Fix uint16 support for quantize_per_tensor.

Differential Revision: D73517183

Pull Request resolved: #10398

Author: hsharma35

backends/cadence/hifi/operators/op_quantize_per_tensor.cpp

@@ -20,11 +20,26 @@ namespace cadence {
 namespace impl {
 namespace HiFi {
 namespace native {
-
+namespace {
 using ::executorch::aten::ScalarType;
 using ::executorch::aten::Tensor;
 using ::executorch::runtime::KernelRuntimeContext;
 
+// Add checks for dtype quant min/max bounds.
+template <typename T>
+void check_quant_min_and_max(
+    KernelRuntimeContext& ctx,
+    const int64_t quant_min,
+    const int64_t quant_max) {
+  ET_KERNEL_CHECK(
+      ctx,
+      std::numeric_limits<T>::min() == quant_min &&
+          std::numeric_limits<T>::max() == quant_max,
+      InvalidArgument, );
+}
+
+} // namespace
+
 // Quantize the input tensor (PT2 version). Note that quant_<min,max> are not
 // used in any computation.
 void quantize_per_tensor_out(
@@ -36,15 +51,43 @@ void quantize_per_tensor_out(
     __ET_UNUSED int64_t quant_max,
     const ScalarType dtype,
     Tensor& out) {
-  // Add checks for dtype quant min/max bounds.
-  ET_SWITCH_REALB_TYPES(
-      out.scalar_type(), ctx, "quantize_per_tensor", OUT_DTYPE, [&]() {
-        ET_KERNEL_CHECK(
-            ctx,
-            std::numeric_limits<OUT_DTYPE>::min() == quant_min &&
-                std::numeric_limits<OUT_DTYPE>::max() == quant_max,
-            InvalidArgument, );
-      });
+  // Check for input scalar type.
+  ET_KERNEL_CHECK_MSG(
+      ctx,
+      input.scalar_type() == ScalarType::Float,
+      InvalidType,
+      ,
+      "Input tensor for quantize_per_tensor.out should be type %s, but got %s",
+      ::torch::executor::toString(ScalarType::Float),
+      ::torch::executor::toString(input.scalar_type()));
+
+  // Check quant min/max for output types.
+  switch (out.scalar_type()) {
+    case ScalarType::Byte:
+      check_quant_min_and_max<uint8_t>(ctx, quant_min, quant_max);
+      break;
+    case ScalarType::Char:
+      check_quant_min_and_max<int8_t>(ctx, quant_min, quant_max);
+      break;
+    case ScalarType::Short:
+      check_quant_min_and_max<int16_t>(ctx, quant_min, quant_max);
+      break;
+    case ScalarType::Bits16:
+    case ScalarType::UInt16:
+      check_quant_min_and_max<uint16_t>(ctx, quant_min, quant_max);
+      break;
+    case ScalarType::Int:
+      check_quant_min_and_max<int32_t>(ctx, quant_min, quant_max);
+      break;
+    default:
+      ET_KERNEL_CHECK_MSG(
+          ctx,
+          false,
+          InvalidType,
+          ,
+          "Unhandled output dtype %s",
+          ::torch::executor::toString(out.scalar_type()));
+  }
 
   const float* input_data = input.const_data_ptr<float>();
   const size_t numel = out.numel();

backends/cadence/hifi/operators/tests/test_op_quantize_per_tensor.cpp

@@ -106,7 +106,7 @@ TEST_F(HiFiQuantizePerTensorTest, ThrowKernelFailureForQuantMaxLessThanLimit) {
           out));
 }
 
-TEST_F(HiFiQuantizePerTensorTest, CheckSingleElementQuantize) {
+TEST_F(HiFiQuantizePerTensorTest, CheckSingleElementIntQuantize) {
   TensorFactory<ScalarType::Float> tf;
   const std::vector<int> sizes{1};
   constexpr ScalarType kOutDtype = ScalarType::Int;
@@ -132,6 +132,32 @@ TEST_F(HiFiQuantizePerTensorTest, CheckSingleElementQuantize) {
   EXPECT_TENSOR_EQ(out, tf_out.make(sizes, {kExpectedOutputValue}));
 }
 
+TEST_F(HiFiQuantizePerTensorTest, CheckSingleElementUInt16Quantize) {
+  TensorFactory<ScalarType::Float> tf;
+  const std::vector<int> sizes{1};
+  constexpr ScalarType kOutDtype = ScalarType::UInt16;
+  TensorFactory<kOutDtype> tf_out;
+  Tensor out = tf_out.zeros(sizes);
+  // Some arbitrary values for scalar args.
+  constexpr double kScale = 0.01;
+  constexpr int64_t kZeroPoint = 32768;
+  constexpr int64_t kQuantMin = std::numeric_limits<uint16_t>::min();
+  constexpr int64_t kQuantMax = std::numeric_limits<uint16_t>::max();
+  constexpr float kInputValue = 100.0f;
+  constexpr uint16_t kExpectedOutputValue =
+      static_cast<uint16_t>(kInputValue / kScale + kZeroPoint);
+
+  quantize_per_tensor_out(
+      tf.make(sizes, {kInputValue}),
+      kScale,
+      kZeroPoint,
+      kQuantMin,
+      kQuantMax,
+      kOutDtype,
+      out);
+  EXPECT_TENSOR_EQ(out, tf_out.make(sizes, {kExpectedOutputValue}));
+}
+
 } // namespace
 } // namespace native
 } // namespace HiFi