Use GraphBuilder in unit tests for ops removal #2.

Differential Revision: D75034439

Pull Request resolved: #11011

Author: eigen-k

backends/cadence/aot/tests/test_remove_ops_passes.py

@@ -235,36 +235,28 @@ def test_remove_zero_arg_cat(self):
         )
 
     def test_remove_clone(self):
-        class Clone(torch.nn.Module):
-            def forward(self, x, y):
-                t1 = x.clone()
-                t2 = y.clone()
-                return t1 + t2
-
-        x = torch.ones(3, 5)
-        y = torch.ones(3, 5)
-        graph_module = export_to_edge(Clone(), (x, y)).exported_program().graph_module
-        new_graph_module = RemoveCloneOpPass()(graph_module).graph_module
-        new_graph_module.graph.eliminate_dead_code()
-        # Assert that t1 and t2 are optimized away
-        self.assertEqual(count_node(new_graph_module, torch.ops.aten.clone.out), 0)
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn([3, 5], dtype=torch.float32))
+        clone = builder.call_operator(op=exir_ops.edge.aten.clone.default, args=(x,))
+        builder.output([clone])
+        original = builder.get_graph_module()
+        graph_after_passes = RemoveCloneOpPass()(original).graph_module
+        self.assertEqual(
+            count_node(graph_after_passes, torch.ops.aten.clone.default), 0
+        )
 
     def test_remove_contiguous(self):
-        class Contiguous(torch.nn.Module):
-            def forward(self, x, y):
-                t1 = x.contiguous()
-                t2 = y.contiguous()
-                return t1 + t2
-
-        x = torch.ones(3, 5)
-        y = torch.ones(3, 5)
-        graph_module = (
-            export_to_edge(Contiguous(), (x, y)).exported_program().graph_module
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn([3, 5], dtype=torch.float32))
+        contiguous = builder.call_operator(
+            op=exir_ops.edge.aten.contiguous.default, args=(x,)
+        )
+        builder.output([contiguous])
+        original = builder.get_graph_module()
+        graph_after_passes = RemoveContiguousOpPass()(original).graph_module
+        self.assertEqual(
+            count_node(graph_after_passes, torch.ops.aten.contiguous.default), 0
         )
-        new_graph_module = RemoveContiguousOpPass()(graph_module).graph_module
-        new_graph_module.graph.eliminate_dead_code()
-        # Assert that t1 and t2 are optimized away
-        self.assertEqual(count_node(new_graph_module, torch.ops.aten.contiguous.out), 0)
 
     @parameterized.expand(
         [
@@ -274,119 +266,129 @@ def forward(self, x, y):
     )
     @torch.no_grad()
     def test_remove_nop_view(self, shape, new_shape):
-        class View(torch.nn.Module):
-            def __init__(self, new_shape):
-                super().__init__()
-                self.new_shape = new_shape
-
-            def forward(self, x: torch.Tensor):
-                return x.view(self.new_shape)
-
-        model = View(new_shape)
-        x = torch.randn(shape)
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-        p = RemoveNopSliceOrViewOpPass()
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-        graph_after_passes.graph.eliminate_dead_code()
-        # Assert that view op was removed
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        view = builder.call_operator(
+            op=exir_ops.edge.aten.view_copy.default, args=(x, new_shape)
+        )
+        builder.output([view])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSliceOrViewOpPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.view_copy.default), 0
         )
 
     def test_remove_nop_slice(self):
-        class Slice(torch.nn.Module):
-            def forward(self, x):
-                return torch.slice_copy(x, dim=0, start=0, step=1)
-
-        x = torch.ones(3, 5)
-        model = Slice()
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-        p = RemoveNopSliceOrViewOpPass()
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-        graph_after_passes.graph.eliminate_dead_code()
-        # Assert that slice op was removed
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(3, 5, dtype=torch.float32))
+        slice_ = builder.call_operator(
+            op=exir_ops.edge.aten.slice_copy.Tensor,
+            args=(
+                x,
+                0,  # dim
+                0,  # start
+                3,  # end
+            ),
+        )
+        builder.output([slice_])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSliceOrViewOpPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.slice_copy.Tensor), 0
         )
 
