Use dynamic quantization in OpenCL™ Direct Convolution tests

This patch calculates the output quantization info based on the inputs'
quantization information. The previous approach was using the same
quantization information for input, weights and output.

This implementation does not cover the cases where we have fused
activation function.

Note that no Neon™ tests are changed since there were not any quantized
Neon Direct Convolution tests.

Resolves: COMPMID-6485
Change-Id: Id32241320acae0b58552546d6d6f665cd5c63044
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10470
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 36 insertions, 1 deletions

diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp
index 2f273e7042..cb4d87601c 100644
--- a/tests/validation/Helpers.cpp
+++ b/tests/validation/Helpers.cpp
@@ -352,8 +352,43 @@ void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &d
     }
 }
 
+QuantizationHint suggest_conv_dst_q_info_and_bias(const QuantizationInfo &in_q_info,
+                                                  const QuantizationInfo &weight_q_info,
+                                                  int32_t height,
+                                                  int32_t width,
+                                                  int32_t channels,
+                                                  DataType data_type,
+                                                  float bias_fraction)
+{
+    /**  Quantization Setup of convolution
+     *
+     *  Just like any other multiply-accummulate, convolution (2D) operation
+     *  multiplies and accumulates the input and weight tensors. This operation
+     *  takes place in three dimensions: height, width and channels. All of them
+     *  belong to the weight tensor.
+     *
+     *  The formula for simple convolution can be written as:
+     *      C = sum_h sum_w sum_c(I[h_offset + h, w_offset + w, c] * W[h, w, c])
+     *
+     *  Here, h_offset and w_offset are the starting positions in the image. Effects
+     *  of paddings are ignored. This accumulation reduces to something like
+     *
+     *  C = sum_m(I_index * W_hwc)
+     *      where m is height x width x channels.
+     *
+     *  Non-unit strides and/or dilations do not change the probabilistic nature of
+     *  this sum because we always iterate as the size of the weight tensor.
+     *
+     *  Paddings may affect this summation, but it's a boundary condition and so is
+     *  neglected for brevity.
+     */
+
+    return suggest_mac_dst_q_info_and_bias(in_q_info, weight_q_info, height * width * channels, data_type, bias_fraction);
+}
+
 QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info,
-                                                    const QuantizationInfo &rhs_q_info, int32_t m, int32_t n, int32_t k, DataType data_type,
+                                                    const QuantizationInfo &rhs_q_info,
+                                                    int32_t m, int32_t n, int32_t k, DataType data_type,
                                                     float bias_fraction)
 {
     ARM_COMPUTE_UNUSED(m, n);