Merge pull request #554 from Xilinx/bump_to_af8514c9

[AutoBump] Merge with af8514c (Jan 27) (160)

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

@@ -13877,6 +13877,34 @@ def Torch_AtenAsStridedOp : Torch_Op<"aten.as_strided", [
   }];
 }
 
+def Torch_Aten_AssertTensorMetadataOp : Torch_Op<"aten._assert_tensor_metadata", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::_assert_tensor_metadata : (Tensor, int[]?, int[]?, int?, Device?, int?) -> ()`";
+  let arguments = (ins
+    AnyTorchTensorType:$a,
+    AnyTorchOptionalListOfTorchIntType:$size,
+    AnyTorchOptionalListOfTorchIntType:$stride,
+    AnyTorchOptionalIntType:$dtype,
+    AnyTorchOptionalDeviceType:$device,
+    AnyTorchOptionalIntType:$layout
+  );
+  let results = (outs
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult Aten_AssertTensorMetadataOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 6, 0);
+    }
+    void Aten_AssertTensorMetadataOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 6, 0);
+    }
+  }];
+  let hasFolder = 1;
+}
+
 def Torch_AtenDiagonalOp : Torch_Op<"aten.diagonal", [
     AllowsTypeRefinement,
     ReadOnly

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -2346,7 +2346,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
         ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
         unsigned inputRank = inputShape.size();
         // only handle 2D, 3D and 5D pooling cases
-        if (inputRank > 5 or inputRank < 3) {
+        if (inputRank > 5 || inputRank < 3) {
           return failure();
         }
         if (!resultType || !resultType.hasSizes()) {
@@ -2454,7 +2454,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                                              "Unimplemented: unranked tensor");
         unsigned rank = *maybeRank;
         // only 1D, 2D and 3D LpPool is supported.
-        if (rank > 5 or rank < 3) {
+        if (rank > 5 || rank < 3) {
           return failure();
         }
 
@@ -3697,11 +3697,9 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
             binder.tensorResultType(resultType))
           return failure();
 
-        // TODO: Add support for non-zero center_point_box value.
-        if (centerPointBox != 0)
+        if (centerPointBox != 0 && centerPointBox != 1)
           return rewriter.notifyMatchFailure(
-              binder.op, "unimplemented: expected center_point_box "
-                         "attribute value to be 0");
+              binder.op, "expected center_point_box attribute to be 0 or 1");
 
         // TODO: Support multiple batches and classes
         // Squeeze the boxes and scores tensor.
@@ -3727,6 +3725,49 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                                              "failed to squeeze scores tensor");
         boxes = squeezedBoxes.value();
         scores = squeezedScores.value();
+        if (centerPointBox == 1) {
+          // When center_point_box is 1, the box data is supplied as
+          // [[x_center, y_center, width, height], ...]. Slice it to
+          // [[x_center, y_center], ...] and [[width, height], ...],
+          // calculate the [[x1, y1], ...] and [[x2, y2], ...], and concatnate
+          // to [[x1, y1, x2, y2], ...]
+          auto boxesTensorType =
+              dyn_cast<Torch::ValueTensorType>(boxes.getType());
+          Value const0 = rewriter.create<Torch::ConstantIntOp>(
+              loc, rewriter.getI64IntegerAttr(0));
+          Value const1 = rewriter.create<Torch::ConstantIntOp>(
+              loc, rewriter.getI64IntegerAttr(1));
+          Value const2 = rewriter.create<Torch::ConstantIntOp>(
+              loc, rewriter.getI64IntegerAttr(2));
+          Value const4 = rewriter.create<Torch::ConstantIntOp>(
+              loc, rewriter.getI64IntegerAttr(4));
+          Value const2F = rewriter.create<Torch::ConstantFloatOp>(
+              loc, rewriter.getF64FloatAttr(2.0));
+
+          // extract scaled ranges for regions of interest
+          auto sliceShape = SmallVector<int64_t>{Torch::kUnknownSize, 2};
+          auto sliceTensorType = rewriter.getType<Torch::ValueTensorType>(
+              sliceShape, boxesTensorType.getDtype());
+          Value centers = rewriter.create<Torch::AtenSliceTensorOp>(
+              loc, sliceTensorType, boxes, const1, const0, const2, const1);
+          Value sizes = rewriter.create<Torch::AtenSliceTensorOp>(
+              loc, sliceTensorType, boxes, const1, const2, const4, const1);
+          Value halfSizes = rewriter.create<Torch::AtenDivScalarOp>(
+              loc, sizes.getType(), sizes, const2F);
+          Value x1y1s = rewriter.create<Torch::AtenSubTensorOp>(
+              loc, centers.getType(), centers, halfSizes, const1);
+          Value x2y2s = rewriter.create<Torch::AtenAddTensorOp>(
+              loc, centers.getType(), centers, halfSizes, const1);
+
+          Type listElemType = boxesTensorType.getWithSizesAndDtype(
+              /*optionalSizes=*/std::nullopt,
+              /*optionalDtype=*/nullptr);
+          Type listType = Torch::ListType::get(listElemType);
+          Value tensorList = rewriter.create<Torch::PrimListConstructOp>(
+              loc, listType, SmallVector<Value>{x1y1s, x2y2s});
+          boxes = rewriter.create<Torch::AtenCatOp>(loc, boxesTensorType,
+                                                    tensorList, const1);
+        }
 
