[ET] correcting cpu ref quantize_per_channel logic to align with ATen

kernels/quantized/cpu/op_quantize.cpp

@@ -6,7 +6,6 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-#include <executorch/kernels/portable/cpu/util/reduce_util.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <algorithm>
 #include <cinttypes>
@@ -282,55 +281,34 @@ Tensor& quantize_per_channel_out(
 
   check_quantize_per_tensor_args(input, quant_min, quant_max, dtype, out);
 
-  // a list contains all dimensions except axis
-  int64_t dims[kTensorDimensionLimit];
-  for (int64_t i = 0; i < input.dim() - 1; i++) {
-    if (i < axis) {
-      dims[i] = i;
-    } else {
-      dims[i] = i - 1;
-    }
-  }
   const double* scale_data = scale.const_data_ptr<double>();
   const int64_t* zero_point_data = zero_point.const_data_ptr<int64_t>();
 
-  std::optional<executorch::aten::ArrayRef<int64_t>> optional_dim_list{
-      executorch::aten::ArrayRef<int64_t>{dims, size_t(input.dim() - 1)}};
-
-  // Actual quantization logic
-  // input, out are the input and output tensors
-  // channel_ix is the index along the axis dimension. 0 <= channel_ix <
-  // input.size(axis).
-  //   i.e. if the tensor has shape (N,C,H,W), axis being 1, then channel_ix
-  //   will be 0, 1, 2, ... C-1
-  // in_ix is the flat index of the element you are quantizing.
-  //   in other words you are quantizing in_data[in_ix]
+  // High-performance single loop with direct channel calculation
 #define QUANTIZE_IMPL(CTYPE_IN, CTYPE_OUT, out_dtype)                          \
-  case ScalarType::out_dtype:                                                  \
-    for (size_t channel_ix = 0; channel_ix < input.size(axis); ++channel_ix) { \
-      double _scale = scale_data[channel_ix];                                  \
-      int64_t _zero_point = zero_point_data[channel_ix];                       \
-      auto* out_data_ptr = out.mutable_data_ptr<CTYPE_OUT>();                  \
-      const auto* input_data_ptr = input.const_data_ptr<CTYPE_IN>();           \
-      apply_over_dim_list(                                                     \
-          [input_data_ptr,                                                     \
-           out_data_ptr,                                                       \
-           _scale,                                                             \
-           _zero_point,                                                        \
-           quant_min,                                                          \
-           quant_max](size_t in_ix) {                                          \
-            out_data_ptr[in_ix] = quantize_val<CTYPE_OUT, CTYPE_IN>(           \
-                _scale,                                                        \
-                _zero_point,                                                   \
-                input_data_ptr[in_ix],                                         \
-                quant_min,                                                     \
-                quant_max);                                                    \
-          },                                                                   \
-          input,                                                               \
-          optional_dim_list,                                                   \
-          channel_ix);                                                         \
+  case ScalarType::out_dtype: {                                                \
+    auto* out_data_ptr = out.mutable_data_ptr<CTYPE_OUT>();                    \
+    const auto* input_data_ptr = input.const_data_ptr<CTYPE_IN>();             \
+    const int64_t input_numel = input.numel();                                 \
+    const int64_t axis_size = input.size(axis);                                \
+    /* Calculate the stride pattern for efficient channel index calculation */ \
+    int64_t axis_block_size = 1;                                               \
+    for (int64_t i = axis + 1; i < input.dim(); i++) {                         \
+      axis_block_size *= input.size(i);                                        \
     }                                                                          \
-    break;
+    /* Single loop over all elements */                                        \
+    for (int64_t i = 0; i < input_numel; i++) {                                \
+      /* Calculate which channel this element belongs to */                    \
+      int64_t channel_idx = (i / axis_block_size) % axis_size;                 \
+      /* Get quantization parameters for this channel */                       \
+      double _scale = scale_data[channel_idx];                                 \
+      int64_t _zero_point = zero_point_data[channel_idx];                      \
+      /* Apply quantization */                                                 \
+      out_data_ptr[i] = quantize_val<CTYPE_OUT, CTYPE_IN>(                     \
+          _scale, _zero_point, input_data_ptr[i], quant_min, quant_max);       \
+    }                                                                          \
+  } break;
+
 #define CALCULATE_FLOAT_TYPE(CTYPE_IN, in_dtype)         \
   case ScalarType::in_dtype:                             \
     switch (out.scalar_type()) {                         \

