add argmax lowering

Add argmax lowering from torch to linalg

e2e_testing/torchscript/argmax.py

@@ -0,0 +1,66 @@
+#  Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+#  See https://llvm.org/LICENSE.txt for license information.
+#  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import torch
+
+from torch_mlir_e2e_test.torchscript.framework import TestUtils
+from torch_mlir_e2e_test.torchscript.registry import register_test_case
+from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
+
+# ==============================================================================
+
+class ArgmaxModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return torch.argmax(a)
+
+
+@register_test_case(module_factory=lambda: ArgmaxModule())
+def ArgmaxModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+# ==============================================================================
+
+class ArgmaxWithDimModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.argmax(a, dim=1)
+
+@register_test_case(module_factory=lambda: ArgmaxWithDimModule())
+def ArgmaxModule_with_dim(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 5))
+
+# ==============================================================================
+
+class ArgmaxKeepDimsModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None, 
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.argmax(a, 0, True)
+
+@register_test_case(module_factory=lambda: ArgmaxKeepDimsModule())
+def ArgmaxModule_keepDim(module, tu: TestUtils):
+    module.forward(tu.rand(4, 6))
+

e2e_testing/torchscript/main.py

@@ -34,6 +34,7 @@
 from . import quantized_models
 from . import elementwise
 from . import reduction
+from . import argmax
 
 def _get_argparse():
     config_choices = ['native_torch', 'torchscript', 'refbackend', 'tosa', 'external']

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

@@ -1349,6 +1349,22 @@ def Torch_AtenArangeStartOp : Torch_Op<"aten.arange.start", [
   let assemblyFormat = "$start `,` $end `,` $dtype `,` $layout `,` $device `,` $pin_memory attr-dict `:` type($start) `,` type($end) `,` type($dtype) `,` type($layout) `,` type($device) `,` type($pin_memory) `->` type($result)";
 }
 
+def Torch_AtenArgmaxOp : Torch_Op<"aten.argmax", [
+    AllowsTypeRefinement,
+    HasValueSemantics
+  ]> {
+  let summary = "Generated op for `aten::argmax : (Tensor, int?, bool) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    TorchOptionalIntType:$dim,
+    Torch_BoolType:$keepdim
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self `,` $dim `,` $keepdim attr-dict `:` type($self) `,` type($dim) `,` type($keepdim) `->` type($result)";
+}
+
 def Torch_AtenContiguousOp : Torch_Op<"aten.contiguous", [
     AllowsTypeRefinement
   ]> {

lib/Conversion/TorchToLinalg/TorchToLinalg.cpp

@@ -800,7 +800,7 @@ class ConvertAtenMmOp : public OpConversionPattern<AtenMmOp> {
     // of *internal* compiler invariants, and for a user manifests as a compiler
     // crash in the worst case (such as we try to canonicalize/fold/print the
     // invalid op before the verifier gets to see it -- also release builds of a
-    // mature copmiler usually have the verifier turned off for compile time
+    // mature compiler usually have the verifier turned off for compile time
     // reasons).
     //
     // The compiler cannot crash even if the user wrote an erroneous program!
@@ -1141,12 +1141,161 @@ static Value createLinalgPayloadCalculationForReduceOp(
   if (isa<AtenSumOp, AtenSumDimIntListOp>(op) &&
       elementType.isa<mlir::FloatType>())
     return b.create<AddFOp>(loc, payloadArgs);
-
   op->emitError("unimplemented lowering in "
                 "createLinalgPayloadCalculationForReduceOp");
   return nullptr;
 }
 