         // TODO: Support score_threshold input
         // Filter out the boxes if the score < score_threshold

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -5462,6 +5462,54 @@ OpFoldResult PrimsConvertElementTypeOp::fold(FoldAdaptor adaptor) {
   return getA();
 }
 
+//===----------------------------------------------------------------------===//
+// Aten_AssertTensorMetadataOp
+//===----------------------------------------------------------------------===//
+
+LogicalResult Aten_AssertTensorMetadataOp::fold(
+    FoldAdaptor adaptor, SmallVectorImpl<::mlir::OpFoldResult> &results) {
+  Value input = getA();
+  auto inputType = cast<BaseTensorType>(input.getType());
+  if (!inputType.hasDtype() || !inputType.hasSizes())
+    return failure();
+
+  // TODO: Add checks for stride, device, and layout when we can extract that
+  // information from the torch tensor. For now, we can only get the shape and
+  // dtype info from the tensor hence adding checks for them.
+
+  // convert size to a list of integers.
+  SmallVector<int64_t> size;
+  if (!isa<Torch::NoneType>(getSize().getType())) {
+    if (!matchPattern(getSize(), m_TorchListOfConstantInts(size))) {
+      return emitOpError("expected dtype to be a constant int");
+    }
+    if (!llvm::all_of(llvm::zip(inputType.getSizes(), size),
+                      [](const auto &pair) {
+                        return std::get<0>(pair) == std::get<1>(pair);
+                      }))
+      return emitOpError("Failed to fold the _assert_tensor_metadata op since "
+                         "the sizes do not match");
+  }
+
+  // convert dtype to an integer.
+  int64_t dtype;
+  if (!isa<Torch::NoneType>(getDtype().getType())) {
+    if (!matchPattern(getDtype(), m_TorchConstantInt(&dtype))) {
+      return emitOpError("expected dtype to be a constant int");
+    }
+    FailureOr<Type> inputDtype =
+        getTypeForScalarType(getContext(), (torch_upstream::ScalarType)dtype);
+    if (failed(inputDtype))
+      return failure();
+    if (inputType.getDtype() != inputDtype)
+      return emitOpError("Failed to fold the _assert_tensor_metadata op since "
+                         "the dtype does not match");
+  }
+
+  getOperation()->erase();
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // AtenMaxPoolWithIndicesOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -11950,7 +11950,17 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.avg_pool1d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.bool, %arg5: !torch.bool) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int0 = torch.constant.int 0\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.aten.ne.int %0#1, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.adaptive_avg_pool2d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>) -> !torch.int {\n"
@@ -11962,11 +11972,31 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.avg_pool2d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.bool, %arg5: !torch.bool, %arg6: !torch.optional<int>) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int0 = torch.constant.int 0\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.aten.ne.int %0#1, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.avg_pool3d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.bool, %arg5: !torch.bool, %arg6: !torch.optional<int>) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int0 = torch.constant.int 0\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.aten.ne.int %0#1, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.batch_norm\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<tuple<int, int>>, %arg2: !torch.optional<tuple<int, int>>, %arg3: !torch.optional<tuple<int, int>>, %arg4: !torch.optional<tuple<int, int>>, %arg5: !torch.bool, %arg6: !torch.float, %arg7: !torch.float, %arg8: !torch.bool) -> !torch.int {\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -9780,16 +9780,16 @@ class DecomposeAtenNllLossForwardOp
     auto targetSizes = targetType.getSizes();
     int64_t selfRank = selfSizes.size();
     int64_t targetRank = targetSizes.size();
-    if (selfRank <= 0 or selfRank > 2) {
+    if (selfRank <= 0 || selfRank > 2) {
       return rewriter.notifyMatchFailure(op, "input tensor should be 1D or 2D");
     }
     if (targetRank > 1) {
       return rewriter.notifyMatchFailure(op,
                                          "target tensor shoule be 0D or 1D!");
     }
 
