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| | import numpy as np |
| | import hypothesis.strategies as st |
| | import unittest |
| | import caffe2.python.hypothesis_test_util as hu |
| | from caffe2.python import core, workspace |
| | from hypothesis import given |
| | import caffe2.python.ideep_test_util as mu |
| |
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|
| | @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") |
| | class TestAdamOps(hu.HypothesisTestCase): |
| | @given(inputs=hu.tensors(n=4), |
| | ITER=st.integers(min_value=0, max_value=10000), |
| | LR=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | beta1=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | beta2=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | epsilon=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | **mu.gcs) |
| | def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): |
| | param, mom1, mom2, grad = inputs |
| | ITER = np.array([ITER], dtype=np.int64) |
| | LR = np.array([LR], dtype=np.float32) |
| | mom2 = np.absolute(mom2) |
| | op = core.CreateOperator( |
| | "Adam", |
| | ["param", "mom1", "mom2", "grad", "lr", "iter"], |
| | ["output_param", "output_mom1", "output_mom2"], |
| | beta1=beta1, beta2=beta2, epsilon=epsilon) |
| | |
| | input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do} |
| |
|
| | self.assertDeviceChecks( |
| | dc, op, |
| | [param, mom1, mom2, grad, LR, ITER], |
| | [0], |
| | input_device_options=input_device_options, |
| | threshold=0.001) |
| |
|
| | @given(inputs=hu.tensors(n=4), |
| | ITER=st.integers(min_value=0, max_value=10000), |
| | LR=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | beta1=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | beta2=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | epsilon=st.floats(min_value=0.01, max_value=0.99, |
| | allow_nan=False, allow_infinity=False), |
| | **mu.gcs) |
| | def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): |
| | param, mom1, mom2, grad = inputs |
| | ITER = np.array([ITER], dtype=np.int64) |
| | LR = np.array([LR], dtype=np.float32) |
| | mom2 = np.absolute(mom2) |
| |
|
| | op = core.CreateOperator( |
| | "Adam", |
| | ["param", "mom1", "mom2", "grad", "lr", "iter"], |
| | ["output_param", "output_mom1", "output_mom2", "output_grad"], |
| | beta1=beta1, beta2=beta2, epsilon=epsilon) |
| |
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| | |
| | input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do} |
| |
|
| | self.assertDeviceChecks( |
| | dc, op, |
| | [param, mom1, mom2, grad, LR, ITER], |
| | [0], |
| | input_device_options=input_device_options, |
| | threshold=0.001) |
| |
|
| | if __name__ == "__main__": |
| | unittest.main() |
| |
|
