Integrate LLVM at a854c266b98468ad4479a7d3c56a3fa76437e30d (#4017)

Update LLVM to
llvm/llvm-project@a854c26
    
TOSA Updates Summary:

[TOSA] Update tosa.mul's shift as input
[TOSA] Update tosa.slice's start and size to !tosa.shape type
[TOSA] Refactor: Use tosaCastTensorToType() function to create tosa.cast

---------

Signed-off-by: Vivek Khandelwal <[email protected]>
Co-authored-by: Justin Ngo <[email protected]>

Author: vivekkhandelwal1

externals/llvm-project

@@ -24,6 +24,7 @@ createOneDimTfIndices(PatternRewriter &rewriter, Operation *op,
                       SmallVector<int64_t> indiceOneDimShape, int32_t dim,
                       ArrayRef<int64_t> indexShape);
 
+// Default function to create TOSA op with shift value
 mlir::tosa::MulOp createMulOpAndCast(PatternRewriter &rewriter, Operation *op,
                                      TensorType outType, Value lhs, Value rhs,
                                      int32_t shift);
@@ -32,8 +33,8 @@ mlir::tosa::MulOp createMulOpAndCast(PatternRewriter &rewriter, Operation *op,
 template <typename TosaOpT>
 TosaOpT createBinaryOpAndCast(PatternRewriter &rewriter, Operation *op,
                               TensorType outType, Value lhs, Value rhs) {
-  lhs = promoteType(rewriter, lhs, outType);
-  rhs = promoteType(rewriter, rhs, outType);
+  lhs = tosa::tosaCastTensorToType(rewriter, lhs, outType).value();
+  rhs = tosa::tosaCastTensorToType(rewriter, rhs, outType).value();
   return CreateOpAndInfer<TosaOpT>(rewriter, op->getLoc(), outType, lhs, rhs);
 }
 

include/torch-mlir/Conversion/TorchToTosa/TosaLegalizeCommon.h

@@ -10,13 +10,14 @@
 #ifndef TORCHMLIR_CONVERSION_TORCHTOTOSA_TOSALEGALIZEUTILS_H
 #define TORCHMLIR_CONVERSION_TORCHTOTOSA_TOSALEGALIZEUTILS_H
 
-#include "mlir/Dialect/Quant/IR/QuantTypes.h"     // from @llvm-project
-#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"   // from @llvm-project
-#include "mlir/IR/BuiltinAttributes.h"            // from @llvm-project
-#include "mlir/IR/BuiltinTypes.h"                 // from @llvm-project
-#include "mlir/IR/PatternMatch.h"                 // from @llvm-project
-#include "mlir/Interfaces/InferTypeOpInterface.h" // from @llvm-project
-#include "mlir/Support/LLVM.h"                    // from @llvm-project
+#include "mlir/Dialect/Quant/IR/QuantTypes.h"        // from @llvm-project
+#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h" // from @llvm-project
+#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"      // from @llvm-project
+#include "mlir/IR/BuiltinAttributes.h"               // from @llvm-project
+#include "mlir/IR/BuiltinTypes.h"                    // from @llvm-project
+#include "mlir/IR/PatternMatch.h"                    // from @llvm-project
+#include "mlir/Interfaces/InferTypeOpInterface.h"    // from @llvm-project
+#include "mlir/Support/LLVM.h"                       // from @llvm-project
 
 namespace mlir {
 namespace tosa {
@@ -45,6 +46,10 @@ bool isScale32(mlir::quant::UniformQuantizedType output_element_type);
 Value getTosaConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
                                   float val);
 
+// Create an int8_t const tosa.mul shift tensor from an int
+Value getTosaMulShiftConstTensor(PatternRewriter &rewriter, Operation *op,
+                                 int32_t shift);
+
 // Create a zero constant tensor of the desired type and shape.
 std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
                                         Operation *op, Type type);
@@ -58,55 +63,24 @@ std::optional<Value> getConstTensor(PatternRewriter &rewriter, Operation *op,
                                     ArrayRef<T> vec, ArrayRef<int64_t> shape,
                                     std::optional<Type> dtype = {});
 
-LogicalResult tosaCastTensorToType(PatternRewriter &rewriter, Operation *op,
-                                   Value src, Type destType, Value &result);
-
-Value promoteType(PatternRewriter &rewriter, Value input, TensorType outType);
+// Default function to create tosa.cast op. This should be called instead of
+// directly calling rewriter.create<tosa::CastOp>.
+std::optional<Value> tosaCastTensorToType(PatternRewriter &rewriter, Value src,
+                                          TensorType destType);
 
 // Creates a TOSA operation and performs shape inference on the individual
 // op. This allows shape inference during the framework to TOSA lowering.
+template <typename TosaOp, typename... Args>
+TosaOp CreateOpAndInfer(ImplicitLocOpBuilder &builder, Type result_ty,
+                        Args &&...args) {
+  return CreateOpAndInferShape<TosaOp>(builder, result_ty, args...);
+}
+
 template <typename TosaOp, typename... Args>
 TosaOp CreateOpAndInfer(PatternRewriter &rewriter, Location loc, Type result_ty,
                         Args &&...args) {
-  auto op = rewriter.create<TosaOp>(loc, result_ty, args...);
-
-  InferShapedTypeOpInterface shapeInterface =
-      dyn_cast<InferShapedTypeOpInterface>(op.getOperation());
-  if (!shapeInterface)
-    return op;
-
-  SmallVector<ShapedTypeComponents> returnedShapes;
-  if (shapeInterface
-          .inferReturnTypeComponents(op.getContext(), op.getLoc(),
-                                     op->getOperands(), op->getAttrDictionary(),
-                                     op->getPropertiesStorage(),
-                                     op->getRegions(), returnedShapes)
-          .failed())
-    return op;
-
-  // We need to use the element type of the existing result type to generate
-  // the new result shaped type. This is because rescale can include a cast to
-  // different bit-width types and does not have a TypeAttr to define the
-  // target type.
-  auto result = op->getResult(0);
-  auto predictedShape = returnedShapes[0];
-  auto currentKnowledge = ValueKnowledge::getKnowledgeFromType(result_ty);
-
-  // Compute the knowledge based on the inferred type.
-  auto inferredKnowledge = ValueKnowledge::getPessimisticValueState();
-  inferredKnowledge.dtype = cast<ShapedType>(result_ty).getElementType();
-  inferredKnowledge.hasRank = predictedShape.hasRank();
-  if (predictedShape.hasRank()) {
-    for (auto dim : predictedShape.getDims()) {
-      inferredKnowledge.sizes.push_back(dim);
-    }
-  }
-
-  // Compute the new type based on the joined version.
-  auto newKnowledge = ValueKnowledge::join(currentKnowledge, inferredKnowledge);
-  auto new_ty = newKnowledge.getType();
-  result.setType(new_ty);
-  return op;
+  ImplicitLocOpBuilder builder(loc, rewriter);
+  return CreateOpAndInfer<TosaOp>(builder, result_ty, args...);
 }
 
 template <typename TosaOp, typename... Args>