text: // Copyright 2020 Google LLC
  //
  // This source code is licensed under the BSD-style license found in the
  // LICENSE file in the root directory of this source tree.
  
  #include <math.h>
  #include <stddef.h>
  #include <stdint.h>
  
  #include <xnnpack.h>
  #include <xnnpack/log.h>
  #include <xnnpack/params.h>
  #include <xnnpack/subgraph.h>
  
  
  static enum xnn_status create_global_average_pooling_operator(
    const struct xnn_node* node,
    const struct xnn_value* values,
    size_t num_values,
    struct xnn_operator_data* opdata)
  {
    assert(node->num_inputs == 1);
    const uint32_t input_id = node->inputs[0];
    assert(input_id != XNN_INVALID_VALUE_ID);
    assert(input_id < num_values);
  
    assert(node->num_outputs == 1);
    const uint32_t output_id = node->outputs[0];
    assert(output_id != XNN_INVALID_VALUE_ID);
    assert(output_id < num_values);
  
    const size_t num_input_dims = values[input_id].shape.num_dims;
    assert(num_input_dims >= 1);
    const size_t channel_dim = values[input_id].shape.dim[num_input_dims - 1];
  
    enum xnn_status status;
    if (values[node->inputs[0]].layout == xnn_layout_type_nchw) {
      assert(node->compute_type == xnn_compute_type_fp32);
      status = xnn_create_global_average_pooling_ncw_f32(
        channel_dim /* channels */,
        node->activation.output_min,
        node->activation.output_max,
        node->flags,
        &opdata->operator_object);
    } else {
      assert(values[node->inputs[0]].layout == xnn_layout_type_nhwc);
      assert(values[node->outputs[0]].layout == xnn_layout_type_nhwc);
      switch (node->compute_type) {
        case xnn_compute_type_fp32:
          status = xnn_create_global_average_pooling_nwc_f32(
            channel_dim /* channels */, channel_dim /* input stride */, channel_dim /* output stride */,
            node->activation.output_min,
            node->activation.output_max,
            node->flags,
            &opdata->operator_object);
          break;
  #ifndef XNN_NO_F16_OPERATORS
        case xnn_compute_type_fp16:
          status = xnn_create_global_average_pooling_nwc_f16(
            channel_dim /* channels */, channel_dim /* input stride */, channel_dim /* output stride */,
            node->activation.output_min,
            node->activation.output_max,
            node->flags,
            &opdata->operator_object);
          break;
  #endif  // !defined(XNN_NO_F16_OPERATORS)
  #ifndef XNN_NO_QS8_OPERATORS
        case xnn_compute_type_qs8:
        {
          const float output_scale = values[output_id].quantization.scale;
          const int32_t output_zero_point = values[output_id].quantization.zero_point;
          const int8_t output_min =
            (int8_t) lrintf(fminf(fmaxf(node->activation.output_min / output_scale + (float) output_zero_point, -128.0f), 127.0f));
          const int8_t output_max =
            (int8_t) lrintf(fminf(fmaxf(node->activation.output_max / output_scale + (float) output_zero_point, -128.0f), 127.0f));
          status = xnn_create_global_average_pooling_nwc_qs8(
            channel_dim /* channels */, channel_dim /* input stride */, channel_dim /* output stride */,
            (int8_t) values[input_id].quantization.zero_point, values[input_id].quantization.scale,
            (int8_t) values[output_id].quantization.zero_point, values[output_id].quantization.scale,
            output_min,
            output_max,
            node->flags,
            &opdata->operator_object);
          break;
        }
  #endif  // !defined(XNN_NO_QS8_OPERATORS)
  #ifndef XNN_NO_QU8_OPERATORS
        case xnn_compute_type_qu8:
        {
          const float output_scale = values[output_id].quantization.scale;
          const int32_t output_zero_point = values[output_id].quantization.zero_point;
          const uint8_t output_min =
            (uint8_t) lrintf(fminf(fmaxf(node->activation.output_min / output_scale + (float) output_zero_point, 0.0f), 255.0f));
          const uint8_t output_max =
            (uint8_t) lrintf(fminf(fmaxf(node->activation.output_max / output_scale + (float) output_zero_point, 0.0f), 255.0f));
          status = xnn_create_global_average_pooling_nwc_qu8(
            channel_dim /* channels */, channel_dim /* input stride */, channel_dim /* output stride */,
            (uint8_t) values[input_id].quantization.zero_point, values[input_id].quantization.scale,
            (uint8_t) values[output_id].quantization.zero_point, values[output_id].quantization.scale,
            output_min,
            output_max,
            node->flags,
            &opdata->operator_object);
          break;
        }
  #endif  // !defined(XNN_NO_QU8_OPERATORS)
        default:
          XNN_UNREACHABLE;
      }
    }
    if (status == xnn_status_success) {
      opdata->batch_size = values[input_id].shape.dim[0];
      opdata->input_width = values[input_id].shape.dim[1] * values[input_id].shape.dim[2];
      opdata->inputs[0] = input_id;
      opdata->outputs[0] = output_id;
    }
    return status;
  }
  
