mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
222 lines
8.5 KiB
Python
222 lines
8.5 KiB
Python
"""
|
|
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 gc
|
|
import time
|
|
from typing import List, Optional
|
|
|
|
import paddle
|
|
import pynvml
|
|
from paddle import nn
|
|
|
|
from fastdeploy import envs
|
|
from fastdeploy.config import FDConfig
|
|
from fastdeploy.engine.request import Request
|
|
from fastdeploy.platforms import current_platform
|
|
from fastdeploy.plugins.model_runner import load_model_runner_plugins
|
|
from fastdeploy.utils import get_logger, set_random_seed
|
|
from fastdeploy.worker.model_runner_base import ModelRunnerBase
|
|
from fastdeploy.worker.output import ModelRunnerOutput
|
|
from fastdeploy.worker.worker_base import WorkerBase
|
|
|
|
logger = get_logger("gpu_worker", "gpu_worker.log")
|
|
|
|
try:
|
|
ModelRunner = load_model_runner_plugins()
|
|
except:
|
|
from fastdeploy.worker.gpu_model_runner import GPUModelRunner as ModelRunner
|
|
|
|
|
|
class GpuWorker(WorkerBase):
|
|
def __init__(
|
|
self,
|
|
fd_config: FDConfig,
|
|
local_rank: int,
|
|
rank: int,
|
|
):
|
|
super().__init__(
|
|
fd_config=fd_config,
|
|
local_rank=local_rank,
|
|
rank=rank,
|
|
)
|
|
pass
|
|
|
|
def init_device(self):
|
|
"""
|
|
Initialize device and construct model runner
|
|
"""
|
|
self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
|
|
if self.device_config.device_type == "cuda" and paddle.device.is_compiled_with_cuda():
|
|
# Set environment variable
|
|
self.device_ids = self.parallel_config.device_ids.split(",")
|
|
self.device = f"gpu:{self.local_rank % self.max_chips_per_node}"
|
|
paddle.device.set_device(self.device)
|
|
paddle.set_default_dtype(self.parallel_config.dtype)
|
|
|
|
gc.collect()
|
|
paddle.device.cuda.empty_cache()
|
|
if (
|
|
not self.parallel_config.disable_custom_all_reduce
|
|
and self.parallel_config.tensor_parallel_size > 1
|
|
and paddle.is_compiled_with_cuda()
|
|
):
|
|
from fastdeploy.distributed.communication import use_custom_allreduce
|
|
|
|
use_custom_allreduce()
|
|
else:
|
|
raise RuntimeError(f"Not support device type: {self.device_config.device}")
|
|
|
|
set_random_seed(self.fd_config.model_config.seed)
|
|
# Construct model runner
|
|
self.model_runner: ModelRunnerBase = ModelRunner(
|
|
fd_config=self.fd_config,
|
|
device=self.device,
|
|
device_id=self.device_ids[self.local_rank % self.max_chips_per_node],
|
|
rank=self.rank,
|
|
local_rank=self.local_rank,
|
|
)
|
|
|
|
def exist_prefill(self):
|
|
"""
|
|
check whether prefill stage exist
|
|
"""
|
|
return self.model_runner.exist_prefill()
|
|
|
|
def determine_available_memory(self) -> int:
|
|
"""
|
|
Profiles the peak memory usage of the model to determine how much
|
|
memory can be used for KV cache without OOMs.
|
|
|
|
The engine will first conduct a profiling of the existing memory usage.
|
|
Then, it calculate the maximum possible number of GPU and CPU blocks
|
|
that can be allocated with the remaining free memory.
|
|
|
|
Tip:
|
|
You may limit the usage of GPU memory
|
|
by adjusting the `gpu_memory_utilization` parameter.
