text: // Copyright 2019 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 <assert.h>
  #include <math.h>
  #include <stdbool.h>
  #include <stddef.h>
  #include <stdint.h>
  #include <stdlib.h>
  #include <string.h>
  
  #include <xnnpack.h>
  #include <xnnpack/allocator.h>
  #include <xnnpack/operator.h>
  #include <xnnpack/common.h>
  #include <xnnpack/log.h>
  #include <xnnpack/math.h>
  #include <xnnpack/params-init.h>
  #include <xnnpack/params.h>
  #include <xnnpack/indirection.h>
  
  
  static inline size_t compute_output_dimension(
      size_t padded_input_dimension,
      size_t kernel_dimension)
  {
    return padded_input_dimension / kernel_dimension;
  }
  
  static const struct argmaxpool_parameters* select_ukernel(
      size_t pooling_size,
      const struct argmaxpool_parameters* ukernel)
  {
    while (ukernel->qr == 0 && ukernel->mr < pooling_size) {
      ukernel++;
    }
    return ukernel;
  }
  
  enum xnn_status xnn_create_argmax_pooling2d_nhwc_f32(
      uint32_t input_padding_top,
      uint32_t input_padding_right,
      uint32_t input_padding_bottom,
      uint32_t input_padding_left,
      uint32_t pooling_height,
      uint32_t pooling_width,
      size_t channels,
      size_t input_pixel_stride,
      size_t output_pixel_stride,
      uint32_t flags,
      xnn_operator_t* argmax_pooling_op_out)
  {
    xnn_operator_t argmax_pooling_op = NULL;
    enum xnn_status status = xnn_status_uninitialized;
  
    if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
      xnn_log_error("failed to create %s operator: XNNPACK is not initialized",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32));
      goto error;
    }
  
    status = xnn_status_invalid_parameter;
  
    const uint32_t pooling_size = pooling_height * pooling_width;
    if (pooling_size == 0) {
      xnn_log_error(
        "failed to create %s operator with %" PRIu32 "x%" PRIu32 " pooling size: "
        "pooling size dimensions must be non-zero",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32), pooling_width, pooling_height);
      goto error;
    }
  
    if (pooling_size == 1) {
      xnn_log_error(
        "failed to create %s operator with 1 pooling element: 1x1 pooling is meaningless",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32));
      goto error;
    }
  
    if (channels == 0) {
      xnn_log_error(
        "failed to create %s operator with %zu channels: number of channels must be non-zero",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32), channels);
      goto error;
    }
  
    if (input_pixel_stride < channels) {
      xnn_log_error(
        "failed to create %s operator with input pixel stride of %zu: "
        "stride must be at least as large as the number of channels (%zu)",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32), input_pixel_stride, channels);
      goto error;
    }
  
    if (output_pixel_stride < channels) {
      xnn_log_error(
        "failed to create %s operator with output pixel stride of %zu: "
        "stride must be at least as large as the number of channels (%zu)",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32), output_pixel_stride, channels);
      goto error;
    }
  
    const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
    if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) {
      if (any_padding) {
        xnn_log_error(
          "failed to create %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: "
          "TensorFlow SAME padding can't be combined with explicit padding specification",
          xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32),
          input_padding_top, input_padding_left, input_padding_bottom, input_padding_right);
        goto error;
      }
    }
  
    status = xnn_status_out_of_memory;
  
    argmax_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
    if (argmax_pooling_op == NULL) {
      xnn_log_error(
        "failed to allocate %zu bytes for %s operator descriptor",
        sizeof(struct xnn_operator), xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32));
      goto error;
    }
  
    argmax_pooling_op->padding_top = input_padding_top;
    argmax_pooling_op->padding_right = input_padding_right;
    argmax_pooling_op->padding_bottom = input_padding_bottom;
    argmax_pooling_op->padding_left = input_padding_left;
  
    argmax_pooling_op->kernel_height = pooling_height;
    argmax_pooling_op->kernel_width = pooling_width;
    argmax_pooling_op->stride_height = pooling_height;
    argmax_pooling_op->stride_width = pooling_width;
    argmax_pooling_op->dilation_height = 1;
    argmax_pooling_op->dilation_width = 1;
    argmax_pooling_op->channels = channels;
    argmax_pooling_op->input_pixel_stride = input_pixel_stride;
    argmax_pooling_op->output_pixel_stride = output_pixel_stride;
  
    argmax_pooling_op->type = xnn_operator_type_argmax_pooling_nhwc_f32;
    argmax_pooling_op->flags = flags;
  
    argmax_pooling_op->state = xnn_run_state_invalid;
  
    *argmax_pooling_op_out = argmax_pooling_op;
    return xnn_status_success;
  
  error:
    xnn_delete_operator(argmax_pooling_op);
    return status;
  }
  
  enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32(
      xnn_operator_t argmax_pooling_op,
      size_t batch_size,
      size_t input_height,
      size_t input_width,
      const float* input,
      float* output,
      uint32_t* index,
      pthreadpool_t threadpool)
  {
    if (argmax_pooling_op->type != xnn_operator_type_argmax_pooling_nhwc_f32) {
      xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32),
        xnn_operator_type_to_string(argmax_pooling_op->type));
      return xnn_status_invalid_parameter;
    }
    argmax_pooling_op->state = xnn_run_state_invalid;
  
    if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
      xnn_log_error("failed to setup %s operator: XNNPACK is not initialized",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32));
      return xnn_status_uninitialized;
    }
  
