Integrate LLVM at https://github.com/llvm/llvm-project/commit/92cb0ce8f814

PiperOrigin-RevId: 315290349
Change-Id: I2405c3505b6a860dd32f32d754d1a6da3f3acd29

tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.cc

@@ -89,12 +89,11 @@ StatusOr<ElementsAttr> ConvertFlatTensor(const Tensor& input_tensor,
 
 ElementsAttr ConvertBf16Tensor(const Tensor& input_tensor,
                                RankedTensorType type) {
-  auto flat = input_tensor.flat<bfloat16>();
-  llvm::SmallVector<llvm::APFloat, 4> floats;
-  floats.reserve(flat.size());
-  for (bfloat16 v : llvm::makeArrayRef(flat.data(), flat.size()))
-    floats.push_back(llvm::APFloat(static_cast<double>(v)));
-  return mlir::DenseElementsAttr::get(type, llvm::makeArrayRef(floats));
+  auto buffer = llvm::makeArrayRef(static_cast<char*>(input_tensor.data()),
+                                   input_tensor.TotalBytes());
+  return mlir::DenseElementsAttr::getFromRawBuffer(
+      type, buffer,
+      /*isSplatBuffer=*/type.getNumElements() == 1);
 }
 
 ElementsAttr ConvertHalfTensor(const Tensor& tensor, RankedTensorType type) {
@@ -280,16 +279,11 @@ void ConvertIntElementsAttr(const mlir::DenseIntElementsAttr attr,
 
 void ConvertBfloat16ElementsAttr(const mlir::DenseFPElementsAttr attr,
                                  protobuf::RepeatedField<int>* output) {
-  // Bfloat16 is internally represented as `double` in MLIR.
   if (attr.isSplat()) {
-    double v = attr.getSplatValue<double>();
-    bfloat16 bf16_val = static_cast<bfloat16>(v);
-    output->Add(absl::bit_cast<int16>(bf16_val));
+    output->Add((*attr.begin()).bitcastToAPInt().getSExtValue());
   } else {
-    for (auto v : attr.getValues<double>()) {
-      bfloat16 bf16_val = static_cast<bfloat16>(v);
-      output->Add(absl::bit_cast<int16>(bf16_val));
-    }
+    for (const llvm::APFloat value : attr.getFloatValues())
+      output->Add(value.bitcastToAPInt().getSExtValue());
   }
 }
 

tensorflow/compiler/mlir/xla/hlo_utils.cc

@@ -44,8 +44,7 @@ template <typename CppType>
 }
 
 mlir::APFloat ConvertToAPFloat(bfloat16 val) {
-  // bfloat16 values are stored as double in MLIR.
-  return llvm::APFloat(static_cast<double>(val));
+  return llvm::APFloat(llvm::APFloat::BFloat(), llvm::APInt(16, val.value));
 }
 
 mlir::APFloat ConvertToAPFloat(half val) {

tensorflow/compiler/mlir/xla/mlir_hlo_to_hlo.cc

@@ -979,10 +979,10 @@ StatusOr<xla::Literal> CreateLiteralFromAttr(ElementsAttr attr) {
       values.reserve(attr.getNumElements());
       for (APFloat val : attr.getValues<APFloat>()) {
         bool loses_info = false;
-        CHECK_EQ(val.convert(llvm::APFloat::IEEEsingle(),
-                             llvm::APFloat::rmTowardZero, &loses_info),
-                 llvm::APFloat::opOK);
-        CHECK(!loses_info);
+        TF_RET_CHECK(val.convert(llvm::APFloat::IEEEsingle(),
+                                 llvm::APFloat::rmTowardZero,
+                                 &loses_info) == llvm::APFloat::opOK);
+        TF_RET_CHECK(!loses_info);
         values.push_back(xla::half(val.convertToFloat()));
       }
       xla::Array<xla::half> source_data(shape.dimensions());
@@ -992,10 +992,15 @@ StatusOr<xla::Literal> CreateLiteralFromAttr(ElementsAttr attr) {
     case xla::PrimitiveType::BF16: {
       xla::Array<double> source_data(shape.dimensions());
       auto attr_values = attr.getValues<APFloat>();
-      std::vector<double> values_double(source_data.num_elements());
-      for (auto index_and_value : llvm::enumerate(attr_values)) {
-        values_double[index_and_value.index()] =
-            index_and_value.value().convertToDouble();
+      std::vector<double> values_double;
+      values_double.reserve(source_data.num_elements());
+      for (APFloat val : attr_values) {
+        bool loses_info = false;
+        TF_RET_CHECK(val.convert(llvm::APFloat::IEEEdouble(),
+                                 llvm::APFloat::rmTowardZero,
+                                 &loses_info) == llvm::APFloat::opOK);
+        TF_RET_CHECK(!loses_info);
+        values_double.push_back(val.convertToDouble());
       }
       source_data.SetValues(values_double);
       return xla::LiteralUtil::ConvertF64ToBF16(

tensorflow/compiler/mlir/xla/tests/convert.mlir

@@ -191,7 +191,7 @@ func @const_f32_bf16() -> tensor<bf16> {
 
 // CHECK-LABEL: func @const_bf16_f64
 func @const_bf16_f64() -> tensor<f64> {
-  // CHECK-NEXT: [[CST:%.+]] = xla_hlo.constant dense<4.2{{0*}}e+00> : tensor<f64>
+  // CHECK-NEXT: [[CST:%.+]] = xla_hlo.constant dense<4.187500e+00> : tensor<f64>
   %cst = xla_hlo.constant dense<4.2> : tensor<bf16>
   %0 = "xla_hlo.convert"(%cst) : (tensor<bf16>) -> tensor<f64>
   // CHECK-NEXT: return [[CST]]

tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc

@@ -22,6 +22,7 @@ limitations under the License.
 #include "absl/base/casts.h"
 #include "absl/strings/match.h"
 #include "absl/strings/str_cat.h"
+#include "llvm/ADT/Triple.h"
 #include "llvm/IR/DerivedTypes.h"
 #include "llvm/IR/GlobalValue.h"
 #include "llvm/IR/GlobalVariable.h"

third_party/mlir/BUILD

@@ -686,25 +686,6 @@ gentbl(
     ],
 )
 
-gentbl(
-    name = "MLIRShapeCanonicalizationIncGen",
-    strip_include_prefix = "include/mlir/Dialect/Shape",
-    tbl_outs = [
-        (
-            "-gen-rewriters",
-            "include/mlir/Dialect/Shape/IR/ShapeCanonicalization.inc",
-        ),
-    ],
-    tblgen = ":mlir-tblgen",
-    td_file = "lib/Dialect/Shape/IR/ShapeCanonicalization.td",
-    td_srcs = [
-        ":StdOpsTdFiles",
-        "include/mlir/Dialect/Shape/IR/ShapeBase.td",
-        "include/mlir/Dialect/Shape/IR/ShapeOps.td",
-        "include/mlir/Interfaces/InferTypeOpInterface.td",
-    ],
-)
-
 cc_library(
     name = "Shape",
     srcs = glob(
@@ -723,7 +704,6 @@ cc_library(
         ":Dialect",
         ":IR",
         ":InferTypeOpInterface",
-        ":MLIRShapeCanonicalizationIncGen",
         ":ShapeOpsIncGen",
         ":SideEffects",
         ":Support",