Add lowering for _convolution.deprecated (#1259)

* Add lowering for _convolution.deprecated

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -3444,6 +3444,40 @@ def Torch_Aten_ConvolutionOp : Torch_Op<"aten._convolution", [
   }];
 }
 
+def Torch_Aten_ConvolutionDeprecatedOp : Torch_Op<"aten._convolution.deprecated", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::_convolution.deprecated : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$input,
+    AnyTorchTensorType:$weight,
+    AnyTorchOptionalTensorType:$bias,
+    AnyTorchListOfTorchIntType:$stride,
+    AnyTorchListOfTorchIntType:$padding,
+    AnyTorchListOfTorchIntType:$dilation,
+    Torch_BoolType:$transposed,
+    AnyTorchListOfTorchIntType:$output_padding,
+    Torch_IntType:$groups,
+    Torch_BoolType:$benchmark,
+    Torch_BoolType:$deterministic,
+    Torch_BoolType:$cudnn_enabled
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult Aten_ConvolutionDeprecatedOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 12, 1);
+    }
+    void Aten_ConvolutionDeprecatedOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 12, 1);
+    }
+  }];
+}
+
 def Torch_AtenFlipOp : Torch_Op<"aten.flip", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -927,13 +927,14 @@ public:
 };
 } // namespace
 
-// Decompose aten.convolution_overrideable to aten.convolution
+// Decompose aten._convolution-like to aten.convolution
 namespace {
-class DecomposeAten_ConvolutionOp
+template<typename ConvolutionLikeOp>
-    : public OpRewritePattern<Aten_ConvolutionOp> {
+class DecomposeAten_ConvolutionLikeOp
+    : public OpRewritePattern<ConvolutionLikeOp> {
 public:
-  using OpRewritePattern::OpRewritePattern;
+  using OpRewritePattern<ConvolutionLikeOp>::OpRewritePattern;
-  LogicalResult matchAndRewrite(Aten_ConvolutionOp op,
+  LogicalResult matchAndRewrite(ConvolutionLikeOp op,
                                 PatternRewriter &rewriter) const override {
 
     rewriter.replaceOpWithNewOp<AtenConvolutionOp>(
@@ -2542,8 +2543,10 @@ public:
     patterns.add<DecomposeAtenNativeBatchNormOp>(context);
     target.addIllegalOp<AtenConvolutionOverrideableOp>();
     patterns.add<DecomposeAtenConvolutionOverrideableOp>(context);
-    target.addIllegalOp<Aten_ConvolutionOp>();
+    target.addIllegalOp<Aten_ConvolutionOp, Aten_ConvolutionDeprecatedOp>();
-    patterns.add<DecomposeAten_ConvolutionOp>(context);
+    patterns.add<DecomposeAten_ConvolutionLikeOp<Aten_ConvolutionOp>,
+                 DecomposeAten_ConvolutionLikeOp<Aten_ConvolutionDeprecatedOp>>(
+        context);
     target.addIllegalOp<AtenConv2dOp>();
     patterns.add<DecomposeAtenConv2dOp>(context);
     patterns.add<DecomposeAtenArangeOp>(context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -712,7 +712,7 @@ void TypeAnalysis::visitOperation(Operation *op,
 
   // Promote the two dtypes assuming non-zero rank.
   if (isa<AtenMmOp, AtenBmmOp, AtenMatmulOp, AtenConv2dOp, AtenConvolutionOp,
-          Aten_ConvolutionOp, AtenConvolutionOverrideableOp>(op)) {
+          Aten_ConvolutionOp, Aten_ConvolutionDeprecatedOp, AtenConvolutionOverrideableOp>(op)) {
     auto knowledge =
         ValueKnowledge::getTensorPessimisticValueState(op->getContext());
     knowledge.dtype = getPromotedResultTypeAssumingNonZeroRank(

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -6341,6 +6341,10 @@ module {
     %0 = call @"__torch_mlir_shape_fn.aten.convolution"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8) : (!torch.list<int>, !torch.list<int>, !torch.optional<list<int>>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int) -> !torch.list<int>
     return %0 : !torch.list<int>
   }
+  func.func @"__torch_mlir_shape_fn.aten._convolution.deprecated"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.optional<list<int>>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.bool, %arg7: !torch.list<int>, %arg8: !torch.int, %arg9: !torch.bool, %arg10: !torch.bool, %arg11: !torch.bool) -> !torch.list<int> {
+    %0 = call @"__torch_mlir_shape_fn.aten.convolution"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8) : (!torch.list<int>, !torch.list<int>, !torch.optional<list<int>>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int) -> !torch.list<int>
+    return %0 : !torch.list<int>
+  }
   func.func @"__torch_mlir_shape_fn.aten.flip"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {
     return %arg0 : !torch.list<int>
   }

