Internal change on quantization.

PiperOrigin-RevId: 351876369
Change-Id: If0990e2fca9e90b4eee6dea23a3e2eeaeb6623fb

tensorflow/lite/kernels/conv.cc

@@ -965,8 +965,8 @@ TfLiteStatus EvalHybrid(TfLiteContext* context, TfLiteNode* node,
       op_params.padding_values.height = data->padding.height;
       op_params.stride_width = params->stride_width;
       op_params.stride_height = params->stride_height;
-      op_params.dilation_width_factor = 1;
-      op_params.dilation_height_factor = 1;
+      op_params.dilation_width_factor = params->dilation_width_factor;
+      op_params.dilation_height_factor = params->dilation_height_factor;
       op_params.float_activation_min = output_activation_min;
       op_params.float_activation_max = output_activation_max;
       optimized_ops::HybridConv(

tensorflow/lite/kernels/conv_test.cc

@@ -1295,6 +1295,69 @@ TEST_P(ConvolutionOpTest, SimpleTestHybridInt8) {
                                  0.16)));
 }
 
+TEST_P(ConvolutionOpTest, SimpleTestHybridInt8WithDilation) {
+  const int stride_width = 1;
+  const int stride_height = 1;
+  const Padding padding = Padding_VALID;
+  const int dilation_width_factor = 2;
+  const int dilation_height_factor = 1;
+
+  HybridConvolutionOpModel m(
+      GetRegistration(), {TensorType_FLOAT32, {2, 2, 4, 1}},
+      {TensorType_INT8, {3, 2, 2, 1}, 0, 0, 4.0 / 127.0, 0},
+      {TensorType_FLOAT32, {}}, stride_width, stride_height, padding,
+      ActivationFunctionType_NONE, dilation_width_factor,
+      dilation_height_factor);
+
+  m.SetInput({
+      // First batch
+      1, 1, 1, 1,  // row = 1
+      2, 2, 2, 2,  // row = 2
+      // Second batch
+      1, 2, 3, 4,  // row = 1
+      1, 2, 3, 4,  // row = 2
+  });
+  m.SetSignedFilter({
+      1, 2, 3, 4,    // first 2x2 filter
+      -1, 1, -1, 1,  // second 2x2 filter
+      -1, -1, 1, 1,  // third 2x2 filter
+  });
+  m.SetBias({1, 2, 3});
+
+  m.Invoke();
+
+  // Example: we get 17.1577 instead of 17.
+  //
+  // Second batch:
+  // 1 2 3 4  -> 32 64 95 127 with scale factor 127/4.
+  // 1 2 3 4     32 64 95 127
+  //
+  // First filter:
+  // 1 2  -> 32 64  with scale factor of 127/4.
+  // 3 4     95 127
+  //
+  // The left half of the input gives us 16288. Multiply by (4/127)^2 for
+  // dequantization and adding 1 for the bias gives us the result. and adding
+  // the bias gives us the result.
+  //
+  // The optimized kernel converts the input into this matrix via Im2Col
+  //
+  // 1 1 2 2
+  // 1 1 2 2
+  // 1 3 1 3
+  // 2 4 2 4
+  //
+  // and multiplies it with the filter directly.
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(
+                                 {
+                                     18, 2, 5,  // first batch, left
+                                     18, 2, 5,  // first batch, right
+                                     23, 6, 3,  // second batch, left
+                                     33, 6, 3,  // second batch, right
+                                 },
+                                 0.16)));
+}
+
 TEST_P(ConvolutionOpTest, SimpleTestHybridInt8Big) {
   // A bigger variant of the simple hybrid test to ensure coverage on
   // optimized paths that are only enabled at larger matrix sizes.

tensorflow/lite/kernels/internal/optimized/optimized_ops.h

@@ -1342,6 +1342,8 @@ inline void HybridConv(const ConvParams& params, float* scaling_factors_ptr,
                        int8_t* im2col_data, CpuBackendContext* context) {
   const int stride_width = params.stride_width;
   const int stride_height = params.stride_height;
+  const int dilation_width_factor = params.dilation_width_factor;
+  const int dilation_height_factor = params.dilation_height_factor;
   const float output_activation_min = params.float_activation_min;
   const float output_activation_max = params.float_activation_max;
   TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
@@ -1352,15 +1354,22 @@ inline void HybridConv(const ConvParams& params, float* scaling_factors_ptr,
   const int filter_width = filter_shape.Dims(2);
   const int filter_height = filter_shape.Dims(1);
 
+  const int input_zero_point = 0;
   const int8_t* gemm_input_data = nullptr;
   int num_input;
+  const bool need_dilated_im2col =
+      dilation_width_factor != 1 || dilation_height_factor != 1;
   const bool need_im2col = stride_width != 1 || stride_height != 1 ||
                            filter_width != 1 || filter_height != 1;
 
-  if (need_im2col) {
+  if (need_dilated_im2col) {
+    DilatedIm2col(params, input_zero_point, input_shape, input_data,
+                  filter_shape, output_shape, im2col_data);
+    gemm_input_data = im2col_data;
+    num_input = im2col_shape.FlatSize();
+  } else if (need_im2col) {
     TFLITE_DCHECK(im2col_data);
     // symmetric quantization assumes zero point of 0.
-    const int input_zero_point = 0;
 
     Im2col(params, filter_height, filter_width, input_zero_point, input_shape,
            input_data, im2col_shape, im2col_data);