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from caffe2.python import core, test_util
from caffe2.proto import caffe2_pb2
import caffe2.python.nomnigraph as ng
from hypothesis import given
import hypothesis.strategies as st
import random
class TestBindings(test_util.TestCase):
def test_simple(self):
nn = ng.NNModule()
dfg = nn.dataFlow
dfg.createNode(ng.NeuralNetData("X"))
dfg.createNode(ng.NeuralNetOperator("FC"))
assert len(nn.dataFlow.getMutableNodes()) == 2
def test_core_net_simple(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
nn = ng.NNModule(net)
for node in nn.dataFlow.getMutableNodes():
if node.isOperator():
assert node.getName() == "FC"
elif node.isTensor():
assert node.getName() in ["X", "W", "Y"]
def test_core_net_controlflow(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
net.Relu(["Y"], ["Z"])
nn = ng.NNModule(net)
assert len(nn.controlFlow) == 2
for instr in nn.controlFlow:
assert instr.getType() == "Operator"
assert nn.controlFlow[0].getName() == "FC"
assert nn.controlFlow[1].getName() == "Relu"
def test_core_net_nn_accessors(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
net.Relu(["Y"], ["Z"])
nn = ng.NNModule(net)
tensors = set()
for t in nn.tensors:
tensors.add(t.name)
assert tensors == set(["X", "W", "Y", "Z"])
ops = set()
for op in nn.operators:
ops.add(op.name)
assert ops == set(["FC", "Relu"])
nodes = set()
for node in nn.nodes:
nodes.add(node.name)
assert nodes == (ops | tensors)
def test_netdef_simple(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
nn = ng.NNModule(net.Proto())
for node in nn.dataFlow.getMutableNodes():
if node.isOperator():
assert node.getOperator().getName() == "FC"
elif node.isTensor():
assert node.getTensor().getName() in ["X", "W", "Y"]
def test_operatordef_simple(self):
nn = ng.NNModule()
dfg = nn.dataFlow
op = core.CreateOperator("Ceil", ["X"], ["Y"], engine="CUDNN")
dfg.createNode(op)
for node in dfg.getMutableNodes():
assert node.isOperator()
assert node.getOperator().getName() == "Ceil"
def test_invalid_node(self):
nn = ng.NNModule()
dfg = nn.dataFlow
with self.assertRaises(Exception):
dfg.createNode(7)
def test_edges_simple(self):
nn = ng.NNModule()
dfg = nn.dataFlow
x = dfg.createNode(ng.NeuralNetData("X"))
w = dfg.createNode(ng.NeuralNetData("W"))
op = dfg.createNode(ng.NeuralNetOperator("Op"))
with self.assertRaises(Exception):
dfg.createEdge(x, w)
dfg.createEdge(op, w)
dfg.createEdge(x, op)
# Dot generation
assert(str(dfg).startswith("digraph G"))
# subgraph
sg = ng.NNSubgraph()
sg.addNode(x)
sg.addNode(op)
sg.induceEdges()
assert len(sg) == 2
# subgraph dot generation
assert(str(sg).startswith("digraph G"))
@given(size=st.sampled_from([10, 50]))
def test_edges_complex(self, size):
random.seed(1337)
nn = ng.NNModule()
dfg = nn.dataFlow
data = []
ops = []
for _ in range(size):
data.append(dfg.createNode(ng.NeuralNetData("X")))
for i in range(size):
ops.append(dfg.createNode(ng.NeuralNetOperator("Op" + str(i))))
for i in range(size):
for j in range(size):
if bool(random.getrandbits(1)):
dfg.createEdge(data[i], ops[j])
def test_traversal(self):
net = core.Net("test")
net.FC(["X", "W"], ["Y"])
net.Relu(["Y"], ["Z"])
nn = ng.NNModule(net)
fc = nn.controlFlow[0]
relu = nn.controlFlow[1]
assert not fc.inputs[0].hasProducer()
assert fc.inputs[0].name == "X"
assert fc.inputs[1].name == "W"
assert relu.outputs[0].name == "Z"
assert relu.inputs[0].name == "Y"
assert relu.inputs[0].hasProducer()
assert relu.inputs[0].producer.name == "FC"
assert fc.outputs[0].consumers[0].name == "Relu"
def test_debug(self):
nn = ng.NNModule()
dfg = nn.dataFlow
dfg.createNode(ng.NeuralNetData("X"))
dfg.createNode(ng.NeuralNetData("W"))
dfg.createNode(ng.NeuralNetOperator("Op"))
ng.render(nn.dataFlow)
def test_match_graph_node(self):
mg = ng.NNMatchGraph()
mg.createNode(ng.NeuralNetOperator("test"))
nn = ng.NNModule()
test = nn.dataFlow.createNode(ng.NeuralNetOperator("test"))
x = nn.dataFlow.createNode(ng.NeuralNetData("X"))
nn.dataFlow.createEdge(x, test)
count = 0
for match in nn.match(mg):
assert len(match) == 1
count += 1
# Dot generation of subgraph
assert(str(match).startswith("digraph G"))
