update e2e tests

projects/pt1/e2e_testing/xfail_sets.py

@@ -969,6 +969,14 @@
     "AtenLinearMatVec_basic",
     "AtenLinearVecMatBias_basic",
     "AtenLinearVecMat_basic",
+    "Aten_BilinearModule1D_basic",
+    "Aten_BilinearModuleND_basic",
+    "Aten_BilinearModule_basic",
+    "Aten_TrilinearModuleSumAllDims_basic",
+    "Aten_TrilinearModuleSumdims_basic",
+    "Aten_TrilinearModuleVaryingRanksUnorderedExpands_basic",
+    "Aten_TrilinearModuleVaryingRanks_basic",
+    "Aten_TrilinearModule_basic",
     "ReduceAminSingleDim_basic",
     "AtenDotModule_basic",
     "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
@@ -1764,6 +1772,9 @@
     "Aten_TrilinearModuleVaryingRanksUnorderedExpands_basic",
     "Aten_TrilinearModuleVaryingRanks_basic",
     "Aten_TrilinearModule_basic",
+    "Aten_BilinearModule1D_basic",
+    "Aten_BilinearModuleND_basic",
+    "Aten_BilinearModule_basic",
     "ElementwiseAddBoolModule_basic",
     "Exp2StaticModule_basic",
     "CosineSimilarityStaticBroadcastModule_basic",
@@ -3339,6 +3350,10 @@
     "Aten_TrilinearModuleVaryingRanks_basic",
     "Aten_TrilinearModuleVaryingRanksUnorderedExpands_basic",
     "Aten_TrilinearModuleZerodDimBug_basic",
+    "Aten_BilinearModule1D_basic",
+    "Aten_BilinearModuleDynamic_basic",
+    "Aten_BilinearModuleND_basic",
+    "Aten_BilinearModule_basic",
 }
 
 if torch_version_for_comparison() < version.parse("2.3.0.dev"):
@@ -4098,6 +4113,10 @@
     "Aten_TrilinearModuleVaryingRanks_basic",
     "Aten_TrilinearModuleVaryingRanksUnorderedExpands_basic",
     "Aten_TrilinearModuleZerodDimBug_basic",
+    "Aten_BilinearModule1D_basic",
+    "Aten_BilinearModuleDynamic_basic",
+    "Aten_BilinearModuleND_basic",
+    "Aten_BilinearModule_basic",
     "AtenTrilModule_basic",
     "AtenTrilWithNegDiagonalModule_basic",
     "AtenTrilWithPosDiagonalModule_basic",

projects/pt1/python/torch_mlir_e2e_test/test_suite/reshape_like.py

@@ -1950,7 +1950,7 @@ def Aten_TrilinearModuleZerodDimBug_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
-class Aten_BilinearModuleStaticShape(torch.nn.Module):
+class Aten_BilinearModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
 
@@ -1968,12 +1968,12 @@ def forward(self, input1, input2, weight, bias):
         return torch.ops.aten.bilinear(input1, input2, weight, bias)
 
 
-@register_test_case(module_factory=lambda: Aten_BilinearModuleStaticShape())
-def Aten_BilinearModuleStaticShape_basic(module, tu: TestUtils):
+@register_test_case(module_factory=lambda: Aten_BilinearModule())
+def Aten_BilinearModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(8, 2), tu.rand(8, 3), tu.rand(4, 2, 3), tu.rand(4))
 
 
-class Aten_BilinearModuleDynamicShape(torch.nn.Module):
+class Aten_BilinearModuleDynamic(torch.nn.Module):
     def __init__(self):
         super().__init__()
 
@@ -1991,8 +1991,8 @@ def forward(self, input1, input2, weight, bias):
         return torch.ops.aten.bilinear(input1, input2, weight, bias)
 
 
-@register_test_case(module_factory=lambda: Aten_BilinearModuleDynamicShape())
-def Aten_BilinearModuleDynamicShape_basic(module, tu: TestUtils):
+@register_test_case(module_factory=lambda: Aten_BilinearModuleDynamic())
+def Aten_BilinearModuleDynamic_basic(module, tu: TestUtils):
     module.forward(tu.rand(8, 2), tu.rand(8, 3), tu.rand(4, 2, 3), tu.rand(4))
 
 
@@ -2004,10 +2004,10 @@ def __init__(self):
     @annotate_args(
         [
             None,
-            ([-1], torch.float32, True),
-            ([-1], torch.float32, True),
-            ([-1, -1, -1], torch.float32, True),
-            ([-1], torch.float32, True),
+            ([2], torch.float32, True),
+            ([3], torch.float32, True),
+            ([4, 2, 3], torch.float32, True),
+            ([4], torch.float32, True),
         ]
     )
     def forward(self, input1, input2, weight, bias):
@@ -2027,10 +2027,10 @@ def __init__(self):
     @annotate_args(
         [
             None,
-            ([-1, -1, -1, -1], torch.float32, True),
-            ([-1, -1, -1, -1], torch.float32, True),
-            ([-1, -1, -1], torch.float32, True),
-            ([-1], torch.float32, True),
+            ([8, 6, 12, 2], torch.float32, True),
+            ([8, 6, 12, 3], torch.float32, True),
+            ([4, 2, 3], torch.float32, True),
+            ([4], torch.float32, True),
         ]
     )
     def forward(self, input1, input2, weight, bias):