optional zero points on dequantize per channel (#2364)

Summary:
X-link: pytorch/pytorch#121724

Pull Request resolved: #2364

bypass-github-export-checks

Reviewed By: mikekgfb

Differential Revision: D54709217

fbshipit-source-id: 2d1efe19a65bba8d014fa35bbb06a1b3a2af97d8

Author: manuelcandales

kernels/quantized/cpu/op_dequantize.cpp

@@ -166,7 +166,7 @@ Tensor& dequantize_per_tensor_tensor_args_out(
 Tensor& dequantize_per_channel_out(
     const Tensor& input,
     const Tensor& scale,
-    const Tensor& zero_point,
+    const optional<Tensor>& opt_zero_points,
     int64_t axis,
     int64_t quant_min,
     int64_t quant_max,
@@ -201,16 +201,19 @@ Tensor& dequantize_per_channel_out(
       ssize_t(scale.numel()),
       ssize_t(input.size(axis)));
 
-  ET_CHECK_MSG(
-      zero_point.scalar_type() == ScalarType::Long,
-      "zero_point.scalar_type() %" PRId8 " is not integer type",
-      static_cast<int8_t>(zero_point.scalar_type()));
+  if (opt_zero_points.has_value()) {
+    auto zero_point = opt_zero_points.value();
+    ET_CHECK_MSG(
+        zero_point.scalar_type() == ScalarType::Long,
+        "zero_point.scalar_type() %" PRId8 " is not integer type",
+        static_cast<int8_t>(zero_point.scalar_type()));
 
-  ET_CHECK_MSG(
-      zero_point.numel() == input.size(axis),
-      "zero_point.numel() %zd != input.size(axis) %zd",
-      ssize_t(zero_point.numel()),
-      ssize_t(input.size(axis)));
+    ET_CHECK_MSG(
+        zero_point.numel() == input.size(axis),
+        "zero_point.numel() %zd != input.size(axis) %zd",
+        ssize_t(zero_point.numel()),
+        ssize_t(input.size(axis)));
+  }
 
   check_dequantize_per_tensor_args(
       input, quant_min, quant_max, dtype, out_dtype, out);
@@ -225,7 +228,12 @@ Tensor& dequantize_per_channel_out(
     }
   }
   const double* scale_data = scale.const_data_ptr<double>();
-  const int64_t* zero_point_data = zero_point.const_data_ptr<int64_t>();
+  const int64_t* zero_point_data;
+  if (opt_zero_points.has_value()) {
+    zero_point_data = opt_zero_points.value().const_data_ptr<int64_t>();
+  } else {
+    zero_point_data = nullptr;
+  }
 
   exec_aten::optional<exec_aten::ArrayRef<int64_t>> optional_dim_list{
       exec_aten::ArrayRef<int64_t>{dims, size_t(input.dim() - 1)}};
@@ -242,7 +250,10 @@ Tensor& dequantize_per_channel_out(
   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];                       \
+      int64_t _zero_point = 0;                                                 \
+      if (zero_point_data != nullptr) {                                        \
+        _zero_point = zero_point_data[channel_ix];                             \
+      }                                                                        \
       apply_over_dim_list(                                                     \
           [input, out, _scale, _zero_point](size_t in_ix) {                    \
             out.mutable_data_ptr<CTYPE_OUT>()[in_ix] = static_cast<CTYPE_OUT>( \
@@ -284,7 +295,7 @@ Tensor& dequantize_per_channel_out(
     RuntimeContext& context,
     const Tensor& input,
     const Tensor& scale,
-    const Tensor& zero_point,
+    const optional<Tensor>& opt_zero_points,
     int64_t axis,
     int64_t quant_min,
     int64_t quant_max,
@@ -295,7 +306,7 @@ Tensor& dequantize_per_channel_out(
   return dequantize_per_channel_out(
       input,
       scale,
-      zero_point,
+      opt_zero_points,
       axis,
       quant_min,
       quant_max,

kernels/quantized/quantized.yaml

@@ -28,7 +28,7 @@
     - arg_meta: null
       kernel_name: torch::executor::quantize_per_channel_out
 
-- func: quantized_decomposed::dequantize_per_channel.out(Tensor input, Tensor scales, Tensor zero_points, int axis, int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None, Tensor(a!) out) -> Tensor(a!)
+- func: quantized_decomposed::dequantize_per_channel.out(Tensor input, Tensor scales, Tensor? zero_points, int axis, int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None, Tensor(a!) out) -> Tensor(a!)
   variants: function
   kernels:
     - arg_meta: null