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
192
193
194
195
196
197
198
199
200
201
|
from thinc.api import Linear, SGD, PyTorchWrapper, PyTorchWrapper_v2, PyTorchWrapper_v3
from thinc.api import xp2torch, torch2xp, ArgsKwargs, use_ops
from thinc.api import chain, get_current_ops, Relu
from thinc.api import CupyOps, MPSOps, NumpyOps
from thinc.backends import context_pools
from thinc.layers.pytorchwrapper import PyTorchWrapper_v3
from thinc.shims.pytorch_grad_scaler import PyTorchGradScaler
from thinc.shims.pytorch import default_deserialize_torch_model
from thinc.shims.pytorch import default_serialize_torch_model
from thinc.compat import has_torch, has_torch_amp
from thinc.compat import has_cupy_gpu, has_torch_mps_gpu
import numpy
import pytest
from thinc.util import get_torch_default_device
from ..util import make_tempdir, check_input_converters
XP_OPS = [NumpyOps()]
if has_cupy_gpu:
XP_OPS.append(CupyOps())
if has_torch_mps_gpu:
XP_OPS.append(MPSOps())
if has_torch_amp:
TORCH_MIXED_PRECISION = [False, True]
else:
TORCH_MIXED_PRECISION = [False]
XP_OPS_MIXED = [
(ops, mixed)
for ops in XP_OPS
for mixed in TORCH_MIXED_PRECISION
if not mixed or isinstance(ops, CupyOps)
]
def check_learns_zero_output(model, sgd, X, Y):
"""Check we can learn to output a zero vector"""
Yh, get_dX = model.begin_update(X)
dYh = (Yh - Y) / Yh.shape[0]
dX = get_dX(dYh)
model.finish_update(sgd)
prev = numpy.abs(Yh.sum())
for i in range(100):
Yh, get_dX = model.begin_update(X)
total = numpy.abs(Yh.sum())
dX = get_dX(Yh - Y) # noqa: F841
model.finish_update(sgd)
assert total < prev
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
@pytest.mark.parametrize("nN,nI,nO", [(2, 3, 4)])
def test_pytorch_unwrapped(nN, nI, nO):
model = Linear(nO, nI).initialize()
X = numpy.zeros((nN, nI), dtype="f")
X += numpy.random.uniform(size=X.size).reshape(X.shape)
sgd = SGD(0.01)
Y = numpy.zeros((nN, nO), dtype="f")
check_learns_zero_output(model, sgd, X, Y)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
@pytest.mark.parametrize("nN,nI,nO", [(2, 3, 4)])
def test_pytorch_wrapper(nN, nI, nO):
import torch.nn
model = PyTorchWrapper(torch.nn.Linear(nI, nO)).initialize()
sgd = SGD(0.001)
X = numpy.zeros((nN, nI), dtype="f")
X += numpy.random.uniform(size=X.size).reshape(X.shape)
Y = numpy.zeros((nN, nO), dtype="f")
Yh, get_dX = model.begin_update(X)
assert isinstance(Yh, numpy.ndarray)
assert Yh.shape == (nN, nO)
dYh = (Yh - Y) / Yh.shape[0]
dX = get_dX(dYh)
model.finish_update(sgd)
assert dX.shape == (nN, nI)
check_learns_zero_output(model, sgd, X, Y)
assert isinstance(model.predict(X), numpy.ndarray)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
@pytest.mark.parametrize("ops_mixed", XP_OPS_MIXED)
@pytest.mark.parametrize("nN,nI,nO", [(2, 3, 4)])
def test_pytorch_wrapper_thinc_input(ops_mixed, nN, nI, nO):
import torch.nn
ops, mixed_precision = ops_mixed
with use_ops(ops.name):
ops = get_current_ops()
pytorch_layer = torch.nn.Linear(nO, nO)
