text: // Copyright (c) Facebook, Inc. and its affiliates.
  // All rights reserved.
  //
  // 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/common.h>
  #include <xnnpack/indirection.h>
  #include <xnnpack/log.h>
  #include <xnnpack/math.h>
  #include <xnnpack/operator.h>
  #include <xnnpack/params-init.h>
  #include <xnnpack/params.h>
  
  
  static inline size_t compute_output_dimension(
      size_t padded_input_dimension,
      size_t kernel_dimension,
      size_t dilation_dimension,
      size_t stride_dimension)
  {
    const size_t effective_kernel_dimension = (kernel_dimension - 1) * dilation_dimension + 1;
    return (padded_input_dimension - effective_kernel_dimension) / stride_dimension + 1;
  }
  
  static inline size_t compute_output_dimension_with_tf_same_padding(
      size_t input_dimension,
      size_t stride_dimension)
  {
    return divide_round_up(input_dimension, stride_dimension);
  }
  
  static enum xnn_status create_max_pooling2d_nhwc(
      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,
      uint32_t stride_height,
      uint32_t stride_width,
      uint32_t dilation_height,
      uint32_t dilation_width,
      size_t channels,
      size_t input_pixel_stride,
      size_t output_pixel_stride,
      uint32_t flags,
      const void* params,
      size_t params_size,
      uint32_t datatype_init_flags,
      enum xnn_operator_type operator_type,
      xnn_operator_t* max_pooling_op_out)
  {
    xnn_operator_t max_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 setup %s operator: XNNPACK is not initialized",
        xnn_operator_type_to_string(operator_type));
      return xnn_status_uninitialized;
    }
  
    status = xnn_status_unsupported_hardware;
  
    if ((xnn_params.init_flags & datatype_init_flags) != datatype_init_flags) {
      xnn_log_error(
        "failed to create %s operator: operations on data type are not supported",
        xnn_operator_type_to_string(operator_type));
      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(operator_type),
        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(operator_type));
      goto error;
    }
  
    if (stride_height == 0 || stride_width == 0) {
      xnn_log_error(
        "failed to create %s operator with %" PRIu32 "x%" PRIu32 " stride: stride dimensions must be non-zero",
        xnn_operator_type_to_string(operator_type), stride_width, stride_height);
      goto error;
    }
  
    if (dilation_height == 0 || dilation_width == 0) {
      xnn_log_error(
        "failed to create %s operator with %" PRIu32 "x%" PRIu32 " dilation: dilation dimensions must be non-zero",
        xnn_operator_type_to_string(operator_type), dilation_width, dilation_height);
      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(operator_type), 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(operator_type), 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(operator_type), 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(operator_type),
          input_padding_top, input_padding_left, input_padding_bottom, input_padding_right);
        goto error;
      }
    }
  
    status = xnn_status_out_of_memory;
  
    max_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
    if (max_pooling_op == NULL) {
      xnn_log_error(
        "failed to allocate %zu bytes for %s operator descriptor",
        sizeof(struct xnn_operator), xnn_operator_type_to_string(operator_type));
      goto error;
    }
  
    max_pooling_op->padding_top = input_padding_top;
    max_pooling_op->padding_right = input_padding_right;
    max_pooling_op->padding_bottom = input_padding_bottom;
    max_pooling_op->padding_left = input_padding_left;
  
    max_pooling_op->kernel_height = pooling_height;
    max_pooling_op->kernel_width = pooling_width;
    max_pooling_op->stride_height = stride_height;
    max_pooling_op->stride_width = stride_width;
    max_pooling_op->dilation_height = dilation_height;
    max_pooling_op->dilation_width = dilation_width;
    max_pooling_op->channels = channels;
    max_pooling_op->input_pixel_stride = input_pixel_stride;
    max_pooling_op->output_pixel_stride = output_pixel_stride;
  
    memcpy(&max_pooling_op->params, params, params_size);
    max_pooling_op->type = operator_type;
    max_pooling_op->ukernel.type = xnn_ukernel_type_max_pooling;
    max_pooling_op->flags = flags;
  
    max_pooling_op->state = xnn_run_state_invalid;
  