  static enum xnn_status setup_global_average_pooling_operator(
    const struct xnn_operator_data* opdata,
    const struct xnn_blob* blobs,
    size_t num_blobs,
    pthreadpool_t threadpool)
  {
    const uint32_t input_id = opdata->inputs[0];
    assert(input_id != XNN_INVALID_VALUE_ID);
    assert(input_id < num_blobs);
  
    const uint32_t output_id = opdata->outputs[0];
    assert(output_id != XNN_INVALID_VALUE_ID);
    assert(output_id < num_blobs);
  
    const struct xnn_blob* input_blob = blobs + input_id;
    const void* input_data = input_blob->data;
    assert(input_data != NULL);
  
    const struct xnn_blob* output_blob = blobs + output_id;
    void* output_data = output_blob->data;
    assert(output_data != NULL);
  
    switch (opdata->operator_object->type) {
      case xnn_operator_type_global_average_pooling_ncw_f32:
        return xnn_setup_global_average_pooling_ncw_f32(
          opdata->operator_object,
          opdata->batch_size,
          opdata->input_width,
          input_data,
          output_data,
          threadpool);
        break;
      case xnn_operator_type_global_average_pooling_nwc_f32:
        return xnn_setup_global_average_pooling_nwc_f32(
          opdata->operator_object,
          opdata->batch_size,
          opdata->input_width,
          input_data,
          output_data,
          threadpool);
        break;
  #ifndef XNN_NO_F16_OPERATORS
      case xnn_operator_type_global_average_pooling_nwc_f16:
        return xnn_setup_global_average_pooling_nwc_f16(
          opdata->operator_object,
          opdata->batch_size,
          opdata->input_width,
          input_data,
          output_data,
          threadpool);
        break;
  #endif  // !defined(XNN_NO_F16_OPERATORS)
  #ifndef XNN_NO_QS8_OPERATORS
      case xnn_operator_type_global_average_pooling_nwc_qs8:
        return xnn_setup_global_average_pooling_nwc_qs8(
          opdata->operator_object,
          opdata->batch_size,
          opdata->input_width,
          input_data,
          output_data,
          threadpool);
        break;
  #endif  // !defined(XNN_NO_QS8_OPERATORS)
  #ifndef XNN_NO_QU8_OPERATORS
      case xnn_operator_type_global_average_pooling_nwc_qu8:
        return xnn_setup_global_average_pooling_nwc_qu8(
          opdata->operator_object,
          opdata->batch_size,
          opdata->input_width,
          input_data,
          output_data,
          threadpool);
        break;
  #endif  // !defined(XNN_NO_QU8_OPERATORS)
      default:
        XNN_UNREACHABLE;
    }
  }
  
  enum xnn_status xnn_define_global_average_pooling_2d(
    xnn_subgraph_t subgraph,
    float output_min,
    float output_max,
    uint32_t input_id,
    uint32_t output_id,
    uint32_t flags)
  {
    if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
      xnn_log_error("failed to define %s operator: XNNPACK is not initialized",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d));
      return xnn_status_uninitialized;
    }
  
    if (isnan(output_min)) {
      xnn_log_error(
        "failed to define %s operator with NaN output lower bound: lower bound must be non-NaN",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d));
      return xnn_status_invalid_parameter;
    }
  