|
|
"""
|
|
# 1. Record memory state before profile run
|
|
start_time = time.perf_counter()
|
|
Gb = 1024**3
|
|
local_rank = self.local_rank % self.max_chips_per_node
|
|
paddle.device.cuda.reset_max_memory_reserved(local_rank)
|
|
paddle.device.cuda.reset_max_memory_allocated(local_rank)
|
|
paddle_reserved_mem_before_run = paddle.device.cuda.max_memory_reserved(local_rank)
|
|
paddle_allocated_mem_before_run = paddle.device.cuda.max_memory_allocated(local_rank) # not reserved
|
|
|
|
pynvml.nvmlInit()
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(int(self.device_ids[local_rank]))
|
|
before_run_meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
|
|
|
|
logger.info(
|
|
(
|
|
"Before running the profile, the memory usage info is as follows:",
|
|
f"\nDevice Total memory: {before_run_meminfo.total / Gb}",
|
|
f"\nDevice used memory: {before_run_meminfo.used / Gb}",
|
|
f"\nDevice free memory: {before_run_meminfo.free / Gb}",
|
|
f"\nPaddle reserved memory: {paddle_reserved_mem_before_run / Gb}",
|
|
f"\nPaddle allocated memory: {paddle_allocated_mem_before_run / Gb}",
|
|
)
|
|
)
|
|
|
|
# 2. Profile run
|
|
self.model_runner.profile_run()
|
|
set_random_seed(self.fd_config.model_config.seed)
|
|
|
|
# 3. Statistical memory information
|
|
paddle_reserved_mem_after_run = paddle.device.cuda.max_memory_reserved(local_rank)
|
|
paddle_allocated_mem_after_run = paddle.device.cuda.max_memory_allocated(local_rank)
|
|
|
|
model_block_memory_used = self.cal_theortical_kvcache()
|
|
paddle_peak_increase = paddle_reserved_mem_after_run - paddle_allocated_mem_before_run
|
|
|
|
paddle.device.cuda.empty_cache()
|
|
|
|
after_run_meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
|
|
pynvml.nvmlShutdown()
|
|
|
|
available_kv_cache_memory = (
|
|
after_run_meminfo.total * self.cache_config.gpu_memory_utilization
|
|
- after_run_meminfo.used
|
|
- paddle_peak_increase
|
|
)
|
|
available_kv_cache_memory += model_block_memory_used * self.parallel_config.total_block_num
|
|
|
|
end_time = time.perf_counter()
|
|
logger.info(
|
|
(
|
|
"After running the profile, the memory usage info is as follows:",
|
|
f"\nDevice Total memory: {after_run_meminfo.total / Gb}",
|
|
f"\nDevice used memory: {after_run_meminfo.used / Gb}",
|
|
f"\nDevice free memory: {after_run_meminfo.free / Gb}",
|
|
f"\nPaddle reserved memory: {paddle_reserved_mem_after_run / Gb}",
|
|
f"\nPaddle allocated memory: {paddle_allocated_mem_after_run / Gb}",
|
|
f"\nAvailable KV Cache meomory: {available_kv_cache_memory / Gb}",
|
|
f"Profile time: {end_time - start_time}",
|
|
)
|
|
)
|
|
|
|
return available_kv_cache_memory # return to calculate the block num in this device
|
|
|
|
def load_model(self) -> None:
|
|
"""Load model"""
|
|
self.model_runner.load_model()
|
|
|
|
def get_model(self) -> nn.Layer:
|
|
"""Get current model"""
|
|
return self.model_runner.get_model()
|
|
|
|
def initialize_cache(self, num_gpu_blocks: int) -> None:
|
|
"""Initizlize the KV Cache with accurate num_gpu_blocks"""
|
|
# accurate cache size
|
|
self.model_runner.update_share_input_block_num(num_gpu_blocks=num_gpu_blocks)
|
|
|
|
def execute_model(
|
|
self,
|
|
model_forward_batch: Optional[List[Request]] = None,
|
|
num_running_request: int = None,
|
|
) -> Optional[ModelRunnerOutput]:
|
|
""" """
|
|
output = self.model_runner.execute_model(model_forward_batch, num_running_request)
|
|
return output
|
|
|
|
def preprocess_new_task(self, req_dicts: List[Request], num_running_requests: int) -> None:
|
|
"""Process new requests and then start the decode loop
|
|
TODO(gongshaotian):The scheduler should schedule the handling of prefill,
|
|
and workers and modelrunners should not perceive it.
|
|
"""
|
|
if envs.ENABLE_V1_KVCACHE_SCHEDULER:
|
|
self.model_runner.insert_tasks_v1(req_dicts=req_dicts, num_running_requests=num_running_requests)
|
|
else:
|
|
self.model_runner.insert_prefill_inputs(req_dicts=req_dicts, num_running_requests=num_running_requests)
|
|
|
|
def graph_optimize_and_warm_up_model(self) -> None:
|
|
"""
|
|
Perform the warm-up and the graph optimization
|
|
"""
|
|
if self.model_runner.graph_opt_level >= 1:
|
|
self.model_runner.sot_warmup()
|
|
# Trigger cuda graph capture
|
|
self.model_runner.capture_model()
|
|
|
|
def check_health(self) -> bool:
|
|
""" """
|
|
return True
|
|
|
|
def cal_theortical_kvcache(self) -> int:
|
|
"""Calculate the block memory required"""
|
|
return self.model_runner.cal_theortical_kvcache()
|