From 7f41f140822a1200eec72845fac403afa2d90ced Mon Sep 17 00:00:00 2001
From: Eric Kunze <eric.kunze@arm.com>
Date: Thu, 20 Jul 2023 21:05:48 -0700
Subject: Update to fix build issues with clang

Signed-off-by: Eric Kunze <eric.kunze@arm.com>
Change-Id: Iae97d2827b697fb900a5f9ca8e47bc6904509ce7
---
 src/TosaDeserialize.cpp | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/TosaDeserialize.cpp b/src/TosaDeserialize.cpp
index 45e1f18..3f8a6ef 100644
--- a/src/TosaDeserialize.cpp
+++ b/src/TosaDeserialize.cpp
@@ -101,12 +101,12 @@ mlir::LogicalResult BuildTensorType(mlir::OpBuilder *op_builder,
 template <class T>
 mlir::DenseElementsAttr BuildDenseI8ElementsAttr(mlir::OpBuilder *op_builder,
                                                  const std::vector<T> &values) {
-  std::vector<int8_t> vec;
+  llvm::SmallVector<int8_t> vec;
   for (auto val : values) {
     vec.push_back(val);
   }
-  auto type =
-      mlir::RankedTensorType::get({vec.size()}, op_builder->getI8Type());
+  auto type = mlir::RankedTensorType::get({static_cast<int64_t>(vec.size())},
+                                          op_builder->getI8Type());
   return mlir::DenseElementsAttr::get(type, llvm::ArrayRef(vec));
 }
 
@@ -114,12 +114,12 @@ template <class T>
 mlir::DenseElementsAttr
 BuildDenseI16ElementsAttr(mlir::OpBuilder *op_builder,
                           const std::vector<T> &values) {
-  std::vector<int16_t> vec;
+  llvm::SmallVector<int16_t> vec;
   for (auto val : values) {
     vec.push_back(val);
   }
-  auto type =
-      mlir::RankedTensorType::get({vec.size()}, op_builder->getI16Type());
+  auto type = mlir::RankedTensorType::get({static_cast<int64_t>(vec.size())},
+                                          op_builder->getI16Type());
   return mlir::DenseElementsAttr::get(type, llvm::ArrayRef(vec));
 }
 
@@ -127,12 +127,12 @@ template <class T>
 mlir::DenseElementsAttr
 BuildDenseI32ElementsAttr(mlir::OpBuilder *op_builder,
                           const std::vector<T> &values) {
-  std::vector<int32_t> vec;
+  llvm::SmallVector<int32_t> vec;
   for (auto val : values) {
     vec.push_back(val);
   }
-  auto type =
-      mlir::RankedTensorType::get({vec.size()}, op_builder->getI32Type());
+  auto type = mlir::RankedTensorType::get({static_cast<int64_t>(vec.size())},
+                                          op_builder->getI32Type());
   return mlir::DenseElementsAttr::get(type, llvm::ArrayRef(vec));
 }
 
@@ -810,8 +810,8 @@ TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const {
   TosaPadAttribute *attr = static_cast<TosaPadAttribute *>(op->GetAttribute());
 
   auto padding_attr = BuildDenseI32ElementsAttr(op_builder, attr->padding());
-  auto padding_type = mlir::RankedTensorType::get({attr->padding().size()},
-                                                  op_builder->getI32Type());
+  auto padding_type = mlir::RankedTensorType::get(
+      {static_cast<int64_t>(attr->padding().size())}, op_builder->getI32Type());
   mlir::Operation *mlir_const_op =
       op_builder->create<mlir::tosa::ConstOp>(loc, padding_type, padding_attr);
   block->push_back(mlir_const_op);
@@ -872,8 +872,8 @@ std::vector<mlir::Value> TosaMlirOperatorBuilder::build<Op_TRANSPOSE>(
   // make a constant op from attr->perms values, of type: { shape = { perms.size
   // }, element_type = I32 }
   const auto perms_values = attr->perms();
-  auto const_type = mlir::RankedTensorType::get({perms_values.size()},
-                                                op_builder->getI32Type());
+  auto const_type = mlir::RankedTensorType::get(
+      {static_cast<int64_t>(perms_values.size())}, op_builder->getI32Type());
   mlir::DenseElementsAttr const_attr =
       BuildDenseI32ElementsAttr(op_builder, perms_values);
   mlir::Operation *mlir_const_op =
@@ -1045,13 +1045,13 @@ TosaMlirOperatorBuilder::build<Op_TABLE>(TosaSerializationOperator *op) const {
       input_val.getType().cast<mlir::ShapedType>().getElementType();
   if (input_element_type.isInteger(8)) {
     // table is signed 8 mode
-    const_type = mlir::RankedTensorType::get({table_values.size()},
-                                             op_builder->getI8Type());
+    const_type = mlir::RankedTensorType::get(
+        {static_cast<int64_t>(table_values.size())}, op_builder->getI8Type());
     const_attr = BuildDenseI8ElementsAttr(op_builder, table_values);
   } else {
     // table is signed 16 mode
-    const_type = mlir::RankedTensorType::get({table_values.size()},
-                                             op_builder->getI16Type());
+    const_type = mlir::RankedTensorType::get(
+        {static_cast<int64_t>(table_values.size())}, op_builder->getI16Type());
     const_attr = BuildDenseI16ElementsAttr(op_builder, table_values);
   }
   mlir::Operation *mlir_const_op =
-- 
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