[AutoBump] Merge with fixes of 5eab669c (Sep 30) (63)

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -892,8 +892,6 @@ class ConvertAtenReductionOp : public OpConversionPattern<AtenOpT> {
     if (!result)
       return failure();
 
-    // TBD - support dtype casting.
-
     rewriter.replaceOp(op, {result.value()});
 
     return success();
@@ -6516,8 +6514,7 @@ ConvertAtenOp<Aten__InterpolateSizeListScaleListOp>::matchAndRewrite(
   return success();
 }
 
-// Template to create support tril mask tensor for aten.tril
-// legalization
+// Template to create supporting tril mask tensor for aten.tril
 template <typename T>
 Value createTrilMask(PatternRewriter &rewriter, Operation *op,
                      ArrayRef<int64_t> shape, int64_t h, int64_t w,
@@ -6540,28 +6537,6 @@ Value createTrilMask(PatternRewriter &rewriter, Operation *op,
   return tosa::getConstTensor<T>(rewriter, op, vec, shape).value();
 }
 
-// Function to get tril mask tensor based on input type
-// for aten.tril legalization
-Value getTrilMask(PatternRewriter &rewriter, Operation *op,
-                  ArrayRef<int64_t> shape, int64_t h, int64_t w,
-                  int64_t diagonal, Type type) {
-  return TypeSwitch<Type, Value>(type)
-      .Case<mlir::FloatType>([&](auto) {
-        return createTrilMask<float>(rewriter, op, shape, h, w, diagonal);
-      })
-      .Case<mlir::IntegerType>([&](auto intType) {
-        switch (intType.getWidth()) {
-        case 1:
-          return createTrilMask<bool>(rewriter, op, shape, h, w, diagonal);
-        case 32:
-          return createTrilMask<int32_t>(rewriter, op, shape, h, w, diagonal);
-        case 64:
-          return createTrilMask<int64_t>(rewriter, op, shape, h, w, diagonal);
-        }
-        llvm_unreachable("Invalid integer width");
-      });
-}
-
 // Legalization for aten.tril
 template <>
 LogicalResult ConvertAtenOp<AtenTrilOp>::matchAndRewrite(
@@ -6609,21 +6584,221 @@ LogicalResult ConvertAtenOp<AtenTrilOp>::matchAndRewrite(
     return rewriter.notifyMatchFailure(op, "Diagonal value is not an integer");
 
   // Define shape for mask tensor based on rank
-  SmallVector<int64_t> constShape;
+  SmallVector<int64_t> maskShape;
   for (auto i = 0; i < selfRank - 2; i++)
-    constShape.push_back(1);
-  constShape.push_back(h);
-  constShape.push_back(w);
-
-  Value trilMask = getTrilMask(rewriter, op, constShape, h, w, diagonal,
-                               resultType.getElementType());
+    maskShape.push_back(1);
+  maskShape.push_back(h);
+  maskShape.push_back(w);
+
+  Value trilMask = TypeSwitch<Type, Value>(resultType.getElementType())
+                       .Case<mlir::FloatType>([&](auto) {
+                         return createTrilMask<float>(rewriter, op, maskShape,
+                                                      h, w, diagonal);
+                       })
+                       .Case<mlir::IntegerType>([&](auto intType) {
+                         switch (intType.getWidth()) {
+                         case 1:
+                           return createTrilMask<bool>(rewriter, op, maskShape,
+                                                       h, w, diagonal);
+                         case 32:
+                           return createTrilMask<int32_t>(
+                               rewriter, op, maskShape, h, w, diagonal);
+                         case 64:
+                           return createTrilMask<int64_t>(
+                               rewriter, op, maskShape, h, w, diagonal);
+                         }
+                         llvm_unreachable("Invalid integer width");
+                       });
 
   rewriter.replaceOpWithNewOp<tosa::MulOp>(op, resultType, self, trilMask,
                                            /*shift=*/0);
 
