Add relu6 and binary broadcasts (#1408)

* Add relu6 and binary broadcasts

e2e_testing/xfail_sets.py

@@ -50,6 +50,32 @@ MHLO_PASS_SET = {
     "ElementwiseUnaryModule_basic",
     "ElementwiseUnsqueezeNegDimsModule_basic",
     "ElementwiseToDtypeF32ToI64Module_basic",
+    "ElementwiseAddModule_basic",
+    "ElementwiseAddScalarFloatModule_basic",
+    "ElementwiseAddScalarInt64Module_basic",
+    "ElementwiseAddScalarIntModule_basic",
+    "ElementwiseDivScalarModule_basic",
+    "ElementwiseEqDiffWidthScalarModule_basic",
+    "ElementwiseEqFloatScalarModule_basic",
+    "ElementwiseEqIntScalarModule_basic",
+    "ElementwiseErfModule_basic",
+    "ElementwiseGeluModule_basic",
+    "ElementwiseGtFloatScalarModule_basic",
+    "ElementwiseGtIntScalarModule_basic",
+    "ElementwiseGtMixed2ScalarModule_basic",
+    "ElementwiseLtDiffWidthScalarModule_basic",
+    "ElementwiseLtFloatScalarModule_basic",
+    "ElementwiseLtIntScalarModule_basic",
+    "ElementwiseMulScalarModule_basic",
+    "ElementwiseMulScalarModule_float",
+    "ElementwiseMulScalarModule_int",
+    "ElementwiseNeFloatTensorModule_basic",
+    "ElementwiseNeIntScalarModule_basic",
+    "ElementwiseReciprocalModule_basic",
+    "ElementwiseRelu6Module_basic",
+    "ElementwiseReluModule_basic",
+    "ElementwiseSubScalarFloatModule_basic",
+    "ElementwiseSubScalarIntModule_basic",
     "ExpandAsIntModule_basic",
     "ExpandModule_basic",
     "FullLikeModuleDefaultDtype_basic",

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

@@ -158,6 +158,51 @@ def Torch_AtenRelu_Op : Torch_Op<"aten.relu_", [
   }];
 }
 
+def Torch_AtenRelu6Op : Torch_Op<"aten.relu6", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::relu6 : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRelu6Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenRelu6Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
+def Torch_AtenRelu6_Op : Torch_Op<"aten.relu6_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::relu6_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRelu6_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenRelu6_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenLeakyReluOp : Torch_Op<"aten.leaky_relu", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToMhlo/Basic.cpp

@@ -184,6 +184,41 @@ public:
 
 } // namespace
 
+// The binary broadcast patterns
+namespace {
+template <typename AtenOpT, typename ChloOpT>
+class ConvertAtenBinaryBroadcastOp : public OpConversionPattern<AtenOpT> {
+public:
+  using OpConversionPattern<AtenOpT>::OpConversionPattern;
+  using OpAdaptor = typename AtenOpT::Adaptor;
+  LogicalResult
+  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    Value lhs = adaptor.self();
+    auto lhsTy = lhs.getType().cast<TensorType>();
+    Value rhs = adaptor.other();
+    auto rhsTy = rhs.getType().cast<TensorType>();
+
+    if (!lhsTy || !rhsTy)
+      return op.emitError("only Tensor types supported");
+
+    auto lhsElemTy = lhsTy.getElementType();
+    auto rhsElemTy = rhsTy.getElementType();
+
+    if (lhsElemTy != rhsElemTy)
+      return op.emitError("input data types mismatched");
+
+    rewriter.replaceOpWithNewOp<ChloOpT>(
+        op,
+        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
+            op.getType()),
+        lhs, rhs,
+        /*broadcast_attr*/ nullptr);
+    return success();
+  }
+};
+} // namespace
+
 // These binary op legalizations are specific to add/sub which have an
 // alpha multiplier.
 namespace {
@@ -1231,4 +1266,13 @@ void mlir::torch::torch_to_mhlo::populateBasicOpPatternsAndLegality(
   INSERT_ATENOP_PATTERN(AtenSizeIntOp);
   INSERT_ATENOP_PATTERN(AtenToDtypeOp);
 #undef INSERT_ATENOP_PATTERN
+
+#define INSERT_BINARY_BROADCAST_PATTERN(AtenOp, MhloOp)                        \
+  target.addIllegalOp<AtenOp>();                                               \
+  patterns.add<ConvertAtenBinaryBroadcastOp<AtenOp, MhloOp>>(typeConverter,    \
+                                                             context)
+  INSERT_BINARY_BROADCAST_PATTERN(AtenMaximumOp, chlo::BroadcastMaxOp);
+  INSERT_BINARY_BROADCAST_PATTERN(AtenMinimumOp, chlo::BroadcastMinOp);
+  INSERT_BINARY_BROADCAST_PATTERN(Aten__And__TensorOp, chlo::BroadcastAndOp);
+#undef INSERT_BINARY_BROADCAST_PATTERN
 }

