问题描述
案例 1
框架:Tensorflow 2.5.0,Intel-Tensorflow 2.5.0
环境:Google Colab
我有一个由 LPOT 量化的成功量化模型,该模型将在不使用 LPOT API 的情况下运行以进行推理,因此我编写了以下推理代码:
with tf.compat.v1.Session() as sess:
tf.compat.v1.saved_model.loader.load(sess,['serve'],model)
output = sess.graph.get_tensor_by_name(output_tensor_name)
predictions = sess.run(output,{input_tensor_name: x})
mse = tf.reduce_mean(tf.keras.losses.mean_squared_error(y,predictions))
print(mse.eval())
运行 predictions = sess.run(output,{input_tensor_name: x})
行时:
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _do_call(self,fn,*args)
1374 try:
-> 1375 return fn(*args)
1376 except errors.OpError as e:
7 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _run_fn(Feed_dict,fetch_list,target_list,options,run_Metadata)
1359 return self._call_tf_sessionrun(options,Feed_dict,-> 1360 target_list,run_Metadata)
1361
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self,run_Metadata)
1452 fetch_list,-> 1453 run_Metadata)
1454
InternalError: Missing 0-th output from {{node model/layer_1/Conv2D_eightbit_requantize}}
During handling of the above exception,another exception occurred:
InternalError Traceback (most recent call last)
<ipython-input-6-2bddd853d111> in <module>()
2 tf.compat.v1.saved_model.loader.load(sess,model)
3 output = sess.graph.get_tensor_by_name(output_tensor_name)
----> 4 predictions = sess.run(output,{input_tensor_name: x[:64]}) # 64,257,60,1
5 mse = tf.reduce_mean(tf.keras.losses.mean_squared_error(y[:64],predictions))
6 print(mse.eval())
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in run(self,fetches,run_Metadata)
966 try:
967 result = self._run(None,options_ptr,--> 968 run_Metadata_ptr)
969 if run_Metadata:
970 proto_data = tf_session.TF_GetBuffer(run_Metadata_ptr)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _run(self,handle,run_Metadata)
1189 if final_fetches or final_targets or (handle and Feed_dict_tensor):
1190 results = self._do_run(handle,final_targets,final_fetches,-> 1191 Feed_dict_tensor,run_Metadata)
1192 else:
1193 results = []
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _do_run(self,run_Metadata)
1367 if handle is None:
1368 return self._do_call(_run_fn,Feeds,targets,-> 1369 run_Metadata)
1370 else:
1371 return self._do_call(_prun_fn,fetches)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _do_call(self,*args)
1392 '\nsession_config.graph_options.rewrite_options.'
1393 'disable_Meta_optimizer = True')
-> 1394 raise type(e)(node_def,op,message)
1395
1396 def _extend_graph(self):
InternalError: Missing 0-th output from node model/layer_1/Conv2D_eightbit_requantize (defined at <ipython-input-6-2bddd853d111>:2)
此错误发生安装或未安装 Intel-Tensorflow==2.5.0
,也不会在显式设置 os.environ['TF_ENABLE_ONednN_OPTS'] = '1'
时解决。
另一方面,当我使用 Python 3.6.8 64-bit base: Conda
在 VS Code 中运行相同的代码时,它返回与案例 2 中相同的错误消息。
案例 2
框架:Tensorflow 2.4.0,Intel-Tensorflow 2.4.0
环境:Google Colab
此案例运行良好并打印出预测的 MSE 损失,但是当我卸载 Intel-Tensorflow 2.4.0
并仅使用官方 Tensorflow 运行时,同时在案例 1 中运行同一行({ {1}}):
predictions = sess.run(output,{input_tensor_name: x})
即使明确设置了 ---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _do_call(self,run_Metadata)
1357 # Ensure any changes to the graph are reflected in the runtime.
-> 1358 self._extend_graph()
1359 return self._call_tf_sessionrun(options,/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _extend_graph(self)
1397 with self._graph._session_run_lock(): # pylint: disable=protected-access
-> 1398 tf_session.ExtendSession(self._session)
1399
InvalidArgumentError: No OpKernel was registered to support Op 'QuantizedMatMulWithBiasAndDequantize' used by {{node model/dense/tensordot/MatMul_eightbit_requantize}} with these attrs: [input_quant_mode="MIN_FirsT",T1=DT_QUINT8,Toutput=DT_FLOAT,T2=DT_QINT8,Tbias=DT_QINT32,transpose_a=false,transpose_b=false]
Registered devices: [cpu]
Registered kernels:
<no registered kernels>
[[model/dense/tensordot/MatMul_eightbit_requantize]]
During handling of the above exception,another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-6-2bddd853d111> in <module>()
2 tf.compat.v1.saved_model.loader.load(sess,message)
1395
1396 def _extend_graph(self):
InvalidArgumentError: No OpKernel was registered to support Op 'QuantizedMatMulWithBiasAndDequantize' used by node model/dense/tensordot/MatMul_eightbit_requantize (defined at <ipython-input-6-2bddd853d111>:2) with these attrs: [input_quant_mode="MIN_FirsT",transpose_b=false]
Registered devices: [cpu]
Registered kernels:
<no registered kernels>
[[model/dense/tensordot/MatMul_eightbit_requantize]]
,错误仍然存在。
结论
我相信这两种情况都是由相同类型的错误引起的,即没有注册 OpKernel 来支持 Op ...
我了解到,安装官方 os.environ['TF_ENABLE_ONednN_OPTS'] = '1'
并设置环境变量 Tensorflow v2.5
(reference) 后,量化模型应该在支持 onednN 的情况下运行。但是在 v2.4 和 v2.5 中似乎都不是这种情况。
我的问题是如何在无需安装 TF_ENABLE_ONednN_OPTS=1
的情况下拥有支持 onednN 的官方 Tensorflow 2.5
环境?或者为什么 Intel-Tensorflow
不起作用?谢谢。
解决方法
LPOT 在英特尔® AI Analytics Toolkit 中发布,可与英特尔 TensorFlow 优化配合使用。 LPOT 可以在任何 Intel CPU 上运行以量化 AI 模型。 Intel Optimized TensorFlow 2.5.0 需要在运行 LPOT 量化或部署量化模型之前设置环境变量 TF_ENABLE_MKL_NATIVE_FORMAT=0
。
有关详细信息,请参阅 this。
能否请您检查一下您是否在 Tensorflow 2.4 中量化了模型并在 Tensorflow 2.5 上运行推理?模型未在 Tensorflow 2.5 中运行而在 Tensorflow 2.4 中运行的合理解释是,支持 Tensorflow 2.5 的算子可能不支持在 Tensorflow 2.4 中创建的模型。