一、問題源起
從以下的異常堆疊可以看到是BLAS程式集初始化失敗，可以看到是執行MatMul的時候發生的異常，基本可以斷定可能資料集太大導致memory不夠用了。
2021-08-10 16:38:04.917501: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2021-08-10 16:38:04.960048: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2021-08-10 16:38:04.986898: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2021-08-10 16:38:04.992366: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2021-08-10 16:38:04.992389: W tensorflow/stream_executor/stream.cc:1455] attempting to perform BLAS operation using StreamExecutor without BLAS support
Traceback (most recent call last):
File "/home/mango/PycharmProjects/DeepLearing/minist_conv.py", line 32, in <module>
model.fit(train_images, train_labels, epochs=5, batch_size=64)
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/keras/engine/training.py", line 1183, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py", line 950, in _call
return self._stateless_fn(*args, **kwds)
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py", line 3023, in __call__
return graph_function._call_flat(
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py", line 1960, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py", line 591, in call
outputs = execute.execute(
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError: Blas xGEMM launch failed : a.shape=[1,64,576], b.shape=[1,576,64], m=64, n=64, k=576
[[node sequential/dense/MatMul (defined at home/mango/PycharmProjects/DeepLearing/minist_conv.py:32) ]] [Op:__inference_train_function_993]
Function call stack:
train_function
二、開發環境
mango@mango-ubuntu:~$ /usr/local/cuda/bin/nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Wed_Jul_14_19:41:19_PDT_2021
Cuda~~ compilation tools, release 11.4, V11.4.100==
Build cuda_11.4.r11.4/compiler.30188945_0
mango@mango-ubuntu:~$ tail -n 10 /usr/include/cudnn_version.h
#ifndef CUDNN_VERSION_H_
#define CUDNN_VERSION_H_
#define CUDNN_MAJOR 8
#define CUDNN_MINOR 2
#define CUDNN_PATCHLEVEL 2
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#endif /* CUDNN_VERSION_H */
mango@mango-ubuntu:~$ python3 --version
Python 3.9.5
mango@mango-ubuntu:~$ nvidia-smi
Tue Aug 10 19:57:58 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| N/A 54C P0 N/A / N/A | 329MiB / 2002MiB | 9% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1818 G /usr/lib/xorg/Xorg 186MiB |
| 0 N/A N/A 2002 G /usr/bin/gnome-shell 45MiB |
| 0 N/A N/A 3435 G ...AAAAAAAAA= --shared-files 75MiB |
| 0 N/A N/A 6016 G python3 13MiB |
+-----------------------------------------------------------------------------+
mango@mango-ubuntu:~$ python3
Python 3.9.5 (default, May 11 2021, 08:20:37)
[GCC 10.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2021-08-10 18:33:05.917520: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
>>> tf.__version__
'2.5.0'
>>>
三、Tensorflow針對GPU記憶體的分配策略
By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmentation.
預設情況下，為了通過減少記憶體碎片更有效地利用裝置上相對寶貴的GPU記憶體資源，TensorFlow程式會使用所有可見的GPU。
In some cases it is desirable for the process to only allocate a subset of the available memory, or to only grow the memory usage as is needed by the process. TensorFlow provides two methods to control this.
在某些情況下，程式只分配可用記憶體的一個子集，或者只根據程式的需要增加記憶體使用量。TensorFlow提供了兩種方法來控制這種情況。
The first option is to turn on memory growth by calling tf.config.experimental.set_memory_growth, which attempts to allocate only as much GPU memory as needed for the runtime allocations: it starts out allocating very little memory, and as the program gets run and more GPU memory is needed, the GPU memory region is extended for the TensorFlow process. Memory is not released since it can lead to memory fragmentation. To turn on memory growth for a specific GPU, use the following code prior to allocating any tensors or executing any ops.
第一種選擇是通過呼叫tf.config.experimental.set_memory_growth來開啟記憶體增長，它嘗試只分配執行時所需的GPU記憶體:它開始分配很少的記憶體，當程式執行時需要更多的GPU記憶體時，GPU記憶體區域會進一步擴充套件增大。記憶體不會被釋放，因為這會導致記憶體碎片。為了開啟特定GPU的記憶體增長，在分配任何張量或執行任何操作之前，使用以下程式碼。
gpus = tf.config.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
Another way to enable this option is to set the environmental variable TF_FORCE_GPU_ALLOW_GROWTH to true. This configuration is platform specific.
啟用該選項的另一種方法是將環境變數TF_FORCE_GPU_ALLOW_GROWTH設定為true。此配置是特定於平臺的。
The second method is to configure a virtual GPU device with tf.config.experimental.set_virtual_device_configuration and set a hard limit on the total memory to allocate on the GPU.
This is useful if you want to truly bound the amount of GPU memory available to the TensorFlow process. This is common practice for local development when the GPU is shared with other applications such as a workstation GUI.