   return success();
 }
 
+// Template to create supporting diagonal mask tensor for aten.diagonal
+template <typename T>
+Value createDiagonalMask(PatternRewriter &rewriter, Operation *op,
+                         ArrayRef<int64_t> shape, int64_t h, int64_t w,
+                         int64_t offset) {
+  SmallVector<T> vec;
+
+  for (int64_t i = 0; i < h; i++) {
+    for (int64_t j = 0; j < w; j++) {
+      // Positive offset value moves above the main diagonal, while negative
+      // diagonal value moves below the main diagonal.
+      if (i + offset == j) {
+        vec.push_back(static_cast<T>(1));
+      } else {
+        vec.push_back(static_cast<T>(0));
+      }
+    }
+  }
+
+  return tosa::getConstTensor<T>(rewriter, op, vec, shape).value();
+}
+
+// Legalization for aten.diagonal
+template <>
+LogicalResult ConvertAtenOp<AtenDiagonalOp>::matchAndRewrite(
+    AtenDiagonalOp op, OpAdaptor adaptor,
+    ConversionPatternRewriter &rewriter) const {
+  auto self = adaptor.getSelf();
+
+  // Not a ranked tensor type
+  auto selfType = dyn_cast<RankedTensorType>(self.getType());
+  if (!selfType)
+    return rewriter.notifyMatchFailure(
+        op, "Only ranked tensor types are supported");
+
+  // Rank below 2 not accepted
+  auto selfRank = selfType.getRank();
+  if (selfRank <= 1)
+    return rewriter.notifyMatchFailure(
+        op, "Rank 0 and 1 are not accepted as they cause underflow");
+
+  if (!selfType.hasStaticShape())
+    return rewriter.notifyMatchFailure(
+        op, "Currently only static shapes are supported");
+
+  const TypeConverter *typeConverter = this->getTypeConverter();
+  RankedTensorType resultType = cast<RankedTensorType>(
+      typeConverter->convertType(op->getResult(0).getType()));
+  if (!resultType)
+    return rewriter.notifyMatchFailure(op, "Result type cannot be empty");
+
+  auto selfElemTy = selfType.getElementType();
+  auto resultElemTy = resultType.getElementType();
+
+  int64_t offset, dim1, dim2;
+  if (!matchPattern(op.getOffset(), m_TorchConstantInt(&offset)))
+    offset = 0;
+
+  if (!matchPattern(op.getDim1(), m_TorchConstantInt(&dim1))) {
+    dim1 = 0;
+  } else {
+    dim1 = toPositiveDim(dim1, selfRank);
+  }
+
+  if (!matchPattern(op.getDim2(), m_TorchConstantInt(&dim2))) {
+    dim2 = 1;
+  } else {
+    dim2 = toPositiveDim(dim2, selfRank);
+  }
+
+  auto selfShape = makeShapeTorchCompatible(selfType.getShape());
+  int64_t h = selfShape[dim1];
+  int64_t w = selfShape[dim2];
+
+  // Overflowing offset not supported
+  if ((offset < 0 && std::abs(offset) >= h) || (offset >= 0 && offset >= w))
+    return rewriter.notifyMatchFailure(
+        op, "Offset greater or equal than shape not supported");
+
+  int64_t targetDim1 = selfRank - 2;
+  int64_t targetDim2 = selfRank - 1;
+
+  Value selfTransposed = self;
+  SmallVector<int64_t> transposedInputShape = selfShape;
+  RankedTensorType transposedInputType = selfType;
+
+  // If (dim1, dim2) != (rank - 2, rank - 1), transpose the input tensor
+  // so that dim1 and dim2 become rank - 2 and rank - 1. We do this so that
+  // we can consistently create the diagonal mask tensor.
+  if (!(dim1 == targetDim1 && dim2 == targetDim2)) {
+    SmallVector<int32_t> transposedDims;
+    transposedInputShape.clear();
+
+    for (int64_t i = 0; i < selfRank; ++i) {
+      if (i == dim1 || i == dim2)
+        continue;
+      transposedDims.push_back(i);
+    }
+    transposedDims.push_back(dim1);
+    transposedDims.push_back(dim2);
+
+    auto transposedDimsConst = tosa::getConstTensor<int32_t>(
+        rewriter, op,
+        /*vec=*/transposedDims,
+        /*shape=*/{static_cast<int32_t>(selfRank)});
+
+    for (auto &dim : transposedDims)
+      transposedInputShape.push_back(selfShape[dim]);
+
+    transposedInputType = RankedTensorType::get(
+        makeShapeLLVMCompatible(transposedInputShape), selfElemTy);
+
+    selfTransposed = rewriter.create<tosa::TransposeOp>(
+        op->getLoc(), transposedInputType, self, transposedDimsConst.value());
+  }
+
+  // Define shape for mask tensor based on rank
+  SmallVector<int64_t> maskShape;
+  for (auto i = 0; i < selfRank - 2; i++)
+    maskShape.push_back(1);
+  maskShape.push_back(h);
+  maskShape.push_back(w);
+
+  Value diagonalMask =
+      TypeSwitch<Type, Value>(resultElemTy)
+          .Case<mlir::FloatType>([&](auto) {
+            return createDiagonalMask<float>(rewriter, op, maskShape, h, w,
+                                             offset);
+          })
+          .Case<mlir::IntegerType>([&](auto intType) {
+            switch (intType.getWidth()) {
+            case 1:
+              return createDiagonalMask<bool>(rewriter, op, maskShape, h, w,
+                                              offset);
+            case 32:
+              return createDiagonalMask<int32_t>(rewriter, op, maskShape, h, w,
+                                                 offset);
+            case 64:
+              return createDiagonalMask<int64_t>(rewriter, op, maskShape, h, w,
+                                                 offset);
+            }
+            llvm_unreachable("Invalid integer width");
+          });
+
+  Value diagonalTensor = rewriter.create<tosa::MulOp>(
+      op->getLoc(), transposedInputType, selfTransposed, diagonalMask,
+      /*shift=*/0);
+
+  auto resultShape = makeShapeTorchCompatible(resultType.getShape());
+  auto targetReduceDim = resultShape[resultType.getRank() - 1];
+
+  // If transposedInputShape[targetDim1] (or h) is greater than the innermost
+  // dim of the result, we won't get the correct shape when we reduce sum along
+  // the innermost dim to get the result. Therefore, we have to slice the
+  // transposed tensor so that transposedInputShape[targetDim1] ==
+  // targetReduceDim.
+  if (h > targetReduceDim) {
+    transposedInputShape[targetDim1] = targetReduceDim;
+    transposedInputType = RankedTensorType::get(
+        makeShapeLLVMCompatible(transposedInputShape), selfElemTy);
+    SmallVector<int64_t> startSlice(selfRank, 0);
+    SmallVector<int64_t> sizeSlice =
+        llvm::to_vector(makeShapeTorchCompatible(transposedInputShape));
+    if (offset < 0)
+      startSlice[targetDim1] = std::abs(offset);
+    diagonalTensor = rewriter.create<tosa::SliceOp>(
+        op->getLoc(), transposedInputType, diagonalTensor,
+        rewriter.getDenseI64ArrayAttr(startSlice),
+        rewriter.getDenseI64ArrayAttr(sizeSlice));
+  }
+
+  // Apply Reduce Sum to get the result
+  auto reduceDimType = RankedTensorType::get({1}, rewriter.getI64Type());
+  auto reduceDimAttr =
+      DenseIntElementsAttr::get(reduceDimType, llvm::ArrayRef({targetDim2}));
+  auto result =
+      mlir::tosa::convertReduceSumOp(rewriter, op, resultType, diagonalTensor,
+                                     reduceDimAttr, /*keep_dims=*/false);
+
+  rewriter.replaceOp(op, result.value());
+
+  return success();
+}
 } // namespace
 
