mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
a47652379f
Summary: Same fix as https://github.com/caffe2/caffe2/pull/249, but for SparseAdagrad. Also update the tests for both ops to test this functionality. Closes https://github.com/caffe2/caffe2/pull/675 Differential Revision: D5148750 Pulled By: akyrola fbshipit-source-id: d30b722429bc547fd53400c1a29e4ee9e2e6ed18
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
import functools
|
|
|
|
import hypothesis
|
|
from hypothesis import given, strategies as st
|
|
import numpy as np
|
|
|
|
from caffe2.python import core
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
|
|
|
|
class TestAdagrad(hu.HypothesisTestCase):
|
|
|
|
@staticmethod
|
|
def ref_adagrad(param_in, mom_in, grad, lr, epsilon):
|
|
mom_out = mom_in + np.square(grad)
|
|
grad_adj = lr * grad / (np.sqrt(mom_out) + epsilon)
|
|
param_out = param_in + grad_adj
|
|
return (param_out, mom_out)
|
|
|
|
@given(inputs=hu.tensors(n=3),
|
|
lr=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),
|
|
**hu.gcs)
|
|
def test_adagrad(self, inputs, lr, epsilon, gc, dc):
|
|
param, momentum, grad = inputs
|
|
lr = np.array([lr], dtype=np.float32)
|
|
|
|
op = core.CreateOperator(
|
|
"Adagrad",
|
|
["param", "momentum", "grad", "lr"],
|
|
["param", "momentum"],
|
|
epsilon=epsilon,
|
|
device_option=gc,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
gc, op,
|
|
[param, momentum, grad, lr],
|
|
functools.partial(self.ref_adagrad, epsilon=epsilon))
|
|
|
|
@given(inputs=hu.tensors(n=3),
|
|
lr=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),
|
|
data_strategy=st.data(),
|
|
**hu.gcs)
|
|
def test_sparse_adagrad(self, inputs, lr, epsilon,
|
|
data_strategy, gc, dc):
|
|
param, momentum, grad = inputs
|
|
momentum = np.abs(momentum)
|
|
lr = np.array([lr], dtype=np.float32)
|
|
|
|
# Create an indexing array containing values which index into grad
|
|
indices = data_strategy.draw(
|
|
hu.tensor(dtype=np.int64,
|
|
elements=st.sampled_from(np.arange(grad.shape[0]))),
|
|
)
|
|
hypothesis.note('indices.shape: %s' % str(indices.shape))
|
|
|
|
# For now, the indices must be unique
|
|
hypothesis.assume(np.array_equal(np.unique(indices.flatten()),
|
|
np.sort(indices.flatten())))
|
|
|
|
# Sparsify grad
|
|
grad = grad[indices]
|
|
|
|
op = core.CreateOperator(
|
|
"SparseAdagrad",
|
|
["param", "momentum", "indices", "grad", "lr"],
|
|
["param", "momentum"],
|
|
epsilon=epsilon,
|
|
device_option=gc)
|
|
|
|
def ref_sparse(param, momentum, indices, grad, lr):
|
|
param_out = np.copy(param)
|
|
momentum_out = np.copy(momentum)
|
|
for i, index in enumerate(indices):
|
|
param_out[index], momentum_out[index] = self.ref_adagrad(
|
|
param[index], momentum[index], grad[i], lr, epsilon)
|
|
return (param_out, momentum_out)
|
|
|
|
self.assertReferenceChecks(
|
|
gc, op,
|
|
[param, momentum, indices, grad, lr],
|
|
ref_sparse)
|