Add tests for GraphConverterWithShape (#3951)

test/ut/retiarii/convert_mixin.py

+import torch
+
+from nni.retiarii.converter.graph_gen import convert_to_graph, GraphConverterWithShape
+
+
+class ConvertMixin:
+    @staticmethod
+    def _convert_model(model, input):
+        script_module = torch.jit.script(model)
+        model_ir = convert_to_graph(script_module, model)
+        return model_ir
+
+
+class ConvertWithShapeMixin:
+    @staticmethod
+    def _convert_model(model, input):
+        script_module = torch.jit.script(model)
+        model_ir = convert_to_graph(script_module, model, converter=GraphConverterWithShape(), example_inputs=input)
+        return model_ir

test/ut/retiarii/test_convert.py

@@ -13,9 +13,10 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import basic_unit
-from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
 
+from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
+
 class MnistNet(nn.Module):
     def __init__(self):
         super(MnistNet, self).__init__()
@@ -48,7 +49,7 @@ class Linear(nn.Module):
         out = self.linear(input.view(size[0] * size[1], -1))
         return out.view(size[0], size[1], -1)
 
-class TestConvert(unittest.TestCase):
+class TestConvert(unittest.TestCase, ConvertMixin):
     @staticmethod
     def _match_state_dict(current_values, expected_format):
         result = {}
@@ -61,8 +62,7 @@ class TestConvert(unittest.TestCase):
         return result
 
     def checkExportImport(self, model, input):
-        script_module = torch.jit.script(model)
-        model_ir = convert_to_graph(script_module, model)
+        model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
 
         exec_vars = {}
@@ -579,3 +579,6 @@ class TestConvert(unittest.TestCase):
             self.checkExportImport(model, (x,))
         finally:
             remove_inject_pytorch_nn()
+
+class TestConvertWithShape(TestConvert, ConvertWithShapeMixin):
+    pass

test/ut/retiarii/test_convert_basic.py

@@ -9,12 +9,13 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import basic_unit
-from nni.retiarii.converter import convert_to_graph
+
+from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 from nni.retiarii.codegen import model_to_pytorch_script
 
 # following pytorch v1.7.1
 
-class TestConvert(unittest.TestCase):
+class TestConvert(unittest.TestCase, ConvertMixin):
     @staticmethod
     def _match_state_dict(current_values, expected_format):
         result = {}
@@ -27,8 +28,7 @@ class TestConvert(unittest.TestCase):
         return result
 
     def checkExportImport(self, model, input, check_value=True):
-        script_module = torch.jit.script(model)
-        model_ir = convert_to_graph(script_module, model)
+        model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
         print(model_code)
 
@@ -188,7 +188,7 @@ class TestConvert(unittest.TestCase):
                 out2 = torch.addmv(x, y, z, beta=0.1, alpha=0.2)
                 return out1, out2
         self.checkExportImport(SimpleOp(), (torch.randn(2), torch.randn(2, 3), torch.randn(3), ))
-        
+
     def test_basic_addr(self):
         class SimpleOp(nn.Module):
             def forward(self, x, y, z):
@@ -204,7 +204,7 @@ class TestConvert(unittest.TestCase):
                 out2 = torch.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False)
                 return out1, out2
         self.checkExportImport(SimpleOp(), (torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08]), ))
-        
+
     def test_basic_angle(self):
         class SimpleOp(nn.Module):
             def forward(self, x):
@@ -229,7 +229,7 @@ class TestConvert(unittest.TestCase):
                 o4 = x.argmin(dim=1, keepdim=True)
                 return out1, out2, out3, out4, out5, o1, o2, o3, o4
         self.checkExportImport(SimpleOp(), (torch.randn(4, 4), ))
-        
+
     def test_basic_argsort(self):
         class SimpleOp(nn.Module):
             def forward(self, x):
@@ -241,7 +241,7 @@ class TestConvert(unittest.TestCase):
         self.checkExportImport(SimpleOp(), (torch.randn(4, 4), ))
 
     # skip backward(gradient=None, retain_graph=None, create_graph=False)
-        
+
     def test_basic_bernoulli(self):
         class SimpleOp(nn.Module):
             def forward(self, x):
@@ -261,7 +261,7 @@ class TestConvert(unittest.TestCase):
                 out4 = x.bincount(weights=y, minlength=2)
                 return out1, out2, out3, out4
         self.checkExportImport(SimpleOp(), (torch.randint(0, 8, (5,), dtype=torch.int64), torch.linspace(0, 1, steps=5), ))
-        
+
     def test_basic_bitwise(self):
         class SimpleOp(nn.Module):
             def forward(self, x, y):
@@ -279,4 +279,8 @@ class TestConvert(unittest.TestCase):
             def forward(self, x):
                 out1 = x.ceil()
                 return out1
-        self.checkExportImport(SimpleOp(), (torch.randn(4), ))
\ No newline at end of file
+        self.checkExportImport(SimpleOp(), (torch.randn(4), ))
+
+
+class TestConvertWithShape(TestConvert, ConvertWithShapeMixin):
+    pass

test/ut/retiarii/test_convert_models.py

@@ -10,11 +10,12 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import serialize
-from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
 
