diff --git a/backends/cadence/aot/tests/test_quantizer_ops.py b/backends/cadence/aot/tests/test_quantizer_ops.py index 99953346b05..4e05a33959c 100644 --- a/backends/cadence/aot/tests/test_quantizer_ops.py +++ b/backends/cadence/aot/tests/test_quantizer_ops.py @@ -55,10 +55,6 @@ CadenceNopQuantizer, # No-op quantizer, doesn't annotate anything CadenceW8A32MixedQuantizer, # TODO: T247438158 Add test coverage CadenceRmsNormNopQuantizer, # No-op quantizer, doesn't annotate anything, preserves rms_norm from decomposition - CadenceWakeWordQuantizer, # TODO: T247438162 Add test coverage - CadenceWith16BitConvActivationsQuantizer, # TODO: T247438221 Add test coverage - CadenceWithLayerNormQuantizer, # TODO: T247438410 Add test coverage - CadenceWithSoftmaxQuantizer, # TODO: T247438418 Add test coverage } @@ -93,6 +89,51 @@ # For linear: [input_activation, weight] [qconfig_A16.input_activation, qconfig_A16.weight], ), + ( + "conv1d_A16", + lambda self: self._build_conv1d_graph(), + CadenceWith16BitConvActivationsQuantizer(), + torch.ops.aten.conv1d.default, + qconfig_A16.output_activation, + # For conv1d: [input_activation, weight] + [qconfig_A16.input_activation, qconfig_A16.weight], + ), + ( + "conv2d_A16", + lambda self: self._build_conv2d_graph(), + CadenceWith16BitConvActivationsQuantizer(), + torch.ops.aten.conv2d.default, + qconfig_A16.output_activation, + # For conv2d: [input_activation, weight] + [qconfig_A16.input_activation, qconfig_A16.weight], + ), + ( + "softmax_A16", + lambda self: self._build_softmax_graph(), + CadenceWithSoftmaxQuantizer(), + torch.ops.aten._softmax.default, + qconfig_A16.output_activation, + # For softmax: only input_activation + [qconfig_A16.input_activation], + ), + ( + "layer_norm_A8W8", + lambda self: self._build_layer_norm_graph(), + CadenceWithLayerNormQuantizer(), + torch.ops.aten.layer_norm.default, + qconfig_A8W8.output_activation, + # For layer_norm: only input_activation (weights/bias are passed as others) + [qconfig_A8W8.input_activation], + ), + ( + "add_A8W8", + lambda self: self._build_add_graph(), + CadenceWakeWordQuantizer(), + torch.ops.aten.add.Tensor, + qconfig_A8W8.output_activation, + # For add: both inputs are activations + [qconfig_A8W8.input_activation, qconfig_A8W8.input_activation], + ), ] # Derive the set of tested quantizer classes from the test cases. @@ -149,6 +190,123 @@ def _build_linear_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: self.assertEqual(len(linear_nodes), 1, "Should find exactly one linear node") return gm, linear_nodes[0] + def _build_conv1d_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: + """Build a simple graph with a conv1d operation (no bias).""" + builder = GraphBuilder() + # Input shape: (batch, in_channels, length) + x = builder.placeholder("x", torch.randn(1, 3, 10)) + # Weight shape: (out_channels, in_channels, kernel_size) + weight = builder.placeholder("weight", torch.randn(6, 3, 3)) + conv1d = builder.call_operator( + op=torch.ops.aten.conv1d.default, + args=(x, weight), + meta=NodeMetadata( + {"source_fn_stack": [("conv1d", torch.ops.aten.conv1d.default)]} + ), + ) + builder.output([conv1d]) + gm = builder.get_graph_module() + + conv1d_nodes = gm.graph.find_nodes( + op="call_function", + target=torch.ops.aten.conv1d.default, + ) + self.assertEqual(len(conv1d_nodes), 1, "Should find exactly one conv1d node") + return gm, conv1d_nodes[0] + + def _build_conv2d_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: + """Build a simple graph with a conv2d operation (no bias).""" + builder = GraphBuilder() + # Input shape: (batch, in_channels, height, width) + x = builder.placeholder("x", torch.randn(1, 3, 8, 8)) + # Weight shape: (out_channels, in_channels, kernel_h, kernel_w) + weight = builder.placeholder("weight", torch.randn(6, 3, 3, 3)) + conv2d = builder.call_operator( + op=torch.ops.aten.conv2d.default, + args=(x, weight), + meta=NodeMetadata( + {"source_fn_stack": [("conv2d", torch.ops.aten.conv2d.default)]} + ), + ) + builder.output([conv2d]) + gm = builder.get_graph_module() + + conv2d_nodes = gm.graph.find_nodes( + op="call_function", + target=torch.ops.aten.conv2d.default, + ) + self.assertEqual(len(conv2d_nodes), 1, "Should find exactly one conv2d node") + return gm, conv2d_nodes[0] + + def _build_softmax_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: + """Build a simple graph with a softmax operation.""" + builder = GraphBuilder() + x = builder.placeholder("x", torch.randn(1, 10)) + softmax = builder.call_operator( + op=torch.ops.aten._softmax.default, + args=(x, -1, False), # dim=-1, half_to_float=False + meta=NodeMetadata( + {"source_fn_stack": [("softmax", torch.ops.aten._softmax.default)]} + ), + ) + builder.output([softmax]) + gm = builder.get_graph_module() + + softmax_nodes = gm.graph.find_nodes( + op="call_function", + target=torch.ops.aten._softmax.default, + ) + self.assertEqual(len(softmax_nodes), 1, "Should find exactly one softmax node") + return gm, softmax_nodes[0] + + def _build_layer_norm_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: + """Build a simple graph with a layer_norm operation.""" + builder = GraphBuilder() + # Input shape: (batch, features) + x = builder.placeholder("x", torch.randn(1, 10)) + # normalized_shape must match the last dimension(s) of input + normalized_shape = [10] + layer_norm = builder.call_operator( + op=torch.ops.aten.layer_norm.default, + args=(x, normalized_shape), + meta=NodeMetadata( + {"source_fn_stack": [("layer_norm", torch.ops.aten.layer_norm.default)]} + ), + ) + builder.output([layer_norm]) + gm = builder.get_graph_module() + + layer_norm_nodes = gm.graph.find_nodes( + op="call_function", + target=torch.ops.aten.layer_norm.default, + ) + self.assertEqual( + len(layer_norm_nodes), 1, "Should find exactly one layer_norm node" + ) + return gm, layer_norm_nodes[0] + + def _build_add_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: + """Build a simple graph with an add operation.""" + builder = GraphBuilder() + x = builder.placeholder("x", torch.randn(1, 10)) + y = builder.placeholder("y", torch.randn(1, 10)) + add = builder.call_operator( + op=torch.ops.aten.add.Tensor, + args=(x, y), + meta=NodeMetadata( + {"source_fn_stack": [("add", torch.ops.aten.add.Tensor)]} + ), + ) + builder.output([add]) + gm = builder.get_graph_module() + + add_nodes = gm.graph.find_nodes( + op="call_function", + target=torch.ops.aten.add.Tensor, + ) + self.assertEqual(len(add_nodes), 1, "Should find exactly one add node") + return gm, add_nodes[0] + @parameterized.expand(QUANTIZER_ANNOTATION_TEST_CASES) def test_quantizer_annotation( self,