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8bd0522c20
Summary: These GPU paths are probably even buggier than the CPU paths for sparse gradients with duplicate indices. Both paths cause multiple momentum updates in a single iteration, but only the GPU path is non-deterministic. Depending on how we decide to address the issues on the CPU path, pooyadavoodi has a good idea for how to match dense behavior with the sparse GPU ops. Closes https://github.com/caffe2/caffe2/pull/254 Reviewed By: bwasti Differential Revision: D4871680 Pulled By: dzhulgakov fbshipit-source-id: 220be57a0f699a22ea85ed4f7022d92d362d06b3
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import functools
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from hypothesis import given, strategies as st
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import numpy as np
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestAdagrad(hu.HypothesisTestCase):
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@staticmethod
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def ref_adagrad(param_in, mom_in, grad, lr, epsilon):
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mom_out = mom_in + np.square(grad)
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grad_adj = lr * grad / (np.sqrt(mom_out) + epsilon)
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param_out = param_in + grad_adj
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return (param_out, mom_out)
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@given(inputs=hu.tensors(n=3),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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def test_adagrad(self, inputs, lr, epsilon, gc, dc):
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param, momentum, grad = inputs
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adagrad",
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["param", "momentum", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc,
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)
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self.assertReferenceChecks(
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gc, op,
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[param, momentum, grad, lr],
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functools.partial(self.ref_adagrad, epsilon=epsilon))
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@given(inputs=hu.tensors(n=3),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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def test_sparse_adagrad(self, inputs, lr, epsilon, gc, dc):
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param, momentum, grad = inputs
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indices = np.arange(grad.shape[0])
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indices = indices[indices % 2 == 0]
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grad = grad[indices]
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"SparseAdagrad",
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["param", "momentum", "indices", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc)
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def ref_sparse(param, momentum, indices, grad, lr):
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param_out = np.copy(param)
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momentum_out = np.copy(momentum)
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for i, index in enumerate(indices):
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param_out[index], momentum_out[index] = self.ref_adagrad(
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param[index], momentum[index], grad[i], lr, epsilon)
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return (param_out, momentum_out)
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self.assertReferenceChecks(
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gc, op,
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[param, momentum, indices, grad, lr],
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ref_sparse)
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