1 files changed, 161 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/arm_conv/depthwise/interleaves/generic_quantized_dot_product.cpp b/src/core/NEON/kernels/arm_conv/depthwise/interleaves/generic_quantized_dot_product.cpp
new file mode 100644
index 0000000000..a6389054d1
--- /dev/null
+++ b/src/core/NEON/kernels/arm_conv/depthwise/interleaves/generic_quantized_dot_product.cpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2022 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "generic_quantized_dot_product.hpp"
+#include <cstdint>
+
+namespace arm_conv {
+namespace depthwise {
+namespace interleaves {
+namespace quantized {
+
+size_t get_storage_size(
+  const DepthwiseArgs &args,
+  const arm_gemm::VLType vl_type,
+  const unsigned int accumulator_depth_vl
+)
+{
+  // We produce VL<int32_t> channels at a time, for each of these blocks of
+  // channels we store a vector of biases, weights (complicated) and
+  // requantize parameters.
+  const unsigned int iter_length = accumulator_depth_vl * arm_gemm::utils::get_vector_length<int32_t>(vl_type);
+  const unsigned int n_iters = args.input_channels * arm_gemm::iceildiv(args.channel_multiplier, iter_length);
+
+  // Compute the cost of storing the weights
+  const unsigned int n_dots_per_kernel_row = arm_gemm::iceildiv(args.kernel_cols, 4u);
+
+  return n_iters * iter_length * (
+    sizeof(int32_t) +  // Bias
+    4 * n_dots_per_kernel_row * args.kernel_rows * sizeof(int8_t) +  // Weights
+    2 * sizeof(int32_t)  // Requantisation parameters
+  );
+}
+
+template <typename T>
+void pack_parameters(
+  void *_buffer, const int32_t *biases,
+  const T *weights, size_t ld_weight_col, size_t ld_weight_row,
+  const DepthwiseArgs &args,
+  const arm_gemm::Requantize32 &qp,
+  const arm_gemm::VLType vl_type,
+  const unsigned int accumulator_depth_vl
+)
+{
+  auto buffer = static_cast<uint8_t *>(_buffer);
+  auto requant_muls = qp.per_channel_muls;
+  auto requant_shifts = qp.per_channel_right_shifts;
+
+  const unsigned int iter_length = accumulator_depth_vl * arm_gemm::utils::get_vector_length<int32_t>(vl_type);
+  const unsigned int n_iters_per_input_channel = arm_gemm::iceildiv(args.channel_multiplier, iter_length);
+  const unsigned int n_dots_per_kernel_row = arm_gemm::iceildiv(args.kernel_cols, 4u);
+
+  const size_t iter_stride = iter_length * (
+      sizeof(int32_t) +  // Bias
+      4 * n_dots_per_kernel_row * args.kernel_rows * sizeof(T) +  // Weights
+      2 * sizeof(int32_t)  // Requantisation parameters
+  );
+
+  ld_weight_col = (ld_weight_col == 0) ? args.input_channels * args.channel_multiplier : ld_weight_col;
+  ld_weight_row = (ld_weight_row == 0) ? args.kernel_cols * ld_weight_col : ld_weight_row;
+
+  for (unsigned int input_channel = 0; input_channel < args.input_channels; input_channel++)
+  {
+    auto buffer_input_channel = buffer + input_channel * n_iters_per_input_channel * iter_stride;
+    auto weights_input_channel = weights + input_channel * args.channel_multiplier;
+
+    for (unsigned int iter = 0; iter < n_iters_per_input_channel; iter++)
+    {
+      // Get a pointer to the start of this portion of the buffer; consequently
+      // derive pointers to the bias, weight and requantisation portions of
+      // this frame.
+      auto buffer_base = buffer_input_channel + iter_stride * iter;
+      auto buffer_biases = reinterpret_cast<int32_t *>(buffer_base);
+      auto buffer_weights = buffer_base + sizeof(int32_t) * iter_length;
+      auto buffer_requant_mul = reinterpret_cast<int32_t *>(
+        buffer_weights + args.kernel_rows * n_dots_per_kernel_row * 4 * iter_length);
+      auto buffer_requant_shift = buffer_requant_mul + iter_length;
+      auto weights_base = weights_input_channel + iter * iter_length;
+
+      // Hence work through the data for this iteration, on a
+      // channel-by-channel basis.
+      const auto this_iter_length = std::min<unsigned int>(
+        iter_length, args.channel_multiplier - iter * iter_length
+      );
+      for (unsigned int i = 0; i < this_iter_length; i++)
+      {
+        auto weights_channel = weights_base + i;
+
+        // Read the bias value, we modify this as we read the weights.
+        auto bias_value = biases == nullptr ? 0 : *(biases++);
+        int32_t elements_sum = 0;
+
+        // Read through the kernel; for each row, marshal together as many dot
+        // product terms as are required.
+        for (unsigned int ki = 0; ki < args.kernel_rows; ki++)
+        {
+          auto buffer_row = buffer_weights + i*4 + ki * 4 * n_dots_per_kernel_row * iter_length;
+          auto weights_row = weights_channel + ki * ld_weight_row;
+
+          unsigned int kj = 0;
+          for (; kj < args.kernel_cols; kj++)
+          {
+            // Determine which element to which we're writing
+            const auto dot = kj / 4;
+            const auto elem = kj % 4;
+
+            // Copy the value; include in the sum
+            const auto val = weights_row[kj * ld_weight_col];
+            buffer_row[dot * 4 * iter_length + elem] = val;
+            elements_sum += val;
+          }
+          for (; kj < 4 * n_dots_per_kernel_row; kj++)
+          {
+            const auto dot = kj / 4;
+            const auto elem = kj % 4;
+            buffer_row[dot * 4 * iter_length + elem] = 0;
+          }
+
+          buffer_row += 4 * n_dots_per_kernel_row * iter_length;
+        }
+
+        // Write back the bias and offset values
+        *(buffer_biases++) =
+          bias_value - qp.a_offset * elements_sum +
+          args.kernel_rows * args.kernel_cols * qp.a_offset * qp.b_offset;
+
+        // Write out the requantisation parameters
+        *(buffer_requant_mul++) = qp.per_channel_requant ? *(requant_muls++) : qp.per_layer_mul;
+        *(buffer_requant_shift++) = qp.per_channel_requant ? *(requant_shifts++) : qp.per_layer_right_shift;
+      }
+    }
+  }
+}
+
+template void pack_parameters(void *, const int32_t *, const int8_t *, size_t, size_t, const DepthwiseArgs &, const arm_gemm::Requantize32 &, arm_gemm::VLType, unsigned int);
+template void pack_parameters(void *, const int32_t *, const uint8_t *, size_t, size_t, const DepthwiseArgs &, const arm_gemm::Requantize32 &, arm_gemm::VLType, unsigned int);
+
+}  // namespace quantized
+}  // namespace interleaves
+}  // namespace depthwise
+}  // namespace arm_conv