/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/adam_test.py (21559B)
import functools
import hypothesis
from hypothesis import given
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
class TestAdam(hu.HypothesisTestCase):
@staticmethod
def ref_adam(param, mom1, mom2, grad, LR, ITER,
beta1, beta2, epsilon, output_grad=False):
t = ITER + 1
corrected_local_rate = np.sqrt(1 - np.power(beta2, t)) / \
(1 - np.power(beta1, t))
mom1_out = (beta1 * mom1) + (1 - beta1) * grad
mom2_out = (beta2 * mom2) + (1 - beta2) * np.square(grad)
grad_out = corrected_local_rate * mom1_out / \
(np.sqrt(mom2_out) + epsilon)
param_out = param + LR * grad_out
if output_grad:
return param_out, mom1_out, mom2_out, grad_out
else:
return param_out, mom1_out, mom2_out
@staticmethod
def ref_smart_decay_adam(param, mom1, mom2, last_seen, grad, LR, ITER,
beta1, beta2, epsilon):
for name in ('param', 'mom1', 'mom2', 'last_seen', 'grad',
'LR', 'ITER', 'beta1', 'beta2', 'epsilon'):
print("{} {} {}".format(name, locals()['name'], type(locals()['name'])))
t = ITER + 1
k = t - last_seen
k = k.flatten()[0]
last_seen_out = t * np.ones_like(last_seen)
# Make up for lost minibatches.
mom2_out = (beta2**k * mom2) + (1 - beta2) * np.square(grad)
param_out = param
mom1_out = mom1
# For catchup
assert k >= 1
for i in range(k):
mom1_out *= beta1
if i == k - 1:
mom1_out += grad * (1 - beta1)
param_out += LR * mom1_out / (np.sqrt(mom2_out) + epsilon)
grad_out = mom1_out / (np.sqrt(mom2_out) + epsilon)
return param_out, mom1_out, mom2_out, last_seen_out
@staticmethod
def ref_row_wise_adam(param, mom1, mom2, grad, LR, ITER,
beta1, beta2, epsilon, output_grad=False):
t = ITER + 1
corrected_local_rate = np.sqrt(1 - np.power(beta2, t)) / \
(1 - np.power(beta1, t))
mom1_out = (beta1 * mom1) + (1 - beta1) * grad
mom2_out = (beta2 * mom2) + (1 - beta2) * np.mean(np.square(grad))
grad_out = corrected_local_rate * mom1_out / (np.sqrt(mom2_out) + epsilon)
param_out = param + LR * grad_out
if output_grad:
return param_out, mom1_out, mom2_out, grad_out
else:
return param_out, mom1_out, mom2_out
@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),
**hu.gcs)
def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
param, mom1, mom2, grad = inputs
mom2 = np.abs(mom2)
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
op = core.CreateOperator(
"Adam",
["param", "mom1", "mom2", "grad", "lr", "iter"],
["output_param", "output_mom1", "output_mom2"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, grad, LR, ITER],
functools.partial(
self.ref_adam,
beta1=beta1, beta2=beta2, epsilon=epsilon),
input_device_options=input_device_options)
@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),
**hu.gcs_cpu_only)
def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
param, mom1, mom2, grad = inputs
mom2 = np.abs(mom2)
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
op = core.CreateOperator(
"Adam",
["param", "mom1", "mom2", "grad", "lr", "iter"],
["output_param", "output_mom1", "output_mom2", "output_grad"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, grad, LR, ITER],
functools.partial(
self.ref_adam,
beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True),
input_device_options=input_device_options)
@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),
data_strategy=st.data(),
**hu.gcs)
def test_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon,
data_strategy, gc, dc):
param, mom1, mom2, grad = inputs
mom2 = np.absolute(mom2)
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
# Create an indexing array containing values which index into grad
indices = data_strategy.draw(
hu.tensor(
max_dim=1,
min_value=1,
max_value=grad.shape[0],
dtype=np.int64,
elements=st.sampled_from(np.arange(grad.shape[0])),
),
)
# Verify that the generated indices are unique
hypothesis.assume(
np.array_equal(
np.unique(indices.flatten()),
np.sort(indices.flatten())))
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"SparseAdam",
["param", "mom1", "mom2", "indices", "grad", "lr", "iter"],
["param", "mom1", "mom2"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
def ref_sparse(param, mom1, mom2, indices, grad, LR, ITER):
param_out = np.copy(param)
mom1_out = np.copy(mom1)
mom2_out = np.copy(mom2)
for i, index in enumerate(indices):
param_out[index], mom1_out[index], mom2_out[index] = \
