[converter] support aten::{maximum, minimum}

docs/op_matrix.md

@@ -111,9 +111,11 @@ Operators that are implemented in Python
 | `aten::matmul` |  |
 | `aten::max` |  |
 | `aten::max_pool2d` | Only dilation == 1 is supported |
+| `aten::maximum` |  |
 | `aten::mean` |  |
 | `aten::meshgrid` | aten::meshgrid for dynamic tensors is not supported |
 | `aten::min` |  |
+| `aten::minimum` |  |
 | `aten::mish` |  |
 | `aten::mm` |  |
 | `aten::mul` |  |

tests/converter_op_test.py

@@ -86,14 +86,14 @@ def test_sign(self):
 
         def model(x):
             return torch.sign(x)
-        
+
         model_path = get_model_path()
         converter = TFLiteConverter(model, dummy_input, model_path, nchw_transpose=False)
         converter.convert()
 
         dummy_output = model(dummy_input)
         tfl_output = tfl_run_model(model_path, dummy_input, dummy_output)
-        assert_close(dummy_output, tfl_output) 
+        assert_close(dummy_output, tfl_output)
 
     def test_masked_fill(self):
         class TestModel(nn.Module):
@@ -188,6 +188,8 @@ def test_binary_elementwise_same_dtype(self):
             (torch, 'eq'),
             (torch, 'ne'),
             (torch, 'rsub'),
+            (torch, 'maximum'),
+            (torch, 'minimum'),
         ]
 
         funcs = [getattr(ns, attr) for ns, attr in func_names if hasattr(ns, attr)]
@@ -224,6 +226,8 @@ def test_binary_elementwise_constant_same_dtype(self):
             (torch, 'eq'),
             (torch, 'ne'),
             (torch, 'rsub'),
+            (torch, 'maximum'),
+            (torch, 'minimum'),
         ]
 
         funcs = [getattr(ns, attr) for ns, attr in func_names if hasattr(ns, attr)]
@@ -259,6 +263,8 @@ def test_binary_elementwise_different_dtype(self):
             (torch, 'eq'),
             (torch, 'ne'),
             (torch, 'rsub'),
+            (torch, 'maximum'),
+            (torch, 'minimum'),
         ]
 
         funcs = [getattr(ns, attr) for ns, attr in func_names if hasattr(ns, attr)]
@@ -295,6 +301,8 @@ def test_binary_elementwise_constant_different_dtype(self):
             (torch, 'eq'),
             (torch, 'ne'),
             (torch, 'rsub'),
+            (torch, 'maximum'),
+            (torch, 'minimum'),
         ]
 
         funcs = [getattr(ns, attr) for ns, attr in func_names if hasattr(ns, attr)]
@@ -5294,7 +5302,7 @@ def model(x):
         dummy_output = model(dummy_input)
         tfl_output = tfl_run_model(model_path, dummy_input, dummy_output)
         assert_close(dummy_output, tfl_output, atol=256.0, rtol=256.0)
-        
+
     @unittest.skipIf(not hasattr(torch, 'norm'), "Norm is not supported")
     def test_norm_p1(self):
         dummy_input = torch.randn(10, 10, dtype=torch.float32)

tinynn/converter/operators/torch/__init__.py

@@ -179,6 +179,8 @@
     "aten::baddbmm": ATenBaddbmmOperator,
     "aten::linalg_vector_norm": ATenLinalgVectorNormOperator,
     "aten::broadcast_tensors": ATenBroadcastTensorsOperator,
+    "aten::maximum": ATenMaximumOperator,
+    "aten::minimum": ATenMinimumOperator,
     # quantized
     "aten::quantize_per_tensor": ATenQuantizePerTensorOperator,
     "aten::fake_quantize_per_tensor_affine": ATenFakeQuantizePerTensorAffineOperator,

tinynn/converter/operators/torch/aten.py

@@ -15,7 +15,7 @@
 
 class AtenSignOperator(ATenSignSchema):
     def parse(self, node, attrs, args, graph_converter):
-        
+
         super().parse(node, attrs, args, graph_converter)
 
         self.run(node)
@@ -3453,6 +3453,28 @@ def parse_common(self, graph_converter, input_idx=0, mask_idx=1, other_idx=2, ou
             graph_converter.add_operator(op)
 
 
+class ATenMaximumOperator(ATenMaximumSchema):
+    def parse(self, node, attrs, args, graph_converter):
+        super().parse(node, attrs, args, graph_converter)
+
+        self.run(node)
+        if type(self.input_tensors[1]) != torch.Tensor:
+            self.input_tensors[1] = self.torch_tensor_from_scalar(self.input_tensors[0], self.input_tensors[1])
+
+        self.elementwise_binary(tfl.MaximumOperator, graph_converter, True)
+
+
+class ATenMinimumOperator(ATenMinimumSchema):
+    def parse(self, node, attrs, args, graph_converter):
+        super().parse(node, attrs, args, graph_converter)
+
+        self.run(node)
+        if type(self.input_tensors[1]) != torch.Tensor:
+            self.input_tensors[1] = self.torch_tensor_from_scalar(self.input_tensors[0], self.input_tensors[1])
+
+        self.elementwise_binary(tfl.MinimumOperator, graph_converter, True)
+
+
 class ATenGtOperator(ATenGtSchema):
     def parse(self, node, attrs, args, graph_converter):
         super().parse(node, attrs, args, graph_converter)
@@ -3792,7 +3814,7 @@ def parse(self, node, attrs, args, graph_converter):
         self.run(node)
         self.parse_common(node, attrs, args, graph_converter)
 
-        
+
 class ATenLinalgVectorNormOperator(ATenLinalgVectorNormSchema):
     def parse(self, node, attrs, args, graph_converter):
         super().parse(node, attrs, args, graph_converter)