[MLIR][TORCH] Add E2E support for aten.upsample_nearest2d_backward.vec op

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>

e2e_testing/xfail_sets.py

@@ -621,4 +621,6 @@ LTC_XFAIL_SET = {
     "Fill_TensorFloat32WithFloat32_basic",
     "Fill_TensorFloat32WithFloat64_basic",
     "Fill_TensorFloat32WithInt64_basic",
+    "UpSampleNearest2dBackwardVec_basic",
+    "UpSampleNearest2dBackwardOutputSizeNone_basic",
 }

include/torch-mlir/Conversion/Utils/Utils.h

@@ -49,6 +49,10 @@ SmallVector<Value>
 castIntVectorToIndexVector(OpBuilder &b, Location loc,
                            SmallVectorImpl<Value> &intValues);
 
+SmallVector<Value>
+castIndexVectorToInt64Vector(OpBuilder &b, Location loc,
+                             SmallVectorImpl<Value> &indexValues);
+
 Value getDimOp(OpBuilder &b, Location loc, Value v, int dim);
 
 SmallVector<Value> getTensorSizesUntilDim(OpBuilder &b, Location loc,

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

@@ -4893,6 +4893,32 @@ def Torch_AtenMseLossOp : Torch_Op<"aten.mse_loss", [
   }];
 }
 
+def Torch_AtenUpsampleNearest2dBackwardVecOp : Torch_Op<"aten.upsample_nearest2d_backward.vec", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::upsample_nearest2d_backward.vec : (Tensor, int[]?, int[], float[]?) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$grad_output,
+    AnyTorchOptionalListOfTorchIntType:$output_size,
+    AnyTorchListOfTorchIntType:$input_size,
+    AnyTorchOptionalListOfTorchFloatType:$scale_factors
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenUpsampleNearest2dBackwardVecOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 4, 1);
+    }
+    void AtenUpsampleNearest2dBackwardVecOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 4, 1);
+    }
+  }];
+}
+
 def Torch_AtenConstantPadNdOp : Torch_Op<"aten.constant_pad_nd", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/IndirectDataMovement.cpp