+namespace {
+// Aten argmax lowering represents the ArgMax op as an linalg.indexed_generic
+// op, producing two output buffers.
+//
+// The first output buffer contains the index of the found maximum value. It is
+// initialized to 0 and is resulting integer type.
+//
+// The second output buffer contains the maximum value found. It is initialized
+// to the minimum representable value of the input element type. After being
+// populated by indexed_generic, this buffer is disgarded as only the index is
+// requested.
+//
+// The indexed_generic op updates both the maximum value and index if the
+// current value exceeds the running max.
+class ConvertAtenArgmaxOp : public OpConversionPattern<AtenArgmaxOp> {
+public:
+  using OpConversionPattern<AtenArgmaxOp>::OpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(AtenArgmaxOp argmaxOp, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+
+    Location loc = argmaxOp.getLoc();
+    AtenArgmaxOp::Adaptor adaptor(operands);
+    Value input = adaptor.self();
+    RankedTensorType resultType =
+        getTypeConverter()
+            ->convertType(argmaxOp.getResult().getType())
+            .cast<RankedTensorType>();
+    RankedTensorType inputType = input.getType().cast<RankedTensorType>();
+    Type outElementType = resultType.getElementType();
+    if (!outElementType.isa<IntegerType>())
+      return rewriter.notifyMatchFailure(
+          argmaxOp,
+          "aten.arg_max to linalg.* requires integer-like result type");
+
+    bool keepDim = false;
+    if (!matchPattern(argmaxOp.keepdim(), m_TorchConstantBool(&keepDim)))
+      return failure();
+
+    int64_t dim;
+    if (!matchPattern(argmaxOp.dim(), m_TorchConstantInt(&dim))) {
+      if (!argmaxOp.dim().getType().isa<Torch::NoneType>())
+        return rewriter.notifyMatchFailure(
+            argmaxOp,
+            "aten.arg_max to linalg.* requires int or NoneType value for Dim");
+      // For pytorch, if the value of Dim is None, argmax
+      // returns the index of the max value of the flattened input tensor,
+      // so here we flatten the input tensor.
+      SmallVector<ReassociationIndices> reassociation(1);
+      for (auto i : llvm::seq<int64_t>(0, inputType.getRank()))
+        reassociation[0].push_back(i);
+      input = rewriter.create<linalg::TensorCollapseShapeOp>(
+          argmaxOp->getLoc(), input, reassociation);
+      // Becomes 0 for flattened tensor.
+      dim = 0;
+      // Recast to fix shape.
+      inputType = input.getType().cast<RankedTensorType>();
+    }
+    Type inElementType = inputType.getElementType();
+    if (!inElementType.isa<mlir::FloatType>()) {
+      return rewriter.notifyMatchFailure(
+          argmaxOp,
+          "aten.arg_max to linalg.* requires Float input element type");
+    }
+
+    // Constant op to account for the reduction along dim.
+    auto c1 = rewriter.create<mlir::ConstantIndexOp>(loc, /*value=*/1);
+    SmallVector<Value> resultShape;
+    for (int64_t i = 0; i < inputType.getRank(); i++) {
+      if (dim != i) {
+        auto currentDimSize = rewriter.create<tensor::DimOp>(loc, input, i);
+        resultShape.push_back(currentDimSize);
+      } else if (keepDim)
+        resultShape.push_back(c1);
+    }
+    // First fill the output buffer for the index.
+    Value filledTensorIdx =
+        createZeroInitTensor(rewriter, loc, resultShape, outElementType);
+
+    // Second fill the output buffer for the running max.
+    Value initTensorMax =
+        rewriter.create<linalg::InitTensorOp>(loc, resultShape, inElementType)
+            .result();
+
+    FloatAttr fillValueMaxAttr = rewriter.getFloatAttr(
+        inElementType,
+        APFloat::getLargest(
+            inElementType.cast<mlir::FloatType>().getFloatSemantics(), true));
+
+    Value fillValueMax = rewriter.create<ConstantOp>(loc, fillValueMaxAttr);
+    Value filledTensorMax =
+        rewriter.create<linalg::FillOp>(loc, fillValueMax, initTensorMax)
+            .result();
+
+    // Create the affine expressions that will be used to
+    // iterate over the input and output tensors.
+    // Here we also set the type of iterator: parallel or reduction.
+    SmallVector<AffineExpr> exprs;
+    SmallVector<StringRef> iteratorTypes;
+    SmallVector<AffineExpr> resultExprs;
+    for (auto size : llvm::enumerate(inputType.getShape())) {
+      exprs.push_back(rewriter.getAffineDimExpr(size.index()));
+
+      if (unsigned(dim) == size.index()) {
+        iteratorTypes.push_back(getReductionIteratorTypeName());
+        // If `keepDim`, create affine map to the first element
+        // in the current dimension.
+        if (keepDim)
+          resultExprs.push_back(rewriter.getAffineConstantExpr(0));
+      } else {
+        iteratorTypes.push_back(getParallelIteratorTypeName());
+        resultExprs.push_back(rewriter.getAffineDimExpr(size.index()));
+      }
+    }
+    auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs});
+    auto linalgOp = rewriter.create<linalg::GenericOp>(
+        loc,
+        ArrayRef<Type>({filledTensorIdx.getType(), filledTensorMax.getType()}),
+        input, ValueRange({filledTensorIdx, filledTensorMax}), maps,
+        iteratorTypes,
+        [&](OpBuilder &nestedBuilder, Location nestedLoc,
+            ValueRange blockArgs) {
+          Value newValue = blockArgs[0];
+          Value oldIndex = blockArgs[1];
+          Value oldValue = blockArgs[2];
+
+          Value newIndex = rewriter.create<IndexCastOp>(
+              nestedLoc, oldIndex.getType(),
+              rewriter.create<linalg::IndexOp>(loc, dim));
+
+          Value predicate;
+          if (inElementType.isa<mlir::FloatType>())
+            predicate = rewriter.create<mlir::CmpFOp>(
+                nestedLoc, CmpFPredicate::OGT, newValue, oldValue);
+          auto resultMax = rewriter.create<mlir::SelectOp>(nestedLoc, predicate,
+                                                           newValue, oldValue);
+          auto resultIndex = rewriter.create<mlir::SelectOp>(
+              nestedLoc, predicate, newIndex, oldIndex);
+          nestedBuilder.create<linalg::YieldOp>(
+              nestedLoc, ValueRange({resultIndex, resultMax}));
+        });
+
+    // This cast is required to fix the shape in the case of keepDim=True
+    rewriter.replaceOpWithNewOp<tensor::CastOp>(argmaxOp, resultType,
+                                                linalgOp.getResult(0));
+    return success();
+  }
+};
+} // namespace
 namespace {
 