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -645,6 +645,20 @@ static Value getRelu6Results(PatternRewriter &rewriter, Location loc,
   return relu6Out;
 }
 
+namespace {
+class DecomposeAtenRelu6Op : public OpRewritePattern<AtenRelu6Op> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenRelu6Op op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value relu6 = getRelu6Results(rewriter, loc, op.self());
+    rewriter.replaceOp(op, relu6);
+    return success();
+  }
+};
+} // namespace
+
 // Hardswish(x) = x * Relu6(x+3)/6
 namespace {
 class DecomposeAtenHardswishOp : public OpRewritePattern<AtenHardswishOp> {
@@ -2907,6 +2921,8 @@ public:
     target.addIllegalOp<AtenRandLikeOp>();
     patterns.add<DecomposeAtenHardsigmoidOp>(context);
     target.addIllegalOp<AtenHardsigmoidOp>();
+    patterns.add<DecomposeAtenRelu6Op>(context);
+    target.addIllegalOp<AtenRelu6Op>();
     patterns.add<DecomposeAtenHardswishOp>(context);
     target.addIllegalOp<AtenHardswishOp>();
     patterns.add<DecomposeAtenSoftplusOp>(context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -677,7 +677,7 @@ void TypeAnalysis::visitOperation(Operation *op,
 
   // Take dtype from first operand.
   if (isa<CopyToValueTensorOp, CopyToNonValueTensorOp, AtenBatchNormOp,
-          AtenReluOp, AtenGeluOp, AtenCeilOp, AtenGeluBackwardOp,
+          AtenReluOp, AtenRelu6Op, AtenGeluOp, AtenCeilOp, AtenGeluBackwardOp,
           AtenBitwiseNotOp, AtenToPrimDeviceOp, AtenCpuOp, AtenContiguousOp,
           AtenFill_ScalarOp, AtenDetachOp, AtenMaskedFill_ScalarOp, AtenCopy_Op,
           AtenCumsumOp, AtenLayerNormOp, AtenClampOp, AtenClampMinOp,

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -6402,6 +6402,10 @@ StringRef mlir::torch::Torch::getShapeLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.relu6\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten._softmax\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.bool) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -7821,4 +7825,4 @@ StringRef mlir::torch::Torch::getShapeLibrary() {
 #ifndef _MSC_VER
 #pragma clang diagnostic pop
 #endif
-}
+}

lib/Dialect/TorchConversion/Transforms/VerifyMhloBackendContract.cpp

@@ -12,6 +12,7 @@
 #include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
 #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/Shape/IR/Shape.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/BuiltinOps.h"
 #include "mlir/IR/OpDefinition.h"
@@ -45,9 +46,10 @@ class VerifyMhloBackendContractPass
     ConversionTarget target(*context);
 
     // Structural operations.
-    target.addDynamicallyLegalOp<ModuleOp, func::FuncOp, func::ReturnOp>(
-        opHasLegalTypes);
-    // Basic scalar operations.
+    target.addDynamicallyLegalOp<ModuleOp, func::FuncOp, func::ReturnOp>(opHasLegalTypes);
+    // Shape operations.
+    target.addDynamicallyLegalOp<shape::ShapeOfOp>(opHasLegalTypes);
+
     target.addLegalDialect<mhlo::MhloDialect>();
     target.addLegalDialect<chlo::ChloDialect>();
     target.addLegalDialect<tensor::TensorDialect>();

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

@@ -397,6 +397,9 @@ def aten〇log(self: List[int]) -> List[int]:
 def aten〇relu(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇relu6(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇_softmax(self: List[int], dim: int, half_to_float: bool) -> List[int]:
     return upstream_shape_functions.unary(self)
 

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

@@ -241,6 +241,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
             "aten::tanh : (Tensor) -> (Tensor)",
             "aten::hardtanh : (Tensor, Scalar, Scalar) -> (Tensor)",
             "aten::relu : (Tensor) -> (Tensor)",
+            "aten::relu6 : (Tensor) -> (Tensor)",
             "aten::leaky_relu : (Tensor, Scalar) -> (Tensor)",
             "aten::log : (Tensor) -> (Tensor)",
             "aten::sigmoid : (Tensor) -> (Tensor)",

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -345,6 +345,28 @@ def ElementwiseReluModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseRelu6Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.relu6(x)
+
+
+@register_test_case(module_factory=lambda: ElementwiseRelu6Module())
+def ElementwiseRelu6Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4, 2) - 0.5)
+
+
+# ==============================================================================
+
+
 class ElementwiseLeakyReluModule(torch.nn.Module):
 
     def __init__(self):