| |
| |
| |
| |
| |
| 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}) |
