Repository: pytorch
Issue: TestAdagrad.test_row_wise_sparse_adagrad intermittently fails health check
Labels: caffe2

Sample log: https://ci.pytorch.org/jenkins/job/caffe2-builds/job/py2-devtoolset7-rocmrpm-centos7.5-test/846//consoleFull

```
18:23:25 =================================== FAILURES ===================================
18:23:25 ___________________ TestAdagrad.test_row_wise_sparse_adagrad ___________________
18:23:25 
18:23:25 self = <caffe2.python.operator_test.adagrad_test.TestAdagrad testMethod=test_row_wise_sparse_adagrad>
18:23:25 
18:23:25     @settings(suppress_health_check=[HealthCheck.filter_too_much])
18:23:25 >   @given(inputs=hu.tensors(n=2),
18:23:25            lr=st.floats(min_value=0.01, max_value=0.99,
18:23:25                         allow_nan=False, allow_infinity=False),
18:23:25            epsilon=st.floats(min_value=0.01, max_value=0.99,
18:23:25                              allow_nan=False, allow_infinity=False),
18:23:25            data_strategy=st.data(),
18:23:25            **hu.gcs)
18:23:25     def test_row_wise_sparse_adagrad(self, inputs, lr, epsilon,
18:23:25                                      data_strategy, gc, dc):
18:23:25 E                                    FailedHealthCheck: Data generation is extremely slow: Only produced 7 valid examples in 1.00 seconds (108 invalid ones and 0 exceeded maximum size). Try decreasing size of the data you're generating (with e.g.max_size or max_leaves parameters).
18:23:25 E                                    See https://hypothesis.readthedocs.io/en/latest/healthchecks.html for more information about this. If you want to disable just this health check, add HealthCheck.too_slow to the suppress_health_check settings for this test.
18:23:25 
18:23:25 /usr/local/caffe2/lib/python2.7/site-packages/caffe2/python/operator_test/adagrad_test.py:168: FailedHealthCheck
18:23:25 ---------------------------------- Hypothesis ----------------------------------
18:23:25 Trying example: test_row_wise_sparse_adagrad(self=<caffe2.python.operator_test.adagrad_test.TestAdagrad testMethod=test_row_wise_sparse_adagrad>, inputs=[array([0.], dtype=float32), array([0.], dtype=float32)], lr=0.01, epsilon=0.01, data_strategy=data(...), gc=, dc=[, device_type: 6])
18:23:25 Trying example: test_row_wise_sparse_adagrad(self=<caffe2.python.operator_test.adagrad_test.TestAdagrad testMethod=test_row_wise_sparse_adagrad>, inputs=[array([[[[ 0.275142,  0.275142,  0.618039, -0.606138,  0.275142]],
18:23:25  
18:23:25          [[ 0.275142,  0.275142,  0.806599,  0.275142,  0.275142]]],
18:23:25  
18:23:25  
18:23:25         [[[ 0.275142,  0.275142, -0.672307,  0.275142,  0.275142]],
18:23:25  
18:23:25          [[-0.692087, -0.71764 ,  0.275142,  0.275142,  0.275142]]]],
18:23:25        dtype=float32),
18:23:25  array([[[[-0.250365, -0.250365, -0.250365, -0.250365, -0.250365]],
18:23:25  
18:23:25          [[-0.250365, -0.250365, -0.250365,  0.260523, -0.250365]]],
18:23:25  
18:23:25  
18:23:25         [[[-0.905285, -0.250365, -0.250365, -0.250365, -0.250365]],
18:23:25  
18:23:25          [[-0.250365, -0.250365, -0.250365, -0.250365, -0.250365]]]],
18:23:25        dtype=float32)], lr=0.07642670198526025, epsilon=0.5954366965838439, data_strategy=data(...), gc=, dc=[, device_type: 6])
```

It's worth figuring out why this is intermittent; our Hypothesis test generation should be deterministic, which means we should ALWAYS fail the health check, if our generation strategy is bad.