[ET] enabling half dtype input for quantization

Pull Request resolved: #11479

# Context
Currently the cpu implementation for the quantization operator (which includes `quantize_per_token`, `quantize_per_tensor`, and `quantize_per_channel`), does not inherently support half (fp16) input scalar types. In order to align with the PyTorch implementation that accepts fp16 and bfp16 inputs, this diff aims to enable half input dtype support for the quantization operators. We will be comparing this implementation against the vulkan operators.

# Changes
As defined in ExecuTorch [scalar_type_util.h](https://github.com/pytorch/executorch/blob/053686242c1687f0d51b3bb8befd14b047d7b025/runtime/core/exec_aten/util/scalar_type_util.h#L190) file, there is a method to enable support simply changing which preprocessor is called to ET_FORALL_FLOATH_TYPES. This enables support for Half (fp16), Float (fp32), and Double (fp64).

I have also included more comprehensive testing against the input dtypes, including adding double testing since it didn't already exist before. Instead of just confirming that all the output dtypes are supported, we also have a check that all input dtypes are supported now as well.
ghstack-source-id: 290376481
@exported-using-ghexport

Differential Revision: [D76053764](https://our.internmc.facebook.com/intern/diff/D76053764/)

Author: morelos

kernels/quantized/cpu/op_quantize.cpp

@@ -150,7 +150,7 @@ Tensor& quantize_per_tensor_out(
     break;
 
   switch (input.scalar_type()) {
-    ET_FORALL_FLOAT_TYPES(CALCULATE_FLOAT_TYPE);
+    ET_FORALL_FLOATH_TYPES(CALCULATE_FLOAT_TYPE);
     default:
       ET_CHECK_MSG(
           false,
@@ -346,7 +346,7 @@ Tensor& quantize_per_channel_out(
     break;
 
   switch (input.scalar_type()) {
-    ET_FORALL_FLOAT_TYPES(CALCULATE_FLOAT_TYPE);
+    ET_FORALL_FLOATH_TYPES(CALCULATE_FLOAT_TYPE);
     default:
       ET_CHECK_MSG(
           false,

kernels/quantized/test/op_quantize_test.cpp

@@ -49,6 +49,32 @@ void test_dtype() {
   EXPECT_TENSOR_EQ(out, expected);
 }
 
+template <ScalarType INPUT_DTYPE>
+void test_input_dtype() {
+  TensorFactory<INPUT_DTYPE> tf_input;
+
+  Tensor input = tf_input.full({3, 5}, 4);
+  double scale = 0.5;
+  int64_t zero_point = 108;
+  int64_t quant_min = 0;
+  int64_t quant_max = 127;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({3, 5});
+  // 4 / 0.5 + 108 = 116
+  Tensor expected = tfo.full({3, 5}, 116);
+  quantize_per_tensor_out(
+      input, scale, zero_point, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, AllInputDtypesSupported) {
+  test_input_dtype<ScalarType::Float>();
+  test_input_dtype<ScalarType::Half>();
+  test_input_dtype<ScalarType::Double>();
+}
+
 TEST(OpQuantizeOutTest, AllDtypesSupported) {
   test_dtype<ScalarType::Byte>();
   test_dtype<ScalarType::Char>();
@@ -58,6 +84,45 @@ TEST(OpQuantizeOutTest, AllDtypesSupported) {
   test_dtype<ScalarType::Int>();
 }
 
+TEST(OpQuantizeOutTest, DoubleInputTest) {
+  TensorFactory<ScalarType::Double> tf_double;
+
+  // Test with a more complex value that might have precision differences
+  Tensor input = tf_double.full({2, 3}, 3.14159265359);
+  double scale = 0.01;
+  int64_t zero_point = -100;
+  int64_t quant_min = 0;
+  int64_t quant_max = 255;
+
+  TensorFactory<ScalarType::Byte> tfo;
+  Tensor out = tfo.zeros({2, 3});
+  // 3.14159265359 / 0.01 - 100 = 214.159265359
+  Tensor expected = tfo.full({2, 3}, 214);
+  quantize_per_tensor_out(
+      input, scale, zero_point, quant_min, quant_max, ScalarType::Byte, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, HalfInputTest) {
+  TensorFactory<ScalarType::Half> tf_half;
+
+  Tensor input = tf_half.full({2, 3}, 2.5);
+  double scale = 0.5;
+  int64_t zero_point = 10;
+  int64_t quant_min = -128;
+  int64_t quant_max = 127;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({2, 3});
+  // 2.5 / 0.5 + 10 = 15
+  Tensor expected = tfo.full({2, 3}, 15);
+  quantize_per_tensor_out(
+      input, scale, zero_point, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
 TEST(OpQuantizeOutTest, TensorArgOverload) {
   TensorFactory<ScalarType::Float> tf_float;
   TensorFactory<ScalarType::Double> tf_double;