    if (input_width == 0 || input_height == 0) {
      xnn_log_error(
        "failed to setup %s operator with %zux%zu input: input dimensions must be non-zero",
        xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32), input_width, input_height);
      return xnn_status_invalid_parameter;
    }
  
    if (batch_size == 0) {
      argmax_pooling_op->state = xnn_run_state_skip;
      return xnn_status_success;
    }
  
    argmax_pooling_op->batch_size = batch_size;
    argmax_pooling_op->input_height = input_height;
    argmax_pooling_op->input_width = input_width;
    argmax_pooling_op->input = input;
  
    const size_t pooling_height = argmax_pooling_op->kernel_height;
    const size_t pooling_width = argmax_pooling_op->kernel_width;
  
    if (argmax_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) {
      argmax_pooling_op->output_height = divide_round_up(input_height, pooling_height);
      argmax_pooling_op->output_width = divide_round_up(input_width, pooling_width);
  
      const uint32_t padding_height = argmax_pooling_op->output_height * pooling_height - input_height;
      const uint32_t padding_width = argmax_pooling_op->output_width * pooling_width - input_width;
      argmax_pooling_op->padding_top = padding_height / 2;
      argmax_pooling_op->padding_left = padding_width / 2;
      argmax_pooling_op->padding_bottom = padding_height - argmax_pooling_op->padding_top;
      argmax_pooling_op->padding_right = padding_width - argmax_pooling_op->padding_left;
    } else {
      argmax_pooling_op->output_height = compute_output_dimension(
          argmax_pooling_op->padding_top + input_height + argmax_pooling_op->padding_bottom,
          argmax_pooling_op->kernel_height);
      argmax_pooling_op->output_width = compute_output_dimension(
          argmax_pooling_op->padding_left + input_width + argmax_pooling_op->padding_right,
          argmax_pooling_op->kernel_width);
    }
  
    const size_t pooling_size = pooling_height * pooling_width;
    const size_t output_height = argmax_pooling_op->output_height;
    const size_t output_width = argmax_pooling_op->output_width;
    const struct argmaxpool_parameters* ukernel = select_ukernel(pooling_size, xnn_params.f32.argmaxpool);
    const uint32_t mr = ukernel->mr;
  
    const size_t step_width = pooling_width;
    const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height;
  
    if (input_height != argmax_pooling_op->last_input_height ||
        input_width != argmax_pooling_op->last_input_width)
    {
      // Micro-kernel may read up to (mr - 1) elements after the end of indirection buffer.
      const size_t indirection_buffer_size = sizeof(void*) * ((mr - 1) + output_height * step_height);
  
      const void** indirection_buffer =
        (const void**) xnn_reallocate_memory(argmax_pooling_op->indirection_buffer, indirection_buffer_size);
      if (indirection_buffer == NULL) {
        xnn_log_error(
          "failed to allocate %zu bytes for %s operator indirection buffer",
          indirection_buffer_size, xnn_operator_type_to_string(xnn_operator_type_argmax_pooling_nhwc_f32));
        return xnn_status_out_of_memory;
      }
      argmax_pooling_op->indirection_buffer = indirection_buffer;
  
      xnn_indirection_init_maxpool2d(argmax_pooling_op, step_height, step_width, 2 /* log2(sizeof(float)) */);
  
      argmax_pooling_op->last_input = input;
      argmax_pooling_op->last_input_height = input_height;
      argmax_pooling_op->last_input_width = input_width;
    }
  
    const size_t channels = argmax_pooling_op->channels;
  
    const size_t indirect_input_height_stride = step_height * sizeof(void*);
    const size_t output_width_stride = argmax_pooling_op->output_pixel_stride * sizeof(float);
    const size_t output_height_stride = output_width * output_width_stride;
    const size_t index_height_stride = output_width * channels * sizeof(uint32_t);
  
    const uint32_t qr = ukernel->qr;
    const size_t multipass_adjustment = qr == 0 ? 0 : round_up(pooling_size - mr, qr) + mr - qr;
    argmax_pooling_op->context.argmax_pooling = (struct argmax_pooling_context) {
      .indirect_input = argmax_pooling_op->indirection_buffer,
      .indirect_input_height_stride = indirect_input_height_stride,
      .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) argmax_pooling_op->last_input),
      .input_batch_stride = input_height * input_width * argmax_pooling_op->input_pixel_stride * sizeof(float),
      .output = output,
      .output_batch_stride = output_height * output_height_stride,
      .output_height_stride = output_height_stride,
      .output_width = output_width,
      .index = index,
      .index_batch_stride = output_height * index_height_stride,
      .index_height_stride = index_height_stride,
      .pooling_size = pooling_size,
      .channels = channels,
      .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
      .output_increment = output_width_stride - channels * sizeof(float),
    };
    argmax_pooling_op->compute.type = xnn_parallelization_type_2d;
    argmax_pooling_op->compute.range[0] = batch_size;
    argmax_pooling_op->compute.range[1] = output_height;
  
    if (pooling_size <= mr) {
      argmax_pooling_op->context.argmax_pooling.unipass_ukernel = ukernel->up;
      argmax_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_argmax_pooling_unipass;
    } else {
      argmax_pooling_op->context.argmax_pooling.multipass_ukernel = ukernel->mp;
      argmax_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_argmax_pooling_multipass;
    }
    argmax_pooling_op->state = xnn_run_state_ready;
  
    return xnn_status_success;
  }
  
  