Update base for Update on "[ET-VK] double, short, and uint16 dtype runtime support"

Creating support for double, short, and uint16 for quantization ops. Registering the short keyword since theres already support.

Also changing the cpu implementation to support half

Differential Revision: [D75959063](https://our.internmc.facebook.com/intern/diff/D75959063/)

[ghstack-poisoned]

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,
@@ -347,7 +347,7 @@ 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;