@@ -896,6 +896,190 @@ public:
 };
 } // namespace
 
+static Value getGradOutputValue(OpBuilder &builder, Location loc,
+                                Value gradOutput, Type gradOutputElemType,
+                                Value numBatch, Value numChannel,
+                                Value inputIndexH, Value inputIndexW,
+                                Value kernelIndexH, Value kernelIndexW,
+                                SmallVector<Value> &gradOutputSizeIndexValues,
+                                SmallVector<Value, 2> &scaleFactorsIntValues) {
+  Value constantOne = builder.create<arith::ConstantIndexOp>(loc, 1);
+
+  Value outputIndexH = builder.create<arith::MulIOp>(
+      loc, inputIndexH, castIntToIndex(builder, loc, scaleFactorsIntValues[0]));
+  outputIndexH = builder.create<arith::AddIOp>(loc, outputIndexH, kernelIndexH);
+
+  Value outputIndexW = builder.create<arith::MulIOp>(
+      loc, inputIndexW, castIntToIndex(builder, loc, scaleFactorsIntValues[1]));
+  outputIndexW = builder.create<arith::AddIOp>(loc, outputIndexW, kernelIndexW);
+
+  // Handling corner cases.
+  Value gradOutputHMinusOne = builder.create<arith::SubIOp>(
+      loc, gradOutputSizeIndexValues[2], constantOne);
+  Value predH = builder.create<arith::CmpIOp>(
+      loc, arith::CmpIPredicate::sle, outputIndexH, gradOutputHMinusOne);
+  outputIndexH = builder.create<arith::SelectOp>(loc, predH, outputIndexH,
+                                                 gradOutputHMinusOne);
+
+  Value gradOutputWMinusOne = builder.create<arith::SubIOp>(
+      loc, gradOutputSizeIndexValues[3], constantOne);
+  Value predW = builder.create<arith::CmpIOp>(
+      loc, arith::CmpIPredicate::sle, outputIndexW, gradOutputWMinusOne);
+  outputIndexW = builder.create<arith::SelectOp>(loc, predW, outputIndexW,
+                                                 gradOutputWMinusOne);
+
+  Value gradOutputValue = builder.create<tensor::ExtractOp>(
+      loc, gradOutput,
+      ValueRange{numBatch, numChannel, outputIndexH, outputIndexW});
+  Value constantZero =
+      builder.create<arith::ConstantOp>(loc, builder.getF32FloatAttr(0.0));
+  Value pred = builder.create<arith::AndIOp>(loc, predH, predW);
+  Value result = builder.create<arith::SelectOp>(
+      loc, pred, gradOutputValue,
+      convertScalarToDtype(builder, loc, constantZero, gradOutputElemType));
+
+  return result;
+}
+
+// The implementation of the `aten.upsample_nearest2d_backward.vec` op's
+// lowering is as follows:
+// gradOutput: Tensor of size [n, c, oh, ow]
+// outTensor: Tensor of size [n, c, ih, iw], initialized with zero
+// kh = ceil(oh/ih), kw = ceil(ow/iw)
+//
+// for i in range(n):
+//   for j in range(c):
+//     for p in range(ih):
+//       for q in range(iw):
+//         for x in range(kh):
+//           for y in range(kw):
+//             outTensor[i, j, p, q] += gradOutput[i, j, (p*kh)+x, (q*kw)+y]
+namespace {
+class ConvertAtenUpsampleNearest2dBackwardVecOp
+    : public OpConversionPattern<AtenUpsampleNearest2dBackwardVecOp> {
+
+public:
+  using OpConversionPattern::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(AtenUpsampleNearest2dBackwardVecOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+
+    Location loc = op->getLoc();
+    Value gradOutput = adaptor.grad_output();
+
+    Type resultType = getTypeConverter()->convertType(op.getResult().getType());
+    auto gradOutputType = gradOutput.getType().cast<RankedTensorType>();
+    auto gradOutputRank = gradOutputType.getRank();
+    Type elementType = gradOutputType.getElementType();
+
+    SmallVector<Value> gradOutputSizeIndexValues =
+        getTensorSizes(rewriter, loc, gradOutput);
+    SmallVector<Value> gradOutputSizeIntValues =
+        castIndexVectorToInt64Vector(rewriter, loc, gradOutputSizeIndexValues);
+    SmallVector<Value, 2> scaleFactorsFloatValues;
+
+    SmallVector<Value, 4> inputSizeTorchInt;
+    if (!getListConstructElements(op.input_size(), inputSizeTorchInt))
+      return rewriter.notifyMatchFailure(
+          op, "unimplemented: the input_size is not constructed from "
+              "ListConstruct");
+    SmallVector<Value, 4> inputSizeIntValues;
+    inputSizeIntValues = getTypeConvertedValues(
+        rewriter, loc, getTypeConverter(), inputSizeTorchInt);
+
+    // The dimension at which the scaling starts.
+    unsigned hDimOffset = 2;
+
+    if (!op.scale_factors().getType().isa<Torch::NoneType>()) {
+      SmallVector<Value, 2> scaleFactorsTorchFloat;