 // -----------------------------------------------------------------------------
@@ -6949,6 +7124,7 @@ class ConvertTorchToTosa : public ConvertTorchToTosaBase<ConvertTorchToTosa> {
     INSERT_ATENOP_PATTERN(AtenIscloseOp);
     INSERT_ATENOP_PATTERN(Aten__InterpolateSizeListScaleListOp);
     INSERT_ATENOP_PATTERN(AtenTrilOp);
+    INSERT_ATENOP_PATTERN(AtenDiagonalOp);
 #undef INSERT_ATENOP_PATTERN
 
 #define INSERT_CLONE_ATENOP_PATTERN(AtenOp)                                    \

projects/pt1/e2e_testing/xfail_sets.py

@@ -1720,6 +1720,8 @@
 # and very few tests work yet.
 TOSA_PASS_SET = {
     "BinaryCrossEntropyWithLogitsStaticModule_basic",
+    "DiagonalWithStaticShapeModule_basic",
+    "EinsumStaticDiagonalDimensionModule_basic",
     "ElementwiseAtenFloorDivideBroadcastModule_basic",
     "ElementwiseAtenFloorDivideScalarModule_basic",
     "ElementwiseAtenFloorDivideScalarNegativeModule_basic",
@@ -3444,6 +3446,7 @@
 }
 
 FX_IMPORTER_TOSA_XFAIL_SET = {
+    "AdaptiveMaxPool1dDimOneStatic_basic",
     "AtenPolarDoubleModule_basic",
     "AtenPolarFloatModule_basic",
     "HstackBasicComplexModule_basic",
@@ -3467,7 +3470,6 @@
     "Conv_Transpose2dStaticModule_basic",
     "Conv_Transpose3dModule_basic",
     "Conv_Transpose3dStaticModule_basic",
-    "EinsumStaticDiagonalDimensionModule_basic",
     "ElementwiseFloatTensorGtIntTensorModule_basic",
     "ElementwiseIntTensorLtFloatTensorModule_basic",
     "ElementwiseRreluEvalModule_basic",
@@ -3638,14 +3640,6 @@
     "DeterminantBatchedModule_F32",
     "DeterminantDynamicModule_F32",
     "DeterminantModule_F32",
-    "DiagonalModule_basic",
-    "DiagonalModule_nonsquare",
-    "DiagonalModule_transposed",
-    "DiagonalModule_with_dims",
-    "DiagonalModule_with_dims_and_offset",
-    "DiagonalModule_with_negative_dims",
-    "DiagonalModule_with_offset",
-    "DiagonalWithStaticShapeModule_basic",
     "DivFloatModule_basic",
     "DivIntModule_basic",
     "DropoutTrainModule_basic",
@@ -4058,11 +4052,7 @@
     "ToCopyWithDTypeModule_basic",
     "TorchPrimLoopForLikeModule_basic",
     "TorchPrimLoopWhileLikeModule_basic",
-    "TraceModule_basic",
     "TraceModule_empty",
-    "TraceModule_nonsquare",
-    "TraceSignedIntModule_basic",
-    "TraceUnsignedIntModule_basic",
     "TraceUnsignedIntModule_empty",
     "TypeConversionI1ToF64Module_basic",
     "TypeConversionI1ToI32Module_basic",
@@ -4098,6 +4088,7 @@
 }
 