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/shape_lib_gen.py

@@ -940,7 +940,10 @@ def aten〇convolution(input: List[int], weight: List[int], bias: Optional[List[
 
 def aten〇_convolution(input: List[int], weight: List[int], bias: Optional[List[int]], stride: List[int], padding: List[int], dilation: List[int], transposed: bool, output_padding: List[int], groups: int, benchmark: bool, deterministic: bool, cudnn_enabled: bool, allow_tf32: bool) -> List[int]:
     return aten〇convolution(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups)
-    
+
+def aten〇_convolution〇deprecated(input: List[int], weight: List[int], bias: Optional[List[int]], stride: List[int], padding: List[int], dilation: List[int], transposed: bool, output_padding: List[int], groups: int, benchmark: bool, deterministic: bool, cudnn_enabled: bool) -> List[int]:
+    return aten〇convolution(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups)
+
 def aten〇flip(self: List[int], dims: List[int]) -> List[int]:
     return self
 

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -337,6 +337,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::convolution : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int) -> (Tensor)")
     emit("aten::convolution_overrideable : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int) -> (Tensor)")
     emit("aten::_convolution : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool, bool) -> (Tensor)")
+    emit("aten::_convolution.deprecated : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool) -> (Tensor)")
     emit("aten::flip : (Tensor, int[]) -> (Tensor)")
     emit(
         "aten::native_batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float) -> (Tensor, Tensor, Tensor)"

python/torch_mlir_e2e_test/test_suite/conv.py

@@ -406,6 +406,118 @@ class _Convolution2DTF32Module(torch.nn.Module):
 def _Convolution2DTF32Module_basic(module, tu: TestUtils):
     module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
 
+class _ConvolutionDeprecated2DAllFalseModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, inputVec, weight):
+        return torch.ops.aten._convolution(inputVec,
+                                           weight,
+                                           bias=None,
+                                           stride=[3, 3],
+                                           padding=[2, 2],
+                                           dilation=[1, 1],
+                                           transposed=False,
+                                           output_padding=[0, 0],
+                                           groups=1,
+                                           benchmark=False,
+                                           deterministic=False,
+                                           cudnn_enabled=False)
+
+@register_test_case(module_factory=lambda: _ConvolutionDeprecated2DAllFalseModule())
+def _ConvolutionDeprecated2DAllFalseModule_basic(module, tu: TestUtils):
+    module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
+
+class _ConvolutionDeprecated2DBenchmarkModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, inputVec, weight):
+        return torch.ops.aten._convolution(inputVec,
+                                           weight,
+                                           bias=None,
+                                           stride=[3, 3],
+                                           padding=[2, 2],
+                                           dilation=[1, 1],
+                                           transposed=False,
+                                           output_padding=[0, 0],
+                                           groups=1,
+                                           benchmark=True,
+                                           deterministic=False,
+                                           cudnn_enabled=False)
+
+@register_test_case(module_factory=lambda: _ConvolutionDeprecated2DBenchmarkModule())
+def _ConvolutionDeprecated2DBenchmarkModule_basic(module, tu: TestUtils):
+    module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
+
+class _ConvolutionDeprecated2DDeterministicModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, inputVec, weight):
+        return torch.ops.aten._convolution(inputVec,
+                                           weight,
+                                           bias=None,
+                                           stride=[3, 3],
+                                           padding=[2, 2],
+                                           dilation=[1, 1],
+                                           transposed=False,
+                                           output_padding=[0, 0],
+                                           groups=1,
+                                           benchmark=False,
+                                           deterministic=True,
+                                           cudnn_enabled=False)
+
+@register_test_case(module_factory=lambda: _ConvolutionDeprecated2DDeterministicModule())
+def _ConvolutionDeprecated2DDeterministicModule_basic(module, tu: TestUtils):
+    module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
+
+class _ConvolutionDeprecated2DCudnnModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, inputVec, weight):
+        return torch.ops.aten._convolution(inputVec,
+                                           weight,
+                                           bias=None,
+                                           stride=[3, 3],
+                                           padding=[2, 2],
+                                           dilation=[1, 1],
+                                           transposed=False,
+                                           output_padding=[0, 0],
+                                           groups=1,
+                                           benchmark=False,
+                                           deterministic=False,
+                                           cudnn_enabled=True)
+
+@register_test_case(module_factory=lambda: _ConvolutionDeprecated2DCudnnModule())
+def _Convolution2DCudnnModule_basic(module, tu: TestUtils):
+    module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
+
 class ConvolutionModule2DGroups(torch.nn.Module):
     def __init__(self):
         super().__init__()