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import functools
from hypothesis import given, settings, HealthCheck
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
class TestStorm(hu.HypothesisTestCase):
@given(inputs=hu.tensors(n=3),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
**hu.gcs_cpu_only)
def test_storm_dense(self, inputs, grad_sq_sum, lr, momentum, beta, gc, dc):
param, moment, grad = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
op = core.CreateOperator(
"Storm",
["param", "moment", "grad_sq_sum", "grad", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc
)
def ref_dense(param, moment, grad_sq_sum, grad, lr, momentum, beta):
grad_sq_sum_out = grad_sq_sum + np.sum(grad * grad)
nlr = lr * np.power(beta + grad_sq_sum_out, -1.0 / 3.0)
alpha = momentum * np.square(nlr)
moment_out = grad + (1 - alpha) * (moment - grad)
param_out = param + nlr * moment_out
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, lr],
functools.partial(ref_dense, momentum=momentum, beta=beta)
)
# Suppress filter_too_much health check.
# Likely caused by `assume` call falling through too often.
@settings(suppress_health_check=[HealthCheck.filter_too_much])
@given(inputs=hu.tensors(n=3),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
**hu.gcs_cpu_only)
def test_storm_sparse(self, inputs, grad_sq_sum, lr,
momentum, beta, gc, dc):
param, moment, grad = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
# Create an indexing array containing values that are lists of indices,
# which index into grad
indices = np.random.choice(np.arange(grad.shape[0]),
size=np.random.randint(grad.shape[0]),
replace=False)
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"SparseStorm",
["param", "moment", "grad_sq_sum", "grad", "indices", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc)
def ref_sparse(param, moment, grad_sq_sum, grad, indices,
lr, momentum, beta):
param_out = np.copy(param)
moment_out = np.copy(moment)
grad_sq_sum_out = np.copy(grad_sq_sum)
grad_sq_sum_out = grad_sq_sum + np.sum(grad * grad)
nlr = lr * np.power(beta + grad_sq_sum_out, -1.0 / 3.0)
alpha = momentum * np.square(nlr)
for i, index in enumerate(indices):
gi = grad[i]
moment_out[index] = gi + (1 - alpha) * (moment[index] - gi)
param_out[index] = param[index] + nlr * moment_out[index]
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, indices, lr],
functools.partial(ref_sparse, momentum=momentum, beta=beta)
)
@given(inputs=hu.tensors(n=2),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
data_strategy=st.data(),
**hu.gcs_cpu_only)
def test_storm_sparse_empty(self, inputs, grad_sq_sum, lr, momentum,
beta, data_strategy, gc, dc):
param, moment = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
indices = np.empty(shape=(0,), dtype=np.int64)
op = core.CreateOperator(
"SparseStorm",
["param", "moment", "grad_sq_sum", "grad", "indices", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc)
def ref_sparse_empty(param, moment, grad_sq_sum, grad, indices,
lr, momentum, beta):
param_out = np.copy(param)
moment_out = np.copy(moment)
grad_sq_sum_out = np.copy(grad_sq_sum)
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, indices, lr],
functools.partial(ref_sparse_empty, momentum=momentum, beta=beta)
)
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