 ONNX_TOSA_XFAIL_SET = {
+    "AdaptiveMaxPool1dDimOneStatic_basic",
     "ScaledDotProductAttentionDifferentCausalModule_basic",
     "HstackBasicComplexModule_basic",
     "HstackBasicFloatModule_basic",
@@ -4127,7 +4118,6 @@
     "Conv_Transpose2dStaticModule_basic",
     "Conv_Transpose3dModule_basic",
     "Conv_Transpose3dStaticModule_basic",
-    "EinsumStaticDiagonalDimensionModule_basic",
     "EinsumStaticModule_basic",
     "ElementwiseFmaxModule_basic",
     "ElementwiseFminModule_basic",

test/Conversion/TorchToTosa/basic.mlir

@@ -2101,3 +2101,29 @@ func.func @torch.aten.bitwise_right_shift.Tensor$basic(%arg0: !torch.vtensor<[?,
   %0 = torch.aten.bitwise_right_shift.Tensor %arg0, %arg1: !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si32> -> !torch.vtensor<[?,?],si32>
   return %0: !torch.vtensor<[?,?],si32>
 }
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.diagonal$basic(
+// CHECK-SAME:                                         %[[VAL_0:.*]]: !torch.vtensor<[3,4,5,6],si32>) -> !torch.vtensor<[5,6,2],si32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,5,6],si32> -> tensor<3x4x5x6xi32>
+// CHECK:           %[[VAL_2:.*]] = torch.constant.int 1
+// CHECK:           %[[VAL_3:.*]] = torch.constant.int 0
+// CHECK:           %[[VAL_4:.*]] = torch.constant.int -2
+// CHECK:           %[[VAL_5:.*]] = "tosa.const"() <{value = dense<[2, 3, 1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
+// CHECK:           %[[VAL_6:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_5]] : (tensor<3x4x5x6xi32>, tensor<4xi32>) -> tensor<5x6x4x3xi32>
+// CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]]]]> : tensor<1x1x4x3xi32>}> : () -> tensor<1x1x4x3xi32>
+// CHECK:           %[[VAL_8:.*]] = tosa.mul %[[VAL_6]], %[[VAL_7]] {shift = 0 : i8} : (tensor<5x6x4x3xi32>, tensor<1x1x4x3xi32>) -> tensor<5x6x4x3xi32>
+// CHECK:           %[[VAL_9:.*]] = tosa.slice %[[VAL_8]] {size = array<i64: 5, 6, 2, 3>, start = array<i64: 0, 0, 2, 0>} : (tensor<5x6x4x3xi32>) -> tensor<5x6x2x3xi32>
+// CHECK:           %[[VAL_10:.*]] = tosa.reduce_sum %[[VAL_9]] {axis = 3 : i32} : (tensor<5x6x2x3xi32>) -> tensor<5x6x2x1xi32>
+// CHECK:           %[[VAL_11:.*]] = tosa.reshape %[[VAL_10]] {new_shape = array<i64: 5, 6, 2>} : (tensor<5x6x2x1xi32>) -> tensor<5x6x2xi32>
+// CHECK:           %[[VAL_12:.*]] = torch_c.from_builtin_tensor %[[VAL_11]] : tensor<5x6x2xi32> -> !torch.vtensor<[5,6,2],si32>
+// CHECK:           return %[[VAL_12]] : !torch.vtensor<[5,6,2],si32>
+// CHECK:         }
+func.func @torch.aten.diagonal$basic(%arg0: !torch.vtensor<[3,4,5,6], si32>) -> !torch.vtensor<[5,6,2], si32> {
+  %dim1 = torch.constant.int 1
+  %dim2 = torch.constant.int 0
+  %offset = torch.constant.int -2
+  %0 = torch.aten.diagonal %arg0, %offset, %dim1, %dim2 : !torch.vtensor<[3,4,5,6],si32>, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[5,6,2],si32>
+  return %0 : !torch.vtensor<[5,6,2],si32>
+}