kernels/quantized/cpu/targets.bzl

@@ -51,12 +51,6 @@ _QUANT_OPS = (
     ),
     op_target(
         name = "op_quantize",
-        deps = [
-            "//executorch/kernels/portable/cpu/util:reduce_util",
-        ],
-        _aten_mode_deps = [
-            "//executorch/kernels/portable/cpu/util:reduce_util_aten",
-        ],
     ),
 )
 

kernels/quantized/test/op_quantize_test.cpp

@@ -206,3 +206,243 @@ TEST(OpQuantizeOutTest, QuantizePerChannel) {
 
   EXPECT_TENSOR_EQ(out, expected);
 }
+
+TEST(OpQuantizeOutTest, QuantizePerChannelAxis0) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  Tensor input = tf_float.full({3, 2}, 4);
+  Tensor scale = tf_double.make({3}, {0.5, 1.0, 2.0});
+  Tensor zero_point = tf_long.make({3}, {100, 50, 25});
+  int64_t quant_min = 0;
+  int64_t quant_max = 255;
+
+  TensorFactory<ScalarType::Byte> tfo;
+  Tensor out = tfo.zeros({3, 2});
+  // Channel 0: 4 / 0.5 + 100 = 108
+  // Channel 1: 4 / 1.0 + 50 = 54
+  // Channel 2: 4 / 2.0 + 25 = 27
+  Tensor expected = tfo.make({3, 2}, {108, 108, 54, 54, 27, 27});
+  quantize_per_channel_out(
+      input, scale, zero_point, 0, quant_min, quant_max, ScalarType::Byte, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannel3D) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  // Test 3D tensor with axis=1 (middle dimension)
+  Tensor input = tf_float.full({2, 3, 4}, 6);
+  Tensor scale = tf_double.make({3}, {0.5, 1.0, 1.5});
+  Tensor zero_point = tf_long.make({3}, {10, 20, 30});
+  int64_t quant_min = -128;
+  int64_t quant_max = 127;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({2, 3, 4});
+  // Channel 0: 6 / 0.5 + 10 = 22
+  // Channel 1: 6 / 1.0 + 20 = 26
+  // Channel 2: 6 / 1.5 + 30 = 34
+  Tensor expected = tfo.make(
+      {2, 3, 4},
+      {
+          22, 22, 22, 22, // First batch, channel 0
+          26, 26, 26, 26, // First batch, channel 1
+          34, 34, 34, 34, // First batch, channel 2
+          22, 22, 22, 22, // Second batch, channel 0
+          26, 26, 26, 26, // Second batch, channel 1
+          34, 34, 34, 34 // Second batch, channel 2
+      });
+  quantize_per_channel_out(
+      input, scale, zero_point, 1, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannel4D) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  // Test 4D tensor with axis=2 (typical conv weight layout: N,C,H,W)
+  Tensor input = tf_float.full({2, 2, 3, 2}, 8);
+  Tensor scale = tf_double.make({3}, {0.25, 0.5, 1.0});
+  Tensor zero_point = tf_long.make({3}, {0, 10, 20});
+  int64_t quant_min = -128;
+  int64_t quant_max = 127;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({2, 2, 3, 2});
+  // Channel 0: 8 / 0.25 + 0 = 32
+  // Channel 1: 8 / 0.5 + 10 = 26
+  // Channel 2: 8 / 1.0 + 20 = 28
+  std::vector<int8_t> expected_data;
+  for (int n = 0; n < 2; n++) {
+    for (int c = 0; c < 2; c++) {
+      for (int h = 0; h < 3; h++) {
+        for (int w = 0; w < 2; w++) {
+          int8_t val = (h == 0) ? 32 : (h == 1) ? 26 : 28;
+          expected_data.push_back(val);
+        }
+      }
+    }
+  }
+  Tensor expected = tfo.make({2, 2, 3, 2}, expected_data);
+  quantize_per_channel_out(
+      input, scale, zero_point, 2, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelNegativeAxis) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  Tensor input = tf_float.full({2, 3}, 5);
+  Tensor scale = tf_double.make({3}, {0.5, 1.0, 2.0});
+  Tensor zero_point = tf_long.make({3}, {0, 10, 20});
+  int64_t quant_min = 0;
+  int64_t quant_max = 255;
+
+  TensorFactory<ScalarType::Byte> tfo;
+  Tensor out = tfo.zeros({2, 3});
+  // Using axis=-1 should be equivalent to axis=1 for 2D tensor
+  // Channel 0: 5 / 0.5 + 0 = 10
+  // Channel 1: 5 / 1.0 + 10 = 15
+  // Channel 2: 5 / 2.0 + 20 = 22 (rounded from 22.5)
+  Tensor expected = tfo.make({2, 3}, {10, 15, 22, 10, 15, 22});
