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, 24 insertions, 2 deletions

diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index 7d53c1de37..5a1e69afbd 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -240,6 +240,27 @@ std::pair<int, int> get_symm_quantized_per_channel_bounds(const QuantizationInfo
  */
 void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC, bool only_right_pad = false);
 
+/** For 2d convolution, given the Lhs/Rhs matrix quantization informations and the convolution dimension,
+ *  calculate a suitable output quantization and suggested bias range for obtaining non-saturated outputs with high probability.
+ *
+ * @param[in] in_q_info     Input matrix quantization info
+ * @param[in] weight_q_info Weights matrix quantization info
+ * @param[in] height        Height of the weights tensor
+ * @param[in] width         Width of the weights tensors
+ * @param[in] channels      Number of input channels
+ * @param[in] data_type     data type, only QASYMM8, QASYMM8_SIGNED are supported
+ * @param[in] bias_fraction see @ref suggest_mac_dst_q_info_and_bias() for explanation
+ *
+ * @return QuantizationHint object containing the suggested output quantization info and min/max bias range
+ */
+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);
+
 /** For a matrix multiplication, given the Lhs/Rhs matrix quantization informations and the matrix multiplication dimensions,
  *  calculate a suitable output quantization and suggested bias range for obtaining non-saturated outputs with high probability.
  *
@@ -249,7 +270,7 @@ void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &d
  * @param[in] n             Number of columns of Rhs Matrix
  * @param[in] k             Number of rows/columns of Rhs/Lhs Matrix
  * @param[in] data_type     data type, only QASYMM8, QASYMM8_SIGNED are supported
- * @param[in] bias_fraction the fraction of bias amplitude compared to integer accummulation. 0 if there is no bias.
+ * @param[in] bias_fraction see @ref suggest_mac_dst_q_info_and_bias() for explanation
  *
  * @return QuantizationHint object containing the suggested output quantization info and min/max bias range
  */
@@ -269,7 +290,8 @@ QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_
  * @return QuantizationHint object containing the suggested output quantization info and min/max bias range
  */
 QuantizationHint suggest_mac_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info,
-                                                 const QuantizationInfo &rhs_q_info, int32_t k, DataType data_type, float bias_fraction);
+                                                 const QuantizationInfo &rhs_q_info, int32_t k, DataType data_type,
+                                                 float bias_fraction);
 } // namespace validation
 } // namespace test
 } // namespace arm_compute