# Initialize with large weights to trigger overflow of FP16 in
# mixed-precision training.
torch.nn.init.uniform_(pytorch_layer.weight, 9.0, 11.0)
device = get_torch_default_device()
model = chain(
Relu(),
PyTorchWrapper_v2(
pytorch_layer.to(device),
mixed_precision=mixed_precision,
grad_scaler=PyTorchGradScaler(
enabled=mixed_precision, init_scale=2.0**16
),
).initialize(),
)
# pytorch allocator is set in PyTorchShim
if isinstance(ops, CupyOps):
assert "pytorch" in context_pools.get()
sgd = SGD(0.001)
X = ops.xp.zeros((nN, nI), dtype="f")
X += ops.xp.random.uniform(size=X.size).reshape(X.shape)
Y = ops.xp.zeros((nN, nO), dtype="f")
model.initialize(X, Y)
Yh, get_dX = model.begin_update(X)
assert isinstance(Yh, ops.xp.ndarray)
assert Yh.shape == (nN, nO)
dYh = (Yh - Y) / Yh.shape[0]
dX = get_dX(dYh)
model.finish_update(sgd)
assert dX.shape == (nN, nI)
check_learns_zero_output(model, sgd, X, Y)
assert isinstance(model.predict(X), ops.xp.ndarray)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
def test_pytorch_roundtrip_conversion():
import torch
xp_tensor = numpy.zeros((2, 3), dtype="f")
torch_tensor = xp2torch(xp_tensor)
assert isinstance(torch_tensor, torch.Tensor)
new_xp_tensor = torch2xp(torch_tensor)
assert numpy.array_equal(xp_tensor, new_xp_tensor)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
def test_pytorch_wrapper_roundtrip():
import torch.nn
model = PyTorchWrapper(torch.nn.Linear(2, 3))
model_bytes = model.to_bytes()
PyTorchWrapper(torch.nn.Linear(2, 3)).from_bytes(model_bytes)
with make_tempdir() as path:
model_path = path / "model"
model.to_disk(model_path)
new_model = PyTorchWrapper(torch.nn.Linear(2, 3)).from_bytes(model_bytes)
new_model.from_disk(model_path)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
@pytest.mark.parametrize(
"data,n_args,kwargs_keys",
[
# fmt: off
(numpy.zeros((2, 3), dtype="f"), 1, []),
([numpy.zeros((2, 3), dtype="f"), numpy.zeros((2, 3), dtype="f")], 2, []),
((numpy.zeros((2, 3), dtype="f"), numpy.zeros((2, 3), dtype="f")), 2, []),
({"a": numpy.zeros((2, 3), dtype="f"), "b": numpy.zeros((2, 3), dtype="f")}, 0, ["a", "b"]),
(ArgsKwargs((numpy.zeros((2, 3), dtype="f"), numpy.zeros((2, 3), dtype="f")), {"c": numpy.zeros((2, 3), dtype="f")}), 2, ["c"]),
# fmt: on
],
)
def test_pytorch_convert_inputs(data, n_args, kwargs_keys):
import torch.nn
model = PyTorchWrapper(torch.nn.Linear(3, 4))
convert_inputs = model.attrs["convert_inputs"]
Y, backprop = convert_inputs(model, data, is_train=True)
check_input_converters(Y, backprop, data, n_args, kwargs_keys, torch.Tensor)
@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
def test_pytorch_wrapper_custom_serde():
import torch.nn
def serialize(model):
return default_serialize_torch_model(model)
def deserialize(model, state_bytes, device):
return default_deserialize_torch_model(model, state_bytes, device)
def get_model():
return PyTorchWrapper_v3(
torch.nn.Linear(2, 3),
serialize_model=serialize,
deserialize_model=deserialize,
)
model = get_model()
model_bytes = model.to_bytes()
get_model().from_bytes(model_bytes)
with make_tempdir() as path:
model_path = path / "model"
model.to_disk(model_path)
new_model = get_model().from_bytes(model_bytes)
new_model.from_disk(model_path)
|