    *max_pooling_op_out = max_pooling_op;
    return xnn_status_success;
  
  error:
    xnn_delete_operator(max_pooling_op);
    return status;
  }
  
  static enum xnn_status setup_max_pooling2d_nhwc(
    xnn_operator_t max_pooling_op,
    size_t batch_size,
    size_t input_height,
    size_t input_width,
    const void* input,
    void* output,
    uint32_t log2_input_element_size,
    uint32_t log2_output_element_size,
    struct maxpool_parameters maxpool[restrict XNN_MIN_ELEMENTS(1)],
    const void* params,
    size_t params_size,
    size_t num_threads)
  {
    max_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(max_pooling_op->type));
      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(max_pooling_op->type), input_width, input_height);
      return xnn_status_invalid_parameter;
    }
  
    if (batch_size == 0) {
      max_pooling_op->state = xnn_run_state_skip;
      return xnn_status_success;
    }
  
    max_pooling_op->input_height = input_height;
    max_pooling_op->input_width = input_width;
    max_pooling_op->input = input;
  
    if (max_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) {
      max_pooling_op->output_height = compute_output_dimension_with_tf_same_padding(
          input_height, max_pooling_op->stride_height);
      max_pooling_op->output_width = compute_output_dimension_with_tf_same_padding(
          input_width, max_pooling_op->stride_width);
  
      const uint32_t effective_kernel_height = (max_pooling_op->kernel_height - 1) * max_pooling_op->dilation_height + 1;
      const uint32_t effective_kernel_width = (max_pooling_op->kernel_width - 1) * max_pooling_op->dilation_width + 1;
      const uint32_t total_padding_height =
        doz((max_pooling_op->output_height - 1) * max_pooling_op->stride_height + effective_kernel_height, input_height);
      const uint32_t total_padding_width =
        doz((max_pooling_op->output_width - 1) * max_pooling_op->stride_width + effective_kernel_width, input_width);
      max_pooling_op->padding_top = total_padding_height / 2;
      max_pooling_op->padding_left = total_padding_width / 2;
      max_pooling_op->padding_bottom = total_padding_height - max_pooling_op->padding_top;
      max_pooling_op->padding_right = total_padding_width - max_pooling_op->padding_left;
    } else {
      max_pooling_op->output_height = compute_output_dimension(
          max_pooling_op->padding_top + input_height + max_pooling_op->padding_bottom,
          max_pooling_op->kernel_height,
          max_pooling_op->dilation_height,
          max_pooling_op->stride_height);
      max_pooling_op->output_width = compute_output_dimension(
          max_pooling_op->padding_left + input_width + max_pooling_op->padding_right,
          max_pooling_op->kernel_width,
          max_pooling_op->dilation_width,
          max_pooling_op->stride_width);
    }
  
    const size_t pooling_height = max_pooling_op->kernel_height;
    const size_t pooling_width = max_pooling_op->kernel_width;
    const size_t pooling_size = pooling_height * pooling_width;
    const size_t output_height = max_pooling_op->output_height;
    const size_t output_width = max_pooling_op->output_width;
    const uint32_t mr = maxpool->mr;
  
    const size_t step_width =
      max_pooling_op->dilation_width > 1 ? pooling_width : min(max_pooling_op->stride_width, pooling_width);
    const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height;
  
    if (input_height != max_pooling_op->last_input_height ||
        input_width != max_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(max_pooling_op->indirection_buffer, indirection_buffer_size);
      if (indirection_buffer == NULL) {
        xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size);
        return xnn_status_out_of_memory;
      }
      max_pooling_op->indirection_buffer = indirection_buffer;
  
      xnn_indirection_init_maxpool2d(max_pooling_op, step_height, step_width, log2_input_element_size);
  
      max_pooling_op->last_input = input;
      max_pooling_op->last_input_height = input_height;
      max_pooling_op->last_input_width = input_width;
    }
  
    const uint32_t qr = maxpool->qr;
    const size_t channels = max_pooling_op->channels;
  
    const size_t indirect_input_height_stride = step_height * sizeof(void*);
    const size_t output_width_stride = max_pooling_op->output_pixel_stride << log2_output_element_size;
    const size_t output_height_stride = output_width * output_width_stride;
    const size_t multipass_adjustment = round_up(doz(pooling_size, mr), qr) + mr;
  
    max_pooling_op->context.max_pooling = (struct max_pooling_context) {
      .indirect_input = max_pooling_op->indirection_buffer,
      .indirect_input_height_stride = indirect_input_height_stride,
      .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) max_pooling_op->last_input),
      .input_batch_stride = (input_height * input_width * max_pooling_op->input_pixel_stride) << log2_input_element_size,
      .output = output,
      .output_batch_stride = output_height * output_height_stride,
      .output_height_stride = output_height_stride,
      .output_width = output_width,
      .pooling_size = pooling_size,
      .channels = channels,
      .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
      .output_increment = output_width_stride - (channels << log2_output_element_size),
      .ukernel = maxpool->ukernel,
    };
    memcpy(&max_pooling_op->context.max_pooling.params, params, params_size);
  
    max_pooling_op->compute.type = xnn_parallelization_type_2d;
    max_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_max_pooling;
    max_pooling_op->compute.range[0] = batch_size;
    max_pooling_op->compute.range[1] = output_height;
    max_pooling_op->state = xnn_run_state_ready;
  