+from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
-class TestModels(unittest.TestCase):
+
+class TestModels(unittest.TestCase, ConvertMixin):
     @staticmethod
     def _match_state_dict(current_values, expected_format):
         result = {}
@@ -27,8 +28,7 @@ class TestModels(unittest.TestCase):
         return result
 
     def run_test(self, model, input, check_value=True):
-        script_module = torch.jit.script(model)
-        model_ir = convert_to_graph(script_module, model)
+        model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
         print(model_code)
 
@@ -89,3 +89,6 @@ class TestModels(unittest.TestCase):
         model = Net(4)
         x = torch.rand((1, 16), dtype=torch.float)
         self.run_test(model, ([x], ))
+
+class TestModelsWithShape(TestModels, ConvertWithShapeMixin):
+    pass

test/ut/retiarii/test_convert_operators.py

@@ -15,13 +15,14 @@ import torch.nn.functional as F
 import torchvision
 
 import nni.retiarii.nn.pytorch as nn
-from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
 
+from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
+
 # following pytorch v1.7.1
 
 
-class TestOperators(unittest.TestCase):
+class TestOperators(unittest.TestCase, ConvertMixin):
     @staticmethod
     def _match_state_dict(current_values, expected_format):
         result = {}
@@ -34,8 +35,7 @@ class TestOperators(unittest.TestCase):
         return result
 
     def checkExportImport(self, model, input, check_value=True):
-        script_module = torch.jit.script(model)
-        model_ir = convert_to_graph(script_module, model)
+        model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
         #print(model_code)
 
@@ -1042,7 +1042,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.tensor([[[[0.0, 1.0, 1.0, 1.0], [2.0, 3.0, 7.0, 7.0]]]], requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_batchnorm(self):
         class SimpleOp(nn.Module):
@@ -1056,7 +1056,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.ones(2, 2, 2, 2, requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_batchnorm_1d(self):
         class SimpleOp(nn.Module):
@@ -1084,7 +1084,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.ones(20, 16, 50, 40, requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
     def test_conv_onnx_irv4_opset8(self):
         # This test point checks that for opset 8 (or lower), even if
         # keep_initializers_as_inputs is set to False, it is ignored,
@@ -1129,7 +1129,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(20, 16, 50)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_maxpool_dilations(self):
         class SimpleOp(nn.Module):
@@ -1143,7 +1143,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(20, 16, 50)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_avg_pool2d(self):
         class SimpleOp(nn.Module):
@@ -1157,7 +1157,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(20, 16, 50, 32)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
     @unittest.skip('jit error: "Return value was annotated as having type Tensor but is actually of type Tuple[Tensor, Tensor]"')
     def test_basic_maxpool_indices(self):
         class SimpleOp(nn.Module):
@@ -1200,7 +1200,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(1, 2, 3, 4, requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_elu(self):
         class SimpleOp(nn.Module):
@@ -1214,7 +1214,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(1, 2, 3, 4, requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_selu(self):
         class SimpleOp(nn.Module):
@@ -1261,7 +1261,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(128, 128, 1, 1, requires_grad=True)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
     def test_embedding_bags(self):
         class SimpleOp(nn.Module):
             def __init__(self):
@@ -1288,7 +1288,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(1, 2, 3, 4)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_prelu(self):
         class SimpleOp(nn.Module):
@@ -1302,7 +1302,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(1, 2, 3, 4)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_log_sigmoid(self):
         class SimpleOp(nn.Module):
@@ -1316,7 +1316,7 @@ class TestOperators(unittest.TestCase):
 
         x = torch.randn(1, 2, 3, 4)
         self.checkExportImport(SimpleOp(), (x, ))
-    
+
 
     def test_basic_linear(self):
         class SimpleOp(nn.Module):
@@ -1385,4 +1385,7 @@ class TestOperators(unittest.TestCase):
                 return out
 
         x = torch.randn(20, 5, 10, 10)
-        self.checkExportImport(SimpleOp(), (x, ))
\ No newline at end of file
+        self.checkExportImport(SimpleOp(), (x, ))
+
+class TestOperatorsWithShape(TestOperators, ConvertWithShapeMixin):
+    pass

test/ut/retiarii/test_convert_pytorch.py

@@ -15,11 +15,12 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import serialize
-from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
 
