1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
|
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, assume, settings
import hypothesis.strategies as st
import numpy as np
import unittest
class TestMomentumSGD(serial.SerializedTestCase):
@given(n=st.integers(4, 8), nesterov=st.booleans(), **hu.gcs)
@settings(deadline=10000)
def test_momentum_sgd(self, n, nesterov, gc, dc):
param = np.random.rand(n).astype(np.float32)
grad = np.random.rand(n).astype(np.float32)
lr = np.random.rand(1).astype(np.float32)
param_momentum = np.random.rand(n).astype(np.float32)
momentum = 0.9
def momentum_sgd(grad, param_momentum, lr, param=None):
if not nesterov:
adjusted_gradient = lr * grad + momentum * param_momentum
if param is None:
return [adjusted_gradient, adjusted_gradient]
else:
paramup = param - adjusted_gradient
return [adjusted_gradient, adjusted_gradient, paramup]
else:
m_new = momentum * param_momentum + lr * grad
grad_new = (1 + momentum) * m_new - momentum * param_momentum
if param is None:
return [grad_new, m_new]
else:
paramup = param - grad_new
return [grad_new, m_new, paramup]
op = core.CreateOperator(
"MomentumSGDUpdate",
["grad", "param_momentum", "lr", "param"],
["grad", "param_momentum", "param"],
momentum=momentum,
nesterov=int(nesterov),
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[grad, param_momentum, lr, param],
reference=momentum_sgd
)
op_noparam = core.CreateOperator(
"MomentumSGD",
["grad", "param_momentum", "lr"],
["grad", "param_momentum"],
momentum=momentum,
nesterov=int(nesterov),
)
self.assertReferenceChecks(
device_option=gc,
op=op_noparam,
inputs=[grad, param_momentum, lr],
reference=momentum_sgd
)
@given(
inputs=hu.tensors(n=3),
momentum=st.floats(min_value=0.1, max_value=0.9),
nesterov=st.booleans(),
lr=st.floats(min_value=0.1, max_value=0.9),
data_strategy=st.data(),
**hu.gcs
)
@settings(deadline=10000)
def test_sparse_momentum_sgd(
self, inputs, momentum, nesterov, lr, data_strategy, gc, dc
):
w, grad, m = inputs
# 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
assume(
np.array_equal(
np.unique(indices.flatten()),
np.sort(indices.flatten())))
# Sparsify grad
grad = grad[indices]
# Make momentum >= 0
m = np.abs(m)
# Convert lr to a numpy array
lr = np.asarray([lr], dtype=np.float32)
op = core.CreateOperator(
"SparseMomentumSGDUpdate", ["grad", "m", "lr", "param", "indices"],
["adjusted_grad", "m", "param"],
momentum=momentum,
nesterov=int(nesterov),
device_option=gc
)
# Reference
def momentum_sgd(grad, m, lr):
lr = lr[0]
if not nesterov:
adjusted_gradient = lr * grad + momentum * m
return (adjusted_gradient, adjusted_gradient)
else:
m_new = momentum * m + lr * grad
return ((1 + momentum) * m_new - momentum * m, m_new)
def sparse(grad, m, lr, param, i):
grad_new, m_new = momentum_sgd(grad, m[i], lr)
m[i] = m_new
param[i] -= grad_new
return (grad_new, m, param)
self.assertReferenceChecks(
gc,
op,
[grad, m, lr, w, indices],
sparse)
@unittest.skip("Test is flaky, see https://github.com/pytorch/pytorch/issues/31368")
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
@given(n=st.integers(4, 8), nesterov=st.booleans(), **hu.gcs)
def test_fp16momentum_sgd(self, n, nesterov, gc, dc):
assume(core.IsGPUDeviceType(gc.device_type))
gpuvers = workspace.GetDeviceProperties(0)["major"]
if gc.device_type == caffe2_pb2.CUDA and gpuvers < 6:
print("No FP16 support because major version {} < 6".format(gpuvers))
return
param = np.random.rand(n).astype(np.float16)
grad = np.random.rand(n).astype(np.float16)
lr = np.random.rand(1).astype(np.float32)
param_momentum = np.random.rand(n).astype(np.float16)
momentum = 0.9
def momentum_sgd(grad, param_momentum, lr, param=None):
if not nesterov:
adjusted_gradient = lr * grad + momentum * param_momentum
paramup = param - adjusted_gradient
return [adjusted_gradient, adjusted_gradient, paramup]
else:
m_new = momentum * param_momentum + lr * grad
grad_new = (1 + momentum) * m_new - momentum * param_momentum
paramup = param - grad_new
return [grad_new, m_new, paramup]
op = core.CreateOperator(
"FP16MomentumSGDUpdate",
["grad", "param_momentum", "lr", "param"],
["grad", "param_momentum", "param"],
momentum=momentum,
nesterov=int(nesterov),
weight_decay=0.0,
)
threshold = 1e-3 if (gc.device_type == caffe2_pb2.HIP) else 1e-4
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[grad, param_momentum, lr, param],
reference=momentum_sgd,
threshold=threshold
)
if __name__ == "__main__":
unittest.main()
|