+  quantize_per_channel_out(
+      input,
+      scale,
+      zero_point,
+      -1,
+      quant_min,
+      quant_max,
+      ScalarType::Byte,
+      out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelSingleChannel) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  Tensor input = tf_float.full({3, 1, 4}, 7);
+  Tensor scale = tf_double.make({1}, {0.5});
+  Tensor zero_point = tf_long.make({1}, {128});
+  int64_t quant_min = 0;
+  int64_t quant_max = 255;
+
+  TensorFactory<ScalarType::Byte> tfo;
+  Tensor out = tfo.zeros({3, 1, 4});
+  // Single channel: 7 / 0.5 + 128 = 142
+  Tensor expected = tfo.full({3, 1, 4}, 142);
+  quantize_per_channel_out(
+      input, scale, zero_point, 1, quant_min, quant_max, ScalarType::Byte, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelDifferentInputTypes) {
+  TensorFactory<ScalarType::Double> tf_double_input;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  Tensor input = tf_double_input.full({2, 2}, 3.14159);
+  Tensor scale = tf_double.make({2}, {0.01, 0.02});
+  Tensor zero_point = tf_long.make({2}, {0, 100});
+  int64_t quant_min = -128;
+  int64_t quant_max = 127;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({2, 2});
+  // Channel 0: 3.14159 / 0.01 + 0 = 314 -> clamped to 127
+  // Channel 1: 3.14159 / 0.02 + 100 = 257 -> clamped to 127
+  Tensor expected = tfo.make({2, 2}, {127, 127, 127, 127});
+  quantize_per_channel_out(
+      input, scale, zero_point, 1, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelDifferentOutputTypes) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  Tensor input = tf_float.full({2, 2}, 10);
+  Tensor scale = tf_double.make({2}, {1.0, 2.0});
+  Tensor zero_point = tf_long.make({2}, {1000, 2000});
+  int64_t quant_min = -32768;
+  int64_t quant_max = 32767;
+
+  // Test with 16-bit output
+  TensorFactory<ScalarType::Short> tfo;
+  Tensor out = tfo.zeros({2, 2});
+  // Channel 0: 10 / 1.0 + 1000 = 1010
+  // Channel 1: 10 / 2.0 + 2000 = 2005
+  Tensor expected = tfo.make({2, 2}, {1010, 2005, 1010, 2005});
+  quantize_per_channel_out(
+      input,
+      scale,
+      zero_point,
+      1,
+      quant_min,
+      quant_max,
+      ScalarType::Short,
+      out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelMixedValues) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  // Test with different input values per position
+  Tensor input = tf_float.make({2, 3}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0});
+  Tensor scale = tf_double.make({3}, {0.5, 1.0, 1.5});
+  Tensor zero_point = tf_long.make({3}, {10, 20, 30});
+  int64_t quant_min = 0;
+  int64_t quant_max = 255;
+
+  TensorFactory<ScalarType::Byte> tfo;
+  Tensor out = tfo.zeros({2, 3});
+  // Row 0: [1.0/0.5+10, 2.0/1.0+20, 3.0/1.5+30] = [12, 22, 32]
+  // Row 1: [4.0/0.5+10, 5.0/1.0+20, 6.0/1.5+30] = [18, 25, 34]
+  Tensor expected = tfo.make({2, 3}, {12, 22, 32, 18, 25, 34});
+  quantize_per_channel_out(
+      input, scale, zero_point, 1, quant_min, quant_max, ScalarType::Byte, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}
+
+TEST(OpQuantizeOutTest, QuantizePerChannelClampingBehavior) {
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  // Test values that will exceed quant_min/quant_max bounds
+  Tensor input = tf_float.make({1, 3}, {-100.0, 0.0, 100.0});
+  Tensor scale = tf_double.make({3}, {1.0, 1.0, 1.0});
+  Tensor zero_point = tf_long.make({3}, {0, 0, 0});
+  int64_t quant_min = -10;
+  int64_t quant_max = 10;
+
+  TensorFactory<ScalarType::Char> tfo;
+  Tensor out = tfo.zeros({1, 3});
+  // Values: [-100, 0, 100] should be clamped to [-10, 0, 10]
+  Tensor expected = tfo.make({1, 3}, {-10, 0, 10});
+  quantize_per_channel_out(
+      input, scale, zero_point, 1, quant_min, quant_max, ScalarType::Char, out);
+
+  EXPECT_TENSOR_EQ(out, expected);
+}