    return xnn_status_success;
  }
  
  enum xnn_status xnn_create_max_pooling2d_nhwc_u8(
      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,
      uint32_t stride_height,
      uint32_t stride_width,
      uint32_t dilation_height,
      uint32_t dilation_width,
      size_t channels,
      size_t input_pixel_stride,
      size_t output_pixel_stride,
      uint8_t output_min,
      uint8_t output_max,
      uint32_t flags,
      xnn_operator_t* max_pooling_op_out)
  {
    if (output_min >= output_max) {
      xnn_log_error(
        "failed to create %s operator with [%" PRIu8 ", %" PRIu8 "] output range: range min must be below range max",
        xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_u8), output_min, output_max);
      return xnn_status_invalid_parameter;
    }
  
    const union xnn_u8_minmax_params params = xnn_init_u8_minmax_params(output_min, output_max);
    return create_max_pooling2d_nhwc(
      input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
      pooling_height, pooling_width,
      stride_height, stride_width,
      dilation_height, dilation_width,
      channels, input_pixel_stride, output_pixel_stride,
      flags,
      &params, sizeof(params), XNN_INIT_FLAG_U8,
      xnn_operator_type_max_pooling_nhwc_u8,
      max_pooling_op_out);
  }
  
  enum xnn_status xnn_create_max_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,
      uint32_t stride_height,
      uint32_t stride_width,
      uint32_t dilation_height,
      uint32_t dilation_width,
      size_t channels,
      size_t input_pixel_stride,
      size_t output_pixel_stride,
      float output_min,
      float output_max,
      uint32_t flags,
      xnn_operator_t* max_pooling_op_out)
  {
    if (isnan(output_min)) {
      xnn_log_error(
        "failed to create %s with NaN output lower bound: lower bound must be non-NaN",
        xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32));
      return xnn_status_invalid_parameter;
    }
  
    if (isnan(output_max)) {
      xnn_log_error(
        "failed to create %s with NaN output upper bound: upper bound must be non-NaN",
        xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32));
      return xnn_status_invalid_parameter;
    }
  
    if (output_min >= output_max) {
      xnn_log_error(
        "failed to create %s with [%.7g, %.7g] output range: lower bound must be below upper bound",
        xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_f32), output_min, output_max);
      return xnn_status_invalid_parameter;
    }
  
    const union xnn_f32_minmax_params params = xnn_init_f32_minmax_params(output_min, output_max);
    return create_max_pooling2d_nhwc(
      input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
      pooling_height, pooling_width,
      stride_height, stride_width,
      dilation_height, dilation_width,
      channels, input_pixel_stride, output_pixel_stride,
      flags,
      &params, sizeof(params), XNN_INIT_FLAG_F32,
      xnn_operator_type_max_pooling_nhwc_f32,
      max_pooling_op_out);
  }
  
  enum xnn_status xnn_setup_max_pooling2d_nhwc_u8(
      xnn_operator_t max_pooling_op,
      size_t batch_size,
      size_t input_height,
      size_t input_width,
      const uint8_t* input,
      uint8_t* output,
      pthreadpool_t threadpool)
  {
    if (max_pooling_op->type != xnn_operator_type_max_pooling_nhwc_u8) {
      xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)",
        xnn_operator_type_to_string(xnn_operator_type_max_pooling_nhwc_u8),
        xnn_operator_type_to_string(max_pooling_op->type));
      return xnn_status_invalid_parameter;
    }
  
    return setup_max_pooling2d_nhwc(
      max_pooling_op,
      batch_size, input_height, input_width,
      input, output,
      0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */,
      0 /* log2(sizeof(output element)) = log2(sizeof(uint8_t)) */,
      &xnn_params.u8.maxpool,
      &max_pooling_op->params.u8_minmax, sizeof(max_pooling_op->params.u8_minmax),
      pthreadpool_get_threads_count(threadpool));
  }
  
  enum xnn_status xnn_setup_max_pooling2d_nhwc_f32(
      xnn_operator_t max_pooling_op,
      size_t batch_size,
      size_t input_height,
      size_t input_width,
      const float* input,
      float* output,
      pthreadpool_t threadpool)
  {
    if (max_pooling_op->type != xnn_operator_type_max_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_max_pooling_nhwc_f32),
        xnn_operator_type_to_string(max_pooling_op->type));
      return xnn_status_invalid_parameter;
    }
  
    return setup_max_pooling2d_nhwc(
      max_pooling_op,
      batch_size, input_height, input_width,
      input, output,
      2 /* log2(sizeof(input element)) = log2(sizeof(float)) */,
      2 /* log2(sizeof(output element)) = log2(sizeof(float)) */,
      &xnn_params.f32.maxpool,
      &max_pooling_op->params.f32_minmax, sizeof(max_pooling_op->params.f32_minmax),
      pthreadpool_get_threads_count(threadpool));
  }
  
  