-    def test_remove_nop_select(self):
-        class SelectFeasible1(torch.nn.Module):
-            def forward(self, x):
-                y = x.select(0, 0)
-                z = y.view([1, 5, 6])
-                return z
-
-        x = torch.ones(1, 5, 6)
-        graph_module = (
-            export_to_edge(SelectFeasible1(), (x,)).exported_program().graph_module
+    def test_remove_nop_select_before_view(self):
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 5, 6, dtype=torch.float32))
+        select = builder.call_operator(
+            op=exir_ops.edge.aten.select_copy.int,
+            args=(
+                x,
+                0,  # dim
+                0,  # index
+            ),
         )
-        self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 1
+        view = builder.call_operator(
+            op=exir_ops.edge.aten.view_copy.default,
+            args=(select, [1, 5, 6]),  # new shape
         )
-        graph_module = RemoveNopSelectOpPass()(graph_module).graph_module
-        # Assert that select op was removed
+        builder.output([view])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSelectOpPass()(original)
+        ).graph_module
         self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 0
+            count_node(graph_after_passes, exir_ops.edge.aten.select_copy.int), 0
         )
 
-        class SelectFeasible2(torch.nn.Module):
-            def forward(self, x, y):
-                x = x.select(0, 0)
-                z = x + y
-                return z
-
-        x = torch.ones(1, 5, 6)
-        y = torch.ones(1, 5, 6)
-        graph_module = (
-            export_to_edge(SelectFeasible2(), (x, y)).exported_program().graph_module
-        )
-        self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 1
+    def test_remove_nop_select_before_add(self):
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 5, 6, dtype=torch.float32))
+        y = builder.placeholder("y", torch.randn(1, 5, 6, dtype=torch.float32))
+        select = builder.call_operator(
+            op=exir_ops.edge.aten.select_copy.int,
+            args=(
+                x,
+                0,  # dim
+                0,  # index
+            ),
         )
-        graph_module = RemoveNopSelectOpPass()(graph_module).graph_module
-        # Assert that select op was removed
+        add = builder.call_operator(op=exir_ops.edge.aten.add.Tensor, args=(select, y))
+        builder.output([add])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSelectOpPass()(original)
+        ).graph_module
         self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 0
+            count_node(graph_after_passes, exir_ops.edge.aten.select_copy.int), 0
         )
 
-        class SelectFeasible3(torch.nn.Module):
-            def forward(self, x, y):
-                x = x.select(0, 0)
-                z = x * y
-                return z
-
-        x = torch.ones(1, 5, 6)
-        y = torch.ones(1, 5, 6)
-        graph_module = (
-            export_to_edge(SelectFeasible3(), (x, y)).exported_program().graph_module
-        )
-        self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 1
+    def test_remove_nop_select_before_mul(self):
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 5, 6, dtype=torch.float32))
+        y = builder.placeholder("y", torch.randn(1, 5, 6, dtype=torch.float32))
+        select = builder.call_operator(
+            op=exir_ops.edge.aten.select_copy.int,
+            args=(
+                x,
+                0,  # dim
+                0,  # index
+            ),
         )
-        graph_module = RemoveNopSelectOpPass()(graph_module).graph_module
-        # Assert that select op was removed
+        mul = builder.call_operator(op=exir_ops.edge.aten.mul.Tensor, args=(select, y))
+        builder.output([mul])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSelectOpPass()(original)
+        ).graph_module
         self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 0
+            count_node(graph_after_passes, exir_ops.edge.aten.select_copy.int), 0
         )
 
-        class SelectFeasible4(torch.nn.Module):
-            def forward(self, x, y):
-                x = x.select(0, 0)
-                z = x / y
-                return z
-
-        x = torch.ones(1, 5, 6)
-        y = torch.ones(1, 5, 6)
-        graph_module = (
-            export_to_edge(SelectFeasible4(), (x, y)).exported_program().graph_module
-        )
-        self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 1
+    def test_remove_nop_select_before_div(self):
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 5, 6, dtype=torch.float32))
+        y = builder.placeholder("y", torch.randn(1, 5, 6, dtype=torch.float32))
+        select = builder.call_operator(
+            op=exir_ops.edge.aten.select_copy.int,
+            args=(
+                x,
+                0,  # dim
+                0,  # index
+            ),
         )
-        graph_module = RemoveNopSelectOpPass()(graph_module).graph_module
-        # Assert that select op was removed
+        div = builder.call_operator(op=exir_ops.edge.aten.div.Tensor, args=(select, y))
+        builder.output([div])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopSelectOpPass()(original)
+        ).graph_module
         self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.select_copy.int), 0
+            count_node(graph_after_passes, exir_ops.edge.aten.select_copy.int), 0
         )
 
     def test_remove_nop_quant_dequant(self):