+from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
-class TestPytorch(unittest.TestCase):
+
+class TestPytorch(unittest.TestCase, ConvertMixin):
     @staticmethod
     def _match_state_dict(current_values, expected_format):
         result = {}
@@ -32,8 +33,7 @@ class TestPytorch(unittest.TestCase):
         return result
 
     def run_test(self, model, input, check_value=True):
-        script_module = torch.jit.script(model)
-        model_ir = convert_to_graph(script_module, model)
+        model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
         print(model_code)
 
@@ -1230,4 +1230,7 @@ class TestPytorch(unittest.TestCase):
                     return torch.arange(input.size(0)), torch.arange(input.size(-1)), torch.ones(input.shape)
 
         x = torch.randn(5, 3, 2)
-        self.run_test(SizeModel(10, 5), (x, ))
\ No newline at end of file
+        self.run_test(SizeModel(10, 5), (x, ))
+
+class TestPytorchWithShape(TestPytorch, ConvertWithShapeMixin):
+    pass

test/ut/retiarii/test_convert_shape.py

+import unittest
+import torch
+
+import nni.retiarii.nn.pytorch as nn
+
+from .convert_mixin import ConvertWithShapeMixin
+
+
+class TestShape(unittest.TestCase, ConvertWithShapeMixin):
+    def test_simple_convnet(self):
+        class ConvNet(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.Conv2d(3, 1, 3)
+                self.relu = nn.ReLU()
+                self.pool = nn.MaxPool2d(kernel_size=2)
+            def forward(self, x):
+                return self.pool(self.relu(self.conv(x)))
+
+        net = ConvNet()
+        input = torch.randn((1, 3, 224, 224))
+        model_ir = self._convert_model(net, input)
+
+        conv_node = model_ir.get_nodes_by_type('__torch__.torch.nn.modules.conv.Conv2d')[0]
+        relu_node = model_ir.get_nodes_by_type('__torch__.torch.nn.modules.activation.ReLU')[0]
+        pool_node = model_ir.get_nodes_by_type('__torch__.torch.nn.modules.pooling.MaxPool2d')[0]
+        self.assertEqual(conv_node.operation.parameters.get('input_shape'), [[1, 3, 224, 224]])
+        self.assertEqual(conv_node.operation.parameters.get('output_shape'), [[1, 1, 222, 222]])
+        self.assertEqual(relu_node.operation.parameters.get('input_shape'), [[1, 1, 222, 222]])
+        self.assertEqual(relu_node.operation.parameters.get('output_shape'), [[1, 1, 222, 222]])
+        self.assertEqual(pool_node.operation.parameters.get('input_shape'), [[1, 1, 222, 222]])
+        self.assertEqual(pool_node.operation.parameters.get('output_shape'), [[1, 1, 111, 111]])
+
+    def test_nested_module(self):
+        class ConvRelu(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.Conv2d(3, 1, 3)
+                self.relu = nn.ReLU()
+            def forward(self, x):
+                return self.relu(self.conv(x))
+
+        class ConvNet(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = ConvRelu()
+                self.pool = nn.MaxPool2d(kernel_size=2)
+            def forward(self, x):
+                return self.pool(self.conv(x))
+
+        net = ConvNet()
+        input = torch.randn((1, 3, 224, 224))
+        model_ir = self._convert_model(net, input)
+
+        # check if shape propagation works
+        cell_node = model_ir.get_nodes_by_type('_cell')[0]
+        self.assertEqual(cell_node.operation.parameters.get('input_shape'), [[1, 3, 224, 224]])
+        self.assertEqual(cell_node.operation.parameters.get('output_shape'), [[1, 1, 222, 222]])
+
+    def test_layerchoice(self):
+        class ConvNet(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.LayerChoice([
+                    nn.Conv2d(3, 1, 3),
+                    nn.Conv2d(3, 1, 5, padding=1),
+                ])
+                self.pool = nn.MaxPool2d(kernel_size=2)
+            def forward(self, x):
+                return self.pool(self.conv(x))
+
+        net = ConvNet()
+        input = torch.randn((1, 3, 224, 224))
+        model_ir = self._convert_model(net, input)
+
+        # check shape info of each candidates
+        conv_nodes = model_ir.get_nodes_by_type('__torch__.torch.nn.modules.conv.Conv2d')
+        self.assertEqual(conv_nodes[0].operation.parameters.get('output_shape'), [[1, 1, 222, 222]])
+        self.assertEqual(conv_nodes[1].operation.parameters.get('output_shape'), [[1, 1, 222, 222]])