Add dynamic shape tests for xnnpack model tests

Add dynamic shape support to xnnpack model tests that previously
only tested with static inputs. This covers DeepLab V3, EDSR,
Inception V3/V4, Emformer RNNT, and MobileBERT using the same
DynamicWrapper + interpolate pattern established by ResNet and ViT.

Addresses #11585

Signed-off-by: Lidang-Jiang <lidangjiang@gmail.com>

Author: Lidang-Jiang

backends/xnnpack/test/models/deeplab_v3.py

@@ -22,13 +22,37 @@ def forward(self, *args):
         return self.m(*args)["out"]
 
 
+class DynamicDL3Wrapper(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.m = deeplabv3_resnet50(
+            weights=deeplabv3.DeepLabV3_ResNet50_Weights.DEFAULT
+        )
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(
+            x,
+            size=(224, 224),
+            mode="bilinear",
+            align_corners=True,
+            antialias=False,
+        )
+        return self.m(x)["out"]
+
+
 class TestDeepLabV3(unittest.TestCase):
     def setUp(self):
         torch._dynamo.reset()
 
     dl3 = DL3Wrapper()
     dl3 = dl3.eval()
     model_inputs = (torch.randn(1, 3, 224, 224),)
+    dynamic_shapes = (
+        {
+            2: torch.export.Dim("height", min=224, max=455),
+            3: torch.export.Dim("width", min=224, max=455),
+        },
+    )
 
     def test_fp32_dl3(self):
 
@@ -40,3 +64,13 @@ def test_fp32_dl3(self):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    def test_fp32_dl3_dynamic(self):
+        (
+            Tester(DynamicDL3Wrapper(), self.model_inputs, self.dynamic_shapes)
+            .export()
+            .to_edge_transform_and_lower()
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )

backends/xnnpack/test/models/edsr.py

@@ -13,12 +13,34 @@
 from torchsr.models import edsr_r16f64
 
 
+class DynamicEDSR(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = edsr_r16f64(2, False).eval()
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(
+            x,
+            size=(224, 224),
+            mode="bilinear",
+            align_corners=True,
+            antialias=False,
+        )
+        return self.model(x)
+
+
 class TestEDSR(unittest.TestCase):
     def setUp(self):
         torch._dynamo.reset()
 
     edsr = edsr_r16f64(2, False).eval()  # noqa
     model_inputs = (torch.randn(1, 3, 224, 224),)
+    dynamic_shapes = (
+        {
+            2: torch.export.Dim("height", min=224, max=455),
+            3: torch.export.Dim("width", min=224, max=455),
+        },
+    )
 
     def test_fp32_edsr(self):
         (
@@ -53,3 +75,13 @@ def test_qs8_edsr_no_calibrate(self):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    def test_fp32_edsr_dynamic(self):
+        (
+            Tester(DynamicEDSR(), self.model_inputs, self.dynamic_shapes)
+            .export()
+            .to_edge_transform_and_lower()
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )

backends/xnnpack/test/models/emformer_rnnt.py

@@ -48,6 +48,24 @@ def test_fp32_emformer_joiner(self):
             .run_method_and_compare_outputs()
         )
 
+    def test_fp32_emformer_joiner_dynamic(self):
+        joiner = self.Joiner()
+        dynamic_shapes = (
+            {0: torch.export.Dim("batch", min=1, max=4)},
+            None,
+            {0: torch.export.Dim("batch", min=1, max=4)},
+            None,
+        )
+        (
+            Tester(joiner, joiner.get_example_inputs(), dynamic_shapes=dynamic_shapes)
+            .export()
+            .to_edge_transform_and_lower()
+            .check(["torch.ops.higher_order.executorch_call_delegate"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )
+
     class Predictor(EmformerRnnt):
         def forward(self, a, b):
             return self.rnnt.predict(a, b, None)
@@ -96,3 +114,23 @@ def test_fp32_emformer_transcriber(self):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    def test_fp32_emformer_transcriber_dynamic(self):
+        transcriber = self.Transcriber()
+        dynamic_shapes = (
+            {0: torch.export.Dim("batch", min=1, max=4)},
+            None,
+        )
+        (
+            Tester(
+                transcriber,
+                transcriber.get_example_inputs(),
+                dynamic_shapes=dynamic_shapes,
+            )
+            .export()
+            .to_edge_transform_and_lower()
+            .check(["torch.ops.higher_order.executorch_call_delegate"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )

backends/xnnpack/test/models/inception_v3.py

@@ -12,12 +12,34 @@
 from torchvision import models
 
 
+class DynamicInceptionV3(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = models.inception_v3(weights="IMAGENET1K_V1").eval()
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(
+            x,
+            size=(224, 224),
+            mode="bilinear",
+            align_corners=True,
+            antialias=False,
+        )
+        return self.model(x)
+
+
 class TestInceptionV3(unittest.TestCase):
     def setUp(self):
         torch._dynamo.reset()
 
     ic3 = models.inception_v3(weights="IMAGENET1K_V1").eval()  # noqa
     model_inputs = (torch.randn(1, 3, 224, 224),)
+    dynamic_shapes = (
+        {
+            2: torch.export.Dim("height", min=224, max=455),
+            3: torch.export.Dim("width", min=224, max=455),
+        },
+    )
 
     all_operators = {
         "executorch_exir_dialects_edge__ops_aten_addmm_default",
@@ -82,3 +104,14 @@ def test_qs8_ic3_no_calibration(self):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    def test_fp32_ic3_dynamic(self):
+        (
+            Tester(DynamicInceptionV3(), self.model_inputs, self.dynamic_shapes)
+            .export()
+            .to_edge_transform_and_lower()
+            .check(["torch.ops.higher_order.executorch_call_delegate"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )

backends/xnnpack/test/models/inception_v4.py

@@ -11,12 +11,34 @@
 from timm.models import inception_v4
 
 
+class DynamicInceptionV4(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = inception_v4(pretrained=False).eval()
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(
+            x,
+            size=(299, 299),
+            mode="bilinear",
+            align_corners=True,
+            antialias=False,
+        )
+        return self.model(x)
+
+
 class TestInceptionV4(unittest.TestCase):
     def setUp(self):
         torch._dynamo.reset()
 
     ic4 = inception_v4(pretrained=False).eval()
     model_inputs = (torch.randn(3, 299, 299).unsqueeze(0),)
+    dynamic_shapes = (
+        {
+            2: torch.export.Dim("height", min=299, max=455),
+            3: torch.export.Dim("width", min=299, max=455),
+        },
+    )
 
     all_operators = {
         "executorch_exir_dialects_edge__ops_aten_addmm_default",
@@ -60,3 +82,14 @@ def test_qs8_ic4(self):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    def test_fp32_ic4_dynamic(self):
+        (
+            Tester(DynamicInceptionV4(), self.model_inputs, self.dynamic_shapes)
+            .export()
+            .to_edge_transform_and_lower()
+            .check(["torch.ops.higher_order.executorch_call_delegate"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )

backends/xnnpack/test/models/mobilebert.py

@@ -31,6 +31,10 @@ def setUp(self):
         "executorch_exir_dialects_edge__ops_aten_constant_pad_nd_default",
     }
 
+    dynamic_shapes = (
+        {1: torch.export.Dim("seq_length", min=2, max=32)},
+    )
+
     def test_fp32_mobilebert(self):
         (
             Tester(self.mobilebert, self.example_inputs)
@@ -53,3 +57,18 @@ def test_qs8_mobilebert(self):
             .serialize()
             .run_method_and_compare_outputs(inputs=self.example_inputs)
         )
+
+    def test_fp32_mobilebert_dynamic(self):
+        (
+            Tester(
+                self.mobilebert,
+                self.example_inputs,
+                dynamic_shapes=self.dynamic_shapes,
+            )
+            .export()
+            .to_edge_transform_and_lower()
+            .check(["torch.ops.higher_order.executorch_call_delegate"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs(inputs=self.example_inputs)
+        )