 // Converts an elementwise op.
@@ -1896,6 +2045,8 @@ class ConvertTorchToLinalg
     patterns.add<ConvertAtenGatherOp>(typeConverter, context);
     target.addIllegalOp<AtenLayerNormOp>();
     patterns.add<ConvertAtenLayerNormOp>(typeConverter, context);
+    target.addIllegalOp<AtenArgmaxOp>();
+    patterns.add<ConvertAtenArgmaxOp>(typeConverter, context);
 
     if (failed(applyPartialConversion(getOperation(), target,
                                       std::move(patterns))))

lib/Dialect/Torch/IR/TorchTypes.cpp

@@ -138,7 +138,6 @@ Type parseTensorType(MLIRContext *context, DialectAsmParser &parser,
         sizes.push_back(-1);
         continue;
       }
-
       int64_t size;
       auto optionalInt = parser.parseOptionalInteger(size);
       if (optionalInt.hasValue()) {

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -270,6 +270,8 @@ class TypeAnalyzer : public ForwardDataFlowAnalysis<ValueKnowledge> {
     } else if (auto meanDim = dyn_cast<AtenMeanDimOp>(op)) {
       return visitReductionAlongDimIntListOp(meanDim, meanDim.dim(),
                                              meanDim.keepdim(), operands);
+    } else if (auto argmax = dyn_cast<AtenArgmaxOp>(op)) {
+      return visitAtenArgmaxOp(argmax, operands);
     } else if (auto anyDim = dyn_cast<AtenAnyDimOp>(op)) {
       return visitAtenAnyDimOp(anyDim, operands);
     } else if (auto view = dyn_cast<AtenViewOp>(op)) {
@@ -397,6 +399,9 @@ class TypeAnalyzer : public ForwardDataFlowAnalysis<ValueKnowledge> {
       Operation *op, Value dim, Value keepdim,
       ArrayRef<LatticeElement<ValueKnowledge> *> operands);
   ChangeResult
+  visitAtenArgmaxOp(AtenArgmaxOp op,
+                    ArrayRef<LatticeElement<ValueKnowledge> *> operands);
+  ChangeResult
   visitAtenAnyDimOp(AtenAnyDimOp op,
                     ArrayRef<LatticeElement<ValueKnowledge> *> operands);
   template <typename OpTy>
@@ -733,8 +738,8 @@ ChangeResult TypeAnalyzer::visitReductionAlongDimIntListOp(
       ValueKnowledge::getNotNonePessimisticValueState(op->getContext());
   knowledge.dtype = input.dtype;
   llvm::SmallVector<int64_t> dimList;
-  bool keepdimBool;
-  if (matchPattern(keepdim, m_TorchConstantBool(&keepdimBool))) {
+  bool keepDim;
+  if (matchPattern(keepdim, m_TorchConstantBool(&keepDim))) {
     knowledge.hasSizes = true;
     int64_t inputRank = input.sizes.size();
     // TODO: This is not safe. Need to check the list users and use aliasing
@@ -745,20 +750,47 @@ ChangeResult TypeAnalyzer::visitReductionAlongDimIntListOp(
       DenseSet<int64_t> dimSet(dimList.begin(), dimList.end());
       for (auto en : llvm::enumerate(input.sizes)) {
         if (dimSet.contains(en.index())) {
-          if (keepdimBool)
+          if (keepDim)
             knowledge.sizes.push_back(1);
         } else {
           knowledge.sizes.push_back(en.value());
