ann-qin-lu opened a new issue #20959:
URL: https://github.com/apache/incubator-mxnet/issues/20959
## Description
GPU memory leak when using gluon.data.DataLoader after upgrading
Cuda-11.1/Cudnn-8.2.x (also tested with latest Cuda11.5+CuDnn8.3.x but still
leaking). Minimal code to repro attached below.
No memory leak with older Cuda version (Cuda-10.1 + CuDnn-7.6.5).
### Error Message
gpu memory keeps increasing during training.
## To Reproduce
(If you developed your own code, please provide a short script that
reproduces the error. For existing examples, please provide link.)
```
import mxnet.gluon as gl
import mxnet as mx
import gc
if __name__ == "__main__":
gpu_ctx = mx.gpu()
model = gl.nn.Embedding(10, 5)
model.initialize(ctx=gpu_ctx)
X = mx.random.uniform(shape=(1000, 3))
dataset = mx.gluon.data.dataset.ArrayDataset(X)
num_workers_list = [0, 4, 8]
for num_workers in num_workers_list:
for epoch in range(5):
dataset = mx.gluon.data.dataset.ArrayDataset(X)
data_loader = gl.data.DataLoader(
dataset,
batch_size=1,
num_workers=num_workers,
)
for batch in data_loader:
# move data to gpu
data_gpu = batch.copyto(mx.gpu())
# forward
l = model(data_gpu)
# force immediate compute
l.asnumpy()
# gc & gpu_ctx.empty_cache
mx.nd.waitall()
del dataset
del data_loader
gc.collect()
gpu_ctx.empty_cache()
mx.nd.waitall()
a, b = mx.context.gpu_memory_info(0)
print(f"num_workers: {num_workers} epoch {epoch}: "
f"current gpu memory {(b - a) / (1024 * 1024 * 1024)} GB, "
f"Total gpu memory {b / (1024 * 1024 * 1024)} GB.")
```
### Steps to reproduce
(Paste the commands you ran that produced the error.)
```
### Output with MXNet-1.9 built with Cuda11.1 CuDnn 8.2.0 (Memory leak when
`num_workers` > 0)
(also tested with the latest Cuda11.5+CuDnn8.3.x)
num_workers: 0 epoch 0: current memory 1.381591796875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 1: current memory 1.381591796875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 2: current memory 1.381591796875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 3: current memory 1.381591796875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 4: current memory 1.381591796875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 0: current memory 1.483154296875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 1: current memory 1.582763671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 2: current memory 1.683349609375 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 3: current memory 1.782958984375 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 4: current memory 1.880615234375 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 0: current memory 1.980224609375 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 1: current memory 2.080810546875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 2: current memory 2.180419921875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 3: current memory 2.281982421875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 4: current memory 2.380615234375 GB, Total memory
15.78173828125 GB.
### Output with MXNet-1.9 built with Cuda10.1 CuDnn 7.6.5 (No memory leak)
num_workers: 0 epoch 0: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 1: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 2: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 3: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 0 epoch 4: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 0: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 1: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 2: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 3: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 4 epoch 4: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 0: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 1: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 2: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 3: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
num_workers: 8 epoch 4: current memory 1.301513671875 GB, Total memory
15.78173828125 GB.
```
## What have you tried to solve it?
1. python gc clean doesn't help
2. upgrade cuda/cudnn to least version doesn't help
## Environment
***We recommend using our script for collecting the diagnostic information
with the following command***
`curl --retry 10 -s
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
| python3`
<details>
<summary>Environment Information</summary>
```
# Paste the diagnose.py command output here
```
```
----------Python Info----------
Version : 3.6.14
Compiler : GCC 7.5.0
Build : ('default', 'Feb 19 2022 10:06:15')
Arch : ('64bit', 'ELF')
------------Pip Info-----------
No corresponding pip install for current python.
----------MXNet Info-----------
Version : 1.9.0
Directory :
/efs-storage/debug_log/test-runtime/lib/python3.6/site-packages/mxnet
Commit hash file
"/efs-storage/debug_log/test-runtime/lib/python3.6/site-packages/mxnet/COMMIT_HASH"
not found. Not installed from pre-built package or built from source.
Library : ['/efs-storage/debug_log/test-runtime//lib/libmxnet.so']
Build features:
✔ CUDA
✔ CUDNN
✖ NCCL
✖ CUDA_RTC
✖ TENSORRT
✔ CPU_SSE
✔ CPU_SSE2
✔ CPU_SSE3
✖ CPU_SSE4_1
✖ CPU_SSE4_2
✖ CPU_SSE4A
✖ CPU_AVX
✖ CPU_AVX2
✔ OPENMP
✖ SSE
✖ F16C
✖ JEMALLOC
✔ BLAS_OPEN
✖ BLAS_ATLAS
✖ BLAS_MKL
✖ BLAS_APPLE
✔ LAPACK
✔ MKLDNN
✔ OPENCV
✖ CAFFE
✖ PROFILER
✖ DIST_KVSTORE
✖ CXX14
✖ INT64_TENSOR_SIZE
✔ SIGNAL_HANDLER
✖ DEBUG
✖ TVM_OP
----------System Info----------
Platform : Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with
system : Linux
node : ip-10-0-10-233.ec2.internal
release : 4.14.232-177.418.amzn2.x86_64
version : #1 SMP Tue Jun 15 20:57:50 UTC 2021
----------Hardware Info----------
machine : x86_64
processor : x86_64
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 2630.103
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.04
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 46080K
NUMA node0 CPU(s): 0-15,32-47
NUMA node1 CPU(s): 16-31,48-63
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge
mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm
constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf
pni pclmulqdq monitor est ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt
tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm
3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2
erms invpcid rtm rdseed adx xsaveopt ida
```
</details>
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