Clean up some parts of the test suite

The purpose of the test suite is to accelerate the development of the
compiler. However, we had various tests there that were not expected to
work, had no in-progress work being tested by the test, and nobody was
actively working on them. Having such tests in our test suite just adds
clutter and slows down development on the compiler.

e2e_testing/xfail_sets.py

@@ -14,10 +14,10 @@
 
 REFBACKEND_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS
 
-EAGER_MODE_XFAIL_SET = {
-    # RefBackend fails
-    "TableBatchEmbeddingModule_basic",
-    "QuantizedMLP_basic",
+EAGER_MODE_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS | {
+    # RefBackend fails for some reason.
+    # These tests pass in the regular RefBackend flow, so it's unclear
+    # why they fail here.
     "Matmul_vecmat",
     "BatchMlpLayerModule_basic",
     "UpSampleNearest2dDynamicFactor_basic",
@@ -619,7 +619,6 @@
     "StdUnbiasedModule_basic",
     "SubFloatModule_basic",
     "SubIntModule_basic",
-    "TableBatchEmbeddingModule_basic",
     "TensorsConcatNegativeDimModule_basic",
     "TensorToBoolZeroRank_basic",
     "TensorToBool_basic",
@@ -645,10 +644,8 @@
     "Fill_TensorFloat32WithInt64_basic",
     "UpSampleNearest2dBackwardVec_basic",
     "UpSampleNearest2dBackwardOutputSizeNone_basic",
-    "ConvolutionBackwardModule1D_basic",
     "ConvolutionBackwardModule2D_basic",
     "ConvolutionBackwardModule2DPadded_basic",
-    "ConvolutionBackwardModule3D_basic",
     "VarMeanCorrectionModule_basic",
     "VarMeanCorrectionNoneModule_basic"
 }

python/torch_mlir_e2e_test/test_suite/__init__.py

@@ -8,15 +8,6 @@
 # to the backend contract.
 COMMON_TORCH_MLIR_LOWERING_XFAILS = {
     "QuantizedMLP_basic",
-    "TableBatchEmbeddingModule_basic",
-    "Convolution3DModule_basic",
-    "Convolution1DModule_basic",
-    "Conv_Transpose3dModule_basic",
-    "Conv_Transpose1dModule_basic",
-    "MaxPool2dWith3dInputModule_basic",
-    "MaxPool2dWithIndicesWith3dInputModule_basic",
-    "ConvolutionBackwardModule1D_basic",
-    "ConvolutionBackwardModule3D_basic",
 }
 
 def register_all_tests():
@@ -47,7 +38,6 @@ def register_all_tests():
     from . import constant_alloc
     from . import threshold
     from . import histogram_binning_calibration
-    from . import table_batch_embedding
     from . import rng
     from . import cast
     from . import index_put

python/torch_mlir_e2e_test/test_suite/backprop.py

@@ -58,41 +58,6 @@ def TanhBackward_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
-
-class ConvolutionBackwardModule1D(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1], torch.float32, True),
-        ([-1, -1, -1], torch.float32, True),
-        ([-1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, grad_out, input_vec, weight):
-        return torch.ops.aten.convolution_backward(
-            grad_out,
-            input_vec,
-            weight,
-            bias_sizes=None,
-            stride=[1],
-            padding=[0],
-            dilation=[1],
-            transposed=False,
-            output_padding=[0],
-            groups=1,
-            output_mask=[True, True, True])
-
-
-@register_test_case(module_factory=lambda: ConvolutionBackwardModule1D())
-def ConvolutionBackwardModule1D_basic(module, tu: TestUtils):
-    with torch.backends.mkldnn.flags(enabled=False):
-        module.forward(tu.rand(3, 3, 3), tu.rand(3, 3, 3),
-                       tu.rand(3, 3, 1))
-
-
 class ConvolutionBackwardModule2D(torch.nn.Module):
 
     def __init__(self):
@@ -161,40 +126,6 @@ def ConvolutionBackwardModule2DPadded_basic(module, tu: TestUtils):
                        tu.rand(2, 2, 3, 3))
 
 
-class ConvolutionBackwardModule3D(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, grad_out, input_vec, weight):
-        return torch.ops.aten.convolution_backward(
-            grad_out,
-            input_vec,
-            weight,
-            bias_sizes=None,
-            stride=[1, 1, 1],
-            padding=[0],
-            dilation=[1, 1, 1],
-            transposed=False,
-            output_padding=[0],
-            groups=1,
-            output_mask=[True, True, True])
-
-
-@register_test_case(module_factory=lambda: ConvolutionBackwardModule3D())
-def ConvolutionBackwardModule3D_basic(module, tu: TestUtils):
-    with torch.backends.mkldnn.flags(enabled=False):
-        module.forward(tu.rand(3, 3, 3, 3, 3), tu.rand(3, 3, 3, 3, 3),
-                       tu.rand(3, 3, 1, 1, 1))
-
-
 # ==============================================================================
 
