Fix a few test files

Signed-off-by: zjgarvey <[email protected]>

Author: zjgarvey

projects/e2e/torch_mlir_e2e_test/test_suite/conv.py

@@ -1088,6 +1088,94 @@ def UpSampleNearest2dStaticFactor_basic(module, tu: TestUtils):
     module.forward(tu.rand(2, 3, 4, 4))
 
 
+class UpSampleNearest2dVecNoneShape(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float64, True),
+        ]
+    )
+    def forward(self, input):
+        return torch.ops.aten.upsample_nearest2d.vec(
+            input, output_size=None, scale_factors=[3.66, 4.2]
+        )
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest2dVecNoneShape())
+def UpSampleNearest2dVecNoneShape_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 6, 12).to(torch.float64))
+
+
+class UpSampleNearest2dVecNoneScales(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float64, True),
+        ]
+    )
+    def forward(self, input):
+        return torch.ops.aten.upsample_nearest2d.vec(
+            input,
+            output_size=[18, 48],
+            scale_factors=None,
+        )
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest2dVecNoneScales())
+def UpSampleNearest2dVecNoneScales_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 6, 12).to(torch.float64))
+
+
+class UpSampleNearest1dVecNoneShape(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float64, True),
+        ]
+    )
+    def forward(self, input):
+        return torch.ops.aten.upsample_nearest1d.vec(
+            input, output_size=None, scale_factors=[3.0]
+        )
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest1dVecNoneShape())
+def UpSampleNearest1dVecNoneShape_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 6).to(torch.float64))
+
+
+class UpSampleNearest1dVecNoneScales(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float64, True),
+        ]
+    )
+    def forward(self, input):
+        return torch.ops.aten.upsample_nearest1d.vec(input, [18], None)
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest1dVecNoneScales())
+def UpSampleNearest1dVecNoneScales_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 6).to(torch.float64))
+
+
 class Conv1dModule(torch.nn.Module):
     def __init__(self):
         super().__init__()

projects/e2e/torch_mlir_e2e_test/test_suite/pooling.py

@@ -180,6 +180,55 @@ def AdaptiveAvgPool2dUnitOutputSizeDynamicModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class MaxPool1dWithIndicesModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.max_pool1d_with_indices(
+            x, kernel_size=[6], stride=[2], padding=[3], dilation=2, ceil_mode=False
+        )
+
+
+@register_test_case(module_factory=lambda: MaxPool1dWithIndicesModule())
+def MaxPool1dWithIndicesModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 64, 112, low=-1))
+
+
+class MaxPool1dWithIndicesCeilModeModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.max_pool1d_with_indices(
+            x, kernel_size=[4], stride=[2], padding=[2], dilation=2, ceil_mode=True
+        )
+
+
+@register_test_case(module_factory=lambda: MaxPool1dWithIndicesCeilModeModule())
+def MaxPool1dWithIndicesCeilModeModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 25, 37, low=-1))
+
+
+# ==============================================================================
+
+
 class MaxPool1dModule(torch.nn.Module):
 
     def __init__(self):