assert count == 1
def test_match_graph_node_strict(self):
mg = ng.NNMatchGraph()
mg.createNode(ng.NeuralNetOperator("test"), strict=True)
nn = ng.NNModule()
test = nn.dataFlow.createNode(ng.NeuralNetOperator("test"))
x = nn.dataFlow.createNode(ng.NeuralNetData("X"))
nn.dataFlow.createEdge(test, x)
count = 0
for match in nn.match(mg):
assert len(match) == 1
count += 1
with self.assertRaises(Exception):
assert count == 1
def test_match_graph(self):
mg = ng.NNMatchGraph()
test2m = mg.createNode(ng.NeuralNetOperator("test2"), strict=True)
xm = mg.createNode(ng.NeuralNetData("X"), strict=True)
testm = mg.createNode(ng.NeuralNetOperator("test"))
mg.createEdge(test2m, xm)
mg.createEdge(xm, testm)
nn = ng.NNModule()
test2 = nn.dataFlow.createNode(ng.NeuralNetOperator("test2"))
x = nn.dataFlow.createNode(ng.NeuralNetData("X"))
test = nn.dataFlow.createNode(ng.NeuralNetOperator("test"))
nn.dataFlow.createEdge(test2, x)
nn.dataFlow.createEdge(x, test)
count = 0
for match in nn.match(mg):
print(len(match))
assert len(match) == 3
count += 1
assert count == 1
def test_delete_subgraph(self):
mg = ng.NNMatchGraph()
test2m = mg.createNode(ng.NeuralNetOperator("test2"), strict=True)
xm = mg.createNode(ng.NeuralNetData("X"), strict=True)
testm = mg.createNode(ng.NeuralNetOperator("test"))
mg.createEdge(test2m, xm)
mg.createEdge(xm, testm)
nn = ng.NNModule()
test2 = nn.dataFlow.createNode(ng.NeuralNetOperator("test2"))
x = nn.dataFlow.createNode(ng.NeuralNetData("X"))
test = nn.dataFlow.createNode(ng.NeuralNetOperator("test"))
nn.dataFlow.createEdge(test2, x)
nn.dataFlow.createEdge(x, test)
for m in nn.match(mg):
match = m
nn.deleteSubgraph(match)
assert len(nn.controlFlow) == 0
def test_replace_subraph(self):
mg = ng.NNMatchGraph()
test2m = mg.createNode(ng.NeuralNetOperator("test2"), strict=True)
xm = mg.createNode(ng.NeuralNetData("X"), strict=True)
testm = mg.createNode(ng.NeuralNetOperator("test"))
mg.createEdge(test2m, xm)
mg.createEdge(xm, testm)
nn = ng.NNModule()
test2 = nn.dataFlow.createNode(ng.NeuralNetOperator("test2"))
x = nn.dataFlow.createNode(ng.NeuralNetData("X"))
test = nn.dataFlow.createNode(ng.NeuralNetOperator("test"))
nn.dataFlow.createEdge(test2, x)
nn.dataFlow.createEdge(x, test)
for m in nn.match(mg):
match = m
new_op = nn.dataFlow.createNode(ng.NeuralNetOperator("new_op"))
nn.replaceSubgraph(match, new_op, [], [])
assert len(nn.controlFlow) == 1
assert nn.controlFlow[0].name == "new_op"
def test_genericGraph(self):
g = ng.Graph()
n1 = g.createNode("hello1")
n2 = g.createNode("hello2")
e = g.createEdge(n1, n2)
ng.render(g)
def test_createUniqueDataNode(self):
net = core.Net("name")
nn = ng.NNModule(net)
n1 = nn.createUniqueDataNode("a")
self.assertEqual(n1.name[0], "a")
n2 = nn.dataFlow.createNode(ng.Operator("test1"))
nn.createEdge(n1, n2)
n3 = nn.createUniqueDataNode("a")
nn.createEdge(n2, n3)
self.assertEqual(n3.name[0], "a")
self.assertNotEqual(n1.name, n3.name)
n1 = nn.createUniqueDataNode("b")
n2 = nn.createUniqueDataNode("b")
self.assertNotEqual(n1.name, n2.name)
def test_convertToProto(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
nn = ng.NNModule(net)
new_netdef = nn.convertToCaffe2Proto()
print(new_netdef)
print(net.Proto())
assert len(new_netdef.op) == len(net.Proto().op)
for i in range(len(new_netdef.op)):
op = net.Proto().op[i]
new_op = new_netdef.op[i]
assert op.type == new_op.type
assert len(op.input) == len(new_op.input)
assert len(op.output) == len(new_op.output)
for a, b in zip(op.input, new_op.input):
assert a == b
for a, b in zip(op.output, new_op.output):
assert a == b
for a, b in zip(new_netdef.external_input, net.Proto().external_input):
assert a == b
for a, b in zip(new_netdef.external_output, net.Proto().external_output):
assert a == b
def test_node_interactions(self):
nn = ng.NNModule()
dfg = nn.dataFlow
test1 = dfg.createNode(ng.Operator("test1"))
test2 = dfg.createNode(ng.Operator("test2"))
x = dfg.createNode(ng.Data("x"))
dfg.createEdge(test1, x)
dfg.createEdge(x, test2)
p = test2.getOperatorPredecessors()
assert len(p) == 1
assert p[0] == test1
# Add another node
test3 = dfg.createNode(ng.Operator("test3"))