self.ref_adam(param[index], mom1[index], mom2[index],
grad[i], LR, ITER,
beta1, beta2, epsilon)
return (param_out, mom1_out, mom2_out)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
ref_sparse,
input_device_options=input_device_options)
@given(inputs=hu.tensors(n=4),
ITER=st.integers(min_value=0, max_value=10),
LR=st.floats(min_value=0.000001, max_value=0.1,
allow_nan=False, allow_infinity=False),
beta1=st.floats(min_value=0.0, max_value=0.99999,
allow_nan=False, allow_infinity=False),
beta2=st.floats(min_value=0.9, max_value=0.999999,
allow_nan=False, allow_infinity=False),
epsilon=st.floats(min_value=0.00001, max_value=0.99,
allow_nan=False, allow_infinity=False),
data_strategy=st.data(),
**hu.gcs)
def test_smart_decay_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon,
data_strategy, gc, dc):
param, mom1, mom2, grad = inputs
mom2 = np.absolute(mom2)
_iter, _lr = ITER, LR # Keep the scalar types for reference
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
# Here we will define the last_seen tensor as being randomly from 0 to ITER
# (the value of t to be tested will be ITER+1)
last_seen = data_strategy.draw(
hypothesis.extra.numpy.arrays(
dtype=np.int64,
shape=(param.shape[0],),
elements=st.integers(min_value=0, max_value=_iter),
unique=False,
)
)
# Create an indexing array containing values which index into grad
indices = data_strategy.draw(
hu.tensor(
max_dim=1,
min_value=1,
max_value=grad.shape[0],
dtype=np.int64,
elements=st.sampled_from(np.arange(grad.shape[0])),
),
)
# Verify that the generated indices are unique
hypothesis.assume(
np.array_equal(
np.unique(indices.flatten()),
np.sort(indices.flatten())))
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"SmartDecaySparseAdam",
["param", "mom1", "mom2", "last_seen", "indices", "grad", "lr", "iter"],
["param", "mom1", "mom2", "last_seen"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
def ref_sparse(param, mom1, mom2, last_seen, indices, grad, LR, ITER):
param_out = np.copy(param)
mom1_out = np.copy(mom1)
mom2_out = np.copy(mom2)
last_seen_out = np.copy(last_seen)
for i, index in enumerate(indices):
param_out[index], mom1_out[index], mom2_out[index], last_seen_out[index] = \
self.ref_smart_decay_adam(param[index], mom1[index], mom2[index], last_seen[index],
grad[i], LR, ITER,
beta1, beta2, epsilon)
return (param_out, mom1_out, mom2_out, last_seen_out)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, last_seen, indices, grad, LR, ITER],
ref_sparse,
input_device_options=input_device_options)
@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),
data_strategy=st.data(),
**hu.gcs)
def test_sparse_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon,
data_strategy, gc, dc):
param, mom1, mom2, grad = inputs
mom2 = np.absolute(mom2)
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
# Create an indexing array containing values which index into grad
indices = data_strategy.draw(
hu.tensor(
max_dim=1,
min_value=1,
max_value=grad.shape[0],
dtype=np.int64,
elements=st.sampled_from(np.arange(grad.shape[0])),
),
)
# Verify that the generated indices are unique
hypothesis.assume(
np.array_equal(
np.unique(indices.flatten()),
np.sort(indices.flatten())))
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"SparseAdam",
["param", "mom1", "mom2", "indices", "grad", "lr", "iter"],
["param", "mom1", "mom2", "output_grad"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
def ref_sparse_output_grad(param, mom1, mom2, indices, grad, LR, ITER,
beta1, beta2, epsilon, output_grad):
param_out = np.copy(param)
mom1_out = np.copy(mom1)
mom2_out = np.copy(mom2)
grad_out = np.copy(grad)
for i, index in enumerate(indices):
param_out[index], mom1_out[index], mom2_out[index], grad_out[i] = \
self.ref_adam(param[index], mom1[index], mom2[index],
grad[i], LR, ITER,
beta1, beta2, epsilon, output_grad)
return (param_out, mom1_out, mom2_out, grad_out)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
functools.partial(
ref_sparse_output_grad,
beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True),
input_device_options=input_device_options)
@given(inputs=hu.tensors(n=3),
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),
data_strategy=st.data(),
**hu.gcs)
def test_row_wise_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon,
data_strategy, gc, dc):
