Rob's atenTensor folder (llvm#2867)

If a tensor is initialized by a list with a single constant integer,
this folder turns it into a torch.vtensor.literal

---------

Co-authored-by: Dave Liddell <dliddell@xilinx.com>

Author: daveliddell

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

@@ -8582,6 +8582,7 @@ def Torch_AtenTensorOp : Torch_Op<"aten.tensor", [
       printDefaultTorchOp(printer, *this, 4, 1);
     }
   }];
+  let hasFolder = 1;
 }
 
 def Torch_AtenTensorBoolOp : Torch_Op<"aten.tensor.bool", [

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -2758,6 +2758,27 @@ void AtenDeviceWithIndexOp::getCanonicalizationPatterns(
   });
 }
 
+//===----------------------------------------------------------------------===//
+// AtenTensorOp
+//===----------------------------------------------------------------------===//
+
+OpFoldResult AtenTensorOp::fold(FoldAdaptor adaptor) {
+  // If a torch.aten.tensor op is initialized by a list with a constant, single
+  // element, fold it into a torch.vtensor.literal
+  auto resultTy = dyn_cast<ValueTensorType>(getType());
+  Type eTy = resultTy.getDtype();
+  ShapedType shapedTy = resultTy.toBuiltinTensor().clone(eTy);
+
+  SmallVector<int64_t> data;
+  if (matchPattern(getData(), m_TorchListOfConstantInts(data)) &&
+      data.size() == 1) {
+    Attribute attribute = IntegerAttr::get(eTy, data[0]);
+    return DenseElementsAttr::get(shapedTy, attribute);
+  }
+
+  return nullptr;
+}
+
 //===----------------------------------------------------------------------===//
 // AtenIntTensorOp
 //===----------------------------------------------------------------------===//

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

@@ -570,7 +570,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::new_zeros : (Tensor, int[], int?, int?, Device?, bool?) -> (Tensor)")
     emit("aten::eye : (int, int?, int?, Device?, bool?) -> (Tensor)")
     emit("aten::eye.m : (int, int, int?, int?, Device?, bool?) -> (Tensor)")
-    emit("aten::tensor : (t[], int?, Device?, bool) -> (Tensor)")
+    emit("aten::tensor : (t[], int?, Device?, bool) -> (Tensor)", has_folder=True)
     emit("aten::tensor.bool : (bool, int?, Device?, bool) -> (Tensor)")
     emit("aten::tensor.int : (int, int?, Device?, bool) -> (Tensor)")
     emit("aten::scalar_tensor : (Scalar, int?, int?, Device?, bool?) -> (Tensor)")

test/Dialect/Torch/canonicalize.mlir

@@ -1461,6 +1461,17 @@ func.func @torch.aten.squeeze.dim$zero_rank(%arg0: !torch.tensor<[],f32>) -> !to
   return %0 : !torch.tensor<[],f32>
 }
 
+// CHECK-LABEL:   func.func @torch.aten.tensor$one_elem(
+// CHECK-NEXT: torch.vtensor.literal(dense<42> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
+func.func @torch.aten.tensor$one_elem() -> (!torch.vtensor<[1],si64>) {
+  %none = torch.constant.none
+  %false = torch.constant.bool false
+  %int42 = torch.constant.int 42
+  %66 = torch.prim.ListConstruct %int42 : (!torch.int) -> !torch.list<int>
+  %67 = torch.aten.tensor %66, %none, %none, %false : !torch.list<int>, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[1],si64>
+  return %67 : !torch.vtensor<[1],si64>
+}
+
 // CHECK-LABEL:   func.func @torch.aten.to.dtype$same_dtype(
 // CHECK-SAME:            %[[ARG:.*]]: !torch.tensor<*,f32>) -> !torch.tensor<*,f32> {
 // CHECK-NEXT:      return %[[ARG]] : !torch.tensor<*,f32>