+      if (!getListConstructElements(op.scale_factors(), scaleFactorsTorchFloat))
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: the scale_factors is not constructed from "
+                "ListConstruct");
+      scaleFactorsFloatValues = getTypeConvertedValues(
+          rewriter, loc, getTypeConverter(), scaleFactorsTorchFloat);
+    } else {
+      for (unsigned i = hDimOffset; i < gradOutputRank; i++) {
+        auto scaleFactorVal = rewriter.create<arith::DivFOp>(
+            loc,
+            convertScalarToDtype(rewriter, loc, gradOutputSizeIntValues[i],
+                                 mlir::Float32Type::get(op->getContext())),
+            convertScalarToDtype(rewriter, loc, inputSizeIntValues[i],
+                                 mlir::Float32Type::get(op->getContext())));
+        scaleFactorsFloatValues.push_back(scaleFactorVal);
+      }
+    }
+
+    SmallVector<Value, 2> scaleFactorsIntValues;
+    for (auto v : scaleFactorsFloatValues)
+      scaleFactorsIntValues.push_back(convertScalarToDtype(
+          rewriter, loc, rewriter.create<math::CeilOp>(loc, v),
+          mlir::IntegerType::get(op->getContext(), 64)));
+
+    Value outTensor = createZeroInitTensor(
+        rewriter, loc,
+        castIntVectorToIndexVector(rewriter, loc, inputSizeIntValues),
+        elementType);
+
+    Value kernelTensor = rewriter.create<tensor::EmptyOp>(
+        loc,
+        getAsOpFoldResult(
+            castIntVectorToIndexVector(rewriter, loc, scaleFactorsIntValues)),
+        elementType);
+    unsigned kernelRank = scaleFactorsIntValues.size();
+
+    SmallVector<AffineExpr> affineExprs;
+    for (unsigned i = 0; i < gradOutputRank; i++)
+      affineExprs.push_back(rewriter.getAffineDimExpr(i));
+
+    AffineMap outputMap =
+        AffineMap::get(gradOutputRank + kernelRank,
+                       /*symbolCount=*/0, affineExprs, op->getContext());
+
+    affineExprs.clear();
+    for (unsigned i = gradOutputRank; i < gradOutputRank + kernelRank; i++)
+      affineExprs.push_back(rewriter.getAffineDimExpr(i));
+
+    AffineMap kernelMap =
+        AffineMap::get(gradOutputRank + kernelRank,
+                       /*symbolCount=*/0, affineExprs, op->getContext());
+
+    SmallVector<AffineMap> indexingMaps{kernelMap, outputMap};
+    SmallVector<StringRef> iteratorTypes(gradOutputRank,
+                                         getParallelIteratorTypeName());
+    iteratorTypes.push_back(getReductionIteratorTypeName());
+    iteratorTypes.push_back(getReductionIteratorTypeName());
+
+    Value finalRes =
+        rewriter
+            .create<linalg::GenericOp>(
+                loc, outTensor.getType(), ValueRange{kernelTensor},
+                ValueRange{outTensor},
+                /*indexingMaps=*/indexingMaps,
+                /*iteratorTypes=*/iteratorTypes,
+                [&](OpBuilder &b, Location loc, ValueRange args) {
+                  Value n = rewriter.create<linalg::IndexOp>(loc, 0);
+                  Value c = rewriter.create<linalg::IndexOp>(loc, 1);
+                  Value ih = rewriter.create<linalg::IndexOp>(loc, 2);
+                  Value iw = rewriter.create<linalg::IndexOp>(loc, 3);
+                  Value kh = rewriter.create<linalg::IndexOp>(loc, 4);
+                  Value kw = rewriter.create<linalg::IndexOp>(loc, 5);
+                  Value accValue = getGradOutputValue(
+                      rewriter, loc, gradOutput, elementType, n, c, ih, iw, kh,
+                      kw, gradOutputSizeIndexValues, scaleFactorsIntValues);
+                  Value outputVal = args[1];
+                  outputVal =
+                      rewriter.create<arith::AddFOp>(loc, outputVal, accValue);
+                  b.create<linalg::YieldOp>(loc, outputVal);
+                })
+            ->getResult(0);
+
+    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, finalRes);
+    return success();
+  }
+};
+} // namespace
+
 void mlir::torch::torch_to_linalg::
     populateIndirectDataMovementPatternsAndLegality(
         TypeConverter &typeConverter, RewritePatternSet &patterns,
@@ -913,4 +1097,6 @@ void mlir::torch::torch_to_linalg::
   patterns.add<ConvertAtenEmbeddingBagPaddingIdxOp>(typeConverter, context);
   target.addIllegalOp<AtenUpsampleNearest2dVecOp>();
   patterns.add<ConvertAtenUpsampleNearest2dVecOp>(typeConverter, context);
+  target.addIllegalOp<AtenUpsampleNearest2dBackwardVecOp>();
+  patterns.add<ConvertAtenUpsampleNearest2dBackwardVecOp>(typeConverter, context);
 }