第二種方法是使用tf.config.experimental.set_virtual_device_configuration配置虛擬GPU裝置,並設定GPU上可分配的總記憶體的硬限制。
如果你想真正將GPU記憶體的數量繫結到TensorFlow程式中，這是非常有用的。當GPU與其他應用程式(如工作站GUI)共享時，這是本地開發的常見做法。
gpus = tf.config.list_physical_devices('GPU')
if gpus:
# Restrict TensorFlow to only allocate 1GB of memory on the first GPU
try:
tf.config.set_logical_device_configuration(
gpus[0],
[tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
logical_gpus = tf.config.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
四、問題分析驗證
通過上邊對TensorFlow文件的分析，預設情況下會佔用所有的GPU記憶體，但是TensorFlow提供了兩種方式可以靈活的控制記憶體的分配策略；
我們可以直接設定GPU記憶體按需動態分配
import tensorflow as tf
physical_gpus = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_gpus[0], True)
通過以下命令可以看到執行過程中GPU記憶體的佔用最高為697M
mango@mango-ubuntu:~$ while true; do nvidia-smi; sleep 0.2; done;
Tue Aug 10 20:30:58 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| N/A 58C P0 N/A / N/A | 1026MiB / 2002MiB | 72% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1818 G /usr/lib/xorg/Xorg 186MiB |
| 0 N/A N/A 2002 G /usr/bin/gnome-shell 45MiB |
| 0 N/A N/A 3435 G ...AAAAAAAAA= --shared-files 73MiB |
| 0 N/A N/A 6016 G python3 13MiB |
| 0 N/A N/A 13829 C /usr/bin/python3.9 697MiB |
+-----------------------------------------------------------------------------+
我們也可以限制最多使用1024M的GPU記憶體
import tensorflow as tf
physical_gpus = tf.config.list_physical_devices('GPU')
tf.config.set_logical_device_configuration(physical_gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
同樣通過命令可以看到執行過程中GPU記憶體的佔用最高為1455M
mango@mango-ubuntu:~$ while true; do nvidia-smi; sleep 0.2; done;
Tue Aug 10 20:31:24 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| N/A 58C P0 N/A / N/A | 1784MiB / 2002MiB | 74% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1818 G /usr/lib/xorg/Xorg 186MiB |
| 0 N/A N/A 2002 G /usr/bin/gnome-shell 46MiB |
| 0 N/A N/A 3435 G ...AAAAAAAAA= --shared-files 72MiB |
| 0 N/A N/A 6016 G python3 13MiB |
| 0 N/A N/A 13570 C /usr/bin/python3.9 1455MiB |
+-----------------------------------------------------------------------------+
五、GPU分配策略分析
通過四中的測試結果可得
- 預設的分配策略會佔用所有的記憶體，並且執行中不會進行釋放，如果訓練資料量比較打很容易記憶體不夠用；
- 限制最大使用記憶體，測試佔用記憶體比設定的大，這個可能跟訓練中間使用的模型和操作的複雜程度有關係，需要根據具體的業務場景設定合適的值；但是要注意不能設定大了，否則還是會報錯，但是設定小了只是執行的慢一些罷了；
- 設定記憶體按需分配可能是一個相對比較中庸的方案，感覺可能是一個更好的方案，不知道TensorFlow為什麼沒有設定為預設值，留作一個問題，後續有新的認知的話再補充；
六、擴充套件
單GPU模擬多GPU環境
當我們的本地開發環境只有一個GPU，但卻需要編寫多GPU的程式在工作站上進行訓練任務時，TensorFlow為我們提供了一個方便的功能，可以讓我們在本地開發環境中建立多個模擬GPU，從而讓多GPU的程式除錯變得更加方便。以下程式碼在實體GPU GPU:0 的基礎上建立了兩個視訊記憶體均為2GB的虛擬GPU。
gpus = tf.config.list_physical_devices('GPU')
if gpus:
# Create 2 virtual GPUs with 1GB memory each
try:
tf.config.set_logical_device_configuration(
gpus[0],
[tf.config.LogicalDeviceConfiguration(memory_limit=1024),
tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
logical_gpus = tf.config.list_logical_devices('GPU')
print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
多GPU的資料並行
使用 tf.distribute.Strategy可以將模型拷貝到每個GPU上，然後將訓練資料分批在不同的GPU上執行，達到資料並行。
tf.debugging.set_log_device_placement(True)
gpus = tf.config.list_logical_devices('GPU')
strategy = tf.distribute.MirroredStrategy(gpus)
with strategy.scope():
inputs = tf.keras.layers.Input(shape=(1,))
predictions = tf.keras.layers.Dense(1)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=predictions)
model.compile(loss='mse',
optimizer=tf.keras.optimizers.SGD(learning_rate=0.2))