-    if (selfRank != 1 or targetRank != 0) {
-      if (!(selfSizes[0] == kUnknownSize and targetSizes[0] == kUnknownSize) and
+    if (selfRank != 1 || targetRank != 0) {
+      if (!(selfSizes[0] == kUnknownSize && targetSizes[0] == kUnknownSize) &&
           selfSizes[0] != targetSizes[0]) {
         return rewriter.notifyMatchFailure(
             op,
@@ -9907,7 +9907,7 @@ class DecomposeAtenNllLossForwardOp
         zeroTensor);
 
     Value totalWeight;
-    if (reduction == 0 and selfRank > 1) {
+    if (reduction == 0 && selfRank > 1) {
       auto zeroFloat =
           rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
       Value twSize = rewriter.create<PrimListConstructOp>(

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -2922,9 +2922,10 @@ def aten〇pixel_shuffle〡dtype(self_rank_dtype: Tuple[int, int], upscale_facto
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
-@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 7)], kernel_size=[2]))
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 7)], kernel_size=[2], error_types={torch.uint8}))
 def aten〇avg_pool1d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0,), ceil_mode: bool = False, count_include_pad: bool = True) -> int:
     self_rank, self_dtype = self_rank_dtype
+    assert self_dtype != torch.uint8
     return self_dtype
 
 @check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7)], output_size=[2, 2]))
@@ -2937,14 +2938,16 @@ def aten〇adaptive_avg_pool3d〡dtype(self_rank_dtype: Tuple[int, int], output_
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
-@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7)], kernel_size=[2, 2]))
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7)], kernel_size=[2, 2], error_types={torch.uint8}))
 def aten〇avg_pool2d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0, 0,), ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> int:
     self_rank, self_dtype = self_rank_dtype
+    assert self_dtype != torch.uint8
     return self_dtype
 
-@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7, 8)], kernel_size=[2, 2, 2]))
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 5, 7, 8)], kernel_size=[2, 2, 2], error_types={torch.uint8}))
 def aten〇avg_pool3d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0, 0, 0,), ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> int:
     self_rank, self_dtype = self_rank_dtype
+    assert self_dtype != torch.uint8
     return self_dtype
 
 # @check_dtype_function(_check_tensors_with_the_same_dtype(

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -1019,6 +1019,10 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::alias : (Tensor) -> (Tensor)", has_folder=True)
     emit("aten::as_strided_copy : (Tensor, int[], int[], int?) -> (Tensor)")
     emit("aten::as_strided : (Tensor, int[], int[], int?) -> (Tensor)")
+    emit(
+        "aten::_assert_tensor_metadata : (Tensor, int[]?, int[]?, int?, Device?, int?) -> ()",
+        has_folder=True,
+    )
     emit("aten::diagonal : (Tensor, int, int, int) -> (Tensor)")
     emit("aten::diagonal_copy : (Tensor, int, int, int) -> (Tensor)")
     emit("aten::expand_copy : (Tensor, int[], bool) -> (Tensor)")

pytorch-hash.txt

@@ -1 +1 @@
-3f159d635772fa2a8fd352d96b95100d885f8169
+37626ee0e6ff5dc1d38664690bd2ff6c790aab0c

pytorch-requirements.txt

@@ -4,4 +4,4 @@
 # release page, and we use this page as an additional source for the wheels.
 -f https://xilinx.github.io/torch-mlir/package-index/
 --pre
-torch==2.6.0.dev20241216
+torch==2.7.0.dev20250120