 

python/torch_mlir_e2e_test/test_suite/conv.py

@@ -142,32 +142,6 @@ def Conv2dWithPaddingDilationStrideStaticModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
-class Convolution1DModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1], torch.float32, True),
-        ([-1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, inputVec, weight):
-        return torch.ops.aten.convolution(inputVec,
-                                          weight,
-                                          bias=None,
-                                          stride=[1],
-                                          padding=[0],
-                                          dilation=[1],
-                                          transposed=False,
-                                          output_padding=[0],
-                                          groups=1)
-
-
-@register_test_case(module_factory=lambda: Convolution1DModule())
-def Convolution1DModule_basic(module, tu: TestUtils):
-    module.forward(torch.randn(3, 3, 10), torch.randn(3, 3, 2))
-
 class Convolution2DModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -193,31 +167,6 @@ def forward(self, inputVec, weight):
 def Convolution2DModule_basic(module, tu: TestUtils):
     module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
 
-class Convolution3DModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, inputVec, weight):
-        return torch.ops.aten.convolution(inputVec,
-                                          weight,
-                                          bias=None,
-                                          stride=[1, 1, 1],
-                                          padding=[0, 0, 0],
-                                          dilation=[1, 1, 1],
-                                          transposed=False,
-                                          output_padding=[0, 0, 0],
-                                          groups=1)
-
-
-@register_test_case(module_factory=lambda: Convolution3DModule())
-def Convolution3DModule_basic(module, tu: TestUtils):
-    module.forward(torch.randn(3, 3, 10, 10, 10), torch.randn(3, 3, 2, 2, 2))
 
 class Convolution2DStaticModule(torch.nn.Module):
     def __init__(self):
@@ -635,33 +584,6 @@ def ConvolutionModule2DTransposeStridedStatic_basic(module, tu: TestUtils):
     module.forward(torch.randn(5, 2, 5, 6), torch.randn(2, 5, 2, 2))
 
 
-class Conv_Transpose1dModule(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1], torch.float32, True),
-        ([-1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, inputVec, weight):
-        return torch.ops.aten.conv_transpose1d(inputVec,
-                                               weight,
-                                               bias=None,
-                                               stride=[2],
-                                               padding=[1],
-                                               dilation=[1],
-                                               output_padding=[0],
-                                               groups=1)
-
-
-@register_test_case(module_factory=lambda: Conv_Transpose1dModule())
-def Conv_Transpose1dModule_basic(module, tu: TestUtils):
-    module.forward(torch.randn(5, 2, 5), torch.randn(2, 5, 2))
-
-
 class Conv_Transpose2dModule(torch.nn.Module):
 
     def __init__(self):
@@ -688,31 +610,6 @@ def forward(self, inputVec, weight):
 def Conv_Transpose2dModule_basic(module, tu: TestUtils):
     module.forward(torch.randn(5, 2, 5, 6), torch.randn(2, 5, 2, 2))
 
-class Conv_Transpose3dModule(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-        ([-1, -1, -1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, inputVec, weight):
-        return torch.ops.aten.conv_transpose3d(inputVec,
-                                               weight,
-                                               bias=None,
-                                               stride=[2, 2, 2],
-                                               padding=[1, 1, 1],
-                                               dilation=[1, 1, 1],
-                                               output_padding=[0, 0, 0],
-                                               groups=1)
-
-
-@register_test_case(module_factory=lambda: Conv_Transpose3dModule())
-def Conv_Transpose3dModule_basic(module, tu: TestUtils):
-    module.forward(torch.randn(5, 2, 5, 6, 4), torch.randn(2, 5, 2, 2, 2))
 
 class UpSampleNearest2d(torch.nn.Module):
 

python/torch_mlir_e2e_test/test_suite/pooling.py

@@ -171,29 +171,6 @@ def MaxPool2dCeilModeTrueModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(1, 1, 20, 20, low=0.5, high=1.0))
 
 
-class MaxPool2dWith3dInputModule(torch.nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.mp2d = torch.nn.MaxPool2d(kernel_size=[6, 8],
-                                       stride=[2, 2],
-                                       padding=[3, 4],
-                                       dilation=2)
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, x):
-        return self.mp2d(x)
-
-
-@register_test_case(module_factory=lambda: MaxPool2dWith3dInputModule())
-def MaxPool2dWith3dInputModule_basic(module, tu: TestUtils):
-    module.forward(tu.rand(1, 20, 20, low=-1))
-
-
 # ==============================================================================
 
 
@@ -435,30 +412,6 @@ def MaxPool2dWithIndicesCeilModeTrueModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(1, 1, 8, 8, low=0.5, high=1.0))
 
 
-class MaxPool2dWithIndicesWith3dInputModule(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_pool2d_with_indices(x,
-                                                      kernel_size=[2, 2],
-                                                      stride=[1, 1],
-                                                      padding=[0, 0],
-                                                      dilation=[1, 1])
-
-
-@register_test_case(
-    module_factory=lambda: MaxPool2dWithIndicesWith3dInputModule())
-def MaxPool2dWithIndicesWith3dInputModule_basic(module, tu: TestUtils):
-    module.forward(tu.rand(1, 8, 8, low=0.5, high=1.0))
-
-
 # ==============================================================================