lib/Conversion/Utils/Utils.cpp

@@ -156,6 +156,15 @@ castIntVectorToIndexVector(OpBuilder &b, Location loc,
   return indexValues;
 }
 
+SmallVector<Value>
+castIndexVectorToInt64Vector(OpBuilder &b, Location loc,
+                             SmallVectorImpl<Value> &indexValues) {
+  SmallVector<Value> intValues;
+  for (Value v : indexValues)
+    intValues.push_back(castIndexToInt64(b, loc, v));
+  return intValues;
+}
+
 Value getDimOp(OpBuilder &b, Location loc, Value v, int dim) {
   return b.createOrFold<tensor::DimOp>(loc, v, dim);
 }

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -700,8 +700,8 @@ void TypeAnalysis::visitOperation(Operation *op,
           AtenMaskedFillScalarOp, AtenFlipOp, PrimAbsScalarOp, AtenNumpyTOp,
           AtenTriuOp, AtenMaskedFillTensorOp, AtenRollOp, AtenPowTensorTensorOp,
           AtenLiftFreshCopyOp, AtenIndexTensorHackedTwinOp,
-          AtenUpsampleNearest2dVecOp, AtenMishOp, AtenRoundOp,
+          AtenUpsampleNearest2dVecOp, AtenMishOp, AtenRoundOp, AtenFillTensorOp,
-          AtenFillTensorOp>(op)) {
+          AtenUpsampleNearest2dBackwardVecOp>(op)) {
     return incorporateKnowledge(op->getResult(0), operands[0]->getValue());
   }
 

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -6076,6 +6076,9 @@ StringRef mlir::torch::Torch::getShapeLibrary() {
 "  func.func @\"__torch_mlir_shape_fn.aten.max_pool2d_with_indices_backward\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.bool, %arg7: !torch.list<int>) -> !torch.list<int> {\n"
 "    return %arg1 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.upsample_nearest2d_backward.vec\"(%arg0: !torch.list<int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.list<int>, %arg3: !torch.optional<list<float>>) -> !torch.list<int> {\n"
+"    return %arg2 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.avg_pool2d\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.bool, %arg5: !torch.bool, %arg6: !torch.optional<int>) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.avg_pool2d(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6) : (!torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.optional<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"

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

@@ -690,6 +690,9 @@ def aten〇max_pool2d_with_indices(self: List[int], kernel_size: List[int], stri
 def aten〇max_pool2d_with_indices_backward(grad_output: List[int], self: List[int], kernel_size: List[int], stride: List[int], padding: List[int], dilation: List[int], ceil_mode: bool, indices: List[int]) -> List[int]:
     return self
 
+def aten〇upsample_nearest2d_backward〇vec(grad_output: List[int], output_size: Optional[List[int]], input_size: List[int], scale_factors: Optional[List[float]]) -> List[int]:
+    return input_size
+
 # TODO: This should be upstreamed.
 # See https://github.com/pytorch/pytorch/pull/76889 for an example.
 def avg_pool2d(input: List[int], kernel_size: List[int], stride: List[int], padding: List[int], ceil_mode: bool, count_include_pad: bool, divisor_override: Optional[int]):

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

@@ -407,6 +407,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::linalg_vector_norm : (Tensor, Scalar, int[]?, bool, int?) -> (Tensor)")
     emit("aten::frobenius_norm.dim : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::mse_loss : (Tensor, Tensor, int) -> (Tensor)")
+    emit("aten::upsample_nearest2d_backward.vec : (Tensor, int[]?, int[], float[]?) -> (Tensor)")
 
     # Misc tensor ops.
     emit("aten::constant_pad_nd : (Tensor, int[], Scalar) -> (Tensor)")

python/torch_mlir_e2e_test/test_suite/basic.py

@@ -3022,3 +3022,50 @@ class SingleTensorTupleReturn(torch.nn.Module):
 @register_test_case(module_factory=lambda: SingleTensorTupleReturn())
 def SingleTensorTupleReturn_basic(module, tu: TestUtils):
     module.forward(torch.randn(2, 4))
+
+
+# ==============================================================================
+
+
+class UpSampleNearest2dBackwardVec(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, input):
+        return torch.ops.aten.upsample_nearest2d_backward(input,
+                                               output_size=[4, 8],
+                                               input_size=[1, 1, 2, 3],
+                                               scale_factors=None)
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest2dBackwardVec())
+def UpSampleNearest2dBackwardVec_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 4, 8))
+
+
+class UpSampleNearest2dBackwardOutputSizeNone(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_backward(input,
+                                               output_size=None,
+                                               input_size=[1, 1, 2, 3],
+                                               scale_factors=[3.0, 4.0])
+
+
+@register_test_case(module_factory=lambda: UpSampleNearest2dBackwardOutputSizeNone())
+def UpSampleNearest2dBackwardOutputSizeNone_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 1, 6, 12).to(torch.float64))