Unit Tests¶
-Unit tests include comparing a forward operator’s implementations on different devices, comparing a backward operator’s implementation on different devices, and a scaling test for the backward operator. Here, we introduce the unit tests for MulOp.
+Unit tests for an operator include
+
+- comparing a forward operator’s implementations on different devices,
+- comparing a backward operator’s implementation on different devices, and
+- a scaling test for the backward operator.
+
+Here, we introduce the unit tests for MulOp.
+
+Testing Forward Operators¶
+A forward operator unit test inherits unittest.TestCase and defines metaclass __metaclass__ = OpTestMeta. More concrete tests are performed in OpTestMeta. Testing a forward operator requires the following:
+
+- Defining input, output and relevant attributes in
setUp method.
+- Generating random input data.
+- Implementing the same computation logic in a Python script:
+
+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+class TestMulOp(unittest.TestCase):
+ __metaclass__ = OpTestMeta
+
+ def setUp(self):
+ self.type = "mul"
+ self.inputs = {
+ 'X': np.random.random((32, 84)).astype("float32"),
+ 'Y': np.random.random((84, 100)).astype("float32")
+ }
+ self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+
+
+Get its output, and compare it with the forward operator’s own output.
+The code above first loads required packages. In addition, we have
+
+self.type = "mul" defines the type that is identical to what the operator’s registered type.self.inputs defines input, with type numpy.array and initializes it.self.outputs defines output and completes the same operator computation in the Python script, and returns its result from the Python script.
+
+
+Testing Backward Operators¶
+A backward operator unit test inherits GradientChecker, which inherits unittest.TestCase. As a result, a backward operator unit test needs to be have the prefix test_.
+class TestMulGradOp(GradientChecker):
+ def setUp(self):
+ self.op = create_op("mul")
+ self.inputs = {
+ 'X': np.random.random((32, 84)).astype("float32"),
+ 'Y': np.random.random((84, 100)).astype("float32")
+ }
+
+ def test_cpu_gpu_compare(self):
+ self.compare_grad(self.op, self.inputs)
+
+ def test_normal(self):
+ # mul op will enlarge the relative error
+ self.check_grad(
+ self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+
+ def test_ignore_x(self):
+ self.check_grad(
+ self.op,
+ self.inputs, ["Y"],
+ "Out",
+ max_relative_error=0.5,
+ no_grad_set={"X"})
+
+ def test_ignore_y(self):
+ self.check_grad(
+ self.op,
+ self.inputs, ["X"],
+ "Out",
+ max_relative_error=0.5,
+ no_grad_set={"Y"})
+
+
+Some key points in the code above include:
+
+create_op("mul") creates the backward operator’s corresponding forward operator.compare_grad compares results between utilizing the CPU and the GPU.test_normal calls check_grad to validate scaling tests’ correctness and stability through numeric methods.
+- The first variable
self.op denotes the forward operator.
+- The second variable
self.inputs denotes the input dictionary, which has its key value identical to its ProtoMaker definitions.
+- The third variable
["X", "Y"] appoints X and Y to be scale tested.
+- The fourth variable
"Out" points to the network’s final output target Out.
+
+test_ignore_x and test_ignore_ybranches test the cases where there is only one scaling input.
+
+
+Compiling and Running¶
+Any new unit testing file of the format test_*.py added to the director python/paddle/v2/framework/tests is automatically added to the project to compile.
+Note that unlike the compile test for Ops, running unit tests requires compiling the entire project and requires compiling with flag WITH_TESTING on i.e. cmake paddle_dir -DWITH_TESTING=ON.
+After successfully compiling the project, run the following command to run unit tests:
+make test ARGS="-R test_mul_op -V"
+
+
+Or,
+ctest -R test_mul_op
+
+
+
+*_op.h (if applicable), *_op.cc, and *_op.cu (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.REGISTER_OP(B, ...) in A_op.cc will cause unit testing failures.*_op.cu file, or else unit tests will fail.*_op.* can be created to store them, such as gather.h.Parameters","Design Doc: ProgramDesc","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Refactorization Overview","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Interaction between C++ and Python","Background","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","GET STARTED","RNN Models","RNN Configuration","Build PaddlePaddle from Source Code and Run Unit Test","Contribute Code","Write New Layers","How to write a new operator","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Cluster bootstrapping tool survey","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Image Classification Tutorial","Model Zoo - ImageNet","TUTORIALS","Quick Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation 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