param, mom1, grad = inputs
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
# Create a 1D row-wise average 2nd moment tensor.
mom2 = data_strategy.draw(
hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0],
elements=hu.elements_of_type(dtype=np.float32))
)
mom2 = np.absolute(mom2)
# Create an indexing array containing values which index into grad
indices = data_strategy.draw(
hu.tensor(
max_dim=1,
min_value=1,
max_value=grad.shape[0],
dtype=np.int64,
elements=st.sampled_from(np.arange(grad.shape[0])),
),
)
# Note that unlike SparseAdam, RowWiseSparseAdam uses a moment
# tensor that is strictly 1-dimensional and equal in length to the
# first dimension of the parameters, so indices must also be
# 1-dimensional.
indices = indices.flatten()
hypothesis.note('indices.shape: %s' % str(indices.shape))
# Verify that the generated indices are unique
hypothesis.assume(np.array_equal(np.unique(indices), np.sort(indices)))
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"RowWiseSparseAdam",
["param", "mom1", "mom2", "indices", "grad", "lr", "iter"],
["param", "mom1", "mom2"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
def ref_row_wise_sparse(param, mom1, mom2, indices, grad, LR, ITER):
param_out = np.copy(param)
mom1_out = np.copy(mom1)
mom2_out = np.copy(mom2)
for i, index in enumerate(indices):
param_out[index], mom1_out[index], mom2_out[index] = \
self.ref_row_wise_adam(param[index], mom1[index], mom2[index],
grad[i], LR, ITER,
beta1, beta2, epsilon)
return (param_out, mom1_out, mom2_out)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertDeviceChecks(
dc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
[0, 1, 2],
input_device_options=input_device_options)
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
ref_row_wise_sparse,
input_device_options=input_device_options)
@given(inputs=hu.tensors(n=3),
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),
data_strategy=st.data(),
**hu.gcs)
def test_row_wise_sparse_adam_output_grad(self, inputs, ITER, LR, beta1, beta2,
epsilon, data_strategy, gc, dc):
param, mom1, grad = inputs
ITER = np.array([ITER], dtype=np.int64)
LR = np.array([LR], dtype=np.float32)
# Create a 1D row-wise average 2nd moment tensor.
mom2 = data_strategy.draw(
hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0],
elements=hu.elements_of_type(dtype=np.float32))
)
mom2 = np.absolute(mom2)
# Create an indexing array containing values which index into grad
indices = data_strategy.draw(
hu.tensor(
max_dim=1,
min_value=1,
max_value=grad.shape[0],
dtype=np.int64,
elements=st.sampled_from(np.arange(grad.shape[0])),
),
)
# Note that unlike SparseAdam, RowWiseSparseAdam uses a moment
# tensor that is strictly 1-dimensional and equal in length to the
# first dimension of the parameters, so indices must also be
# 1-dimensional.
indices = indices.flatten()
hypothesis.note('indices.shape: %s' % str(indices.shape))
# Verify that the generated indices are unique
hypothesis.assume(np.array_equal(np.unique(indices), np.sort(indices)))
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"RowWiseSparseAdam",
["param", "mom1", "mom2", "indices", "grad", "lr", "iter"],
["param", "mom1", "mom2", "output_grad"],
beta1=beta1, beta2=beta2, epsilon=epsilon)
def ref_row_wise_sparse_output_grad(param, mom1, mom2, indices, grad, LR, ITER,
beta1, beta2, epsilon, output_grad):
param_out = np.copy(param)
mom1_out = np.copy(mom1)
mom2_out = np.copy(mom2)
grad_out = np.copy(grad)
for i, index in enumerate(indices):
param_out[index], mom1_out[index], mom2_out[index], grad_out[i] = \
self.ref_row_wise_adam(param[index], mom1[index], mom2[index],
grad[i], LR, ITER,
beta1, beta2, epsilon, output_grad)
return (param_out, mom1_out, mom2_out, grad_out)
# Iter lives on the CPU
input_device_options = {'iter': hu.cpu_do}
self.assertDeviceChecks(
dc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
[0, 1, 2, 3],
input_device_options=input_device_options)
self.assertReferenceChecks(
gc, op,
[param, mom1, mom2, indices, grad, LR, ITER],
functools.partial(
ref_row_wise_sparse_output_grad,
beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True),
input_device_options=input_device_options)
if __name__ == "__main__":
import unittest
unittest.main()