y = dfg.createNode(ng.Data("y"))
dfg.createEdge(test3, y)
dfg.createEdge(y, test2)
p = test2.getOperatorPredecessors()
assert len(p) == 2
assert test1 in p
assert test3 in p
# Successors
assert len(test2.getOperatorSuccessors()) == 0
assert len(test1.getOperatorSuccessors()) == 1
assert test1.getOperatorSuccessors()[0] == test2
# Check all the nodes are valid (pybind ownership test)
for node in [test1, test2, test3]:
assert node.isOperator()
for node in [x, y]:
assert node.isTensor()
def test_delete_node(self):
nn = ng.NNModule()
node = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
nn.dataFlow.deleteNode(node)
assert len(nn.dataFlow.getMutableNodes()) == 0
def test_replace_producer(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
nn = ng.NNModule(net)
fc = nn.controlFlow[0]
test_op = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
nn.replaceProducer(fc.outputs[0], test_op)
nn.deleteNode(fc)
assert len(nn.controlFlow) == 1
assert nn.controlFlow[0].name == "TestOp"
def test_replace_all_uses_with(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
net.FC(["X", "W2"], ["Y2"])
nn = ng.NNModule(net)
fc = nn.controlFlow[0]
test_tensor = nn.dataFlow.createNode(ng.NeuralNetData("T"))
nn.replaceAllUsesWith(fc.inputs[0], test_tensor)
for op in nn.controlFlow:
assert op.inputs[0].name == "T"
def test_replace_as_consumer(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
nn = ng.NNModule(net)
fc = nn.controlFlow[0]
test_op = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
nn.replaceAsConsumer(fc, test_op)
nn.deleteNode(fc)
assert len(nn.controlFlow) == 1
assert nn.controlFlow[0].name == "TestOp"
assert nn.controlFlow[0].inputs[0].name == "X"
assert nn.controlFlow[0].inputs[1].name == "W"
def test_annotation_basic(self):
annot = ng.Annotation()
annot.setDevice("woot")
assert annot.getDevice() == "woot"
annot.setDeviceType(7)
assert annot.getDeviceType() == 7
def test_annotation_from_graph(self):
nn = ng.NNModule()
node = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
annot = node.getAnnotation()
annot.setDeviceType(7)
node.setAnnotation(annot)
new_annot = node.getAnnotation()
assert new_annot.getDeviceType() == 7
def test_annotation_operator_def(self):
nn = ng.NNModule()
opdef = core.CreateOperator("Conv", [], [], engine="SENTINEL")
node = nn.dataFlow.createNode(opdef)
assert node.annotation.operator_def.engine == "SENTINEL"
opdef = core.CreateOperator("Conv", [], [], engine="NEW_SENTINEL")
node.annotation.operator_def = opdef
netdef = nn.convertToCaffe2Proto()
assert len(netdef.op) == 1
assert netdef.op[0].engine == "NEW_SENTINEL"
def test_annotation_device_option(self):
nn = ng.NNModule()
node = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
d = caffe2_pb2.DeviceOption()
d.node_name = "test"
node.annotation.device_option = d
# access in a different way
d_2 = nn.controlFlow[0].annotation.device_option
assert d == d_2
def test_has_device_option(self):
nn = ng.NNModule()
node = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
assert not node.annotation.hasDeviceOption()
d = caffe2_pb2.DeviceOption()
node.annotation.device_option = d
assert node.annotation.hasDeviceOption()
def test_distributed_annotations(self):
nn = ng.NNModule()
key = nn.dataFlow.createNode(ng.NeuralNetData("key"))
length = nn.dataFlow.createNode(ng.NeuralNetData("length"))
node = nn.dataFlow.createNode(ng.NeuralNetOperator("TestOp"))
annot = ng.Annotation()
annot.setKeyNode(key)
annot.setLengthNode(length)
annot.setComponentLevels(["", "test", "woot"])
node.setAnnotation(annot)
new_annot = node.getAnnotation()
#assert new_annot.getLengthNode() == length
assert new_annot.getKeyNode() == key
assert len(new_annot.getComponentLevels()) == 3
assert new_annot.getComponentLevels()[0] == ""
assert new_annot.getComponentLevels()[2] == "woot"
def test_distributed_device_map(self):
net = core.Net("name")
net.FC(["X", "W"], ["Y"])
d = caffe2_pb2.DeviceOption()
nn = ng.NNModule(net, {"X": d, "W": d})
with self.assertRaises(Exception):
nn = ng.NNModule(net, {"X": d, "Fake": d})
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