         }
       }
     } else if (auto listConstruct = dim.getDefiningOp<PrimListConstructOp>()) {
       auto sizes = listConstruct.elements();
-      knowledge.sizes.resize(keepdimBool ? inputRank : inputRank - sizes.size(),
+      knowledge.sizes.resize(keepDim ? inputRank : inputRank - sizes.size(),
                              kUnknownSize);
     }
   }
   return getLatticeElement(op->getResult(0)).join(knowledge);
 }
+ChangeResult TypeAnalyzer::visitAtenArgmaxOp(
+    AtenArgmaxOp op, ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
+  auto input = operands[0]->getValue();
+  auto knowledge = ValueKnowledge::getPessimisticValueState(op->getContext());
+  knowledge.dtype = IntegerType::get(op->getContext(), 64, IntegerType::Signed);
+  int64_t dim;
+  bool keepDim;
+  if (matchPattern(op.keepdim(), m_TorchConstantBool(&keepDim))) {
+    int64_t inputRank = input.sizes.size();
+    knowledge.hasSizes = true;
+    if (matchPattern(op.dim(), m_TorchConstantInt(&dim))) {
+      knowledge.sizes = input.sizes;
+      dim = toPositiveDim(dim, inputRank);
+      if (isValidDim(dim, inputRank)) {
+        if (keepDim)
+          knowledge.sizes[dim] = 1;
+        else
+          knowledge.sizes.erase(knowledge.sizes.begin() + dim);
+      }
+    } else if (op.dim().getType().isa<IntegerType>())
+     knowledge.sizes.resize(keepdimBool ? inputRank : inputRank - 1,
+                             kUnknownSize);
+  } 
+  // If dim is no kind of Integer, keepDim is ignored, 
+  // and the result will bea rank-0 tensor
+  return getLatticeElement(op->getResult(0)).join(knowledge);
+}
 
 ChangeResult TypeAnalyzer::visitAtenAnyDimOp(
     AtenAnyDimOp op, ArrayRef<LatticeElement<ValueKnowledge> *> operands) {
@@ -767,22 +799,21 @@ ChangeResult TypeAnalyzer::visitAtenAnyDimOp(
       ValueKnowledge::getNotNonePessimisticValueState(op->getContext());
   knowledge.dtype = input.dtype;
   int64_t dim;
-  bool keepdimBool;
-  if (matchPattern(op.keepdim(), m_TorchConstantBool(&keepdimBool))) {
+  bool keepDim;
+  if (matchPattern(op.keepdim(), m_TorchConstantBool(&keepDim))) {
     int64_t inputRank = input.sizes.size();
     knowledge.hasSizes = true;
     if (matchPattern(op.dim(), m_TorchConstantInt(&dim))) {
       knowledge.sizes = input.sizes;
       dim = toPositiveDim(dim, inputRank);
       if (isValidDim(dim, inputRank)) {
-        if (keepdimBool)
+        if (keepDim)
           knowledge.sizes[dim] = 1;
         else
           knowledge.sizes.erase(knowledge.sizes.begin() + dim);
       }
     } else {
-      knowledge.sizes.resize(keepdimBool ? inputRank : inputRank - 1,
-                             kUnknownSize);
+      knowledge.sizes.resize(keepDim ? inputRank : inputRank - 1, kUnknownSize);
     }
   }
   return getLatticeElement(op->getResult(0)).join(knowledge);