    if (isnan(output_max)) {
      xnn_log_error(
        "failed to define %s operator with NaN output upper bound: upper bound must be non-NaN",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d));
      return xnn_status_invalid_parameter;
    }
  
    if (output_min >= output_max) {
      xnn_log_error(
        "failed to define %s operator with [%.7g, %.7g] output range: lower bound must be below upper bound",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), output_min, output_max);
      return xnn_status_invalid_parameter;
    }
  
    if (input_id >= subgraph->num_values) {
      xnn_log_error(
        "failed to define %s operator with input ID #%" PRIu32 ": invalid Value ID",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), input_id);
      return xnn_status_invalid_parameter;
    }
  
    const struct xnn_value* input_value = &subgraph->values[input_id];
    if (input_value->type != xnn_value_type_dense_tensor) {
      xnn_log_error(
        "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value type %d (expected dense tensor)",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), input_id, input_value->type);
      return xnn_status_invalid_parameter;
    }
  
    switch (input_value->datatype) {
      case xnn_datatype_fp32:
  #ifndef XNN_NO_QS8_OPERATORS
      case xnn_datatype_qint8:
  #endif  // !defined(XNN_NO_QS8_OPERATORS)
  #ifndef XNN_NO_QU8_OPERATORS
      case xnn_datatype_quint8:
  #endif  // !defined(XNN_NO_QU8_OPERATORS)
        break;
      default:
        xnn_log_error(
          "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
          xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), input_id,
          xnn_datatype_to_string(input_value->datatype), input_value->datatype);
        return xnn_status_invalid_parameter;
    }
  
    if (output_id >= subgraph->num_values) {
      xnn_log_error(
        "failed to define %s operator with output ID #%" PRIu32 ": invalid Value ID",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), output_id);
      return xnn_status_invalid_parameter;
    }
  
    const struct xnn_value* output_value = &subgraph->values[output_id];
    if (output_value->type != xnn_value_type_dense_tensor) {
      xnn_log_error(
        "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value type %d (expected dense tensor)",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), output_id, output_value->type);
      return xnn_status_invalid_parameter;
    }
  
    enum xnn_compute_type compute_type = xnn_compute_type_invalid;
    switch (output_value->datatype) {
      case xnn_datatype_fp32:
        compute_type = xnn_compute_type_fp32;
        break;
  #ifndef XNN_NO_QS8_OPERATORS
      case xnn_datatype_qint8:
        compute_type = xnn_compute_type_qs8;
        break;
  #endif  // !defined(XNN_NO_QS8_OPERATORS)
  #ifndef XNN_NO_QU8_OPERATORS
      case xnn_datatype_quint8:
        compute_type = xnn_compute_type_qu8;
        break;
  #endif  // !defined(XNN_NO_QU8_OPERATORS)
      default:
        xnn_log_error(
          "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
          xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), output_id,
          xnn_datatype_to_string(output_value->datatype), output_value->datatype);
        return xnn_status_invalid_parameter;
    }
  
    if (input_value->datatype != output_value->datatype) {
      xnn_log_error(
        "failed to define %s operator with input ID #%" PRIu32 " and output ID #%" PRIu32
        ": mismatching datatypes across input (%s) and output (%s)",
        xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), input_id, output_id,
        xnn_datatype_to_string(input_value->datatype),
        xnn_datatype_to_string(output_value->datatype));
      return xnn_status_invalid_parameter;
    }
  
    struct xnn_node* node = xnn_subgraph_new_node(subgraph);
    if (node == NULL) {
      return xnn_status_out_of_memory;
    }
  
    node->type = xnn_node_type_global_average_pooling_2d;
    node->compute_type = compute_type;
    node->activation.output_min = output_min;
    node->activation.output_max = output_max;
    node->num_inputs = 1;
    node->inputs[0] = input_id;
    node->num_outputs = 1;
    node->outputs[0] = output_id;
    node->flags = flags;
  
    node->create = create_global_average_pooling_operator;
    node->setup = setup_global_average_pooling_operator;
  
    return xnn_status_success;
  }
  