,  # default False
        rope_theta=1e6,  # default 1e6
        sliding_window=4096,  # default 4096
        attention_dropout=0.0,  # default 0.0
        num_experts_per_tok=2,  # default 2
        num_local_experts=expert_num,  # default 8
        output_router_logits=False,  # default False
        router_aux_loss_coef=0.001,  # default 0.001
    )

position_ids = None
if use_flash:
    configuration._attn_implementation = "flash_attention_2"
    position_ids = torch.arange(args.seq_len).unsqueeze(0).expand(args.batch_size, args.seq_len).cuda()
else:
    configuration._attn_implementation = "eager"

def mixtral_mlp_run():
    # vllm_model = MixtralDecoderLayer(configuration).half().cuda()
    ss_model = MixtralMoE(
        num_experts=configuration.num_local_experts,
        top_k=configuration.num_experts_per_tok,
        hidden_size=configuration.hidden_size,
        intermediate_size=configuration.intermediate_size).half().cuda()
    ss_model.eval()

    input = torch.rand((n, k)).half().cuda()

    for i in range(ITER + WARMUP):
        if i == WARMUP:
            torch.cuda.synchronize()
            start = time.time()
        output = ss_model(input)
    torch.cuda.synchronize()
    end = time.time()
    print("Mixtral,mlp,vLLM,%d,%d,%d,%d,%d,%d,%s,%s" %
          (ITER, args.batch_size, args.seq_len, args.hidden_size, args.intermediate_size, args.experts, (end - start) * 1000, configuration._attn_implementation))

    if args.profile:
        prof = torch.profiler.profile(
            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
            schedule=torch.profiler.schedule(wait=1, warmup=10, active=10, repeat=1),
            on_trace_ready=torch.profiler.tensorboard_trace_handler(
                './outputs/profiler/Mixtral_vLLM_MLP_' + torch.cuda.get_device_name().split(' ')[1].split('-')[
                    0] + '_' + str(args.batch_size)),
            record_shapes=True,
            profile_memory=True,
            with_stack=True
        )
        prof.start()

        for i in range(ITER):
            # 执行过程
            output = ss_model(input)
            prof.step()

        prof.stop()

def mixtral_decoder_layer_run():
    ss_model = MixtralDecoderLayer(configuration, 0)
    ss_model = ss_model.half().cuda()
    
    ss_model.eval()
    
    input = torch.rand((args.batch_size, args.seq_len, k)).half().cuda()

    for i in range(ITER + WARMUP):
        if i == WARMUP:
            torch.cuda.synchronize()
            start = time.time()
        output = ss_model(input, position_ids=position_ids)
    torch.cuda.synchronize()
    end = time.time()
    print("Mixtral,layer,vLLM,%d,%d,%d,%d,%d,%d,%s,%s" %
          (ITER, args.batch_size, args.seq_len, args.hidden_size, args.intermediate_size, args.experts, (end - start) * 1000, configuration._attn_implementation))

    if args.profile:
        prof = torch.profiler.profile(
            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
            schedule=torch.profiler.schedule(wait=1, warmup=10, active=10, repeat=1),
            on_trace_ready=torch.profiler.tensorboard_trace_handler(
                './outputs/profiler/Mixtral_vLLM_layer_' + torch.cuda.get_device_name().split(' ')[1].split('-')[
                    0] + '_' + str(args.batch_size)),
            record_shapes=True,
            profile_memory=True,
            with_stack=True
        )
        prof.start()

        for i in range(ITER):
            # 执行过程
            output = ss_model(input, position_ids=position_ids)
            prof.step()

        prof.stop()

if __name__ == "__main__":
    # 设置随机数种子
    torch.manual_seed(123)

    # 对于CUDA，还需要设置随机数种子
    torch.cuda.manual_seed(123)
    torch.cuda.manual_seed_all(123)

    torch.set_grad_enabled(False)

    os.environ["MASTER_ADDR"] = "127.0.0.1"
    os.environ["MASTER_PORT"] = "9834"
    torch.distributed.init_process_group(world_size=1, rank=0)
    initialize_model_parallel()

    if args.mlp:
        mixtral_mlp_run()
    if args.layer:
        mixtral_decoder_layer_run()
    if args.model:
        pass